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Peter Drucker is credited with positing “you cannot improve what you do not measure”. Misdiagnosis is difficult to measure in operational metrics but tied to significant costs. In the age of AI, clinician decision support can not only improve outcomes but also significantly reduce the hidden costs of misdiagnosis. 

Healthcare operations teams live and breathe KPIs. Metrics like Medical Loss Ratio (MLR) and Relative Value Units (RVUs) are foundational to how performance is measured and decisions are made. These metrics are well understood, widely benchmarked, and deeply embedded in operations. If it moves MLR or RVUs in the right direction then it’s considered a win.

Operational teams naturally optimize for RVUs and/or MLR because they are so visible and tied to revenue and margin. However, knowing what drives these metrics is somewhat of a dark art. Several factors affect the bottom line but aren’t directly measured in operations data. For example, long wait times for specialists such as dermatologists can cause loss of revenue from patient leakage and increased cost from delayed treatment. Misdiagnosis is measured in clinical studies but generally not tracked in operational metrics. However, misdiagnosis may be one of the most significant drivers of cost. 

Skin conditions affect 30-70% of individuals in all geographies and age groups.1 A two year study at the University of Miami showed that 36% of primary care patients report at least one skin condition.2 Primary care is generally on the front line of providing initial evaluation, diagnosis, and triage of skin conditions.3 However, non-specialists receive limited dermatology training. As a consequence, there is low concordance between non-specialists and specialists for diagnosis and treatment2,4. Moreover, studies have shown a low concordance between dermatologists, and a panel of dermatologists.5 

A recent systematic review found that misdiagnosis in dermatology is associated with significant costs in the billions of U.S. dollars and an unintuitively high percentage of health system costs.6 Misdiagnosis of melanoma contributes ~$673 million in excess costs in the U.S.7 Up to $515 million can be attributed to misdiagnosis of cellulitis.8 Dermatology is on the front line of diagnosing lyme disease from erythema migrans.9 61% of patients in one survey indicate misdiagnosis or delayed diagnosis of lyme disease which can result in several thousand dollars of additional healthcare costs per case.10  

Multiple studies have shown the ability of clinical decision support to reduce clinical errors and associated costs.11–13 The greatest potential for AI in the short term is integrating with existing workflows to solve real-world pain points. This is exactly the approach of Dermatic health. By providing the right information at the right time within the clinical workflow, we empower primary care to address low acuity skin issues, streamline workflows for specialists, and generally serve as a “double check” to help reduce misdiagnosis and associated costs. While costs from dermatology misdiagnosis may not be well measured or well understood, the impact is very real. Implementing Dermatic can help mitigate these costs in addition to driving revenue through improved workflows. 

1. Hay RJ, Johns NE, Williams HC, et al. The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions. J Invest Dermatol. 2014;134(6):1527-1534. doi:10.1038/jid.2013.446 

2. Lowell BA, Froelich CW, Federman DG, Kirsner RS. Dermatology in primary care: Prevalence and patient disposition. J Am Acad Dermatol. 2001;45(2):250-255. doi:10.1067/mjd.2001.114598 

3. Lim HW, Collins SAB, Resneck JS, et al. The burden of skin disease in the United States. J Am Acad Dermatol. 2017;76(5):958-972.e2. doi:10.1016/j.jaad.2016.12.043 

4. Bae GH, Hartman RI, Joyce C, Mostaghimi A. Comparing dermatology referral patterns and diagnostic accuracy between nonphysician providers, physician trainees, and attending physicians. J Am Acad Dermatol. 2016;75(1):226-227. doi:10.1016/j.jaad.2016.02.1213 

5. Liu Y, Jain A, Eng C, et al. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020;26(6):900-908. doi:10.1038/s41591-020-0842-3 

6. Lehmann L, Appelbaum S, Ostermann T, Weber B, Hofmann SC. Medical error analysis in dermatology according to the reports of the North Rhine Medical Association from 2004 to 2018. JDDG J Dtsch Dermatol Ges. 2022;20(12):1603-1611. doi:10.1111/ddg.14899 

7. Bhattacharya A, Young A, Wong A, Stalling S, Wei M, Hadley D. Precision Diagnosis Of Melanoma And Other Skin Lesions From Digital Images. AMIA Summits Transl Sci Proc. 2017;2017:220-226. 

8. Weng QY, Raff AB, Cohen JM, et al. Costs and Consequences Associated With Misdiagnosed Lower Extremity Cellulitis. JAMA Dermatol. 2017;153(2):141-146. doi:10.1001/jamadermatol.2016.3816 

9. https://fyra.io. Lyme Disease Update. Practical Dermatology. Accessed January 2, 2023. https://practicaldermatology.com/articles/2017-oct/lyme-disease-update 

10. admin. Lyme & Tick-Borne Disease Misdiagnosis | IGeneX Tick Talk. IGeneX | Tick Talk. January 26, 2018. Accessed January 2, 2023. https://igenex.com/tick-talk/the-high-cost-of-misdiagnosis-for-patients-with-lyme-disease-and-other-tick-borne-diseases/ 

11. Delvaux N, Piessens V, Burghgraeve TD, et al. Clinical decision support improves the appropriateness of laboratory test ordering in primary care without increasing diagnostic error: the ELMO cluster randomized trial. Implement Sci IS. 2020;15:100. doi:10.1186/s13012-020-01059-y 

12. Zuccotti G, Maloney FL, Feblowitz J, Samal L, Sato L, Wright A. Reducing Risk with Clinical Decision Support. Appl Clin Inform. 2014;5(3):746-756. doi:10.4338/ACI-2014-02-RA-0018 

13. Taylor RA, Sangal RB, Smith ME, et al. Leveraging artificial intelligence to reduce diagnostic errors in emergency medicine: Challenges, opportunities, and future directions. Acad Emerg Med. 2025;32(3):327-339. doi:10.1111/acem.15066