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1.
J Emerg Med ; 67(1): e89-e98, 2024 Jul.
Article En | MEDLINE | ID: mdl-38824039

BACKGROUND: To help improve access to care, section 507 of the VA MISSION (Maintaining Internal Systems and Strengthening Integrated Outside Networks) Act of 2018 mandated a 2-year trial of medical scribes in the Veterans Health Administration (VHA). OBJECTIVE: The impact of scribes on provider productivity and patient throughput time in VHA emergency departments (EDs) was evaluated. METHODS: A clustered randomized trial was designed using intent-to-treat difference-in-differences analysis. The intervention period was from June 30, 2020 to July 1, 2022. The trial included six intervention and six comparison ED clinics. Two ED providers who volunteered to participate in the trial were assigned two scribes each. Scribes assisted providers with documentation and visit-related activities. The outcomes were provider productivity and patient throughput time per clinic-pay period. RESULTS: Randomization to intervention resulted in decreased provider productivity and increased patient throughput time. In adjusted regression models, randomization to scribes was associated with a decrease of 8.4 visits per full-time equivalent (95% confidence interval [CI] 12.4-4.3; p < 0.001) and 0.5 patients per day per provider (95% CI 0.8-0.3; p < 0.001). Intervention was associated with increases in length of stay of 29.1 min (95% CI 21.2-36.9 min; p < 0.001), 6.3 min in door to doctor (95% CI 2.9-9.6 min; p < 0.001), 19.5 min in door to disposition (95% CI 13.2-25.9 min; p < 0.001), and 13.7 min in doctor to disposition (95% CI 8.8-18.6 min; p < 0.001). CONCLUSIONS: Scribes were associated with decreased provider productivity and increased patient throughput time in VHA EDs. Although scribes may have contributed to improvements in other dimensions of quality, further examination of the ways in which scribes were used is advisable before widespread adoption in VHA EDs.


Efficiency, Organizational , Emergency Service, Hospital , United States Department of Veterans Affairs , Humans , Emergency Service, Hospital/organization & administration , Emergency Service, Hospital/statistics & numerical data , United States Department of Veterans Affairs/organization & administration , United States Department of Veterans Affairs/statistics & numerical data , United States , Efficiency, Organizational/statistics & numerical data , Efficiency , Documentation/methods , Documentation/statistics & numerical data , Documentation/standards , Time Factors , Female
2.
J Healthc Manag ; 69(3): 178-189, 2024.
Article En | MEDLINE | ID: mdl-38728544

GOAL: A lack of improvement in productivity in recent years may be the result of suboptimal measurement of productivity. Hospitals and clinics benefit from external benchmarks that allow assessment of clinical productivity. Work relative value units have long served as a common currency for this purpose. Productivity is determined by comparing work relative value units to full-time equivalents (FTEs), but FTEs do not have a universal or standardized definition, which could cause problems. We propose a new clinical labor input measure-"clinic time"-as a substitute for using the reported measure of FTEs. METHODS: In this observational validation study, we used data from a cluster randomized trial to compare FTE with clinic time. We compared these two productivity measures graphically. For validation, we estimated two separate ordinary least squares (OLS) regression models. To validate and simultaneously adjust for endogeneity, we used instrumental variables (IV) regression with the proportion of days in a pay period that were federal holidays as an instrument. We used productivity data collected between 2018 and 2020 from Veterans Health Administration (VA) cardiology and orthopedics providers as part of a 2-year cluster randomized trial of medical scribes mandated by the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018. PRINCIPAL FINDINGS: Our cohort included 654 unique providers. For both productivity variables, the values for patients per clinic day were consistently higher than those for patients per day per FTE. To validate these measures, we estimated separate OLS and IV regression models, predicting wait times from the two productivity measures. The slopes from the two productivity measures were positive and small in magnitude with OLS, but negative and large in magnitude with IV regression. The magnitude of the slope for patients per clinic day was much larger than the slope for patients per day per FTE. Current metrics that rely on FTE data may suffer from self-report bias and low reporting frequency. Using clinic time as an alternative is an effective way to mitigate these biases. PRACTICAL APPLICATIONS: Measuring productivity accurately is essential because provider productivity plays an important role in facilitating clinic operations outcomes. Most importantly, tracking a more valid productivity metric is a concrete, cost-effective management tactic to improve the provision of care in the long term.


