Heartbeat Health is a venture-funded health tech startup in New York City

Heartbeat Health

The advent of machine learning (ML) and artificial intelligence (AI) in the realm of healthcare has been akin to a new dawn. Amidst their vast array of applications, one transformative change stands tall: the reinvention of healthcare reimbursement processes. With a synergetic convergence of ML, AI, and cloud computing, healthcare's financial landscape is undergoing a seismic shift, making remote health a reality for millions who once found it out of reach. The Automation Revolution in Reimbursement Traditionally, the reimbursement process within healthcare has been a byzantine affair. Codes to be entered, forms to be filled, and the ever-looming threat of human error meant that the journey from services rendered to payment secured was arduous. Enter ML and AI—technologies that thrive on complexity. By analyzing patterns within vast data troves, algorithms now predict, with increasing accuracy, the correct coding for procedures, thereby reducing the administrative load. Research published by the American Medical Association indicates that administrative tasks are one of the leading causes of doctor burnout, with insurance-related tasks particularly culpable. AI is a beacon of hope here, with its capacity to streamline these processes, thus freeing healthcare professionals to focus on patient care. Precision and Speed: A Quantitative Leap Error rates in manual claims processing are a notorious challenge, with the American Academy of Family Physicians highlighting that the average error rate for medical billing is around 7-10%. This is not just a statistic; it translates to delays in payments, increased costs, and financial uncertainty for healthcare providers. The introduction of ML and AI into cloud-based systems has dramatically altered this landscape. ML algorithms learn from every claim, becoming more adept over time at flagging discrepancies and potential errors. They can process and analyze claims at a speed no human team can match, reducing processing times from weeks to mere days or hours, ensuring that the financial heartbeat of healthcare keeps a steady and reliable tempo. Cost Savings and Fraud Minimization Financial fraud in healthcare is a multi-billion-dollar problem. According to the National Health Care Anti-Fraud Association, healthcare fraud costs the United States about $68 billion annually. AI-driven systems are instrumental in identifying and preventing fraudulent claims. Their ability to detect anomalous patterns and predict potential fraud has saved the healthcare industry significant amounts by catching false claims before they are paid out. This not only prevents losses but also acts as a deterrent against the perpetration of fraud. Bridging the Remote Health Divide One of the most poignant narratives in the rise of end-to-end cloud solutions in healthcare is their impact on remote health. The World Health Organization underscores the importance of telehealth, particularly in remote areas, where access to healthcare is often limited by geographical barriers. AI and ML have been pivotal in this context. By efficiently managing the backend financial transactions, they have enabled healthcare providers to scale their services remotely. No longer do distance and isolation preclude patients from receiving expert care. Virtual consultations, AI-assisted diagnostics, and cloud-managed patient data are not just conveniences but lifelines for those in remote regions. The Future Is Here: An Era of Unbounded Potential The journey of integrating ML, AI, and cloud computing into healthcare's financial workflows is an ongoing one, but its trajectory points unmistakably towards a horizon of hope and improvement. As these technologies mature and their integration becomes more seamless, we are looking at a future where the entire healthcare experience — from diagnosis to treatment, from billing to follow-up — is enhanced. Remote health, once shackled by the chains of cumbersome administrative processes, is now flourishing under the aegis of these smart solutions. We are witnessing a democratization of healthcare, where quality services are not a privilege but a given, regardless of a patient's location. The power of ML and AI in healthcare is not merely in their technological prowess but in their profound impact on humanity. They stand not as cold, inanimate algorithms but as the beating heart of a system striving for equity, efficiency, and excellence in patient care. As these technologies continue to evolve, the potential for further innovation is boundless. Imagine a future where AI not only processes reimbursements but also predicts patient outcomes and personalizes treatment plans. Picture a healthcare system unburdened by financial inefficiencies, where every resource is optimized for patient wellness, and where remote health is not a distant possibility but a present reality for all. In the final analysis, ML and AI in the cloud are not just reshaping healthcare's financial backbone; they are redefining the very essence of how care is provided and managed across the globe. This revolution is not just about technology; it's about people, health, and the immeasurable value of both.

