Preparing to integrate the social determinants of health

about 1 year ago

We know social determinants of health (SDH) have a major effect on health outcomes and cost. Numerous observational studies of spending patterns show large savings, but few have captured data-driven examples to isolate portable methods for success. The reason is that the medicine and social sciences are miles apart in science, approach and attitude. To organize in ways that reduce costs for everyone, we must do three things.

  1. Manage at the community level
  2. Recognize the difference between a community and a population is a game changer
  3. Enable community collaborations that adapt to achieve shared outcomes and rewards

Managing the whole

Healthcare delivery runs in an environment of social and biologic complexity. This environment continuously adapts and reorganizes itself in ways that are impossible to fully understand or predict. Feedback loops alter and multiply effects so that what comes back may not be recognized in terms of whatever causal events kicked it off. Excessive cost and unexpected changes seem to come from nowhere because understanding causality can be very difficult.

The nature of the environment cannot be changed, but we can change the scope of our solution to include all its important parts. We do this by managing as a community.

A community is not a population 

Medicine has become population focused with a strong reliance on statistical analysis. We use statistics to measure levels of certainty and the predictive value of results. We take information learned from narrow cohorts of a membership base and apply it to the real world on the implicit assumption that the findings apply everywhere. This creates a false sense of stability and reliability, and worse, implies a mechanical view of a world that is in fact, Complex, and an altogether different animal.

Statistics based on such gaussian or standard normal probability distributions are based in a population perspective defined as any whole group of things having at least one characteristic in common. The population is a set of identical objects having random variability. This ignores the network effects that define and empower community. While this mathematical model is wonderful, the underlying assumptions do not fit with the network reality of healthcare delivery.

Standard model statistical tools are essential for research to create scientific knowledge. They work using experimental conditions that isolate what we study. In actual practice, we cannot assume a normal distribution is present or that the error will be small because natural systems tend to have Complexity driven power law distributions. An example of an inverse power law distribution and a normal distribution is shown in figure 1.

Figure 1: Comparison of Normal Distribution and Inverse Power Law †

When we compare the statistical population based distribution (the blue line) with empiric reality of a natural human system and community networks (the orange line), our comfort zone should disappear. Useful averages and standard deviations are gone. There are no confidence intervals. There are many more events in the statistical tails than we now recognize or manage. This is a wicked problem and it needs a strategy based in complex system science. This is a fundamental change in perspective. To manage healthcare costs, we must focus and collaborate in real time around every person. Fortunately, this is easier than it sounds because inside complexity are simple rules.

Making it work in your community

Modern network and computing infrastructure allows us to create systems capable of matching the complexity of the environments we serve. This concept comes from Ashby’s Law of Requisite Variety later updated by Boisot and McKelvey’s to the Law of Requisite Complexity.†† In short, unless we can adapt in step with the environment, we cannot achieve our goals. To see that in action, look at Amazon®, Facebook®, LinkedIn® and many more. It isn’t the technology or the internet that makes them successful, their success comes because they use simple rules to harness technology, enabling them to match the complex human environment they serve and make it simple for customers. 

Healthcare can use the same principles and supportive technology to focus on each person with connections to all points of care – medical and social. Moving from static metrics and retrospective analytics to real-time measures that act as in-flight instrumentation is key to enable community collaborations that can adapt to achieve shared outcomes and rewards. 

I’ll describe this in more detail in my next article and will present key points of a new pilot we are working on at the State of Reform conference in Portland, Oregon, October 11, 2017. 

I hope to see you there. Together we can deliver real value and heal healthcare.

† For a more thorough discussion of this see “Mathematical Principles: Tales of Tails” by Bruce J. West Chief Scientist Mathematics Army Research, in the Handbook of Systems and Complexity in Health, 2013, DOI: 10.1007/9781-4614-4998-0 Springer Science and Business media.

†† “Complexity and Organization—Environment Relations: Revisiting Ashby’s Law of Requisite Variety,” Max Boisot and Bill McKelvey, The SAGE handbook of Complexity and Management