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Jinesh Kumar Chinnathambi, a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received "Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence," "Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps," “Harnessing Data Analytics and Artificial Intelligence in Healthcare,” and “Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration.” His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event. The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients. Jinesh Kumar Chinnathambi , a Solution Architect at a leading health insurance company, is using his real-world experience in the fields of Machine Learning (ML) and Artificial Intelligence (AI) to make the healthcare sector more patient-centric and improve patient care. Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi People pursuing a career in healthcare IT are often driven by the aspiration to have a significant impact on people's lives. For Jinesh, the best way to do this in 2024 is by utilizing the problem-solving capabilities of technology to improve and streamline the healthcare industry. As he says, "The ever-changing healthcare landscape, with advancements such as Electronic Health Records (EHR), telemedicine, artificial intelligence, and predictive analytics transforming patient care, presents captivating challenges and opportunities for innovation. It provides the satisfaction of knowing that work in this field directly contributes to enhancing the quality, accessibility, and effectiveness of patient care, ultimately making healthcare more patient-centric." Combining the innovations and technologies in this healthcare IT, such as AI-driven diagnostics, predictive analytics, and personalized treatment plans, with the soft skills required in Healthcare roles, including problem-solving, analytical thinking, communication, and understanding of healthcare operations, has proven incredibly beneficial to both patients and healthcare professionals. Introducing Jinesh Kumar Chinnathambi Jinesh Kumar Chinnathambi's education began with a Bachelor's degree in computer science engineering, which gave him a strong foundation for his work in healthcare IT. During his studies, Jinesh focused on subjects at the intersection of technology and healthcare, including databases, data analysis, and healthcare systems. After graduating, he gained practical IT experience through entry-level roles and tech positions in the healthcare industry. He achieved further specialization in healthcare IT through certifications offered by AHIP (America's Health Insurance Plan) and has kept updated on new healthcare regulations, emerging technologies, and industry best practices to stay ahead of the curve. Networking with professionals, attending industry conferences, and keeping abreast of healthcare IT journals have provided valuable insights and helped him garner success in his career. As he says himself, "The journey into Healthcare IT involves a combination of education, practical experience, ongoing learning, and a dedication to enhancing healthcare delivery through technology." Jinesh has published numerous publications and pieces of academic research, including the well-received " Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence ," " Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps ," “ Harnessing Data Analytics and Artificial Intelligence in Healthcare ,” and “ Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration .” Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Effective Cancer Recurrence Prediction using Healthcare Data Analytics with Machine Learning and Artificial Intelligence Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Leveraging Data Analytics with Artificial Intelligence to Detect and Close Health Care Gaps Harnessing Data Analytics and Artificial Intelligence in Healthcare Harnessing Data Analytics and Artificial Intelligence in Healthcare Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration His wide range of professional certifications and important achievements demonstrate his expertise in a highly competitive field. He is the only one of Elevance Health’s approximately 100,000 employees to hold four AWS certifications, including AWS Certified DevOps Professional, AWS Certified Solutions Architect Associate, AWS Certified Developer Associate, and AWS Certified SysOps Administrator Associate. He also holds a Sun Certified Java Professional (SCJP) certification and multiple America's Health Insurance Plans (AHIP) certifications, including Fundamentals of Healthcare Parts A and B and Basics of Managed Care Part A. In addition to all of this, Jinesh has been recognized with a 2024 Global Recognition Award for his notable healthcare and IT industry achievements. Global Recognition Award Global Recognition Award In August 2024, he also won the Cloud Innovator of the Year Award at the Business Innovation Awards 2024 in the Health Care/Information Technology category. This award recognizes outstanding achievements in cloud innovation. In addition to being awarded for his work, Jinesh was also named a judge for the Globee® Awards for Leadership in 2024, signifying a formal recognition of his commitment and significant contributions to his field, and received recognition for his role as judge at the Virginia Tech College Hackathon event . Globee® Awards for Leadership Globee® Awards for Leadership Virginia Tech College Hackathon event Virginia Tech College Hackathon event The Role of AI, ML, Data Analytics, Data Warehouses & Cloud Migration in Healthcare IT One of the major benefits of implementing technological advancements like AI and ML into healthcare IT is how they can be utilized to analyze vast amounts of healthcare data, accurately predict patient outcomes and improve treatment plans. A pivotal moment in Jinesh's IT career came with the advent of cloud computing, which reshaped the entire technology landscape. It was here, at the precipice of such a monumental technological change, that Jinesh realized just how beneficial such systems could be to the work he was doing. "Before the rise of cloud computing, businesses were required to establish, manage, and uphold their own expensive IT infrastructures,” recalls Jinesh. “Companies like Amazon, Google, and Microsoft began providing platforms that allowed access to databases, servers, software, and analytics via the internet. This led to a substantial reduction in the cost and complexity of IT operations, hastened innovation, and offered the flexibility to scale resources as needed. It empowered businesses to concentrate more on their core operations while leveraging state-of-the-art technology. Furthermore, cloud computing paved the way for a multitude of other technological advancements seen today, such as big data analytics and AI, which I am currently working on. It stands as a milestone that has redefined the possibilities of IT and continues to shape the future of technology." All of this works in tandem with the dedication of people in the healthcare field to craft a more patient-centric approach, providing personalized and timely care with revolutionary speed and precision. Real-World Studies and Applications By implementing these technological advancements into his established healthcare IT system, Jinesh has been able to successfully develop predictive analytics products to enhance patient care. His work has even been able to identify the signs and likelihood of cancer recurrence far earlier than previously thought possible, all thanks to data analytics, AI, and ML. By configuring AI models to run seamlessly on the cloud, Jinesh has contributed to accelerating predictive diagnoses, enhancing treatment plans, automating administrative tasks, and improving patient care. The strategic designs and technical expertise of solution architects are a vital part of shaping the future of AI-enabled, cloud-based healthcare systems. The impact of these projects has improved patient outcomes, reduced costs, improved efficiency, and helped to deliver more personalized care than ever before. Jinesh has worked tirelessly to guide and develop technology and tool solutions that improve data lineage adoption and governance by collaborating with technology partners to create a cloud-based data operation strategy. He oversaw a comprehensive cloud migration and data governance framework at Elevance Health, ensuring alignment with business objectives and HIPAA regulatory requirements, and also established a data council of senior executives across the enterprise to facilitate efficient collaboration on data initiatives. Furthermore, during his time at Elevance, he worked closely with the enterprise infrastructure and technology team to explore a cloud-based lake-house solution. Jinesh led the implementation of a data migration framework, including issue research, resolution processes, root cause analysis, reporting, data classifications and standards, and data quality validation and controls. He also developed a data governance framework to align with business objectives and HIPAA regulatory requirements, which supported the implementation of monolithic microservices for various use cases. He researched and recommended the appropriate Snowflake warehouse sizes after conducting several proofs of concept, resulting in optimal performance for middleware queries and reducing annual costs by 30%. One study estimated the national cost-savings in the United States from early diagnosis to be $26 billion per year. One study estimated the national cost-savings One study estimated the national cost-savings The Future of Healthcare Technology Jinesh's vision for the future of healthcare technology is one full of promise, continued improvement, and the potential to create a more patient-centric healthcare system that benefits everyone. He plans to work at the convergence of healthcare and technology to improve patient care by introducing new and advanced solutions and developing and managing Electronic Health Records (EHRs), health information exchanges, telemedicine services, and other initiatives to enhance healthcare coordination and accessibility, ultimately benefiting local communities. This will contribute to early disease detection, effective monitoring, decreased medical errors, and better communication between healthcare providers and patients.

