Psychometric properties of the Treatment Effectiveness Assessment in methamphetamine use disorder.

INTRODUCTION: The ability for people living with stimulant use disorder to live meaningful lives requires not only abstinence from addictive substances, but also healthy engagement with their community, lifestyle practices, and overall health. The Treatment Effectiveness Assessment (TEA) assesses components of recovery consisting of four functional domains: substance use, health, lifestyle, and community. This secondary data analysis of 403 participants with severe methamphetamine use disorder tested the reliability and validity of the TEA. METHODS: Participants were enrolled in the Accelerated Development of Additive Pharmacotherapy Treatment (ADAPT-2) for methamphetamine use disorder. The study used total TEA and domain scores at baseline to assess factor structure and internal consistency, as well as construct validity related to substance cravings (visual analog scale [VAS]), quality of life (quality-of-life assessment [QoL]), mental health (Patient Health Questionnaire-9 [PHQ-9], Concise Health Risk Tracking Scale Self-Report [CHRT-SR16]), and social support (CHRT-SR16). RESULTS: Individual TEA items showed moderate to large correlations with each other (r = 0.27-0.51; p < .001), and strong correlations to the total score (r = 0.69-0.78; p < .001). Internal consistency was strong (coefficient α = 0.73 [0.68-0.77]; coefficient ω = 0.73 [0.69-0.78]). Construct validity was acceptable, with the strongest correlation between the TEA Health item and the general health status item on the QoL (r = 0.53, p < .001). CONCLUSIONS: TEA has acceptable levels of reliability and validity supporting prior similar findings in a sample of participants with moderate to severe methamphetamine use disorder. Results from this study provide support for its use in assessing clinically meaningful changes beyond simply reduced substance use.

A Data-Driven Assessment of the U.S. Health Informatics Programs and Job Market.

BACKGROUND: Health Informatics (HI) is an interdisciplinary field, integrating health sciences, computer science, information science, and cognitive science to assist health information management, analysis, and utilization. As the HI field is broad, it is impossible that a student will be able to master all the diverse HI topics. Thus, it is important to train the HI students based on the offering of the various HI programs and needs of the current market. This project will study the U.S. HI programs, training materials, HI job market, the skillset required by the employers, competencies taught in HI programs, and comparisons between them. METHODS: We collected the training information for the 238 U.S. universities that offered MS, PhD, or postbaccalaureate certificate programs in HI or related professions. Next, we explored the HI job market by randomly checking 200 jobs and their required skillsets and domain knowledge. Then, we compared these skillsets with those offered by the HI programs and identified the gaps and overlaps for program enhancements. RESULTS: Among the 238 U.S. universities, 94 universities offer HI programs: 92 universities with MS (Master of Science), 43 with doctoral, 42 with both MS and doctoral, and 54 with certificate programs. The most offered HI courses are related to practicum, data analytics, research, and ethics. For the HI job postings, the three most technical skillsets required in HI job posting are data analysis, database management, and knowledge of electronic health records. However, only 58% of HI programs offer courses in database management and analytics. Compared with American Medical Informatics Association's recommended 10 fundamental domains, the HI curriculum generally lacks training in socio-technical systems, social-behavioral aspects of health, and interprofessional collaborative practice. CONCLUSION: There are gaps between the industry expectations of HI and the training received in HI programs. Advance level technical courses are needed in HI programs to meet industry expectations.

Enhancing an AI-Empowered Periodontal CDSS and Comparing with Traditional Perio-risk Assessment Tools.

Periodontal disease (PD) is one of the most prevalent dental diseases. Fortunately, it can be prevented if identified early, especially for high-risk patients. Dental electronic health records (EHRs) could help develop a data-driven personalized prediction model using advanced machine learning development of clinical decision support system (CDSS) as in our Phase I, II AMIA-AI showcase. In phase II, we created a CDSS, the Perio-Risk Scoring system (PRSS), to help clinicians generate perio-scores and diagnoses and identify the influential factors. In Phase III (this study), we implemented and compared the patient's risk factors information in five periodontal risk assessment tools [periodontal risk assessment (PRA), PreViser, Sonicare, Cigna, and Periodontal Risk Scoring System (PRSS)]. We examined 1) agreement between the risk scores provided by each of the five risk assessment tools of 20 patients' information and 2) compare the risk scores provided by each tool to the original outcomes (five years outcomes). Fleiss Kappa, Cohen's Kappa, and percentage agreements were performed to determine the agreements between risk scores and original outcomes. We found a -1.24 Kappa value which indicates disagreement between the risk scores provided by five risk assessment tools. Compared to the original outcomes (five-year disease outcomes), PRSS provided the most accurate prediction (70%), followed by Previser (55%), PRA (35%), Phillips (35%), and Cigna (25%). We conclude that using advanced state-of-the-art informatics methods could help us utilize EHR data optimally to represent the current patient populations and their risk factors to provide the most accurate disease risk score. This may promote preventive strategies at the chairside, hoping to reduce PD prevalence, improve quality of life, and reduce healthcare costs.