Wearable device, artificial intelligence, mobile application

HealthX

healthx

The Life and Love of the Insect by Jean-Henri Fabre is part of the HackerNoon Books Series. Read this book online for free on HackerNoon! : The Life and Love of the Insect Title : Jean-Henri Fabre Author : Alexander Teixeira de Mattos Translator : September 11, 2022 [eBook #68974] Release date : English Language Table of Links CHAPTER I. THE SACRED BEETLE II. THE SACRED BEETLE: THE PEAR III. THE SACRED BEETLE: THE MODELLING IV. THE SACRED BEETLE: THE GRUB, THE METAMORPHOSIS, THE HATCHING CHAMBER V. THE SPANISH COPRIS VI. THE ONTHOPHAGI VII. A BARREN PROMISE VIII. A DUNG BEETLE OF THE PAMPAS IX. THE GEOTRUPES: THE PUBLIC HEALTH X. THE MINOTAURUS TYPHŒUS XI. THE TWO BANDED SCOLIA XII. THE RINGED CALICURGUS XIII. THE OLD WEEVILS XIV. LEAF ROLLERS XV. THE HALICTI XVI. THE HALICTI: THE PORTRESS XVII. THE LANGUEDOCIAN SCORPION XVIII. THE LANGUEDOCIAN SCORPION: THE FAMILY About HackerNoon Book Series: We bring you the most important technical, scientific, and insightful public domain books. This book is part of the public domain. Jean-Henri Fabre (2022). The Life and Love of the Insect. Urbana, Illinois: Project Gutenberg. Retrieved https://www.gutenberg.org/cache/epub/68974/pg68974-images.html This eBook is for the use of anyone anywhere at no cost and with almost no restrictions whatsoever. You may copy it, give it away or re-use it under the terms of the Project Gutenberg License included with this eBook or online at www.gutenberg.org , located at https://www.gutenberg.org/policy/license.html .

The Life and Love of the Insect by Jean-Henri Fabre - Table of Links

Golem (GNT): A Deep Dive into The AirBnB of Computers

Digital Health: LLMs for Prompt and Quality Sleep?

Tips for Setting Healthy Boundaries At Work

Surveys show that most healthcare stakeholders consider investments in insights and analytics as a priority funding nowadays. They point out that their organizations are increasingly betting on unified business intelligence (BI) solutions that help implement predictive analytics. Building such a data-driven approach in healthcare propels this domain forward, as 94% of respondents believe. They emphasized the top advantage doctors and patients can leverage from implementing healthcare BI tools and data analytics: a more personalized treatment path. Now, we do see progress in this market. For instance, the Deloitte survey found that customers are more satisfied with how fast and easy they can share their data via wearables than they were a few years ago. Nevertheless, transforming these data into valuable insights is still a perennial challenge from clinicians’ perspective, let alone acting on it promptly. I would say that data is one of the most underutilized assets in healthcare now. Storing Data Does Not Mean Benefiting From Them Many healthcare organizations lack dedicated teams or analytical tools to interpret this volume of data. Clinicians complain about being overloaded by a huge number of irrelevant records. So, only well-implemented electronic health records (EHR) systems with built-in analytical software, served by professionals, can quickly extract information with decision-making potential. Despite focusing on a data-driven approach in the majority of interviewed organizations, only 16% define themselves as mature players in this race. They can gather and analyze data from various sources and leverage findings. The rest of the healthcare institutions are struggling with different chain links of this process. Working with healthcare solutions suppliers, I see how often their customers demand customized software with advanced BI tools to not only react urgently but also use its forecast capabilities. Inhibitory Factors While the mentioned statistics show the healthcare sector has an optimistic view of using BI and DA solutions, some roadblocks stop capitalizing on this opportunity. Forty-three percent of interviewees listed disparate or incompatible systems as a top handicap healthcare organizations deal with. The bottom line is that such organizations need a solution to connect all these data storage, parse the information, and visualize ready-to-use findings. Preparing this infrastructure is a multi-step process that demands resources. It is not just about the investments. As I highlighted before, healthcare stakeholders are ready to adopt these technologies. I would say they know about this trend and would like to jump on this opportunity, but subconsciously, such projects may seem like a burden with an uncertain return on investment (ROI) still for many managers. How to Overcome Limitations In fact, healthcare management wants to know how the new technology aligns with the KPIs and helps boost performance. For example, medical organizations are waiting for more engagement from patients and a higher number of positive reviews, cost optimization, scaling, and better security. From a doctor's perspective, automating some paperwork and decreasing diagnosis time matter. A B2B vendor that provides a SaaS EHR solution would like to compete with major players and increase its market share, gain a new round of investments, meet tight deadlines and high investor expectations, or ultimately, enter new markets and even go for an IPO. In the U.S., there is a noticeable trend toward burnout and dissatisfaction among clients and doctors. Recently, the U.S. healthcare system faced its largest cyber attack. Gartner warned about such concerns last year. Such serious problems in the healthcare domain uncover big opportunities for health tech players, especially with SaaS products. The medical system needs new qualitative solutions to revolutionize its operations or thoroughly modernize the exciting ones. All you need is to show your clients how an EHR solution with BI tools will address their specific problems or help them achieve their goals. The second most common limitation I often observe in such projects is the scope of work and time frames, which is overwhelming for many stakeholders. As a micro-changing approach mature adopter, we recommend an incremental legacy modernization of your EHR system (of course, into your decision-making assistant and a business driver). How It Works In Practice We have had a client who offers EHR as a SaaS solution. Initially, they used this system for internal purposes. Later, they saw the growing demand for such software and decided to get their share of the pie. The organization faced many limitations. Their old system wasn’t scalable enough and couldn’t cope with increased users and data. Additionally, the owners were afraid of security concerns, data leaks, and damaging their reputations. The main challenge was to isolate customers’ data from each other in one environment. As a solution, we created a cloud multi-tenant architecture when different clinics’ records are operated absolutely independently. Also, cloud migration enables the client to scale the resources according to their actual use. For instance, seasonal influenza outbreaks cause higher patient activities and increased requests. The system adjusts resource consumption for such cases and scales them down after the peak periods. Moreover, the solution can predict such events. The system transforms historical data and information extracted in real-time from various databases into conclusions. Then, it automatically visualizes them on BI monitoring panels and notifies decision-makers. Hence, healthcare organizations can prepare their personnel and infrastructure in advance. A clear business strategy based on the modernized solution helped our client gain investments and government program support. By relying on our step-by-step upgrading approach, they overcame the main limitations. In Conclusion When we talk about adopting BI and data analysis in healthcare, it goes not only about technical or funding limitations. The key inhibitor we have to cope with is switching managers' business mindset and encouraging the new corporate culture. It is not enough just to have innovative tools. They won’t revolutionize your performance if they don’t have organizational leaders' support. While many companies are still implementing technologies for technologies, the most successful market players see their purposes and reach them. Also published here. Surveys show that most healthcare stakeholders consider investments in insights and analytics as a priority funding nowadays. They point out that their organizations are increasingly betting on unified business intelligence (BI) solutions that help implement predictive analytics. consider Building such a data-driven approach in healthcare propels this domain forward, as 94% of respondents believe. They emphasized the top advantage doctors and patients can leverage from implementing healthcare BI tools and data analytics : a more personalized treatment path. data analytics Now, we do see progress in this market. For instance, the Deloitte survey found that customers are more satisfied with how fast and easy they can share their data via wearables than they were a few years ago. Nevertheless, transforming these data into valuable insights is still a perennial challenge from clinicians’ perspective, let alone acting on it promptly. I would say that data is one of the most underutilized assets in healthcare now. Storing Data Does Not Mean Benefiting From Them Many healthcare organizations lack dedicated teams or analytical tools to interpret this volume of data. Clinicians complain about being overloaded by a huge number of irrelevant records. So, only well-implemented electronic health records (EHR) systems with built-in analytical software, served by professionals, can quickly extract information with decision-making potential. Despite focusing on a data-driven approach in the majority of interviewed organizations, only 16% define themselves as mature players in this race. They can gather and analyze data from various sources and leverage findings. The rest of the healthcare institutions are struggling with different chain links of this process. Working with healthcare solutions suppliers, I see how often their customers demand customized software with advanced BI tools to not only react urgently but also use its forecast capabilities. Inhibitory Factors While the mentioned statistics show the healthcare sector has an optimistic view of using BI and DA solutions, some roadblocks stop capitalizing on this opportunity. Forty-three percent of interviewees listed disparate or incompatible systems as a top handicap healthcare organizations deal with. The bottom line is that such organizations need a solution to connect all these data storage, parse the information, and visualize ready-to-use findings. Preparing this infrastructure is a multi-step process that demands resources. It is not just about the investments. As I highlighted before, healthcare stakeholders are ready to adopt these technologies. I would say they know about this trend and would like to jump on this opportunity, but subconsciously, such projects may seem like a burden with an uncertain return on investment (ROI) still for many managers. How to Overcome Limitations In fact, healthcare management wants to know how the new technology aligns with the KPIs and helps boost performance. For example, medical organizations are waiting for more engagement from patients and a higher number of positive reviews, cost optimization, scaling, and better security. From a doctor's perspective, automating some paperwork and decreasing diagnosis time matter. A B2B vendor that provides a SaaS EHR solution would like to compete with major players and increase its market share, gain a new round of investments, meet tight deadlines and high investor expectations, or ultimately, enter new markets and even go for an IPO. In the U.S., there is a noticeable trend toward burnout and dissatisfaction among clients and doctors. Recently, the U.S. healthcare system faced its largest cyber attack. Gartner warned about such concerns last year. faced warned Such serious problems in the healthcare domain uncover big opportunities for health tech players, especially with SaaS products. The medical system needs new qualitative solutions to revolutionize its operations or thoroughly modernize the exciting ones. All you need is to show your clients how an EHR solution with BI tools will address their specific problems or help them achieve their goals. The second most common limitation I often observe in such projects is the scope of work and time frames, which is overwhelming for many stakeholders. As a micro-changing approach mature adopter, we recommend an incremental legacy modernization of your EHR system (of course, into your decision-making assistant and a business driver). incremental legacy modernization How It Works In Practice We have had a client who offers EHR as a SaaS solution. Initially, they used this system for internal purposes. Later, they saw the growing demand for such software and decided to get their share of the pie. The organization faced many limitations. Their old system wasn’t scalable enough and couldn’t cope with increased users and data. Additionally, the owners were afraid of security concerns, data leaks, and damaging their reputations. The main challenge was to isolate customers’ data from each other in one environment. As a solution, we created a cloud multi-tenant architecture when different clinics’ records are operated absolutely independently. Also, cloud migration enables the client to scale the resources according to their actual use. For instance, seasonal influenza outbreaks cause higher patient activities and increased requests. The system adjusts resource consumption for such cases and scales them down after the peak periods. Moreover, the solution can predict such events. The system transforms historical data and information extracted in real-time from various databases into conclusions. Then, it automatically visualizes them on BI monitoring panels and notifies decision-makers. Hence, healthcare organizations can prepare their personnel and infrastructure in advance. A clear business strategy based on the modernized solution helped our client gain investments and government program support. By relying on our step-by-step upgrading approach, they overcame the main limitations. In Conclusion When we talk about adopting BI and data analysis in healthcare, it goes not only about technical or funding limitations. The key inhibitor we have to cope with is switching managers' business mindset and encouraging the new corporate culture. It is not enough just to have innovative tools. They won’t revolutionize your performance if they don’t have organizational leaders' support. business mindset While many companies are still implementing technologies for technologies, the most successful market players see their purposes and reach them. Also published here . here here

Business Intelligence Is So Important For Development In Healthcare: Here's Why

Overview Company: WELL3 Token ticker: $WELL Blockchain: Multichain Sector: Decentralized Physical Infrastructure Network Business Model: a DePIN and AI platform in the wellness industry (DePIN) and Decentralized Identity (DID) What does WELL3 do? WELL3 — a pioneering force reshaping health and wellness through Decentralized Physical Infrastructure Network (DePIN), Decentralized Identity (DID) and integrated AI systems. With over 1 million pre-registered users eagerly anticipating its launch, WELL3’s mission is to enhance well-being through secure, data-empowered health journeys. By leveraging DePIN, blockchain, and AI analytics, WELL3 ensures a personalized user experience that is securely authenticated, while fostering a community where millions unite in their pursuit of well-being. This approach allows users to elevate data ownership and security standards, seamlessly blending AI insights to tailor wellness solutions that align with their vision of a secure and health-conscious future. Key Milestones and Achievements The WELL3 community is vibrant and expansive. With their help, WELL3 has achieved numerous record-breaking achievements. Public Sale – In just seven days, the public sale registered a total value locked (TVL) of 15,237.2 ETH or $55M USD. Moreover, over $5 Million was raised through seed round and NFT sales.