Cloud Empowerment: How AI and ML Are Reshaping Healthcare's Financial Backbone

Scaling Without Imploding

Data is becoming increasingly important in almost every conceivable field and area. From business and healthcare to law enforcement and sports, data is central to their operations. It’s not enough to simply collect information, however. Instead, you need to make good use of it, and this is where comes into play. Anomaly detection is one of the most interesting applications of data science, and in this article, I will provide a brief overview of what it is, and how it can be used. data science What Is Anomaly Detection? involves identifying the differences, deviations, and exceptions from the norm in a dataset. It’s sometimes referred to as outlier detection (i.e., looking at a dataset to identify any outlying or unusual datapoints, data groups, or activity). Anomaly detection For example, credit card companies collect data on everything we purchase, including the amount of money we spend, where we spend it, what we spend it on, how frequently we make purchases, and more. Anomaly detection makes this data not only useful but powerful. This is because anomaly detection algorithms analyse all the data above to identify fraudulent credit card activity within seconds of a transaction taking place. What Are the Applications of Anomaly Detection? There are many applications for anomaly detection: – Network intrusion is a prominent example. One way an anomaly detection algorithm would do this would be by monitoring traffic to establish normal levels and then identifying anything that falls outside this norm. Cybersecurity – This was mentioned above with the credit card example. Fraud detection – To get a better understanding of user activity and engagement on social media as well as other forms of digital marketing and advertising, anomaly detection might identify that searches for a particular topic spike at certain times of the year, enabling advertisers and marketers to allocate their budgets accordingly. Social media monitoring – technologies are a good example in this instance. A digital twin is an exact digital replica of a real-world machine, process, or piece of equipment. Anomaly detection can identify deviations in performance in the digital twin that are early warning signals of an impending failure in the real-world machine. This makes it possible to schedule maintenance of the machine before the failure occurs, reducing downtime and improving productivity. Machine performance Digital twin – This is everything from identifying abnormal patterns or occurrences in an individual (such as an irregular heartbeat) to identifying health-related anomalies in groups of people, such as the unusual spread of a disease over a short period of time in a particular geographical area. Medical monitoring Of course, there are many more applications of anomaly detection than those listed above. The crucial point is the fact that anomaly detection is becoming increasingly important and that it enables data to be used in ways it never was before. 3 Different Types of Anomalies in Anomaly Detection Not all anomalies are equal. In fact, they can be split into three broad categories: Point anomalies, Collective anomalies, Contextual anomalies Let’s look at each in more detail. 1. Point Anomalies A point anomaly is where a single datapoint stands out from the expected pattern, range, or norm. In other words, the datapoint is unexpected. Examples of point anomalies. We can again turn to credit card spending as an example. A point anomaly would be a single, high-value, and unusual spend on a credit card. For example, normal spending on a credit card might be small to medium-sized amounts, typically within the same geographical area and usually to purchase a similar group of products. A point anomaly would be if that credit card was then used to make a single high-value purchase for an item not previously purchased and purchased in a location where the credit card has not been used before. This point anomaly could indicate fraudulent activity. has good support for point anomaly detection algorithms, such as . Scikit-learn LOF 2. Collective Anomalies A collective anomaly occurs where single datapoints looked at in isolation appear normal. When you look at a group of these datapoints, however, unexpected patterns, behaviours, or results become clear. An irregular heart beat is an example of a collective anomaly. Those unexpected occurrences could be, for example, events occurring in an order or in a combination that is unexpected. We can use potential credit card fraud as our example again. This would occur where multiple purchases appear to fit within normal spending activity when looked at individually. When you look at these purchases as a group, however, unusual patterns and behaviour can appear. 3. Contextual Anomalies Instead of looking at specific datapoints or groups of data, an algorithm looking for contextual anomalies will be interested in unexpected results that come from what appears to be normal activity. The crucial element here is context: Are the results out of context? Example of contextual anomaly detection using the Twitter AnomalyDetection package in R A good example in this instance is a network intrusion attempt. An algorithm looking for contextual anomalies will have a baseline of activity that provides it with normal parameters. This could, for example, show the expected levels of traffic accessing the network at various times of the day. Traffic might be at its lowest in the early hours of the morning. Therefore, a spike in traffic at 3 a.m. is a contextual anomaly that warrants further action and/or investigation as it could indicate a network intrusion attempt. In other words, accessing the network in itself was a completely normal event. Having such high levels of traffic at three in the morning, however, was unusual; it was an anomaly. Anomaly detection is one of the most interesting applications of data science. There are many applications of anomaly detection like fraud detection, social media monitoring, and more. Anomaly detection makes this data not only useful but powerful.

