This article first appeared in JAMA Network. It is co-authored with Ezekiel J. Emanuel, MD, PhD.

Diagnostic excellence is a priority of both patients and individual clinicians, yet does not seem to be afforded the same attention by health care systems. Autopsy data from 2 Swedish hospitals revealed that 30% of 2410 cases had clinically significant undiagnosed diseases.¹ A review of methods used to estimate the rate of diagnostic error suggested that diagnostic errors are more common than medication errors.² Even for conditions that are considered “easy” to detect, such as hypertension, 1 estimate suggested that 10% of US adults may have high blood pressure that is undiagnosed.³ Failure to make diagnoses expeditiously leads to prolonged uncertainty and may result in costly unnecessary tests and procedures, delayed treatment, and increased risk of morbidity and mortality. If diagnoses are forgone in favor of empirical treatment, patients may receive ineffective or potentially harmful treatments. Given this, a key question is why physicians would take shortcuts on diagnostic workups and rely on guesses and intuition when initiating treatments when it is possible to make accurate diagnoses and deliver evidence-based care.

An important factor that may be contributing to these issues regarding diagnosis is incentives. The US fee-for-service health system pays for tests and treatments, not for diagnostic reasoning or accuracy, and does not make a distinction about whether payment is for the appropriate diagnostic tests or whether treatment selections are based on a correct diagnosis. Virtually all activities related to testing and treatment, including those associated with errors, are reimbursable. Fee-for-service reimbursement rewards empirical treatment; even if the initial treatment is incorrect, the next treatment is reimbursable too. It also rewards nonparsimonious diagnostic workups and consultations. A fee-for-service system does not pay more when decision support tools are used and does not consider timeliness of diagnosis and initiation of efficacious treatment in how it pays. Unlike purchases of other goods and services, there are no refunds for ordering the wrong service or executing it poorly, only additional charges.

Innovation and knowledge translation are slow in health care, and the lack of incentives may impede the uptake and use of potentially beneficial advancements for diagnosis. For the past 20 years, there have been claims that artificial intelligence (AI) will surpass physicians, particularly for diagnosis. Over this time, there have been many attempts to create automated tools to support clinician decision-making, but few have gained widespread adoption. Radiology decision support tools that read mammograms have no discernable effect on radiologist productivity, which makes use of these tools economically irrational.⁴ A similar pattern has been observed with digital pathology diagnostic tools.⁵ Several attempts have been made for the integration of diagnostic decision support tools into electronic medical records for emergency physicians and primary care physicians; most of these attempts have been limited by low usage by clinicians because the tools have not improved productivity or quality.⁶

A major challenge to developing new technologies to improve diagnostic quality is poorly aligned economic incentives. To become a viable business, a company needs to create a diagnostic tool that is highly sensitive and specific and also improves clinical care enough to generate demand at a high enough price to make it attractive to investors. To command a price sufficient to support commercializing, a diagnostic innovation also needs to generate a large and rapid economic return that typically exceeds what a customer is willing to pay.

Tests that predict the chemotherapy responsiveness of various cancers (such as Oncotype DX, MammaPrint, and Prosigna) were created and adopted because they could potentially generate large savings for payers in the costs of chemotherapy. Because clinicians are often not sharing in the economic value created by these innovations, widespread adoption is often costly and slow. This makes it more expensive and slower to commercialize new technologies and ultimately leads to less investment in innovative technologies that could improve the speed and accuracy of diagnoses.

Even technologies that are likely to change or refine diagnoses, like genetic testing for cancer, have not been widely adopted outside academic cancer centers. Genetic testing improves diagnostic precision and prognostication and often should alter treatment decisions. However, in 1 study of patients in California and Georgia, only 25% of the 77 000 predominantly White women diagnosed as having breast cancer and 31% of 6000 predominantly White women diagnosed as having ovarian cancer had undergone genetic testing as part of their diagnostic workups despite guidelines that recommend testing for most patients.⁷

While there may be promising breakthroughs in diagnostic speed and accuracy through technologies like genetic testing and AI, these tools also may reduce the incentive for clinicians to improve their diagnostic acumen. Just as advances in electronic stethoscopes and mobile echocardiography reduce the need for physicians to hone their auscultation skills, improvement in genetics- and AI-driven decision supports may reduce the incentive for clinicians to maintain some diagnostic skills.⁸

Creating incentives that improve the quality of diagnosis should be a priority. Most important, rapid and accurate diagnoses should be financially rewarded. One approach to accomplish this objective could involve providing reimbursement for use of clinical decision support, requiring complete diagnostic workups before initiating elective treatments, penalizing nonparsimonious outpatient workups and empirical treatment, and not providing reimbursement for serial trial-and-error approaches in cases for which a definitive diagnosis can be rendered. One policy could be to institute a reimbursement modifier for treatment that is initiated without the diagnostic tests delineated in professional society guidelines.⁹ For instance, for rheumatological diseases like rheumatoid arthritis, polymyalgia rheumatica, and giant cell arteritis, there might be a reimbursement modifier that leads to higher reimbursement for patients for whom a diagnosis is made before empirical treatment with steroids.