WELL ID – After the announcement of the launch of WELL3 Ring – a pioneering wearable that converts personal wellness metrics into on-chain data, over 900,000 users have signed up for its corresponding NFT whitelist, and registered on the WELL3 ecosystem.


WELL3 NFT project – Since its launch on February 10th, 2024, Well3NFT has rapidly ascended to become the largest NFT collection on the opBNB chain, with over 324,000 owners and 8 million NFTs created by April 27, 2024.


$17M transactions have been created on WELL3.


WELL3 has established partnerships with leading entities including Animoca Brands, AWS, Samsung, The Spartan Group, Blocore, Fenbushi Capital, Newman Group, Soul Capital, XY Finance and Lumoz.


Strong strategic alliance – In pursuit of interoperability and a seamless user experience across multichain, WELL3 has formed a strategic alliance with leading chains such as Polygon, Solana, Base, Ton, Avalanche, Arbitrum, BNB Chain, Sei, Algorand, Blast and telos. This collaboration ensures that users benefit from a multichain and scalable ecosystem, enhancing the overall functionality and reach of WELL3’s offerings. Key features of WELL3 In a nutshell, WELL3 is leading a transformative approach in health data management under the DePIN framework. Data decentralization: Health data is securely processed within the DePIN system This process within Trusted Executive Environments (TEE) powered by Amazon Web Services (AWS) ensures user data remains private and secured.


Rewarding Wearables for Enhanced Wellness: WELL3 introduces an innovative line of wearables, starting with a smart ring equipped with state-of-art sensors and algorithms that provides a comprehensive view of an individual's health. Users can earn $WELL tokens through active participation and consenting to share their anonymized health data. This token-based ecosystem not only boosts user engagement by rewarding healthy behaviors and data sharing but also ensures that users have a stake in the governance and ongoing development of the platform.


Complete ownership over personal data: By leveraging WELL3's DePIN and DID technologies, users not only have complete ownership over their personal information and data, but are also safeguarded against unauthorized access, data breaches, and frauds.


Multichain: WELL3’s multichain framework spans multiple blockchain platforms including Bitcoin, Ethereum, Polygon, Solana, Blast, and TON. This broad horizontal coverage allows WELL3 to leverage the strengths of various networks, ensuring that users can interact with the WELL3 ecosystem across multiple platforms without barriers, facilitating a seamless and integrated user experience. Additionally, this approach can significantly reduce transaction costs by allowing WELL3 to choose the most efficient blockchain for specific actions.


Interoperability Across Ecosystems: Designed to seamlessly integrate across various platforms and services, WELL ID eliminates the hassle of multiple passwords and redundant verification processes. Whether it’s accessing health services, participating in wellness programs, or engaging with partner ecosystems, WELL ID provides a unified identity solution that simplifies digital interactions while enhancing security. What is $WELL token? And what does it do? The WELL token acts as both a utility and governance token within the WELL3 Network, embodying the core principles of Wellness, Health, and Governance. It powers a dynamic network of health ecosystem partners and their users, functioning seamlessly across all Layer 1 (L1) and Layer 2 (L2) chains. More specifically, the following are the use cases for the $WELL token in the WELL3 ecosystem: Supports the WELL3 Ecosystem as DePin Ecosystem Rewards, health data sharing and activity-based incentives.


As a reward for contributing to the WELL3’s decentralized network.


Identity creation, management, and KYC-as-a-service via DID.


Act as a currency for both digital and physical purchases in the WELL3 Ecosystem. Distribution of the $WELL token is as follows: Max supply: 42,000,000,000
71% of tokens to community initiatives
Lock up: The unlock schedule is spread across different timeframes to align long-term interests and promote stability. $WELL will unlock over the course of 24 months per the Token Release Schedule. Overview Overview Company : WELL3 Company Token ticker : $WELL Token ticker Blockchain : Multichain Blockchain Sector : Decentralized Physical Infrastructure Network Sector Business Model : a DePIN and AI platform in the wellness industry Business Model ( DePIN ) and Decentralized Identity ( DID ) DePIN DID What does WELL3 do? What does WELL3 do? WELL3 — a pioneering force reshaping health and wellness through Decentralized Physical Infrastructure Network (DePIN), Decentralized Identity (DID) and integrated AI systems. With over 1 million pre-registered users eagerly anticipating its launch, WELL3’s mission is to enhance well-being through secure, data-empowered health journeys. By leveraging DePIN, blockchain, and AI analytics, WELL3 ensures a personalized user experience that is securely authenticated, while fostering a community where millions unite in their pursuit of well-being. This approach allows users to elevate data ownership and security standards, seamlessly blending AI insights to tailor wellness solutions that align with their vision of a secure and health-conscious future. Key Milestones and Achievements Key Milestones and Achievements The WELL3 community is vibrant and expansive. With their help, WELL3 has achieved numerous record-breaking achievements. Public Sale – In just seven days, the public sale registered a total value locked (TVL) of 15,237.2 ETH or $55M USD. Moreover, over $5 Million was raised through seed round and NFT sales. WELL ID – After the announcement of the launch of WELL3 Ring – a pioneering wearable that converts personal wellness metrics into on-chain data, over 900,000 users have signed up for its corresponding NFT whitelist, and registered on the WELL3 ecosystem. WELL3 NFT project – Since its launch on February 10th, 2024, Well3NFT has rapidly ascended to become the largest NFT collection on the opBNB chain, with over 324,000 owners and 8 million NFTs created by April 27, 2024. $17M transactions have been created on WELL3. WELL3 has established partnerships with leading entities including Animoca Brands, AWS, Samsung, The Spartan Group, Blocore, Fenbushi Capital, Newman Group, Soul Capital, XY Finance and Lumoz. Strong strategic alliance – In pursuit of interoperability and a seamless user experience across multichain, WELL3 has formed a strategic alliance with leading chains such as Polygon, Solana, Base, Ton, Avalanche, Arbitrum, BNB Chain, Sei, Algorand, Blast and telos. This collaboration ensures that users benefit from a multichain and scalable ecosystem, enhancing the overall functionality and reach of WELL3’s offerings. Public Sale – In just seven days, the public sale registered a total value locked (TVL) of 15,237.2 ETH or $55M USD. Moreover, over $5 Million was raised through seed round and NFT sales. Public Sale – In just seven days, the public sale registered a total value locked (TVL) of 15,237.2 ETH or $55M USD. Moreover, over $5 Million was raised through seed round and NFT sales. Public Sale – WELL ID – After the announcement of the launch of WELL3 Ring – a pioneering wearable that converts personal wellness metrics into on-chain data, over 900,000 users have signed up for its corresponding NFT whitelist, and registered on the WELL3 ecosystem. WELL ID – After the announcement of the launch of WELL3 Ring – a pioneering wearable that converts personal wellness metrics into on-chain data, over 900,000 users have signed up for its corresponding NFT whitelist, and registered on the WELL3 ecosystem. WELL ID – WELL3 NFT project – Since its launch on February 10th, 2024, Well3NFT has rapidly ascended to become the largest NFT collection on the opBNB chain, with over 324,000 owners and 8 million NFTs created by April 27, 2024. WELL3 NFT project – Since its launch on February 10th, 2024, Well3NFT has rapidly ascended to become the largest NFT collection on the opBNB chain, with over 324,000 owners and 8 million NFTs created by April 27, 2024. WELL3 NFT project – $17M transactions have been created on WELL3. $17M transactions have been created on WELL3. $17M transactions WELL3 has established partnerships with leading entities including Animoca Brands, AWS, Samsung, The Spartan Group, Blocore, Fenbushi Capital, Newman Group, Soul Capital, XY Finance and Lumoz. WELL3 has established partnerships with leading entities including Animoca Brands, AWS, Samsung, The Spartan Group, Blocore, Fenbushi Capital, Newman Group, Soul Capital, XY Finance and Lumoz. WELL3 Strong strategic alliance – In pursuit of interoperability and a seamless user experience across multichain, WELL3 has formed a strategic alliance with leading chains such as Polygon, Solana, Base, Ton, Avalanche, Arbitrum, BNB Chain, Sei, Algorand, Blast and telos. This collaboration ensures that users benefit from a multichain and scalable ecosystem, enhancing the overall functionality and reach of WELL3’s offerings. Strong strategic alliance – In pursuit of interoperability and a seamless user experience across multichain, WELL3 has formed a strategic alliance with leading chains such as Polygon, Solana, Base, Ton, Avalanche, Arbitrum, BNB Chain, Sei, Algorand, Blast and telos. This collaboration ensures that users benefit from a multichain and scalable ecosystem, enhancing the overall functionality and reach of WELL3’s offerings. Strong strategic alliance Key features of WELL3 Key features of WELL3 In a nutshell, WELL3 is leading a transformative approach in health data management under the DePIN framework. Data decentralization: Health data is securely processed within the DePIN system This process within Trusted Executive Environments (TEE) powered by Amazon Web Services (AWS) ensures user data remains private and secured. Rewarding Wearables for Enhanced Wellness: WELL3 introduces an innovative line of wearables, starting with a smart ring equipped with state-of-art sensors and algorithms that provides a comprehensive view of an individual's health. Users can earn $WELL tokens through active participation and consenting to share their anonymized health data. This token-based ecosystem not only boosts user engagement by rewarding healthy behaviors and data sharing but also ensures that users have a stake in the governance and ongoing development of the platform. Complete ownership over personal data: By leveraging WELL3's DePIN and DID technologies, users not only have complete ownership over their personal information and data, but are also safeguarded against unauthorized access, data breaches, and frauds. Multichain: WELL3’s multichain framework spans multiple blockchain platforms including Bitcoin, Ethereum, Polygon, Solana, Blast, and TON. This broad horizontal coverage allows WELL3 to leverage the strengths of various networks, ensuring that users can interact with the WELL3 ecosystem across multiple platforms without barriers, facilitating a seamless and integrated user experience. Additionally, this approach can significantly reduce transaction costs by allowing WELL3 to choose the most efficient blockchain for specific actions. Interoperability Across Ecosystems: Designed to seamlessly integrate across various platforms and services, WELL ID eliminates the hassle of multiple passwords and redundant verification processes. Whether it’s accessing health services, participating in wellness programs, or engaging with partner ecosystems, WELL ID provides a unified identity solution that simplifies digital interactions while enhancing security. Data decentralization: Health data is securely processed within the DePIN system This process within Trusted Executive Environments (TEE) powered by Amazon Web Services (AWS) ensures user data remains private and secured. Data decentralization: Health data is securely processed within the DePIN system This process within Trusted Executive Environments (TEE) powered by Amazon Web Services (AWS) ensures user data remains private and secured. Data decentralization: Rewarding Wearables for Enhanced Wellness: WELL3 introduces an innovative line of wearables, starting with a smart ring equipped with state-of-art sensors and algorithms that provides a comprehensive view of an individual's health. Users can earn $WELL tokens through active participation and consenting to share their anonymized health data. This token-based ecosystem not only boosts user engagement by rewarding healthy behaviors and data sharing but also ensures that users have a stake in the governance and ongoing development of the platform. Rewarding Wearables for Enhanced Wellness: WELL3 introduces an innovative line of wearables, starting with a smart ring equipped with state-of-art sensors and algorithms that provides a comprehensive view of an individual's health. Users can earn $WELL tokens through active participation and consenting to share their anonymized health data. This token-based ecosystem not only boosts user engagement by rewarding healthy behaviors and data sharing but also ensures that users have a stake in the governance and ongoing development of the platform. Rewarding Wearables for Enhanced Wellness: Complete ownership over personal data: By leveraging WELL3's DePIN and DID technologies, users not only have complete ownership over their personal information and data, but are also safeguarded against unauthorized access, data breaches, and frauds. Complete ownership over personal data: By leveraging WELL3's DePIN and DID technologies, users not only have complete ownership over their personal information and data, but are also safeguarded against unauthorized access, data breaches, and frauds. Complete ownership over personal data: Multichain: WELL3’s multichain framework spans multiple blockchain platforms including Bitcoin, Ethereum, Polygon, Solana, Blast, and TON. This broad horizontal coverage allows WELL3 to leverage the strengths of various networks, ensuring that users can interact with the WELL3 ecosystem across multiple platforms without barriers, facilitating a seamless and integrated user experience. Additionally, this approach can significantly reduce transaction costs by allowing WELL3 to choose the most efficient blockchain for specific actions. Multichain: WELL3’s multichain framework spans multiple blockchain platforms including Bitcoin, Ethereum, Polygon, Solana, Blast, and TON. This broad horizontal coverage allows WELL3 to leverage the strengths of various networks, ensuring that users can interact with the WELL3 ecosystem across multiple platforms without barriers, facilitating a seamless and integrated user experience. Additionally, this approach can significantly reduce transaction costs by allowing WELL3 to choose the most efficient blockchain for specific actions. Multichain: Interoperability Across Ecosystems: Designed to seamlessly integrate across various platforms and services, WELL ID eliminates the hassle of multiple passwords and redundant verification processes. Whether it’s accessing health services, participating in wellness programs, or engaging with partner ecosystems, WELL ID provides a unified identity solution that simplifies digital interactions while enhancing security. Interoperability Across Ecosystems: Designed to seamlessly integrate across various platforms and services, WELL ID eliminates the hassle of multiple passwords and redundant verification processes. Whether it’s accessing health services, participating in wellness programs, or engaging with partner ecosystems, WELL ID provides a unified identity solution that simplifies digital interactions while enhancing security. Interoperability Across Ecosystems: What is $WELL token? And what does it do? What is $WELL token? And what does it do? The WELL token acts as both a utility and governance token within the WELL3 Network, embodying the core principles of Wellness, Health, and Governance. It powers a dynamic network of health ecosystem partners and their users, functioning seamlessly across all Layer 1 (L1) and Layer 2 (L2) chains. More specifically, the following are the use cases for the $WELL token in the WELL3 ecosystem: Supports the WELL3 Ecosystem as DePin Ecosystem Rewards, health data sharing and activity-based incentives. As a reward for contributing to the WELL3’s decentralized network. Identity creation, management, and KYC-as-a-service via DID. Act as a currency for both digital and physical purchases in the WELL3 Ecosystem. Supports the WELL3 Ecosystem as DePin Ecosystem Rewards, health data sharing and activity-based incentives. Supports the WELL3 Ecosystem as DePin Ecosystem Rewards, health data sharing and activity-based incentives. As a reward for contributing to the WELL3’s decentralized network. As a reward for contributing to the WELL3’s decentralized network. Identity creation, management, and KYC-as-a-service via DID. Identity creation, management, and KYC-as-a-service via DID. Act as a currency for both digital and physical purchases in the WELL3 Ecosystem. Act as a currency for both digital and physical purchases in the WELL3 Ecosystem. Distribution of the $WELL token is as follows: Max supply: 42,000,000,000 71% of tokens to community initiatives Lock up: The unlock schedule is spread across different timeframes to align long-term interests and promote stability. $WELL will unlock over the course of 24 months per the Token Release Schedule. Max supply: 42,000,000,000 71% of tokens to community initiatives Lock up: The unlock schedule is spread across different timeframes to align long-term interests and promote stability. $WELL will unlock over the course of 24 months per the Token Release Schedule . Token Release Schedule Token Release Schedule

