ArticlePreventing chronic disease2018
Linking Data From Health Surveys and Electronic Health Records: A Demonstration Project in Two Chicago Health Center Clinics.
Article in Preventing chronic disease, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review.Journal of the American Medical Informatics Association : JAMIA · 2020Pooled it
- A mixed-methods study comparing clinical and patient-reported perspectives on long COVID: insights from electronic health records and narrative journal entries.BMC infectious diseases · 2026Observational
- Impact of Constipation on Quality of Life, Work Productivity, and Treatment Practices: Findings from the CONNECT Web-Based Survey.Digestion · 2026Article
- Access to care affects electronic health record reliability and AI-driven disease prediction.Nature health · 2026Article
- Accuracy of owner-reported diagnoses for dogs enrolled in the Dog Aging Project as compared to veterinary electronic medical records.PloS one · 2026Article
- Cross-sectional survey of 43,517 dogs in the Dog Aging Project identifies owner-reported lifetime prevalence and characteristics of gastrointestinal disease.Journal of the American Veterinary Medical Association · 2024Article
- The role and limitations of electronic medical records versus patient interviews for determining symptoms of, underlying comorbidities of, and medication use by patients with COVID-19.American journal of epidemiology · 2024Article
- Utilization of Electronic Health Records for Chronic Disease Surveillance: A Systematic Literature Review.Cureus · 2023Review
- Observational
- Comparing medical history data derived from electronic health records and survey answers in the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Survey and electronic health record-based medication use agreement in children with cystic fibrosis: A retrospective cross-sectional study.International journal of paediatric dentistry · 2021Article
- Comparison of physical examination and laboratory data between a clinical study and electronic health records.PloS one · 2020Article
- Electronic Health Record Use in Public Health Infectious Disease Surveillance, USA, 2018-2019.Current infectious disease reports · 2019Review
- Towards augmenting structured EHR data: a comparison of manual chart review and patient self-report.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.