How AI and Machine Learning Are Making the Healthcare Sector More Patient-centric

እንዴት AI እና ማሽን መማር የጤና አጠባበቅ ሴክተሩን የበለጠ ታጋሽ ተኮር እያደረጉት ነው።

Süni intellekt və Maşın Öyrənməsi Səhiyyə Sektorunu necə daha xəstə mərkəzli edir

Com la IA i l'aprenentatge automàtic estan fent que el sector sanitari sigui més centrat en el pacient

Jak umělá inteligence a strojové učení dělají sektor zdravotnictví více zaměřený na pacienta

Cómo la IA y el aprendizaje automático están haciendo que el sector sanitario esté más centrado en el paciente

Comment l'IA et l'apprentissage automatique rendent le secteur de la santé plus centré sur le patient

Como a IA e a aprendizaxe automática están facendo que o sector sanitario sexa máis centrado no paciente

कैसे AI और मशीन लर्निंग स्वास्थ्य सेवा क्षेत्र को अधिक रोगी-केंद्रित बना रहे हैं

AIと機械学習が医療分野をより患者中心に変える

AI жана машинаны үйрөнүү кантип саламаттыкты сактоо секторун сабырдуураак кылат

Ndenge nini AI mpe Machine Learning Ezali Kosala ete Secteur ya Soins de Santé ezala mingi na ba malades

Paano Ginagawa ng AI at Machine Learning ang Sektor ng Pangangalagang Pangkalusugan na Mas Nakasentro sa Pasyente