Meet Well3, the Multichain Framework Transforming Health Data Management

মিলন Well3, মাল্টিচেন ফ্রেমওয়ার্ক ট্রান্সফর্মিং হেলথ ডেটা ম্যানেজমেন্ট

Lernen Sie Well3 kennen, das Multichain-Framework, das das Gesundheitsdatenmanagement revolutioniert

Conozca Well3, el marco multicadena que transforma la gestión de datos de salud

Découvrez Well3, le cadre multichaîne qui transforme la gestion des données de santé

मिलिए Well3 से, जो स्वास्थ्य डेटा प्रबंधन को बदलने वाला मल्टीचेन फ्रेमवर्क है

健康データ管理を変革するマルチチェーンフレームワーク Well3 をご紹介します

건강 데이터 관리를 혁신하는 멀티체인 프레임워크 Well3을 만나보세요

Conheça Well3, a estrutura Multichain que transforma o gerenciamento de dados de saúde

Встречайте Well3, мультичейн-платформу, преобразующую управление медицинскими данными

Sağlık Veri Yönetimini Dönüştüren Çok Zincirli Çerçeve Well3 ile tanışın

Gặp gỡ Well3, Khung đa chuỗi chuyển đổi quản lý dữ liệu sức khỏe

认识 Well3，改变健康数据管理的多链框架

Corporate Downfalls: Why Healthcare Is Losing The Battle Against Ransomware Attacks

Authors:
(1) TIMNIT GEBRU, Black in AI;
(2) JAMIE MORGENSTERN, University of Washington;
(3) BRIANA VECCHIONE, Cornell University;
(4) JENNIFER WORTMAN VAUGHAN, Microsoft Research;
(5) HANNA WALLACH, Microsoft Research;
(6) HAL DAUMÉ III, Microsoft Research; University of Maryland;
(7) KATE CRAWFORD, Microsoft Research. Table of Links 1 Introduction 1.1 Objectives 2 Development Process 3 Questions and Workflow 3.1 Motivation 3.2 Composition 3.3 Collection Process 3.4 Preprocessing/cleaning/labeling 3.5 Uses 3.6 Distribution 3.7 Maintenance 4 Impact and Challenges Acknowledgments and References Appendix 3.7 Maintenance As with the questions in the previous section, dataset creators should provide answers to these questions prior to distributing the dataset. The questions in this section are intended to encourage dataset creators to plan for dataset maintenance and communicate this plan to dataset consumers. • Who will be supporting/hosting/maintaining the dataset? • How can the owner/curator/manager of the dataset be contacted (e.g., email address)? • Is there an erratum? If so, please provide a link or other access point. • Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)? • If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. • Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers. • If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description. • Any other comments? This paper is available on arxiv under CC 4.0 license. Authors: (1) TIMNIT GEBRU, Black in AI; (2) JAMIE MORGENSTERN, University of Washington; (3) BRIANA VECCHIONE, Cornell University; (4) JENNIFER WORTMAN VAUGHAN, Microsoft Research; (5) HANNA WALLACH, Microsoft Research; (6) HAL DAUMÉ III, Microsoft Research; University of Maryland; (7) KATE CRAWFORD, Microsoft Research. Authors: Authors: (1) TIMNIT GEBRU, Black in AI; (2) JAMIE MORGENSTERN, University of Washington; (3) BRIANA VECCHIONE, Cornell University; (4) JENNIFER WORTMAN VAUGHAN, Microsoft Research; (5) HANNA WALLACH, Microsoft Research; (6) HAL DAUMÉ III, Microsoft Research; University of Maryland; (7) KATE CRAWFORD, Microsoft Research. Table of Links 1 Introduction 1 Introduction 1.1 Objectives 1.1 Objectives 2 Development Process 2 Development Process 3 Questions and Workflow 3 Questions and Workflow 3.1 Motivation 3.1 Motivation 3.2 Composition 3.2 Composition 3.3 Collection Process 3.3 Collection Process 3.4 Preprocessing/cleaning/labeling 3.4 Preprocessing/cleaning/labeling 3.5 Uses 3.5 Uses 3.6 Distribution 3.6 Distribution 3.7 Maintenance 3.7 Maintenance 4 Impact and Challenges 4 Impact and Challenges Acknowledgments and References Acknowledgments and References Appendix Appendix 3.7 Maintenance As with the questions in the previous section, dataset creators should provide answers to these questions prior to distributing the dataset. The questions in this section are intended to encourage dataset creators to plan for dataset maintenance and communicate this plan to dataset consumers. • Who will be supporting/hosting/maintaining the dataset? • Who will be supporting/hosting/maintaining the dataset? • How can the owner/curator/manager of the dataset be contacted (e.g., email address)? • How can the owner/curator/manager of the dataset be contacted (e.g., email address)? • Is there an erratum? If so, please provide a link or other access point. • Is there an erratum? • Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to dataset consumers (e.g., mailing list, GitHub)? • Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? • If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. • If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were the individuals in question told that their data would be retained for a fixed period of time and then deleted)? • Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to dataset consumers. • Will older versions of the dataset continue to be supported/hosted/maintained? • If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/verified? If so, please describe how. If not, why not? Is there a process for communicating/distributing these contributions to dataset consumers? If so, please provide a description. • If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? • Any other comments? • Any other comments? This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Ensuring Dataset Health: Strategies for Effective Maintenance and Support

Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike, a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029, the brainpower it needs. The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety. "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. Move over, Dr. House. There's a new medical genius in town, and it doesn't even have a stethoscope. Spike , a Silicon Valley startup that focuses on data technology and artificial intelligence (AI), just landed $3.5 million in seed funding. Its mission? To give healthcare AI, which is set to reach $148 billion by 2029 , the brainpower it needs. Spike set to reach $148 billion by 2029 The oversubscribed investment was co-led by Practica Capital and TheVentureCity, and a who's who of tech investors joined the party, including CEAS Investments, Plug & Play Ventures, Geek Ventures, and Axel Springer Porsche (APX). Spike is ready to supercharge product development, expand into new markets, and cement its status as a medical director in the AI health technology field. So, what's Spike's secret remedy? Its B2B solution allows developers to add AI superpowers to their healthcare apps. Spike aims to cut development time and costs to a fraction of what they used to be and connect to the data collected by hundreds of medical, wearable, and IoT devices. Think of Spike's API as a health data buffet, offering insights from over 300 sources – from your trusty Fitbit to high-tech medical devices. It's the fuel that powers Spike's AI algorithms to deliver personalized healthcare solutions smarter than a med school grad (well, almost). "Advancements in retrieval-augmented generation AI mean that LLMs are no longer taking a 'best guess,' but are now able to seek out specialized context, producing outputs that are much more accurate and considerably less prone to hallucinations," Povilas Gudzius, CEO and co-founder of Spike, said. "By incorporating this technology on top of Spike's high-quality health data API, we're enabling our clients to easily leverage these tools and improve the health of millions of end users." Spike vs. the Goliaths, with a RAG, not a Stone Spike vs. the Goliaths, with a RAG, not a Stone Sure, Google, Amazon, and Microsoft have their AI health data solutions. But let's be honest: They're like old-school doctors who still use pagers. Spike is the young, energetic upstart shaking things up. Google Cloud Healthcare API provides access to healthcare data and offers AI and machine learning tools for building healthcare applications. Amazon HealthLake is a HIPAA-eligible service that helps healthcare organizations store, transform, query, and analyze health data. And Microsoft Azure API for FHIR enables the exchange of healthcare data using the FHIR standard and offers AI tools for building healthcare applications. So while the megaliths in the room are busy managing massive cloud infrastructures, Spike is laser-focused on healthcare. They're not just aggregating data; they're curating it, ensuring it's top-notch and tailor-made for the unique needs of the medical world. And Spike uses retrieval-augmented generation (RAG), a technique for enhancing the accuracy and reliability of generative AI models with facts fetched from external sources. RAG helps AI models avoid embarrassing "hallucination" moments by cross-checking their answers with real-world facts. Think of it as a reality check for AI, ensuring its answers are as accurate as a doctor's diagnosis. This makes AI more reliable, less prone to making things up, and ultimately, much more useful in the healthcare world. "We are utilizing health expertise networks and curated in specialized context databases in RAG AI infrastructure," Gudzius said. "RAG AI allows us to incorporate these problem-specific sources effectively and makes the LLM responses more grounded. The design of the RAG AI infrastructure and domain-expert curated medical content allows us to further innovate in developing the 'Trustworthy Health AI.'" All of this technology doesn't mean Spike can scrimp on security and privacy. Like the Big 3, it has to make strong commitments to regulatory protection, especially with searches for AI in healthcare being orders of magnitude higher than searches for AI safety . searches for AI in healthcare being orders of magnitude higher than searches for AI safety "Spike takes data security and privacy very seriously, so our infrastructure products follow strict GDRP, HIPAA, and CCPA regulatory guidelines," Gudzius said. "From the technical perspective, we are also adopting specific techniques to prioritize data privacy. For example, we have built our data access layers that provide limited control to the LLM agent while handling data acquisition logic and complying with regulatory requirements. As the quality and volume of individuals' health data grows, there's a huge need to develop and innovate regulatory-compliant techniques for private health data-to-LLM interactions like that." The Future of Healthcare: Spike's Prescription The Future of Healthcare: Spike's Prescription This $3.5 million isn't just pocket change; it's the catalyst for Spike's grand vision. They're not just building AI tools but trustworthy AI tools that are as reliable as your favorite family doctor (but hopefully with a better bedside manner). They're expanding their reach to include everything from electronic health records to genomics data. "We believe in a future where health data volume will grow exponentially driven by the improvements in sensor hardware," Gudzius said. "These sensors include optical, electrical, and electromechanical sensors from wearables, IoT devices, and other sources like EHRs and lab tests. We are well positioned to capture this potential because our team has extensive experience working with data acquisition and a scientific background in Health AI. Our edge is ensuring we provide the capabilities of these emerging non-diagnostic technologies to our clients in a safe and regulatory compliant way." Spike imagines a future where AI isn't just a buzzword; it's an integral part of healthcare, improving patient care and making those dreaded doctor's visits a little less dreadful. A version of this article was originally published on Dataconomy and is republished with permission. A version of this article was originally published on Dataconomy and is republished with permission. published on Dataconomy

