The quality of social determinants data in the electronic health record: a systematic review

Cook, Lily A.; Sachs, Jonathan; Weiskopf, Nicole G.

doi:10.1093/jamia/ocab199

Cited by 95 publications

(49 citation statements)

References 64 publications

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“…Nevertheless, very few articles in the medical informatics and AI literature mention any aspects of SDoH in the context of AI fairness and bias, suggesting that SDoH considerations are not yet routine in the development of AI for health. A potential contributor to the lack of SDoH representation in datasets are limitations posed by EHR designs, poor data quality, and similar to intersectionality, challenges in operationalizing the complex concept of SDoH into a format that is amenable to documentation in EHRs [3,37]. Fluid definitions of SDoH and the lack of standards for the capture and representation of SDoH stand as substantial challenges [37].…”

Section: Discussionmentioning

confidence: 99%

“…Improved representation of the features of underrepresented population subgroups in datasets used for AI development is one component towards AI fairness. It is commonly suggested that improved and more complete capture of sensitive features of underrepresented groups (e.g., demographic characteristics) and social determinants of health (SDoH) in AI datasets are necessary towards the development of less biased algorithms [2,3]. Beyond providing a foundation for less biased algorithm development at the outset, diverse datasets are also necessary to enable computational approaches to mitigation of bias in AI; these approaches rely, to a large extent, on data quality.…”

Section: Introductionmentioning

confidence: 99%

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The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

co-author

Alhuwail

et al. 2022

Yearb Med Inform

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Objectives: The objective of this paper is to draw attention to the currently underused potential of clinical documentation by nursing and allied health professions to improve the representation of social determinants of health (SDoH) and intersectionality data in electronic health records (EHRs), towards the development of equitable artificial intelligence (AI) technologies. Methods: A rapid review of the literature on the inclusion of nursing and allied health data and the nature of health equity information representation in the development and/or use of artificial intelligence approaches alongside expert perspectives from the International Medical Informatics Association (IMIA) Student and Emerging Professionals Working Group. Results: Consideration of social determinants of health and intersectionality data are limited in both the medical AI and nursing and allied health AI literature. As a concept being newly discussed in the context of AI, the lack of discussion of intersectionality in the literature was unsurprising. However, the limited consideration of social determinants of health was surprising, given its relatively longstanding recognition and the importance of representation of the features of diverse populations as a key requirement for equitable AI. Conclusions: Leveraging the rich contextual data collected by nursing and allied health professions has the potential to improve the capture and representation of social determinants of health and intersectionality. This will require addressing issues related to valuing AI goals (e.g., diagnostics versus supporting care delivery) and improved EHR infrastructure to facilitate documentation of data beyond medicine. Leveraging nursing and allied health data to support equitable AI development represents a current open question for further exploration and research.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

co-author

Alhuwail

et al. 2022

Yearb Med Inform

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“…Many SDH data included in the studies were obtained through EHRs. Despite widespread acknowledgement that SDH data are frequently missing or inaccurate in EHRs and large datasets (60), SDH data quality, including characteristics of completeness, correctness, and consistency, was seldom addressed in the studies included in this review-three out of four studies failed to report handling of missing data, and no study reported applying any validation method for SDH data. Measurement error may explain the mixed findings of independent associations between some SDH and postsepsis outcomes reported among our studies.…”

Section: Measurement Errormentioning

confidence: 99%

The Assessment of Social Determinants of Health in Postsepsis Mortality and Readmission: A Scoping Review

