Dataset Information

Association of Work Measures and Specialty With Assigned Work Relative Value Units Among Surgeons.

ABSTRACT:

Importance

The primary data sources used to generate and update work relative value units (RVUs) are surveys of small groups of specialists who are asked to estimate the time and intensity needed to perform surgical procedures. Because these surveys are conducted by specialty societies and rely on subjective data, these sources have been challenged as potentially biased.

Objective

To assess whether objective work measures are associated with a surgical procedure's assigned work RVUs and whether differences exist by surgical specialty.

Design, setting, and participants

This cross-sectional study obtained data from the 2016 and 2017 participant use files of the American College of Surgeons National Surgical Quality Improvement Program. The 2017 physician fee schedule of the Centers for Medicare & Medicaid Services was a secondary data source. Procedures were included if they had at least 100 patient-level observations over the 2-year period. Data were analyzed from August 29, 2018, to April 2, 2019.

Main outcomes and measures

The dependent variable was a procedure's assigned work RVU. Independent variables of work RVUs were 4 procedure-level work measures (median operative time, median postoperative length of stay, all-cause 30-day readmission rate, and all-cause 30-day reoperation rate) and surgeon specialty (10-level category using general surgery as the reference).

Results

The data set included 628 unique Current Procedural Terminology (CPT) codes and 726 CPT-specialty combinations from 1 239 991 patient observations. Statistically significant associations were found between each work measure and assigned work RVU, as follows: median operative time (R2 = 0.74; 95% CI, 0.71-0.78), postoperative length of stay (R2 = 0.42; 95% CI, 0.36-0.48), rate of readmission (R2 = 0.18; 95% CI, 0.13-0.23), and rate of reoperation (R2 = 0.15; 95% CI, 0.10-0.20). Including all 4 measures explained 80.2% (95% CI, 77.3%-83.1%) of the variation. Adding the surgical specialty improved the overall fit of the model (likelihood ratio test χ2 = 231.27; P < .001). Cardiac (7.78; 95% CI, 4.25-11.31; P < .001) and neurosurgery (2.46; 95% CI, 1.08-3.83; P < .001) had higher work RVUs compared with general surgery, whereas orthopedics (-1.53; 95% CI, -2.48 to -0.59; P = .002), urology (-1.58; 95% CI, -2.88 to -0.29; P = .02), plastics (-2.70; 95% CI, -4.39 to -1.01; P = .002), and otolaryngology (-3.05; 95% CI, -4.69 to -1.42; P < .001) had lower work RVUs compared with general surgery.

Conclusions and relevance

Objective work measures appeared to be associated with assigned work RVUs, predominantly with operative time; registry data can be used to augment and inform the generation and updating processes of the work RVUs.

SUBMITTER: Childers CP

PROVIDER: S-EPMC6647362 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionAcademic medicine continues to struggle in its efforts to compensate scholarly productivity. Academic achievements receive less recognition compared to clinical work, evidenced by a lack of reduced clinical hours or financial incentive. Core departmental education responsibilities are often distributed inequitably across academic departments. An approach using an incentive program, which emphasizes transparency, equity, and consensus may help academic departments share core education responsibilities and reward scholarly activity.MethodsWe launched a two-stage approach to confront the inequitable distribution of educational responsibilities and to recognize the scholarly work among our faculty. In the first stage, baseline education expectations were implemented for all faculty members, which included accountability procedures tied to a financial incentive. The second stage involved the creation of an aAcademic rRelative vValue uUnit (ARVU) system which contained additional activities that were derived and weighted based on stakeholder consensus. The points earned in the ARVU system were applied towards additional financial incentive at academic year-end. We compared education contributions before and after implementation as well as total points earned in the ARVU system.ResultsIn the first year of implementing education expectations, 87% of faculty fulfilled requirements. Those with a heavier clinical load made up the majority of deficient faculty. Those who did not meet education expectations were notified and had their year-end incentive reduced to reflect this. Faculty conference attendance increased by 21% (P<.001) and the number of resident assessments completed increased by 30% (P<.001) compared to the previous year. To date, faculty across the department have logged a total of 1,240 academic activities in the database, which will be converted into financial bonus amounts at year-end.ConclusionWe have seen significant increases in faculty participation in educational activities and learner assessments as well as documentation of activities in the ARVU system. A similar system using different specialty-specific activities may be generalizable and employed at other institutions.

Project description:Although medical school programs oriented toward postgraduate specialty training have the potential to reduce the duration and cost of medical school for US medical students, success depends on the ability of students to predict their postgraduate specialties. It is clear that first-year choices are poorly predictive, but it is not known when predictions become sufficiently reliable to support specialty-oriented learning programs. We therefore examined the predictive value of specialty preferences expressed at the ends of the first, second and third years of medical school and asked whether concurrent expressions of confidence in choices improved predictive ability. We also investigated the possibility that discrepancies between predicted and actual postgraduate specialty training were related to scores on an examination of knowledge in basic biomedical sciences required for US medical school graduation (the United States Medical Licensing Examination (USLME) Step 1 examination).We calculated positive and negative predictive values (PPV and NPV, respectively) for specialty choices and the sensitivity and specificity of asking for choices for 634 University of Colorado School of Medicine students who trained in 23 accredited residencies from 2011 through 2015. We examined the effect of confidence in first choices in 609 students, and in 334 students, sought an association between USMLE Step 1 scores and switching from postgraduate training specialties predicted at the end of year 2.The PPV of first choices improved from years 1 through 3. NPV was high throughout. PPVs of year 3 first choices ranged from 79% in Anesthesiology to 95% in Psychiatry. Expressions of confidence in first choices did not improve PPV. Sensitivity of asking for first choices increased with time; specificity was consistently high. USLME Step 1 scores were higher for students who ultimately trained in specialties more competitive than first-choice specialties at the end of year 2.Specialty-oriented learning programs during medical school must accommodate students who change career plans. The PPV of specialty first choices improves each year, but even year 3 predictions can be inaccurate with potential loss of students from specialty-specific programs. USMLE Step 1 scores appeared to affect career plans expressed at the end of year 2.