Population health management and medication adherence
By Rajib Ghosh
In my last article I highlighted opportunities for health analytics entrepreneurs in the field of radiology services and revenue cycle management. This article focuses on two more opportunities, which are also part of the Centers for Medicare & Medicaid Services’ (CMS) reportable quality measures for provider organizations: managing population health and medication adherence. Some healthcare organizations have already achieved early success using analytics, but many have yet to adopt it. During the next three to five years, I predict we will see increased adoption of analytics in these two areas, driven by the need of compliance and the search for profitability.
Healthcare’s Microeconomics Problem
The Federal Health Insurance Exchange (Healthcare.gov) is at the center of an ongoing controversy – website glitches, broken promises, customer dissatisfaction and general skepticism about the Affordable Care Act (ACA). While we can argue on both sides, the opening of the marketplace to allow consumers to buy their own insurance after comparing plans is surely a paradigm shift in the healthcare industry. However, the bigger question is, what’s next?
According to White House officials, the overarching goal of the ACA is to enroll more than 10 percent of the country’s population who are currently uninsured into the umbrella of “affordable” healthcare through insurance. So what happens when all those people successfully buy insurance through their state or federal insurance marketplaces and seek care? Chances are many of the newly insured people have complex chronic conditions for which they rarely received care in the past. Is the chronic care delivery model equipped to handle that rising demand?
Hospitals with an average of 4 percent operating margin  are consolidating at an accelerated rate to stay viable. In addition, nearly 50 percent of physicians and 45 percent of nurses are over 50 and are close to retirement. With a looming shortage of primary care physicians and nurses (Figure 1), who historically are the key providers for the chronic care patients, the health systems in the United States are about to face a daunting task: keeping patients out of hospitals and other healthcare facilities while maintaining high patient satisfaction! This might sound like an oxymoron but that’s exactly what is needed. If providers cannot do that effectively, overcrowding in clinics and emergency rooms is inevitable, adding to the chaos. This brings us to the healthcare’s microeconomics problem: If demand outstrips supply, should we see direct cost rise to achieve equilibrium? Since there will be an artificial price ceiling, could it create more shortages and a decline in quality and patient satisfaction? Effective population health management is the answer, and therein lies the opportunity for analytics.
Figure 1: National supply and demand projections for FTE registered nurses (2018-2025).
Population Health Management
Population health management is about improving health outcomes for a group of individuals and removing health inequities. Caring for a population’s health is anything but trivial. A 1961 report, which was vindicated 40 years later in 2001, showed that only 27 percent of the patient population reports any health issue to clinical providers . This means a health system managing a large patient population having one or more chronic conditions often does not have access to accurate information about its patients. It takes patient data, interoperability and analytics to address this problem.
|Figure 2: Steps in population health management.
(Source: Population Health Management, Institute for Health Technology Transformation)
As Figure 2 demonstrates, many steps and tasks are involved in managing population health effectively. The application of health IT systems such as electronic medical records (EMR), telehealth, disease registries and analytics is a mandatory requirement for success. Any given population can be divided into three main clusters: 1) patients with high utilization of health care resources, 2) patients with two or more primary diseases, typically overweight and a smoker, and 3) patients who are relatively healthy with low utilization of the health care resources.
The middle group is the largest group, and Lisa Bielamowicz, M.D, chief medical officer of the Advisory Board Company, described them as the “rising risk” patients. It’s just a matter of time before they become part of a high-cost patient cluster. Effective management of this group is important to reduce the health system’s total-cost risk exposure. Finding out who they are and when they need intervention is a key to managing a risk-based payment model . Unlike bringing patients into the medical facilities, this approach requires predictive insights and, therefore, presents a big opportunity for analytic solutions. Providers and thought leaders have been talking about this use case for healthcare analytics for quite some time. The looming migration toward a risk-based payment model where keeping patients in good health is rewarded by CMS through the Shared Savings program is now acting as the key driver. Some commercial payers (e.g., WellPoint, Aetna) have followed suit.
Medication Non-Adherence: The $290 Billion Problem
In a research report, the New England Health Institute (NEHI) has shown that patients not adhering to their medication regimen is a $290 billion problem in U.S. healthcare . Medication management and improved adherence are not only critical tools to ensure hospital readmission rates stay under control and providers avoid penalties, but also to address compliance needs and financial incentives of stakeholders in the healthcare value chain. Providers who also have pharmacies need EHNAC (Electronic Healthcare Network Accreditation Commission) certification, and medication adherence is an important part of certification. The HEDIS (Healthcare Effectiveness Data and Information Set) score that measures medication management is another key metric that payors pay attention to because it impacts their health plans and ultimately their profits.
Payors who offer Medicare Advantage plans, for example, are concerned about CMS provided-STAR ratings for their plans because more stars mean a higher bonus amount (tens of millions of dollars for large plans). Medication adherence is a key aspect for STAR ratings. Although better medication adherence can marginally increase a payor’s cost per patient, better outcomes and cost savings from preventable disease exacerbations save money in the long run. Analytics play a major role in both predicting patient cohorts who are at risk of non-adherence and the kind of intervention that works best for them. Analytic models can consider hundreds of different factors in a patient population and improve accuracy of the prediction.
Medication adherence is also a key component of achieving effective population health management, so it makes sense for providers, payors or accountable care organizations to apply predictive analytics to address both problems at the same time. Forward-looking health care organizations such as Partners Healthcare, Baylor Healthcare System, Kaiser Permanente and Intermountain Healthcare have already done that. The next few years will be a bit chaotic but exciting for the healthcare industry. Data and analytics will be harnessed like never before to deliver better care
for the population as a whole, as well as individual patients.
Rajib Ghosh (email@example.com) is an independent consultant and business advisor with 20 years of technology experience in various industry verticals where he had senior level management roles in software engineering, program management, product management and business and strategy development. Ghosh spent a decade in the U.S. healthcare industry as part of a global ecosystem of medical device manufacturers, medical software companies and telehealth and telemedicine solution providers. He’s held senior positions at Hill-Rom, Solta Medical and Bosch Healthcare. His recent work interest includes public health and the field of IT-enabled sustainable healthcare delivery in the United States as well as emerging nations. Follow Ghosh on twitter @ghosh_r.
1. AHA Research Report, “Aggregate Total Hospital Margins, Operating Margins, and Patient Margins, 1991-2011”; http://www.aha.org/research/reports/tw/chartbook/ch4.shtml
2. L.A. Green, et al., 2001, “The Ecology of Medical Care Revisited,” New England Journal of Medicine, Vol., 344, No. 26, June 2001; www.nejm.org/doi/pdf/10.1056/NEJM200106283442611
3. Mark Hagland, “Slicing and Dicing the Populations Within Population Health: One Industry Expert’s View”; www.healthcare-informatics.com/article/slicing-and-dicing-populations-within-population-health-one-industry-expert-s-view
4. NEHI, “Improving Patient Medication Adherence: A $290 Billion Opportunity”; www.nehi.net/bendthecurve/sup/documents/Medication_Adherence_Brief.pdf