From predictions to prescriptions: A data-driven response to COVID-19.
Health Care Manag Sci
; 24(2): 253-272, 2021 Jun.
Article
in English
| MEDLINE | ID: covidwho-1085646
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Machine Learning
/
COVID-19
/
COVID-19 Drug Treatment
Type of study:
Cohort study
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Topics:
Vaccines
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Health Care Manag Sci
Journal subject:
Health Services
Year:
2021
Document Type:
Article
Affiliation country:
S10729-020-09542-0
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