Development and Performance of a Clinical Decision Support Tool to Inform Resource Utilization for Elective Operations.

Importance: Hospitals ceased most elective procedures during the height of coronavirus disease 2019 (COVID-19) infections. As hospitals begin to recommence elective procedures, it is necessary to have a means to assess how resource intensive a given case may be. Objective: To evaluate the development and performance of a clinical decision support tool to inform resource utilization for elective procedures. Design, Setting, and Participants: In this prognostic study, predictive modeling was used on retrospective electronic health records data from a large academic health system comprising 1 tertiary care hospital and 2 community hospitals of patients undergoing scheduled elective procedures from January 1, 2017, to March 1, 2020. Electronic health records data on case type, patient demographic characteristics, service utilization history, comorbidities, and medications were and abstracted and analyzed. Data were analyzed from April to June 2020. Main Outcomes and Measures: Predicitons of hospital length of stay, intensive care unit length of stay, need for mechanical ventilation, and need to be discharged to a skilled nursing facility. These predictions were generated using the random forests algorithm. Predicted probabilities were turned into risk classifications designed to give assessments of resource utilization risk. Results: Data from the electronic health records of 42 199 patients from 3 hospitals were abstracted for analysis. The median length of stay was 2.3 days (range, 1.3-4.2 days), 6416 patients (15.2%) were admitted to the intensive care unit, 1624 (3.8%) received mechanical ventilation, and 2843 (6.7%) were discharged to a skilled nursing facility. Predictive performance was strong with an area under the receiver operator characteristic ranging from 0.76 to 0.93. Sensitivity of the high-risk and medium-risk groupings was set at 95%. The negative predictive value of the low-risk grouping was 99%. We integrated the models into a daily refreshing Tableau dashboard to guide decision-making. Conclusions and Relevance: The clinical decision support tool is currently being used by surgical leadership to inform case scheduling. This work shows the importance of a learning health care environment in surgical care, using quantitative modeling to guide decision-making.

Identified themes of interactive visualizations overlayed onto EHR data: an example of improving birth center operating room efficiency.

OBJECTIVE: While electronic health record (EHR) systems store copious amounts of patient data, aggregating those data across patients can be challenging. Visual analytic tools that integrate with EHR systems allow clinicians to gain better insight and understanding into clinical care and management. We report on our experience building Tableau-based visualizations and integrating them into our EHR system. MATERIALS AND METHODS: Visual analytic tools were created as part of 12 clinician-initiated quality improvement projects. We built the visual analytic tools in Tableau and linked it within our EPIC environment. We identified 5 visual themes that spanned the various projects. To illustrate these themes, we choose 1 exemplary project which aimed to improve obstetric operating room efficiency. RESULTS: Across our 12 projects, we identified 5 visual themes that are integral to project success: scheduling & optimization (in 11/12 projects); provider assessment (10/12); executive assessment (8/12); patient outcomes (7/12); and control and goal charts (2/12). DISCUSSION: Many visualizations share common themes. Identification of these themes has allowed our internal team to be more efficient and directed in developing visualizations for future projects. CONCLUSION: Organizing visual analytics into themes can allow informatics teams to more efficiently provide visual products to clinical collaborators.

No-Shows in Adult Urology Outpatient Clinics: Economic and Operational Implications.

INTRODUCTION: We analyzed trends and explored implications of no-show rates in adult urology from provider related characteristics at an academic program. METHODS: No-show rates were determined from electronic health records of appointments in adult urology at Duke University Medical Center and affiliated clinics between January 2014 and December 2016. t-Test, Wilcoxon rank sum and ANOVA were employed. RESULTS: Of 72,571 total appointments 13,219 (18.2%) were no-shows. The no-show rates per provider related characteristic were provider type (physician 22.1% vs advanced primary provider 34.0%), visit category (new 26.9% vs return 25.6% vs procedure 17.5%), faculty status (assistant 22.9% vs associate 21.9% vs professor 21.4%) and specialty (oncology 26.7% vs reconstructive 22.9% vs stones 25.4%). Average lead times of advanced primary practitioners and physicians were 47 and 62 days, respectively. There was a statistically significant difference in mean no-show rates by provider type (p <0.01) and new patient by provider type (p <0.01). However, there was no statistical difference in mean rates by specialty, faculty status, provider bump history, provider based visit types and average lead time. The potential loss in revenue from outpatient no-shows is at least $429,810 annually. CONCLUSIONS: Provider type and new patient visits by provider type have statistically different no-show rates. Missed appointments are costly and affect clinical efficiency, access to care and potentially patient outcomes. Given the shift toward value based care and future workforce changes, further investigations are needed to determine interventions to help reduce no-show rates. Models to predict and adjust clinics should be developed and deployed.

Disabled healthcare professionals' diverse, embodied, and socially embedded experiences.

Disabled people are underrepresented within healthcare professions, although their participation has potential benefits for them personally, and for broader society. Disabled peoples' participation in healthcare professions is limited by assumptions about disability. Little research explores how healthcare professions can be organized to support disabled peoples' employment. Within a critical realist paradigm influenced by grounded theory, this study used interviews to explore the experiences of 56 disabled healthcare clinicians and students, and advance a conceptual taxonomy of disability experience within healthcare professions. Participants describe their experiences of disability in the healthcare professional context in terms of characteristics and dimensions of disability-how characteristics interact with factors within healthcare training and practice environments. We profile two particularly salient dimensions of the disability experience: visibility and onset of disability. These are developed to describe complexity and specificity of the experiences of individuals negotiating the healthcare context. Among participants there is extensive heterogeneity related to the experience of disability in healthcare professional contexts. Despite some having similar disability characteristics, no two individuals experience the same combination of characteristics and dimensions of disability. Given the complexity of experiences for disabled healthcare professionals/students, a taxonomy for conceptualizing this experience is presented. Readers are encouraged to consider the taxonomy through which they might conceptualize individual, embodied, and socially embedded experiences of disabled healthcare professionals and students. Stakeholders involved in healthcare professions and education should consider this shift in perspective, with a view to increasing access of disabled people to health professional practice.