Healthcare experts find it difficult to use Patient Feedback appropriately in improving the overall Patient Experience. But with the help of patient feedback tools, one of the most resourceful tools used in the healthcare industry, it's easy to capture Patient Feedback. The imperative work starts after the post-collection of data, which consists of analyzing the results, understanding service gaps, and creating and implementing strategies to enhance efficiency. Medical Group Management Association (MGMA) found that approximately 80% of the practices identified as “better performing” in the healthcare industry used patient surveys. The Importance of Using Patient Surveys Healthcare & Patient Surveys not only gauge Patient Feedback, but it helps in improving the overall performance and communication with the patients. Nowadays, the healthcare industry is more relying on Patient Survey App to evaluate their performance as a provider. Implementation of patient surveys can help hospitals understand patient requirements and fulfill them.
It will help you understand what is essential to your patients which will further help you deliver quality services.
If you care about your patient’s opinions, then it will give them a solid reason to keep you as their medical care professional and will ultimately lead to word-of-mouth recommendations.
Patient Surveys can help you with the right data to improve patient satisfaction. As per Accenture, “61% of respondents surveyed would switch health care providers for the ability to get an appointment quickly when needed.” Make Use of the CG-CAHPS Survey The CAHPS (Consumer Assessment of Healthcare Providers and Systems) survey is an industry-recognized survey that is globally known for measuring Patient Satisfaction and capturing patient insights. The survey is created by the Agency for Healthcare Research and Quality under the guidance of experts from the healthcare industry. There are numerous versions available of the CAHPS survey depending upon the medical providers. CG-CAHPS, also known as, Clinician and Group Consumer Assessment of Healthcare Providers and Systems, is another version of the CAHPS survey, which is used to measure patient perceptions regarding care delivered in a hospital. As per Press Ganey, a renowned company that is used for improving patient satisfaction, 5 essential questions from a CH-CAHPS survey should be included in surveys, which are the key pointers for determining Patient Satisfaction and Patient Loyalty. How would you rate your confidence in the healthcare provider?
How well does the staff coordinate in providing exceptional care?
How much concern was shown from the healthcare providers’ end for your queries?
During your recent visit, how carefully did the healthcare provider hear your problems?
How would you like to rate the friendliness of the hospital? Best Practices for Patient Surveys Surveying your patients will help you understand their preferences and what they are looking for in a particular service, be it patient registration, doctor appointment, specific healthcare treatment, etc. It can help you enhance the overall healthcare processes. It’s not necessary that patients will prefer making use of patient surveys; being a healthcare provider, you need to give a compelling reason to the respondents and ensure to include the best patient survey practices. Let them know why you care about their opinion and how it can help you offer a seamless patient experience. Best Practices for Patient Surveys Distribute Surveys on Multiple Platforms
Distribute Survey at the Right Time (post interaction)
Make use of Open-Ended Questions and Close-Ended Questions
Keep your Surveys Short and Simple Distribute Surveys on Multiple Platforms There is always a question that comes to the mind of most healthcare providers which is: What should be the medium to collect surveys? Whether online or offline? To get a high response rate from your patients, it’s always recommended to distribute patient surveys on different platforms, be it online or offline. It can include social media networking sites, websites, emails, kiosks, etc. For example, an inpatient post-discharge could be emailed a survey about his visit and experience with the hospital. On the other hand, an outpatient can be presented with a kiosk to take real-time feedback while he is at the healthcare center. Distribute Survey at the Right Time (post interaction) It’s another vital factor to capture patient feedback. Capturing it after every interaction will help you analyze what services can be improved. Patients might not be able to share useful insights if the surveys are sent to them after long intervals. Make use of Open-Ended Questions and Close-Ended Questions Open-ended questions are those questions where patients don’t have to write anything. They will get multiple-choice answer options which would be pre-populated. Close-ended questions are those questions where patients will get the opportunity to answer questions in a more descriptive format. Using both open-ended and close-ended questions will help you capture maximum patient insights. Keep your Surveys Short and Simple It has been observed that most of the time, patients hesitate to take surveys or leave them in between if they are too long. Try to keep them precise, short, and simple. Respect their time, and it will result in high response rates. Conclusion Nowadays, technology has become an integral part of most industries, and healthcare should not be left behind. To improve Patient Satisfaction, collecting feedback at a particular time plays an important role. If you want to provide incredible services to your patients, then making use of a real-time Patient Feedback System with tracking capabilities can be helpful. Making use of surveys will help you capture patient complaints, which should be addressed immediately to make them happy and satisfied. Healthcare experts find it difficult to use Patient Feedback appropriately in improving the overall Patient Experience. But with the help of patient feedback tools, one of the most resourceful tools used in the healthcare industry, it's easy to capture Patient Feedback. patient feedback tool s, patient feedback tool The imperative work starts after the post-collection of data, which consists of analyzing the results, understanding service gaps, and creating and implementing strategies to enhance efficiency. Medical Group Management Association (MGMA) found that approximately 80% of the practices identified as “better performing” in the healthcare industry used patient surveys. Medical Group Management Association (MGMA) The Importance of Using Patient Surveys The Importance of Using Patient Surveys Healthcare & Patient Surveys not only gauge Patient Feedback, but it helps in improving the overall performance and communication with the patients. Nowadays, the healthcare industry is more relying on Patient Survey App to evaluate their performance as a provider. Implementation of patient surveys can help hospitals understand patient requirements and fulfill them. It will help you understand what is essential to your patients which will further help you deliver quality services. If you care about your patient’s opinions, then it will give them a solid reason to keep you as their medical care professional and will ultimately lead to word-of-mouth recommendations. Patient Surveys can help you with the right data to improve patient satisfaction. Implementation of patient surveys can help hospitals understand patient requirements and fulfill them. It will help you understand what is essential to your patients which will further help you deliver quality services. If you care about your patient’s opinions, then it will give them a solid reason to keep you as their medical care professional and will ultimately lead to word-of-mouth recommendations. Patient Surveys can help you with the right data to improve patient satisfaction . improve patient satisfaction improve patient satisfaction As per Accenture, “61% of respondents surveyed would switch health care providers for the ability to get an appointment quickly when needed.” As per Accenture, “61% of respondents surveyed would switch health care providers for the ability to get an appointment quickly when needed.” Make Use of the CG-CAHPS Survey Make Use of the CG-CAHPS Survey The CAHPS (Consumer Assessment of Healthcare Providers and Systems) survey is an industry-recognized survey that is globally known for measuring Patient Satisfaction and capturing patient insights. The survey is created by the Agency for Healthcare Research and Quality under the guidance of experts from the healthcare industry. measuring Patient Satisfaction Agency for Healthcare Research and Quality There are numerous versions available of the CAHPS survey depending upon the medical providers. CG-CAHPS, also known as, Clinician and Group Consumer Assessment of Healthcare Providers and Systems, is another version of the CAHPS survey, which is used to measure patient perceptions regarding care delivered in a hospital. Clinician and Group Consumer Assessment of Healthcare Providers and Systems, As per Press Ganey , a renowned company that is used for improving patient satisfaction, 5 essential questions from a CH-CAHPS survey should be included in surveys, which are the key pointers for determining Patient Satisfaction and Patient Loyalty. Press Ganey How would you rate your confidence in the healthcare provider? How well does the staff coordinate in providing exceptional care? How much concern was shown from the healthcare providers’ end for your queries? During your recent visit, how carefully did the healthcare provider hear your problems? How would you like to rate the friendliness of the hospital? How would you rate your confidence in the healthcare provider? How well does the staff coordinate in providing exceptional care? How much concern was shown from the healthcare providers’ end for your queries? During your recent visit, how carefully did the healthcare provider hear your problems? How would you like to rate the friendliness of the hospital? Best Practices for Patient Surveys Best Practices for Patient Surveys Surveying your patients will help you understand their preferences and what they are looking for in a particular service, be it patient registration, doctor appointment, specific healthcare treatment, etc. It can help you enhance the overall healthcare processes. It’s not necessary that patients will prefer making use of patient surveys; being a healthcare provider, you need to give a compelling reason to the respondents and ensure to include the best patient survey practices. Let them know why you care about their opinion and how it can help you offer a seamless patient experience. Best Practices for Patient Surveys Distribute Surveys on Multiple Platforms Distribute Survey at the Right Time (post interaction) Make use of Open-Ended Questions and Close-Ended Questions Keep your Surveys Short and Simple Distribute Surveys on Multiple Platforms Distribute Survey at the Right Time (post interaction) Make use of Open-Ended Questions and Close-Ended Questions Keep your Surveys Short and Simple Distribute Surveys on Multiple Platforms Distribute Surveys on Multiple Platforms Distribute Surveys on Multiple Platforms There is always a question that comes to the mind of most healthcare providers which is: What should be the medium to collect surveys? Whether online or offline? To get a high response rate from your patients, it’s always recommended to distribute patient surveys on different platforms, be it online or offline. It can include social media networking sites, websites, emails, kiosks, etc. For example, an inpatient post-discharge could be emailed a survey about his visit and experience with the hospital. On the other hand, an outpatient can be presented with a kiosk to take real-time feedback while he is at the healthcare center. Distribute Survey at the Right Time (post interaction) Distribute Survey at the Right Time (post interaction) Distribute Survey at the Right Time (post interaction) It’s another vital factor to capture patient feedback. Capturing it after every interaction will help you analyze what services can be improved. Patients might not be able to share useful insights if the surveys are sent to them after long intervals. Make use of Open-Ended Questions and Close-Ended Questions Make use of Open-Ended Questions and Close-Ended Questions Make use of Open-Ended Questions and Close-Ended Questions Open-ended questions are those questions where patients don’t have to write anything. They will get multiple-choice answer options which would be pre-populated. Close-ended questions are those questions where patients will get the opportunity to answer questions in a more descriptive format. Using both open-ended and close-ended questions will help you capture maximum patient insights. Keep your Surveys Short and Simple Keep your Surveys Short and Simple Keep your Surveys Short and Simple It has been observed that most of the time, patients hesitate to take surveys or leave them in between if they are too long. Try to keep them precise, short, and simple. Respect their time, and it will result in high response rates. Conclusion Nowadays, technology has become an integral part of most industries, and healthcare should not be left behind. To improve Patient Satisfaction, collecting feedback at a particular time plays an important role. If you want to provide incredible services to your patients, then making use of a real-time Patient Feedback System with tracking capabilities can be helpful. Making use of surveys will help you capture patient complaints, which should be addressed immediately to make them happy and satisfied.

The Growing Importance of Patient Surveys

রোগীর সমীক্ষার ক্রমবর্ধমান গুরুত্ব

Die wachsende Bedeutung von Patientenbefragungen

La creciente importancia de las encuestas a pacientes

L’importance croissante des enquêtes auprès des patients

रोगी सर्वेक्षणों का बढ़ता महत्व

患者調査の重要性の高まり

환자 설문조사의 중요성 증가

A crescente importância das pesquisas com pacientes

Растущая важность опросов пациентов

Hasta Anketlerinin Artan Önemi

Tầm quan trọng ngày càng tăng của khảo sát bệnh nhân

患者调查的重要性日益增加

Medical Software Testing 101: Advice to Consider for Upgrading Patient Experiences 

