Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

Berg, Kelsey; Doktorchik, Chelsea; Quan, Hude; Saini, Vipin

doi:10.21203/rs.2.16433/v1

Cited by 2 publications

(2 citation statements)

References 31 publications

(56 reference statements)

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“…On the other hand, less complex classification methods have been used as well to identify specific groups of patients, risk assessment or aid in validating structured annotation 25–27. A recent scoping review found that although practitioners collect a variety of SBDH data at point of care through EHR, the overall use of automated technology is limited to date 28…”

Section: Introductionmentioning

confidence: 99%

Simplified data science approach to extract social and behavioural determinants: a retrospective chart review

Teng

Wilcox

2022

BMJ Open

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ObjectivesWe aim to extract a subset of social factors from clinical notes using common text classification methods.DesignRetrospective chart review.SettingWe collaborated with a local level I trauma hospital located in an underserved area that has a housing unstable patient population of about 6.5% and extracted text notes related to various social determinants for acute care patients.ParticipantsNotes were retrospectively extracted from 43 798 acute care patients.MethodsWe solely use open source Python packages to test simple text classification methods that can potentially be easily generalisable and implemented. We extracted social history text from various sources, such as admission and emergency department notes, over a 5-year timeframe and performed manual chart reviews to ensure data quality. We manually labelled the sentiment of the notes, treating each text entry independently. Four different models with two different feature selection methods (bag of words and bigrams) were used to classify and predict housing stability, tobacco use and alcohol use status for the extracted clinical text.ResultsFrom our analysis, we found overall positive results and metrics in applying open-source classification techniques; the accuracy scores were 91.2%, 84.7%, 82.8% for housing stability, tobacco use and alcohol use, respectively. There were many limitations in our analysis including social factors not present due to patient condition, multiple copy-forward entries and shorthand. Additionally, it was difficult to translate usage degrees for tobacco and alcohol use. However, when compared with structured data sources, our classification approach on unstructured notes yielded more results for housing and alcohol use; tobacco use proved less fruitful for unstructured notes.

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Section: Introductionmentioning

confidence: 99%

Simplified data science approach to extract social and behavioural determinants: a retrospective chart review

Teng

Wilcox

2022

BMJ Open

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“…4 Given the significant impact on health outcomes, several large healthcare systems are taking steps to integrate SDOH screenings into the electronic medical record and to connect patients with local services. [5][6][7][8] Existing literature seeks to characterize SDOH and resource connection programs. In a systematic review of 37 socioeconomic needs programs, Gottlieb and colleagues included interventions that provided patients with community resources.…”

Section: Introductionmentioning

confidence: 99%

Development and Evaluation of Electronic Social Needs Assessment and Resource Connection Tool in Facilitating Utilization of Community Services That Address Upstream Health

et al. 2021

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Background: Social determinants of health (SDOH) put some groups who access care at student-run free clinics (SRFCs) at higher risk for adverse medical outcomes. Free or low-cost community services supplement clinic-based interventions, but access is limited by knowledge, transportation, language, and/or trust. Social needs assessments may be electronically paired with resource connection tools to connect patients to local, validated resources. The objective of this pilot study was to evaluate the SDOH screen and resource connection tool developed at a SRFC. Methods: The tool was piloted with a convenience sample of 40 patients with scheduled outpatient appointments at a SRFC in Miami, Florida. Participants were given information about a variety of services and screened for high-risk situations such as human trafficking. Follow-up survey via telephone at 2-4 weeks identified survey acceptability, successful connections, and barriers to access. Results: Forty participants completed the assessment. All participants were counseled regarding exercise and nutrition and requested information about more than one resource. Sixty percent (n=24) were successfully contacted for follow-up. Of these, 29% (n=7) were able to connect with one or more recommended resources. Reasons given for failure to access resources included lack of time or transportation, health issues, and lack of response from contacted organizations. Conclusions: Social needs assessments may be implemented in SRFCs to identify high-risk needs, facilitate linkage to local organizations that meet these needs, gather data to guide future programming, and provide education and counseling. Stronger connections with local organizations and closed loop referrals may be needed to facilitate connection to community resources.

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Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

Abstract: Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health

Cited by 2 publications

References 31 publications

Simplified data science approach to extract social and behavioural determinants: a retrospective chart review

Simplified data science approach to extract social and behavioural determinants: a retrospective chart review

Development and Evaluation of Electronic Social Needs Assessment and Resource Connection Tool in Facilitating Utilization of Community Services That Address Upstream Health

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