Extracting Medical Information from Paper COVID-19 Assessment Forms.

OBJECTIVE: This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center. METHODS: An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to validate were calculated. A subset of forms was validated by multiple participants to calculate agreement between participants. RESULTS: The OMR/OCR tools correctly extracted data from scanned forms fields with an average accuracy of 70% and median accuracy of 78% when the OMR/OCR results were compared with the results from crowd validation. Scanned forms were crowd-validated at a mean rate of 157 seconds per document and a volume of approximately 108 documents per day. A randomly selected subset of documents was reviewed by multiple participants, producing an interobserver agreement of 97% for documents when narrative-text fields were included and 98% when only Boolean and multiple-choice fields were considered. CONCLUSION: Due to the COVID-19 pandemic, it may be challenging for health care workers wearing personal protective equipment to interact with electronic health records. The combination of OMR/OCR technology, a novel UI, and crowdsourcing data-validation processes allowed for the efficient extraction of a large volume of paper medical documents produced during the COVID-19 pandemic.

Visualization of aggregate perioperative data improves anesthesia case planning: A randomized, cross-over trial.

STUDY OBJECTIVE: A challenge in reducing unwanted care variation is effectively managing the wide variety of performed surgical procedures. While an organization may perform thousands of types of cases, privacy and logistical constraints prevent review of previous cases to learn about prior practices. To bridge this gap, we developed a system for extracting key data from anesthesia records. Our objective was to determine whether usage of the system would improve case planning performance for anesthesia residents. DESIGN: Randomized, cross-over trial. SETTING: Vanderbilt University Medical Center. MEASUREMENTS: We developed a web-based, data visualization tool for reviewing de-identified anesthesia records. First year anesthesia residents were recruited and performed simulated case planning tasks (e.g., selecting an anesthetic type) across six case scenarios using a randomized, cross-over design after a baseline assessment. An algorithm scored case planning performance based on care components selected by residents occurring frequently among prior anesthetics, which was scored on a 0-4 point scale. Linear mixed effects regression quantified the tool effect on the average performance score, adjusting for potential confounders. MAIN RESULTS: We analyzed 516 survey questionnaires from 19 residents. The mean performance score was 2.55 ± SD 0.32. Utilization of the tool was associated with an average score improvement of 0.120 points (95% CI 0.060 to 0.179; p < 0.001). Additionally, a 0.055 point improvement due to the "learning effect" was observed from each assessment to the next (95% CI 0.034 to 0.077; p < 0.001). Assessment score was also significantly associated with specific case scenarios (p < 0.001). CONCLUSIONS: This study demonstrated the feasibility of developing of a clinical data visualization system that aggregated key anesthetic information and found that the usage of tools modestly improved residents' performance in simulated case planning.

CYP2C19 Phenotype and Risk of Proton Pump Inhibitor-Associated Infections.

OBJECTIVES: Proton pump inhibitors (PPIs) are often used in pediatrics to treat common gastrointestinal disorders, and there are growing concerns for infectious adverse events. Because CYP2C19 inactivates PPIs, genetic variants that increase CYP2C19 function may decrease PPI exposure and infections. We tested the hypothesis that CYP2C19 metabolizer phenotypes are associated with infection event rates in children exposed to PPIs. METHODS: This retrospective biorepository cohort study included individuals aged 0 to 36 months at the time of PPI exposure. Respiratory tract and gastrointestinal tract infection events were identified by using International Classification of Diseases codes in the year after the first PPI mention. Variants defining CYP2C19 *2, *3, *4, *8, *9, and *17 were genotyped, and all individuals were classified as CYP2C19 poor or intermediate, normal metabolizers (NMs), or rapid or ultrarapid metabolizers (RM/UMs). Infection rates were compared by using univariate and multivariate analyses. RESULTS: In all, 670 individuals were included (median age 7 months; 44% girls). CYP2C19 NMs (n = 267; 40%) had a higher infection rate than RM/UMs (n = 220; 33%; median 2 vs 1 infections per person per year; P = .03). There was no difference between poor or intermediate (n = 183; 27%) and NMs. In multivariable analysis of NMs and RM/UMs adjusting for age, sex, PPI dose, and comorbidities, CYP2C19 metabolizer status remained a significant risk factor for infection events (odds ratio 0.70 [95% confidence interval 0.50-0.97] for RM/UMs versus NMs). CONCLUSIONS: PPI therapy is associated with higher infection rates in children with normal CYP2C19 function than in those with increased CYP2C19 function, highlighting this adverse effect of PPI therapy and the relevance of CYP2C19 genotypes to PPI therapeutic decision-making.

Crowdsourcing to delineate skin affected by chronic graft-vs-host disease.

BACKGROUND: Estimating the extent of affected skin is an important unmet clinical need both for research and practical management in many diseases. In particular, cutaneous burden of chronic graft-vs-host disease (cGVHD) is a primary outcome in many trials. Despite advances in artificial intelligence and 3D photography, progress toward reliable automated techniques is hindered by limited expert time to delineate cGVHD patient images. Crowdsourcing may have potential to provide the requisite expert-level data. MATERIALS AND METHODS: Forty-one three-dimensional photographs of three cutaneous cGVHD patients were delineated by a board-certified dermatologist. 410 two-dimensional projections of the raw photos were each annotated by seven crowd workers, whose consensus performance was compared to the expert. RESULTS: The consensus delineation by four of seven crowd workers achieved the highest agreement with the expert, measured by a median Dice index of 0.7551 across all 410 images, outperforming even the best worker from the crowd (Dice index 0.7216). For their internal agreement, crowd workers achieved a median Fleiss's kappa of 0.4140 across the images. The time a worker spent marking an image had only weak correlation with the surface area marked, and very low correlation with accuracy. Percent of pixels selected by the consensus exhibited good correlation (Pearson R = 0.81) with the patient's affected surface area. CONCLUSION: Crowdsourcing may be an efficient method for obtaining demarcations of affected skin, on par with expert performance. Crowdsourced data generally agreed with the current clinical standard of percent body surface area to assess cGVHD severity in the skin.

Feasibility Assessment of a Pre-Hospital Automated Sensing Clinical Documentation System.

Clinical documentation in the pre-hospital setting is challenged by limited resources and fast-paced, high-acuity. Military and civilian medics are responsible for performing procedures and treatments to stabilize the patient, while transporting the injured to a trauma facility. Upon arrival, medics typically give a verbal report from memory or informal source of documentation such as a glove or piece of tape. The development of an automated documentation system would increase the accuracy and amount of information that is relayed to the receiving physicians. This paper discusses the 12-week deployment of an Automated Sensing Clinical Documentation (ASCD) system among the Nashville Fire Department EMS paramedics. The paper examines the data collection methods, operational challenges, and perceptions surrounding real-life deployment of the system. Our preliminary results suggest that the ASCD system is feasible for use in the pre-hospital setting, and it revealed several barriers and their solutions.