The Electronic Health Record as the Primary Data Source in a Pragmatic Trial: A Case Study

2022 ◽  
pp. 0272989X2110699
Author(s):  
Louise B. Russell ◽  
Qian Huang ◽  
Yuqing Lin ◽  
Laurie A. Norton ◽  
Jingsan Zhu ◽  
...  

Introduction. Pragmatic clinical trials test interventions in patients representative of real-world medical practice and reduce data collection costs by using data recorded in the electronic health record (EHR) during usual care. We describe our experience using the EHR to measure the primary outcome of a pragmatic trial, hospital readmissions, and important clinical covariates. Methods. The trial enrolled patients recently discharged from the hospital for treatment of heart failure to test whether automated daily monitoring integrated into the EHR could reduce readmissions. The study team used data from the EHR and several data systems that drew on the EHR, supplemented by the hospital admissions files of three states. Results. Almost three-quarters of enrollees’ readmissions over the 12-mo trial period were captured by the EHRs of the study hospitals. State data, which took 7 mo to more than 2 y from first contact to receipt of first data, provided the remaining one-quarter. Considerable expertise was required to resolve differences between the 2 data sources. Common covariates used in trial analyses, such as weight and body mass index during the index hospital stay, were available for >97% of enrollees from the EHR. Ejection fraction, obtained from echocardiograms, was available for only 47.6% of enrollees within the 6-mo window that would likely be expected in a traditional trial. Discussion. In this trial, patient characteristics and outcomes were collected from existing EHR systems, but, as usual for EHRs, they could not be standardized for date or method of measurement and required substantial time and expertise to collect and curate. Hospital admissions, the primary trial outcome, required additional effort to locate and use supplementary sources of data. Highlights Electronic health records are not a single system but a series of overlapping and legacy systems that require time and expertise to use efficiently. Commonly measured patient characteristics such as weight and body mass index are relatively easy to locate for most trial enrollees but less common characteristics, like ejection fraction, are not. Acquiring essential supplementary data—in this trial, state data on hospital admission—can be a lengthy and difficult process.

2012 ◽  
Vol 42 (4) ◽  
pp. 342-347 ◽  
Author(s):  
Beverly B. Green ◽  
Melissa L. Anderson ◽  
Andrea J. Cook ◽  
Sheryl Catz ◽  
Paul A. Fishman ◽  
...  

2018 ◽  
Author(s):  
Azraa Amroze ◽  
Terry S Field ◽  
Hassan Fouayzi ◽  
Devi Sundaresan ◽  
Laura Burns ◽  
...  

BACKGROUND Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. OBJECTIVE This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. METHODS We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. RESULTS We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. CONCLUSIONS EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study.


2016 ◽  
Vol 39 (9) ◽  
pp. 1271-1288 ◽  
Author(s):  
Jennifer B. Seaman ◽  
Anna C. Evans ◽  
Andrea M. Sciulli ◽  
Amber E. Barnato ◽  
Susan M. Sereika ◽  
...  

The electronic health record is a potentially rich source of data for clinical research in the intensive care unit setting. We describe the iterative, multi-step process used to develop and test a data abstraction tool, used for collection of nursing care quality indicators from the electronic health record, for a pragmatic trial. We computed Cohen’s kappa coefficient (κ) to assess interrater agreement or reliability of data abstracted using preliminary and finalized tools. In assessing the reliability of study data ( n = 1,440 cases) using the finalized tool, 108 randomly selected cases (10% of first half sample; 5% of last half sample) were independently abstracted by a second rater. We demonstrated mean κ values ranging from 0.61 to 0.99 for all indicators. Nursing care quality data can be accurately and reliably abstracted from the electronic health records of intensive care unit patients using a well-developed data collection tool and detailed training.


2019 ◽  
Vol 26 (5) ◽  
pp. 1156-1163
Author(s):  
Danielle S Chun ◽  
Aimee Faso ◽  
Hyman B Muss ◽  
Hanna K Sanoff ◽  
John Valgus ◽  
...  

Background Pharmacist-led medication reconciliation (PMR) ensures adequate recording and use of medications by patients. PMR may be important for cancer patients initiating new therapies, as they have a high burden of medication use and are more susceptible to inadvertent medication discrepancies. To describe medication changes (additions, discontinuations, and modifications) made to the electronic health record during a PMR among cancer patients initiating chemotherapy. Methods From October 2011 to March 2012, 397 cancer patients initiating chemotherapy underwent a PMR at the University of North Carolina Cancer Hospital. Self-reported medications and those in the patients’ electronic health record were reviewed. Log-binomial regression models were used to estimate adjusted prevalence ratios and 95% confidence intervals for the associations between patient characteristics and medication changes made to the electronic health record. Results Mean age at time of the PMR was 58. Median number of medications taken prior to the PMR was 10 and median time to PMR completion was 11 min. Vitamins and herbal supplements accounted for the largest proportion of medication additions (38%) and modifications (20%). Antimicrobials accounted for the largest share of discontinuations (15%). After adjustment for all other covariates, patients aged 60–69 years were more likely to have additions than those aged 50 and under (aPR = 1.47, 95%CI: 1.10–1.97). Patients 70 years and over were more likely to have modifications (aPR = 1.74, 95%CI: 1.07–2.82). Conclusion Our results show that most cancer patients had a medication change in the electronic health record. A brief oncology PMR can accurately capture and improve medication safety by preventing prescribing and administration errors.


Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Dan Riskin ◽  
Keri L Monda ◽  
Ricardo Dent ◽  
A. Reshad Garan

Introduction: Real world evidence (RWE) is increasingly used for regulatory and market access decision-making. In heart failure (HF), typical structured datasets have limitations in data accuracy and identifying relevant patient characteristics. Understanding which characteristics require enhancement from unstructured data and how to validly apply extraction methods will improve the definition of complex patient cohorts. Hypothesis: Augmenting structured with unstructured electronic health record (EHR) data may overcome challenges in accurately identifying relevant HF patient characteristics. Methods: Using EHR data from 4,288 primary care encounters, 20 clinical concepts were defined a priori by 3 HF experts. A reference standard was generated through chart abstraction, with each record reviewed by at least two annotators. Inter-rater reliability (IRR) was measured by Cohen’s kappa. EHR structured data (EHR-S) extracted with traditional query techniques and EHR unstructured (EHR-U) data extracted with artificial intelligence (AI) technologies were tested for accuracy against the reference standard. Results: In EHR-S, recall ranged from 0-95.1% and precision from 52.9-100%. In EHR-U data processed using AI, recall ranged from 80.4-99.7% and precision from 82.3-100%. Results demonstrated a 45.1% absolute difference and 98.1% relative increase in F1-score (Table). Reference standard IRR was 95.3%. Conclusions: RWE credibility and applicability relies on accurate identification of a patient cohort. This study suggests that readily available data sources may not accurately identify patient phenotypes in HF. Novel means of using AI with EHR-U may improve such efforts, particularly for conditions and symptoms. This approach offers a pathway for defining highly accurate HF cohorts that may be useful in studies with narrowly defined or complex phenotypes, such as those where inclusion and exclusion criteria are specific and outcomes require validity.


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