Spike Aims to Give Healthcare AI a Brain Boost and Outsmart the Tech Giants

With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google, GE Healthcare, Siemens Healthineers, and many other leading AI startups, these AI dreams are achieving more feasibility. However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements. With an alarming 46 percent statistical concern about access to treatment in the healthcare system around the world, AI is seeking to democratize and simplify the process of diagnosis and patient treatment. With companies like Google , GE Healthcare , Siemens Healthineers , and many other leading AI startups, these AI dreams are achieving more feasibility. 46 percent statistical concern Google GE Healthcare Siemens Healthineers However, how will AI’s health solution be future-proof if privacy concerns and data breaches are still lurking? Overview of AI advancements Overview of AI advancements GE Healthcare has been able to develop a technology that assists with ultra-sound imaging, and high-level accurate scans like PET, MRI, and CT. it is also in the U.S. Food and Drug Administration-approved AI medical devices having 58 out of 692 entries. GE Healthcare Siemens Healthineers claims to have more than 80 AI-based healthcare solutions with 40 hardware devices approved lately. The focus of this company is the algorithmic mechanism that automates the post-processing of images and helps radiologists handle heavy workflows and routine tasks. Siemens Healthineers According to an article on VKTR by Neil Savage, “it includes modules for chest CT, which can highlight lung nodules and estimate tumor burden; brain MR, which divides the brain into different segments and makes volumetric measurements” VKTR A host of other startups are promising in harnessing the potential of the intelligent models in addressing the human problems with the current healthcare system. host of other startups Can we trust AI with our healthcare data? Can we trust AI with our healthcare data? AI by default makes decisions and recommendations based on the data it has been trained with and all forms have an underlying principle of large datasets in finding patterns. This leads us to our first question on how the benefits be balanced with the disadvantages in utilizing people’s healthcare data for training and decisions. The concerns are in two categories. The first concern is the privacy of the data used to train these models, second is the model’s response based on the input of the patient’s data in the working process—or decision-making process of the model. AI’s advancements in the healthcare venture have received ovations with its impressive abilities ranging from medical evaluations, automation, chest x-rays, smart wearables, medical diagnosis, imaging processing, and robotic surgery—which has been touted as a hypothetical possibility with recent innovations and on a higher level rather than the current level with tiny incisions. In Health IT security’s words, “Artificial intelligence (AI) in healthcare has the potential to revolutionize clinical research and care delivery. AI can be used to sift through massive databases and may have applications in mental healthcare and cancer detection.” Over time, the medical profession has prioritized the oath of secrecy in the medical process of diagnosis, treatments, and prescriptions. However, a data-oriented system solving the measure of traditional problems while maintaining the status quo remains a vital cornerstone to the utility of this innovation. Definitive Healthcare has identified about 80 percent of health data breaches due to hacking or IT incidents. Therefore, there’s still a lot to be addressed when fears abound about the possibility of weaponized models, poisoning, and other sort of invasive possibilities on the technology that was originally meant to assist and automate tasks. Definitive identified invasive This compounds the problems and leaves gaps as the healthcare AI application accelerates at an unprecedented pace. Several approaches may mitigate problems and tie up the loose ends which will be detailed in succeeding paragraphs. Looking forward: The turning point and AI promises Looking forward: The turning point and AI promises A technical approach such as anonymizing, encryption, and creating privacy-oriented algorithms in storing and retrieval of health care data. An example is blockchain technology detailed in this paper by the NLM. paper A second way can be developing legal frameworks in protecting the medical records of individuals and according to Valentin Kuzmenco, in an article on Hyperight, “We need robust legal frameworks to safeguard our privacy in the digital age. Stringent data protection laws like GDPR and CCPA offer a starting point,… protecting our data from unauthorized access and misuse, regardless of geographical borders.” GDPR and CCPA There’s a longtime perpetual concern of big tech with people’s data and their collateral cost of internet surfing, and recommendation algorithms. Patients can be educated on best privacy practices, and providing consent forms—because not all forms of medical data may be deemed private Thirdly, there is ongoing research in addressing the underlying concerns and a continual progression will address technological challenges with data privacy and automative algorithms. Being on the lookout while supervising automation for progressive steps will go a long way. In conclusion, the impact of AI in the healthcare industry is promising and has lots of potential to mitigate current and future healthcare problems and automate tasks to lower costs and democratize healthcare. However, best security practices, compliance regulatory frameworks, and continual research would enhance a futuristic possibility with innovations and advancements.

As Healthcare AI Advances, How Do we Balance the Benefits With Privacy Concerns?

Revolutionizing Healthcare: Andy Schoonover's Playbook for Disrupting Entrenched Industries

বিপ্লবী স্বাস্থ্যসেবা: প্রবেশ করা শিল্পগুলিকে ব্যাহত করার জন্য অ্যান্ডি শুনোভারের প্লেবুক

Revolutionierung des Gesundheitswesens: Andy Schoonovers Strategie zur Umwälzung etablierter Branchen

Revolucionando la atención médica: el manual de Andy Schoonover para revolucionar industrias arraigadas

Révolutionner les soins de santé : le manuel d'Andy Schoonover pour perturber les industries bien établies

स्वास्थ्य सेवा में क्रांति: स्थापित उद्योगों में बदलाव के लिए एंडी स्कूनओवर की कार्यपुस्तिका

ヘルスケアの革命: 定着した業界を変革するためのアンディ・シューノーバーの戦略

의료 혁신: 고착화된 산업을 혁신하기 위한 Andy Schoonover의 플레이북

Revolucionando a saúde: o manual de Andy Schoonover para desestabilizar indústrias entrincheiradas

Революция в здравоохранении: книга Энди Шуновера по разрушению устоявшихся отраслей

Sağlık Hizmetinde Devrim Yaratmak: Andy Schoonover'ın Yerleşik Endüstrileri Yıkmaya Yönelik Başucu Kitabı

Cách mạng hóa hoạt động chăm sóc sức khỏe: Sách hướng dẫn cách phá vỡ các ngành công nghiệp cố thủ của Andy Schoonover

医疗保健革命：安迪·斯库诺弗颠覆根深蒂固行业的策略

The Quiet Crisis That Is Stealing Our Health (and How to Get It Back) - Insights From Dr. Eric Berg

The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author The rise of electronic health records and the growing use of data analytics in healthcare have made data governance a key focus area for improving healthcare services to be safer, more efficient, and better tailored to individual patient needs. The stakes are high, as a single breach in healthcare data can jeopardize patient confidentiality and weaken trust in the entire healthcare system. Astra Security reports that 51% of healthcare organizations have seen an uptick in data breaches since 2019, with hospitals facing increasing amounts of breaches over the last 12 years. Data governance is important not just for security but for ensuring data quality, integrity, and accessibility – all critical for informed decision-making in healthcare. The HIPAA (Health Insurance Portability and Accountability Act) Journal revealed that a whopping 133 million records were exposed or mishandled in 2023 alone, underscoring the need for proper data governance. 51% of healthcare organizations 51% of healthcare organizations 133 million records 133 million records Healthcare data leaders like Nithin Narayan Koranchirath are rising to the challenge by championing proper data governance strategies in the healthcare industry. Nithin’s belief is that more awareness of the essential role data governance plays in healthcare will lead to increased adoption by the industry. Managing Data in Healthcare Managing Data in Healthcare Data governance is not a box to tick and then be forgotten about; it is a strategic necessity in the healthcare industry. With the increasing amount of Electronic Health Records (EHRs) and medical research data, healthcare institutions must guarantee that data remains precise, accessible, and secure. With inconsistencies in data across different EHR systems, there are more administrative tasks and potential risks to patient well-being than ever before. The challenges for healthcare providers in handling data are maintaining privacy and security standards, complying with regulations, managing data sources effectively, and ensuring seamless communication between different systems. Establishing a data governance program can enhance the delivery of healthcare services, foster innovation and research, improve decision-making in healthcare, and ensure compliance with laws like HIPAA and GDPR (General Data Protection Regulation). An Authority on Healthcare Data Management An Authority on Healthcare Data Management Nithin Narayan Koranchirath, a professional in healthcare data management, stresses the significance of data governance. He has years of expertise overseeing various healthcare data systems, and emphasizes that "efficient data management goes beyond compliance, it aims at enhancing outcomes and streamlining healthcare services." Data governance might appear as a subject typically reserved exclusively for IT experts, but its impact extends to everyone involved in the healthcare network. At its core, data governance entails managing data from the collection phase to its end use in patient care or research. Nithin’s pivotal moment came when leading the Data and Analytics platform at his current organization, one of the United States’s largest healthcare providers, where he witnessed the transformative impact of data-driven insights. "This role allowed me to witness firsthand the power of data-driven insights in revolutionizing data analytics delivery and administration," Nithin reflects, solidifying his commitment to analytics and data-driven decision-making. Grasping proper data governance practices empowers healthcare professionals with the knowledge to make informed decisions. This fundamental understanding reduces the chances of mistakes that could jeopardize safety. For patients, it offers reassurance that their personal data is handled with care and confidentiality. Additionally, implementing data governance measures such as utilizing Enterprise Master Patient Indexes (EMPI) can significantly enhance healthcare quality by providing an overview of patient records. Implementing a Data Governance Framework Tailored for Healthcare Implementing a Data Governance Framework Tailored for Healthcare Healthcare professionals frequently encounter obstacles with data, such as difficulty accessing and safeguarding data, disorganized and isolated data sets, limited resource availability, and insufficient staff collaboration and training. Overcoming these challenges involves defining roles for adopting security measures, selecting a data management solution, allocating resources for data experts, and educating staff on effective data management practices. When assessing options for data governance solutions, healthcare institutions should take into account several factors, including integration with existing systems, compliance with requirements, scalability, and ease of use. Platforms like Big Data, AWS, and Snowflake offer data governance capabilities for overseeing data governance processes while effectively addressing security concerns and regulatory demands. offer data governance capabilities offer data governance capabilities Nithin’s work in implementing a comprehensive data governance framework at his organization via Big Data, AWS, and Snowflake underscores the practical benefits of such knowledge. "The implementation of robust data governance practices enabled us to achieve greater compliance with regulatory requirements and mitigate risks associated with data management," he explains. For healthcare professionals, this translates to better decision-making capabilities, and for patients, it means the assurance of data privacy and security. AWS and Snowflake are cloud-based data management solutions that align seamlessly with the needs of the healthcare industry. It offers functionalities like data discovery, traceability, and search capabilities to help organizations streamline their approach to data governance while simultaneously enhancing security and compliance measures efficiently. At the end of the day, leaders such as C-suite executives and IT managers ultimately bear responsibility for ensuring data governance practices are followed in an organization. However, every individual working within the healthcare sector plays a role in its success. Effective leadership should ensure that all staff members know the importance of data governance, while data experts should have a grasp on how data impacts staff operations. Future Outlook for Data Governance in Healthcare Future Outlook for Data Governance in Healthcare In an era defined by numerous technological advancements in healthcare, the importance of data governance is only set to grow. As we look towards the future, data governance in healthcare is poised to become a cornerstone of the United States healthcare system, playing a pivotal role in shaping economic outcomes. The U.S. healthcare sector, one of the largest contributors to the national economy, faces rising costs, inefficiencies, and demands for improved patient outcomes and privacy. Nithin’s hard work in solving these issues will have lasting positive effects not only on the healthcare industry but on the tech sector and the U.S. economy at large. Additionally, with the standardization of data practices, interoperability across different healthcare systems can be improved. This improvement facilitates better coordination of care, especially for chronic disease management, which is a significant expenditure for the U.S. economy. Data governance addresses these challenges by enhancing the quality, accessibility, and security of healthcare data. The path toward establishing effective data governance within healthcare is challenging but critical. As Nithin Narayan Koranchirath says himself, "Data governance is the backbone of a trustworthy healthcare system." By ensuring the integrity, accessibility, and security of healthcare data, the U.S. can improve its healthcare outcomes, reduce costs, and foster innovation, thereby enhancing its economic stability and growth. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Why Data Governance in Healthcare Matters in 2024 With Nithin Narayan Koranchirath

