Population health best practices: Diet, exercise, and data
As provider organizations of all sizes become more directly and more financially accountable for patient outcomes and clinical quality, they must also increasingly count on each other—and on each other’s data. This is true for hospitals and health systems that acquire or affiliate with community practices, and equally true for small to medium-sized practices banding together under the umbrella of independent physician associations or as part of an ACO.
So what are the keys to effective population health management? And given that patient outcomes and clinical quality will increasingly determine reimbursement, how can you optimize the processes and the systems where population health management and revenue cycle management intersect?
Hint: there’s more to it than a predominantly plant-based diet and an attractive personal trainer.
Integration, aggregation, and normalization
Population health management depends on multiple “levels” of data:
- Comprehensive patient data, in the form of complete longitudinal records of care
- Population/disease cohort data
- Clinical outcome data
Longitudinal records of care form the foundation of effective population health management—they’re the only way to efficiently identify gaps in care, and they’re critical for pinpointing and reliably tracking the best practices that lead to improved patient outcomes. They’re also the only source of evidence that you’re meeting quality measures such as smoking-cessation counseling and diabetic eye exams.
Creating and maintaining longitudinal records of care requires consistent commitment to detailed documentation across care settings and across the entire continuum of care. Given the number of providers involved in caring for patients with complex or chronic conditions—and the fact that all those providers may use different PM/EHR/HIS systems—gathering data about each episode of care is a challenge in and of itself. But that challenge can be a prelude to an excruciatingly manual and cumbersome normalization process if there’s no software in place to clean up and organize the data as it’s aggregated. In short, the process starts with detailed documentation—but providers’ diligence and attention to detail only pays off if organizations implement the right technology.
People who aren’t familiar with the challenges and the sunk costs of healthcare IT might ask why all providers don’t simply use the same system or software. That sounds nice, but it isn’t feasible—and it isn’t even necessarily preferable given that some systems are better suited for hospitals while others are tailor-made for specialists or primary care physicians.
That’s why—much like patient engagement and clinical communications solutions—the effectiveness and the power of population health management solutions will depend on their interoperability. The question isn’t “which systems will the software work with?” It’s “will this software work well with every system we interact with now and every system we might interact with in the future?”
Analytics and actionable insight
Data aggregation and normalization are the building blocks—analytics, in turn, forms the ever-evolving blueprint that shows you how to build a financially sound, high-quality continuum of care that optimizes reimbursement and patient outcomes.
The term analytics gets thrown around a lot, but for organizations that want to optimize their ability to manage population health, comprehensive clinical and financial analytics are the keys to staying on the forward edge of changes that are reshaping both revenue cycle and the delivery of care.
One of the most important distinctions among clinical analytics solutions isn’t whether the information is available in real-time—it’s whether some of the insights get delivered ahead of time. Providers need clear reporting on possible gaps in care for the patients who have appointments that day or in the near future. If reporting only identifies these gaps after patients come in for their appointment, it adds a layer of inefficiency and decreases the likelihood that the gaps can be filled in a timely manner—or at all.
Let’s say an otherwise healthy patient who happens to be a smoker comes in for their annual physical. If smoking-cessation counseling isn’t included, what are the chances of dragging that patient back in just so you can remind them to kick the habit? Or consider patients with chronic conditions who’ve recently gone through a dozen tests and been prescribed a half-dozen medicines. Will they be enthusiastic about returning for additional tests that could have been conducted during previous office visits—especially if they’re not sure what their additional financial obligation will be?
That last example touches on just how interconnected financial and clinical issues have become. Quality and utilization will both play a role in determining reimbursement—on the back end and the front end (for example: failing to meet quality measures will result in lower overall reimbursement rates or significant financial penalties). The ability to meet those measures will depend partly on patient engagement and effective communication around patient financial obligation, which is becoming both more complex and potentially more fraught as more patients find themselves dealing with skyrocketing deductibles. Without clear, comprehensive reporting on financial and clinical performance—the cause and the effect of quality, cost, and utilization—it won’t be possible to figure out what’s going wrong or how to fix the problems until long after they’ve mushroomed into full-blown disasters.
I’m not discounting diet and exercise
They’re critical to maintaining good health and to improving health over the long term. But for providers, software to aggregate and normalize data such as vital signs, test results, and episodes of care is just as important. Effective aggregation and analytics solutions are the best way to track whether patients are taking care of themselves—and the only way to track how well you’re taking care of your patients.
Mary Hardy is the Director of Health Data Analytics at ZirMed. She has over 24 years of healthcare experience, half of which she spent leading very successful Channel Partner teams. Prior to joining ZirMed, Mary was the VP Sales at Alere Analytics (formerly DiagnosisOne) where she led the Sales and Partner team and doubled their revenue year over year by designing a solid go-to-market strategy. Before joining Alere Analytics, Mary worked at GE Healthcare for 21 years, holding various successful sales positions.