Efficiency, Organizational , Humans , United States , Efficiency , United States Department of Veterans Affairs , Benchmarking , Female , Relative Value Scales , Male
3.
Value Health ; 27(6): 713-720, 2024 Jun.
Article En | MEDLINE | ID: mdl-38462222

OBJECTIVES: To improve access, the VA Maintaining Internal Systems and Strengthening Integrated Outside Networks (MISSION) Act of 2018 mandated a 2-year study of medical scribes in Veterans Health Administration specialty clinics and emergency departments. Medical scribes are employed in clinical settings with the goals of increasing provider productivity and satisfaction by minimizing physicians' documentation burden. Our objective is to quantify the economic outcomes of the MISSION Act scribes trial. METHODS: A cluster-randomized trial was designed with 12 Department of Veterans Affairs (VA) medical centers randomized into the intervention. We estimated the total cost of the trial, cost per scribe-year, and projected cost of hiring additional physicians to achieve the observed scribe productivity benefits in relative value units and visits per full-time-equivalent over the 2-year intervention period (June 30, 2020 to July 1, 2022). RESULTS: The estimated cost of the trial was $4.6 million, below the Congressional Budget Office estimate of $5 million. A full-time scribe-year cost approximately $74 600 through contracting and $62 900 through VA hiring. Randomization into the trial led to an approximate 30% increase in productivity in cardiology and 20% in orthopedics. The projected incremental cost of using additional physicians instead of scribes to achieve the same productivity benefits was nearly $1.7 million more, or 75% higher, than the observed cost of scribes in cardiology and orthopedics. CONCLUSIONS: As the largest randomized trial of scribes to date, the MISSION Act scribes trial provides important evidence on the costs and benefits of scribes. Improving productivity enhances access and scribes may give VA a new tool to improve productivity in specialty care at a lower cost than hiring additional providers.


Efficiency, Organizational , United States Department of Veterans Affairs , United States , Humans , Documentation/economics , Cost-Benefit Analysis , Efficiency , Hospitals, Veterans/economics , Delivery of Health Care, Integrated/economics , Delivery of Health Care, Integrated/organization & administration
4.
J Gen Intern Med ; 38(Suppl 3): 878-886, 2023 07.
Article En | MEDLINE | ID: mdl-37340268

BACKGROUND: Section 507 of the VA MISSION Act of 2018 mandated a 2-year pilot study of medical scribes in the Veterans Health Administration (VHA), with 12 VA Medical Centers randomly selected to receive scribes in their emergency departments or high wait time specialty clinics (cardiology and orthopedics). The pilot began on June 30, 2020, and ended on July 1, 2022. OBJECTIVE: Our objective was to evaluate the impact of medical scribes on provider productivity, wait times, and patient satisfaction in cardiology and orthopedics, as mandated by the MISSION Act. DESIGN: Cluster randomized trial, with intent-to-treat analysis using difference-in-differences regression. PATIENTS: Veterans using 18 included VA Medical Centers (12 intervention and 6 comparison sites). INTERVENTION: Randomization into MISSION 507 medical scribe pilot. MAIN MEASURES: Provider productivity, wait times, and patient satisfaction per clinic-pay period. KEY RESULTS: Randomization into the scribe pilot was associated with increases of 25.2 relative value units (RVUs) per full-time equivalent (FTE) (p < 0.001) and 8.5 visits per FTE (p = 0.002) in cardiology and increases of 17.3 RVUs per FTE (p = 0.001) and 12.5 visits per FTE (p = 0.001) in orthopedics. We found that the scribe pilot was associated with a decrease of 8.5 days in request to appointment day wait times (p < 0.001) in orthopedics, driven by a 5.7-day decrease in appointment made to appointment day wait times (p < 0.001), and observed no change in wait times in cardiology. We also observed no declines in patient satisfaction with randomization into the scribe pilot. CONCLUSIONS: Given the potential improvements in productivity and wait times with no change in patient satisfaction, our results suggest that scribes may be a useful tool to improve access to VHA care. However, participation in the pilot by sites and providers was voluntary, which could have implications for scalability and what effects could be expected if scribes were introduced to the care process without buy-in. Cost was not considered in this analysis but is an important factor for future implementation. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04154462.


Cardiology , Orthopedics , Humans , Waiting Lists , Patient Satisfaction , Pilot Projects , Documentation/methods
5.
Stat Med ; 42(13): 2029-2043, 2023 06 15.
Article En | MEDLINE | ID: mdl-36847107

Extending (i.e., generalizing or transporting) causal inferences from a randomized trial to a target population requires assumptions that randomized and nonrandomized individuals are exchangeable conditional on baseline covariates. These assumptions are made on the basis of background knowledge, which is often uncertain or controversial, and need to be subjected to sensitivity analysis. We present simple methods for sensitivity analyses that directly parameterize violations of the assumptions using bias functions and do not require detailed background knowledge about specific unknown or unmeasured determinants of the outcome or modifiers of the treatment effect. We show how the methods can be applied to non-nested trial designs, where the trial data are combined with a separately obtained sample of nonrandomized individuals, as well as to nested trial designs, where the trial is embedded within a cohort sampled from the target population.


Research Design , Humans , Bias , Causality
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