How to Craft Inclusive AI Prompts That Mitigate Bias

Building a Data Strategy: Aligning Data With Business Goals for Maximum Impact

HackerNoon: What is your company in 2–5 words? Lewis Johnson: AI-powered human learning technology Why is now the time for your company to exist? We at Alelo became experts in applying conversational AI to education and training long before AI became fashionable. Now that the world has woken up to the potential of generative AI, people increasingly come to us for AI-powered learning solutions. What do you love about your team, and why are you the ones to solve this problem? I love our team’s shared passion and commitment for what we do, which is to use conversational AI to create quality learning products that make a difference in people’s lives. We understand the power of conversation to promote learning, which gives us a unique perspective. A key focus of ours is ensuring that our AI-powered solutions are trustworthy. I see too many AI-powered products out there that make mistakes and are unreliable. If you weren’t building your startup, what would you be doing? My other passion is singing, and if I were weren’t building my startup I would be making more music instead. At the moment, how do you measure success? What are your metrics? We measure success by the number of successful learners and happy customers. We provide solutions to organizations with large numbers of learners, so that we can have as much impact as possible. 500,000 learners to date, with potential for rapid increase. In a few sentences, what do you offer to whom? Our primary industry focus right now is healthcare. We work with health systems to create Virtual Patient Navigators that answer patient questions and point them to educational resources, thereby reducing burdens on care teams. We also offer training solutions to training organizations and health systems, that let health professionals practice their skills with simulated patients and caregivers. We also get demand from pharmaceutical companies and other providers of educational content, to create Navigators that support their customers**.** What’s most exciting about your traction to date? What’s exciting is to me is that we are acquiring customers with really large numbers of learners. One of our customers has a site that has reached 100 million downloads. That translates into really large numbers of learners, generating lots of recurring revenue for us. Where do you think your growth will be next year? I think our growth next year will be 100% YoY, and the year after that 1,000% YoY. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was the US Government. We reached a lot of learners by working with the Government, but it was mainly through fixed contracts instead of recurring revenue. The new generative AI platform that we are developing has much more recurring revenue potential.  We project $2M next year, with an exponential growth curve kicking in toward the end of next year. What’s your biggest threat? Our biggest threats are our biggest suppliers: the large language model developers such as Google and OpenAI. They provide foundational technology for us for now, but they might try to offer competing solutions in the future. So far, their offerings in healthcare are untrustworthy and even unsafe, which gives us an advantage. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. Would you like to take a stab at answering some of these questions? The link for the template is HERE. HackerNoon : What is your company in 2–5 words? HackerNoon Lewis Johnson : AI-powered human learning technology Lewis Johnson Lewis Johnson Why is now the time for your company to exist? We at Alelo became experts in applying conversational AI to education and training long before AI became fashionable. Now that the world has woken up to the potential of generative AI, people increasingly come to us for AI-powered learning solutions. Alelo What do you love about your team, and why are you the ones to solve this problem? I love our team’s shared passion and commitment for what we do, which is to use conversational AI to create quality learning products that make a difference in people’s lives. We understand the power of conversation to promote learning, which gives us a unique perspective. A key focus of ours is ensuring that our AI-powered solutions are trustworthy. I see too many AI-powered products out there that make mistakes and are unreliable. If you weren’t building your startup, what would you be doing? My other passion is singing, and if I were weren’t building my startup I would be making more music instead. At the moment, how do you measure success? What are your metrics? We measure success by the number of successful learners and happy customers. We provide solutions to organizations with large numbers of learners, so that we can have as much impact as possible. 500,000 learners to date, with potential for rapid increase. In a few sentences, what do you offer to whom? Our primary industry focus right now is healthcare. We work with health systems to create Virtual Patient Navigators that answer patient questions and point them to educational resources, thereby reducing burdens on care teams. We also offer training solutions to training organizations and health systems, that let health professionals practice their skills with simulated patients and caregivers. We also get demand from pharmaceutical companies and other providers of educational content, to create Navigators that support their customers**.** What’s most exciting about your traction to date? What’s exciting is to me is that we are acquiring customers with really large numbers of learners. One of our customers has a site that has reached 100 million downloads. That translates into really large numbers of learners, generating lots of recurring revenue for us. Where do you think your growth will be next year? I think our growth next year will be 100% YoY, and the year after that 1,000% YoY. Tell us about your first paying customer and revenue expectations over the next year. Our first paying customer was the US Government. We reached a lot of learners by working with the Government, but it was mainly through fixed contracts instead of recurring revenue. The new generative AI platform that we are developing has much more recurring revenue potential.  We project $2M next year, with an exponential growth curve kicking in toward the end of next year. What’s your biggest threat? Our biggest threats are our biggest suppliers: the large language model developers such as Google and OpenAI. They provide foundational technology for us for now, but they might try to offer competing solutions in the future. So far, their offerings in healthcare are untrustworthy and even unsafe, which gives us an advantage. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke’s ten questions for startup founders. This startup founder interview template is based on HackerNoon Founder & CEO David Smooke ’s ten questions for startup founders . HackerNoon Founder & CEO David Smooke ten questions for startup founders 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.

