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JMIR Med Inform. 2019 Aug 2;7(3):e13802. doi: 10.2196/13802.

Assessing the Availability of Data on Social and Behavioral Determinants in Structured and Unstructured Electronic Health Records: A Retrospective Analysis of a Multilevel Health Care System.

Hatef E1,2, Rouhizadeh M3, Tia I4, Lasser E1, Hill-Briggs F5,6,7,8,9, Marsteller J8,9,10,11, Kharrazi H1,4,9,10,11.

Author information

1
Center for Population Health IT, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
2
Johns Hopkins Center for Health Disparities Solutions, Baltimore, MD, United States.
3
Center for Clinical Data Analysis, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.
4
Division of Health Sciences Informatics, Johns Hopkins School of Medicine, Baltimore, MD, United States.
5
Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, United States.
6
Department of Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
7
Department of Acute and Chronic Care, Johns Hopkins School of Nursing, Baltimore, MD, United States.
8
Welch Center for Prevention, Epidemiology & Clinical Research, Johns Hopkins University, Baltimore, MD, United States.
9
Behavioral, Social and Systems Sciences Translational Research Community, Institute for Clinical and Translational Research, Johns Hopkins School of Medicine, Baltimore, MD, United States.
10
Center for Health Services and Outcomes Research, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
11
Armstrong Institute for Patient Safety and Quality, Johns Hopkins School of Medicine, Baltimore, MD, United States.

Abstract

BACKGROUND:

Most US health care providers have adopted electronic health records (EHRs) that facilitate the uniform collection of clinical information. However, standardized data formats to capture social and behavioral determinants of health (SBDH) in structured EHR fields are still evolving and not adopted widely. Consequently, at the point of care, SBDH data are often documented within unstructured EHR fields that require time-consuming and subjective methods to retrieve. Meanwhile, collecting SBDH data using traditional surveys on a large sample of patients is infeasible for health care providers attempting to rapidly incorporate SBDH data in their population health management efforts. A potential approach to facilitate targeted SBDH data collection is applying information extraction methods to EHR data to prescreen the population for identification of immediate social needs.

OBJECTIVE:

Our aim was to examine the availability and characteristics of SBDH data captured in the EHR of a multilevel academic health care system that provides both inpatient and outpatient care to patients with varying SBDH across Maryland.

METHODS:

We measured the availability of selected patient-level SBDH in both structured and unstructured EHR data. We assessed various SBDH including demographics, preferred language, alcohol use, smoking status, social connection and/or isolation, housing issues, financial resource strains, and availability of a home address. EHR's structured data were represented by information collected between January 2003 and June 2018 from 5,401,324 patients. EHR's unstructured data represented information captured for 1,188,202 patients between July 2016 and May 2018 (a shorter time frame because of limited availability of consistent unstructured data). We used text-mining techniques to extract a subset of SBDH factors from EHR's unstructured data.

RESULTS:

We identified a valid address or zip code for 5.2 million (95.00%) of approximately 5.4 million patients. Ethnicity was captured for 2.7 million (50.00%), whereas race was documented for 4.9 million (90.00%) and a preferred language for 2.7 million (49.00%) patients. Information regarding alcohol use and smoking status was coded for 490,348 (9.08%) and 1,728,749 (32.01%) patients, respectively. Using the International Classification of Diseases-10th Revision diagnoses codes, we identified 35,171 (0.65%) patients with information related to social connection/isolation, 10,433 (0.19%) patients with housing issues, and 3543 (0.07%) patients with income/financial resource strain. Of approximately 1.2 million unique patients with unstructured data, 30,893 (2.60%) had at least one clinical note containing phrases referring to social connection/isolation, 35,646 (3.00%) included housing issues, and 11,882 (1.00%) had mentions of financial resource strain.

CONCLUSIONS:

Apart from demographics, SBDH data are not regularly collected for patients. Health care providers should assess the availability and characteristics of SBDH data in EHRs. Evaluating the quality of SBDH data can potentially enable health care providers to modify underlying workflows to improve the documentation, collection, and extraction of SBDH data from EHRs.

KEYWORDS:

electronic health record; multi-level health care system; natural language processing; social and behavioral determinants of health; structured data; unstructured data

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