3 Types of Anomalies in Anomaly Detection

Unlike many others, I ended up in the blockchain thanks to my passion for writing and creating online content. My writer friends recommended Steemit, I tried it and started blogging there. Then I ended up with crypto and no clue what it was all about. That in its turn led me to fall down the rabbit hole. Since then I read and learned a lot and have even been working in the blockchain field since then… But this post is not about that. It’s not about the technology. It’s not about the cryptocurrency. It’s not even about how the two that I mentioned are gonna change the world in the future. No. This post is about the human aspects of the story. It’s about what’s been extremely important since day 1. And that’s the people and the communities they form. Communities have always been at the epicenter of the blockchain and crypto. The small group of passionate rebels were the ones who set things up. We all were about creating a better, safer and decentralized space, where everyone will have a voice, no one will be dictating rules and where we would get back the control and freedom. Steemit, like many others in the space, was always community-centric. Steemit community has always been strong and it represented the platform’s heartbeat. It was a healthy community with people who were building true friendships with each other. Place where like-minded people could speak up and get support, rant, and be understood. The community that rewarded the good quality content creators and protecting it from all those toxic and crappy stuff. Steemit had become a place where many people felt home… And then… we all know what happened… And I get it. At the end of the day for someone, it was a business. And when that’s the case, it’s just a matter of zero-s. So I am not blaming anyone. What I am trying to do instead is - illustrate how powerful can communities be. What happened next proved that one can buy the company but one can never buy its community, because communities can not be bought. What many don’t understand is that the love of the community is earned… but it’s not earned only once. You need to nurture it, you need to take care of your relationship with your community… because it’s not a one-time win or a one-way street. When things go wrong, the community will hate you with the same strength they loved you before. What’s the beauty behind the Steemit community story is that they realized that they have reached the point where the platform does not define them anymore. They’ve got each other, so if their “home” no longer feels like home - just like with any relationship that no longer works, what you do is you leave. Now they have a new place. Their new home looks even cozier than their old one. What I like most about the whole story is that they had the last say. I can’t predict the future of Steemit or Hive. I don’t know how things are gonna be developed. But what I know for sure is that any company, brand, or online platform that does not have a loyal community behind it is doomed to fail. So don’t underestimate the power of the community and don’t treat them as numbers. Behind each number, there is a real human being - please don’t forget that.

