Linking Data From Health Surveys and Electronic Health Records: A Demonstration Project in Two Chicago Health Center Clinics

Abstract

IntroductionMonitoring and understanding population health requires conducting health-related surveys and surveillance. The objective of our study was to assess whether data from self-administered surveys could be collected electronically from patients in urban, primary-care, safety-net clinics and subsequently linked and compared with the same patients’ electronic health records (EHRs).MethodsData from self-administered surveys were collected electronically from a convenience sample of 527 patients at 2 Chicago health centers from September through November, 2014. Survey data were linked to EHRs.ResultsA total of 251 (47.6%) patients who completed the survey consented to having their responses linked to their EHRs. Consenting participants were older, more likely to report fair or poor health, and took longer to complete the survey than those who did not consent. For 8 of 18 categorical variables, overall percentage of agreement between survey data and EHR data exceeded 80% (sex, race/ethnicity, pneumococcal vaccination, self-reported body mass index [BMI], diabetes, high blood pressure, medication for high blood pressure, and hyperlipidemia), and of these, the level of agreement was good or excellent (κ ≥0.64) except for pneumococcal vaccination (κ = 0.40) and hyperlipidemia (κ = 0.47). Of 7 continuous variables, agreement was substantial for age and weight (concordance coefficients ≥0.95); however, with the exception of calculated survey BMI and EHR–BMI (concordance coefficient = 0.88), all other continuous variables had poor agreement.ConclusionsSelf-administered and web-based surveys can be completed in urban, primary-care, safety-net clinics and linked to EHRs. Linking survey and EHR data can enhance public health surveillance by validating self-reported data, completing gaps in patient data, and extending sample sizes obtained through current methods. This approach will require promoting and sustaining patient involvement.

Survey and electronic health record‐based medication use agreement in children with cystic fibrosis: A retrospective cross‐sectional study

“…A previous study reported discrepancies between self‐reported and EHR‐based antibiotic use about 8.3% of the time in adults with CF 20 . Consistent with our findings, non‐CF studies have reported a range of kappa values associated with the two methods for different types of medications: 0.18 for antibiotics, 0.53‐0.64 for chronic medications, and 0.67 for vaccines 21‐23 . A study of pregnant women reported relatively high kappa values for chronic medications (>0.80) and lower kappa values for acute medications (0.35) 24 …”

Integration of patient experience factors improves readmission prediction

“…Only in more recent years have institutions begun to start incorporating patient response data related to experience or even social determinants of health. [32] Given the extensive resources dedicated to clinical outcomes, dedicating even a fraction of this attention to the collection and integration of patient experience data would be an impactful first step in our more rigorous understanding the patient experience with respect to hospital readmissions and ways to positively impact patient outcomes. The pairwise comparisons within the factor analysis did demonstrate both some expected and unexpected findings.…”

Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review

“…Studies that included the prevalence of disease patterns, the prevalence of common diagnostic categories, disease incidence estimates, and measures of concordance between EHR and another data source, all in the context of chronic disease surveillance, were also included [8][9][10][11]. We excluded all studies conducted outside of the U.S.…”

“…We excluded all studies conducted outside of the U.S. The number of studies included for qualitative synthesis in the final review was 17 [5,6,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25].…”