কেন নিথিন নারায়ণ কোরানচিরথের সাথে 2024 সালে স্বাস্থ্যসেবা বিষয়ে ডেটা গভর্নেন্স

Warum Data Governance im Gesundheitswesen im Jahr 2024 wichtig ist – mit Nithin Narayan Koranchirath

Por qué es importante la gobernanza de datos en la atención sanitaria en 2024 con Nithin Narayan Koranchirath

Pourquoi la gouvernance des données dans le secteur de la santé est importante en 2024 avec Nithin Narayan Koranchirath

2024 में स्वास्थ्य सेवा में डेटा गवर्नेंस क्यों मायने रखता है, नितिन नारायण कोरानचिराथ के साथ

2024 年に医療におけるデータ ガバナンスが重要な理由 (ニティン ナラヤン コランチラート氏)

Nithin Narayan Koranchirath와 함께 2024년에 의료 분야의 데이터 거버넌스가 중요한 이유

Por que a governança de dados na área da saúde é importante em 2024 com Nithin Narayan Koranchirath

Почему управление данными в здравоохранении имеет значение в 2024 году с Нитин Нараян Коранчират

Nithin Narayan Koranchirath ile 2024'te Sağlık Hizmetlerinde Veri Yönetişimi Neden Önemli?

Tại sao quản trị dữ liệu trong lĩnh vực chăm sóc sức khỏe lại quan trọng vào năm 2024 với Nithin Narayan Koranchirath

Nithin Narayan Koranchirath 谈 2024 年医疗保健数据治理为何如此重要

Every year, people around the world find themselves unable to cover basic medical costs. In fact, about half of U.S. adults currently have trouble affording healthcare. This is caused by issues like the high cost of hospital care and a lack of pharmaceutical price transparency, resulting in a faulty system that continues to leave millions out. However, industry experts like Mandhir seek to address these issues. As a senior lead engineer at Elevance Health, Mandhir has been instrumental in using patient data to create a unique, AI-powered system that helps users access personalized medical services. He’s also worked to establish a broader vision of patient care that not only considers physical factors but also takes into account social, behavioral, and mental elements. Learn more about Mandhir’s work and how he’s worked to make medical costs more affordable for millions of people worldwide. The Current Challenges in Healthcare Most problems in the American healthcare industry can be traced to a lack of transparency. Under the current system, patients have no way of knowing the full cost of their treatments until the bill is already due, whether it’s hospital fees, prescription drugs, or insurance coverage. This leaves patients susceptible to surprise medical bills that can quickly wipe out savings. Additionally, most facilities don’t have the proper infrastructure to store and organize the massive amounts of patient data that the healthcare industry generates every day. This makes it difficult for providers to access vital records, but it also means they can get inconsistent or incomplete data, making it harder to get the full picture of a patient’s health problems. Without a unified communication system among providers that keeps data synchronized and all in one place, patient care suffers as doctors inaccurately assess their needs (due to a lack of good data). It also leads to conflicting reports and unnecessary additional expenses, significantly contributing to a healthcare system that’s already rife with disparities in price and quality. Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Mandhir has understood the importance of healthcare accessibility since he was a child. Raised in a remote village in Punjab, India, he saw firsthand how families around him struggled to afford basic health treatments, even going as far as to sell their property. When he moved to the United States, he was impressed by many advancements in healthcare, but he was even more surprised that healthcare was almost just as inaccessible despite its innovations. After receiving his Bachelor of Technology from the Punjab Technical University, he worked for over 13 years as a technology architect for the second-largest Indian IT company, Infosys. During this time, he worked on claim adjudication systems for Anthem, one of the largest health companies in the US, and gained great insight into the inner workings of the country’s healthcare sector. This experience eventually led Mandhir to work directly for Anthem, now called Elevance Health. As a senior lead engineer, he’s used his position and talents over the years to spearhead significant developments in data-driven healthcare technology. How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance Fueled by a passion rooted in personal experience, Mandhir began addressing healthcare’s shortcomings by incorporating concepts like machine learning and artificial intelligence into Elevance Health’s patient data systems. Some of the ways he’s contributed to a better healthcare system include: The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality.


The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. Thanks to these efforts, Mandhir has worked toward making medicine more affordable. For example, the insights gained from data-powered health monitoring allow patients to start precautionary treatments earlier and reduce unwanted expenses like hospital stays. They also help providers calculate costs more accurately based on a patient’s health plan. Whole Health: A Holistic Approach to Healthcare Data Improving the quality of patient health records was only the first step toward making quality medical services more accessible to patients. Mandhir has tailored his machine learning systems to fit the “whole health” approach to healthcare. Whole health is a mindset that recognizes that a patient’s health consists of more than just their physical condition — it includes their mental well-being, behavioral conditions, and social factors like their living situation. Additionally, according to this philosophy, all of these factors should be considered when addressing a patient’s access to health services. To this end, Mandhir assisted in developing the Whole Health Index, a tool that gathers patient data from multiple sources (like health records, surveys, and community organizations) and creates a thorough overview of a patient’s health profile and financial situation. This holistic approach has been key to enhancing patient-provider communication. By understanding what the patient can and can’t afford, both parties can work to find a cost-effective, high-quality treatment option. Not only does that build patient trust in the healthcare industry, but it also leads to improved health outcomes. Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery By seizing the latest developments in emerging technology like AI, Mandhir plans to reinforce data security and privacy in platforms like Health OS to build further trust with providers. He also aims to make the whole health approach a fixed industry standard to change the public perception of the healthcare system. Mandhir also keeps an open dialogue with policymakers and stakeholders to advocate for price disclosure and accessibility as a broader societal change. Mandhir believes that healthcare should not be a privilege but a right. And with his technical expertise, in-depth knowledge of the healthcare system, and personal life experience, he’s dedicated his career to turning this vision into a reality. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author Every year, people around the world find themselves unable to cover basic medical costs. In fact, about half of U.S. adults currently have trouble affording healthcare . This is caused by issues like the high cost of hospital care and a lack of pharmaceutical price transparency, resulting in a faulty system that continues to leave millions out. about half of U.S. adults currently have trouble affording healthcare about half of U.S. adults currently have trouble affording healthcare However, industry experts like Mandhir seek to address these issues. As a senior lead engineer at Elevance Health, Mandhir has been instrumental in using patient data to create a unique, AI-powered system that helps users access personalized medical services. He’s also worked to establish a broader vision of patient care that not only considers physical factors but also takes into account social, behavioral, and mental elements. Learn more about Mandhir’s work and how he’s worked to make medical costs more affordable for millions of people worldwide. The Current Challenges in Healthcare The Current Challenges in Healthcare Most problems in the American healthcare industry can be traced to a lack of transparency. Under the current system, patients have no way of knowing the full cost of their treatments until the bill is already due, whether it’s hospital fees, prescription drugs, or insurance coverage. This leaves patients susceptible to surprise medical bills that can quickly wipe out savings. Additionally, most facilities don’t have the proper infrastructure to store and organize the massive amounts of patient data that the healthcare industry generates every day. This makes it difficult for providers to access vital records, but it also means they can get inconsistent or incomplete data, making it harder to get the full picture of a patient’s health problems. Without a unified communication system among providers that keeps data synchronized and all in one place, patient care suffers as doctors inaccurately assess their needs (due to a lack of good data). It also leads to conflicting reports and unnecessary additional expenses, significantly contributing to a healthcare system that’s already rife with disparities in price and quality. Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Advocating for Transparency: Mandhir’s Commitment to a Better Healthcare Ecosystem Mandhir has understood the importance of healthcare accessibility since he was a child. Raised in a remote village in Punjab, India, he saw firsthand how families around him struggled to afford basic health treatments, even going as far as to sell their property. When he moved to the United States, he was impressed by many advancements in healthcare, but he was even more surprised that healthcare was almost just as inaccessible despite its innovations. After receiving his Bachelor of Technology from the Punjab Technical University, he worked for over 13 years as a technology architect for the second-largest Indian IT company, Infosys. During this time, he worked on claim adjudication systems for Anthem, one of the largest health companies in the US, and gained great insight into the inner workings of the country’s healthcare sector. This experience eventually led Mandhir to work directly for Anthem, now called Elevance Health. As a senior lead engineer, he’s used his position and talents over the years to spearhead significant developments in data-driven healthcare technology. How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance How Mandhir’s Work Uses AI-Driven Data Systems to Improve Patient Assistance Fueled by a passion rooted in personal experience, Mandhir began addressing healthcare’s shortcomings by incorporating concepts like machine learning and artificial intelligence into Elevance Health’s patient data systems. Some of the ways he’s contributed to a better healthcare system include: The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Implementation of Data-Driven Decision-Making: Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Implementation of Data-Driven Decision-Making : Mandhir created an AI-powered system that generates insights and recommendations for providers based on patient data. This has helped physicians get a better sense of their patients’ situations and use more personalized approaches. As a result, Elevance Health saw a 20% improvement in data quality. The Implementation of Data-Driven Decision-Making The Creation of the Health OS Platform: Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Creation of the Health OS Platform : Mandhir was also the lead behind the Health Operating System (Health OS), a virtual platform that integrates patients’ health information into a single “lifetime” patient record. This system streamlines tasks like filling out paperwork for each different provider by having all relevant information in one centralized place. It also allows health teams to better plan a patient’s treatment over time, aided by the system’s insights. The Creation of the Health OS Platform Thanks to these efforts, Mandhir has worked toward making medicine more affordable. For example, the insights gained from data-powered health monitoring allow patients to start precautionary treatments earlier and reduce unwanted expenses like hospital stays. They also help providers calculate costs more accurately based on a patient’s health plan. Whole Health: A Holistic Approach to Healthcare Data Whole Health: A Holistic Approach to Healthcare Data Improving the quality of patient health records was only the first step toward making quality medical services more accessible to patients. Mandhir has tailored his machine learning systems to fit the “whole health” approach to healthcare. Whole health is a mindset that recognizes that a patient’s health consists of more than just their physical condition — it includes their mental well-being, behavioral conditions, and social factors like their living situation. Additionally, according to this philosophy, all of these factors should be considered when addressing a patient’s access to health services. To this end, Mandhir assisted in developing the Whole Health Index, a tool that gathers patient data from multiple sources (like health records, surveys, and community organizations) and creates a thorough overview of a patient’s health profile and financial situation. This holistic approach has been key to enhancing patient-provider communication. By understanding what the patient can and can’t afford, both parties can work to find a cost-effective, high-quality treatment option. Not only does that build patient trust in the healthcare industry, but it also leads to improved health outcomes. Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery Mandhir’s Continued Efforts to Bridge Gaps in Healthcare Delivery By seizing the latest developments in emerging technology like AI, Mandhir plans to reinforce data security and privacy in platforms like Health OS to build further trust with providers. He also aims to make the whole health approach a fixed industry standard to change the public perception of the healthcare system. Mandhir also keeps an open dialogue with policymakers and stakeholders to advocate for price disclosure and accessibility as a broader societal change. Mandhir believes that healthcare should not be a privilege but a right. And with his technical expertise, in-depth knowledge of the healthcare system, and personal life experience, he’s dedicated his career to turning this vision into a reality. This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

