Our Perspective
Population health is hardly a new concept… but the imperative to change – and the availability of technological resources to enable it – has never been greater.
For decades, all healthcare stakeholders – from providers to insurers and government entities – have been moving toward a more nuanced understanding of healthcare that considers social dynamics. Clinical interactions are one of many factors that influence health outcomes. Often, improving outcomes is about influencing population-level change rather than treating an individual. For provider organizations responsible for a bolus of their community’s health outcomes, that means public health interventions. For healthcare delivery organizations, leveraging these insights in service of improved care is a matter of population health management (PHM).
A proper working definition of population health management (PHM) is “the aggregation of patient data across multiple health information technology resources, the analysis of that data into a single, actionable patient record, and the actions through which care providers can improve both clinical and financial outcomes.” [LS1] While the clinical interventions may take place in the context of individual patient relationships, robust population health management depends on gaining a more comprehensive set of data points. Capturing information about the factors that could impact each patient's health in the defined population and using that data to identify and proactively treat the highest-risk patients and patient population subgroups is the key to unlocking demonstrable results.
Ultimately, population health management seeks to improve patient outcomes through a better care experience that is also financially sustainable. This aligns with value-based care models and the incentives driving accountable care organizations (ACOs).
Population health is hardly a new concept – healthcare entities have been trying to move in this direction for at least a decade. But the imperative to change – and the availability of technological resources to enable it – has never been greater.
Reimagining Population Health: The Data Imperative
Aside from the obvious process and technology requirements of population health management (new clinical rules and workflows, etc.), data is a crucial enabler of PHM. Numerous data points – from social media posts to income levels, access to behavioral healthcare, and incarceration records – could influence clinical decisions and care plans. Some of this data is already accessible, while some may never be accessible due to regulatory restrictions. However, in the middle, there is a vast reservoir of data that could potentially be tapped by healthcare providers and integrated with EHRs. The big question is: How do you effectively feed that data to a care provider in a way that provides actionable insights?
Increasingly, the answers will need to come via AI. Public agencies can theoretically supply care providers with numerous data points, and APIs can facilitate data portability across public and private entities. However, care providers must elevate and act on the correct data at the right time to avoid a confusing data deluge. AI’s ability to organize and prioritize data will be essential when incorporating new data points into care delivery.
Technology is the enabler, not the starting point. Plenty of population health management tools are on the market today—enough to cause any healthcare leader confusion and fatigue when selecting the right tool to enable the targeted outcome. Two key factors to consider when choosing the platform to make a population health effort effective are data accessibility and interoperability.
Data Accessibility and Interoperability for Population Health Management
To advance population health management, providers must understand their patients' identities, what they need, and how to effectively influence behaviors outside the clinical setting. That means drawing from multiple systems and sources, including electronic health records (EHRs), claims, labs, and benefits, and gathering socio-economic and geographical data. All this information must be cleansed and represented in a format helpful to clinicians before making it available.
Given the significant differences in healthcare IT infrastructure across organizations, this aggregation – the starting point – can be the biggest hurdle. Information needs to be exchanged between institutions within a single healthcare system and with accountable care organizations, health information exchanges (HIEs), and other healthcare partnerships. Data collection must extend outside the institution. Interoperability with other providers and health systems is essential to build a model of care around population health management.
The key is to put the patient’s needs at the center of the interoperability effort. If a patient goes to multiple facilities and has to request and carry records from one to the other and needs duplicate tests, that’s costly. Addressing these patient challenges can help reduce administrative costs and improve health and medical outcomes. Continuing with this example, the challenge here is to ensure that a patient’s care is continued through referrals and follow-ups, irrespective of the caregiver’s facility. As technology advances, healthcare systems can better access patient and health data across the care continuum, and automated triggers and algorithms can play an increasingly intelligent role in aligning and enabling the flow of data, insight, and actions.
Payer and provider organizations are beginning to realize the benefits of adopting intelligent, data-driven population health initiatives. For example, one of Wipro’s clients uses a commercial PHM platform to aggregate data, segment the population into focused categories based on risk profile, develop and document individual care plans, and coordinate care beyond the “four walls” using a clinical care coordination team of registered nurses, local vocational nurses, and certified medical assistants.
