Determinants of outpatient substance use disorder treatment length-of-stay and completion: the case of a treatment program in the southeast U.S.

Successful outcomes of outpatient substance use disorder treatment result from many factors for clients-including intersections between individual characteristics, choices made, and social determinants. However, prioritizing which of these and in what combination, to address and provide support for remains an open and complex question. Therefore, we ask: What factors are associated with outpatient substance use disorder clients remaining in treatment for > 90 days and successfully completing treatment? To answer this question, we apply a virtual twins machine learning (ML) model to de-identified data for a census of clients who received outpatient substance use disorder treatment services from 2018 to 2021 from one treatment program in the Southeast U.

Gender Effects on the Impact of Colorectal Cancer Risk Calculators on Screening Intentions: Experimental Study.

Background: According to a 2020 study by the American Cancer Society, colorectal cancer (CRC) represents the third leading cause of cancer both in incidence and death in the United States. Nonetheless, CRC screening remains lower than that for other high-risk cancers such as breast and cervical cancer. Risk calculators are increasingly being used to promote cancer awareness and improve compliance with CRC screening tests.

Use of machine learning to examine disparities in completion of substance use disorder treatment.

The objective of this work is to examine disparities in the completion of substance use disorder treatment in the U.S. Our data is from the Treatment Episode Dataset Discharge (TEDS-D) datasets from the U.

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis.

Objective: The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.

Materials And Methods: The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics.

Results: The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic).

Telehealth Adoption and Discontinuation by US Hospitals: Results From 2 Quasi-Natural Experiments.

Background: Prior US hospital telehealth (video visit) studies have focused on describing factors that influence telehealth adoption or performance effects for specific patient segments, hospital systems, or geographic regions. To our knowledge, a larger-scale, national-level (US) study has yet to be conducted on the causal impacts of hospital telehealth adoption as well as discontinuation.

Objective: The aim of this study is to understand the causal impact of US hospital telehealth adoption or discontinuation on hospital performance from 2016 to 2018.

Stakeholder bias in best practice advisories: an ethical perspective.

Clinicians are increasingly being asked to heed and follow the guidance provided by "best practice advisories." Such advisories, often in the form of electronic reminders or alerts, are meant to increase the efficiency and effectiveness of evidence-based medical practice. However, we argue that best practice advisories can sometimes be infused with stakeholder bias, even if inadvertently.

Why primary care practices should become digital health information hubs for their patients.

Background: Two interesting health care trends are currently occurring: 1) patient-facing technologies, such as personal health records, patient portals, and mobile health apps, are being adopted at rapid rates, and 2) primary care, which includes family practice, is being promoted as essential to reducing health care costs and improving health care outcomes. While these trends are notable and commendable, both remain subject to significant fragmentation and incentive misalignments, which has resulted in significant data coordination and value generation challenges. In particular, patient-facing technologies designed to increase care coordination, often fall prey to the very digital fragmentation issues they are supposed to overcome.

An economic framework for transitioning to capacity building.

Global Health Organizations (GHOs) often focus on resource provisioning strategies to assist communities in need, especially when disaster strikes. While such strategies are commendable, how should GHOs approach the challenge of developing sustainable strategic objectives after critical needs have been addressed? Leveraging the context of GHOs partnering with communities in need of support after disaster strikes, we propose an economic framework for use in strategic assessment and transition planning. We focus on a strategic process by which GHOs can systematically assess and manage the temporal shift from resource provisioning to capacity building strategies.

Understanding determinants of consumer mobile health usage intentions, assimilation, and channel preferences.

Background: Consumer use of mobile devices as health service delivery aids (mHealth) is growing, especially as smartphones become ubiquitous. However, questions remain as to how consumer traits, health perceptions, situational characteristics, and demographics may affect consumer mHealth usage intentions, assimilation, and channel preferences.

Objective: We examine how consumers' personal innovativeness toward mobile services (PIMS), perceived health conditions, health care availability, health care utilization, demographics, and socioeconomic status affect their (1) mHealth usage intentions and extent of mHealth assimilation, and (2) preference for mHealth as a complement or substitute for in-person doctor visits.

Corporate governance and the adoption of health information technology within integrated delivery systems.

Background: Although several previous studies have found "system affiliation" to be a significant and positive predictor of health information technology (IT) adoption, little is known about the association between corporate governance practices and adoption of IT within U.S. integrated delivery systems (IDSs).