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Journal articles on the topic 'Physician Productivity'

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Published: 5 June 2025

Last updated: 15 July 2025

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1

Greenfield, Alan R. "Physician productivity." Journal of Ambulatory Care Management 12, no. 1 (1989): 6–10. http://dx.doi.org/10.1097/00004479-198902000-00004.

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2

Storfa, Amy H., and Michael L. Wilson. "Physician Productivity." American Journal of Clinical Pathology 143, no. 1 (2015): 6–9. http://dx.doi.org/10.1309/ajcp5m1famlcynpx.

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3

Wong, C., S. Lu, D. Wang, S. Dowling, and E. Lang. "P003: Productivity patterns in early-career physicians: a multi-center analysis of administrative emergency department operations data." CJEM 22, S1 (2020): S65. http://dx.doi.org/10.1017/cem.2020.211.

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Introduction: Physician metrics extracted from an electronic medical records (EMR) system can be utilized for practice improvement. One key metric analyzed at many emergency departments (EDs) is ‘patients per hour’ (pts/hr), a proxy for physician productivity. It is often believed that early-career physicians experience rapid growth in efficiency as they acclimatize to a hospital system and develop clinical confidence. This is the first study to evaluate the following question: Do early-career ED physicians increase their productivity when beginning practice? Methods: We performed a retrospective review of EMR data of early-career ED physicians working at one or more urban, academic centers. Early-career physicians must have started practice within three months of residency completion, and were identified by privileging records and provincial medical college registration. Physicians were excluded if they did not have at least 36 months of continuous data. Monthly productivity data (pts/hr) was extracted for each physician for their first 36-months of practice. A ‘performance curve’ or graph with a trendline of productivity as a moving average was created for each physician. Each performance curve was visually evaluated by two independent reviewers to qualitatively identify the general trend as upward, downward, or stable, with disagreements resolved by conference. Each physician's first and third year average productivity was compared quantitatively as well, with a significant upward or downward trend defined as a difference of at least 0.2 pts/hr. Results: A total of 41 physicians met the inclusion and exclusion criteria. Overall monthly pts/hr averages ranged from 1.08 to 7.65. Upon visual inspection, six (14.6%) physicians had upward trends, five (12.2%) had downward trends, and 30 (73.2%) had no discernable pattern. The quantitative analysis comparing first year to third year productivity matched the qualitative inspection exactly, with the same six physicians showing increased productivity, five with decreased, and 30 without significant change. Notably, the majority (30/41) of physicians demonstrated radical productivity variations over short periods with no discernable long-term trends. Conclusion: The majority of early career physicians do not demonstrate sustained early-career productivity changes. Of those that do, an approximately equal number will become faster and slower.

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Baerlocher, Mark Otto, Jason Noble, and Allan S. Detsky. "Impact of physician income source on productivity." Clinical & Investigative Medicine 30, no. 1 (2007): 42. http://dx.doi.org/10.25011/cim.v30i1.448.

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Based on data from the 2004 National Physician Survey, physicians whose primary payment method was fee-for-service saw more patients per week than physicians remunerated by other methods, including salary or blended payments. This result did not change when examined according to specialty or specialty grouping (Table 1), physician age (Table 2) Family physicians versus specialists, type of practice (office-based versus hospital-based; data not shown), or practice setting (urban versus rural; data not shown). Overall, fee-for-service (FFS) physicians saw approximately twice the number of patients per week as salaried physicians. These data provide convincing evidence that FFS physicians see substantially more patients.

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Friedman, Eliot L., and Teri U. Guidi. "Measuring Physician Productivity." Oncology Issues 20, no. 1 (2005): 38–41. http://dx.doi.org/10.1080/10463356.2005.11883234.

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6

Hurdle, Sylvia, and Gregory C. Pope. "Improving physician productivity." Journal of Ambulatory Care Management 12, no. 1 (1989): 11–26. http://dx.doi.org/10.1097/00004479-198902000-00005.

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7

SHAPIRO, ROBYN S., KRISTEN A. TYM, DAN EASTWOOD, ARTHUR R. DERSE, and JOHN P. KLEIN. "Managed Care, Doctors, and Patients: Focusing on Relationships, Not Rights." Cambridge Quarterly of Healthcare Ethics 12, no. 3 (2003): 300–307. http://dx.doi.org/10.1017/s0963180103123134.

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For over a decade, managed care has profoundly altered how healthcare is delivered in the United States. There have been concerns that the patient-physician relationship may be undermined by various aspects of managed care, such as restrictions on physician choice, productivity requirements that limit the time physicians may spend with patients, and the use of compensation formulas that reward physicians for healthcare dollars not spent. We have previously published data on the effects of managed care on the physician-patient relationship from the physician's perspective. In 1999, we collected data on the impact of managed care arrangements on the physician-patient relationship from the patient's perspective. This article discusses our collective findings.

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8

Payson, Norman C. "Physician productivity and capitation." Journal of Ambulatory Care Management 12, no. 1 (1989): 1–5. http://dx.doi.org/10.1097/00004479-198902000-00003.

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9

Vogt, William F. "Physician clinical productivity comparison." Journal of Ambulatory Care Management 12, no. 1 (1989): 79. http://dx.doi.org/10.1097/00004479-198902000-00011.

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10

MALOUIN, J. "Physician productivity and reimbursement." Clinics in Family Practice 5, no. 4 (2003): 991–1007. http://dx.doi.org/10.1016/s1522-5720(03)00082-5.

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11

Smith, David M., Douglas K. Martin, Carl D. Langefeld, Michael E. Miller, and Jay A. Freedman. "Primary care physician productivity." Journal of General Internal Medicine 10, no. 9 (1995): 495–503. http://dx.doi.org/10.1007/bf02602400.

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12

Stewart, F. Marc, Robert L. Wasserman, Clara D. Bloomfield, et al. "Benchmarks in Clinical Productivity: A National Comprehensive Cancer Network Survey." Journal of Oncology Practice 3, no. 1 (2007): 2–8. http://dx.doi.org/10.1200/jop.0712001.