Transforming Healthcare: Mandhir's Quest for Affordable Medical Access with AI

ট্রান্সফর্মিং হেলথ কেয়ার: এআই সহ সাশ্রয়ী মূল্যের চিকিৎসা অ্যাক্সেসের জন্য মন্দিরের কোয়েস্ট

Transformation des Gesundheitswesens: Mandhirs Streben nach erschwinglichem Zugang zur medizinischen Versorgung mithilfe von KI

Transformando la atención médica: la búsqueda de Mandhir de un acceso médico asequible con inteligencia artificial

Transformer les soins de santé : la quête de Mandhir pour un accès médical abordable grâce à l'IA

स्वास्थ्य सेवा में बदलाव: एआई के साथ सस्ती चिकित्सा पहुंच के लिए मंधीर की खोज

ヘルスケアの変革: AI を活用した手頃な医療アクセスの実現を目指す Mandhir の取り組み

의료 혁신: AI를 통한 저렴한 의료 접근성을 위한 Mandhir의 탐구

Transformando a saúde: a busca de Mandhir por acesso médico acessível com IA

Преобразование здравоохранения: стремление Мандира к доступному медицинскому доступу с помощью ИИ

Sağlık Hizmetlerini Dönüştürmek: Mandhir'in Yapay Zeka ile Uygun Fiyatlı Tıbbi Erişim Arayışı

Chuyển đổi chăm sóc sức khỏe: Nhiệm vụ của Mandhir về khả năng tiếp cận y tế giá cả phải chăng với AI

医疗转型：Mandhir 寻求利用人工智能实现平价医疗服务

Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. 1. What is your company in 2–5 words? Digital product agency disrupting healthcare. 2. Why is now the time for your company to exist? There is no denying that the pace of innovation and technology has accelerated in the last decade. However, the healthcare industry has not evolved as quickly as other industries. It faces unique challenges, like a lack of agility, strict regulations, antiquated processes, and slow innovation. Although there are many people in the sector working to improve quality and accessibility, systemic problems and financial interests limit what can be achieved. Our strategy is to bring agility to this setting by using our knowledge of technology, user-centric approach, and in-depth knowledge of the business to create software that is strategically planned, scalable, and cleverly constructed. 3. What do you love about your team, and why are you the ones to solve this problem? When we first set out, we were quite excited about developing digital solutions that prioritized the needs and wants of users and delivered genuine value. By then, we collaborated with businesses in every sector of the economy, including real estate, retail, food, entertainment, agronomy, retail, and finance. Even though we've produced incredible products and gained a lot of knowledge from the process, we believed that focus and mission were still lacking. In 2020, during the pandemic, we made a strategic shift and decided to focus 100% on healthcare, allowing us to fully immerse ourselves in the industry, gain specialized knowledge, and, most importantly, have a shared, meaningful purpose. This change gave our entire team a compelling mission and a defined path. Who wouldn't find satisfaction in working on initiatives for a sector of the economy that ultimately saves lives? I guess that’s what I love most about the team and why we are the ones who solve problems. We’re a team of focused, knowledgeable, and creative people with a shared purpose: improving healthcare. 4. If you weren’t building your startup, what would you be doing? I would probably be building another startup. I’ve been an entrepreneur since I was a teenager, so I’m sure that if Light-it had not succeeded, I would be building something else. 5. At the moment, how do you measure success? What are your metrics? We work with OKRs (Objectives and Key Results), and they are measured quarterly. We have company-wide OKRs and OKRs per department. They help us stay aligned with the same goals quarter to quarter. 6. In a few sentences, what do you offer to whom? In broad terms, we partner with our clients to help them ideate, design, and develop healthcare digital products. The challenge we resolve is the lack of expertise in developing healthcare software with a product mindset and keeping strategy, budget, compliance, and user experience in mind.  There are hundreds of software development firms with no experience in healthcare, which can lead to short-term gains at the expense of long-term scalability and maintenance. We at Light-itadopt a unique strategy. We follow industry best practices, implementing scalable models across several platforms and providing end-to-end product development services with extensive user experience support and consulting. Since we have an agile approach, startups are our bread and butter. Nevertheless, our interactions with health systems have also been excellent. Our introduction of an agile vision and innovative mindset has been extremely effective in these circumstances, given their customary bureaucratic and slower ways. Finding a reliable partner is usually difficult for a healthcare organization, but finding someone who can offer both high-tech development and strategic advice is much more difficult. That's what makes us unique. 7. What’s most exciting about your traction to date? **Light-it'**s traction has been steadily growing year after year, an incredibly encouraging trend. Last year was particularly exciting for us as we forged partnerships with industry giants like ABInBev Foundation, the world's largest beer company, and Inviza, a groundbreaking RPM and DTX product for the US Air Force.  These collaborations are not only about business and transactions – they validate our position as leaders in our field and serve as a powerful encouragement to continue our hard work. 8. Where do you think your growth will be next year? We have been collaborating with startups, supporting them through the entire product development process, from launch to scaling. Now that we are collaborating with Health Systems, we plan to continue doing so because we have seen such a positive impact. We don't intend to expand the company too quickly because, as a team, we want to keep our boutique approach. We aim to keep our crew size between one hundred and two hundred. We always have new projects coming up. In my opinion, one of the biggest promises in health technology is generative AI. Puppeteeris a spinoff company we launched last year, and it is the result of the realization that 2024 was going to be the year of generative AI and that humanity will either have to accept it or perish. Our first round is presently being raised, and we have high hopes for it this year. Regarding our long-term future, Light-it's DNA will always embrace new technologies and pathways, so who knows what else might emerge! "You win, or you learn" strikes a chord with me. Every new endeavor, whether it succeeds or fails, adds to our development and resiliency. I'm committed to establishing a positive work environment that prioritizes curiosity, boldness, ownership, and dedication to ongoing development while also considering the well-being of our team. 9. Tell us about your first paying customer and revenue expectations over the next year. The first paying customers we had were family members and friends building e-commerces. Since then, we’ve worked with startups from pre-seed to Series D, with Fortune 500s and some of the biggest health systems in the world! In terms of revenue, we expect to keep growing at the rate of 50% per year. 10. What’s your biggest threat? We have all heard that AI will replace software engineers, so I guess that should be my biggest thread as a software development company owner. Still, I’m convinced that our team has minds that no AI can substitute: Empathy, strategy, and creativity are some of the skills needed in a product team that AI can't substitute. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. Welcome to HackerNoon ’s Writing Prompts! Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon The link for the template is HERE. 1. What is your company in 2–5 words? Digital product agency disrupting healthcare. 2. Why is now the time for your company to exist? There is no denying that the pace of innovation and technology has accelerated in the last decade. However, the healthcare industry has not evolved as quickly as other industries. It faces unique challenges, like a lack of agility, strict regulations, antiquated processes, and slow innovation. Although there are many people in the sector working to improve quality and accessibility, systemic problems and financial interests limit what can be achieved. Our strategy is to bring agility to this setting by using our knowledge of technology, user-centric approach, and in-depth knowledge of the business to create software that is strategically planned, scalable, and cleverly constructed. 3. What do you love about your team, and why are you the ones to solve this problem? When we first set out, we were quite excited about developing digital solutions that prioritized the needs and wants of users and delivered genuine value. By then, we collaborated with businesses in every sector of the economy, including real estate, retail, food, entertainment, agronomy, retail, and finance. Even though we've produced incredible products and gained a lot of knowledge from the process, we believed that focus and mission were still lacking. In 2020, during the pandemic, we made a strategic shift and decided to focus 100% on healthcare, allowing us to fully immerse ourselves in the industry, gain specialized knowledge, and, most importantly, have a shared, meaningful purpose. This change gave our entire team a compelling mission and a defined path. Who wouldn't find satisfaction in working on initiatives for a sector of the economy that ultimately saves lives? I guess that’s what I love most about the team and why we are the ones who solve problems. We’re a team of focused, knowledgeable, and creative people with a shared purpose: improving healthcare. 4. If you weren’t building your startup, what would you be doing? I would probably be building another startup. I’ve been an entrepreneur since I was a teenager, so I’m sure that if Light-it had not succeeded, I would be building something else. Light-it Light-it 5. At the moment, how do you measure success? What are your metrics? We work with OKRs (Objectives and Key Results), and they are measured quarterly. We have company-wide OKRs and OKRs per department. They help us stay aligned with the same goals quarter to quarter. 6. In a few sentences, what do you offer to whom? In broad terms, we partner with our clients to help them ideate, design, and develop healthcare digital products. The challenge we resolve is the lack of expertise in developing healthcare software with a product mindset and keeping strategy, budget, compliance, and user experience in mind.  There are hundreds of software development firms with no experience in healthcare, which can lead to short-term gains at the expense of long-term scalability and maintenance. We at Light-it adopt a unique strategy. We follow industry best practices, implementing scalable models across several platforms and providing end-to-end product development services with extensive user experience support and consulting. Light-it Since we have an agile approach, startups are our bread and butter. Nevertheless, our interactions with health systems have also been excellent. Our introduction of an agile vision and innovative mindset has been extremely effective in these circumstances, given their customary bureaucratic and slower ways. Finding a reliable partner is usually difficult for a healthcare organization, but finding someone who can offer both high-tech development and strategic advice is much more difficult. That's what makes us unique. 7. What’s most exciting about your traction to date? **Light-it'**s traction has been steadily growing year after year, an incredibly encouraging trend. Last year was particularly exciting for us as we forged partnerships with industry giants like ABInBev Foundation, the world's largest beer company, and Inviza, a groundbreaking RPM and DTX product for the US Air Force.  These collaborations are not only about business and transactions – they validate our position as leaders in our field and serve as a powerful encouragement to continue our hard work. 8. Where do you think your growth will be next year? We have been collaborating with startups, supporting them through the entire product development process, from launch to scaling. Now that we are collaborating with Health Systems, we plan to continue doing so because we have seen such a positive impact. We don't intend to expand the company too quickly because, as a team, we want to keep our boutique approach. We aim to keep our crew size between one hundred and two hundred. We always have new projects coming up. In my opinion, one of the biggest promises in health technology is generative AI. Puppeteer is a spinoff company we launched last year, and it is the result of the realization that 2024 was going to be the year of generative AI and that humanity will either have to accept it or perish. Our first round is presently being raised, and we have high hopes for it this year. Puppeteer Regarding our long-term future, Light-it's DNA will always embrace new technologies and pathways, so who knows what else might emerge! "You win, or you learn" strikes a chord with me. Every new endeavor, whether it succeeds or fails, adds to our development and resiliency. I'm committed to establishing a positive work environment that prioritizes curiosity, boldness, ownership, and dedication to ongoing development while also considering the well-being of our team. 9. Tell us about your first paying customer and revenue expectations over the next year. The first paying customers we had were family members and friends building e-commerces. Since then, we’ve worked with startups from pre-seed to Series D, with Fortune 500s and some of the biggest health systems in the world! In terms of revenue, we expect to keep growing at the rate of 50% per year. 10. What’s your biggest threat? We have all heard that AI will replace software engineers, so I guess that should be my biggest thread as a software development company owner. Still, I’m convinced that our team has minds that no AI can substitute: Empathy, strategy, and creativity are some of the skills needed in a product team that AI can't substitute. Would you like to take a stab at answering some of these questions? The link for the template is HERE. Would you like to take a stab at answering some of these questions? The link for the template is HERE. The link for the template is HERE.

