Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists.

OBJECTIVE: To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. METHODS: A sequential explanatory mixed methods study from 2019 to 2020 was performed. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. RESULTS: Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. CONCLUSION: Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.

Using the Kitagawa Decomposition to Measure Overall-and Individual Facility Contributions to-Within-facility and Between-facility Differences: Analyzing Racial and Ethnic Wait Time Disparities in the Veterans Health Administration.

BACKGROUND: Identifying whether differences in health care disparities are due to within-facility or between-facility differences is key to disparity reductions. The Kitagawa decomposition divides the difference between 2 means into within-facility differences and between-facility differences that are measured on the same scale as the original disparity. It also enables the identification of facilities that contribute most to within-facility differences (based on facility-level disparities and the proportion of patient population served) and between-facility differences. OBJECTIVES: Illustrate the value of a 2-stage Kitagawa decomposition to partition a disparity into within-facility and between-facility differences and to measure the contribution of individual facilities to each type of difference. SUBJECTS: Veterans receiving a new outpatient consult for cardiology or orthopedic services during fiscal years 2019-2021. MEASURES: Wait time for a new-patient consult. METHODS: In stage 1, we predicted wait time for each Veteran from a multivariable model; in stage 2, we aggregated individual predictions to determine mean adjusted wait times for Hispanic, Black, and White Veterans and then decomposed differences in wait times between White Veterans and each of the other groups. RESULTS: Noticeably longer wait times were experienced by Hispanic Veterans for cardiology (2.32 d, 6.8% longer) and Black Veterans for orthopedics (3.49 d, 10.3% longer) in both cases due entirely to within-facility differences. The results for Hispanic Veterans using orthopedics illustrate how positive within-facility differences (0.57 d) can be offset by negative between-facility differences (-0.34 d), resulting in a smaller overall disparity (0.23 d). Selecting 10 facilities for interventions in orthopedics based on the largest contributions to within-in facility differences instead of the largest disparities resulted in a higher percentage of Veterans impacted (31% and 12% of Black and White Veterans, respectively, versus 9% and 10% of Black and White Veterans, respectively) and explained 21% of the overall within-facility difference versus 11%. CONCLUSIONS: The Kitagawa approach allows the identification of disparities that might otherwise be undetected. It also allows the targeting of interventions at those facilities where improvements will have the largest impact on the overall disparity.

Strong for Surgery: Association Between Bundled Risk Factors and Outcomes After Major Elective Surgery in the VA Population.

BACKGROUND: Strong for Surgery (S4S) is a public health campaign focused on optimizing patient health prior to surgery by identifying evidence-based modifiable risk factors. The potential impact of S4S bundled risk factors on outcomes after major surgery has not been previously studied. This study tested the hypothesis that a higher number of S4S risk factors is associated with an escalating risk of complications and mortality after major elective surgery in the VA population. METHODS: The Veterans Affairs Surgical Quality Improvement Program (VASQIP) database was queried for patients who underwent major non-emergent general, thoracic, vascular, urologic, and orthopedic surgeries between the years 2008 and 2015. Patients with complete data pertaining to S4S risk factors, specifically preoperative smoking status, HbA1c level, and serum albumin level, were stratified by number of positive risk factors, and perioperative outcomes were compared. RESULTS: A total of 31,285 patients comprised the study group, with 16,630 (53.2%) patients having no S4S risk factors (S4S0), 12,323 (39.4%) having one (S4S1), 2,186 (7.0%) having two (S4S2), and 146 (0.5%) having three (S4S3). In the S4S1 group, 60.3% were actively smoking, 35.2% had HbA1c > 7, and 4.4% had serum albumin < 3. In the S4S2 group, 87.8% were smokers, 84.8% had HbA1c > 7, and 27.4% had albumin < 3. Major complications, reoperations, length of stay, and 30-day mortality increased progressively from S4S0 to S4S3 groups. S4S3 had the greatest adjusted mortality risk (adjusted odds radio [AOR] 2.56, p = 0.04) followed by S4S2 (AOR 1.58, p = 0.02) and S4S1 (AOR 1.34, p = 0.02). CONCLUSION: In the VA population, patients who had all three S4S risk factors, namely active smoking, suboptimal nutritional status, and poor glycemic control, had the greatest risk of postoperative mortality compared to patients with fewer S4S risk factors.

Exploring Trajectories of Health Care Utilization Before and After Surgery.

BACKGROUND: Long-term trajectories of health care utilization in the context of surgery have not been well characterized. The objective of this study was to examine health care utilization trajectories among surgical patients and identify factors associated with high utilization that could possibly be mitigated after surgical admissions. STUDY DESIGN: Hospital medical and surgical admissions within 2 years of an index inpatient surgery in the Veterans Health Administration (October 1, 2007 to September 30, 2014) were identified. Group-based trajectory analysis identified 5 distinct trajectories of inpatient admissions around surgery. Characteristics of trajectories of utilization were compared across groups using bivariate statistics and multivariate logistic regression. RESULTS: Of 280,681 surgery inpatients, most underwent orthopaedic (29.2%), general (28.4%), or peripheral vascular procedures (12.2%). Five trajectories of health care utilization were identified, with 5.2% of patients among consistently high inpatient users accounting for 34.0% of inpatient days. Male (95.4% vs 93.5%, p < 0.01), African-American (21.6% vs 17.3%, p < 0.01), or unmarried patients (61.6% vs 52.5%, p < 0.01) were more likely to be high health care users as compared with other trajectories. High users also had a higher comorbidity burden and a strikingly higher burden of mental health diagnoses (depression: 30.3% vs 16.3%; bipolar disorder: 5.3% vs 2.1%, p < 0.01), social/behavioral risk factors (smoker: 41.1% vs 33.6%, p < 0.01; alcohol use disorder: 28.9% vs 12.9%, p < 0.01), and chronic pain (6.4% vs 2.8%, p < 0.01). CONCLUSIONS: Mental health, social/behavioral, and pain-related factors are independently associated with high pre- and postoperative health care utilization in surgical patients. Connecting patients to social workers and mental health care coordinators around the time of surgery may mitigate the risk of postoperative readmissions related to these factors.

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis.

BACKGROUND: Statistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks. METHODS: Over 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced. RESULTS: A total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63). CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass.

Common statistical and research design problems in manuscripts submitted to high-impact medical journals.

BACKGROUND: To assist educators and researchers in improving the quality of medical research, we surveyed the editors and statistical reviewers of high-impact medical journals to ascertain the most frequent and critical statistical errors in submitted manuscripts. FINDINGS: The Editors-in-Chief and statistical reviewers of the 38 medical journals with the highest impact factor in the 2007 Science Journal Citation Report and the 2007 Social Science Journal Citation Report were invited to complete an online survey about the statistical and design problems they most frequently found in manuscripts. Content analysis of the responses identified major issues. Editors and statistical reviewers (n = 25) from 20 journals responded. Respondents described problems that we classified into two, broad themes: A. statistical and sampling issues and B. inadequate reporting clarity or completeness. Problems included in the first theme were (1) inappropriate or incomplete analysis, including violations of model assumptions and analysis errors, (2) uninformed use of propensity scores, (3) failing to account for clustering in data analysis, (4) improperly addressing missing data, and (5) power/sample size concerns. Issues subsumed under the second theme were (1) Inadequate description of the methods and analysis and (2) Misstatement of results, including undue emphasis on p-values and incorrect inferences and interpretations. CONCLUSIONS: The scientific quality of submitted manuscripts would increase if researchers addressed these common design, analytical, and reporting issues. Improving the application and presentation of quantitative methods in scholarly manuscripts is essential to advancing medical research.