Patient Segmentation for Population Health Management
Why is segmentation so vital? Informed segmentation helps healthcare providers better understand the challenges these sub-segments face and their needs for care. To impact a patient population, look beyond their medical records to social and economic welfare. What do you know about how and where they live? Is your clinic primarily servicing a community where English is the second language? Is a high percentage of your local population also on welfare support? Is your area mainly a “vacation” community? Are your patients single parents, working mothers, or elderly? Population health is most effective in the “real world” – where we work and live.
An example is Wipro’s collaboration with government agencies and academia to develop a low-cost diabetes management solution that spans glucose sensing, insulin delivery, data exchange between patients, providers, and counselors, and a 24/7 call center for patient monitoring and assistance to ensure adherence to treatment plans. Our efforts have resulted in better data collection, less manual intervention, and better insights into the specific challenges facing populations of patients within a certain disease state or geography.
Segmentation will be the key to turning new datasets into concrete decisions. For this reason, one study of various population health data initiatives in the US recommends shifting away from simple data aggregation and visualization toward data hypothesis and insight. “We should mine data to derive insights to guide interventions by clinicians on the frontlines,” the authors write, “…focusing clinical care to specifically underserved patients or partnering with local-level welfare workers and other professionals to change some of the underlying determinants of health.” On the provider side, a vital enabler of the enablers of population health management will be platforms that can capture the community context of healthcare to segment patient sub-populations and associated interventions at scale effectively.
Finding the Right Platform
With so many IT platforms and vendors offering PHM services, what should organizations consider as they implement population health management initiatives?
The correct population health management platform should, first and foremost, help providers better understand patients. It should combine data from increasingly diverse sources, allowing providers to stratify the patient population into meaningful categories.
Based on this patient segmentation, the platform should also promote more efficient service delivery. It should automate work that doesn’t need to be done by people (for example, sending targeted behavioral reminders. that promote the well-being of specific subgroups), freeing staff to focus on treating patients who need in-depth clinical care. Increasingly, a PHM platform should be able to predict or propose the following best action, going far beyond simply sending automated appointment reminders. The bottom line: It comes down to usability and relevance. The technology should solve problems for doctors, business users, caregivers, and patients.
Automation and artificial intelligence (AI) are two increasingly relevant technology considerations. For example, in the field of cancer services, automation frees clinicians and caregivers to spend more time on patients’ care plans as digital assistants take care of scheduling appointments, reminders, and notifications. As PHM platforms embrace GenAI, they should also augment clinical workflows, for example, via chatbots that provide natural language querying for scheduling/rescheduling appointments, checking on claims status, searching for premiums due and service locations, etc. Meanwhile, by running sophisticated machine learning algorithms on patient population data, these platforms should be able to make proactive recommendations for how and when to reach out to patients with interventions.
The Future of Population Health Management
Chronic diseases like hypertension, obesity, diabetes, and asthma represent not just a significant portion of the overall healthcare spending but also an opportunity for healthcare systems to better collaborate and meet patients’ needs. Focusing on population health management will enable healthcare systems to improve health outcomes and the patient experience by more strategically deploying healthcare resources.
Healthcare organizations can mine data to identify impactful strategies unique to specific patient sub-categories and chronic diseases by aligning the right people, processes, and technologies. This segmentation will lead to evidence-based care management plans while evolving PHC platforms equip hospital employees, patients, and their extended care teams with the tools to easily access the information they need to follow care plans better. Fundamentally, these positive outcomes will be utterly reliant on good data. Healthcare organizations will need to accelerate their PHC journeys by connecting previously overlooked or inaccessible data points to EHR systems and then unlocking the value of those data points with AI.
To learn more about how Wipro Healthcare can help your healthcare organization reap the benefits of successful population health management platforms, visit the website at https://www.wipro.com/healthcare/
About the Author
Philip Handal
Senior Partner, Health Consulting
Phil has over 20 years of experience in healthcare technology consulting and care delivery. He is a leader in Wipro’s Payor Strategic Consulting services, focusing on transforming technology to deliver value for the future of healthcare delivery. He has worked extensively with payors, providers, and life sciences organizations to develop strategic initiatives, implement technology, and execute data-driven digital solutions.