Communities Rule: What Hive vs Steemit is All About

Welcome to the exciting world of Analytics! As developers, we all know the value of data-driven decision-making, but did you know that Unity Analytics can unveil surprising insights that can supercharge your game's success? game development with Unity In this article, I'll explore six unexpected and game-changing revelations that Unity Analytics can provide. Retention and Churn Analysis are like a heartbeat monitor for your game's health. They help you understand how many players stick around after the initial excitement wears off. Retention rates With Unity Analytics, you can easily track retention by monitoring the percentage of players who return to your game over specific time intervals. Imagine this: you release a new level, and players flock to check it out. But how many of them return the next day? The week after? Tracking retention helps you gauge the staying power of your content and identify patterns in player behavior. Churn - the dreaded enemy of game developers. It's when players bid farewell to your game and never look back. But why does it happen? can provide invaluable insights into the reasons behind churn. Unity Analytics Maybe your game becomes too challenging too quickly, or perhaps players get stuck on a certain level. By studying churn patterns, you can pinpoint pain points and address them with updates or better tutorials. Understanding why players leave is the key to keeping them hooked. Improvement is the name of the game. Armed with retention and churn data, you can tweak game mechanics, enhance onboarding experiences, and roll out fresh content that aligns with player preferences. And don't forget the power of personalized engagement – reaching out to dormant players might just rekindle their interest. In-App Purchase Patterns Imagine wielding a magical crystal ball that reveals how players spend within your game. Unity Analytics empowers us to gain invaluable insights into player behavior and hard-earned cash investments. Discovering Player Spending Habits: Dive deep into data to gauge in-app purchase effectiveness. Understand the percentage of paying users to optimize strategies. Conversion Rates: Unveil sought-after items to boost visibility and sales. Promote what players crave. Popular Purchases: Explore player spending patterns – frequent splurges or steady small purchases? Tailor offerings for maximum revenue. Tailoring to Spending Habits: Enhance gaming experiences through attractive in-app purchases. Value-driven offerings lead to more purchases. Player-Friendly Value: Encourage urgency with transparent timed deals. Clearly state benefits and durations. Timed and Limited Offers: Experiment with pricing and offers, analyze results, and iterate. Find the sweet spot for player satisfaction. Test, Analyze, Repeat: Our goal as developers is to create captivating games. drive continuous improvement and new experiences." Captivate and Entertain: In-app purchases Level up your game development with Unity Analytics and make every purchase a magical experience. Level Difficulty Assessment When crafting game levels, we want players to stay immersed in the experience and overcome challenges at a steady pace. Level Difficulty Assessment allows us to analyze how players interact with our levels, track their progress, and identify any potential roadblocks that hinder completion. By carefully examining player data, we can pinpoint sections that are causing frustration, leading to high dropout rates. Whether it's a tricky jump, an overly difficult enemy encounter, or a confusing puzzle, understanding these pain points is invaluable for improving level design. Balancing the difficulty of game levels is like fine-tuning an instrument. Too easy, and players get bored; too hard, and they might abandon the game out of frustration. Level Difficulty Assessment enables us to strike that sweet spot where players feel challenged, yet the satisfaction of overcoming obstacles keeps them coming back for more. Through this process, we can adjust enemy AI, tweak platforming elements, or refine puzzles to ensure a smooth and enjoyable progression curve. It's all about providing players with a sense of accomplishment while maintaining a healthy dose of challenge. Time-Based Patterns Crafting an exciting in-game event is just the beginning. Don't let it go to waste due to poor timing. With time-based patterns from Unity Analytics, you can identify peak playing hours and plan your events, challenges, and rewards strategically. Imagine dynamically fine-tuning your game, delivering fresh content precisely when players are most active. Unity Analytics lets you track player activity trends, empowering you to align updates and events strategically for maximum engagement. Weekdays might call for smaller, engaging events, while weekends and holidays are perfect for captivating your audience with larger events and exciting features. Seize the opportunity to create positive interactions and keep players eagerly anticipating what's next in your game. Effectiveness of Marketing Campaigns Launching your game is just the beginning; marketing efforts are in full swing! But, how can you be sure that all those clicks and downloads translate into actual players? Enter user acquisition, the breadcrumb trail leading to your game's doorstep. It reveals how users discover your game via channels like social media, ads, or app stores, helping gauge the success of marketing strategies. Conversion rates are your hidden weapon in this marketing adventure. They disclose the percentage of users who not only discovered your game but also engaged and played it. This metric paints a clear picture of your game's appeal to potential players. "Data is gold," especially in marketing. Evaluating your marketing efforts unveils what's effective and what's not. Imagine running two ad campaigns, one on social media and the other through in-app ads. By comparing user acquisition and conversion rates, you pinpoint which approach resonates best with your target audience. But there's more! Dive into the data ocean for meaningful insights. Understanding user behavior allows you to optimize marketing strategies and tailor your game to specific audience preferences. Social Sharing and Virality As game developers, we invest countless hours crafting unique and engaging gameplay experiences. Yet, if nobody knows about our masterpiece, its potential remains untapped. Enter social sharing - the game-changer. By integrating social sharing features into your game, you empower players to effortlessly spread the word. Imagine this: a player achieves an epic high score or unlocks a rare achievement in your game. They'd naturally want to flaunt their success to friends and followers. Cleverly placing social sharing buttons at key moments taps into this human instinct, spreading achievements, victories, and memorable gameplay. Watch your game's presence grow organically in the social media landscape. Virality is the holy grail of game marketing – rocket fuel for your game's success. When a game goes viral, it ignites like wildfire, drawing an ever-expanding audience without expensive campaigns. The secret lies in shareable content! Funny memes, jaw-dropping gameplay clips, intriguing teasers – bite-sized pieces shared across social platforms generate curiosity and interest. But here's the real magic: the ripple effect. Players share your content, and their friends do the same. The cycle continues, and soon, your game is the talk of the town. Wrapping Up Unity Analytics offers game developers a treasure trove of surprising insights. By diving into the data provided, developers can uncover valuable information about player behavior, preferences, and engagement patterns. These unexpected revelations pave the way for informed decision-making, optimized gameplay, and successful marketing strategies. Embracing Unity Analytics unlocks the potential to create more captivating and enjoyable gaming experiences, taking game development to new heights of greatness.

