How healthcare organizations can succeed in population health management.
With the right analytics, population health management can work for your health system.
One of the core functions in population health management (PHM) is proactively managing care gaps in an effort to drive healthier outcomes. But the process itself often has its own type of gap that can cause PHM efforts to be less effective.
Knowledge gaps result from the way data are gathered. The usual method is to go through claims data to see which actions have been taken for a population and which must still be done to meet evidence-based best practices.
The problem is, there can be as much as a three-month lag between when care is given and when claims data become available. During that time, there’s no telling how many of those gaps have been filled, which means any efforts to close them after the fact are wasted. Additionally, looking at claims data alone does not tell the complete story of a patient, nor does it provide a comprehensive view of health status.
A more thorough approach is to combine claims with clinical data to create a more timely and accurate view of the patient’s current health status and care gaps. This information can then be used to create risk scores at multiple levels, which help healthcare organizations assign their limited care management resources based on where they will deliver the best results.
These risk scores can be made even more accurate by adding in outside data such as social determinants of health, psychographic and other types of data that offer insights into who these patients are, how they live and what motivates them. This information can help break down barriers and improve engagement, driving greater success.
Making this level of PHM precision happen starts with having a strong data strategy driven by robust, next-generation analytics that use aggregated data to build a longitudinal view of each patient’s current health, risk and cost profile. These profiles are then matched to pre-built personas that have been constructed based on similar attributes, such as age, income, education level and so on.
Using personas makes it easier to assign an impactability score to each patient that shows which care gaps to close first and the value of doing so. Placing these scores in a dashboard view further helps healthcare organizations set priorities based on the clinical and financial effects of dedicating care management resources to certain areas.
For example, they can see that getting a morbidly obese patient with chronic hypertension to participate actively in a weight management program can pay significant dividends. At the same time, they will also see where dedicating resources to closing certain care gaps in another patient doesn’t make much sense because it won’t have a significant impact on the patient’s health outcomes or a provider’s cost avoidance.
This same approach of developing analytics that incorporate claims and clinical data can also help healthcare organizations address one of their greatest ongoing financial challenges: managing the transition from fee-for-service to value-based reimbursement.
It starts with gaining visibility into the revenue the organization is receiving through mechanisms such as Medicare’s risk adjustment factor (RAF) programs for managing at-risk populations. Leadership can then use this information to understand whether current investments are delivering the expected results and establish priorities for improvement if they are falling short. The organization can also avoid missteps in those improvement programs by using predictive analytics to determine whether the effects of proposed changes will be positive or negative before they are implemented. Prospective analytics offer even greater guidance by showing the expected outcomes of several courses of action, helping leadership make better decisions.
Resource use by population and facility is another area where next-generation analytics can be invaluable. Suppose the analytics discover that the emergency department in a particular hospital is seeing higher-than-average numbers of asthma patients compared to others in the system or national benchmarks. Rather than continuing to overspend on treatment, the organization may decide to assign more resources to education and prevention to lower its risk exposure.
Closing care gaps is critical to PHM and ensuring success with value-based care. Before they can be closed, however, organizations must discover where they are.
Using robust predictive and prescriptive analytics to combine clinical, claims and nontraditional data will help healthcare organizations close those knowledge gaps. They’ll then be in a better position to increase quality by improving outcomes and efficiency while also reducing costs and managing their provider and network utilization more effectively. It’s a win all the way around.
David Hom is an internationally-recognized expert in the field of consumer engagement through programs such as Value Based Benefits and Employee Wellness. He joined SCIO Health Analytics® in 2009 after more than 25 years with Pitney-Bowes Corporation, where he was responsible for introducing their leading-edge programs in value-based wellness and responsible for reducing medical trend by 50% of the industry average each year for 15 years.
Get the best insights in healthcare analytics directly to your inbox.