Revolutionizing Learning with AI: An Interview with Alelo's President & CEO

The artificial intelligence landscape is undergoing a seismic shift. While text-based Large Language Models (LLMs) have dominated headlines and captured our imagination, a new paradigm is emerging that promises to revolutionize how we interact with AI: multimodal artificial intelligence. This evolution marks a fundamental change in how machines process and understand our world. The Natural Evolution: From Text to Multiple Modalities Humans don't communicate solely through text – we interpret facial expressions, analyze tone of voice, and process visual information simultaneously. This multifaceted approach to communication is what makes human interaction so rich and nuanced. Now, AI is following suit, evolving beyond the constraints of text-only models to embrace a more holistic approach to understanding and generating content. Multimodal AI systems can process and generate multiple forms of data – text, images, audio, video, and documents – in unified models that more closely mirror human cognitive processes. We're already seeing impressive demonstrations of this technology, from foundational models that can generate videos or short films from written descriptions such as Meta’s Movie Gen, Pika Labs, or Runway’s Gen-3 Alpha, to platforms capable of real-time voice conversations that understand both words and emotional undertones like OpenAI’s GPT-4o. These developments aren't just technical achievements – they're reshaping how we think about artificial intelligence and its capabilities. Multimodal AI Potential to Transforming Industries The potential applications of multimodal AI are vast and transformative. In healthcare, systems that can simultaneously analyze medical images, patient voice recordings, and clinical documentation could revolutionize diagnostic processes. Early research shows promising results in detecting cognitive conditions like Alzheimer's disease by combining analysis of speech patterns with medical imaging data. Multimodal models utilizing data from radiology images, histopathology slides, genomics, and clinical data have shown potential to outperform unimodal approaches in predicting radiotherapy treatment outcomes for non-small-cell lung cancer patients. Multimodal AI has the potential to revolutionize robotics by enabling machines to perceive, understand, and interact with the world in ways that more closely mimic human capabilities. By integrating data from multiple sensors such as cameras, microphones, and touch sensors, robots can achieve enhanced environmental awareness, more natural human-robot interactions, and improved decision-making in complex scenarios. This technology paves the way for more adaptable and versatile robots capable of learning from demonstration, performing advanced manipulation tasks, and navigating diverse environments with greater ease. The creative industry stands to benefit significantly as well. Content creators can now envision AI platforms that generate coordinated audio-visual experiences from simple text descriptions. Film production workflows could be streamlined with AI-generated b-roll footage, while virtual assistants could become more empathetic by understanding not just words, but tone and facial expressions. The Data Challenge: Managing Multimodal Complexity However, the journey toward truly effective multimodal AI isn't without its hurdles. The primary challenge lies in data management – specifically, the need for diverse, high-quality datasets that span multiple modalities. While traditional LLMs could rely on text sources like Wikipedia and books, multimodal systems require a much broader spectrum of data, including podcasts, videos, medical imaging, and even biometric data from wearable devices. This expanded data requirement introduces new complexities in data quality control. Poor-quality inputs in any modality can compromise the entire system's performance. For instance, mislabeled video data might confuse visual recognition capabilities, while degraded audio could impair speech recognition accuracy. The old programming adage "garbage in, garbage out" takes on new significance in the multimodal era.\ Introducing Encord - the multimodal data development platform As the industry evolves, new platforms are emerging to address the unique challenges of multimodal AI development. At Encord, we're excited to announce our contribution to this revolution by launching a truly multimodal AI data development platform that enables teams to manage, curate, and label multiple data types within a single unified interface. Our platform addresses one of the most significant pain points in multimodal AI development: the fragmentation of data preparation workflows. Instead of juggling multiple tools for different data types, teams can now handle images, videos, audio files, documents, and DICOM files in one seamless environment. This consolidation significantly reduces development time and improves data quality consistency. A standout feature is our multimodal annotation and workflow orchestration capability, which allows teams to view and annotate multiple file types simultaneously, all in one platform. This unlocks a variety of use cases that previously were only possible through cumbersome workarounds.  A few of these include analyzing PDF reports alongside images, videos, or DICOM files to improve the accuracy and efficiency of annotation workflows by empowering labelers with extreme context. The platform also supports human-in-the-loop workflows and natively integrates with state-of-the-art models like SAM for automated labeling, making it ideal for both traditional AI development and emerging applications like RLHF (Reinforcement Learning from Human Feedback) for generative AI. The Road Ahead The transition to multimodal AI represents more than just a technological advancement – it's a fundamental shift in how we think about artificial intelligence. As these systems become more sophisticated in processing multiple data streams simultaneously, they move us closer to achieving more general forms of artificial intelligence that can understand and interact with the world in ways that feel more natural and human-like. However, success in this new era will depend heavily on our ability to manage and curate diverse data types effectively. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. The future of AI is multimodal, and it's arriving faster than many might have anticipated. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. How about you and your AI team - are you ready? The artificial intelligence landscape is undergoing a seismic shift. While text-based Large Language Models (LLMs) have dominated headlines and captured our imagination, a new paradigm is emerging that promises to revolutionize how we interact with AI: multimodal artificial intelligence. This evolution marks a fundamental change in how machines process and understand our world. The Natural Evolution: From Text to Multiple Modalities The Natural Evolution: From Text to Multiple Modalities Humans don't communicate solely through text – we interpret facial expressions, analyze tone of voice, and process visual information simultaneously. This multifaceted approach to communication is what makes human interaction so rich and nuanced. Now, AI is following suit, evolving beyond the constraints of text-only models to embrace a more holistic approach to understanding and generating content. Multimodal AI systems can process and generate multiple forms of data – text, images, audio, video, and documents – in unified models that more closely mirror human cognitive processes. We're already seeing impressive demonstrations of this technology, from foundational models that can generate videos or short films from written descriptions such as Meta’s Movie Gen, Pika Labs, or Runway’s Gen-3 Alpha, to platforms capable of real-time voice conversations that understand both words and emotional undertones like OpenAI’s GPT-4o. These developments aren't just technical achievements – they're reshaping how we think about artificial intelligence and its capabilities. Multimodal AI Potential to Transforming Industries The potential applications of multimodal AI are vast and transformative. In healthcare, systems that can simultaneously analyze medical images, patient voice recordings, and clinical documentation could revolutionize diagnostic processes. Early research shows promising results in detecting cognitive conditions like Alzheimer's disease by combining analysis of speech patterns with medical imaging data . Multimodal models utilizing data from radiology images, histopathology slides, genomics, and clinical data have shown potential to outperform unimodal approaches in predicting radiotherapy treatment outcomes for non-small-cell lung cancer patients. by combining analysis of speech patterns with medical imaging data by combining analysis of speech patterns with medical imaging data predicting radiotherapy treatment outcomes predicting radiotherapy treatment outcomes Multimodal AI has the potential to revolutionize robotics by enabling machines to perceive, understand, and interact with the world in ways that more closely mimic human capabilities. By integrating data from multiple sensors such as cameras, microphones, and touch sensors, robots can achieve enhanced environmental awareness, more natural human-robot interactions, and improved decision-making in complex scenarios. This technology paves the way for more adaptable and versatile robots capable of learning from demonstration, performing advanced manipulation tasks, and navigating diverse environments with greater ease. revolutionize robotics revolutionize robotics The creative industry stands to benefit significantly as well. Content creators can now envision AI platforms that generate coordinated audio-visual experiences from simple text descriptions. Film production workflows could be streamlined with AI-generated b-roll footage, while virtual assistants could become more empathetic by understanding not just words, but tone and facial expressions. The Data Challenge: Managing Multimodal Complexity However, the journey toward truly effective multimodal AI isn't without its hurdles. The primary challenge lies in data management – specifically, the need for diverse, high-quality datasets that span multiple modalities. While traditional LLMs could rely on text sources like Wikipedia and books, multimodal systems require a much broader spectrum of data, including podcasts, videos, medical imaging, and even biometric data from wearable devices. This expanded data requirement introduces new complexities in data quality control. Poor-quality inputs in any modality can compromise the entire system's performance. For instance, mislabeled video data might confuse visual recognition capabilities, while degraded audio could impair speech recognition accuracy. The old programming adage "garbage in, garbage out" takes on new significance in the multimodal era.\ Introducing Encord - the multimodal data development platform Encord Encord As the industry evolves, new platforms are emerging to address the unique challenges of multimodal AI development. At Encord, we're excited to announce our contribution to this revolution by launching a truly multimodal AI data development platform that enables teams to manage, curate, and label multiple data types within a single unified interface. Our platform addresses one of the most significant pain points in multimodal AI development: the fragmentation of data preparation workflows. Instead of juggling multiple tools for different data types, teams can now handle images, videos, audio files, documents, and DICOM files in one seamless environment. This consolidation significantly reduces development time and improves data quality consistency. A standout feature is our multimodal annotation and workflow orchestration capability, which allows teams to view and annotate multiple file types simultaneously, all in one platform. This unlocks a variety of use cases that previously were only possible through cumbersome workarounds.  A few of these include analyzing PDF reports alongside images, videos, or DICOM files to improve the accuracy and efficiency of annotation workflows by empowering labelers with extreme context. The platform also supports human-in-the-loop workflows and natively integrates with state-of-the-art models like SAM for automated labeling, making it ideal for both traditional AI development and emerging applications like RLHF (Reinforcement Learning from Human Feedback) for generative AI. The Road Ahead The transition to multimodal AI represents more than just a technological advancement – it's a fundamental shift in how we think about artificial intelligence. As these systems become more sophisticated in processing multiple data streams simultaneously, they move us closer to achieving more general forms of artificial intelligence that can understand and interact with the world in ways that feel more natural and human-like. However, success in this new era will depend heavily on our ability to manage and curate diverse data types effectively. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. The future of AI is multimodal, and it's arriving faster than many might have anticipated. Organizations that invest in robust data infrastructure and adopt comprehensive multimodal development platforms will be best positioned to capitalize on these emerging opportunities. How about you and your AI team - are you ready?

