Development of a Machine Learning-Based Prescriptive Tool to Address Racial Disparities in Access to Care After Penetrating Trauma.

Importance: The use of artificial intelligence (AI) in clinical medicine risks perpetuating existing bias in care, such as disparities in access to postinjury rehabilitation services. Objective: To leverage a novel, interpretable AI-based technology to uncover racial disparities in access to postinjury rehabilitation care and create an AI-based prescriptive tool to address these disparities. Design, Setting, and Participants: This cohort study used data from the 2010-2016 American College of Surgeons Trauma Quality Improvement Program database for Black and White patients with a penetrating mechanism of injury. An interpretable AI methodology called optimal classification trees (OCTs) was applied in an 80:20 derivation/validation split to predict discharge disposition (home vs postacute care [PAC]). The interpretable nature of OCTs allowed for examination of the AI logic to identify racial disparities. A prescriptive mixed-integer optimization model using age, injury, and gender data was allowed to "fairness-flip" the recommended discharge destination for a subset of patients while minimizing the ratio of imbalance between Black and White patients. Three OCTs were developed to predict discharge disposition: the first 2 trees used unadjusted data (one without and one with the race variable), and the third tree used fairness-adjusted data. Main Outcomes and Measures: Disparities and the discriminative performance (C statistic) were compared among fairness-adjusted and unadjusted OCTs. Results: A total of 52 468 patients were included; the median (IQR) age was 29 (22-40) years, 46 189 patients (88.0%) were male, 31 470 (60.0%) were Black, and 20 998 (40.0%) were White. A total of 3800 Black patients (12.1%) were discharged to PAC, compared with 4504 White patients (21.5%; P < .001). Examining the AI logic uncovered significant disparities in PAC discharge destination access, with race playing the second most important role. The prescriptive fairness adjustment recommended flipping the discharge destination of 4.5% of the patients, with the performance of the adjusted model increasing from a C statistic of 0.79 to 0.87. After fairness adjustment, disparities disappeared, and a similar percentage of Black and White patients (15.8% vs 15.8%; P = .87) had a recommended discharge to PAC. Conclusions and Relevance: In this study, we developed an accurate, machine learning-based, fairness-adjusted model that can identify barriers to discharge to postacute care. Instead of accidentally encoding bias, interpretable AI methodologies are powerful tools to diagnose and remedy system-related bias in care, such as disparities in access to postinjury rehabilitation care.

COVID-19 mortality risk assessment: An international multi-center study.

Timely identification of COVID-19 patients at high risk of mortality can significantly improve patient management and resource allocation within hospitals. This study seeks to develop and validate a data-driven personalized mortality risk calculator for hospitalized COVID-19 patients. De-identified data was obtained for 3,927 COVID-19 positive patients from six independent centers, comprising 33 different hospitals. Demographic, clinical, and laboratory variables were collected at hospital admission. The COVID-19 Mortality Risk (CMR) tool was developed using the XGBoost algorithm to predict mortality. Its discrimination performance was subsequently evaluated on three validation cohorts. The derivation cohort of 3,062 patients has an observed mortality rate of 26.84%. Increased age, decreased oxygen saturation (≤ 93%), elevated levels of C-reactive protein (≥ 130 mg/L), blood urea nitrogen (≥ 18 mg/dL), and blood creatinine (≥ 1.2 mg/dL) were identified as primary risk factors, validating clinical findings. The model obtains out-of-sample AUCs of 0.90 (95% CI, 0.87-0.94) on the derivation cohort. In the validation cohorts, the model obtains AUCs of 0.92 (95% CI, 0.88-0.95) on Seville patients, 0.87 (95% CI, 0.84-0.91) on Hellenic COVID-19 Study Group patients, and 0.81 (95% CI, 0.76-0.85) on Hartford Hospital patients. The CMR tool is available as an online application at covidanalytics.io/mortality_calculator and is currently in clinical use. The CMR model leverages machine learning to generate accurate mortality predictions using commonly available clinical features. This is the first risk score trained and validated on a cohort of COVID-19 patients from Europe and the United States.

Balancing Efficiency and Fairness in Liver Transplant Access: Tradeoff Curves for the Assessment of Organ Distribution Policies.

