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Background: Polypharmacy (taking ≥ five medications) is common and associated with adverse drug events, medication nonadherence, and increased mortality and healthcare costs. Primary care providers are uniquely positioned to deprescribe unnecessary medications, yet little is known about factors that influence their deprescribing decisions. Objectives: We sought to identify factors that influence primary care providers' recommendations to deprescribe potentially unnecessary medications. Methods: This study represents a secondary data analysis from a national, cross‐sectional survey assessing providers' beliefs, attitudes, and experiences regarding medication deprescribing. A random sample of 2475 providers in Veterans Affairs (VA) primary care clinics were invited; 411 responded (16.6% response rate). Respondents included physicians (n = 304), nurse practitioners/physician assistants (n = 68), and clinical pharmacists (n = 39). Respondents estimated the proportion of their patients taking a potentially unnecessary medication. Of these patients, providers then identified the proportion for whom they recommended medication deprescribing (response options: <20%, 20%‐39%, 40%‐59%, 60%‐79%, ≥80%). We used multivariable logistic regression to determine factors associated with recommending deprescribing to ≥80% of candidate patients. Results: Although most respondents indicated having patients taking unnecessary medications, less than one‐third reported recommending deprescribing to most (≥80%) of their candidate patients. In adjusted analyses, factors significantly associated with increased likelihood of recommending deprescribing included having more patients who asked for medication information, providers' self‐rated comfort with deprescribing, and having support to monitor patients after deprescribing. When the indication for a medication was unclear, providers reported being less likely to recommend deprescribing. Conclusion: Most providers indicated caring for patients taking potentially unnecessary medications, yet they did not consistently say they recommended deprescribing to these patients. Our findings suggest that requiring prescriptions to include their reason for use and models of primary care designed to strengthen interdisciplinary teamwork and communication, such as the patient‐centered medical home, could address barriers providers face to deprescribing medications. [ABSTRACT FROM AUTHOR]

Abstract Background Providers use risk-adjustment systems to help manage healthcare costs. Typically, ordinary least squares (OLS) models on either untransformed or log-transformed cost are used. We examine the predictive ability of several statistical models, demonstrate how model choice depends on the goal for the predictive model, and examine whether building models on samples of the data affects model choice. Methods Our sample consisted of 525,620 Veterans Health Administration patients with mental health (MH) or substance abuse (SA) diagnoses who incurred costs during fiscal year 1999. We tested two models on a transformation of cost: a Log Normal model and a Square-root Normal model, and three generalized linear models on untransformed cost, defined by distributional assumption and link function: Normal with identity link (OLS); Gamma with log link; and Gamma with square-root link. Risk-adjusters included age, sex, and 12 MH/SA categories. To determine the best model among the entire dataset, predictive ability was evaluated using root mean square error (RMSE), mean absolute prediction error (MAPE), and predictive ratios of predicted to observed cost (PR) among deciles of predicted cost, by comparing point estimates and 95% bias-corrected bootstrap confidence intervals. To study the effect of analyzing a random sample of the population on model choice, we re-computed these statistics using random samples beginning with 5,000 patients and ending with the entire sample. Results The Square-root Normal model had the lowest estimates of the RMSE and MAPE, with bootstrap confidence intervals that were always lower than those for the other models. The Gamma with square-root link was best as measured by the PRs. The choice of best model could vary if smaller samples were used and the Gamma with square-root link model had convergence problems with small samples. Conclusion Models with square-root transformation or link fit the data best. This function (whether used as transformation or as a link) seems to help deal with the high comorbidity of this population by introducing a form of interaction. The Gamma distribution helps with the long tail of the distribution. However, the Normal distribution is suitable if the correct transformation of the outcome is used.

Importance The existing readmission quality metric does not meaningfully distinguish readmissions associated with surgical quality from those that are not associated with surgical quality and thus may not reflect the quality of surgical care. Objective To compare a quality metric that classifies readmissions associated with surgical quality with the existing metric of any unplanned readmission in a surgical population. Design, Setting, and Participants Cohort study using US nationwide administrative data collected on 4 high-volume surgical procedures performed at 103 Veterans Affairs hospitals from October 1, 2007, through September 30, 2014. Data analysis was conducted from October 1, 2017, to January 24, 2019. Main Outcomes and Measures Hospital-level rates of unplanned readmission (existing metric) and surgical readmissions associated with surgical quality (new metric) in the 30 days following hospital discharge for an inpatient surgical procedure. Results The study population included 109 258 patients who underwent surgery at 103 hospitals. Patients were majority male (94.1%) and white (78.2%) with a mean (SD) age of 64.0 (10.0) years at the time of surgery. After case-mix adjustment, 30-day surgical readmissions ranged from 4.6% (95% CI, 4.5%-4.8%) among knee arthroplasties to 11.1% (95% CI, 10.9%-11.3%) among colorectal resections. The new surgical readmission metric was significantly correlated with facility-level postdischarge complications for all procedures, with p coefficients ranging from 0.33 (95% CI, 0.13-0.51) for cholecystectomy to 0.52 (95% CI, 0.38-0.68) for colorectal resection. Correlations between postdischarge complications and the new surgical readmission metric were higher than correlations between complications and the existing readmission metric for all procedures examined (knee arthroplasty: 0.50 vs 0.48; hip replacement: 0.44 vs 0.18; colorectal resection: 0.52 vs 0.42; and cholecystectomy: 0.33 vs 0.10). When compared with using the existing readmission metric, using the new surgical readmission metric could change hip replacement-associated payment penalty determinations in 28.4% of hospitals and knee arthroplasty-associated penalties in 26.0% of hospitals. Conclusions and Relevance In this study, surgical quality-associated readmissions were more correlated with postdischarge complications at a higher rate than were unplanned readmissions. Thus, a metric based on such readmissions may be a better measure of surgical care quality. This work provides an important step in the development of future value-based payments and promotes evidence-based quality metrics targeting the quality of surgical care. [ABSTRACT FROM AUTHOR]

