Ethics-by-design: efficient, fair and inclusive resource allocation using machine learning.

The distribution of crucial medical goods and services in conditions of scarcity is among the most important, albeit contested, areas of public policy development. Policymakers must strike a balance between multiple efficiency and fairness objectives, while reconciling disparate value judgments from a diverse set of stakeholders. We present a general framework for combining ethical theory, data modeling, and stakeholder input in this process and illustrate through a case study on designing organ transplant allocation policies. We develop a novel analytical tool, based on machine learning and optimization, designed to facilitate efficient and wide-ranging exploration of policy outcomes across multiple objectives. Such a tool enables all stakeholders, regardless of their technical expertise, to more effectively engage in the policymaking process by developing evidence-based value judgments based on relevant tradeoffs.

From predictions to prescriptions: A data-driven response to COVID-19.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers make difficult decisions on patient triage, treatment and care management on a daily basis. Policy makers have imposed social distancing measures to slow the disease, at a steep economic price. We design analytical tools to support these decisions and combat the pandemic. Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact. By leveraging cohort-level clinical data, patient-level hospital data, and census-level epidemiological data, we develop an integrated four-step approach, combining descriptive, predictive and prescriptive analytics. First, we aggregate hundreds of clinical studies into the most comprehensive database on COVID-19 to paint a new macroscopic picture of the disease. Second, we build personalized calculators to predict the risk of infection and mortality as a function of demographics, symptoms, comorbidities, and lab values. Third, we develop a novel epidemiological model to project the pandemic's spread and inform social distancing policies. Fourth, we propose an optimization model to re-allocate ventilators and alleviate shortages. Our results have been used at the clinical level by several hospitals to triage patients, guide care management, plan ICU capacity, and re-distribute ventilators. At the policy level, they are currently supporting safe back-to-work policies at a major institution and vaccine trial location planning at Janssen Pharmaceuticals, and have been integrated into the US Center for Disease Control's pandemic forecast.

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.