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Purpose Oncologists in academic cancer centers usually generate professional fees that are insufficient to cover salaries and other expenses, despite significant clinical activity; therefore, supplemental funding is frequently required in order to support competitive levels of physician compensation. Relative value units (RVUs) allow comparisons of productivity across institutions and practice locations and provide a reasonable point of reference on which funding decisions can be based. Methods We reviewed the clinical productivity and other characteristics of oncology physicians practicing in 13 major academic cancer institutions with membership or shared membership in the National Comprehensive Cancer Network (NCCN). The objectives of this study were to develop tools that would lead to better-informed decision making regarding practice management and physician deployment in comprehensive cancer centers and to determine benchmarks of productivity using RVUs accrued by physicians at each institution. Three hundred fifty-three individual physician practices across the 13 NCCN institutions in the survey provided data describing adult hematology/medical oncology and bone marrow/stem-cell transplantation programs. Data from the member institutions participating in the survey included all American Medical Association Current Procedural Terminology (CPT®) codes generated (billed) by each physician during each organization's fiscal year 2003 as a measure of actual clinical productivity. Physician characteristic data included specialty, clinical full-time equivalent (CFTE) status, faculty rank, faculty track, number of years of experience, and total salary by funding source. The average adult hematologist/medical oncologist in our sample would produce 3,745 RVUs if he/she worked full-time as a clinician (100% CFTE), compared with 4,506 RVUs for a 100% CFTE transplant oncologist. Results and Conclusion Our results suggest specific clinical productivity targets for academic oncologists and provide a methodology for analyzing potential factors associated with clinical productivity and developing clinical productivity targets specific for physicians with a mix of research, administrative, teaching, and clinical salary support.

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Susser, Leah C., Alyson Gorun, Alison Hermann, Jason Chua, and Judy Tung. "Identifying the Motivations, Barriers, and Factors Associated with Mentoring among Academic Physicians." Chronicle of Mentoring & Coaching 9, no. 1 (2025): 21–30. https://doi.org/10.62935/y3844w.

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Mentoring is crucial for career advancement and retention of physicians. Understanding factors, motivations, and challenges associated with mentoring can help facilitate physician mentoring at academic institutions. This report examined characteristics of physicians associated with mentoring more, identified the motivations for physicians to mentor, and revealed obstacles that hinder physicians from mentoring. The authors recruited physicians to voluntarily participate in an anonymous survey using faculty listservs. Group differences were compared using Chi-Square and Fisher Exact tests. Two hundred and fifty-five physicians from sixteen departments responded to the survey. Mentoring productivity, defined as the number of mentees mentored per year since training, was associated with career track, prior mentoring training, and how much the physician publishes. The most common motivations to mentor included personal fulfillment, talent development, to give back, and advocacy. Time constraint was the most frequent challenge in mentoring. The authors identified differences in motivations and challenges for physician-subgroups. By understanding factors associated with mentoring productivity, motivations for physicians to mentor, and barriers encountered by mentors, academic institutions can design initiatives, customized to specific physician subgroups, to better facilitate mentoring.

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Manning, Chrissy. "Optimizing physician productivity in an employed physician model." Current Orthopaedic Practice 28, no. 4 (2017): 444–45. http://dx.doi.org/10.1097/bco.0000000000000533.

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Evans, John Harry, Kyonghee Kim, Nandu J. Nagarajan, and Sukesh Patro. "Nonfinancial Performance Measures and Physician Compensation." Journal of Management Accounting Research 22, no. 1 (2010): 31–56. http://dx.doi.org/10.2308/jmar.2010.22.1.31.

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ABSTRACT: This study utilizes a national survey of physicians in the United States, administered four times between 1996 and 2005, to examine the use of nonfinancial performance measures in physician compensation contracts. Consistent with agency theory, we find that nonfinancial measures are used more frequently when the measures are more informative; when alternative control mechanisms are complements rather than substitutes; and when external pressures for quality of care and cost containment are greater. Further, we find that contractual relationships in the health care value chain are interrelated; nonfinancial measures are more likely to be used to evaluate physician performance when the physicians’ practice is compensated based on fixed rate payments (i.e., capitation). We also find that physicians’ compensation contracts are more likely to incorporate nonfinancial performance measures when productivity in revenue generation is also used to evaluate performance. Taken together, the results suggest that nonfinancial performance measures play a significant role in physician compensation, acting to balance incentives tied to individual physician productivity.

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Itauma, Omosalewa, and Itauma Itauma. "AI Scribes: Boosting Physician Efficiency in Clinical Documentation." International Journal on Bioinformatics & Biosciences 14, no. 1 (2024): 09–18. http://dx.doi.org/10.5121/ijbb.2024.14102.

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The increasing demand on healthcare systems has amplified the burden on physicians and other healthcare professionals, with a huge portion of time dedicated to documenting patient encounters. Prolonged charting periods not only contribute to decreased physician productivity but also emerge as a prominent factor in physician burnout. This study investigates the potential of Artificial Intelligence (AI) to mitigate this challenge, focusing on AI-powered medical scribing as a solution to alleviate the burden of traditional charting methods in documentation of patient encounters and improve overall physician productivity. This research contributes to the ongoing discourse on the role of AI in healthcare and seeks to inform healthcare professionals, administrators, and policymakers about the potential benefits of integrating AI-powered medical scribing to improve physician efficiency and mitigate the impact of extensive charting on overall productivity and well-being.

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17

EMERY, SANFORD E., and CAROLYN GREGORY. "PHYSICIAN INCENTIVES FOR ACADEMIC PRODUCTIVITY." Journal of Bone and Joint Surgery-American Volume 88, no. 9 (2006): 2049–56. http://dx.doi.org/10.2106/00004623-200609000-00020.

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18

Lazarus, Steven S., Mary Alice Krill, David N. Gans, and Anna Bergstrom. "Physician productivity measurement and comparison." Journal of Ambulatory Care Management 12, no. 1 (1989): 38–51. http://dx.doi.org/10.1097/00004479-198902000-00007.

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19

P. Hudak, LTC Ronald. "Physician productivity in ambulatory care." Journal of Ambulatory Care Management 13, no. 3 (1990): 7–16. http://dx.doi.org/10.1097/00004479-199007000-00005.

