BACKGROUND: Tobacco kills more than 8 million people each year, mostly in low- and middle-income countries. In Vietnam, 1 in every 2 male adults smokes tobacco. Vietnam has set up telephone Quitline counseling that is available to all smokers, but it is underused. We previously developed an automated and effective motivational text messaging system to support smoking cessation among US smokers. OBJECTIVE: The aim of this study is to adapt the aforementioned system for rural Vietnamese smokers to promote cessation of tobacco use, both directly and by increasing the use of telephone Quitline counseling services and nicotine replacement therapy. Moreover, we seek to enhance research and health service capacity in Vietnam. METHODS: We are testing the effectiveness of our culturally adapted motivational text messaging system by using a community-based randomized controlled trial design (N=600). Participants were randomly allocated to the intervention (regular motivational and assessment text messages) or control condition (assessment text messages only) for a period of 6 months. Trial recruitment took place in four communes in the Hung Yen province in the Red River Delta region of Vietnam. Recruitment events were advertised to the local community, facilitated by community health workers, and occurred in the commune health center. We are assessing the impact of the texting system on 6-month self-reported and biochemically verified smoking cessation, as well as smoking self-efficacy, uptake of the Quitline, and use of nicotine replacement therapy. In addition to conducting the trial, the research team also provided ongoing training and consultation with the Quitline during the study period. RESULTS: Site preparation, staff training, intervention adaptation, participant recruitment, and baseline data collection were completed. The study was funded in August 2017; it was reviewed and approved by the University of Massachusetts Medical School Institutional Review Board in 2017. Recruitment began in November 2018. A total of 750 participants were recruited from four communes, and 700 (93.3%) participants completed follow-up by March 2021. An analysis of the trial results is in progress; results are expected to be published in late 2022. CONCLUSIONS: This study examines the effectiveness of mobile health interventions for smoking in rural areas in low- and middle-income countries, which can be implemented nationwide if proven effective. In addition, it also facilitates significant collaboration and capacity building among a variety of international partners, including researchers, policy makers, Quitline counselors, and community health workers. TRIAL REGISTRATION: ClinicalTrials.gov NCT03567993; https://clinicaltrials.gov/ct2/show/NCT03567993. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/30947.

Objective: Early identification of individuals who are at risk for suicide is crucial in supporting suicide prevention. Machine learning is emerging as a promising approach to support this objective. Machine learning is broadly defined as a set of mathematical models and computational algorithms designed to automatically learn complex patterns between predictors and outcomes from example data, without being explicitly programmed to do so. The model's performance continuously improves over time by learning from newly available data. Method: This concept paper explores how machine learning approaches applied to healthcare data obtained from electronic health records, including billing and claims data, can advance our ability to accurately predict future suicidal behavior. Results: We provide a general overview of machine learning concepts, summarize exemplar studies, describe continued challenges, and propose innovative research directions. Conclusion: Machine learning has potential for improving estimation of suicide risk, yet important challenges and opportunities remain. Further research can focus on incorporating evolving methods for addressing data imbalances, understanding factors that affect generalizability across samples and healthcare systems, expanding the richness of the data, leveraging newer machine learning approaches, and developing automatic learning systems.

BACKGROUND: Patient satisfaction is a focus for emergency department (ED) and hospital administrators. ED patient satisfaction studies have tended to be single site and focused on patient and clinician factors. Inclusion of satisfaction scores in a large, national operations database provided an opportunity to conduct an investigation that included diverse operational factors. METHODS: We performed a retrospective analysis of the 2019 Academy of Administrators in Academic Emergency Medicine/Association of Academic Chairs of Emergency Medicine (AAAEM/AACEM) benchmarking survey to identify associations between operational factors and patient satisfaction. We identified 59 database variables as potential predictors of Press Ganey likelihood-to-recommend and physician overall scores. Using random forest modeling, we identified the top eight predictors in the models and described their associations. RESULTS: Forty-three (57.3%) academic departments responding to the AAAEM/AACEM survey reported patient satisfaction scores for 78 EDs. Likelihood to recommend ranged from 30.0 to 93.0 (median = 74.8) and was associated with ED length of stay, boarding, use of hallway spaces, hospital annual admissions, faculty base clinical hours, proportion of patients leaving before treatment complete (LBTC), and provider in triage hours per day. Physician overall score ranged from 53.3 to 93.4 (median = 81.9) and was associated with faculty base clinical hours, x-ray utilization, annual ED arrivals, LBTC, use of hallway spaces, arrivals per attending hour, and CT utilization. CONCLUSIONS: ED patient satisfaction was associated with intrinsic and extrinsic factors, some being potentially manageable within the ED but others being relatively fixed or outside the control of ED operations. For likelihood to recommend, patient flow was dominant, with erosion of satisfaction observed with increased boarding and longer LOS. Factors associated with physician overall score were more varied. The use of hallway spaces and base clinical hours greater than 1,500 per year were associated with both lower likelihood-to-recommend and lower physician overall scores.

BACKGROUND: Validated secondary screeners are needed to stratify suicide risk among those with nonnegligible risk. This study tested the predictive utility of the Emergency Department Safety Assessment and Follow-up Evaluation (ED-SAFE) Secondary Screener (ESS), one of the screeners listed by The Joint Commission's Patient Safety Goal 15 resources as a potential secondary screener for acute care settings. METHODS: The researchers performed secondary analyses of data collected for the ED-SAFE study. Data were collected during an emergency department (ED) visit for 1,376 patients who endorsed active suicide ideation or a suicide attempt in the past week. Participants were followed for 12 months using telephone-based assessments, review of health care records, and National Death Index query. The study examined the predictive validity of the individual items, total score, and a scoring algorithm using the total score and critical items. Bivariable analyses, multivariable logistic regression, and test operating characteristics were calculated. RESULTS: Of the 1,376 patients enrolled, most were positive for at least one indicator. Four of the indicators were significantly associated with several outcomes. Based on score and critical items, the patients were trichotomized: The three strata were associated with significantly different rates of prospective suicidal behavior, with 52% of the high-risk group engaging in suicidal behavior within 12 months. CONCLUSION: The ESS possesses adequate operating characteristics for triage purposes. The researchers recommend validation in new samples to confirm its operating characteristics and potentially reduce its length by removing the substance and agitation items, which offered little predictive utility in this study. reserved.