6 Surprising Insights You Can Gain From Unity Analytics

In this somewhat satirical bonus post, I want to cover office/tech/workplace jargon and terms. Most of these were completely new to me, and too often now I find myself using them fluently, and for that, I hate myself. Junior Dev Diaries The Terms — This means understand it after looking at it for a while. Example Across It “Oh, I’m not across that new work that Jane did on the project yet”. — Have a conversation. “ Catch Up I’d like to catch up next week to discuss the project” — Have a conversation. Check In “I’d like to check in next week to discuss the project” — Have a conversation, but you want to be a creep about it. Touch Base “I’d like to touch base next week to discuss the project” — You didn’t get your way, sometimes with a reason. Push Back “They pushed back on that approach, they wanted us to find a cheaper way”. — Tell you you’re wrong, maybe with reasons. Usually for your own benefit or your arguments benefit (you may not have explained it right). Challenge It/You “I might just challenge you on that, what if we used a 3rd party solution instead?” — The work to do, and what you don’t need to do. “ Scope Oh that’s out of scope.” — The that are outputs of the work, such as code or a document. Deliverable things “Can you add the documentation to the project deliverables? Ta!” — The positive impact you add to the company or product. Value Add “Yeah, but what’s the value add here? I dont know if the new client will benefit from that!?” — Work together on something, because two people will make it go faster…right? Pair “I’ll pair with you on that document”. — Stressing out about something, but a bit more calmly. Something might be bugging them and it’s bugging them that it’s not bugging you. Conscious Of “I’m conscious of the time left on the project and the remaining deliverables do be done. How can I help?” — Forming a coherent argument for something that isn’t trivial. Make a Case “That idea sounds great, Sam. I’ll get you to make a case for it at the next meeting.” — When something is being released to the public. Nothing to do with a biological heartbeat. Go Live “The new health and safety legislation is going live next week — you’ll see press about it…” — The stuff we need to figure out but we’re trying to stay positive about it. “Brad, I’m keen to hear about the challenges a two new team members will bring!” Challenges — False hope of benefits by the under-informed. Opportunities “I’m really excited by the opportunities Artificial Intelligence will bring to our company” — Sentance sugar for that makes you sound so much smarter. Often combined with previous jargon. In This Space “here” “I’m really excited about cryptocurrency. There’s a lot of opportunity in this space.” – Failing on a commitment but trying to get away with it. Somewhat of a “basket of deliverables” metaphor I think? Fall out “I’m afraid that the video feature will probably fall out of this sprint.” – Failing on a commitment but trying to get away with it. Descoping “I’m afraid that the video feature will probably be descoped for this task.” So that’s a few terms to start learning and using around the office. You can mix and mash them too. Get creative. How many can you jam into a single phrase? Remember, . Think about it — If you’re not both translating in and out of this corporate wankery — are you working? the smarter you sound, the better you are really Better living everyone. Thanks to my good friends in my life who I have a bit of a laugh about this with every now and then. They helped me form this most wonderful list. ❤

