Privacy-protecting, reliable response data discovery using COVID-19 patient observations.
J Am Med Inform Assoc
; 28(8): 1765-1776, 2021 07 30.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1246728
ABSTRACT
OBJECTIVE:
To utilize, in an individual and institutional privacy-preserving manner, electronic health record (EHR) data from 202 hospitals by analyzing answers to COVID-19-related questions and posting these answers online. MATERIALS ANDMETHODS:
We developed a distributed, federated network of 12 health systems that harmonized their EHRs and submitted aggregate answers to consortia questions posted at https//www.covid19questions.org. Our consortium developed processes and implemented distributed algorithms to produce answers to a variety of questions. We were able to generate counts, descriptive statistics, and build a multivariate, iterative regression model without centralizing individual-level data.RESULTS:
Our public website contains answers to various clinical questions, a web form for users to ask questions in natural language, and a list of items that are currently pending responses. The results show, for example, that patients who were taking angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers, within the year before admission, had lower unadjusted in-hospital mortality rates. We also showed that, when adjusted for, age, sex, and ethnicity were not significantly associated with mortality. We demonstrated that it is possible to answer questions about COVID-19 using EHR data from systems that have different policies and must follow various regulations, without moving data out of their health systems. DISCUSSION ANDCONCLUSIONS:
We present an alternative or a complement to centralized COVID-19 registries of EHR data. We can use multivariate distributed logistic regression on observations recorded in the process of care to generate results without transferring individual-level data outside the health systems.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
Algoritmos
/
Redes de Comunicación de Computadores
/
Procesamiento de Lenguaje Natural
/
Almacenamiento y Recuperación de la Información
/
Confidencialidad
/
Registros Electrónicos de Salud
/
COVID-19
Tipo de estudio:
Estudio observacional
/
Estudio pronóstico
Límite:
Femenino
/
Humanos
/
Masculino
Idioma:
Inglés
Revista:
J Am Med Inform Assoc
Asunto de la revista:
Informática Médica
Año:
2021
Tipo del documento:
Artículo
País de afiliación:
Jamia
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