Thought Experiment: Applying an EBM Disease Model to Defensive Medicine

Thought Experiment: Applying an EBM Disease Model to Defensive Medicine

But, if a lawyer were to review your chart, how would you defend that decision?

We have all had attendings say this to us when we present a plan for a patient, perpetuating the next generation of defensive medical practitioners.

What if we were to apply an evidence-based disease model to the practice of defensive medicine? In this post, we will do just that, as originated by Dr. Peter Tepler, one of our 2019 EM/IM graduates, in his senior lecture. This framework allows us to think about the utility (and harms) of defensive medicine beyond anecdotes and fears passed down from our attendings.

Defensive medicine occurs when: “Doctors order tests, procedures, or visits, or avoid high-risk patients or procedures, primarily (but not necessarily solely) to reduce their exposure to malpractice liability” (1). Ninety percent of all physicians believe that malpractice concerns result in unnecessary testing (2). Sixty-five percent of emergency physicians stated that avoiding malpractice was “almost always” or “often” a contributing factor to ordering medically unnecessary advanced imaging (3).

Evidence-based medicine is the “conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients” (4). When we practice evidence-based clinical medicine, we recommend an intervention to patients based on the likelihood of benefit versus harm of that particular intervention. Statistically, the evidence of benefits and harms for a particular intervention can be summarized with the Number Needed to Treat (NNT) and Number Needed to Harm (NNH), the number of patients needed to receive an intervention to benefit or harm one person.

The Malpractice Disease

Consider “being sued” like a disease for doctors for which certain interventions can help you avoid the outcome of being sued. One-third to half of all physicians have been named in a lawsuit (5,6). In one large national emergency physician group, one in 11 was named in a claim over 4.5 years (7). Depending on the source, Emergency Medicine ranks as the 3rd to 8th most frequently sued specialty (5-7). Lawsuits cost time, money, and emotional energy for the physician, but only 2% of cases across all specialties result in a judge/jury verdict against the physician (6).

So with that in mind, let’s now consider the number of medically unwarranted CT scans, additional minutes of talking, or troponins that a physician needs to perform to avoid one lawsuit. Let’s call this statistic “Number of Useless Tests to avoid one Suit” – NUTS (Tepler P, unpublished presentation, 2019).

Sample Calculation: NUTS for Troponin Testing

How many troponins for chest pain does an emergency physician have to send to avoid one suit for a missed acute myocardial infarction (MI)? (For simplicity, we’ll consider patients presenting with chest discomfort or equivalent in which MI is in the differential). MI is one of the leading diagnoses in malpractice claims and a persistent source of litigious fear for emergency physicians (8).

Basic Definitions
NNT α NUTS = 1/ARR
ARR = Absolute Risk Reduction of 1 troponin rule-out on risk of 1 Suit = (Risk of suit WITHOUT troponin) – (Risk of suit WITH troponin)

Before we venture any further, we have to acknowledge the significant limitations of this calculation as every input includes multiple assumptions.

Let’s assume, annually:
10,000 suits [a]
7,500,000 chest pain ED visits (9)
5% of claims for missed MI [b]

Therefore:
5% missed MI claims x 10,000 suits = 500 claims for missed MI
500 missed MI/7,500,500 MI visits = 0.006% absolute risk of suit for a chest pain visit

Assume that one could perform 100% sensitive and specific perfect troponin rule-outs that will catch all MIs and prevent all potential lawsuits for all adults with suspected MI. In other words, the absolute risk of suit for chest pain visit is 0% if you perform a perfect troponin rule-out.

NUTS is calculated as:
ARR = (Risk of suit WITHOUT troponin) – (Risk of suit WITH troponin) = 0.006% – 0.0% = 0.006% absolute risk reduction for sending perfect troponin rule-out for one chest pain visit
NUTS = 1/ARR = 1/0.006% = 15,000 perfect troponin rule-outs to prevent 1 suit [c]

Is it worth your personal energy, your patient’s days off work, or your ED resources to rule-out 15,000 patients to prevent one missed MI lawsuit? Also, consider that as troponin tests become more sensitive they lose specificity which ironically creates more diagnostic uncertainty. Considering high-sensitivity troponins as an example, about half of patients with positive troponin are not having an acute MI, and these patients may be subjected to the risks of cardiac catheterization after a false positive test (10). Before you decide on your testing and treatment thresholds, as with any intervention, make sure you have responsibly weighed the potential downstream harms (11).

