Key Points Question Can an automated patient dashboard and nudges to physicians increase guideline-appropriate statin prescription rates among patients who were not previously receiving statin therapy? Findings In this cluster randomized clinical trial of 96 primary care physicians from 32 practice sites including 4774 patients, an automated patient dashboard using active choice framing and peer comparison feedback led to a modest but significant increase in guideline-appropriate statin prescribing rates compared with usual care. Meaning Nudges to physicians offer an effective, low-cost, and scalable approach to increase use of automated patient dashboards to improve guideline-concordant prescribing behaviors, but these approaches may need to be designed to better fit within clinician workflow or be combined with other approaches to further increase their impact.

Abstract Objective We developed and evaluated a privacy-preserving One-shot Distributed Algorithm to fit a multicenter Cox proportional hazards model (ODAC) without sharing patient-level information across sites. Materials and Methods Using patient-level data from a single site combined with only aggregated information from other sites, we constructed a surrogate likelihood function, approximating the Cox partial likelihood function obtained using patient-level data from all sites. By maximizing the surrogate likelihood function, each site obtained a local estimate of the model parameter, and the ODAC estimator was constructed as a weighted average of all the local estimates. We evaluated the performance of ODAC with (1) a simulation study and (2) a real-world use case study using 4 datasets from the Observational Health Data Sciences and Informatics network. Results On the one hand, our simulation study showed that ODAC provided estimates nearly the same as the estimator obtained by analyzing, in a single dataset, the combined patient-level data from all sites (ie, the pooled estimator). The relative bias was <0.1% across all scenarios. The accuracy of ODAC remained high across different sample sizes and event rates. On the other hand, the meta-analysis estimator, which was obtained by the inverse variance weighted average of the site-specific estimates, had substantial bias when the event rate is <5%, with the relative bias reaching 20% when the event rate is 1%. In the Observational Health Data Sciences and Informatics network application, the ODAC estimates have a relative bias <5% for 15 out of 16 log hazard ratios, whereas the meta-analysis estimates had substantially higher bias than ODAC. Conclusions ODAC is a privacy-preserving and noniterative method for implementing time-to-event analyses across multiple sites. It provides estimates on par with the pooled estimator and substantially outperforms the meta-analysis estimator when the event is uncommon, making it extremely suitable for studying rare events and diseases in a distributed manner.

Many health care professionals find it irritating when management gurus recommend solving health care's problems with approaches they would "copy and paste" from unrelated industries -a former chief executive of a manufacturing company claims that the same simple lessons that enabled him to transform his own industry can improve value in health care, or a business-school professor offers an eight-point leadership plan that she's translated into health care as easily as if she'd translated it into French. Many people who work in health care value outside perspectives and are open to new approaches -and yet bristle at facile recommendations emerging from these translations.

BackgroundPatient portals are frequently used in modern health care systems as an engagement and communication tool. An increased focus on the potential value of these communication channels to improve health outcomes is warranted.ObjectiveThis paper aimed to quantify the impact of portal use on patients’ preventive health behavior and chronic health outcomes.MethodsWe conducted a retrospective, observational cohort study of 10,000 patients aged 50 years or older who were treated at the University of Pennsylvania Health System (UPHS) from September 1, 2014, to October 31, 2016. The data were sourced from the UPHS electronic health records. We investigated the association between patient portal use and patients’ preventive health behaviors or chronic health outcomes, controlling for confounders using a novel cardinality matching approach based on propensity scoring and a subsequent bootstrapping method to estimate the variance of association estimates.ResultsPatient-level characteristics differed substantially between portal users, comprising approximately 59.32% (5932/10000) of the cohort, and nonusers. On average, users were more likely to be younger (63.46 years for users vs 66.08 years for nonusers), white (72.77% [4317/5932] for users vs 52.58% [2139/4068] for nonusers), have commercial insurance (60.99% [3618/5932] for users vs 40.12% [1632/4068] for nonusers), and have higher annual incomes (US $74,172/year for users vs US $62,940/year for nonusers). Even after adjusting for these potential confounders, patient portal use had a positive and clinically meaningful impact on patients’ preventive health behaviors but not on chronic health outcomes.ConclusionsThis paper contributes to the understanding of the impact of patient portal use on health outcomes and is the first study to identify a meaningful subgroup of patients’ health behaviors that improved with portal use. These findings may encourage providers to promote portal use to improve patients’ preventive health behaviors.