Your Ultimate Guide to Corporate Jargon

If you have worked with software systems long enough you have surely worked with crons. Cron is a time-based task scheduler in UNIX-like operating systems. We use to run some task/script periodically, for example, every day at 9:30 AM. Setting up and running crons has changed in the past years from a crontab command to now running it on Kubernetes as CronJob, still, the basics remain the same and it is very important to monitor cron jobs. You don’t want to wake up to some client not being paid because the CronJob did not run. This post is going to highlight one cool bash trick which simplifies cron monitoring. The solution is language and framework agnostic as it is done in the command itself. Let’s dive deeper into this way of monitoring crons. Monitoring Crons is important We were working on a project that had two CronJobs one running every 6 hours and the other one running every fortnight. This project was done in Symfony and the crons were set up as Symfony commands executed on Kubernetes as CronJob. As we monitor our applications with New Relic, we monitor and get notified if the Crons don’t run in time with OpsGenie Heartbeat Monitoring.  You can use Healthchecks or Cronitor, anything that suits your needs, the main thing is getting an alert when crons don’t run in the expected time and frequency. So all good till now, but when I looked into the code to ping OpsGenie I saw multiple ways of doing it. Mostly it was like get the latest OpsGenie API/SDK and call it from the application. Someone had even built a wrapper library to do this heartbeat ping and other things. The Problem Following were the problems with this approach: The solution was to call the Heartbeat REST API from the application which resulted in extra dependencies and extra code for the application Calling the API after a successful cron run was language/framework-specific, for example, the code written for PHP won’t work for NodeJs/Typescript. If the API was to upgrade to a new version a different set of change would be needed to each application as the call depended on the application code The Solution The solution to all the above problems is very simple, use bash with the && operator, so that the ping only runs if the cron run is successful. “&&” is used to chain commands together, such that the next command is run if and only if the preceding command exited without errors (or, more accurately, exits with a return code of 0). - Source We ran our Symfony cron command with ping to OpsGenie like below: bin/console our-cron: \
&& curl -X GET --header command 'https://api.opsgenie.com/v2/heartbeats/our-cron/ping' 'Authorization: GenieKey ' " " $OPSGENIE_API_KEY '' We did a bit different variation to the above command and created a bash script where the command and name of the heartbeat were sent as parameters. We are using OpsGenie but there are other SAAS providers in the cron monitoring space too. One you can use for a single monitor for free is . In addition to , Cronitor provides simple for websites and APIs. It also has a handy CLI tool to import your existing crontab and send it to Cronitor. Another SAAS Cronitor.io cron monitoring uptime monitoring So what issues did it solve? There is no need to pull an extra library/package just to do a curl. Like OpsGenie PHP client which I had considered pulling in initially :) The solution is language/framework agnostic. As long as the docker container has bash/sh or any shell (which it generally has) the solution works for PHP, NodeJs (typescript), Python or even GoLang. If the API upgrades the change is the same across all repositories. Conclusion Try to look for solutions out of the box. If we were to solve the problem as it was done earlier we would have either pulled in a new library or built our own OpsGenie Client of some sort. It would solve the problem but give us more dependencies and code to maintain for the future. When trying to find a fix for a problem, step back and think of solutions that are not very obvious.

