Background: Malpractice payments and state medical board discipline are often assumed to identify the same physicians, yet that assumption has never been tested nationally. Methods: National retrospective cohort study using the National Practitioner Data Bank Public Use Data File (March 31, 2026 release). A total of 76 353 allopathic and osteopathic physicians without prior state licensure actions were followed from their first observed paid malpractice report (2004–2018) through March 2026. The primary outcome was any substantive new state licensure action; secondary outcomes classified actions as quality-of-care–related, nonclinical, or severe. All outcomes describe the first subsequent action in a time-to-first-event design. Results: Overall, 71 372 physicians (93.5%) had no subsequent state licensure action during observed follow-up; a severe first action occurred in 2.1% within 15 years, including license revocation in 0.2%. Among the 4981 physicians (6.5%) with a subsequent action, the first action occurred a median of 5 years after the index report, and 43.7% involved exclusively nonclinical conduct. The 15-year cumulative incidence was 2.54% for quality-of-care actions and 2.94% for nonclinical actions. Severe injury or death in the index report was not associated with subsequent quality-of-care discipline (adjusted hazard ratio, 1.01; 95% CI, 0.90-1.12). Conclusions: Board discipline following a paid malpractice report was uncommon, delayed, and frequently unrelated to clinical care. These accountability systems appear to serve complementary rather than interchangeable roles in assessing physician quality.
Background: Clinic workflow efficiency influences patient and employee satisfaction, impacting effective delivery of care. Utilizing lean methodology, we aim to increase the percentage of patients seen within 15 minutes of scheduled appointment to 75% and reduce nonvalue-added time (NVT) per visit by half within 6 months. Methods: Frontline stakeholders observed workflow inefficiencies in an outpatient general surgery clinic, leading to long wait times and generalized dissatisfaction. From August to September 2023, patient and staff flow was captured and appointment component time intervals observed. Only 35.8% of patients were seen within 15 minutes of appointment, with median appointment-to-seen time (AST) 19.00 minutes and NVT 74%. Interventions were implemented through progressive plan-do-study-act (PDSA) cycles, including changes to workflow, rooming, and scheduling template. Appointment time interval data were collected and analyzed using run charts. Results: PDSA 1 yielded median AST of 15.68 minutes, with 51.7% of patients seen within 15 minutes of appointment. PDSA 2 resulted in a median AST of 8.00 minutes, with 66.4% of patients seen within 15 minutes of appointment and reduced median NVT from 74% to 55.9%. Late in PDSA 2 in December, when the clinic was short-staffed, both median AST and NVT increased. PDSA 3 resulted in reduced median AST of 5.00 minutes, with 82.9% of patients seen within 15 minutes of appointment and reduced median NVT to 51.5%, with improved performance stability particularly with respect to AST. Conclusions: Utilization of lean methodology with multiple targeted workflow and scheduling interventions resulted in improved efficiency in an outpatient general surgery clinic.
As increasing numbers of states adopt aid in dying legislation, health care system infrastructure to support clinical practice and state legal requirements remains limited. This article describes how UCHealth, a combined academic and community health care system in Colorado, implemented a unique program utilizing electronic health record tools and a provider-facing navigator to provide real-time assistance for this infrequent but high-risk clinical care.
BACKGROUND:Improved patient experience is associated with enhanced clinical outcomes. The Hospital Consumer Assessment of Healthcare Providers and Systems Overall Rating of the Hospital (ORH) can be considered a quality metric for health systems. We hypothesized that machine learning models could identify actionable predictors of a less-than-top-box ORH score across a multihospital system and sought to determine whether these predictors were consistent across model architectures and hospital size. METHODS:We analyzed 86 351 Hospital Consumer Assessment of Healthcare Providers and Systems surveys from 12 hospitals within a New Jersey health system (January 2023 to May 2026). Four machine learning models (logistic regression, decision tree, random forest, and XGBoost) were developed to predict a less-than-top-box ORH score. Feature importance was compared across the 3 highest-performing models, and SHapley Additive exPlanations (SHAP) analysis was performed on the XGBoost model. All analyses were repeated separately across hospitals stratified by size (large, medium, and small). RESULTS:XGBoost, logistic regression, and random forest demonstrated excellent, comparable discrimination (area under the curve 0.871-0.883); the decision tree model underperformed and was excluded. Feature importance and SHAP analysis converged on the same 3 predictor domains: nursing communication, physician communication, and hospital environment, which jointly accounted for 67% of total SHAP importance. This 3-domain pattern remained stable across hospital-size strata, with only modest shifts in relative ranking. CONCLUSIONS:Across multiple machine learning architectures, analytic methods, and hospital sizes, nursing communication, physician communication, and hospital environment consistently emerged as the primary, modifiable drivers of overall hospital rating. These convergent, generalizable findings identify clear strategic priorities for health systems seeking to improve patient experience.
