Leveraging Data Analytics and Technology to Address Population Health Challenges and Identify Trends

Though population health has been a concern for a long time, it really became a topic at the forefront of healthcare with the COVID-19 Pandemic. Through the pandemic, we not only put more emphasis on being able to track the spread of illnesses by location but also on identifying the challenges a community faces in staying safe and healthy. For example, our quick push towards a virtual world highlighted the struggles rural communities face with reliable network coverage that is needed to achieve a virtual world. So now as we come out of the pandemic with a greater awareness of population health challenges, we must now use the technology and data analytics we have to address these challenges and ideally identify trends so that we can fix them before it becomes a larger problem.

We reached out to our incredible Healthcare IT Today Community with the question – How are health organizations leveraging data analytics and technology to address population health challenges and identify trends that can inform public health interventions? The following are their answers.

Paulo Pinho, Chief Medical and Strategy Officer at Discern Health
Health organizations are increasingly leveraging data and technology to address population health challenges. Predictive analytics plays a crucial role in this effort, by providing the ability to forecast health outcomes and target interventions accordingly. A new generation of companies are at the forefront of this movement, using data analytics to identify high-risk older adult patients and enable targeted interventions to mitigate both medical and social factors that drive health risks. By analyzing electronic health records, claims data, and other sources, they can predict patient outcomes, such as hospitalization risk, and enable healthcare providers to take proactive steps to prevent adverse events. This data-driven approach has the potential to significantly improve health outcomes, reduce healthcare costs, and enhance the overall quality of care for vulnerable populations.

Jay Anders, MD, Chief Medical Officer at Medicomp Systems
With the release of HTI-1 and now HTI-2, the ONC is laying the groundwork for harmonizing communication in the now fragmented public health space. The Covid pandemic taught us that sharing public health data is critical in managing the response to any large public health crisis. In addition, this new initiative to share data will help manage the more routine public health issues such as immunizations and allow for a better understanding of how data like SDOH can be used to enhance healthcare and give providers the tools they need to better manage their patients. On the flip side, we need to monitor the use of this data so that it cannot be used for data mining to target vulnerable populations.

Vatsala Kapur, Vice President, External Affairs at Bamboo Health
It’s an exciting time in the behavioral health space as health organizations leverage data analytics and technology to improve care coordination. Take, for example, individuals with co-occurring mental health and substance use challenges — of the 20.3 million adults with substance use disorders, 37.9% also had mental health conditions. Historically, many initiatives have focused on treating only one piece of the puzzle – either substance use disorder or mental health. However, we know these are often interconnected issues. Previously, a shortage and lack of knowledge about behavioral health services and community-based alternatives have been barriers to treatment for those in need of care, particularly individuals in crisis.

Now, health organizations are beginning to use technology to centralize behavioral treatment resources, expand access, and improve the timeliness of quality care. Real-time technology solutions allow health organizations and state governments to see which programs have capacity, making connecting patients with available treatment resources easier. Solutions for the 988 Suicide and Crisis lifeline can now align call center and mobile response team efforts to connect people in crisis to appropriate care services. Increased interconnectivity between primary care providers and emergency departments through state-level technology solutions creates a cohesive system for behavioral healthcare that increases access to appropriate care for underserved populations. If we can close gaps in care and reduce unnecessary utilization through real-time patient data, we will reduce costs and offer patients the right level of care during pivotal moments in their care journey. With $1.5 trillion spent on the U.S. opioid epidemic in 2020 and 50 percent of adults with symptoms of anxiety and/or depressive disorder reporting an unmet need for counseling or therapy, it’s clear there’s a need for these solutions now.

Salvatore Viscomi, Cofounder and CEO at Carna Health
Health organizations are increasingly leveraging data analytics and technology to address chronic kidney disease (CKD) and improve health outcomes. Predictive analytics and artificial intelligence (AI) play a key role by identifying individuals at risk of CKD before severe symptoms arise, allowing for targeted screening, early interventions, and personalized care plans. This approach not only enhances patient outcomes but also reduces healthcare costs. Combining digital technologies with point-of-care testing further refines real-time diagnostics and treatment adjustments. Additionally, upskilling medical professionals ensures that patients receive specialized care. This integrated strategy, melding predictive analytics, advanced technology, and enhanced professional expertise – is essential for effectively managing CKD and optimizing public health interventions.