Light-it: Empowering Healthcare through Purpose-Driven Digital Solutions

A successful community relies on the efforts of the community manager at every stage: During the challenging initial phase, the community manager overcomes the difficulty of acquiring customers, ensuring a stable customer base for the community. In the growth phase, the community manager addresses member activation issues, converting community members into product users and improving conversion rates. With stable customer acquisition and high conversion rates, the community enters a mature stage. However, three new challenges await the community manager: RRR. The 3R Challenges The concept of 3R originates from the AARRR model. In the initial and growth stages, community managers accomplish the first two tasks: Acquisition and Activation. The subsequent challenge is the 3R hurdles. Retention:Encouraging users to continue using the product or service and maintaining their loyalty.Revenue:Generating income through user behavior.Referral: Encouraging existing users to recommend the product or service to others, thereby achieving secondary propagation. As the community matures, some users may lose interest in the community or product, leading to a decrease in engagement. Additionally, as the community scales and complexity increases, conflicts may arise between new and existing members. These factors can all contribute to a decline in community user retention rates. This article will focus on the first key issue among these three challenges: community retention rates, aiming to provide insights for community managers. The Quantity and Duration of Consensus The quantity of consensus affects how many high-retention users a community can have, while the duration of consensus impacts how long users stay in the community. The formation of consensus relies on user expectations, which can be related to product features or pricing. For projects that have already issued tokens, user expectations, and outcomes typically manifest in asset prices. When users anticipate that project development will increase the value of their assets, they are more likely to confidently hold or trade these assets, thereby increasing the circulation and holding volume of the project, creating a positive cycle. For projects that have not yet issued tokens, user expectations are usually sustained through Token Generation Events (TGEs). Users anticipate future airdrops or token distributions, thus choosing to stay in the community to await future rewards. How to Trigger User Expectations? Any dynamic can serve as an influencing factor. Collaboration information, product updates, and the status of proposals, among others, all contribute. When user expectations are continuously met, consensus gradually forms, thereby strengthening user retention and engagement. Many community managers regularly post tweets within the community, encouraging community users to retweet and like for this reason. It's important to maximize the weight of information and increase its dissemination to subtly instill new expectations in users. This is a mechanical operational behavior, and many community managers use bots to reduce repetitive work: bots can automatically forward project information to the community. However, bots have a downside: they cannot stimulate user-sharing behavior. Why not turn this information into personalized tasks? This approach can not only expand the reach of information but also promote community interaction. TaskOn's Smart Task feature can automatically fetch updates from the project's official account and turn them into various tasks, expanding the dissemination of information and fostering expectations among more users. Continuous Interaction: Frequency Forms Habits Habits are formed based on frequency rather than duration. There's a very common phrase: "It takes 21 days to form a habit." However, it's actually the repeated behavior every day for 21 days that form the habit. To improve user retention rates, the question facing community managers becomes: How many interactions do users need to develop a habit? Successful projects in the past have focused on encouraging users to use their products every day: Centralized exchanges host continuous trading competitions.


L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems.


dApps constantly expand the reach of their projects. TaskOn, a community solution platform, recognizes the importance of frequency. In the community task template, there's an important task-setting option: Recurrence. When setting community tasks, community managers can specify the frequency at which community members should complete the tasks. Currently, there are options for Once/Daily/Weekly/Monthly. Once set, the tasks automatically refresh, saving community managers from repetitive operations. To improve retention rates, community managers need to string together enough user actions until these behaviors are firmly embedded in users' minds. Perfecting the Incentive System There's a potential disconnect between timely rewards and delayed rewards that needs attention. For projects that haven't issued tokens yet, a Token Generation Event (TGE) can be seen as a form of delayed reward. During the wait for rewards, some users may feel that their actions lack meaning due to the lack of timely feedback, leading them to give up on continued participation. One way to address this issue is to provide some immediate, visible incentives or feedback to encourage ongoing participation. For example, project teams can incentivize continued participation before the TGE by setting milestone rewards or periodic incentives. These rewards can take the form of virtual rewards, badges of honor, or elevation in community status to make users feel that their contributions are recognized and give them motivation to continue participating. Currently, there's a trend in the market: the introduction of a points system as an immediate incentive, where users' contributions are measured in points. I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. (How Points System Affects Web3 Community Development & Engagement) Healthy Growth Some rely on advertising for growth, others on third-party growth platforms, and some on Key Opinion Leaders (KOLs) for growth. While pouring a lot of money into advertising and marketing can quickly expand a community in the short term, once the investment stops, the number of community users can decrease rapidly. For mature communities, increasing user retention is crucial for healthy growth. Integrating the product into users' daily lives and making product usage a habit is key. A successful community relies on the efforts of the community manager at every stage: During the challenging initial phase, the community manager overcomes the difficulty of acquiring customers, ensuring a stable customer base for the community. In the growth phase, the community manager addresses member activation issues, converting community members into product users and improving conversion rates. With stable customer acquisition and high conversion rates, the community enters a mature stage. However, three new challenges await the community manager: RRR. The 3R Challenges The concept of 3R originates from the AARRR model. In the initial and growth stages, community managers accomplish the first two tasks: Acquisition and Activation. The subsequent challenge is the 3R hurdles. Retention: Encouraging users to continue using the product or service and maintaining their loyalty. Revenue: Generating income through user behavior. Referral: Encouraging existing users to recommend the product or service to others, thereby achieving secondary propagation. Retention: Revenue: Referral: As the community matures, some users may lose interest in the community or product, leading to a decrease in engagement. Additionally, as the community scales and complexity increases, conflicts may arise between new and existing members. These factors can all contribute to a decline in community user retention rates. This article will focus on the first key issue among these three challenges: community retention rates, aiming to provide insights for community managers. The Quantity and Duration of Consensus The quantity of consensus affects how many high-retention users a community can have, while the duration of consensus impacts how long users stay in the community. The formation of consensus relies on user expectations, which can be related to product features or pricing. For projects that have already issued tokens, user expectations, and outcomes typically manifest in asset prices. When users anticipate that project development will increase the value of their assets, they are more likely to confidently hold or trade these assets, thereby increasing the circulation and holding volume of the project, creating a positive cycle. For projects that have not yet issued tokens, user expectations are usually sustained through Token Generation Events (TGEs). Users anticipate future airdrops or token distributions, thus choosing to stay in the community to await future rewards. How to Trigger User Expectations? How to Trigger User Expectations? Any dynamic can serve as an influencing factor. Collaboration information, product updates, and the status of proposals, among others, all contribute. When user expectations are continuously met, consensus gradually forms, thereby strengthening user retention and engagement. Many community managers regularly post tweets within the community, encouraging community users to retweet and like for this reason. It's important to maximize the weight of information and increase its dissemination to subtly instill new expectations in users. This is a mechanical operational behavior, and many community managers use bots to reduce repetitive work: bots can automatically forward project information to the community. However, bots have a downside: they cannot stimulate user-sharing behavior. Why not turn this information into personalized tasks? This approach can not only expand the reach of information but also promote community interaction. TaskOn's Smart Task feature can automatically fetch updates from the project's official account and turn them into various tasks, expanding the dissemination of information and fostering expectations among more users. Continuous Interaction: Frequency Forms Habits Habits are formed based on frequency rather than duration. There's a very common phrase: "It takes 21 days to form a habit." However, it's actually the repeated behavior every day for 21 days that form the habit. To improve user retention rates, the question facing community managers becomes: How many interactions do users need to develop a habit? Successful projects in the past have focused on encouraging users to use their products every day: Centralized exchanges host continuous trading competitions. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. dApps constantly expand the reach of their projects. Centralized exchanges host continuous trading competitions. Centralized exchanges host continuous trading competitions. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. L1/L2 projects consistently support projects within their ecosystems, integrate more dApps, and encourage users to explore their ecosystems. dApps constantly expand the reach of their projects. dApps constantly expand the reach of their projects. TaskOn, a community solution platform, recognizes the importance of frequency. In the community task template, there's an important task-setting option: Recurrence. When setting community tasks, community managers can specify the frequency at which community members should complete the tasks. Currently, there are options for Once/Daily/Weekly/Monthly. Once set, the tasks automatically refresh, saving community managers from repetitive operations. To improve retention rates, community managers need to string together enough user actions until these behaviors are firmly embedded in users' minds. Perfecting the Incentive System There's a potential disconnect between timely rewards and delayed rewards that needs attention. For projects that haven't issued tokens yet, a Token Generation Event (TGE) can be seen as a form of delayed reward. During the wait for rewards, some users may feel that their actions lack meaning due to the lack of timely feedback, leading them to give up on continued participation. One way to address this issue is to provide some immediate, visible incentives or feedback to encourage ongoing participation. For example, project teams can incentivize continued participation before the TGE by setting milestone rewards or periodic incentives. These rewards can take the form of virtual rewards, badges of honor, or elevation in community status to make users feel that their contributions are recognized and give them motivation to continue participating. Currently, there's a trend in the market: the introduction of a points system as an immediate incentive, where users' contributions are measured in points. I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. (How Points System Affects Web3 Community Development & Engagement) I once wrote an article on points systems, detailing the benefits and how to quickly set up such a system. You can refer to it for more information. ( How Points System Affects Web3 Community Development & Engagement ) How Points System Affects Web3 Community Development & Engagement How Points System Affects Web3 Community Development & Engagement ) Healthy Growth Some rely on advertising for growth, others on third-party growth platforms, and some on Key Opinion Leaders (KOLs) for growth. While pouring a lot of money into advertising and marketing can quickly expand a community in the short term, once the investment stops, the number of community users can decrease rapidly. For mature communities, increasing user retention is crucial for healthy growth. Integrating the product into users' daily lives and making product usage a habit is key.

