I have written hundreds of learning objectives at this point. I even wrote a book trying to address frequently asked questions about the process, The Learning Objective: Identifying appropriate metrics for improving medical education.
I don't have to tell you the rate of type 2 diabetes is ridiculous and continues to rise. Globally it is hard to agree on cohesive recommendations as geography informs adherence to HbA1c levels of varying control benchmarks and other metrics.
The patient-centric approach to a heterogeneous clinical presentation such as type 2 diabetes (T2D) treatment involves interactions between biologic, psychologic, and behavioral determinants, requiring integrated clinical efforts and disease management to reduce health and economic burdens.
The learning objectives (LOs) I see don't reflect this context. What I see are industry pipeline reports and marketing opportunities. My role as a collaborative partner is to educate about health policy, economics, and the clinical perspective of the clinician. Where are the true gaps? Unfortunately they don't always sit in a pretty box with a bow at the intersection of successfully acquired request for proprosals (RFPs) and clinical necessity at the patient level.
My inbox this morning alone contains several programs on how to approach the global scurge. Because as you can imagine, there is "gold" in them there insulin deprived hills. Here are just a few of the LOs:
The problem, as I see it, is we have yet to have meaningful conversations about the scope of glycemic control, ongoing discussions of risk factor management and actual disease prevention, how tight control parameters should be, presence or absence of quality evidence. As you can imagine, a learning objective focused on "outlining data" that might be influenced by a billion dollar industry might leave one with an arched-eyebrow.
What do we know about "value" in diabetes care? What about cost effectiveness? Marginal increases in efficacy? What about lifestyle interventions?
Well-meaning educational programs targeting professionals managing patients at risk or diagnosed with diabetes are scientifically short-sighted. Where is the context? I listened to a webinar this morning where non-inferiority and superiority studies were discussed like we are all born with an innate understanding of how these different RCT designs differ--and why that matters. Sample size requirements vary between the two and so does the importance of the extent of the difference.
The first thing that came to mind while reviewing the latest findings about novel and emerging GLP-1 receptor agonists, DPP-4 inhibitors, SGLT2 inhibitors, and/or injectable fixed-dose combinations was that study designs, study populations, and variable outcomes would be impossible to decipher at the individual patient level for the majority of practicing clinicians.
Improving Numeracy in Medicine attempts to create an informed dialogue but I recognize the limitations as ongoing questions continue to surface. I discussed p-values and their limitations on clinical significance but I am now reminded that 2-tail tests, like the null value often simplify and ignore the importance of one-sided clinical signficance.
For example, the “null hypothesis,” demands no detectable difference between a new and standard of care treatment or placebo. Two-sided p values tell us the probability the results are compatible with the null hypothesis. When that probability is small (<5%), we “reject” the null hypothesis and “accept” the “alternative hypothesis” that the difference we’ve observed is not zero. There is an actual difference between two treatment arms.
The problem is that we haven't described the distinction of the novel treatment as being better, on the one hand, or worse/harmful, on the other,compared to a standard of care or placebo. When performing “2-sided” tests of statistical significance, we know nothing of superiority and non-inferiority. We also frequently misinterpret failure to reject the null hypothesis (based on 2-sided p values >5%, or 95% confidence intervals that include zero) as negative instead of inconclusive.
“It is never correct to claim that treatments have no effect or that there is no difference in the effects of treatments. It is impossible to prove ... that two treatments have the same effect. There will always be some uncertainty surrounding estimates of treatment effects, and a small difference can never be excluded."--Alderson and Chalmers
As a writer transitioned to writing primarily for societies or physician groups (not pharmaceutical companies directly), there is a noticable gap of where the funding dollars are going, what medical education companies are salvating to capture, and the physician at the point-of-care left to sort out a mess of contradictory research findings, media soundbytes, and now computerized algorithms that order the next test based on a single laboratory finding.
Devoid of the clinical expertise of the physician recognizing the uncertainty of a small increase in HbA1c creating a patient with a diagnosis, EMRs won't suggest stages of treatment such as watchful waiting or less aggressive management. What has really happened is that a risk factor has been identified but a disease is being treated. If you are a physician in a quality improvement institution the ICD10 code now starts a cascade of interventions and most likely financial incentives.
Medical education in diabetes care demands driving down HbA1c levels, although other risk factors such as blood pressure and cholesterol level appear to be more effective targets for reducing cardiovascular risk. Care that Matters: Quality Measurement and Health Care reports that glycemic control with drugs other than metformin may actually cause harmful hypoglycemia but fail to appreciably reduce morbidity and mortality. Other diabetes medications are treating numbers and risk factors but not actual patients.
If the goal is shared-decision making and helping patients live longer and higher quality lives we need the right metrics. Instead of measuring tight blood glucose control perhaps we should focus on reducing smoking rates, hypertension, and hyperlipidemia--unless we are becoming evidence resistant.
Thoughtful discussions about content development and outcomes analytics that apply the principles and frameworks of health policy and economics to persistent and perplexing health and health care problems.
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In a world of "evidence-based" medicine I am a bigger fan of practice-based evidence.
Remember the quote by Upton Sinclair...
“It is difficult to get a man to understand something, when his salary depends upon his not understanding it!”
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