Objective: To assess differences in risk (measured by expected costs associated with sociodemographic and clinical profiles) between Veterans receiving outpatient services through two community care (CC) programs: the Fee program ("Fee") and the Veterans Choice Program ("Choice").Data Sources/study Setting: Administrative data from VHA's Corporate Data Warehouse in fiscal years (FY) 2014-2015.Study Design: We compared the clinical characteristics of Veterans across three groups (Fee only, Choice only, and Fee & Choice). We classified Veterans into risk groups based on Nosos risk scores and examined the relationship between type of outpatient utilization and risk within each CC group. We also examined changes in utilization of VHA and CC in FY14-FY15. We used chi-square tests, t tests, and ANOVAs to identify significant differences between CC groups.Principal Findings: Of the 1,400,977 Veterans using CC in FY15, 91.4 percent were Fee-only users, 4.4 percent Choice-only users, and 4.2 percent Fee & Choice users. Mean concurrent risk scores were higher for Fee only and Fee & Choice (1.9, SD = 2.7; 1.8, SD = 2.2) compared to Choice-only users (1.0, SD = 1.2) (p < .0001). Most CC users were "dual users" of both VHA and CC in FY14-FY15.Conclusions: As care transitions from VHA to CC, VHA should consider how best to coordinate care with community providers to reduce duplication of efforts, improve handoffs, and achieve the best outcomes for Veterans. [ABSTRACT FROM AUTHOR]

Objective: Develop and validate a surveillance model to identify outpatient surgical adverse events (AEs) based on previously developed electronic triggers.Data Sources: Veterans Health Administration's Corporate Data Warehouse.Study Design: Six surgical AE triggers, including postoperative emergency room visits and hospitalizations, were applied to FY2012-2014 outpatient surgeries (n = 744,355). We randomly sampled trigger-flagged and unflagged cases for nurse chart review to document AEs and measured positive predictive value (PPV) for triggers. Next, we used chart review data to iteratively estimate multilevel logistic regression models to predict the probability of an AE, starting with the six triggers and adding in patient, procedure, and facility characteristics to improve model fit. We validated the final model by applying the coefficients to FY2015 outpatient surgery data (n = 256,690) and reviewing charts for cases at high and moderate probability of an AE.Principal Findings: Of 1,730 FY2012-2014 reviewed surgeries, 350 had an AE (20 percent). The final surveillance model c-statistic was 0.81. In FY2015 surgeries with >0.8 predicted probability of an AE (n = 405, 0.15 percent), PPV was 85 percent; in surgeries with a 0.4-0.5 predicted probability of an AE, PPV was 38 percent.Conclusions: The surveillance model performed well, accurately identifying outpatient surgeries with a high probability of an AE. [ABSTRACT FROM AUTHOR]

Objective: To examine factors associated with 0- to 7-day admission after outpatient surgery in high-volume specialties: general surgery, orthopedics, urology, ear/nose/throat, and podiatry.Study Design: We calculated rates and assessed diagnosis codes for 0- to 7-day admission after outpatient surgery for Centers for Medicare and Medicaid Services (CMS) and Veterans Health Administration (VA) dually enrolled patients age 65 and older. We also estimated separate multilevel logistic regression models to compare patient, procedure, and facility characteristics associated with postoperative admission.Data Collection: 2011-2013 surgical encounter data from the VA Corporate Data Warehouse; geographic data from the Area Health Resources File; CMS enrollment and hospital admission data.Principal Findings: Among 63,585 outpatient surgeries in 124 facilities, 0- to 7-day admission rates ranged from 5 percent (podiatry) to 28 percent (urology); nearly 66 percent of the admissions occurred on the day of surgery. Only 97 admissions were detected in the CMS data (1 percent). Surgical complications were diagnosed in 4 percent of admissions. Procedure complexity, measured by relative value units or anesthesia risk score, was associated with admission across all specialties.Conclusion: As many as 20 percent of VA outpatient surgeries result in an admission. Complex procedures are more likely to be followed by admission, but more evidence is required to determine how many of these reflect potential safety or quality problems. [ABSTRACT FROM AUTHOR]

Surveillance bias may threaten the accuracy of inpatient complication measures. A systematic literature review was conducted to examine whether surveillance bias influences the validity of selected Patient Safety Indicator- and health care associated infection-related measures. Ten venous thromboembolism (VTE) articles were identified: 7 trauma related, 3 postoperative, and 1 central line-associated bloodstream infection (CLABSI) article. Nine VTE articles found positive associations between deep vein thrombosis imaging and VTE diagnoses. Because imaging also may be symptom driven, most studies performed additional analyses to corroborate findings. Six trauma-related and 2 postoperative VTE studies concluded that surveillance bias was present, the latter based on circumstantial evidence. The non-VTE study found a significant positive correlation between surveillance aggressiveness and intensive care unit CLABSI rates. Even considering VTE, relatively little is known about the impact of surveillance bias on inpatient complication measures. Given the implications of misclassifying hospitals on quality, this issue requires further investigation using more direct measurement methods. [ABSTRACT FROM AUTHOR]