BACKGROUND: Current distribution policies have resulted in persistent geographic disparity in access to donated livers across the country for waitlisted candidates. METHODS: Using mathematical optimization, and subsequently the Liver Simulation Allocation Model, the following organ distribution concepts were assessed: (1) current policy, (2) proposed alternative models, and (3) a novel continuous distribution model. A number of different scenarios for each policy distribution concept were generated and analyzed through efficiency-fairness tradeoff curves. RESULTS: The continuous distribution concept allowed both for the greatest reduction in patient deaths and for the most equitable geographic distribution across comparable organ transportation burden. When applied with an Optimized Prediction of Mortality allocation scheme, continuous distribution allowed for a significant reduction in number of deaths-on the order of 500 lives saved annually (https://livervis.github.io/). CONCLUSIONS: Tradeoff curves allow for a visualized understanding on the efficiency/fairness balance, and have demonstrated that liver candidates awaiting transplant would benefit from a model employing continuous distribution as this holds the greatest advantage for mortality reduction. Development and implementation of continuous distribution models for all solid organ transplants may allow for minimization of the geographic disparity in organ distribution, and allow for efficient and fair access to a limited national resource for all candidates.

Clinical benefit, toxicity and cost of metastatic breast cancer therapies: systematic review and meta-analysis.

PURPOSE: Oncologists, clinical trialists, and guideline developers need tools that enable them to efficiently review the settings and results of previous studies testing metastatic breast cancer (MBC) drug therapies. METHODS: We searched the literature to identify clinical trials testing MBC drug therapies. Key eligibility criteria included at least 90% of patients enrolled in the trial having MBC, therapeutic clinical trials, and Phase II-III studies. Studies were stratified based on patients' tumor receptor statuses and prior exposure to therapy. Survival and toxicity of each drug therapy were estimated from randomized controlled trials using network meta-analysis and from all studies using meta-analysis. These results, along with estimated drug costs, are presented in a web-based visualization tool. RESULTS: We included 1865 studies containing 2676 treatment arms and 184,563 patients in the tool ( http://www.cancertrials.info ). Meta-analysis-based efficacy and toxicity estimates are available for 85 HER-2-directed therapies, 84 hormonal therapies, and 442 undirected therapies. Network meta-analysis-based estimates are available for 16 HER-2-directed therapies, 26 hormonal therapies, and 131 undirected therapies. CONCLUSIONS: In this era of increasing choices of MBC therapeutic agents and no superior approach to choosing a treatment regimen, the ability to compare multiple therapies based on survival, toxicity and cost would enable treating physicians to optimize therapeutic choices for patients. For investigators, it can point them in research directions that were previously non-obvious and for guideline designers, enable them to efficiently review the MBC clinical trial literature and visualize how regimens compare in the key dimensions of clinical benefit, toxicity, and cost.

Scheduling, revenue management, and fairness in an academic-hospital radiology division.

RATIONALE AND OBJECTIVES: Physician staff of academic hospitals today practice in several geographic locations including their main hospital. This is referred to as the extended campus. With extended campuses expanding, the growing complexity of a single division's schedule means that a naive approach to scheduling compromises revenue. Moreover, it may provide an unfair allocation of individual revenue, desirable or burdensome assignments, and the extent to which the preferences of each individual are met. This has adverse consequences on incentivization and employee satisfaction and is simply against business policy. MATERIALS AND METHODS: We identify the daily scheduling of physicians in this context as an operational problem that incorporates scheduling, revenue management, and fairness. Noting previous success of operations research and optimization in each of these disciplines, we propose a simple unified optimization formulation of this scheduling problem using mixed-integer optimization. RESULTS: Through a study of implementing the approach at the Division of Angiography and Interventional Radiology at the Brigham and Women's Hospital, which is directed by one of the authors, we exemplify the flexibility of the model to adapt to specific applications, the tractability of solving the model in practical settings, and the significant impact of the approach, most notably in increasing revenue by 8.2% over previous operating revenue while adhering strictly to a codified fairness and objectivity. CONCLUSIONS: We found that the investment in implementing such a system is far outweighed by the large potential revenue increase and the other benefits outlined.

Depression and costs of health care.

BACKGROUND: In spite of its global importance, the interaction between depression and chronic comorbid diseases remains incompletely understood with regard to prevalence, severity of disease, and potential causative factors mediating this interaction. OBJECTIVE: The authors sought to compare overall medical costs in nondepressed and depressed individuals. METHOD: Insurance claims for 618,780 patients were examined for total annual non-mental health cost of care in 11 chronic diseases. In each disease cohort, median annual non-mental health cost was calculated for individuals with and without depression. RESULTS: Patients with depression had higher median per-patient annual non-mental health costs than patients without depression in all 11 diseases studied. There was a higher-than-random comorbidity between depression and all 11 chronic comorbid diseases. CONCLUSION: Even when controlling for number of chronic comorbid diseases, depressed patients had significantly higher costs than non-depressed patients, in a magnitude consistent across 11 chronic comorbid diseases.