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

Author(s): Elham Hatef , MD, MPH ¹ ^, ² ^, , Masoud Rouhizadeh , MSc, PhD ³ , Iddrisu Tia , MD, MSc ⁴ , Elyse Lasser , MSc ¹ , Felicia Hill-Briggs , PhD ⁵ ^, ⁶ ^, ⁷ ^, ⁸ ^, ⁹ , Jill Marsteller , PhD ⁸ ^, ⁹ ^, ¹⁰ ^, ¹¹ , Hadi Kharrazi , MD, PhD ¹ ^, ⁴ ^, ⁹ ^, ¹⁰ ^, ¹¹

Editor(s): Jennifer Hefner

Other contributor(s): Rachel Gols (Reviewer), Ming Huang (Reviewer), Clayton Rothwell (Reviewer)

Publication date (Electronic): 02 August 2019

Journal: JMIR Medical Informatics

Publisher: JMIR Publications

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

Read this article at

ScienceOpenPublisher PMC

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

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.

Related collections

Most cited references 42

Record: found
Abstract: found
Article: found

Is Open Access

Methods and dimensions of electronic health record data quality assessment: enabling reuse for clinical research

Nicole Weiskopf, Chunhua Weng (2013)

Objective To review the methods and dimensions of data quality assessment in the context of electronic health record (EHR) data reuse for research. Materials and methods A review of the clinical research literature discussing data quality assessment methodology for EHR data was performed. Using an iterative process, the aspects of data quality being measured were abstracted and categorized, as well as the methods of assessment used. Results Five dimensions of data quality were identified, which are completeness, correctness, concordance, plausibility, and currency, and seven broad categories of data quality assessment methods: comparison with gold standards, data element agreement, data source agreement, distribution comparison, validity checks, log review, and element presence. Discussion Examination of the methods by which clinical researchers have investigated the quality and suitability of EHR data for research shows that there are fundamental features of data quality, which may be difficult to measure, as well as proxy dimensions. Researchers interested in the reuse of EHR data for clinical research are recommended to consider the adoption of a consistent taxonomy of EHR data quality, to remain aware of the task-dependence of data quality, to integrate work on data quality assessment from other fields, and to adopt systematic, empirically driven, statistically based methods of data quality assessment. Conclusion There is currently little consistency or potential generalizability in the methods used to assess EHR data quality. If the reuse of EHR data for clinical research is to become accepted, researchers should adopt validated, systematic methods of EHR data quality assessment.

0 comments Cited 306 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: not found
Article: not found

Accountable Health Communities--Addressing Social Needs through Medicare and Medicaid.

Dawn E. Alley, Chisara N Asomugha, Patrick H Conway … (2016)

0 comments Cited 201 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Impact of social factors on risk of readmission or mortality in pneumonia and heart failure: systematic review.

Linda Calvillo-King, Danielle Arnold, Kathryn J Eubank … (2013)

Readmission and mortality after hospitalization for community-acquired pneumonia (CAP) and heart failure (HF) are publically reported. This systematic review assessed the impact of social factors on risk of readmission or mortality after hospitalization for CAP and HF-variables outside a hospital's control. We searched OVID, PubMed and PSYCHINFO for studies from 1980 to 2012. Eligible articles examined the association between social factors and readmission or mortality in patients hospitalized with CAP or HF. We abstracted data on study characteristics, domains of social factors examined, and presence and magnitude of associations. Seventy-two articles met inclusion criteria (20 CAP, 52 HF). Most CAP studies evaluated age, gender, and race and found older age and non-White race were associated with worse outcomes. The results for gender were mixed. Few studies assessed higher level social factors, but those examined were often, but inconsistently, significantly associated with readmissions after CAP, including lower education, low income, and unemployment, and with mortality after CAP, including low income. For HF, older age was associated with worse outcomes and results for gender were mixed. Non-Whites had more readmissions after HF but decreased mortality. Again, higher level social factors were less frequently studied, but those examined were often, but inconsistently, significantly associated with readmissions, including low socioeconomic status (Medicaid insurance, low income), living situation (home stability rural address), lack of social support, being unmarried and risk behaviors (smoking, cocaine use and medical/visit non-adherence). Similar findings were observed for factors associated with mortality after HF, along with psychiatric comorbidities, lack of home resources and greater distance to hospital. A broad range of social factors affect the risk of post-discharge readmission and mortality in CAP and HF. Future research on adverse events after discharge should study social determinants of health.

0 comments Cited 112 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Elham Hatef:

Center for Population Health ITDepartment of Health Policy and ManagementJohns Hopkins Bloomberg School of Public Health624 N Broadway, Room 502Baltimore, MD, 21205United States1 44328722841 4432872284ehatef1@jhu.edu

ORCID: http://orcid.org/0000-0003-2535-8191

Journal

Journal ID (nlm-ta): JMIR Med Inform

Journal ID (iso-abbrev): JMIR Med Inform

Journal ID (publisher-id): JMI

Title: JMIR Medical Informatics

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2291-9694

Publication date Collection: Jul-Sep 2019

Publication date (Electronic): 02 August 2019

Volume: 7

Issue: 3

Electronic Location Identifier: e13802

Affiliations

[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

Author notes

Corresponding Author: Elham Hatef ehatef1@ 123456jhu.edu

Author information

Elham Hatef http://orcid.org/0000-0003-2535-8191

Masoud Rouhizadeh http://orcid.org/0000-0002-9006-6112

Iddrisu Tia http://orcid.org/0000-0002-4265-4649

Elyse Lasser http://orcid.org/0000-0002-1758-9822

Felicia Hill-Briggs http://orcid.org/0000-0002-2804-3278

Jill Marsteller http://orcid.org/0000-0002-8458-954X

Hadi Kharrazi http://orcid.org/0000-0003-1481-4323

Article

Publisher ID: v7i3e13802

DOI: 10.2196/13802

PMC ID: 6696855

PubMed ID: 31376277

SO-VID: d38a75ef-8028-42b8-82c0-e7a3002bcc10

Copyright © ©Elham Hatef, Masoud Rouhizadeh, Iddrisu Tia, Elyse Lasser, Felicia Hill-Briggs, Jill Marsteller, Hadi Kharrazi. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 02.08.2019.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/.as well as this copyright and license information must be included.

History

Date received : 22 February 2019

Date revision requested : 11 March 2019

Date revision received : 3 May 2019

Date accepted : 30 May 2019

Comments

Comment on this article

scite_

Cited by 34

See all cited by

Most referenced authors 319

See all reference authors

Submit your digital health research with an established publisher
- celebrating 25 years of open access