Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

External change agents can play an essential role in healthcare organizational change efforts. This systematic review examines the role that external change agents have played within the context of multifaceted interventions designed to promote organizational change in healthcare—specifically, in primary care settings. We searched PubMed, CINAHL, Cochrane, Web of Science, and Academic Search Premier Databases in July 2016 for randomized trials published (in English) between January 1, 2005 and June 30, 2016 in which external agents were part of multifaceted organizational change strategies. The review was conducted according to PRISMA guidelines. A total of 477 abstracts were identified and screened by 2 authors. Full text articles of 113 studies were reviewed. Twenty-one of these studies were selected for inclusion. Academic detailing (AD) is the most prevalently used organizational change strategy employed as part of multi-component implementation strategies. Out of 21 studies, nearly all studies integrate some form of audit and feedback into their interventions. Eleven studies that included practice facilitation into their intervention reported significant effects in one or more primary outcomes. Our results demonstrate that practice facilitation with regular, tailored follow up is a powerful component of a successful organizational change strategy. Academic detailing alone or combined with audit and feedback alone is ineffective without intensive follow up. Provision of educational materials and use of audit and feedback are often integral components of multifaceted implementation strategies. However, we didn’t find examples where those relatively limited strategies were effective as standalone interventions. System-level support through technology (such as automated reminders or alerts) is potentially helpful, but must be carefully tailored to clinic needs.

Purpose. This study reports the prevalence and types of sexual dysfunction in a sample of men on methadone maintenance for opioid dependence, and describes factors which may contribute to sexual dy...

Background: The United States spends more than US $100 billion annually on the impact of medication misuse. Serious games are effective and innovative digital tools for educating patients about positive health behaviors. There are limited systematic reviews that examine the prevalence of serious games that incorporate medication use. Objective: This systematic review aimed to identify (1) serious games intended to educate patients about medication adherence, education, and safety; (2) types of theoretical frameworks used to develop serious games for medication use; and (3) sampling frames for evaluating serious games on medication use. Methods: PubMed, Scopus, and Web of Science databases were searched for literature about medication-based serious games for patients. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed for article selection. Results: Using PRISMA guidelines, 953 publications and 749 unique titles were identified from PubMed, Scopus, and Web of Science. A total of 16 studies featuring 12 unique serious games were included with components of medication adherence, education, and safety, published from 2003 to 2019. Of the 12 games included, eight serious games were tested in adolescents, three games were tested in young adults, and one game was tested in adults. Most studies (n=11) used small sample sizes to test the usability of serious games. Theoretical frameworks identified in the 12 serious games included information, motivation, and behavior theory; social cognitive theory; precede-proceed model; middle-range theory of chronic illness; adult learning theory; experiential learning theory; and the theory of reasoned action. Existing reviews explore serious games focused on the management of specific disease states, such as HIV, diabetes, and asthma, and on the positive impact of serious game education in each respective disease state. Although other reviews target broad topics such as health care gamification and serious games to educate health care workers, no reviews focus solely on medication use. Serious games were mainly focused on improving adherence, whereas medication safety was not widely explored. Little is known about the efficacy and usability of medication-focused serious games often because of small and nonrepresentative sample sizes, which limit the generalizability of existing studies. Conclusions: Limited studies exist on serious games for health that incorporate medication use. The findings from these studies focus on developing and testing serious games that teach patients about medication use and safety. Many of these studies do not apply a theoretical framework in the design and assessment of these games. In the future, serious game effectiveness could be improved by increasing study sample size and diversity of study participants, so that the results are generalizable to broader populations. Serious games should describe the extent of theoretical framework incorporated into game design and evaluate success by testing the player’s retention of learning objectives.

Background: The clinical narrative in electronic health records (EHRs) carries valuable information for predictive analytics; however, its free-text form is difficult to mine and analyze for clinical decision support (CDS). Large-scale clinical natural language processing (NLP) pipelines have focused on data warehouse applications for retrospective research efforts. There remains a paucity of evidence for implementing NLP pipelines at the bedside for health care delivery. Objective: We aimed to detail a hospital-wide, operational pipeline to implement a real-time NLP-driven CDS tool and describe a protocol for an implementation framework with a user-centered design of the CDS tool. Methods: The pipeline integrated a previously trained open-source convolutional neural network model for screening opioid misuse that leveraged EHR notes mapped to standardized medical vocabularies in the Unified Medical Language System. A sample of 100 adult encounters were reviewed by a physician informaticist for silent testing of the deep learning algorithm before deployment. An end user interview survey was developed to examine the user acceptability of a best practice alert (BPA) to provide the screening results with recommendations. The planned implementation also included a human-centered design with user feedback on the BPA, an implementation framework with cost-effectiveness, and a noninferiority patient outcome analysis plan. Results: The pipeline was a reproducible workflow with a shared pseudocode for a cloud service to ingest, process, and store clinical notes as Health Level 7 messages from a major EHR vendor in an elastic cloud computing environment. Feature engineering of the notes used an open-source NLP engine, and the features were fed into the deep learning algorithm, with the results returned as a BPA in the EHR. On-site silent testing of the deep learning algorithm demonstrated a sensitivity of 93% (95% CI 66%-99%) and specificity of 92% (95% CI 84%-96%), similar to published validation studies. Before deployment, approvals were received across hospital committees for inpatient operations. Five interviews were conducted; they informed the development of an educational flyer and further modified the BPA to exclude certain patients and allow the refusal of recommendations. The longest delay in pipeline development was because of cybersecurity approvals, especially because of the exchange of protected health information between the Microsoft (Microsoft Corp) and Epic (Epic Systems Corp) cloud vendors. In silent testing, the resultant pipeline provided a BPA to the bedside within minutes of a provider entering a note in the EHR. Conclusions: The components of the real-time NLP pipeline were detailed with open-source tools and pseudocode for other health systems to benchmark. The deployment of medical artificial intelligence systems in routine clinical care presents an important yet unfulfilled opportunity, and our protocol aimed to close the gap in the implementation of artificial intelligence–driven CDS. Trial Registration: ClinicalTrials.gov NCT05745480; https://www.clinicaltrials.gov/ct2/show/NCT05745480