Limitations

While the NUTS framework allows us to use an evidence-based lens to examine defensive medicine based on currently available data, any specific estimates are limited by the quality of the evidence. In the case of defensive medicine, the evidence has unique limitations. This includes lack of nationally representative samples (e.g. studies from a single or several insurers), reliance on proprietary data from malpractice insurers and therefore inability to reproduce results, under-reporting of malpractice events to the legally mandated National Practitioner Data Bank, lack of data specific to emergency medicine physicians, and use of data pulled from non-concurrent years for a single calculation.

So, did you send a troponin?

Balancing our exposure to litigation is a reality of our healthcare system that feels at odds with the mission of medicine. As a trainee without her own license, it is easy for defensive medicine to feel exhausting rather than protective. By using a NUTS framework, we can practice less superstitiously by examining the best available data.

Will that extra testing protect you every time? Based on the prevalence of lawsuits, we should all expect to be named in a suit as we accumulate experience and exposure (7). You probably won’t expect a lawsuit when it comes (58% were very surprised). It might be for a missed diagnosis (about one-third of the time). You probably won’t think it was warranted (89% thought not) (6). In the face of so much uncertainty and paucity of data, the most sustainable and practical thing to do is to stay true to our mission and do right by your patient.

Footnotes

[a] All specialties. Estimate chosen based on writer’s counting of 6,000 claims from the 2014 National Practitioner Data Bank public use data set and 12,744 annualized paid claims from 1994-2012 in the Schaffer et al. paper (12).

[b] Based on a 2010 study from insurers participating in the Physician Insurers Association of America (PIAA), a trade association insuring 60% of practicing physician. 5% of claims associated with AMI diagnosis. This is an overestimate as 19% of claims studied named an emergency physician as the primary defendant and not all claims may not have resulted from missed diagnosis (13).

[c] This number is sensitive to the number of suits assumed annually. For example, NUTS ranges from 7,500-25,000 depending on annual claims 6,000-20,000.

References

1. Defensive Medicine and Medical Malpractice [Internet]. U.S. Congress, Office of Technology Assessment; 1994 [cited 2019 Oct 21]. Available from: https://biotech.law.lsu.edu/policy/9405.pdf

2. Bishop TF, Federman AD, Keyhani S. Physicians’ views on defensive medicine: a national survey. Arch Intern Med 2010;170(12):1081–3.

3. Kanzaria HK, Hoffman JR, Probst MA, Caloyeras JP, Berry SH, Brook RH. Emergency physician perceptions of medically unnecessary advanced diagnostic imaging. Acad Emerg Med 2015;22(4):390–8.

4. Sackett DL, Rosenberg WMC, Gray JAM, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t [Internet]. BMJ. 1996;312(7023):71–2. Available from: http://dx.doi.org/10.1136/bmj.312.7023.71

5. Medical liability market research [Internet]. American Medical Association. [cited 2019 Sep 22];Available from: https://www.ama-assn.org/practice-management/sustainability/medical-liability-market-research

6. Medscape: Medscape Access [Internet]. [cited 2019 Sep 22];Available from: https://www.medscape.com/slideshow/2017-malpractice-report-6009206

7. Carlson JN, Foster KM, Pines JM, et al. Provider and Practice Factors Associated With Emergency Physicians’ Being Named in a Malpractice Claim [Internet]. Annals of Emergency Medicine. 2018;71(2):157–64.e4. Available from: http://dx.doi.org/10.1016/j.annemergmed.2017.06.023

8. Katz DA, Williams GC, Brown RL, et al. Emergency physicians’ fear of malpractice in evaluating patients with possible acute cardiac ischemia. Ann Emerg Med 2005;46(6):525–33.

9. National Hospital Ambulatory Medical Care Survey (2016). [cited 2019 Sep 22];Available from: https://www.cdc.gov/nchs/data/nhamcs/web_tables/2016_ed_web_tables.pdf

10. Hollander JE. High-Sensitivity Troponin: Time to Implement. Ann. Emerg. Med. 2018;72(6):665–7.

11. Schiff GD, Kroenke K, Lambert BL, Sanders L, Sheikh A. Ten Principles for More Conservative, Care-Full Diagnosis. Ann Intern Med 2019;170(11):823–4.

12. Schaffer AC, Jena AB, Seabury SA, Singh H, Chalasani V, Kachalia A. Rates and Characteristics of Paid Malpractice Claims Among US Physicians by Specialty, 1992-2014. JAMA Intern Med 2017;177(5):710–8.

13. Brown TW, McCarthy ML, Kelen GD, Levy F. An Epidemiologic Study of Closed Emergency Department Malpractice Claims in a National Database of Physician Malpractice Insurers [Internet]. Academic Emergency Medicine. 2010;17(5):553–60. Available from: http://dx.doi.org/10.1111/j.1553-2712.2010.00729.x