Chris Mazzanti, Chief Operating Officer at Carta Healthcare
Data is the single most important ingredient for improving healthcare. Clinical registries organize data, make it accessible through analytics, and are primarily used by clinicians to improve healthcare. Registries play an important role in managing populations, especially those with chronic diseases such as diabetes or cancer. Unfortunately, the data required to populate a registry is most often buried within the clinical notes in an EHR or other systems in unstructured formats. With advances in artificial intelligence, and particularly generative AI, there is a substantial opportunity to leverage AI to harvest data for registries much faster, at a lower cost, and with higher quality, thereby making population health analytics even more accessible, timely, accurate, and relevant than ever before.

Kim Perry, Chief Growth Officer at emtelligent
Population health initiatives are crucial for improving the overall well-being of communities by uncovering key health trends and identifying the root causes of health disparities. Artificial Intelligence (AI) technologies like natural language processing (NLP) play a pivotal role in population health by transforming the 80% of unstructured healthcare data found in patient records into actionable insights — identifying community-based health trends, uncovering key social determinants, and unlocking critical feedback to guide new clinical interventions. By making this vast data accessible, medical NLP contributes to the development of evidence-based best practices for wellness and treatment programs, ultimately driving measurable improvements in quality, accessibility, and cost of care at scale.

Ahzam Afzal, Co-Founder and CEO at Puzzle Healthcare
A very significant challenge across post-acute care settings is patients being readmitted to the hospital for a previously treated condition. Skilled nursing facilities are struggling to care for patients with chronic illnesses like COPD, diabetes, and heart disease, among others. A shortage of nurses and clinicians, as well as a shortage of hospital staff to track patients’ progress post-discharge, results in gaps in transitions of care between facilities, which can result in patients experiencing worse health outcomes and being readmitted to the hospital. Better use of real-time patient health monitoring and tracking data can enable care coordination teams to more effectively identify patients who are at risk due to chronic illnesses, and deliver timely interventions that lead to better health outcomes, avoiding a readmission. As an example, when a patient in an SNF setting experiences a decrease in mobility or is showing the warning signs that their condition is being exacerbated, the care coordination team can immediately deliver focused care that addresses their complex health needs. Data analytics and remote monitoring technology are vital tools to reduce patient readmissions and enhance the overall quality of care in post-acute settings.

Laura Ferrara, Chief Population Health Officer at Intus Care
Health organizations are increasingly, and should be, harnessing data analytics and technology to identify population health outcomes and trends in order to optimize care management strategies. Within healthcare, there is a lot of data across different healthcare settings, and it is critically important for the data to be timely and accurate so organizations can use it for process improvements, looking for trends and ways to reduce undesired outcomes, both in terms of patient care and costs. A data-driven approach coupled with the expertise of care teams and what they are seeing in real life allow organizations to get ahead of the trends and identify areas of risk for individuals and populations at large to intervene early and prevent adverse health events. This approach supports targeted interventions, reduces unnecessary hospitalizations and other undesired events, and promotes efficient use of healthcare resources.

Evan Floden, Co-Founder and CEO at Seqera
Public organizations are increasingly leveraging data analytics and technology to address population health challenges, particularly using large-scale genomic analysis. The integration of genomic data allows these organizations to track trends and identify public health interventions in real time. For instance, genomic surveillance by the CDC has played a key role in monitoring the spread of infectious diseases like COVID-19 and mpox, providing vital data that informs vaccine development and public health strategies. Genomics England has demonstrated how large-scale data sharing can improve healthcare outcomes by identifying genetic variations that influence disease predisposition and response to treatments. Open science across researchers accelerates this process, enabling precision medicine to become more accessible and effective for diverse populations.

We are seeing how better software drives better science, resulting in improved healthcare outcomes. Open-source tools, such as workflow managers, empower researchers to process and analyze vast datasets with greater speed and accuracy, breaking down computational barriers that often slow down scientific discovery. With platforms that facilitate scalable, reproducible research, we contribute to an ecosystem of open science where collaboration and shared data resources allow for rapid identification of health trends and precision medicine applications. This synergy between advanced technology and open collaboration ensures that discoveries are quickly translated into actionable health interventions that benefit broader populations​.

Kerry Love, Founder, CEO, and President at Sunflower Therapeutics
Addressing public health challenges and reducing disparities requires a focus on innovation, accessibility, and efficiency. Just as advanced manufacturing technologies streamline the production of biologic medicines, data analytics, and technology can transform how we identify health trends and inform interventions. By reimagining traditional methods—whether in biologics or public health strategies—we can enhance the accessibility and scalability of health solutions. Leveraging these advancements allows us to better address social determinants of health, improving outcomes and reducing disparities by ensuring timely and effective responses to emerging needs.

