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Hospital Readmission and Social Risk Factors Identified from Physician Notes.
Navathe, Amol S; Zhong, Feiran; Lei, Victor J; Chang, Frank Y; Sordo, Margarita; Topaz, Maxim; Navathe, Shamkant B; Rocha, Roberto A; Zhou, Li.
Afiliação
  • Navathe AS; Division of Health Policy, University of Pennsylvania, Philadelphia, PA.
  • Zhong F; CMC Philadelphia VA Medical Center, Philadelphia, PA.
  • Lei VJ; Leonard Davis Institute of Health Economics, The Wharton School, University of Pennsylvania, Philadelphia, PA.
  • Chang FY; Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
  • Sordo M; Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
  • Topaz M; Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
  • Navathe SB; Clinical Informatics, Partners eCare, Partners Healthcare Inc., Boston, MA.
  • Rocha RA; Division of General Internal Medicine and Primary Care, Brigham & Women's Hospital, Harvard Medical School, Boston, MA.
  • Zhou L; Clinical Informatics, Partners eCare, Partners Healthcare Inc., Boston, MA.
Health Serv Res ; 53(2): 1110-1136, 2018 04.
Article em En | MEDLINE | ID: mdl-28295260
ABSTRACT

OBJECTIVE:

To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions. STUDY

SETTING:

A multihospital academic health system in southeastern Massachusetts. STUDY

DESIGN:

An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics. DATA COLLECTION/EXTRACTION

METHODS:

All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry. PRINCIPAL

FINDINGS:

All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).

CONCLUSIONS:

The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Readmissão do Paciente / Médicos / Documentação / Registros Eletrônicos de Saúde Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País como assunto: America do norte Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Readmissão do Paciente / Médicos / Documentação / Registros Eletrônicos de Saúde Tipo de estudo: Etiology_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Adolescent / Adult / Aged / Aged80 / Female / Humans / Male / Middle aged País como assunto: America do norte Idioma: En Ano de publicação: 2018 Tipo de documento: Article