“Biology is about systems and processes… but the process of interacting with a patient, with a physician, is actually more complex (than the central dogma).” – Michael Liebman
Patients, caregivers, and physicians trying to manage a difficult medical journey are desperately seeking resolution, but often they encounter frustration and failure instead. While some patients follow the prescribed route and have great outcomes, others progress down the first-line, second-line and beyond treatment alleyways, yet what they see in front of them always seems to be another alley, or worse, a wall.
Dr. Michael Liebman’s thesis is that we are led down these frustrating alleys because we have overly simple conceptions of the process we are in, and do not understand the nuances as they affect us. For example:
- Disease Treatment: A patient has symptoms of disease, gets a diagnosis, a treatment, and has an outcome. But this process is actually much more complex. It depends on who the patient is, how engaged they are in their treatment, what they bring to the physician, the environment where the physician works, and the country and culture which surround them. It also depends on who is looking at the process, i.e., the patient, caregiver, physician, pharma, regulator, payer, or researcher. Each stakeholder has their biases. For example, physicians have to be very operational, especially based on economics and time pressures. They are also considering what needs to be done for reimbursement, which may not follow guidelines. Patients want an answer before they leave the doctor’s office. And the treatment process depends on when the clock starts. Usually the clock starts when the patient shows up in the doctor’s office with symptoms, but there is active research for earlier markers that aren’t being used.
- Disease Biomarkers: While each of us generates a practically infinite set of measurements continually, what is actually measured is an eentsy subset of that – only the accepted things which can be easily and affordably measured – and even these are only done episodically on say, the next visit to the clinic. It’s like trying to explain a day-long wild melee with a couple of grainy pictures. While they capture something, much is lost. Two patients may present with the same biomarkers or clinical variables, but progress very differently over time. And two patients that are progressing exactly the same may not present with the same value because they were observed in the doctor’s office at different points in time. Patients with similar symptoms may have very different illnesses.
- Drug Development: The target for drug development should be a mechanism, something that enhances our understanding of an individual’s path through a disease, but we’re frequently dealing with phenotypes (attributes of the patient). For example, a clinical trial showed no effect over six years with a very large population, and analysis of subgroups did not find any groups for which this drug was appropriate. The problem was the definition of the phenotype, and then using that phenotype to enroll patients into that trial. Subsequent analysis identified five completely different diseases.
Dr. Liebman challenges us to consider patient testing and treatment decisions as part of an enormously large and complex multidimensional system. This perspective will lead us to consider ways in which the conventional learnings we get from much simpler analyses (like the gold-standard randomized clinical trials, with their own serious biases) can lead us astray. Instead he asks us to build more robust models of disease, using techniques well-known to the modeling profession (e.g., mathematics, Bayesian and other statistical techniques). Then, looking creatively back at existing data, and creatively forward to generate ideas for new measurements, see how these models can be applied to give more granular views of each person’s condition, and therefore more specific ideas for treatment.
This is a grand vision. In his talk, which is included below, he discusses the problems and opportunities of models of systems and processes, giving concrete examples of their application and many ways in which the conventional wisdom relies on some dubious foundations. In this, he is encouraging patients to not be afraid to (respectfully) challenge these learnings and perhaps find a better path for their own care.
Key Principles to Avoid the Frustrations of a Simplistic Process Model
- Expand the Scope: Think in terms of systems and processes that are bigger than your main focus and link them to everything else. Include temporal relationships that are commonly ignored. For example, how early should we identify a person as entering a given disease path, and what systems, including more peripheral effects, might be relevant? Consider integrated health providers.
- Test the Model: Enable clinicians to evaluate models retrospectively against their patient populations so they can test performance and see if their patient population may exhibit unique characteristics that through feedback could further enhance the model. Individual physician practices may encounter unique patient populations or more diverse populations and for them to gain confidence that a new algorithm might be appropriate, they should evaluate it against previous patients. If it is not effective, then feedback to the developer can aid in refining it, and/or identify unique characteristics of that physician’s patient population.
- Humility: The physician likely does not have all the answers and should share their degree of confidence in any specific decision or recommendation in a spirit of collaboration. Transparency and willingness to accept some degree of uncertainty can help form better patient-physician relationships/interactions and lead to better and more informed decision-making. Physicians are focused on finding the right drug for a patient with a diagnosis. Some physicians will admit that they don’t trust a diagnosis, but they don’t have the option to try to change that. This is an opportunity for empowering the patient and for research