Hilton

Hauschildt

Shah

et al. 2022

Critical Care Explorations

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To summarize knowledge and identify gaps in evidence about the relationship between social determinants of health (SDH) and postsepsis outcomes. DATA SOURCES:We conducted a comprehensive search of PubMed/Medical Literature Analysis and Retrieval System Online, Excerpta Medica database, and the Cochrane Library. STUDY SELECTION:We identified articles that evaluated SDH as risk factors for mortality or readmission after sepsis hospitalization. Two authors independently screened and selected articles for inclusion. DATA EXTRACTION:We dual-extracted study characteristics with specific focus on measurement, reporting, and interpretation of SDH variables.DATA SYNTHESIS: Of 2,077 articles screened, 103 articles assessed risk factors for postsepsis mortality or readmission. Of these, 28 (27%) included at least one SDH variable. Inclusion of SDH in studies assessing postsepsis adverse outcomes increased over time. The most common SDH evaluated was race/ethnicity (n = 21, 75%), followed by payer type (n = 10, 36%), and income/wealth (n = 9, 32%). Of the studies including race/ethnicity, nine (32%) evaluated no other SDH. Only one study including race/ethnicity discussed the use of this variable as a surrogate for social disadvantage, and none specifically discussed structural racism. None of the studies specifically addressed methods to validate the accuracy of SDH or handling of missing data. Eight (29%) studies included a general statement that missing data were infrequent. Several studies reported independent associations between SDH and outcomes after sepsis discharge; however, these findings were mixed across studies. CONCLUSIONS:Our review suggests that SDH data are underutilized and of uncertain quality in studies evaluating postsepsis adverse events. Transparent and explicit ontogenesis and data models for SDH data are urgently needed to support research and clinical applications with specific attention to advancing our understanding of the role racism and racial health inequities in postsepsis outcomes.

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“…The health care standard only recommends a structure for how information about patient race and ethnicity should be stored; in practice, there are wide variations in how health care systems collect this information. Studies have documented that it is frequently missing from the patient record, and when it is collected, it is often of poor quality [18][19][20][21][22][23]. Our objective was to explore variations in how health care systems collect and report information about the race and ethnicity of their patients.…”

Section: Introductionmentioning

confidence: 99%

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave

Cook¹,

Espinoza²,

Weiskopf³

et al. 2022

JMIR Med Inform

Self Cite

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Background The adverse impact of COVID-19 on marginalized and under-resourced communities of color has highlighted the need for accurate, comprehensive race and ethnicity data. However, a significant technical challenge related to integrating race and ethnicity data in large, consolidated databases is the lack of consistency in how data about race and ethnicity are collected and structured by health care organizations. Objective This study aims to evaluate and describe variations in how health care systems collect and report information about the race and ethnicity of their patients and to assess how well these data are integrated when aggregated into a large clinical database. Methods At the time of our analysis, the National COVID Cohort Collaborative (N3C) Data Enclave contained records from 6.5 million patients contributed by 56 health care institutions. We quantified the variability in the harmonized race and ethnicity data in the N3C Data Enclave by analyzing the conformance to health care standards for such data. We conducted a descriptive analysis by comparing the harmonized data available for research purposes in the database to the original source data contributed by health care institutions. To make the comparison, we tabulated the original source codes, enumerating how many patients had been reported with each encoded value and how many distinct ways each category was reported. The nonconforming data were also cross tabulated by 3 factors: patient ethnicity, the number of data partners using each code, and which data models utilized those particular encodings. For the nonconforming data, we used an inductive approach to sort the source encodings into categories. For example, values such as “Declined” were grouped with “Refused,” and “Multiple Race” was grouped with “Two or more races” and “Multiracial.” Results “No matching concept” was the second largest harmonized concept used by the N3C to describe the race of patients in their database. In addition, 20.7% of the race data did not conform to the standard; the largest category was data that were missing. Hispanic or Latino patients were overrepresented in the nonconforming racial data, and data from American Indian or Alaska Native patients were obscured. Although only a small proportion of the source data had not been mapped to the correct concepts (0.6%), Black or African American and Hispanic/Latino patients were overrepresented in this category. Conclusions Differences in how race and ethnicity data are conceptualized and encoded by health care institutions can affect the quality of the data in aggregated clinical databases. The impact of data quality issues in the N3C Data Enclave was not equal across all races and ethnicities, which has the potential to introduce bias in analyses and conclusions drawn from these data. Transparency about how data have been transformed can help users make accurate analyses and inferences and eventually better guide clinical care and public policy.

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The quality of social determinants data in the electronic health record: a systematic review

Cited by 95 publications

References 64 publications

The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

The Untapped Potential of Nursing and Allied Health Data for Improved Representation of Social Determinants of Health and Intersectionality in Artificial Intelligence Applications: A Rapid Review

The Assessment of Social Determinants of Health in Postsepsis Mortality and Readmission: A Scoping Review

Issues With Variability in Electronic Health Record Data About Race and Ethnicity: Descriptive Analysis of the National COVID Cohort Collaborative Data Enclave

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