3 research outputs found
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Enabling the Reuse of Electronic Health Record Data through Data Quality Assessment and Transparency
With the increasing adoption of health information technology and the growth in the resulting electronic repositories of clinical data, the secondary use of electronic health record data has become one of the most promising approaches to enabling and speeding clinical research. Unfortunately, electronic health record data are known to suffer from significant data quality problems. Awareness of the problem of electronic health record data quality is growing, but methods for measuring data quality remain ad hoc. Clinical researchers must handle this complicated problem without systematic or validated methods. The lack of appropriate or trustworthy electronic health record data quality assessment methodology limits the validity of research performed with electronic health record data.
This dissertation documents the development of a data quality assessment framework and guideline for clinical researchers engaged in the secondary use of electronic health record data for retrospective research. Through a systematic literature review and interviews with key stakeholders, we identified core constructs of data quality, as well as priorities for future approaches to electronic health record data quality assessment. We used a data-driven approach to demonstrate that data quality is task-dependent, indicating that appropriate data quality measures must be selected, applied, and interpreted within the context of a specific study. On the basis of these results, we developed and evaluated a dynamic guideline for data quality measures in order to help researchers choose data quality measures and methods appropriately within the context of reusing electronic health record data for research
Hidden in plain sight: bias towards sick patients when sampling patients with sufficient electronic health record data for research
Background: To demonstrate that subject selection based on sufficient laboratory results and medication orders in electronic health records can be biased towards sick patients. Methods: Using electronic health record data from 10,000 patients who received anesthetic services at a major metropolitan tertiary care academic medical center, an affiliated hospital for women and children, and an affiliated urban primary care hospital, the correlation between patient health status and counts of days with laboratory results or medication orders, as indicated by the American Society of Anesthesiologists Physical Status Classification (ASA Class), was assessed with a Negative Binomial Regression model. Results: Higher ASA Class was associated with more points of data: compared to ASA Class 1 patients, ASA Class 4 patients had 5.05 times the number of days with laboratory results and 6.85 times the number of days with medication orders, controlling for age, sex, emergency status, admission type, primary diagnosis, and procedure. Conclusions: Imposing data sufficiency requirements for subject selection allows researchers to minimize missing data when reusing electronic health records for research, but introduces a bias towards the selection of sicker patients. We demonstrated the relationship between patient health and quantity of data, which may result in a systematic bias towards the selection of sicker patients for research studies and limit the external validity of research conducted using electronic health record data. Additionally, we discovered other variables (i.e., admission status, age, emergency classification, procedure, and diagnosis) that independently affect data sufficiency
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A Real-Time Screening Alert Improves Patient Recruitment Efficiency
The scarcity of cost-effective patient identification methods represents a significant barrier to clinical research. Research recruitment alerts have been designed to facilitate physician referrals but limited support is available to clinical researchers. We conducted a retrospective data analysis to evaluate the efficacy of a real-time patient identification alert delivered to clinical research coordinators recruiting for a clinical prospective cohort study. Data from log analysis and informal interviews with coordinators were triangulated. Over a 12-month period, 11,295 were screened electronically, 1,449 were interviewed, and 282 were enrolled. The enrollment rates for the alert and two other conventional methods were 4.65%, 2.01%, and 1.34% respectively. A taxonomy of eligibility status was proposed to precisely categorize research patients. Practical ineligibility factors were identified and their correlation with age and gender were analyzed. We conclude that the automatic prescreening alert improves screening efficiency and is an effective aid to clinical research coordinators