Building a Successful Web3 Community: Healthy Growth

Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study, the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations, responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust. Innovations in artificial intelligence (AI) have revolutionized the field of medicine in numerous ways. AI technologies are emerging as a powerful tool to transform various aspects of healthcare, such as diagnosis, research, patient care, treatment, and clinical outcomes. Advanced AI models can be used to analyze complex medical data and generate insights based on patterns in training data, accelerating the decision-making process with accuracy. However, akin to any other innovative technology, it has its share of risks and challenges. As per an IBM study , the global average cost resulting from a data breach was $4.45 million in 2023. The study estimated the average cost of a healthcare data breach was the highest among industries, at $10.93 million. IBM study Compliance with the Health Insurance Portability and Accountability Act (HIPAA) is a critical consideration when implementing AI into healthcare services. Here, we will discuss some of the privacy and security risks posed by AI systems in healthcare, along with possible mitigation strategies for responsible and ethical implementation under HIPAA regulations. What Is HIPAA? HIPAA, or the Health Insurance Portability and Accountability Act, is a critical federal health privacy law in the US that sets the standard for protecting the health information of patients. The law focuses on the Privacy Rule that administers the protection of individually identifiable health information, referred to as PHI, generated by healthcare providers, insurance companies, and other entities. Hence, entities that generate health information digitally must comply with HIPAA rules. Additionally, business partners performing PHI-related activities on behalf of a covered entity, such as administrative, financial, management, and legal tasks, are also subject to HIPAA regulations. HIPAA However, de-identified health information that can’t be used to identify an individual is not individually identifiable health information, and there is no restriction on the use or disclosure of such data under HIPAA. Entities Covered by HIPAA HIPAA compliance applies only to certain types of organizations that generate protected health information (PHI) electronically or entities that perform tasks involving PHI on their behalf. To understand the impact of HIPAA on the use of AI, it is essential to recognize HIPAA-covered entities. Whether HIPAA would be applied to an entity is not determined based on someone's professional role but rather on activities performed by them. When healthcare providers, insurance companies, and other organizations carry out transactions that require HIPAA compliance, they are considered covered entities under HIPAA regulations. When incorporating AI in their healthcare operations, these entities, alongside their business associates, must follow HIPAA rules. Transactions that involve only direct payments and don’t include handling electronic health records, like billing insurance, would not be subject to HIPAA regulations. HIPAA and AI in Healthcare: Ensuring Patient Privacy The HIPAA Privacy Rule ensures the protection of patient information, especially when AI technology is integrated into healthcare services, by governing the use and disclosure of protected health information (PHI). There are specific situations where disclosing health information is not prohibited under HIPAA, such as for payment, treatment, healthcare operations, and certain research conditions. When incorporating AI in healthcare research or operations , responsible handling of PHI becomes critical. The process involves de-identifying patient data and receiving patient consent for the use of data, strengthening privacy and trust. However, the law incorporates the concept of a ‘limited data set’, allowing access to certain non-direct identifiers, such as ZIP codes or service dates, for research purposes, provided the data agreement adheres to HIPAA compliance. Use of direct identifiers like names or social security numbers is strictly prohibited. AI in healthcare research or operations The HIPAA safe harbor method outlines 18 specific identifiers, such as name, address, social security numbers, biometric identifiers, and many more, that must be removed from data. After de-identification, the remaining information should not be capable of re-identifying the individuals, even in combination with other data. The de-identification process needs to be robust enough, especially when the data is used for AI purposes, as AI algorithms can potentially analyze data and discover hidden patterns for re-identification. Staying HIPAA Compliant The rapid advancements in artificial intelligence within the healthcare industry present new challenges to stay compliant with HIPAA regulations. Healthcare organizations need to adapt to the changes by working closely with AI developers to understand the algorithms of AI applications and ensure they meet HIPAA privacy standards. This necessitates regular updates to policies and procedures along with the implementation of robust security measures to effectively ensure compliance and manage potential threats. Additionally, educating healthcare professionals about the implications of AI for data privacy is critically important. This empowers them to use AI tools responsibly and obtain consent for data use. Conclusions Implementing AI in healthcare offers immense potential to transform various aspects, from diagnosis and treatment planning to patient care and research, benefiting both patients and healthcare providers. However, integrating data-intensive AI tools poses serious privacy and security challenges. To proactively address these challenges, healthcare organizations must implement safe and effective data protection measures that adhere to HIPAA compliance. Healthcare organizations need to make efforts to keep abreast of the latest developments in AI and their potential privacy implications. Essential strategies such as risk assessment protocols, robust governance frameworks, and compliance monitoring can help prevent breaches of PHI, mitigate risks, and maintain public trust.

Navigating HIPAA Compliance in the Age of AI: Privacy and Security Considerations in Healthcare

How Did Mahesh Kambala Contribute to the Fight Against COVID-19? During the global COVID-19 pandemic, the healthcare industry faced unprecedented challenges that highlighted the importance of healthcare analytics in addressing data gaps and managing resources. Prioritizing and integrating analytics with healthcare objectives was crucial for saving lives, addressing weaknesses, and improving preparedness for future disruptions. With more than 88% of healthcare experts acknowledging a heightened requirement for analytics, the emergency underscored the value of data in handling public health crises. Mahesh Kambala,  with 15 years of professional experience in information technology, has honed his data analytics, ETL, machine learning, and AI skills. He has held significant positions in renowned organizations spanning diverse industries such as healthcare, banking, telecom, and retail. Kambala's knowledge in healthcare analytics proved invaluable while developing tools like the "COVID Dashboards." These dashboards empowered doctors and healthcare providers by equipping them with real-time patient data, enabling them to swiftly identify and address COVID-19 cases. His contributions during the pandemic underscore the vital role of technology and data analytics in healthcare, particularly in times of crisis. This article will explore the significance of analytics in healthcare amid the COVID-19 epidemic and showcase Mahesh Kambala's impact on the healthcare industry. The Need for Next-Gen Healthcare Analytics The COVID-19 pandemic has shown how critical healthcare data and analytics are in handling a global health crisis. It has revealed areas for improvement in the healthcare industry's preparedness and system integration. At the start of the pandemic, it became clear that there was a significant lack of reliable data and adequate analytics capabilities, which are crucial for understanding the spread and impact of the disease. Healthcare organizations faced operational overhauls to address missing data, revealing inefficiencies. The financial instability and well-being of frontline staff, who were dealing with fatigue and burnout engaging with patients, highlighted the importance of a robust analytics infrastructure for effective resource management even more. The next generation of healthcare analytics, which utilizes advanced technologies like AI and Big Data Analytics (BDA), presents an answer to these challenges. By incorporating sophisticated algorithms and machine learning, next-gen analytics can improve the accuracy and speed of data analysis, allowing for real-time tracking of disease spread and identification of at-risk populations. These analytics can streamline data collection and integration, ensuring that healthcare providers have access to timely and reliable information for decision-making. Additionally, predictive analytics can forecast outbreaks and inform proactive strategies for resource allocation, potentially alleviating financial strains. Mahesh Kambalan's Contribution to Next-Gen Healthcare Analytics Mahesh Kambala has introduced solutions to address the challenges brought upon the healthcare industry during the COVID-19 pandemic. His leadership in developing "COVID Dashboards," specifically designed for healthcare providers, has improved healthcare analytics by providing real-time insights into patients' data affected by COVID-19. Kambala's proactive approach and expertise have helped make informed care decisions, representing a significant leap forward in utilizing technology to manage health crises effectively. His work demonstrates his dedication to harnessing technology for the betterment of society and emphasizes the vital importance of timely and accurate data in crisis management. The COVID Dashboards boast several vital features, including the ability to visualize real-time data, identify patient risks based on their medical history, and enable healthcare providers to deliver necessary care promptly. The advantages of these dashboards are manifold. Healthcare providers can now swiftly detect and respond to emerging COVID-19 cases, ensuring targeted interventions and optimizing the allocation of medical resources. By enabling early detection and intervention, these dashboards have significantly enhanced patient care, reducing hospitalizations and improving operational efficiency within healthcare facilities. For instance, Kambala's innovative tool facilitated the quick identification of at-risk individuals, ensuring timely care and potentially saving lives. The tool is a testament to Kambala's approach to utilizing technology to serve humanity, showcasing how advanced healthcare analytics can improve patient care and operational dynamics in healthcare settings during and beyond the pandemic. The Future of Next-Gen Healthcare Analytics In the fight against COVID-19, Mahesh Kambala has made a mark in healthcare analytics by developing "COVID Dashboards." These innovative tools have harnessed the power of artificial intelligence (AI) and machine learning (ML) to provide real-time insights into the pandemic's spread. These dashboards have improved patient care and operational efficiency, enabling healthcare providers to make informed decisions swiftly. Through Kambala's tireless efforts and the utilization of cutting-edge analytics tools like the "COVID Dashboards," the ability of technology to serve humanity, particularly during crises, becomes increasingly apparent. His work is a powerful testament to the tangible difference advanced analytics can make in the healthcare industry. -Written by Belle Villaluz This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author How Did Mahesh Kambala Contribute to the Fight Against COVID-19? During the global COVID-19 pandemic, the healthcare industry faced unprecedented challenges that highlighted the importance of healthcare analytics in addressing data gaps and managing resources. Prioritizing and integrating analytics with healthcare objectives was crucial for saving lives, addressing weaknesses, and improving preparedness for future disruptions. With more than 88% of healthcare experts acknowledging a heightened requirement for analytics, the emergency underscored the value of data in handling public health crises. 88% of healthcare experts 88% of healthcare experts Mahesh Kambala ,  with 15 years of professional experience in information technology, has honed his data analytics, ETL, machine learning, and AI skills. He has held significant positions in renowned organizations spanning diverse industries such as healthcare, banking, telecom, and retail. Kambala's knowledge in healthcare analytics proved invaluable while developing tools like the "COVID Dashboards." These dashboards empowered doctors and healthcare providers by equipping them with real-time patient data, enabling them to swiftly identify and address COVID-19 cases. His contributions during the pandemic underscore the vital role of technology and data analytics in healthcare, particularly in times of crisis. Mahesh Kambala Mahesh Kambala This article will explore the significance of analytics in healthcare amid the COVID-19 epidemic and showcase Mahesh Kambala's impact on the healthcare industry. The Need for Next-Gen Healthcare Analytics The COVID-19 pandemic has shown how critical healthcare data and analytics are in handling a global health crisis. It has revealed areas for improvement in the healthcare industry's preparedness and system integration. At the start of the pandemic, it became clear that there was a significant lack of reliable data and adequate analytics capabilities, which are crucial for understanding the spread and impact of the disease. Healthcare organizations faced operational overhauls to address missing data, revealing inefficiencies. The financial instability and well-being of frontline staff, who were dealing with fatigue and burnout engaging with patients, highlighted the importance of a robust analytics infrastructure for effective resource management even more. The next generation of healthcare analytics, which utilizes advanced technologies like AI and Big Data Analytics (BDA) , presents an answer to these challenges. By incorporating sophisticated algorithms and machine learning, next-gen analytics can improve the accuracy and speed of data analysis, allowing for real-time tracking of disease spread and identification of at-risk populations. These analytics can streamline data collection and integration, ensuring that healthcare providers have access to timely and reliable information for decision-making. Additionally, predictive analytics can forecast outbreaks and inform proactive strategies for resource allocation, potentially alleviating financial strains. AI and Big Data Analytics (BDA) AI and Big Data Analytics (BDA) Mahesh Kambalan's Contribution to Next-Gen Healthcare Analytics Mahesh Kambala has introduced solutions to address the challenges brought upon the healthcare industry during the COVID-19 pandemic. His leadership in developing "COVID Dashboards," specifically designed for healthcare providers, has improved healthcare analytics by providing real-time insights into patients' data affected by COVID-19. Kambala's proactive approach and expertise have helped make informed care decisions, representing a significant leap forward in utilizing technology to manage health crises effectively. His work demonstrates his dedication to harnessing technology for the betterment of society and emphasizes the vital importance of timely and accurate data in crisis management. The COVID Dashboards boast several vital features, including the ability to visualize real-time data, identify patient risks based on their medical history, and enable healthcare providers to deliver necessary care promptly. The advantages of these dashboards are manifold. Healthcare providers can now swiftly detect and respond to emerging COVID-19 cases, ensuring targeted interventions and optimizing the allocation of medical resources. By enabling early detection and intervention, these dashboards have significantly enhanced patient care, reducing hospitalizations and improving operational efficiency within healthcare facilities. For instance, Kambala's innovative tool facilitated the quick identification of at-risk individuals, ensuring timely care and potentially saving lives. The tool is a testament to Kambala's approach to utilizing technology to serve humanity, showcasing how advanced healthcare analytics can improve patient care and operational dynamics in healthcare settings during and beyond the pandemic. The Future of Next-Gen Healthcare Analytics In the fight against COVID-19, Mahesh Kambala has made a mark in healthcare analytics by developing "COVID Dashboards." These innovative tools have harnessed the power of artificial intelligence (AI) and machine learning (ML) to provide real-time insights into the pandemic's spread. These dashboards have improved patient care and operational efficiency, enabling healthcare providers to make informed decisions swiftly. Through Kambala's tireless efforts and the utilization of cutting-edge analytics tools like the "COVID Dashboards," the ability of technology to serve humanity, particularly during crises, becomes increasingly apparent. His work is a powerful testament to the tangible difference advanced analytics can make in the healthcare industry. -Written by Belle Villaluz -Written by Belle Villaluz -Written by Belle Villaluz This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author This story was distributed by Jon Stojan Media under HackerNoon’s Brand As An Author Program. Learn more about the program here: https://business.hackernoon.com/brand-as-author https://business.hackernoon.com/brand-as-author

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