It's no secret that healthcare is changing. Traditional fee-for-service payment models are going away in favor of quality-based payment models. However, with this transition comes the challenge of positioning providers for success under these new models.
As a first step, it will be critical for all stakeholders to be prepared by aligning care delivery and payment models to support population health management to ensure they are delivering the highest value of care to patient populations and individual patients. The purpose is to create an environment that incents both plans and providers to manage the health of their patients by stratifying the population based on risk, outreach and engagement of those high-risk patients, as well as proactively improving the health of the overall population.
This sounds like common sense and an easy fix, but this transition actually comes with many challenges. There are new technologies, laws and regulations, lack of expertise and the inherent difficulties of change management to contend with, so all players supporting the transformation are spread thin. The results are worth it, though, as proactive management of high-risk patients has shown to drastically reduce costs by driving down ED utilization, preventing costly visits before they occur and actively managing chronic conditions. With these results, it is clear that population health is not a fad — it is the future of healthcare.
To simplify population health management and what it takes to support it, it's important to first define what it is. Arcadia Health Solutions defines it as the provider's ability to answer the following questions with certainty and having the data to support these answers:
1. Who are my patients?
2. How sick are my patients?
3. Am I effectively caring for my patients?
Attribution: Who are your patients?
Maintaining a high degree of precision around defining a patient population has never been as important as it is today. Historically, patients scheduled visits, came in for their appointments, and follow-ups were conducted as necessary. With new payment and delivery models, such as accountable care organizations and patient-centered medical homes, provider reimbursements are increasingly dependent on the quality of care delivered and overall risk of their patient population. Providers are now forced to better manage the health of their patient population by proactively reaching out and engaging their highest risk patients while appropriately managing the health of the rest of their population, as well as proving that better health management with high-quality data from their electronic health records. As a result, the ability of health plans or other communities (such as health information exchanges) to successfully attribute patients to their primary care providers, and all parties being able to correctly answer the question, "Who are my patients?" is absolutely critical.
In theory, this seems like a simple task: Assign patients to a PCP, document it and reconcile as needed, However, because of the disconnected nature of healthcare and numerous parties involved in the attribution process, aligning this data and creating a 'single source of truth' can be extremely challenging.
Attribution is where all data analytics, quality improvement and PCMH transformation projects should begin. Defining and placing precise parameters and definition around a provider's patient population is a critical foundational element of any of these engagements, and having a sound reconciliation process in place is a critical success factor under new payment models.
The most effective way to do this is by integrating clinical and demographic data from EHRs with claims membership and eligibility data. Arcadia does this this by tapping into the back-end of the EHR, pulling claims data and loading all data onto a centralized data warehouse. Data is then scrubbed and merged utilizing a master patient index, creating a single record for all patients. This rich view of a patient's activity gives providers the level of detail captured in the EHR, as well as the breadth of data from claims, which allows them to see patient activity across the entire care continuum. The initial attribution process involves reconciling conflicts between a provider's perception of attribution (from EHR data) and a plan's perception (from claims data). This process inevitably requires some degree of manual intervention by both payers and providers but only needs to happen once. After the initial attribution process is complete, a recurring, automated process is implemented requiring far less manual intervention.
Once an attribution process is in place, the value of any reporting, quality improvement, PCMH transformation or overall population health initiative will increase dramatically. Providers will have a clear view of their patient population, creating the foundation to begin tacklingchallenges and position themselves for success in the new era of healthcare.
Measurement: How sick are your patients? Are claims telling the whole story?
Of the patient population a healthcare provider identifies, how many of these patients are diabetic? Hypertensive? How many are at risk for becoming hypertensive? Is the organization certain that it can identify all of them? Being able to answer these questions and having the data to back it is crucial, and more difficult than it seems. First, it is important to be able to identify patients with chronic or other high-risk conditions in order to proactively manage those conditions. Second, new fee-for-value reimbursement models are directly dependent on the level of risk associated with the patient population; by not identifying high-risk patients, reimbursement dollars are being left on the table. Finally, hospitals and health systems must be able to prove the scope and impact of the interventions they deliver, resulting in better outcomes.
