Matching Patients to Accelerate Clinical Trials (MPACT): Enabling Technology for Oncology Clinical Trial Workflow.

Clinical trial enrollment is impeded by the significant time burden placed on research coordinators screening eligible patients. With 50,000 new cancer cases every year, the Veterans Health Administration (VHA) has made increased access for Veterans to high-quality clinical trials a priority. To aid in this effort, we worked with research coordinators to build the MPACT (Matching Patients to Accelerate Clinical Trials) platform with a goal of improving efficiency in the screening process. MPACT supports both a trial prescreening workflow and a screening workflow, employing Natural Language Processing and Data Science methods to produce reliable phenotypes of trial eligibility criteria. MPACT also has a functionality to track a patient's eligibility status over time. Qualitative feedback has been promising with users reporting a reduction in time spent on identifying eligible patients.

An application to support COVID-19 occupational health and patient tracking at a Veterans Affairs medical center.

OBJECTIVE: Reducing risk of coronavirus disease 2019 (COVID-19) infection among healthcare personnel requires a robust occupational health response involving multiple disciplines. We describe a flexible informatics solution to enable such coordination, and we make it available as open-source software. MATERIALS AND METHODS: We developed a stand-alone application that integrates data from several sources, including electronic health record data and data captured outside the electronic health record. RESULTS: The application facilitates workflows from different hospital departments, including Occupational Health and Infection Control, and has been used extensively. As of June 2020, 4629 employees and 7768 patients and have been added for tracking by the application, and the application has been accessed over 46 000 times. DISCUSSION: Data captured by the application provides both a historical and real-time view into the operational impact of COVID-19 within the hospital, enabling aggregate and patient-level reporting to support identification of new cases, contact tracing, outbreak investigations, and employee workforce management. CONCLUSIONS: We have developed an open-source application that facilitates communication and workflow across multiple disciplines to manage hospital employees impacted by the COVID-19 pandemic.

Hypersphere clustering to characterize healthcare providers using prescriptions and procedures from Medicare claims data.

We consider the task of producing a useful clustering of healthcare providers from their clinical action signature- their drug, procedure, and billing codes. Because high-dimensional sparse count vectors are challenging to cluster, we develop a novel autoencoder framework to address this task. Our solution creates a low-dimensional embedded representation of the high-dimensional space that preserves angular relationships and assigns examples to clusters while optimizing the quality of this clustering. Our method is able to find a better clustering than under a two-step alternative, e.g., projected K means/medoids, where a representation is learned and then clustering is applied to the representation. We demonstrate our method's characteristics through quantitative and qualitative analysis of real and simulated data, including in several real-world healthcare case studies. Finally, we develop a tool to enhance exploratory analysis of providers based on their clinical behaviors.

Case misclassification in studies of spinal manipulation and arterial dissection.

BACKGROUND: Spinal manipulation has been associated with cervical arterial dissection and stroke but a causal relationship has been questioned by population-based studies. Earlier studies identified cases using International Classification of Diseases Ninth Revision (ICD-9) codes specific to anatomic stroke location rather than stroke etiology. We hypothesize that case misclassification occurred in these previous studies and an underestimation of the strength of the association. We also predicted that case misclassification would differ by patient age. METHODS: We identified cases in the Veterans Health Administration database using the same strategy as the prior studies. The electronic medical record was then screened for the word "dissection." The presence of atraumatic dissection was determined by medical record review by a neurologist. RESULTS: Of 3690 patients found by ICD-9 codes over a 30-month period, 414 (11.2%) had confirmed cervical artery dissection with a positive predictive value of 10.5% (95% confidence interval [CI] 9.6%-11.5%). The positive predictive value was higher in patients less than 45 years of age vs 45 years of age or older (41% vs 9%, P < .001). We reanalyzed a previous study, which reported no association between spinal manipulation and cervical artery dissection (odds ratio [OR] = 1.12, 95% CI .77-1.63) and recalculated an odds ratio of 2.15 (95% CI .98-4.69). For patients less than 45 years of age, the OR was 6.91 (95% CI 2.59-13.74). CONCLUSIONS: Prior studies grossly misclassified cases of cervical dissection and mistakenly dismissed a causal association with manipulation. Our study indicates that the OR for spinal manipulation exposure in cervical artery dissection is higher than previously reported.