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20

Somaratne, CJK, and DJS Fernando. "Specialist physician productivity: An effective measure for NHS." International Journal of Recent Research in Commerce Economics and Management 9, no. 2 (2022): 110–15. https://doi.org/10.5281/zenodo.6641356.

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<strong>Abstract:</strong> The founding principles of the NHS England, NHS Scotland, NHS Wales and the affiliated Health and Social Care in Northern Ireland were the provision of comprehensive, universal healthcare which is free at the point of delivery without based on the ability to pay.  The NHS systems are 98.8% funded from general taxation and National Insurance contributions, plus small amounts from patient charges for some services. About 10% of GDP is spent on health and most is spent in the public sector. The 2008/9 budget roughly equates to a contribution of £1,980 per person in the UK. The 2018/2018 NHS England budget was about £ 114.6 Billion. Fundamentally, productivity represents the level of output produced for a given level of inputs. Some scholars define productivity as the effective output or quality output produced by the allocated amount of inputs. Theoretically, the productivity could be enhanced by the reducing the wastes, maximizing the output of the existing inputs, reducing the inputs for the current level of output or improving the output of given level of inputs. However, healthcare productivity is assessed in various terms as physician productivity, healthcare institution productivity and health system productivity.  Physician productivity is the level of healthcare output produced by the physicians for a given level of inputs such as staff, infrastructure, medical equipment and infrastructure. This basic definition implies that the validity of a productivity assessment will depend on the corresponding validity of one’s specification and measurement of all relevant inputs and output(s). Conceptually, physician “productivity” is the result of a physician’s labor. It is a measure of the physician’s work or output. Physician productivity measured in different health systems depends on the objective of the measurement. For example, in United States Medicare health system physician productivity is measured to remunerate the physicians while NHS implements various performance targets and indicators to improve the financial efficiency of the Trust hospitals hence enhanced productivity. In NHS Trust hospitals, the physicians are being paid a fixed salary with variable component for on-call and emergency services provided. On the other hand, they must follow the standards and guidelines provided by the NHS England, National Institute for Health and Care Excellence (NICE), Care Quality Commission and other relevant organizations.  Some of these targets include referral to treatment target (RTT), a maximum four-hour wait in A&E from arrival to admission, transfer or discharge, Ambulance response times, New cancer waiting time standard and waiting time standards for mental health services. Therefore, the physicians employed in the Trust hospitals have a limited capacity to cater the services as they are bound to provide services according to the set standards. It is seen that various treatment targets have been set by the NHS organizations in order to cover the financial target and improving the Trust hospital productivity. But this managerial decision should be considered in a broad perspective as the ultimate objective should be to improve the productivity of the NHS. Some management decisions implemented January 2011 has shown that the decision taken in-favour of increasing hospital income by altering the “new to follow-up ratio” has resulted in reducing the health outcome of the patients while increasing the burden on the NHS health system. Some studies have shown that improving number of patients seen (efficiency) could hinder the quality of care and measuring   productivity without regard to quality or value is a risky foundation for wise policy.  The NHS faces major hurdles and is engaged in innovations such as foundation trusts; the new, performance-based contract for general practitioners; a massive investment in information technology; and some dabbling in health care imported from offshore organizations. These innovations are far more consequential than changes in the type of productivity reported by the Office of National Statistics (ONS), and they need to be carefully managed, treated as social experiments, adjusted as time passes, and assessed objectively. Their proper assessment requires that policy makers rely not on simple, potentially misleading metrics of numerical throughput but rather seek answers to the tougher and far more important question of value for money. The people of the UK should be not asking, “How many events for the pound?” but rather, “How much health for the pound?”. Therefore, the NHS organizations should carefully apply the physician productivity concept in the hospitals as it could lead to the negative productivity in wider sense even though it shows that it would improve the performance of the Trust hospitals. <strong>Keywords:</strong> Physician Productivity, Patient Satisfaction, Staff Satisfaction, New to Follow-up Ratio. <strong>Title:</strong> Specialist physician productivity: An effective measure for NHS <strong>Author:</strong> Somaratne CJK, Fernando DJS <strong>International Journal of Recent Research in Commerce Economics and Management (IJRRCEM)</strong> <strong>ISSN 2349-7807</strong> <strong>Vol. 9, Issue 2, April 2022 - June 2022</strong> <strong>Page No: 110-115</strong> <strong>Paper Publications  </strong> <strong>Website: www.paperpublications.org</strong> <strong>Published Date: 14-June-2022</strong> <strong>DOI: </strong><strong>https://doi.org/10.5281/zenodo.6641356</strong> <strong>Paper Download Link (Source)</strong> <strong>https://www.paperpublications.org/upload/book/Specialist%20physician%20productivity-14062022-2.pdf</strong>

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Anastasio, Albert T., Anthony N. Baumann, Kempland C. Walley, et al. "Academic productivity correlates with industry earnings in foot and ankle fellowship programs in the United States: A retrospective analysis." World Journal of Orthopedics 15, no. 2 (2024): 129–38. http://dx.doi.org/10.5312/wjo.v15.i2.129.

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BACKGROUND The study investigates the connection between academic productivity and industry earnings in foot and ankle orthopedic surgery fellowships. Utilizing metrics like the H-index and Open Payments Database (OPD) data, it addresses a gap in understanding the relationship between scholarly achievements and financial outcomes, providing a basis for further exploration in this specialized medical field. AIM To elucidate the trends between academic productivity and industry earnings across foot and ankle orthopedic surgery fellowship programs in the United States. METHODS This study is a retrospective analysis of the relationship between academic productivity and industry earnings of foot and ankle orthopedic surgery fellowships at an individual faculty and fellowship level. Academic productivity was defined via H-index and recorded from the Scopus website. Industry earnings were recorded from the OPD. RESULTS Forty-eight foot and ankle orthopedic surgery fellowships (100% of fellowships) in the United States with a combined total of 165 physicians (95.9% of physicians) were included. Mean individual physician (n = 165) total life-time earnings reported on the OPD website was United States Dollar (USD) 451430.30 ± 1851084.89 (range: USD 25.16-21269249.85; median: USD 27839.80). Mean physician (n = 165) H-index as reported on Scopus is 14.24 ± 12.39 (range: 0-63; median: 11). There was a significant but weak correlation between individual physician H-index and individual physician total life-time earnings (P < 0.001; Spearman’s rho = 0.334) and a significant and moderate positive correlation between combined fellowship H-index and total life-time earnings per fellowship (P = 0.004, Spearman’s rho = 0.409). CONCLUSION There is a significant and positive correlation between academic productivity and industry earnings at foot and ankle orthopedic surgery fellowships in the United States. This observation is true on an individual physician level as well as on a fellowship level.