Paul L Wilder, Executive Director at CommonWell Health Alliance
The biggest shift we’ve seen in population health is a move toward focusing on individual health within a population, rather than relying on broad population-wide programs. Each person now represents their own program, tailored to their specific needs. Years ago, you might target a condition like diabetes, gather aggregate data from provider practices, and develop outreach and intervention programs. Today, we’re assessing each individual against norms and best practices, intervening in real time where needed. While the best practices driving this approach were built from years of aggregate data sharing, access to individual data has become easier thanks to interoperability. For decades, I’ve said that good population health turns into great individual health. Now, we’re often skipping that first step because it’s easier than ever to gather, process, and analyze individual records. I believe this trend will continue, especially on the provider side, as risk is more commonly shared and more people gain access to preventive care through proper insurance coverage.

Eyal Zimlichman, Chief Transformation Officer and Chief Innovation Officer at Sheba Medical Center
It is currently not possible for an individual physician or health system alone to accurately predict health deterioration across a large population. However, by adopting three key tools we can design more effective and informed public health interventions.

First, we need to understand the social determinants of health. Data on demographics, education, environment, and social contexts all contribute to the effectiveness of a health intervention. By combining this data with medical records and family history, we can accurately predict which of the population is likely to develop a chronic condition.

Second, AI and big data tools are important for analyzing these combined social determinants and medical records data and designing the intervention. A streamlined early prediction tool when integrated into a health system can help patients receive earlier diagnoses and treatment. By expanding the use of these tools to cover the whole population we can improve efficiency, save costs, and improve overall health.

Finally, to make public health interventions as impactful as possible, we need to scale at a low cost. Digital health tools that are widely used, including smartphones and wearables, can scale up and make the interventions easily accessible. However, it is the responsibility of the healthcare system to improve digital literacy and ensure that patients know how to properly use these digital health tools.

Dani Arousi, Global Commercial Director, dbMotion, at Altera Digital Health
We have seen a significant shift in the last 5 years in population and public health reporting. Many healthcare organizations are transitioning from weekly, monthly, and/or quarterly reporting intervals to leveraging real-time analytics and predictive AI reporting where possible. This partially stems from growing interest among government and government-funded agencies in health surveillance and monitoring due to the COVID-19 pandemic and financial pressures. With timely reporting and monitoring tools in place, organizations partnering with governments can detect infectious disease outbreaks more quickly. This then leads to a more proactive response, including identifying cohorts who are at high risk and aligning with various immunization programs that are available.

David Schweppe, Chief Analytics Officer at MedeAnalytics
I believe that data analytics is revolutionizing population health by helping us identify high-risk groups, predict health trends, and monitor disease outbreaks. With technologies like EHRs, SDOH, big data platforms, and AI/ML, we can analyze comprehensive data and make accurate predictions. By using AI and advanced techniques, we can help identify patients with certain risk factors and engage them in timely preventive strategies for both physical and mental health conditions. Essentially, advanced analytics can help predict and find individuals at higher risk, enabling proactive care to hopefully reduce the burden of disease and help people live longer healthier lives.

There are a lot of good insights here! Huge thank you to Paulo Pinho, Chief Medical and Strategy Officer at Discern Health, Jay Anders, MD, Chief Medical Officer at Medicomp Systems, Vatsala Kapur, Vice President, External Affairs at Bamboo Health, Salvatore Viscomi, Co-Founder and CEO at Carna Health, Chris Mazzanti, Chief Operating Officer at Carta Healthcare, Kim Perry, Chief Growth Officer at emtelligent, Ahzam Afzal, Co-Founder and CEO at Puzzle Healthcare, Laura Ferrara, Chief Population Health Officer at Intus Care, Evan Floden, Co-Founder and CEO at Seqera, Kerry Love, Founder, CEO, and President at Sunflower Therapeutics, Paul L Wilder, Executive Director at CommonWell Health Alliance, Eyal Zimlichman, Chief Transformation Officer and Chief Innovation Officer at Sheba Medical Center, Dani Arousi, Global Commercial Director, dbMotion, at Altera Digital Health, and David Schweppe, Chief Analytics Officer at MedeAnalytics for taking the time out of your day to submit a quote in to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think health organizations are leveraging data analytics and technology to address population health challenges and identify trends that can inform public health interventions? Let us know either in the comments down below or over on social media! We’d love to hear from all of you!