How to Simplify Cron Monitoring With a Simple Bash Trick

The Internet of Bodies (IoB) term was coined in 2016. It describes connected devices that monitor the human body, collect physiological, biometric, or behavioral data, and exchange information over a wireless or hybrid network. Standalone mobile apps that analyze physical activity and health-related data, such as heartbeat, blood pressure, and sleep cycles, can also be considered part of the IoB cohort. However, we've deliberately excluded them from our classification to avoid confusion with . mHealth The Internet of Bodies falls under the broader umbrella. But as the name implies, IoB devices ensure an even closer synergy between humans and gadgets than connected thermostats, refrigerators, and curtains. IoT solutions IoB products come in various forms, ranging in complexity from smartwatches and fitness trackers, which , to implantable insulin delivery systems, ingestible sensors, and brain stimulation gadgets. are used by approximately 21% of Americans The benefits of implementing IoB solutions at scale include better diagnosis and treatment of health conditions, personalized insurance plans, increased productivity, and improved public safety, to name a few. But the growing Internet of Bodies adoption could also result in unauthorized access to sensitive information by third parties, income-based health disparities, and the installment of a global surveillance state. This article will take a closer look at IoB benefits and applications in the healthcare and wellness domains and identify privacy and security concerns surrounding the Internet of Bodies and . So let's dive right in! medical IoT solutions What is the Internet of Bodies Exactly? Just like other members of the Internet of Things family, IoB devices : operate on four levels with limited or advanced computing capabilities. IoB devices are enhanced with embedded software and an array of sensors measuring human-generated data (step count, pulse, oxygen levels, hematological parameters, facial features, or daily routines.) Depending on a gadget's processing power, sensor data can be stored and analyzed either on the device or in the cloud. It is possible to combine invasive and wearable IoB devices into a wireless body area network (WBAN). Hardware , which can be wireless or hybrid. Connectivity technologies allow IoB systems to securely exchange data with each other and a central hub at preset intervals or in real-time. Networks , which encompasses data storage, , and . When it comes to IoB, the "infrastructure" team can also refer to a support system ensuring a gadget's uninterrupted operation, such as a team of healthcare specialists ready to intervene should a personal help button be pressed. Back-end infrastructure analytics visualization solutions that allow individuals to configure IoB devices, connect them to other hardware and apps, and view sensor data over a given period. These apps often run on mobile devices, although voice interfaces are also getting traction in the Internet of Things domain. End-user applications To classify as the Internet of Bodies product, connected devices must perform one or both of these functions: Gather health, behavioral, or biometric data Alter the human body's functions Most IoB products come in direct contact with the human body, are physically attached to it, or, as in the case of ingestible and implantable devices, are swallowed or surgically inserted into a patient's system. Subsequently, the U.S. Food and Drug Administration (FDA) heavily regulates the development, manufacturing, and use of the Internet of Bodies solutions. Even consumer wearables, which previously were not considered medical devices unless manufacturers labeled them so, now often require FDA clearance. While this the launch of certain IoB products and features, such as the oximeter functionality introduced in the Apple Watch when the pandemic struck, the decision also prevents flawed devices from entering the market and causing damage to people's health. might slow down Examples of IoB Products For your convenience, we've divided the Internet of Bodies solutions into two categories — i.e., consumer-grade and medical devices. Consumer IoB Devices Connected Home Solutions Security cameras and door locks with face recognition capabilities Voice-controlled smart home devices Automatic pill dispensers Hearing aids Indoor navigation systems for the visually impaired In-home remote monitoring systems for the elderly and patients with chronic conditions Fitness Devices Wearable physical activity trackers, including innovative accessories with computer vision functionality Fitness mirrors Connected fabrics and apparel Training and weight measuring equipment enhanced with sensors Wellness Technology Baby tech solutions: camera and sensor-based monitors, smart cribs, intelligent baby formula makers, and connected body temperature thermometers and nebulizers : smart pads, tampons, and menstruation cups; connected bras and breast cups, hardware-based fertility trackers, pelvic floor training tools Femtech products Vitals monitoring devices: wearables for heart rate, blood pressure, and oxygen monitoring : smart glucose meters, wearable insulin pumps, and sensor-infused medication storage devices Diabetes management systems Medical IoB Devices Smart Hospital Solutions for patients and hospital staff Biometric-based identification systems Connected hospital beds with vitals monitoring functionality Point-of-care testing equipment Stationary medication dispensers Integrated IoT systems for inpatient, outpatient, and remote patient care Implantable and ingestible devices Organ systems: heart implants, artificial pancreas systems, smart stents, cochlear implants for individuals with hearing loss, artificial retina implants IoT-based prosthetic limb systems Neurological IoB solutions: brain-computer interfaces (BCI) with implantable sensors, deep brain stimulation solutions, seizure monitors Digital pills for non-invasive diagnostic procedures and medication intake monitoring How the Internet of Bodies Revolutionizes Healthcare Although the Internet of Bodies can transform nearly every industry, healthcare professionals are the immediate beneficiaries of the IoB revolution. The advantages of adopting IoB products in the medical care span: . Sensor and camera-based IoT devices allow caregivers to spot the slightest changes in patients' physiological data, anticipating their decline and taking immediate action in emergency cases. Additionally, IoB technologies help reduce physical interactions between patients and medical personnel, thus preventing contagious diseases from spreading. 