An additional approach for improving the speed and accuracy of diagnoses, advocated by the National Academy of Medicine, would be to track diagnostic success and publicly report performance.¹⁰ This approach could work similarly to how readmission rates are reported for surgeons. Clinicians could have their diagnostic performance reported on the dimensions of accuracy and timeliness for the most common diseases they treat. This could be done in primary care for chronic diseases like type 2 diabetes, hypercholesterolemia, and hypertension because these conditions could often be documented but undiagnosed from screening laboratory tests or vital sign data, often are not treated in accordance with clinical guidelines, and often have suboptimal clinical outcomes. For specialists, reporting could be based on completing diagnostic workups efficiently and quickly and instituting guideline consistent care plans, for example, appropriate use of advanced imaging for back pain and guideline-consistent use of interventions like spine surgery, implantable defibrillators, and antibiotics. Similar to measures such as emergency department arrival to balloon time for acute myocardial infarction, measures could be developed to assess timeliness of accurate diagnoses for patients presenting for other situations, like sepsis, substance abuse treatment, HIV infection, and initial cancer diagnosis, for which the speed of initiating the correct treatment positively affects outcomes.

Similarly, it would be informative to report on how well clinicians perform for uncommon diagnoses based on relative complication rates and costs for these patients compared with expert diagnosticians for these diseases. For this to be possible, new investments will be needed develop diagnostic quality measures. While the Centers for Medicare & Medicaid Services has developed more than 1400 quality metrics, most are process-oriented measures. Process integrity is not a good measure of quality if the diagnosis is incorrect. Fortunately, reporting on metrics like these should be feasible if data exchange standards are enforced because electronic medical records and medical claims offer a longitudinal data set including diagnostic tests and results, when diagnoses are made, how diagnoses change over time, treatments recommended and delivered to patients, and clinical outcomes.

For many aspects of health care, results are directly related to what is paid for. While making the correct diagnosis quickly and cost-effectively is a fundamental tenet of medicine, paradoxically, diagnostic accuracy may not matter economically and data on clinician performance is neither collected nor transparent. To succeed at improving diagnostic performance in the US, a most important first step will require a focus on aligning the economic incentives to reward more accurate and timely diagnoses and substantially improving the ability to assess current diagnostic performance and opportunities for improvement.

Key Points for Diagnostic Excellence

1. Fee-for-service reimbursement does not reward diagnostic excellence.
2. Misaligned economic incentives undermine the development and adoption of technologies that can improve diagnostic quality.
3. Innovations that improve diagnostic quality also need to generate economic value to gain adoption.
4. Changing economic incentives to reward diagnostic excellence along with developing metrics and reporting performance could lead to improvement in diagnostic quality.

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Article Information

Corresponding Author: Bob Kocher, MD, USC Schaeffer Center, 635 Downey Way, Verna and Peter Dauterive Hall (VPD), Los Angeles, CA 90089 (bkocher@venrock.com).

Published Online: April 11, 2022. doi:10.1001/jama.2022.4594

Conflict of Interest Disclosures: Dr Kocher reported that he is a partner at the venture capital firm Venrock and invests in health care technology and services businesses; is on the boards of Devoted Health, Lyra Health, Aledade, Need Health, Virta Health, Sitka Health, Accompany Health, and Premera Blue Cross; and is a board observer at SmithRx, Public Health Company, Stride Health, and Suki. Neither he nor Venrock have any current investments in clinical diagnostics or clinical decision support businesses. Dr Emanuel reported personal fees, nonfinancial support, or both from companies, organizations, and professional health care meetings and being a venture partner at Oak HC/FT; a partner at Embedded Healthcare LLC, ReCovery Partners LLC, and COVID-19 Recovery Consulting; and an unpaid board member of Village MD and Oncology Analytics. Dr Emanuel owns no stock in pharmaceutical, medical device companies, or health insurers. No other disclosures were reported.

Additional Contributions: We thank Daniel Yang, MD, Karen Cosby, MD, and Harvey Fineberg, MD, PhD, of the Gordon and Betty Moore Foundation, for their feedback and intellectual contributions. No compensation was received.

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