Powering Population Health Initiatives with Technology

There’s been a lot of discussion about population health in the health and life sciences (HLS) industry recently. Population health is an approach to healthcare that targets subpopulations with chronic conditions for proactive intervention. These populations tend to be sicker, higher risk, or costly to care for, so finding ways to engage and help them be healthier is important to the HLS industry. It makes financial sense too. Risk-based contracting and pay-for-performance agreements are providing financial incentives to deliver preventative care and to track those patients across the care continuum. Healthcare organizations need to invest in the technologies that can enable proactive management of specific individuals.

We should expect that these patients receive the right level of care to meet their exact needs, which will hopefully prevent unnecessary trips back to the hospital and other avoidable issues in the future.

Population health relies on identifying those who are at the highest risk, having access to the right data about those patients, creating actionable insights about them, and coaching them towards making healthier choices or intervening proactively to prevent patient decline. That requires data collection, analytics, and connected health tools to collect personal health data, which is then used to gain insights about these groups. This is where technology comes in. Technologies including data analytics, machine learning, and artificial intelligence (AI) can provide insights to improve health outcomes for these populations, reduce costs, and help healthcare organizations operate better. As a result, we’re seeing significant investments in technology for population health.

Interpreting Data Effectively

Healthcare organizations gather huge amounts of data every day, so there’s a big opportunity for technology to step in to help use that data effectively. Based on data gathered in clinics, and in some cases gathered remotely via connected monitoring devices, tools like machine learning and artificial intelligence can identify patterns in patient data.

Diabetes, congestive heart failure, smoking cessation, obesity, avoidable readmissions, and unnecessary emergency department use are all examples of population health initiatives that could save money and produce measurable clinical improvements.

For example, these technologies are being used to identify patients currently in hospitals who are at risk of being readmitted after release. It can be challenging for hospital staff to identify all these patients, but machine learning and AI can look at a group of data points for a patient and flag them if they meet certain criteria. Then the hospital develops a treatment plan that will prevent the patient from leaving the hospital too soon or without proper care instructions

Analytics data provides plenty of other insights too, such as which patients are most susceptible to hospital-acquired infections and how best to help patients manage certain chronic conditions. There are many possibilities for data application in our industry, and advanced software applications can help leverage these insights.

Value-Based Care and Technology

The intersection of technology, population health, and value-based care is also worth discussing. Value-based care refers to a system where healthcare providers are reimbursed based on the quality of care and patient outcomes. That means these providers have even more incentive to provide the exact care patients need, especially patients with challenging health problems.

In these organizations, analytics tools play a key role in helping providers identify high-risk patients. Once patients are identified, providers can allocate resources accordingly and use the appropriate treatments. This use of analytics leads to more positive outcomes, which benefits both healthcare providers and patients.

In addition to analytics tools, telemedicine and remote patient-monitoring devices are supporting population health initiatives. They allow care teams to stay in touch with patients and provide continuous, flexible, personalized care.

All of these technologies — analytics tools, AI, machine learning, and remote patient-monitoring devices — work together and deliver insights that help identify and treat at-risk populations in the best way possible.

Technology Will Lead to Healthier Populations

Technology plays an integral role in population health. It finds trends, identifies patients who need attention, and allows doctors and other healthcare providers to do their jobs better. This leads to healthier patients and care facilities that function more effectively.

Intel supports population health initiatives. We’ve worked with Sharp HealthCare, for example, using analytics to identify patients who were most at risk of suddenly declining. This enabled the organization to intelligently place medical emergency teams at key points in hospitals and intervene before situations become life-threatening. This is one of many ways this kind of technology can support healthcare providers and patients.

Find out more about Intel’s work in healthcare technology at Intel’s healthcare page, or keep up with us on the IT Peer Network.

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Jennifer Esposito

About Jennifer Esposito

I believe that technology has the power to accelerate the transformation of healthcare and to improve health, quality of life, safety and security worldwide. Follow me on Twitter @Jennifer_Espo or Flipboard @jesposito. Executive with over 20 years of experience in the global healthcare IT, health and life sciences industry. Jennifer worked for over 13 years at GE Healthcare and is now General Manager of Health and Life Sciences at Intel Corporation. Jennifer has led commercial, sales, marketing and service operations, P&Ls as well as both upstream and downstream strategy and marketing. Jennifer has extensively traveled the globe, regularly meeting with top leaders in industry and government. She is active in initiatives on global health, identifying novel ways technology can be used to advance the SDGs and IHRs. Jennifer has a graduate degree in Epidemiology and Biostatistics from Dartmouth College. She is a full member of the American Association of Physicists in Medicine. Jennifer is a member of the Working Group on Digital Health for the Broadband Commission. She also serves on the Steering Committee of the Global Health Security Agenda Private Sector Roundtable and chairs their working group on Technology and Analytics.