Smart Systems Thinking Applied to Healthcare.
Summary
In 2001, the Institute of Medicine (IOM), a division of the National Academy of Sciences, published Crossing the Quality Chasm (http://www.nationalacademies.org) a report, crafted by dozens of independent experts, who sought answers to the question: how can we make America’s healthcare system more efficient and effective? The report concluded that our healthcare system was so convoluted and fragmented, it could not be “fixed” by merely tweaking parts of the system in isolation. It asserted that the very foundation of healthcare policy, paying for services based on the time and training it took to offer them, was, relative to sound public policy, a fundamental design flaw. By contrast, it advocated for a payment system based on qualitative outcomes. However, America’s healthcare infrastructure offered no training, expertise or incentive to design or build such a model. It is 23 years after the IOM report was published. Its conclusions have been largely ignored or forgotten altogether. Based on the title of the IOM report, in 2001 it was already clear that operationalizing an entirely new framework for the delivery and payment of patient care based on qualitative assessments was a Herculean task. Fourteen years and ~$30 trillion spent on chronic care services since the passage of the ACA, it remains true. Clearly, we are missing something.
Almost fifteen years ago, the author began a brain trust to independently examine the requirements of the IOM’s re-engineering imperative. Solving the conundrum of an inefficient and ineffective healthcare system was going to take more than strong business acumen. It was going to require a synthesis of expertise in the natural sciences, clinical medicine, IT, finance, systems thinking, non-linear mathematics and data sciences. The reputation of that brain trust spread. In 2015 the author served as a technical advisor to the United States Air Force Surgeon General’s office. Then, in 2016, the USAF Medical Service retained the author as the technical lead to design and build an informatics platform capable of operationalizing a new healthcare ecosystem – one that fulfilled the requirements of the IOM’s report. In 2017 with the USAF project in our portfolio, we began to seek funding for a major Demonstration project. Funding options were limited as typical private sector investors were averse to complex ecosystem development. In addition, existing delivery system entities were uninterested in a project that could materially disrupt their cash flows and render their legacy systems less valuable. It became clear that identifying a funding source to operationalize a large-scale Demonstration project was the last but necessary piece in fulfilling the IOM’s vision for a fundamental re-engineering of American healthcare into a coherent system – one with purpose. The COVID phenomenon terminated the effort to secure funding.
Architecture
That new ecosystem was grounded in the application of systems thinking to clinical medicine. It is based on the principle that public policy compels the fulfillment of two objectives: (1) identify the most cost-effective patient care strategies for any given diagnosis and (2) establish a payment apparatus that is data centric, includes feedback loops (it can “learn”) and compensates providers based on the quality of patient outcomes. Identifying what is cost-effective in clinical medicine implies that a comparison be made. That comparison is not established by observing the delivery of a clinical protocol by similarly trained personnel at different locations. Rather, it is established by contrasting expert delivery of different modalities of patient care for a given diagnostic code. The author has chosen to contrast conventional patient care methods with Integrative and/or Functional Medicine (IM/FM) patient care methods because both modalities rely upon “expert opinion” for credibility [1] … reference is at botttom of file.
At its core, conventional patient care has the intention of providing patients relief from troublesome symptoms.
It reflects the historical influence of: (1) a reductionist scientific framework, and (2) various parties with conflicts of interest embedded in existing healthcare operations. By contrast, IM/FM patient care seeks to identify, treat, and eliminate the root cause of patient ailments. It is built on a “systems” (a.k.a. holistic) framework which recognizes that the whole is both complex (more than a two-variable phenomenon) and greater than the sum of its parts. While conventional patient care views health as the absence of disease, IM/FM-centric care views health as an ‘emergent property’ that reflects the operational status of a complex multi-variant system.
Background
Historically, as a matter of public policy, healthcare services in the United States were never designed to keep people healthy. Though there has been some movement, the goal of getting patients well was never embedded in the architecture, design or operation of the industry, nor within the regulatory apparatus that monitors it. Without that foundation, striving to get patients well by utilizing the most cost-effective care available, has never been possible. In America, healthcare providers aren’t accountable for the efficacy of their work - they get paid whether their patients get well or not. There is no financial accountability embedded in the operation of the system, or in how the system allocates resources to pay for the care services delivered. In our current healthcare system, if a particular therapeutic intervention doesn’t work well the first time, it can be tried repeatedly and still be paid for, regardless of its failure rate.
Until something is proven to work, its effectiveness, in relative or absolute terms, isn’t known. However, once its effectiveness is known, its cost-effectiveness can then be assessed. Identification of effectiveness, being a measurement that could reasonably be expected to vary with the time frame of the assessment, cannot be made without capturing the appropriate data at the front end of the care delivery service. If that data isn’t collected as a requirement for a payment being made, the process of driving efficiency into healthcare stops there. The question then arises as to when could the appropriate data be collected and where it might it come?
Conventional patient care methods are documented (albeit not rigorously) within the healthcare payment system by virtue of HCPCS billing codes, their incorporated algorithms, and the data standards (ANSI and HIPAA) embedded in the use of electronic medical claims. By contrast, without comprehensive billing codes, the practices of IM/FM-centric clinicians are not incorporated into the existing data capture apparatus or the healthcare payment infrastructure. Therefore, a comparison of “Data Inputs” (the ‘care’ provided to a patient) has never been possible between the two clinical disciplines. Further complicating the construction of the desired matrix, data necessary for an assessment of “Outcomes Data”, (did the intervention(s) improve the patient’s well-being) in the context of either conventional or IM/FM-centric care, is not captured at all. The box below summarizes the conundrum through an informatics lens.
Some of the data that belongs in the yellow highlighted upper-left quadrant is available via electronic medical claims, but data that belongs in the other three quadrants does not exist.
· Therefore, comparatively we don’t know which approach to patient care works best for any given diagnostic code.
We designed a technical answer to this problem and created a business model to accelerate its adoption in the marketplace.
A comparative effectiveness assessment of these two patient care disciplines cannot occur until all four quadrants are robust with information. In an informatics context, this must be a prime goal of any effort to transform healthcare in America. When sufficiently robust, the data collected can be used to determine the comparative cost-effectiveness of multiple patient protocols for any given diagnostic code. A complete data matrix can provide new types of analysis and insights. Those insights can empower the development of healthcare policy that champions the efficient allocation of healthcare capital. There are a multitude of business, finance and clinical applications for such information. Ultimately, that data is the intellectual foundation for an entirely new healthcare ecosystem – one that is systems-based, patient-centric, and data-driven.
For additional information contact the author: Wayne Miller (512 787 5028)
- In the scientific literature, CLASS “A” evidence is the highest grade of evidence. “Expert opinion” is the lowest grade of evidence. Only 11% of clinical guidelines are based on CLASS “A” evidence. SEE: Joseph D. Feuerstein, MD; et. al. Systematic Analysis Underlying the Quality of the Scientific Evidence and Conflicts of Interest in Interventional Medicine Subspecialty Guidelines. Mayo Clinic Proc 2014**:** 89(1): 16-24.
The matrix referred to in thid document cannot be uploaded in file format. Reach out to author if you want to see it.