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BackgroundProfessional society guidelines are emerging for cardiovascular care in cancer patients. However, it is not yet clear how effectively the cancer survivor population is screened and treated for cardiomyopathy in contemporary clinical practice. As electronic health records (EHRs) are now widely used in clinical practice, we tested the hypothesis that an EHR-based cardio-oncology registry can address these questions. ObjectiveThe aim of this study was to develop an EHR-based pragmatic cardio-oncology registry and, as proof of principle, to investigate care gaps in the cardiovascular care of cancer patients. MethodsWe generated a programmatically deidentified, real-time EHR-based cardio-oncology registry from all patients in our institutional Cancer Population Registry (N=8275, 2011-2017). We investigated: (1) left ventricular ejection fraction (LVEF) assessment before and after treatment with potentially cardiotoxic agents; and (2) guideline-directed medical therapy (GDMT) for left ventricular dysfunction (LVD), defined as LVEF

AIMS: To evaluate the performance of the WATCH-DM risk score, a clinical risk score for heart failure (HF), in patients with dysglycaemia and in combination with natriuretic peptides (NPs). METHODS AND RESULTS: Adults with diabetes/pre-diabetes free of HF at baseline from four cohort studies (ARIC, CHS, FHS, and MESA) were included. The machine learning- [WATCH-DM(ml)] and integer-based [WATCH-DM(i)] scores were used to estimate the 5-year risk of incident HF. Discrimination was assessed by Harrells concordance index (C-index) and calibration by the Greenwood-Nam-DAgostino (GND) statistic. Improvement in model performance with the addition of NP levels was assessed by C-index and continuous net reclassification improvement (NRI). Of the 8938 participants included, 3554 (39.8%) had diabetes and 432 (4.8%) developed HF within 5 years. The WATCH-DM(ml) and WATCH-DM(i) scores demonstrated high discrimination for predicting HF risk among individuals with dysglycaemia (C-indices = 0.80 and 0.71, respectively), with no evidence of miscalibration (GND P ≥0.10). The C-index of elevated NP levels alone for predicting incident HF among individuals with dysglycaemia was significantly higher among participants with low/intermediate (

ObjectiveTo develop and validate a novel, machine learning-derived model to predict the risk of heart failure (HF) among patients with type 2 diabetes mellitus (T2DM).Research design and methodsUsing data from 8,756 patients free at baseline of HF, with

Constructing multi-site specialty registries typically proves time-consuming. Electronic health record (EHR) data collected during clinical care affords a pragmatic approach to accelerating registry implementation. Heart failure with preserved ejection fraction (HFpEF) is an increasingly common and morbid condition. Building a multi-site registry for HFpEF proved feasible using EHR data coded in standard terminologies (SNOMED CT, LOINC) and shared via Health Information Exchanges.

Even the most innovative healthcare technologies provide patient benefits only when adopted by clinicians and/or patients in actual practice. Yet realizing optimal positive impact from a new technology for the widest range of individuals who would benefit remains elusive. In software and new product development, iterative rapid-cycle "agile" methods more rapidly provide value, mitigate failure risks, and adapt to customer feedback. Co-development between builders and customers is a key agile principle. But how does one accomplish co-development with busy clinicians? In this paper, we discuss four practical agile co-development practices found helpful clinically: (1) User stories for lightweight requirements; (2) Time-boxed development for collaborative design and prompt course correction; (3) Automated acceptance test driven development, with clinician-vetted specifications; and (4) Monitoring of clinician interactions after release, for rapid-cycle product adaptation and evolution. In the coming wave of innovation in healthcare apps ushered in by open APIs to EHRs, learning rapidly what new product features work well for clinicians and patients will become even more crucial.

The transformation of the American healthcare payment system from fee-for-service to value-based care increasingly makes it valuable to develop patient registries for specialized populations, to better assess healthcare quality and costs. Recent widespread adoption of Electronic Health Records (EHRs) in the U.S. now makes possible construction of EHR-based specialty registry data collection tools and reports, previously unfeasible using manual chart abstraction. But the complexities of specialty registry EHR tools and measures, along with the variety of stakeholders involved, can result in misunderstood requirements and frequent product change requests, as users first experience the tools in their actual clinical workflows. Such requirements churn could easily stall progress in specialty registry rollout. Modeling a system's requirements and solution design can be a powerful way to remove ambiguities, facilitate shared understanding, and help evolve a design to meet newly-discovered needs. "Agile Modeling" retains these values while avoiding excessive unused up-front modeling in favor of iterative incremental modeling. Using Agile Modeling principles and practices, in calendar year 2015 one institution developed 58 EHR-based specialty registries, with 111 new data collection tools, supporting 134 clinical process and outcome measures, and enrolling over 16,000 patients. The subset of UML and non-UML models found most consistently useful in designing, building, and iteratively evolving EHR-based specialty registries included User Stories, Domain Models, Use Case Diagrams, Decision Trees, Graphical User Interface Storyboards, Use Case text descriptions, and Solution Class Diagrams.