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A Model of More Effective Chronic Condition Management

At ProjectVision, we talk a lot about two elements that are essential to building condition management programs and interventions that not only retain patients, but keep them actively engaged in behavior change:

  • Personalization
  • Quantifying environmental barriers to behavior change

Because of the difficulty in gathering complete behavioral data, many chronic condition management programs focus “personalization” based on clinical risk – A1C level, BMI, blood pressure, etc. Using historic demographic data, they can predict with reasonable accuracy when a patient will shift from Pre-Diabetes to full Type 2 Diabetes.

However, clinical risk doesn’t tell us the “why” in the same way that behavior risk does. Focusing on clinical risk also pushes providers into a mental trap of treating patients with the same clinical risk and demographic characteristics as monolithic. Studies have shown that not more than 20% of patients who successfully lose weight in a structured program are able to maintain that weight loss in the long term.

The unsatisfactory long term outcomes of current condition management programs points to three key issues with the current models of chronic condition management:

  • The program is disconnected from the social, cultural, and environmental reality for most of the patients
  • Behavior risk stratification is not used to further tailor the program to each patient’s unique psychological needs
  • The environmental barriers faced by patients are beyond the scope of clinicians and care providers

So let’s dive deeper into each of these three issues:

Programs Disconnected From Patient Reality

In early November, I presented a webinar centered on more effectively engaging low-income and minority patients in health behavior change. I started that presentation by analyzing the rapidly growing amount of money retailers were spending on digital ads, making the point that in an industry where failing to understand your customers deeply means going out of business, companies make a big investment in tools to help them understand and speak directly to their customers.

A big problem in healthcare is that biases about certain patient groups – the overweight, the poor, the non-white – are allowed to drive program development without being acknowledged and confronted. Failures to recruit patients in adequate numbers or keeping patients engaged in programs often comes down to incongruent messaging on the part of providers. For example, using pictures of thin, white women doing yoga when advertising a condition management program to mothers of color in lower-income communities with high obesity prevalence will not create the necessary connection with those we are trying to reach.

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Programs that don’t acknowledge and build behavior change around existing deep structures within a community can expect to continue to get unimpressive results. Making this change requires letting go of assumptions about how people in certain communities think and feel, and instead, relying on observations from actual data gathering.

Behavior stratification

Simply put, a good program is responsive to psychological variance within the participating patient population. In an inpatient setting, the frequency and intensity of care is based on the clinical risk faced by the patient. Good chronic condition programs should be able to similarly moderate the level of care provided based on the behavioral risk faced by each patient. We need to move away from one-size-fits-all models of intervention. There are many different paths a person can take from being healthy to becoming diabetic.

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Effective Interventions Are More Than Just Clinical

We are shaped by our environment, and we generally take the path of least resistance when making day to day decisions. If a patient lives in a healthy environment – where they have walkable access to healthy food options that they can afford, with clean air, low crime, and easy access to recreation – they are more likely to be healthy. Obesity rate has a very strong negative correlation with income.

By the same token, it’s not within a hospital’s scope to provide adequate healthy food options in a food desert, or job resources for unemployed males in high crime neighborhoods, or for addressing a myriad of other social determinants of health that are not strictly clinical.

In order to be more effective, chronic condition programs run by clinicians need to engage with non-clinical resources in direct partnership. They need to work in concert with food banks, legal services, community health programs like the YMCA, and others to properly address the environmental issues that are causing their patients to fail in more traditional programs.

This more networked approach helps address the two key issues above around being disconnected from the patients’ reality and lacking true behavior stratification. If we understand what each patient’s personal goals are as well as their unique barriers, we can better understand the norms that shape their life and the specific type of support they need to get the most out of a clinical behavior change program. By partnering with non-clinical resources, we can now start actually tailoring the services around those unique needs in a scalable fashion.

By building networks of resources to support patients, we can start leveraging network effects to exponentially increase the value of condition management programs to patients as more resources and more patients become part of the network. You start to have opportunities to leverage past participants as peer advocates as well as your non-clinical partners to bring in billable, reimbursable patient cases.

Bringing Things Together

Current condition management programs have long-term success rates that are anything but impressive. Given the cost concerns of CMS, it is imperative that we aggressively identify new models that can more reliably support behavior change.

Successful new models will address implicit bias and involving high-risk patients more directly in care planning, focus data collection on behavioral risk stratification, and build close partnerships with non-clinical resources.

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