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22

Ariste, Ruolz. "Fee-Scheduleincreasesincanada: Implication for Service Volumes among Family and Specialist Physicians." Journal of Health and Human Services Administration 38, no. 3 (2015): 381–410. http://dx.doi.org/10.1177/107937391503800304.

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Physician spending has substantially increased over the last few years in Canada to reach $27.4 billion in 2010. Total clinical payment to physicians has grown at an average annual rate of 7.6% from 2004 to 2010. The key policy question is whether or not this additional money has bought more physician services. So, the purpose of this study is to understand if we are paying more for the same amount of medical services in Canada or we are getting more bangs for our buck. At the same time, the paper attempts to find out whether or not there is a productivity difference between family physician services and surgical procedures. Using the Baumol theory and data from the National Physician Database for the period 2004–2010, the paper breaks down growth in physician remuneration into growth in unit cost and number of services, both from the physician and the payer perspectives. After removing general inflation and population growth from the 7.6% growth in total clinical payment, we found that real payment per service and volume of services per capita grew at an average annual rate of 3.2% and 1.4% respectively, suggesting that payment per service was the main cost driver of physician remuneration at the national level. Taking the payer perspective, it was found that, for the fee-for-service (FFS) scheme, volume of services per physician decreased at an average annual rate of -0.6%, which is a crude indicator that labour productivity of physicians on FFS has fallen during the period. However, the situation differs for the surgical procedures. Results also vary by province. Overall, our finding is consistent with the Baumol theory, which hypothesizes higher productivity growth in technology-driven sectors.

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Contratto, Erin, Katherine Romp, Carlos A. Estrada, April Agne, and Lisa L. Willett. "Physician Order Entry Clerical Support Improves Physician Satisfaction and Productivity." Southern Medical Journal 110, no. 5 (2017): 363–68. https://doi.org/10.14423/smj.0000000000000645.

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Lim, Andy, Namankit Gupta, Alvin Lim, Wei Hong, and Katie Walker. "Description of the effect of patient flow, junior doctor supervision and pandemic preparation on the ability of emergency physicians to provide direct patient care." Australian Health Review 44, no. 5 (2020): 741. http://dx.doi.org/10.1071/ah20180.

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ObjectiveA pilot study to: (1) describe the ability of emergency physicians to provide primary consults at an Australian, major metropolitan, adult emergency department (ED) during the COVID-19 pandemic when compared with historical performance; and (2) to identify the effect of system and process factors on productivity. MethodsA retrospective cross-sectional description of shifts worked between 1 and 29 February 2020, while physicians were carrying out their usual supervision, flow and problem-solving duties, as well as undertaking additional COVID-19 preparation, was documented. Effect of supervisory load, years of Australian registration and departmental flow factors were evaluated. Descriptive statistical methods were used and regression analyses were performed. ResultsA total of 188 shifts were analysed. Productivity was 4.07 patients per 9.5-h shift (95% CI 3.56–4.58) or 0.43 patients per h, representing a 48.5% reduction from previously published data (P&lt;0.0001). Working in a shift outside of the resuscitation area or working a day shift was associated with a reduction in individual patient load. There was a 2.2% (95% CI: 1.1–3.4, P&lt;0.001) decrease in productivity with each year after obtaining Australian medical registration. There was a 10.6% (95% CI: 5.4–15.6, P&lt;0.001) decrease in productivity for each junior physician supervised. Bed access had no statistically significant effect on productivity. ConclusionsEmergency physicians undertake multiple duties. Their ability to manage their own patients varies depending on multiple ED operational factors, particularly their supervisory load. COVID-19 preparations reduced their ability to see their own patients by half. What is known about the topic?An understanding of emergency physician productivity is essential in planning clinical operations. Medical productivity, however, is challenging to define, and is controversial to measure. Although baseline data exist, few studies examine the effect of patient flow and supervision requirements on the emergency physician’s ability to perform primary consults. No studies describe these metrics during COVID-19. What does this paper add?This pilot study provides a novel cross-sectional description of the effect of COVID-19 preparations on the ability of emergency physicians to provide direct patient care. It also examines the effect of selected system and process factors in a physician’s ability to complete primary consults. What are the implications for practitioners?When managing an emergency medical workforce, the contribution of emergency physicians to the number of patients requiring consults should take into account the high volume of alternative duties required. Increasing alternative duties can decrease primary provider tasks that can be completed. COVID-19 pandemic preparation has significantly reduced the ability of emergency physicians to manage their own patients.

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Tufano, James T., Douglas A. Conrad, and Su-Ying Liang. "Addressing Physician Compensation and Practice Productivity." Journal of Ambulatory Care Management 22, no. 3 (1999): 47–57. http://dx.doi.org/10.1097/00004479-199907000-00009.

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Goldfield, Norbert. "Physician Productivity and Quality—A Commentary." Journal of Ambulatory Care Management 23, no. 4 (2000): 60–66. http://dx.doi.org/10.1097/00004479-200010000-00008.

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Lopez, Kevin, Huan Li, Hyung Paek, et al. "Predicting physician departure with machine learning on EHR use patterns: A longitudinal cohort from a large multi-specialty ambulatory practice." PLOS ONE 18, no. 2 (2023): e0280251. http://dx.doi.org/10.1371/journal.pone.0280251.