Welcome to the Multimodal AI Era

Why does the mind stay sharper than muscles as age advances? Why do professional athletes in several sports retire after 30? What is the difference between genes expressed elsewhere in the body and those expressed in the brain, by neurons? Why does muscle mass decrease after 30? If the mind controls muscle movement through life, does the mind have a role in muscle growth? An offspring does not inherit the language of the parent, but an offspring inherits anatomic features of the parent. What is the difference between the mechanism for language and those for the features of the parent? Both are made possible by genes, but one can be said to be indirect and the other say direct. Genes are present in all cells. They act as engines--with instructions--to make determinations. They work in muscle cells. They work across several cells and are expressed across functions. However, what is the relationship between when genes are expressed in several functions and when the brain is said to regulate those functions? Why is it possible that nurture for an offspring can result in huge differences from the parent, even if physical features are similar? What is the relationship between genes and the mind? Genes contain instructions, but it is postulated that they are almost totally regimented. Many of their instructions, conceptually, are not that flexible, so what they have to express is almost the same way it has to be expressed, without much changes--so to speak. It is theorized that what makes several functions--or options--possible is the mind. This means that genes are near fixed, but the mind runs the flexibility. A second difference is that the ability for the mind to give and receive information is greater than genes, that mostly send out instructions, though they receive information, it is not often as much as the mind, conceptually. Also, for what genes control directly and what the mind does too, directly, how do genes and the mind work together. What is the human mind? The mind is not genes nor neurons. The candidate for the human mind, conceptually, are the electrical and chemical signals of neurons, with their interactions and features in sets, in clusters of neurons across the central and peripheral nervous systems. Sets of signals have configurations [or arrangements] for which the basis of functions is held. This means that a smell, a sound, and so forth are all configurations of signals. Configuration templates arrive by the instruction of genes, but the contents are made by signals. However, there are several cases, conceptually, where genes are responsible for situations of the mind by anomalies in templates. This means that some individuals that have certain susceptibilities may have a template-induced configuration problem, such that when the problem takes place, with signals, it is at the error of genes. Simply, genes make it possible to have templates for which signals can build configurations. These templates--for several functions are generally similar--but anomalies exist within some, which may also be inherited, responsible for some kinds of addictions or mind disorders. A question is that for some of these template problems, are they from scratch or are they activated at a stage during development? Since genes are involved across cells, for development through life, it is possible to theorize that they also play roles in some of the templates for the configurations of mind, through adulthood, in many ways. This means that there could be continuous instruction passages about what templates should be, for certain functions, so that signals can then build configurations into them. In summary, genes can be said to make carvings, while signals enable a fill for those, as configurations. How Do Genes Work With The Mind? The mind is the front face of interaction with the world after birth, though the mind has already formed, conceptually before birth, it has no direct interface with the world. The mind becomes the operational source for many functions. Though genes in cells send out growth instructions, shaping cells for tissues, organs, and systems with timelines and extents, it is signals that operate at availability, while genes build availability. For example, the hand at any growth stage is by the genes, but its control or operation is by the mind. Genes can be said to have caps, theoretically, while the mind does not have caps. Even though genes lay out the possibility for what the mind can fill or build configurations for, the mind seems limitless with what it can learn, what it can be trained to do, though within the capacity of genes, but far beyond the allowance that genes have. It is theorized that a reason why sports retirement is common after 30, for many athletes, is that the stretch of genes becomes capped for certain activities around that age, even though the mind is still as sharp. Genes are also theorized to be responsible for aging, since their lack of continuous instruction for growth, unlike previously, may result in a peak then decline, by cells, of the tissue or organ. However, the mind, in use, often make it possible to keep functions active, even when growth by genes have stalled, conceptually. Mind and genes work closely together, but there is a lot that the mind decides. For example, what is the role of the mind in diseases? How do they relate to genes? Even though there are genetic anomalies responsible for some conditions, how does the mind factor? It is possible to use a conceptual model for the mind towards mapping the reach of genes against diseases. There is a recent feature, Gene therapy protects against motor neuron disease in rats, stating that, "University of Wisconsin–Madison researchers targeting a group of hereditary neurodegenerative diseases have found success using a gene therapy treatment in an animal model. The approach, which uses CRISPR-Cas9 genome editing technology, offers a unique and promising strategy that could one day treat rare but debilitating motor neuron diseases in humans. Hereditary spastic paraplegia, or HSP, is a group of movement disorders that cause progressive weakness and stiffness in the legs of people with certain inherited genetic mutations. The rare disorders usually lead to physical limitations and use of a wheelchair. Studying disease processes in animal models is an indispensable part of developing and testing any new treatments before they’re offered to humans. But scientists had historically struggled to replicate HSP’s symptoms and disease progression in animal models." There is another recent story, Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine, stating that, " Researchers at Washington University School of Medicine in St. Louis have developed a way to capture the effects of aging in the development of Alzheimer’s disease. They have devised a method to study aged neurons in the lab without a brain biopsy, an advancement that could contribute to a better understanding of the disease and new treatment strategies. The scientists transformed skin cells taken from patients with late-onset Alzheimer’s disease into brain cells called neurons. Late-onset Alzheimer’s develops gradually over many decades and only starts to show symptoms at age 65 or older. For the first time, these lab-derived neurons accurately reproduced the hallmarks of this type of dementia, including the amyloid beta buildup, tau protein deposits and neuronal cell death. By studying these cells, the researchers identified aspects of cells’ genomes — called retrotransposable elements, which change their activity as we age — in the development of late-onset Alzheimer’s disease. The findings suggest new treatment strategies targeting these factors." Why does the mind stay sharper than muscles as age advances? Why do professional athletes in several sports retire after 30? What is the difference between