Though population health has been a concern for a long time, it really became a topic at the forefront of healthcare with the COVID-19 Pandemic. Through the pandemic, we not only put more emphasis on being able to track the spread of illnesses by location but also on identifying the challenges a community faces in staying safe and healthy. For example, our quick push towards a virtual world highlighted the struggles rural communities face with reliable network coverage that is needed to achieve a virtual world. So now as we come out of the pandemic with a greater awareness of population health challenges, we must now use the technology and data analytics we have to address these challenges and ideally identify trends so that we can fix them before it becomes a larger problem.

We reached out to our incredible Healthcare IT Today Community with the question – How are health organizations leveraging data analytics and technology to address population health challenges and identify trends that can inform public health interventions? The following are their answers.

Paulo Pinho, Chief Medical and Strategy Officer at Discern Health
Health organizations are increasingly leveraging data and technology to address population health challenges. Predictive analytics plays a crucial role in this effort, by providing the ability to forecast health outcomes and target interventions accordingly. A new generation of companies are at the forefront of this movement, using data analytics to identify high-risk older adult patients and enable targeted interventions to mitigate both medical and social factors that drive health risks. By analyzing electronic health records, claims data, and other sources, they can predict patient outcomes, such as hospitalization risk, and enable healthcare providers to take proactive steps to prevent adverse events. This data-driven approach has the potential to significantly improve health outcomes, reduce healthcare costs, and enhance the overall quality of care for vulnerable populations.

Jay Anders, MD, Chief Medical Officer at Medicomp Systems
With the release of HTI-1 and now HTI-2, the ONC is laying the groundwork for harmonizing communication in the now fragmented public health space. The Covid pandemic taught us that sharing public health data is critical in managing the response to any large public health crisis. In addition, this new initiative to share data will help manage the more routine public health issues such as immunizations and allow for a better understanding of how data like SDOH can be used to enhance healthcare and give providers the tools they need to better manage their patients. On the flip side, we need to monitor the use of this data so that it cannot be used for data mining to target vulnerable populations.

Vatsala Kapur, Vice President, External Affairs at Bamboo Health
It’s an exciting time in the behavioral health space as health organizations leverage data analytics and technology to improve care coordination. Take, for example, individuals with co-occurring mental health and substance use challenges — of the 20.3 million adults with substance use disorders, 37.9% also had mental health conditions. Historically, many initiatives have focused on treating only one piece of the puzzle – either substance use disorder or mental health. However, we know these are often interconnected issues. Previously, a shortage and lack of knowledge about behavioral health services and community-based alternatives have been barriers to treatment for those in need of care, particularly individuals in crisis.

Now, health organizations are beginning to use technology to centralize behavioral treatment resources, expand access, and improve the timeliness of quality care. Real-time technology solutions allow health organizations and state governments to see which programs have capacity, making connecting patients with available treatment resources easier. Solutions for the 988 Suicide and Crisis lifeline can now align call center and mobile response team efforts to connect people in crisis to appropriate care services. Increased interconnectivity between primary care providers and emergency departments through state-level technology solutions creates a cohesive system for behavioral healthcare that increases access to appropriate care for underserved populations. If we can close gaps in care and reduce unnecessary utilization through real-time patient data, we will reduce costs and offer patients the right level of care during pivotal moments in their care journey. With $1.5 trillion spent on the U.S. opioid epidemic in 2020 and 50 percent of adults with symptoms of anxiety and/or depressive disorder reporting an unmet need for counseling or therapy, it’s clear there’s a need for these solutions now.

Salvatore Viscomi, Cofounder and CEO at Carna Health
Health organizations are increasingly leveraging data analytics and technology to address chronic kidney disease (CKD) and improve health outcomes. Predictive analytics and artificial intelligence (AI) play a key role by identifying individuals at risk of CKD before severe symptoms arise, allowing for targeted screening, early interventions, and personalized care plans. This approach not only enhances patient outcomes but also reduces healthcare costs. Combining digital technologies with point-of-care testing further refines real-time diagnostics and treatment adjustments. Additionally, upskilling medical professionals ensures that patients receive specialized care. This integrated strategy, melding predictive analytics, advanced technology, and enhanced professional expertise – is essential for effectively managing CKD and optimizing public health interventions.