Traditionally, tools used to stratify the population and calculate risk have been primarily based on claims data. High-risk patients were identified based on the diagnosis codes for chronic or high risk conditions. Outreach and engagement strategies leveraged this data to proactively manage these conditions. However, claims data does not always tell the whole story. What if the condition was not relevant to the visit and was not properly coded? What if the patient has not been in for a visit in that calendar year? What if the patient is at risk for becoming hypertensive, but that risk is only captured in specific lab result or diagnostic values, not just in the order? These are all examples of cases where claims data may not be telling the whole story, and all present opportunities to both improve quality of care and maximize risk reimbursement.
One effective way to identify these patients and maximize risk reimbursement is by leveraging EHR data; integrating claims with EHR data gives providers a richer view of the patient population. Things EHR data can tell you that claims will often miss include:
Vitals signs. Who are my patients that have not yet been diagnosed but are at high risk for becoming hypertensive?
Medical history. Who are my patients that may not have had a visit in the last year but are considered to be high-risk?
Transitions of care. By integrating EHR data with claims, providers can see patients activity across the care continuum. For instance, they can see if any patients have recently been to the ED.
These are only examples of a few of the questions an integrated data set can answer, but the value of this is impressive. This level of transparency allows providers to more effectively manage the health of their population by proactively managing their actual high-risk patients and managing those at risk of falling into the ‘high-risk' category.
Intervention and Tracking: Are you effectively caring for your patients?
Are you doing what you need to effectively care for your patient population? Chances are, you probably are – but do you have the data to back it up? An integrated, high quality data set gives hospitals the data required to support the answer to this question and provides the foundational tools needed to identify opportunities and drive improvement.
Once the patient population has been identified, the health and quality of care has been measured, and the population has been stratified based on risk, providers and care teams can begin driving more effective population health initiatives in their organizations. Visibility into the specific needs of the patient population allow providers to begin providing proactive, preventive care to manage the health of their high-risk patients and engage patients that are in danger of becoming high-risk. Additionally, visibility into the quality of care being delivered informs the business on specific areas in which providers and care teams can improve and ensure they are delivering the highest quality of care to their population. Ultimately, these tools combine to allow provider organizations to maximize the value of their ambulatory networks by aligning delivery models with fee-for-value payment models and by managing cost by providing proactive care instead of costly ED visits or 'reactive care'.
As provider organizations begin to transform and adopt population health-based tools and methodologies, the improved level of visibility into the organization and patient population fuels opportunities to drive sustainable change and continuous improvement. With the shift from fee-for-service to fee-for-value payment models, the topic of population health is getting more and more attention, and hopefully these tips will help everyone involved be better prepared.
Major contributions to this article were made by Stephen Bognar, a consultant at Arcadia Healthcare Solutions.
Greg Chittim is a senior director at Arcadia Healthcare Solutions. During his tenure at Arcadia, he has had responsibility for strategic marketing, analytics product management and as an account director working with health system, health plan and ONC statewide grantees as a member of the HIE Technical Assistance team. Prior to his time at Arcadia, he was a case team leader with Monitor Group. Greg is a graduate of the Thayer School of Engineering and of Dartmouth College.
Stephen Bognar is a consultant with Arcadia Healthcare Solutions. During his tenure at Arcadia, he has worked with Medicaid managed care organizations and statewide HIEs on issues of analytics and performance management. He is a graduate of Washington State University.
More Articles on Population Health:
How Disruptive Change is Blurring the Lines Between Providers and Payers
The Illness to Wellness Path: Increasing Defined Patient Population Outcomes and Financial Viability
3 Challenges to Integrating Population Health Into Healthcare