An effective population health management program must include three systems: Healthcare Analytics, Best Practice, and Adoption. Organizations with only one or two of these systems often display symptoms of weak and ineffective capability for population health management. But when you have a analytics foundation based upon a data warehouse, combined with evidence-based practices contained in a best practice system, and the ability to deploy and implement systematic changes to healthcare processes, health systems are truly prepared to manage population of patients.
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Risk stratification is essential to effective population health management. To know which patients require what level of care, a platform for separating patients into high-risk, low-risk, and rising-risk is necessary. Several methods for stratifying a population by risk include: Hierarchical Condition Categories (HCCs), Adjusted Clinical Groups (ACG), Elder Risk Assessment (ERA), Chronic Comorbidity Count (CCC), Minnesota Tiering, and Charlson Comorbidity Measure. At Health Catalyst, we use an analytics application called the Risk Model Analyzer to stratify patients into risk categories. This becomes a powerful tool for filtering populations to find higher-risk patients.
Patient engagement is critical as we move toward population health—as patients who engage in their own care by following medical recommendations and making healthy nutrition and lifestyle choices will have better outcomes and experiences. There isn’t, however, a clear path to successful patient engagement. Fortunately, public health can lend several established principles that may help us better involve patients in their own care:
- Using systematic, population-level solutions that require less individual effort.
- Engaging patients on interpersonal and community levels as well as personal.
- Identifying root-cause, assessing and capitalizing on strengths, and engaging stakeholders.
- Using strategies from behavioral economics to help individuals make good choices.
- Anticipating failure and learning from it.
This article examines how to define population health through a review of the top analytics research firms. It lands on a single theme, but in the process it uncovers six common categories of IT capabilities required to successfully manage population health:
- Data Aggregation
- Patient Stratification
- Care Coordination
- Patient Engagement
- Performance Reporting
Given the variety of payment contractors and models, ACOs have their hands full when it comes to assessing risk and managing population health. EMRs are one source of data for painting a partial picture of the population; claims and HIE data are limited, and the promise of FHIR is still a work in progress. To complete the picture, external data sources are necessary. Those are available in a variety of ways, including demographic analysis, and external EMRs from physician practices independent of the ACO. There are many challenges to stratifying risk, but there also many creative ways to pull in the data for accurately identifying the patient population, improving their health, and reducing costs.
Two variables are required in the value-based healthcare equation if it is to add up to a profitable contract. One variable, optimizing the care for the patient population, is commonly included and is a focus for most healthcare systems involved in managing population health. However, a second variable, getting the right dollars in order to care for that population, is often overlooked. And yet this variable is easier to attain. It’s a matter of appropriately assessing the risk of the population by addressing inaccurate diagnoses coding. Here, we offer four methods for solving this variable: identifying high-risk gaps over time, persistent diagnosis tracking, identifying code adequacy, and identifying likely diagnoses.
Population health strategy can borrow a lot from public health. However, health systems haven’t had to deal with patient socioeconomic issues and need to build new skills and use new data. The skills can be adapted from the public health sphere, with hospitals developing health interventions alongside law enforcement, community-based social support, etc. The most important data are patient-reported outcomes data, social determinants of health data, and activity-based costing data. With this approach, the fundamental equation for population health would be Return on Engagement, that is the clinical outcome achieved divided by the total patient investment.
Population Health can mean many different things depending on whom you ask or what you read. The one common element among all the definitions is the focus on outcomes. These outcomes can be related to quality (successfully treating the patient), experience (the patient’s satisfaction with the care that was provided), or cost outcomes (reducing errors and a decline in length of stay). In the end it means delivering the highest quality care for patients at the lowest possible cost over and over again. To ensure these outcomes are tangible, sustainable, and transferable a three-system approach is necessary: a best practices system (to determine what should be done), an analytic system (to tell the organization how it’s doing versus the goal), and an adoption system (to report the results to the organization).
If EHR data have the breadth and depth of a pond, then claims data are just lily pads on the surface. In other words, the volume of EHR data is far more substantial than that of claims data. Population health strategists should adopt the AND-BOTH approach, rather than the EITHER-OR, when it comes to working with both types of data. This best-of-both-worlds tactic offers data that is standardized, accessible, discrete, AND real-time, detailed, and physician based. Given what some might view as an overwhelming volume of data when working with both, it’s wise to develop a data governance framework supported by some type of flexible data management platform, such as an enterprise data warehouse (EDW).
Early this year, CMS began a per member per month reimbursement for Medicare beneficiaries with two or more chronic conditions. It immediately validated the need for care management programs. Three models are used to measure the savings of an effective care management program:
- Historical or intent-to-treat design
- Matching comparison design
- Randomized control design
In the brave new world of value-based healthcare, investing in population health management (PHM) is a requirement for success. Defining PHM isn’t easy, but there is one common term that appears among all the diverse interpretations—outcomes. Assessing the potential ROI for investments in PHM using a clear, understandable framework, can help organizations methodically identify and prioritize their PHM investments. While not every PHM intervention makes sense for every situation, it is important to determine which programs provide the most benefit, as well as determining when the investment will begin paying dividends, to achieve success in the era of PHM.