24/7 patient monitoring . While sensor data collected by a smartwatch can provide valuable insights into a patient's past, current, and future health conditions, IoB also offers a variety of tools for non-invasive and highly efficient medical diagnosis. , for example, serve as a viable alternative to gastroscopy and colonoscopy. A can monitor lung function and flag abnormalities early on. And by analyzing data collected through , physicians can diagnose genetic disorders and better treat diabetes. Non-invasive diagnosis Camera-based ingestible pills sleek wearable gadget worn at the lower part of the ribcage wearable sweat sensors . Unlike wearables, which merely collect data, implantable and embedded IoB products can change and restore the body's functions that have been affected by physical trauma or disease. Some examples of such devices include that transmit data to a dedicated mobile app, returning partial vision to people with retinal diseases, and monitoring blood sugar levels in real-time. Improved quality of life for patients with chronic conditions connected pacemakers microelectronic retina prostheses automated insulin delivery systems . IoB devices could help healthcare professionals identify recurring patterns in health data and devise personalized medicine and treatment plans tailored to the needs of a particular patient or patient group. For this purpose, electronic health records (EHRs) could be augmented with sensor data and . Precision medicine analyzed using artificial intelligence algorithms . The data obtained from the Internet of Bodies products allows health insurance companies to adopt a more granular approach to risk profiling and optimize insurance plans based on an individual's medical history, occupation, and lifestyle. Besides driving and encouraging users to pursue a healthy lifestyle, the growing adoption of IoB solutions in medical insurance could reduce hospitalization rates and by up to 40%. Personalized health insurance plans patient engagement cut claims processing and administrative costs A Rundown of IoB Challenges and Risks — And Ways to Address Them Privacy In addition to users' whereabouts, IoB devices can track various body parameters, such as cardiac rhythms, sleep patterns, and menstrual cycles. And it's not completely clear who can access and use this information. The biometric data captured by an implantable cardiac device, for example, could serve as evidence indisputable criminal cases, like the . In 2016, the police obtained information from Mr. Compton's cardiac pacing devices when suspicions around whether a critically ill man could escape a fire while neatly packing his belongings into a suitcase had arisen. infamous Ross Compton lawsuit Another example comes from Amazon. The company's questionable treatment of employees during the pandemic hardly comes as a surprise following the for warehouse workers. The intrusive technology would potentially allow Amazon to detect idling employees and collect personal information, such as the frequency of their bathroom breaks. introduction of arm motion trackers A comprehensive legal framework is necessary to establish crystal-clear guidelines for collecting, analyzing, and using physiological, behavioral, and biometric data produced by IoB devices. Until then, the Internet of Bodies vendors with HIPAA, FTC, FIPPs, FCRA, and GLBA regulations. are expected to comply Security Being part of the larger Internet of Things family, IoB devices may contain the very same security vulnerabilities as talking in weird voices and that fall victim to malware attacks and attempt to bring the whole Internet down. These a cumbersome software updates installation process, hard-coded and easy-to-guess passwords, the use of insecure or outdated software and hardware components, and a failure to encrypt data traversing the network, among others. And while it literally won't kill you if your fitness tracker joins a newly formed botnet, a or is a totally different story. compromised baby monitors hacked CCTV cameras vulnerabilities may span hacked cardiac implant insulin pump To prevent businesses, cybercriminals, and foreign governments from accessing sensitive data and launching high-profile cyberattacks on the US IT infrastructure, the FDA must devise a comprehensive IoB cybersecurity framework, which could be based on the corresponding framework developed by the National Institute of Standards and Technology. Additionally, the FDA could impose stricter security requirements on IoT companies from the fitness and wellness segments. Ethics Without proper regulations, individuals who use IoB devices may not fully own biometric data. In addition, when implemented in public settings like schools and hospitals, the Internet of Bodies solutions might inadvertently monitor other people surrounding the user, which violates their privacy. And as more healthcare providers and insurance companies incorporate wearable data into treatment plans and health coverage, individuals with lower incomes and limited access to technology may end up missing out on IoB benefits. One way to tackle these problems could be to conduct extensive research on IoB risks and benefits and make the information publicly available, thus filtering out the marketing hype. Closer collaboration between policymakers and device manufacturers could also help address data privacy concerns and make IoB products more affordable. What's In Store for the Internet of Bodies Solutions? The pandemic sparked the general public's interest in digital healthcare solutions, and healthcare providers have followed suit. Driven by telehealth, remote patient monitoring, and healthcare analytics solutions, the digital health market by 2026. is on track to top $220 billion And it's only a matter of time before the Internet of Bodies goes mainstream too. On the one hand, we're witnessing the emergence and at-scale implementation of innovative networking technologies, including satellite Internet, Wi-Fi 6, and 5G. The latter, for instance, up to one million devices per square kilometer (up from 4,000 devices per square kilometer for 4G networks.) could potentially support On the other hand, we could soon expect greater interoperability between IoT and IoB devices, which will exchange data in a unified format and at a faster speed. Several critical steps, such as the , have already been made in this direction. establishment of the global oneM2M initiative All of this would create next-gen cyber-physical systems where connected thermostats feed off data collected through sensors embedded into smart apparel, automatically adjust their temperature settings, and notify an endocrinologist if glucose concentration in your sweat goes up. Whether you need help creating hardware and software components for a custom IoB solution or deploying an Internet of Bodies system across your lab or hospital wards, ITRex Group can assist you with that. to discuss your IoB project! Drop ITRex developers a line

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