Chris Mazzanti, Chief Operating Officer at Carta Healthcare
Data is the single most important ingredient for improving healthcare. Clinical registries organize data, make it accessible through analytics, and are primarily used by clinicians to improve healthcare. Registries play an important role in managing populations, especially those with chronic diseases such as diabetes or cancer. Unfortunately, the data required to populate a registry is most often buried within the clinical notes in an EHR or other systems in unstructured formats. With advances in artificial intelligence, and particularly generative AI, there is a substantial opportunity to leverage AI to harvest data for registries much faster, at a lower cost, and with higher quality, thereby making population health analytics even more accessible, timely, accurate, and relevant than ever before.

Kim Perry, Chief Growth Officer at emtelligent
Population health initiatives are crucial for improving the overall well-being of communities by uncovering key health trends and identifying the root causes of health disparities. Artificial Intelligence (AI) technologies like natural language processing (NLP) play a pivotal role in population health by transforming the 80% of unstructured healthcare data found in patient records into actionable insights — identifying community-based health trends, uncovering key social determinants, and unlocking critical feedback to guide new clinical interventions. By making this vast data accessible, medical NLP contributes to the development of evidence-based best practices for wellness and treatment programs, ultimately driving measurable improvements in quality, accessibility, and cost of care at scale.

Ahzam Afzal, Co-Founder and CEO at Puzzle Healthcare
A very significant challenge across post-acute care settings is patients being readmitted to the hospital for a previously treated condition. Skilled nursing facilities are struggling to care for patients with chronic illnesses like COPD, diabetes, and heart disease, among others. A shortage of nurses and clinicians, as well as a shortage of hospital staff to track patients’ progress post-discharge, results in gaps in transitions of care between facilities, which can result in patients experiencing worse health outcomes and being readmitted to the hospital. Better use of real-time patient health monitoring and tracking data can enable care coordination teams to more effectively identify patients who are at risk due to chronic illnesses, and deliver timely interventions that lead to better health outcomes, avoiding a readmission. As an example, when a patient in an SNF setting experiences a decrease in mobility or is showing the warning signs that their condition is being exacerbated, the care coordination team can immediately deliver focused care that addresses their complex health needs. Data analytics and remote monitoring technology are vital tools to reduce patient readmissions and enhance the overall quality of care in post-acute settings.

Laura Ferrara, Chief Population Health Officer at Intus Care
Health organizations are increasingly, and should be, harnessing data analytics and technology to identify population health outcomes and trends in order to optimize care management strategies. Within healthcare, there is a lot of data across different healthcare settings, and it is critically important for the data to be timely and accurate so organizations can use it for process improvements, looking for trends and ways to reduce undesired outcomes, both in terms of patient care and costs. A data-driven approach coupled with the expertise of care teams and what they are seeing in real life allow organizations to get ahead of the trends and identify areas of risk for individuals and populations at large to intervene early and prevent adverse health events. This approach supports targeted interventions, reduces unnecessary hospitalizations and other undesired events, and promotes efficient use of healthcare resources.

Evan Floden, Co-Founder and CEO at Seqera
Public organizations are increasingly leveraging data analytics and technology to address population health challenges, particularly using large-scale genomic analysis. The integration of genomic data allows these organizations to track trends and identify public health interventions in real time. For instance, genomic surveillance by the CDC has played a key role in monitoring the spread of infectious diseases like COVID-19 and mpox, providing vital data that informs vaccine development and public health strategies. Genomics England has demonstrated how large-scale data sharing can improve healthcare outcomes by identifying genetic variations that influence disease predisposition and response to treatments. Open science across researchers accelerates this process, enabling precision medicine to become more accessible and effective for diverse populations.

We are seeing how better software drives better science, resulting in improved healthcare outcomes. Open-source tools, such as workflow managers, empower researchers to process and analyze vast datasets with greater speed and accuracy, breaking down computational barriers that often slow down scientific discovery. With platforms that facilitate scalable, reproducible research, we contribute to an ecosystem of open science where collaboration and shared data resources allow for rapid identification of health trends and precision medicine applications. This synergy between advanced technology and open collaboration ensures that discoveries are quickly translated into actionable health interventions that benefit broader populations​.

Kerry Love, Founder, CEO, and President at Sunflower Therapeutics
Addressing public health challenges and reducing disparities requires a focus on innovation, accessibility, and efficiency. Just as advanced manufacturing technologies streamline the production of biologic medicines, data analytics, and technology can transform how we identify health trends and inform interventions. By reimagining traditional methods—whether in biologics or public health strategies—we can enhance the accessibility and scalability of health solutions. Leveraging these advancements allows us to better address social determinants of health, improving outcomes and reducing disparities by ensuring timely and effective responses to emerging needs.