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Physician turnover places a heavy burden on the healthcare industry, patients, physicians, and their families. Having a mechanism in place to identify physicians at risk for departure could help target appropriate interventions that prevent departure. We have collected physician characteristics, electronic health record (EHR) use patterns, and clinical productivity data from a large ambulatory based practice of non-teaching physicians to build a predictive model. We use several techniques to identify possible intervenable variables. Specifically, we used gradient boosted trees to predict the probability of a physician departing within an interval of 6 months. Several variables significantly contributed to predicting physician departure including tenure (time since hiring date), panel complexity, physician demand, physician age, inbox, and documentation time. These variables were identified by training, validating, and testing the model followed by computing SHAP (SHapley Additive exPlanation) values to investigate which variables influence the model’s prediction the most. We found these top variables to have large interactions with other variables indicating their importance. Since these variables may be predictive of physician departure, they could prove useful to identify at risk physicians such who would benefit from targeted interventions.

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Molitor, David. "The Evolution of Physician Practice Styles: Evidence from Cardiologist Migration." American Economic Journal: Economic Policy 10, no. 1 (2018): 326–56. http://dx.doi.org/10.1257/pol.20160319.

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Physician treatment choices for observably similar patients vary dramatically across regions. This paper exploits cardiologist migration to disentangle the role of physician-specific factors such as preferences and learned behavior versus environment-level factors such as hospital capacity and productivity spillovers on physician behavior. Physicians starting in the same region and subsequently moving to dissimilar regions practice similarly before the move. After the move, physician behavior in the first year changes by 0.6–0.8 percentage points for each percentage point change in practice environment, with no further changes over time. This suggests environment factors explain between 60–80 percent of regional disparities in physician behavior. (JEL H75, I11, I12, I18)

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Garcia Mosqueira, Adrian, Meredith Rosenthal, and Michael L. Barnett. "The Association Between Primary Care Physician Compensation and Patterns of Care Delivery, 2012-2015." INQUIRY: The Journal of Health Care Organization, Provision, and Financing 56 (January 2019): 004695801985496. http://dx.doi.org/10.1177/0046958019854965.

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As health systems seek to incentivize physicians to deliver high-value care, the relationship between physician compensation and health care delivery is an important knowledge gap. To examine physician compensation nationally and its relationship with care delivery, we examined 2012-2015 cross-sectional data on ambulatory primary care physician visits from the National Ambulatory Medical Care Survey. Among 175 762 office visits with 3826 primary care physicians, 15.4% of primary care physicians reported salary-based, 4.5% productivity-based, and 12.9% “mixed” compensation, while 61.4% were practice owners. After adjustment, delivery of out-of-visit/office care was more common for practice owners and “mixed” compensation primary care physicians, while there was little association between compensation type and rates of high- or low-value care delivery. Despite early health reform efforts, the overall landscape of physician compensation has remained strongly tethered to fee-for-service. The lack of consistent association between compensation and care delivery raises questions about the potential impact of payment reform on individual physicians’ behavior.

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Cheung, Matthew C., Maureen E. Trudeau, Helen Mackay, et al. "The impact of interruptions on physician workflow, productivity, and delivery of care." Journal of Clinical Oncology 35, no. 8_suppl (2017): 201. http://dx.doi.org/10.1200/jco.2017.35.8_suppl.201.

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201 Background: Physician interruptions during clinic and non-clinic hours can lead to medical errors, provider fatigue, prolonged clinic times, reduced academic output and poor job satisfaction. Repetitive interruptions can hamper the ability of physicians to deliver high quality patient-centered care. This study aims to evaluate the type, frequency, duration and self-reported physician response interruptions physicians experience in clinic. Methods: A work observation study was conducted at the Odette Cancer Centre, Sunnybrook Health Sciences Centre in Toronto, Canada. In-clinic data were collected from September 22 to October 6, 2016 using time-motion analyses by shadowing multiple oncologists in clinic. Interruption data were collected and categorized as follows: type of interruption, length of interruption, reason for interruption and role of interrupter. Physicians were asked to record and track themselves regarding interruptions they experienced during non-clinic hours using the same criteria. Results: Over a 2-week period, 5 medical oncology clinics (median 4 hours (hrs) per clinic), were observed and tracked. The clinic physicians averaged 22 interruptions per block, equating to 6 interruptions/hr (one interruption every 10 minutes (mins)). Over the 5 sessions, 112 data points were collected totaling over 1 hr 48 mins of interrupted time. Interruptions averaged 80 seconds (range of 4 to 517) in length with a positive skewed distribution. This calculates to approximately 30 mins of cumulative interrupted time per clinic session. Most interruptions were under 4 mins in length (4.1 at 95th percentile). The type of interruption varied but was most commonly in-person (67), email (24) and text message (10). Conclusions: Interruptions account for approximately 30 mins of physician time during a 4-hour clinic. An assessment of the type and frequency of requests proved highly variable, creating inconsistent ways messages are delivered to physicians. Interruptions potentially impact on patient care and disrupt the workflow of the clinic. These data provide future directions for exploring efficient clinic workflows and establishing standardized means of communicating with physicians during clinic hours.

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Whaley, Christopher M., Daniel J. Crespin, and Tisamarie B. Sherry. "Smartphone Application Allowing Physicians to Call Patients Associated with Increased Physician Productivity." Journal of General Internal Medicine 36, no. 8 (2021): 2307–14. http://dx.doi.org/10.1007/s11606-021-06663-2.

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Erus, Burcay, and Ozan Hatipoglu. "Physician payment schemes and physician productivity: Analysis of Turkish healthcare reforms." Health Policy 121, no. 5 (2017): 553–57. http://dx.doi.org/10.1016/j.healthpol.2017.02.012.

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Krzyzaniak, S., T. G. Dschaak, R. Frederick, J. Hafner, and H. Wang. "22 Making it Work: Correlating Nursing Perception of Physician Productivity to Physician Interpersonal Skills and Actual Productivity." Annals of Emergency Medicine 64, no. 4 (2014): S9. http://dx.doi.org/10.1016/j.annemergmed.2014.07.047.

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Malko, Andrei, and Vaughn Huckfeldt. "Physician Shortage in Canada: A Review of Contributing Factors." Global Journal of Health Science 9, no. 9 (2017): 68. http://dx.doi.org/10.5539/gjhs.v9n9p68.

