Revenue Calculator for Physician Retention
Calculate how many physician resignations you can predict and prevent, and how much associated revenue you can protect
Are you an Academic Medical Center?
ⓘ Owned by or closely affiliated with a medical school and not considered a community hospital.
How many physician resignations you can prevent with predictive analytics
Resignation Breakdown
Service Line
Preventable Resignations
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Revenue Loss from Preventable Resignations
ⓘ (Sum of Time to Fill, Productivity Loss, [for PC only: Indirect Referral Downstream Loss] * Total Estimated Resignations) * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Primary Care
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Indirect Referral Downstream Loss:
ⓘ (Number of Days to Fill for Primary Care/365) * Average Panel Size of Primary Care Physician Per Year * Average Referral Rate of Primary Care Physician * Average Value of New Patient for Medical Specialties
Surgical Specialty
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Medical Specialty
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Hospital Based
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Revenue Opportunity from Predictable Resignations
How many physician resignations you can prevent with predictive analytics
Resignation Breakdown
Service Line
ⓘ Total Estimated Turnover * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations
Revenue Loss from Preventable Resignations
ⓘ (Sum of Time to Fill, Productivity Loss, [for PC only: Indirect Referral Downstream Loss] * Total Estimated Turnover) * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Primary Care
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Indirect Referral Downstream Loss:
ⓘ (Number of Days to Fill for Primary Care/365) * Average Panel Size of Primary Care Physician Per Year * Average Referral Rate of Primary Care Physician * Average Value of New Patient for Medical Specialties
Surgical Specialty
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Medical Specialty
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Hospital Based
Total Estimated Resignations:
ⓘ Total Number of Physicians * Turnover Rate * Percentage of Physicians of Health System in Service Line [as seen in Atalan’s client data–differs depending on whether the health system is an academic medical center or a community health system] * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Preventable Resignations:
ⓘ Total Estimated Resignations * 77% [percentage of non-resident, non-retirement age resignations as seen in Atalan’s client data]
Lost Revenue from Time to Fill:
ⓘ (Revenue by Service Line - Salary by Service Line) * (Number of Days to Fill by Service Line/365)
Lost Revenue from Delays in Ramping up Replacement:
ⓘ Revenue by Service Line * 25%
Revenue Opportunity from Predictable Resignations
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Note: Unavoidable resignations include physicians of retirement age and residents
See your health system’s true revenue impact
Savings Calculation
References
For All Service Lines:
Breakdown of physicians by Service Line at a health system: Atalan’s Health System Client Data (can be replaced with your health system’s specific breakdown)
Number of Days to Fill by Category (can be replaced with your health system’s specific recruitment data): Association for Advancing Physician and Provider Recruitment (AAPPR). 2025 AAPPR Physician and Provider Recruitment Benchmarking Report. https://aappr.org/research/benchmarking/
Productivity Loss During Onboarding: 20% over 12 months (based on interviews with health system financial leaders)
Revenue by Service Line: AMN Healthcare. 2023. 2023 Physician Billing Report. https://www.amnhealthcare.com/amn-insights/physician/whitepapers/2023-physician-billing-report/ (can be adjusted by your health system’s payor mix)
Average Salary by Service Line: AMN Healthcare. (2023). 2023 Physician Billing Report. https://www.amnhealthcare.com/amn-insights/physician/whitepapers/2023-physician-billing-report/
National Average Turnover Rate: Association for Advancing Physician and Provider Recruitment (AAPPR). 2025 AAPPR Physician and Provider Recruitment Benchmarking Report. https://aappr.org/research/benchmarking/
For Primary Care Service Line Only:
Average Referral Rate of Primary Care Physician: El Ayadi H, Desai A, Jones RE, Mercado E, Williams M, Rooks B, Carek PJ. Referral Rates Vary Widely Between Family Medicine Practices. J Am Board Fam Med. 2021 Nov-Dec;34(6):1183-1188. doi: 10.3122/jabfm.2021.06.210213. PMID: 34772773.
Average Panel Size of Primary Care Physician Per Year: Harrington C. Considerations for Patient Panel Size. Dela J Public Health. 2022 Dec 31;8(5):154-157. doi: 10.32481/djph.2022.12.034. PMID: 36751598; PMCID: PMC9894066.
Average Value of New Patient for Medical Specialties: Zocdoc. (n.d.). Industry benchmarks: Comparing new patient value by practice size and specialty. Industry Benchmarks: Comparing New Patient Value by Practice Size and Specialty. https://www.zocdoc.com/resources/blog/article/industry-benchmarks-comparing-new-patient-value-by-practice-size-and-specialty