Do hospital-based emergency Medicaid programs benefit trauma centers? A mixed-methods analysis.

INTRODUCTION: Hospital Presumptive Eligibility (HPE) is a temporary Medicaid insurance at hospitalization, which can offset patient costs of care, increase access to postdischarge resources, and provide a path to sustain coverage through Medicaid. Less is known about the implications of HPE programs on trauma centers (TCs). We aimed to describe the association with HPE and hospital Medicaid reimbursement and characterize incentives for HPE participation among hospitals and TCs. We hypothesized that there would be financial, operational, and mission-based incentives. METHODS: We performed a convergent mixed methods study of HPE hospitals in California (including all verified TCs). We analyzed Annual Financial Disclosure Reports from California's Department of Health Care Access and Information (2005-2021). Our primary outcome was Medicaid net revenue. We also conducted thematic analysis of semistructured interviews with hospital stakeholders to understand incentives for HPE participation (n = 8). RESULTS: Among 367 California hospitals analyzed, 285 (77.7%) participate in HPE, 77 (21%) of which are TCs. As of early 2015, 100% of TCs had elected to enroll in HPE. There is a significant positive association between HPE participation and net Medicaid revenue. The highest Medicaid revenues are in HPE level I and level II TCs. Controlling for changes associated with the Affordable Care Act, HPE enrollment is associated with increased net patient Medicaid revenue ( b = 6.74, p < 0.001) and decreased uncompensated care costs ( b = -2.22, p < 0.05). Stakeholder interviewees' explanatory incentives for HPE participation included reduction of hospital bad debt, improved patient satisfaction, and community benefit in access to care. CONCLUSION: Hospital Presumptive Eligibility programs not only are a promising pathway for long-term insurance coverage for trauma patients but also play a role in TC viability. Future interventions will target streamlining the HPE Medicaid enrollment process to reduce resource burden on participating hospitals and ensure ongoing patient engagement in the program. LEVEL OF EVIDENCE: Economic And Value Based Evaluations; Level II.

State-Level Variability in Hospital Presumptive Eligibility Programs.

This cross-sectional study examines state-level variability in hospital presumptive eligibility programs to understand discrepancies in access by Medicaid expansion status.

Don't Fear the Artificial Intelligence: A Systematic Review of Machine Learning for Prostate Cancer Detection in Pathology.

CONTEXT.­: Automated prostate cancer detection using machine learning technology has led to speculation that pathologists will soon be replaced by algorithms. This review covers the development of machine learning algorithms and their reported effectiveness specific to prostate cancer detection and Gleason grading. OBJECTIVE.­: To examine current algorithms regarding their accuracy and classification abilities. We provide a general explanation of the technology and how it is being used in clinical practice. The challenges to the application of machine learning algorithms in clinical practice are also discussed. DATA SOURCES.­: The literature for this review was identified and collected using a systematic search. Criteria were established prior to the sorting process to effectively direct the selection of studies. A 4-point system was implemented to rank the papers according to their relevancy. For papers accepted as relevant to our metrics, all cited and citing studies were also reviewed. Studies were then categorized based on whether they implemented binary or multi-class classification methods. Data were extracted from papers that contained accuracy, area under the curve (AUC), or κ values in the context of prostate cancer detection. The results were visually summarized to present accuracy trends between classification abilities. CONCLUSIONS.­: It is more difficult to achieve high accuracy metrics for multiclassification tasks than for binary tasks. The clinical implementation of an algorithm that can assign a Gleason grade to clinical whole slide images (WSIs) remains elusive. Machine learning technology is currently not able to replace pathologists but can serve as an important safeguard against misdiagnosis.

Examination of national survey data regarding academic foundation training during the COVID-19 pandemic second wave.

Aim: We aimed to examine the effect of the second wave of the COVID-19 pandemic on Academic Foundation Programme (AFP) trainees. Methods: A voluntary, anonymous questionnaire was circulated to all UK AFP doctors. Data were collected from February 2021 to April 2021 then analysed. Results: Of a possible 1,096 trainees, 149 responded to the survey: 48% of respondents were at least partially redeployed, 31% lost academic time and 47% had projects cancelled or postponed. In free-text responses, despite some research opportunities, frustration at lost research time and opportunities were common themes. Trainees also highlighted communication and wellbeing issues. Conclusion: These results demonstrate that the overall effect of COVID-19 on this cohort cannot be underestimated. We propose that a series of measures are implemented to protect and support academic trainees. We hope that these measures would encourage high-quality academic output and help secure the development of the academic clinical workforce.

Quantitative and qualitative analysis of exception reporting at a district general hospital.

BACKGROUND/AIMS: Exception reporting is a function by which junior doctors report when their work has varied from expected. This study analysed the reporting at the authors' hospital. METHODS: The authors analysed 204 reports submitted across 12 months to investigate the nature and pattern of the exception reports. RESULTS: The majority of reports (86%) were for 'hours and rest', 5% for education and 9% for both. On average doctors reported an additional 1.32 hours of work per report. The most common response was time off in lieu, but 13% of reports were never responded to. Qualitative analysis showed the most common reasons for reporting were 'work outside of rostered hours', 'workload' and 'staffing issues'. Over 10% of the reports discussed an educational issue. CONCLUSIONS: The data were not specific and there was fewer than one report per junior doctor in the period analysed. It is therefore unlikely that the reports submitted represent the additional work done by junior doctors at the hospital. Guardians should investigate local attitudes to exception reporting and educate both seniors and juniors on the importance of submitting accurate exception reports.