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The physician shortage in Canada is multifactorial. It is important to identify potential factors and policies contributing to the problem. An extensive literature review to retrieve primary source articles was performed using the PubMed database. Other sources of information included reports identified using the websites of organizations, associations, government bodies and Google scholar, as well as additional primary source articles identified using reference lists of retrieved articles and reports. Healthcare policy changes in the 1990’s limited the growth of physician supply through the reduction of medical school enrolment, restrictions on recruitment of international medical graduates into the workforce, redistribution of family physician and specialist mix and loss of physicians to the US. Inadequate supply of primary care physicians is reflected in the low interest among medical students in a family medicine career and the shortage of physicians in rural areas. Reduction of physician productivity is characterized by an aging physician population, greater proportion of women in the workforce and the reduction of direct patient care hours among the new generation of physicians. The problem is further exacerbated by inefficiencies in healthcare expenditures, judging from high healthcare spending and low physician-to-population ratio. An understanding of factors contributing to the physician shortage is essential in order to develop successful strategies to alleviate inadequate physician supply.

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Sarangarm, Dusadee, Gregory Lamb, Steven Weiss, Amy Ernst, and Lorraine Hewitt. "Implementation of electronic charting is not associated with significant change in physician productivity in an academic emergency department." JAMIA Open 1, no. 2 (2018): 227–32. http://dx.doi.org/10.1093/jamiaopen/ooy022.

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Abstract Objectives To compare physician productivity and billing before and after implementation of electronic charting in an academic emergency department (ED). Materials and methods This retrospective, blinded, observational study compared the 6 months pre-implementation (January to June 2012) with the 6 months post-implementation 1 year later (January to June 2013). Thirty-one ED physicians were recruited, with each physician acting as his/her own control in a before-after design. Productivity was measured via total number of encounters and “productivity index” defined as worked relative value units divided by the clinical full-time equivalent. Values for charges, encounters, and productivity index were determined during each study period and separately for procedures, observational stays, and critical care. Results No differences were found for total productivity index per month (758 [623-876] pre-group vs. 756 [673-886] post-group; P = 0.30). There was, however, a 9% decrease in total encounters per month (138 [101-163] pre-group vs. 125 [99-159] post-group; P = 0.01). Significant decreases were seen across all observation stay categories. Conversely, significant increases were seen across all critical care categories. There was no difference in total charges per month. Discussion This is one of few studies to demonstrate minimal disruption in physician productivity after transitioning to electronic documentation. The reasons for these findings are likely multi-factorial. Conclusion In this study, implementation of electronic charting was not associated with decreases in productivity or billing for total ED care, but may be associated with increases for critical care and decreases for observational stays.

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Leung, A., G. Puri, B. Chen, et al. "LO56: Novel role of physician navigators on performance indicators in the emergency department." CJEM 19, S1 (2017): S47. http://dx.doi.org/10.1017/cem.2017.118.

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Introduction: Burnout rates for emergency physicians (EP) continue to be amongst the highest in medicine. One of the commonly cited sources of stress contributing to disillusionment is bureaucratic tasks that distract EPs from direct patient care in the emergency department (ED). The novel position of Physician Navigator was created to help EPs decrease their non-clinical workload during shifts, and improve productivity. Physician Navigators are non-licensed healthcare team members that assist in activities which are often clerical in nature, but directly impact patient care. This program was implemented at no net-cost to the hospital or healthcare system. Methods: In this retrospective study, 6845 clinical shifts worked by 20 EPs over 39 months from January 1, 2012 to March 31, 2015 were evaluated. The program was implemented on April 1, 2013. The primary objective was to quantify the effect of Physician Navigators on measures of EP productivity: patient seen per hour (Pt/hr), and turn-around-time (TAT) to discharge. Secondary objectives included examining the impact of Physician Navigators on measures of ED throughput for non-resuscitative patients: emergency department length of stay (LOS), physician-initial-assessment times (PIA), and left-without-being-seen rates (LWBS). A mixed linear model was used to evaluate changes in productivity measures between shifts with and without Physician Navigators in a clustered design, by EP. Autoregressive modelling was performed to compare ED throughput metrics before and after the implementation of Physician Navigators for non-resuscitative patients. Results: Across 20 EPs, 2469 shifts before, and 4376 shifts after April 1, 2013 were analyzed. Daily patient volumes increased 8.7% during the period with Physician Navigators. For the EPs who used Physician Navigators, Pt/hr increased by 1.07 patients per hour (0.98 to 1.16, p&lt;0.001), and TAT to discharge decreased by 10.6 minutes (-13.2 to -8.0, p&lt;0.001). After the implementation of the Physician Navigators, overall LOS for non-resuscitative patients decreased by 2.6 minutes (1.0%, p=0.007), and average PIA decreased by 7.4 minutes (12.0%, p&lt;0.001). LBWS rates decreased by 43.9% (0.50% of daily patient volume, p&lt;0.001). Conclusion: The use of a Physician Navigator was associated with increased EP productivity as measured by Pt/hr, and TAT to discharge, and reductions in ED throughput metrics for non-resuscitative patients.

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Brook, Christopher, Alexandra Chomut, and Rebecca Jeanmonod. "Physician Assistants Contribution to Emergency Department Productivity." Western Journal of Emergency Medicine 13, no. 2 (2012): 181–85. http://dx.doi.org/10.5811/westjem.2011.6.6746.

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Leung, Alexander K., Gaurav Puri, Bingshu E. Chen, et al. "Impact of Physician Navigators on productivity indicators in the ED." Emergency Medicine Journal 35, no. 1 (2017): 5–11. http://dx.doi.org/10.1136/emermed-2017-206809.

