Understanding Pattern Formation in Clinical Care – who will benefit, who will be harmed, and for whom does treatment represent waste?

For essentially every test and treatment we have in health care, there are basically three subpopulations of patients who undergo a test or receive a treatment. First, there is a group that benefits from the test or treatment, but there is also a group that does not benefit (this is waste in our system), and finally, there is a group of people who are harmed as a result of that test or treatment.
Until now, our simplistic thinking has allowed us to rationalize that the waste and harm was just a necessary evil to help those patients who benefit from a test or treatment. Who could argue that e.g. a few unnecessary mammograms/PSA tests/screening tests for hypertension or diabetes are justified to save a patient’s life? But systems science principles argue, and the data from many decades of screening have shown, that it is not so simple, and we are perpetrating a degree of waste and harm in patient care that is not sustainable and not ethical (the problem has now received Cochrane recognition with the formation of the “sustainable healthcare” group - https://sustainablehealthcare.cochrane.org/ ).
To understand how to define the subpopulations that are harmed and those who do not benefit (waste), a more complete understanding of systems science and data analysis is required. The ultimate goal of systems science is to improve outcomes that measure the value of care for any definable, whole patient process. This is achieved by discovering the patient factors and treatment factors that most impact outcomes that measure value and applying insight from the analysis of data to improve these outcomes.
Over time, the analysis of data can produce algorithms to identify these subpopulations. With insight from multiple feedback loops, the clinical team can implement ideas for improvement which can result in lowered costs while outcomes are improved over time. For example, when enough data is accumulated and analyzed appropriately, a subpopulation can be identified who would likely be harmed and another subpopulation that would have no benefit from screening or interventions.
At the same time, another subpopulation of people could be identified who would benefit, some of whom would not otherwise be receiving a test or treatment. With these subpopulations better identified, the costs for disease screening and treatment could plummet while other quality measures would improve, resulting in better value for patients and the system-as-a-whole.