genes expressed elsewhere in the body and those expressed in the brain, by neurons? Why does muscle mass decrease after 30? If the mind controls muscle movement through life, does the mind have a role in muscle growth? An offspring does not inherit the language of the parent, but an offspring inherits anatomic features of the parent. What is the difference between the mechanism for language and those for the features of the parent? Both are made possible by genes, but one can be said to be indirect and the other say direct. Genes are present in all cells. They act as engines--with instructions--to make determinations. They work in muscle cells. They work across several cells and are expressed across functions. However, what is the relationship between when genes are expressed in several functions and when the brain is said to regulate those functions? Why is it possible that nurture for an offspring can result in huge differences from the parent, even if physical features are similar? What is the relationship between genes and the mind ? What is the relationship between genes and the mind Genes contain instructions, but it is postulated that they are almost totally regimented. Many of their instructions, conceptually, are not that flexible, so what they have to express is almost the same way it has to be expressed, without much changes--so to speak. It is theorized that what makes several functions--or options--possible is the mind. This means that genes are near fixed, but the mind runs the flexibility. A second difference is that the ability for the mind to give and receive information is greater than genes, that mostly send out instructions, though they receive information, it is not often as much as the mind, conceptually. Also, for what genes control directly and what the mind does too, directly, how do genes and the mind work together. What is the human mind? The mind is not genes nor neurons. The candidate for the human mind, conceptually, are the electrical and chemical signals of neurons, with their interactions and features in sets, in clusters of neurons across the central and peripheral nervous systems. Sets of signals have configurations [or arrangements] for which the basis of functions is held. This means that a smell, a sound, and so forth are all configurations of signals. Configuration templates arrive by the instruction of genes, but the contents are made by signals. templates However, there are several cases, conceptually, where genes are responsible for situations of the mind by anomalies in templates. This means that some individuals that have certain susceptibilities may have a template-induced configuration problem, such that when the problem takes place, with signals, it is at the error of genes. templates. template-induced Simply, genes make it possible to have templates for which signals can build configurations. These templates--for several functions are generally similar--but anomalies exist within some, which may also be inherited, responsible for some kinds of addictions or mind disorders. A question is that for some of these template problems, are they from scratch or are they activated at a stage during development? Since genes are involved across cells, for development through life, it is possible to theorize that they also play roles in some of the templates for the configurations of mind, through adulthood, in many ways. templates This means that there could be continuous instruction passages about what templates should be, for certain functions, so that signals can then build configurations into them. templates In summary, genes can be said to make carvings, while signals enable a fill for those, as configurations . while signals enable a fill for those, as configurations How Do Genes Work With The Mind? How Do Genes Work With The Mind? The mind is the front face of interaction with the world after birth, though the mind has already formed, conceptually before birth, it has no direct interface with the world. The mind becomes the operational source for many functions. Though genes in cells send out growth instructions, shaping cells for tissues, organs, and systems with timelines and extents, it is signals that operate at availability, while genes build availability. For example, the hand at any growth stage is by the genes, but its control or operation is by the mind. Genes can be said to have caps, theoretically, while the mind does not have caps. Even though genes lay out the possibility for what the mind can fill or build configurations for, the mind seems limitless with what it can learn, what it can be trained to do, though within the capacity of genes, but far beyond the allowance that genes have. It is theorized that a reason why sports retirement is common after 30, for many athletes, is that the stretch of genes becomes capped for certain activities around that age, even though the mind is still as sharp. Genes are also theorized to be responsible for aging, since their lack of continuous instruction for growth, unlike previously, may result in a peak then decline, by cells, of the tissue or organ. However, the mind, in use, often make it possible to keep functions active, even when growth by genes have stalled, conceptually. Mind and genes work closely together, but there is a lot that the mind decides. For example, what is the role of the mind in diseases? How do they relate to genes? Even though there are genetic anomalies responsible for some conditions, how does the mind factor? It is possible to use a conceptual model for the mind towards mapping the reach of genes against diseases. There is a recent feature, Gene therapy protects against motor neuron disease in rats , stating that, "University of Wisconsin–Madison researchers targeting a group of hereditary neurodegenerative diseases have found success using a gene therapy treatment in an animal model. The approach, which uses CRISPR-Cas9 genome editing technology, offers a unique and promising strategy that could one day treat rare but debilitating motor neuron diseases in humans. Hereditary spastic paraplegia, or HSP, is a group of movement disorders that cause progressive weakness and stiffness in the legs of people with certain inherited genetic mutations. The rare disorders usually lead to physical limitations and use of a wheelchair. Studying disease processes in animal models is an indispensable part of developing and testing any new treatments before they’re offered to humans. But scientists had historically struggled to replicate HSP’s symptoms and disease progression in animal models." Gene therapy protects against motor neuron disease in rats There is another recent story, Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine , stating that, " Researchers at Washington University School of Medicine in St. Louis have developed a way to capture the effects of aging in the development of Alzheimer’s disease. They have devised a method to study aged neurons in the lab without a brain biopsy, an advancement that could contribute to a better understanding of the disease and new treatment strategies. The scientists transformed skin cells taken from patients with late-onset Alzheimer’s disease into brain cells called neurons. Late-onset Alzheimer’s develops gradually over many decades and only starts to show symptoms at age 65 or older. For the first time, these lab-derived neurons accurately reproduced the hallmarks of this type of dementia, including the amyloid beta buildup, tau protein deposits and neuronal cell death. By studying these cells, the researchers identified aspects of cells’ genomes — called retrotransposable elements, which change their activity as we age — in the development of late-onset Alzheimer’s disease. The findings suggest new treatment strategies targeting these factors." Aging-related genomic culprit found in Alzheimer’s disease – WashU Medicine