Paul L Wilder, Executive Director at CommonWell Health Alliance
The biggest shift we’ve seen in population health is a move toward focusing on individual health within a population, rather than relying on broad population-wide programs. Each person now represents their own program, tailored to their specific needs. Years ago, you might target a condition like diabetes, gather aggregate data from provider practices, and develop outreach and intervention programs. Today, we’re assessing each individual against norms and best practices, intervening in real time where needed. While the best practices driving this approach were built from years of aggregate data sharing, access to individual data has become easier thanks to interoperability. For decades, I’ve said that good population health turns into great individual health. Now, we’re often skipping that first step because it’s easier than ever to gather, process, and analyze individual records. I believe this trend will continue, especially on the provider side, as risk is more commonly shared and more people gain access to preventive care through proper insurance coverage.

Eyal Zimlichman, Chief Transformation Officer and Chief Innovation Officer at Sheba Medical Center
It is currently not possible for an individual physician or health system alone to accurately predict health deterioration across a large population. However, by adopting three key tools we can design more effective and informed public health interventions.

First, we need to understand the social determinants of health. Data on demographics, education, environment, and social contexts all contribute to the effectiveness of a health intervention. By combining this data with medical records and family history, we can accurately predict which of the population is likely to develop a chronic condition.

Second, AI and big data tools are important for analyzing these combined social determinants and medical records data and designing the intervention. A streamlined early prediction tool when integrated into a health system can help patients receive earlier diagnoses and treatment. By expanding the use of these tools to cover the whole population we can improve efficiency, save costs, and improve overall health.

Finally, to make public health interventions as impactful as possible, we need to scale at a low cost. Digital health tools that are widely used, including smartphones and wearables, can scale up and make the interventions easily accessible. However, it is the responsibility of the healthcare system to improve digital literacy and ensure that patients know how to properly use these digital health tools.

Dani Arousi, Global Commercial Director, dbMotion, at Altera Digital Health
We have seen a significant shift in the last 5 years in population and public health reporting. Many healthcare organizations are transitioning from weekly, monthly, and/or quarterly reporting intervals to leveraging real-time analytics and predictive AI reporting where possible. This partially stems from growing interest among government and government-funded agencies in health surveillance and monitoring due to the COVID-19 pandemic and financial pressures. With timely reporting and monitoring tools in place, organizations partnering with governments can detect infectious disease outbreaks more quickly. This then leads to a more proactive response, including identifying cohorts who are at high risk and aligning with various immunization programs that are available.

David Schweppe, Chief Analytics Officer at MedeAnalytics
I believe that data analytics is revolutionizing population health by helping us identify high-risk groups, predict health trends, and monitor disease outbreaks. With technologies like EHRs, SDOH, big data platforms, and AI/ML, we can analyze comprehensive data and make accurate predictions. By using AI and advanced techniques, we can help identify patients with certain risk factors and engage them in timely preventive strategies for both physical and mental health conditions. Essentially, advanced analytics can help predict and find individuals at higher risk, enabling proactive care to hopefully reduce the burden of disease and help people live longer healthier lives.

There are a lot of good insights here! Huge thank you to Paulo Pinho, Chief Medical and Strategy Officer at Discern Health, Jay Anders, MD, Chief Medical Officer at Medicomp Systems, Vatsala Kapur, Vice President, External Affairs at Bamboo Health, Salvatore Viscomi, Co-Founder and CEO at Carna Health, Chris Mazzanti, Chief Operating Officer at Carta Healthcare, Kim Perry, Chief Growth Officer at emtelligent, Ahzam Afzal, Co-Founder and CEO at Puzzle Healthcare, Laura Ferrara, Chief Population Health Officer at Intus Care, Evan Floden, Co-Founder and CEO at Seqera, Kerry Love, Founder, CEO, and President at Sunflower Therapeutics, Paul L Wilder, Executive Director at CommonWell Health Alliance, Eyal Zimlichman, Chief Transformation Officer and Chief Innovation Officer at Sheba Medical Center, Dani Arousi, Global Commercial Director, dbMotion, at Altera Digital Health, and David Schweppe, Chief Analytics Officer at MedeAnalytics for taking the time out of your day to submit a quote in to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.

How do you think health organizations are leveraging data analytics and technology to address population health challenges and identify trends that can inform public health interventions? Let us know either in the comments down below or over on social media! We’d love to hear from all of you!