The healthcare industry is in the throes of a major change. The Patient Protection and Affordable Care Act (PPACA), lovingly dubbed “Obamacare”, is pushing many healthcare systems away from a relatively simple fee-for-service (FFS) reimbursement model towards an Accountable Care model. This model is being supported and funded by the Center for Medicare and Medicaid Services (CMS) in the form of several shared savings and payment initiative programs.
Identify Key Business DriversBetter outcomes, in the world of ACOs, is not just about a patient getting well; getting well is just the beginning. For health systems, the real key to better outcomes is maintaining health over time. In this case, any time a customer is a patient in the health system is a cost, not a revenue. Since the only time that they health system can control behavior is while the patient is within the walls of the health system, health systems will need to start being more deliberate and creative in understanding their customer’s behavior in a broader scale. This is where customer analytics comes in.
The key business drivers in the ACO model are not how many services the customer consumes, but rather the health behaviors that they engage in outside of the hospital. These vary from behaviors that obviously impact health – smoking and illicit drug use behavior, diet and exercise, etc – to less direct, but still important, behaviors such as seat-belt use and time spent sitting per day. All of these behaviors are harder for health systems to measure and even harder to control. But, once a health system identifies the behaviors that lead to increased health service usage and associated increased costs, the goal is to engage their customers in an individualized health behavior changes, and ensure that their perception is measured as a key business driver in conjunction with the intervention, but more on that later.
Predict Customer Behavior
In ACO business models, the goal is to maintain and improve health in order to reduce utilization of expensive healthcare services. This goal is intractably linked to the behaviors of healthcare customers (patients) – and are not easily modified population perspective. A more granular, personalized, approach is necessary. This is where customer analytics can play an important role
Customer analytics is driven by data and the traditional approach for health systems segmenting and implementing health behavior change has been through parsing data in EMRs. A new and more powerful approach, much in line with concepts of Analytics 2.0, is to take that information in context of the individual as a whole. This includes a complex array of data; including customer interaction information, personal behavior and habits (i.e. eating, exercise), as well as medical record information in the form of EHR’s. The more granular you can make the information, the more personalized the interventions, and the better the outcomes.
Make your Customers Happy
When developing these interventions, it is important to keep the customer’s perception of your company in mind. When implementing programs with patients, health systems need to be very aware of how they approach it. According to several studies, when programs are perceived as imposed, outcomes are worse, compliance is worse, and the risks of liability that the health system incur are higher than in a cases where patients feel that they are part of the decision and partners in the change.
Measuring and managing this perception is a key component of successfully implementing programs with patients. This concept, called customer satisfaction, is well established in customer analytics and is a key component of understanding and predicting customer behavior for better business outcomes.
Please let me know what you think in the comments section below, and come back next week for my next installment.
For more information, see the following links (also imbedded in the text):
- Fee For Service:
- ACO Programs and Definitions:
- Customer Analytics:
- "What Customers Really Want"
This blog was written as an assignment for a course in Digital Analytics at the University of Utah', Spring 2013