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ObjectivesWe created Physician Navigators in our ED to help improve emergency physician (EP) productivity. We aimed to quantify the effect of Physician Navigators on measures of EP productivity: patient seen per hour (Pt/hr), and turn-around time (TAT) to discharge. Secondary objectives included examining their impact on measures of ED throughput for non-resuscitative patients: ED length of stay (LOS), door-to-physician time and left-without-being-seen rates (LWBS).MethodsIn this retrospective study, 6845 clinical shifts worked by 20 EPs at a community ED in Newmarket, Canada from 1 January 2012 to 31 March 2015 were evaluated. Using a clustered design, we compared productivity measures between shifts with and without Physician Navigators, by physician. We used a linear mixed model to examine mean changes in Pt/hr and TAT to discharge for EPs who employed Physician Navigators. For secondary objectives, autoregressive modelling was performed to compare ED throughput metrics before and after the implementation of Physician Navigators for non-resuscitative patients.ResultsPatient volumes increased by 20 patients per day (p<0.001). Mean Pt/hr increased by 1.07 patients per hour (0.98 to 1.16, p<0.001). The mean TAT to discharge decreased by 10.6 min (−13.2 to −8.0, p<0.001). After implementation of the Physician Navigator programme, overall mean LOS for non-resuscitative patients decreased by 2.6 min (p=0.007), and mean door-to-physician time decreased by 7.4 min (p<0.001). LBWS rates decreased from 1.13% to 0.63% of daily patient volume (p<0.001).ConclusionDespite an ED volume increase, the use of a Physician Navigator was associated with significant improvements in EP productivity, and significant reductions in ED throughput times.

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Toussi, Nima, Caroline Zhang, Jocelyn Kang, and Edward J. Licitra. "Impact of AI medical scribes on physician productivity and satisfaction in medical oncology." Journal of Clinical Oncology 43, no. 16_suppl (2025): 11167. https://doi.org/10.1200/jco.2025.43.16_suppl.11167.

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11167 Background: AI Scribes are a leading example of AI implementation in clinical settings, with Oncology practices demonstrating exponential uptake since their introduction. Despite their ever-increasing usage, there are limited studies which directly interrogate the impact of AI Scribes on physician productivity metrics, and few which assess qualitative interpretations of the technology. Methods: This single-center, multi-site study enrolled 27 Medical Oncologists and 3 Primary Care Physicians randomly assigned in a 1:2 ratio to exposure to the Knowtex AI scribe in the initial phase (Phase 1) or control phase (Phase 2). Billing data was collected for 6 months prior to Phase 1 onboarding with Knowtex and for 16 weeks afterward—all within the 2024 fiscal year. During the same period, Phase 2 physicians billing data served as a non-exposed comparison group. Physicians completed opt-in surveys at Week 0 and Week 8 post-exposure assessing confidence and motivation to use the AI Scribe, documentation burden, documentation quality, and experience with the electronic medical record (EMR). Results: All providers adopted the Knowtex AI scribe during their study phase. 4 Phase 2 physicians were excluded from data analysis due to incomplete 2024 fiscal year data. Phase 1 physicians exhibited an increase in mean units (t(10) = 4.44, p < 0.01, d = 1.34, CI [0.90, 2.72]) and mean total billings per working day (t(10) = 4.30, p < 0.01, d = 1.28, CI [$377.55, $1206.75]), a pattern not observed in Phase 2 during the same period. There was no change in the number of diagnostic codes per unit amongst Phase 1 physicians. No learned effect emerged over time in Phase 1 billing metrics or diagnostic coding. Survey findings revealed a strong positive association between Week 0 self-assessed Knowtex understanding and increased units (r(13) = .579, p = 0.024). Physicians reported increased satisfaction with documentation workflow, a reduction in-clinic hours spent on documentation, and increased time spent with patients. Physicians' net impression of EMR challenges markedly decreased following the implementation of the AI scribe (U = 274.5, z=4.054, p < 0.0001). Conclusions: Adoption of an AI Scribe in oncology may enhance certain billing metrics and positively shift physician perceptions of EMR interactions, without affecting the quality of documentation. These findings highlight potential benefits of AI Scribes in improving physician productivity and satisfaction. As AI Scribes trend towards delivering multimodal clinical support tools, future research may focus on the adjunctive effects of AI scribes on procedural efficiencies, such as consistency in billing codes.

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Liang, Su-Ying, Laura Eaton, Sukyung Chung, and Harold Luft. "Impact of Physician Practice Style on Costs, Clinical Quality, Patient Experience, Physician Productivity, and Physician Time." Journal of Patient-Centered Research and Reviews 4, no. 3 (2017): 153–54. http://dx.doi.org/10.17294/2330-0698.1480.

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Armstrong, Melissa J. "Improving email strategies to target stress and productivity in clinical practice." Neurology: Clinical Practice 7, no. 6 (2017): 512–17. http://dx.doi.org/10.1212/cpj.0000000000000395.

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AbstractPhysician burnout is gaining increased attention in medicine and neurology and often relates to hours worked and insufficient time. One component of this is administrative burden, which relates to regulatory requirements and electronic health record tasks but may also involve increased time spent processing emails. Research in academic medical centers demonstrates that physicians face increasing inbox sizes related to mass distribution emails from various sources on top of emails required for patient care, research, and teaching. This commentary highlights the contribution of administrative tasks to physician burnout, research to date on email in medical contexts, and corporate strategies for reducing email burden that are applicable to neurology clinical practice. Increased productivity and decreased stress can be achieved by limiting the amount one accesses email, managing inbox size, and utilizing good email etiquette. Department and practice physician leaders have roles in decreasing email volume and modeling good practice.

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Buswell, Lori A., Patricia Reid Ponte, and Lawrence N. Shulman. "Provider Practice Models in Ambulatory Oncology Practice: Analysis of Productivity, Revenue, and Provider and Patient Satisfaction." Journal of Oncology Practice 5, no. 4 (2009): 188–92. http://dx.doi.org/10.1200/jop.0942006.

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Physicians, nurse practitioners, and physician assistants often work in teams to deliver cancer care in ambulatory oncology practices. This is likely to become more prevalent as the demand for oncology services rises, and the number of providers increases only slightly.

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Tengilimoğlu, Dilaver, Wafaa Menawi, Metin Dinçer, Adnan Kisa, and M. Z. Younis. "Evaluation of the Family Medicine Practice in Ankara Province by Family Physicians." Journal of Health and Human Services Administration 39, no. 2 (2016): 186–216. http://dx.doi.org/10.1177/107937391603900202.

