Hospital Readmission and Social Risk Factors Identified from Physician Notes

被引:125
作者
Navathe, Amol S. [1 ,2 ,3 ,4 ]
Zhong, Feiran [4 ]
Lei, Victor J. [4 ]
Chang, Frank Y. [5 ]
Sordo, Margarita [4 ,5 ]
Topaz, Maxim [4 ]
Navathe, Shamkant B. [6 ]
Rocha, Roberto A. [4 ,5 ]
Zhou, Li [4 ,5 ]
机构
[1] Univ Penn, Div Hlth Policy, 1108 Blockley Hall,423 Guardian Dr, Philadelphia, PA 19146 USA
[2] CMC Philadelphia VA Med Ctr, Philadelphia, PA USA
[3] Univ Penn, Wharton Sch, Leonard Davis Inst Hlth Econ, Philadelphia, PA 19104 USA
[4] Harvard Med Sch, Brigham & Womens Hosp, Div Gen Internal Med & Primary Care, Boston, MA USA
[5] Partners Healthcare Inc, Partners eCare, Clin Informat, Boston, MA USA
[6] Georgia Inst Technol, Coll Comp, Sch Comp Sci, Atlanta, GA 30332 USA
关键词
Social determinants of health; readmissions; natural language processing; quality of care; ADMINISTRATIVE CLAIMS MEASURE; ACUTE MYOCARDIAL-INFARCTION; ELECTRONIC HEALTH RECORDS; CONGESTIVE-HEART-FAILURE; SOCIOECONOMIC-STATUS; MEDICARE BENEFICIARIES; 30-DAY READMISSION; UNPLANNED READMISSION; ELDERLY-PATIENTS; OLDER-ADULTS;
D O I
10.1111/1475-6773.12670
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
ObjectiveTo 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 SettingA multihospital academic health system in southeastern Massachusetts. Study DesignAn 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 MethodsAll-payer claims, EHR data, and physician notes extracted from a centralized clinical registry. Principal FindingsAll 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). ConclusionsThe 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.
引用
收藏
页码:1110 / 1136
页数:27
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