Secondary Analysis of Electronic Health Records

Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex decisions. Unfortunately, the current state of medical knowledge does not provide the guid- ance to make the majority of clinical decisions on the basis of evidence. According to the 2012 Institute of Medicine Committee Report, only 10–20 % of clinical decisions are evidence based. The problem even extends to the creation of clinical practice guidelines (CPGs). Nearly 50 % of recommendations made in specialty society guidelines rely on expert opinion rather than experimental data. Furthermore, the creation process of CPGs is “marred by weak methods and financial conflicts of interest,” rendering current CPGs potentially less trustworthy. The present research infrastructure is inefficient and frequently produces unre- liable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor-intensive, slow, and can return results that are seldom generalizable to every patient population. It is impossible for a tightly controlled RCT to capture the full, interactive, and contextual details of the clinical issues that arise in real clinics and inpatient units. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a “data desert” when it comes to making decisions. Electronic health record (EHR) data are frequently digitally archived and can subsequently be extracted and analyzed. Between 2011 and 2019, the prevalence of EHRs is expected to grow from 34 to 90 % among office-based practices, and the majority of hospitals have replaced or are in the process of replacing paper systems with comprehensive, enterprise EHRs. The power of scale intrinsic to this digital transformation opens the door to a massive amount of currently untapped infor- mation. The data, if properly analyzed and meaningfully interpreted, could vastly improve our conception and development of best practices.

• Tipo de recurso: Book
• Fecha de publicación: 2016
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/18740
• Acceso en línea: https://directory.doabooks.org/handle/20.500.12854/35747; http://hdl.handle.net/20.500.12010/18740
• Palabra clave: Health Informatics; Data Mining and Knowledge Discovery; Statistics for Life Sciences; Historias clínicas - Control de acceso; Ética médica; Secreto médico
• License: Abierto (Texto Completo)