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Turkey's family physician or practice system was established in the beginning of the 2010 across Turkey's 81 provinces and provides low-cost health care, preventive and curative basic medical services to the population. Public health centers across Turkey have now become Family Health Centers (ASMs) as part of Turkey's efforts to harmonize its health care system with that of the European Union. The aim of This studyis to analyz and evaluate the implementation and performance of Family Practice in Ankara province by family physicians. A questionairre form of 42 question was designed and used to determine opinions of the physicians about effective service & quality improvement, patient-physician relationship, efficiency in the area of responsibility, productivity, job satisfaction and equity. The result of the study shows that family physicians were defined to be generally satisfied with the system and performance implementation and significant differences were found according to work seniority, gender and productivity of the participants. Finally this study should be taken within it's limiation. The work seniority and gender was one of the most important factor to improve satfications and productivity for family physcains in Turkey. The sample size was represenative for the country, however, one limitation might be cosnidred the increase of sample size in future research if appropriate funding becaome avlaible in the future. This study did not have any souirce of funding.

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Morozova, Elena V., Maria V. Barabanshchikova, Tatyana I. Ionova, and Boris V. Afanasyev. "Attitudes to the disease and therapy in patients with chronic Ph-negative myeloproliferative neoplasms: results of the physician and patient surveys in Russia as a part of International Landmark Study." Cellular Therapy and Transplantation 9, no. 2 (2020): 28–39. http://dx.doi.org/10.18620/ctt-1866-8836-2020-9-2-28-39.

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The aim of this paper was to present evaluation and synthesis of data derived from a survey of Russian patients and physicians, performed as a part of the international Landmark study for the emerging market countries designed to specify problems and areas of concern in management of patients with chronic Ph-negative myeloproliferative neoplasms (MPN). The online survey forms were filled by 40 adult patients with Ph(-) MPNs (PV, 42.5%; MF, 37.5%; ET, 20%) and 30 physicians with sufficient experience in the Ph(-) MPNs treatment. As a part of this survey, patients and physicians answered questions related to perception of the disease symptoms and their impact on quality of life, daily activities and work productivity of patients, as well as their attitude to main treatment goals and various aspects of the patient-physician communication. The results revealed a number of differences between patient and physician perception of the disease and treatment, thus complementing the data of the Landmark Survey in other countries. It was shown that the patients with different variants of Ph(-) MPNs encounter sufficient disease-related difficulties in everyday life, impaired quality of life and reduced work productivity. Lack of coincidence revealed between the physician and patient assessment of the disease burden and treatment indicates the need for new ways of improving quality of clinical care provided to this category of patients. Further research in this area would be an important step towards implementation

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Weisman, Carol S., and Martha A. Teitelbaum. "The Work-Family Role System and Physician Productivity." Journal of Health and Social Behavior 28, no. 3 (1987): 247. http://dx.doi.org/10.2307/2136844.

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Balbay, Yucel. "The Successful Physician: A Productivity Handbook for Practitioners." Annals of Internal Medicine 130, no. 11 (1999): 952. http://dx.doi.org/10.7326/0003-4819-130-11-199906010-00025.

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Menachemi, Nir, Valerie A. Yeager, Elisabeth Welty, and Bryn Manzella. "Are Physician Productivity and Quality of Care Related?" Journal for Healthcare Quality 37, no. 2 (2015): 93–101. http://dx.doi.org/10.1111/jhq.12038.

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Walker, Deborah L. "Physician Compensation: Rewarding Productivity of the Knowledge Worker." Journal of Ambulatory Care Management 23, no. 4 (2000): 48–59. http://dx.doi.org/10.1097/00004479-200010000-00007.

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Campbell, S., and S. Weerasinghe. "LO68: Patterns and predictors of emergency physician productivity." CJEM 20, S1 (2018): S30—S31. http://dx.doi.org/10.1017/cem.2018.130.

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Introduction: Emergency Physician (EP) performance comprises both quality of care and quantity of patients seen in a set time. Emergency Department (ED) overcrowding increases the importance of the ability of EPs to see patients as rapidly as is safely possible. Maximizing efficiency requires an understanding of variables that are associated with individual physician performance. While using the incidence of return visits within 48 hours as a quality measure is controversial, repeat visits do consume ED resources. Methods: We analysed the practice variables of 85 EPs working at a single academic ED, for the period from June 1, 2013 to May 31, 2017, using data from an emergency department information system (EDIS). Variables analysed included: number of shifts worked, number of patients seen per hour (pt/hr), an adjusted workload measurement (assigning a higher score to CTAS 1-3 patients), percentage of patients whose care involved an ED learner, and the percentage of patients who returned to the ED within 48 hours of ED discharge. Resource utilization was measured by percentage of diagnostic imaging (ultra sound (US), CT scan (CT), x-ray (XR)) ordered and percentage of patients referred to consulting services. We performed principal component analyses to identify bench marks of resource use, demographic (age, EM qualification, gender) and other practice related predictors of performances. Results: Mean pt/hr differed significantly by EM Qualification for CTAS 2-4, with 1.71/hr (95% Confidence Interval=1.63-1.77) by FRCPS physicians, compared to 1.89/hr by CCFP(EM) (CI=1.81-1.97). There were no differences for CTAS 1 and 5. Other variables associated with a significantly lower pt/hr, included a greater use of imaging, (CT: p=0.0003, XR: p=0.0008) although this was did not reach statistical significance with US (p=0.06%). Female gender, older age, number of patient consultations for CTAS 3 and more patients seen by a learner were all associated with lower pt/hr. Pt/hr was a better predictor (R2=45%) for EP resource utilization than adjusted workload measurement (R2 =35%). Higher use of CT was associated with fewer return visits in <48 hrs (0.13% lower). Male gender, younger age, number of patient consultation for CTAS 3 and fewer patients seen by a learner were all associated with an increase in return visits. Conclusion: We found a significant difference in pt/hr rates and return visits within 48 hours between EPs with different age ranges, gender, and EM certification. Increased use of CT scan and x-ray, and consultation for patients CTAS 3 were associated with lower pt/hr. Return visit rates also varied in association with diagnostic imagine use, age, gender and number of patients seen by a learner. Further research is needed to assess the association with these variables on quality of care.

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Rosenfeld, J. A. "The Successful Physician: A Productivity Handbook for Practitioners." JAMA: The Journal of the American Medical Association 281, no. 13 (1999): 1234–35. http://dx.doi.org/10.1001/jama.281.13.1234.

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