Genes or Mind? Which is More Flexible? Roles in Aging, Sports Retirement and Growth

Imagine discovering an investment that goes up 50% on average each year with occasional big drops that you can buy for even further gains. Would you buy it? No. What if that asset went up 10% on average each year? Would you buy it? Yes. Chances are, you already have. I’m talking about Bitcoin. You pay a high price for a cheery consensus That may sound weird. Stupid, even. Who would turn down a 50% opportunity in exchange for a 10% opportunity? The US stock market only gives you 7% annualized. I agree. Yet, that’s what people do when they buy Bitcoin during bull markets like today’s. They don’t buy Bitcoin for $16,000, $32,000, $48,000, or $64,000. Instead, they buy it for $80,000 or more. They heard some people got ridiculously rich with Bitcoin and think they can do it, too. Two years ago, maybe. That was during the depths of a wretched crypto bear market. Now? One Bitcoin is worth more than the average US worker’s annual salary. Bitcoin’s market cap is almost $2 trillion. Most people can’t afford to buy enough Bitcoin to get rich, and those who already have Bitcoin don’t have enough upside to see the massive multiples necessary to retire their bloodlines. A letdown, if you want it to be You won’t find such realistic expectations on social media or the email solicitations you get from other crypto gurus. Some tell you “banana zone will send prices to the moon” and “S2F” predicts a $500,000 bitcoin in 2025. Others admit the chance to get rich with Bitcoin has passed, but tell you you need to buy smaller cryptos with bloated market caps and massive selling from insiders. Nobody will pay $2,500 for a newsletter subscription or spend 50 hours reading investment blogs for a 50% annualized return, even though that beats most traders and every professional fund manager. They’ll gladly pay as much or more for five altcoin picks that will make you $5 million or a secret strategy to make a quick fortune. Only one outcome is realistic, but you pay more for the fantasy. Cryptocurrency is a marathon masquerading as a sprint. They sell you on the sprint, but the real gains come from the marathon. Your best bet This is not what you're supposed to believe. Consensus opinion told you to buy above $50,000 in 2021, sell at $40,000 in 2022, and buy again at $32,000 in 2023. With that approach, a $50,000 investment would have grown to $75,000 by July 2024. That’s a 50% return in three years! Way better than the S&P 500's 16% return over that time. At today’s price of $90,000, you’re at 60% gains. Boom! You still come out way ahead of the S&P 500’s 25% return to date. Feels great, right? Your best financial advisors can barely beat the stock market. You crushed them. You gained 60% in three years! Fun fact: If you had bought Bitcoin blindly, on a fixed schedule, in a fixed amount, regardless of circumstances, you would have bought at an average price of roughly $40,000 for a 123% gain. My plan beat both approaches for a return of 160% over that time. You bought at an average price of roughly $34,000. If you sold when I told you to, you did even better. https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true Maybe not rich but pretty, pretty, pretty good While 160% is better than 123%, it's not the huge, life-changing returns you have in your head. You might get that with altcoins, but with 50,000 to choose from, you're going to have to get lucky and get your timing perfect. Mark, past performance doesn’t dictate future results! True, but it’s the only way we can set realistic expectations. Can you get rich with Bitcoin by doubling your money every few years? Yes, if you start with a decent amount of money, stick around for years, buy the big crashes and bear markets, and buy a lot when you buy. In other words, you need time, money, and patience. Most likely, you're getting into crypto now because you want to make money quickly, with as little effort as possible and don't have much capital to invest. No time, money, or patience. The good news? You can beat 50% annualized returns with Bitcoin. How? Wait for better opportunities. Subscribe to my newsletter, Crypto is Easy, so you know when those opportunities come. You'll also get occasional trading opportunities and altcoin reports, as well as access to my plan and everything else you get with your subscription. Timing is everything With Bitcoin, you may not end up with fabulous riches, a garage full of Lambo's, and enough money to build a nuclear bunker, but you'll do better than all of your friends. Wall Street is on board. Trump’s a fan. Bitcoin’s network continues to grow. If you’re patient, you will have tons of upside. It's all about planning properly. That may seem disappointing. It's not what your favorite guru tells you in their email solicitations. But honestly, is it so bad to wait for a happy, easy way to buy into an asset class that has outpaced every other investment of the past 10 years and offers unlimited growth in the future? Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon. Learn more about him in his bio and connect with him on Tealfeed. Imagine discovering an investment that goes up 50% on average each year with occasional big drops that you can buy for even further gains. Would you buy it? No. What if that asset went up 10% on average each year? Would you buy it? Yes. Chances are, you already have. I’m talking about Bitcoin. You pay a high price for a cheery consensus That may sound weird. Stupid, even. Who would turn down a 50% opportunity in exchange for a 10% opportunity? The US stock market only gives you 7% annualized. I agree. Yet, that’s what people do when they buy Bitcoin during bull markets like today’s. They don’t buy Bitcoin for $16,000, $32,000, $48,000, or $64,000. Instead, they buy it for $80,000 or more. They heard some people got ridiculously rich with Bitcoin and think they can do it, too. Two years ago, maybe. That was during the depths of a wretched crypto bear market. Now? One Bitcoin is worth more than the average US worker’s annual salary. Bitcoin’s market cap is almost $2 trillion. Most people can’t afford to buy enough Bitcoin to get rich, and those who already have Bitcoin don’t have enough upside to see the massive multiples necessary to retire their bloodlines. A letdown, if you want it to be You won’t find such realistic expectations on social media or the email solicitations you get from other crypto gurus. Some tell you “banana zone will send prices to the moon” and “S2F” predicts a $500,000 bitcoin in 2025. Others admit the chance to get rich with Bitcoin has passed, but tell you you need to buy smaller cryptos with bloated market caps and massive selling from insiders. Nobody will pay $2,500 for a newsletter subscription or spend 50 hours reading investment blogs for a 50% annualized return, even though that beats most traders and every professional fund manager. They’ll gladly pay as much or more for five altcoin picks that will make you $5 million or a secret strategy to make a quick fortune. Only one outcome is realistic, but you pay more for the fantasy. Cryptocurrency is a marathon masquerading as a sprint. They sell you on the sprint, but the real gains come from the marathon. Your best bet This is not what you're supposed to believe. Consensus opinion told you to buy above $50,000 in 2021, sell at $40,000 in 2022, and buy again at $32,000 in 2023. With that approach, a $50,000 investment would have grown to $75,000 by July 2024. That’s a 50% return in three years! Way better than the S&P 500's 16% return over that time. At today’s price of $90,000, you’re at 60% gains. Boom! You still come out way ahead of the S&P 500’s 25% return to date. Feels great, right? Your best financial advisors can barely beat the stock market. You crushed them. You gained 60% in three years! You gained 60% in three years! You gained 60% in three years! Fun fact: If you had bought Bitcoin blindly, on a fixed schedule, in a fixed amount, regardless of circumstances, you would have bought at an average price of roughly $40,000 for a 123% gain . 123% gain My plan beat both approaches for a return of 160% over that time. You bought at an average price of roughly $34,000. If you sold when I told you to, you did even better. My plan My plan a return of 160% https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true https://cryptoiseasy.beehiiv.com/p/plan-buying-selling-bitcoin?embedable=true Maybe not rich but pretty, pretty, pretty good While 160% is better than 123%, it's not the huge, life-changing returns you have in your head. You might get that with altcoins, but with 50,000 to choose from, you're going to have to get lucky and get your timing perfect. Mark, past performance doesn’t dictate future results! Mark, past performance doesn’t dictate future results! True, but it’s the only way we can set realistic expectations. Can you get rich with Bitcoin by doubling your money every few years? Yes, if you start with a decent amount of money, stick around for years, buy the big crashes and bear markets, and buy a lot when you buy. In other words, you need time, money, and patience. Most likely, you're getting into crypto now because you want to make money quickly, with as little effort as possible and don't have much capital to invest. No time, money, or patience. The good news? You can beat 50% annualized returns with Bitcoin. How? Wait for better opportunities. Subscribe to my newsletter, Crypto is Easy , so you know when those opportunities come. Crypto is Easy Crypto is Easy Crypto is Easy You'll also get occasional trading opportunities and altcoin reports, as well as access to my plan and everything else you get with your subscription . my plan my plan everything else you get with your subscription Timing is everything With Bitcoin, you may not end up with fabulous riches, a garage full of Lambo's, and enough money to build a nuclear bunker, but you'll do better than all of your friends. Wall Street is on board. Trump’s a fan. Bitcoin’s network continues to grow. If you’re patient, you will have tons of upside. It's all about planning properly. That may seem disappointing. It's not what your favorite guru tells you in their email solicitations. But honestly, is it so bad to wait for a happy, easy way to buy into an asset class that has outpaced every other investment of the past 10 years and offers unlimited growth in the future? Mark Helfman publishes the Crypto is Easy newsletter. He is also the author of three books and a top Bitcoin writer on Medium and Hacker Noon . Learn more about him in his bio and connect with him on Tealfeed . Crypto is Easy Crypto is Easy Crypto is Easy Medium Medium Medium Hacker Noon Hacker Noon Hacker Noon bio bio Tealfeed Tealfeed

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