Disparities in patient record completeness with respect to the health care utilization project

Nasir, Ayan; Liu, Xinliang; Gurupur, Varadraj P.; Qureshi, Zaeem

doi:10.1177/1460458217716005

Cited by 9 publications

(7 citation statements)

References 22 publications

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“…Ten of these (66%) identified that the incomplete data led to validity issues, most commonly that these data were not missing at random and had the potential to introduced bias. 12 , 34 , 43–50 …”

Section: Resultsmentioning

confidence: 99%

“… 10 , 60 , 62 , 76 Some studies tested data quality assessment tools such as the Data Quality Assessment Tool, created to evaluate patient records at Community Health Centers, and the Data Completeness Analysis Package. 12 , 77 …”

Section: Resultsmentioning

confidence: 99%

“… 10 Also included were several large databases compiled from electronic health record EHR data, such as the Biomedical Translational Research Information System repository, 11 and the Healthcare Cost and Utilization Project’s State Inpatient Database. 4 , 12–14 Research using datasets from large cohort studies such as the National Birth Defect Prevention Study were included if the study’s dataset was drawn directly from the participant’s medical records. 15 , 16 …”

Section: Methodsmentioning

confidence: 99%

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

Cook

Sachs

Weiskopf

2021

Journal of the American Medical Informatics Association

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Objective The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH). Materials and Methods We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process. Results The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully. Discussion The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups. Conclusion Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

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

Cook

Sachs

Weiskopf

2021

Journal of the American Medical Informatics Association

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“…For studies reported as full-text articles, we determined cohort data source and duration of follow-up for outcome. We assessed the following characteristics and quality of SDH data: 1) type of SDH, 2) source of SDH data, 3) reporting and handling of SDH data missingness, 3) validity checks of SDH data (e.g., cross-checking multiple data sources or use of a validated data quality assessment software tool) (24), and 4) level of SDH assessment (e.g., individual, neighborhood, and county). The SDH reported as independent risk factors for mortality and rehospitalization were identified only from studies that used methods to account for confounders.…”

Section: Assessment Of Methodological Qualitymentioning

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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“…In this invention data completeness is defined using the Record Strength Score (RSS) score by first developing a concept map of an ideal complete electronic health record and then measuring the missing elements. This algorithm explained by Nasir et al (34,35) happens to be generic in nature and will lead to more sophisticated algorithms depending on the scenario, data lifecycle, data transmission process, or specialization under consideration.…”

Section: A Key Observation In Electronic Health Records Affecting Mhe...mentioning

confidence: 99%

Key observations in terms of management of electronic health records from a mHealth perspective

Gurupur¹

2022

mHealth

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The article is a narrative review that briefly describes some of the recent advances in healthcare data management that will have positive effect on mHealth. The advances described in this article are in fact innovation introduced by the author to the field of data management with respect to electronic health records. The research delineated is transdisciplinary in nature and will potentially have positive impact on healthcare outcomes. Also, the article illustrates the necessity for an out of the box thinking approach to improve mHealth while discussing the current impending issues related to data incompleteness of electronic health records and the much-needed decision support systems for mHealth. It is to be noted that most of the electronic health records are now accessed by patients through mobile devices. These mobile devices will run as clients while much of the heavy computing is performed using servers. Here it is important to discuss some of the important technologies and methods used for decision making. The article attempts to present a discussion on how this myriad of intertwining technologies support this decision making with respect to electronic health records. More importantly it is these processes that assist in decision making and efficiency for both mHealth users and providers. In this respect, the article first provides insights on the complexities of decision making involved with electronic health records. This is followed by a discussion on the problem of data incompleteness of electronic health records. Finally, the author provides some insights into the gravity of the problem of data incompleteness in terms of revenue loss/gain for healthcare providers.

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Disparities in patient record completeness with respect to the health care utilization project

Cited by 9 publications

References 22 publications

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

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

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

Key observations in terms of management of electronic health records from a mHealth perspective

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