As the healthcare industry moves closer to value-based care, there are a lot of projections about the changes that will occur in the near future. This article discusses seven of the top trends the industry is focused on: (1) physicians start to feel the financial impact of CMS’s rules; (2) the use of technology in healthcare is exploding; (3) financial viability is a key concern for CEOs; (4) reducing exposure to risk performance is becoming more important; (5) interest in population health management continues to grow; (6) outcomes improvements will continue to increase; and (7) collaboration between providers and payers will increase.
Healthcare will undergo a number of changes in 2015, particularly as organizations look to manage population health. Dr. David A. Burton outlines what he believes will happen in terms of at-risk contracting, risk evaluation, network optimization, quality and safety, cost reduction, and infrastructure, and how 2015 can develop into opportunity for all.
A staple of inflight magazines, the “Best Doctors” ad showcases individual doctors for specialties in healthcare. Yet, there are no “Best Pilots” ads. That’s because healthcare functions as a craftsmanship practice, while aviation operates using a standard of production. The craftsmanship mentality in medicine leads to a wide variation in results for patients, even those facing the same diagnoses. To improve population health systematically, three systems are required: 1. The Best Practice System (including best practices identified and agreed upon), 2. The Adoption System (meaning how those practices are used across the enterprise), and 3. The Analytics System (in part, measuring how well those best practices are being implemented). Taken together, these systems will move healthcare toward an effective system of production and improve outcomes for patients.
While many organizations use patient registries from EMRs to determine their patient population, there is a better way. Using GIS location technology, a health system can identify its care population based on geography and drive times. Health Catalyst uses Dartmouth Atlas hospital referral regions, a hierarchy of facility levels with appropriate drive time isochrones, and medical specialties-based central place theory to develop a more comprehensive view of a health system’s minimum bounding geometry. Using this method, ACOs derive a better understanding of their enrolled patients and eligible payer groups resulting a better basis for strategy and decision making.
The shift from volume-based to value-based purchasing and the emergence of accountable care organizations are creating a focus on comprehensive management of the health and well-being of patient populations. This doesn’t mean that individual patient care processes are unimportant or unnecessary. It does mean that healthcare systems need to improve both individual patient care processes, while at the same time learning how to manage the entire population of patients they serve. Learn more about population health management and how it is interrelated, but different than public health.
There seem to be a lot of definitions for population health management and population health analytics. But all these definitions share one thing: outcomes. The goal is to provide quality care outcomes with good patient experience outcomes at a low cost outcome. So, how can organizations systematically improve their outcomes? The answer lies in three key questions: What should be done to provide optimal care? How well are those best practices being followed? And how do those best practices move into everyday care for patients? Using a systematic approach to answering these three questions will lead organizations toward becoming an outcomes improvement machine.
Measuring is where successful population health management starts. You can’t do much to manage your diabetes population if you can’t accurately identify that population or see how your population is trending. Identifying diabetes patients and measuring compliance has been difficult in the past—but today, with a healthcare enterprise data warehouse (EDW) and analytics tools, it doesn’t have to be. (Your EMR won’t be enough.) By establishing an EDW, you create a data foundation that enables you to manage your diabetes population in sophisticated ways.
Are you looking for the best way to prioritize your population health efforts? Population health management involves improving and maintaining the health of a defined subset, or cohort, of patients. Effective population health management starts with clearly defining those cohorts and determining on which clinical processes to focus improvement efforts. The Health Catalyst Key Process Analysis (KPA) application applies Pareto analysis to each health system’s data to identify the care processes, care process families and clinical programs that offer the greatest clinical, cost and safety improvement opportunities. The application determines the highest variation and highest resource consumption by integrating and analyzing clinical and financial data.
One of our clients recently launched a Population Health Management initiative in just eight weeks to ten percent of their clinics, potentially improving care delivery for approximately 2,300 patients. When the initiative is completed it will impact nearly 50,000 patients. The interdisciplinary team of clinicians, IT, care coordinators, and business analysts now have a single source of truth and near-real time results to proactively engage and work with their patients to manage care. According to their Director of Clinical Business Analytics, “What we’ve accomplished with Population Health is something we’ve been trying to do for over 20 years with our various clinics. We used to manually pull together reports, all with varying data, and we had no way to proactively monitor our populations. Now, we have near real-time data that enables our care coordinators to drive preventive care and ultimately lower our population health costs.”
Population health management is definitely a hot topic in healthcare today, so I’m excited for the opportunity to weigh in on it with this commentary. In a series of posts, I’ll explore: 1) The evolution of population health management, 2) Data needs for effective population health management, 3) Population health business models, 4) Vendor solutions.
Based on 25 years of experience, first as a senior executive at Intermountain Healthcare and later as the Chairman of the Board of Health Catalyst, Dr. Burton shares his in-depth learnings about how to systematically implement population health management in a long-term, sustainable way.