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BACKGROUND: Digital health technologies (DHTs), such as electronic health records and prescribing systems, are transforming health care delivery around the world. The quality of information in DHTs is key to the quality and safety of care. We developed a novel clinical information quality (CLIQ) framework to assess the quality of clinical information in DHTs. OBJECTIVE: This study explored clinicians' perspectives on the relevance, definition, and assessment of information quality dimensions in the CLIQ framework. METHODS: We used a systematic and iterative eDelphi approach to engage clinicians who had information governance roles or personal interest in information governance; the clinicians were recruited through purposive and snowball sampling techniques. Data were collected using semistructured online questionnaires until consensus was reached on the information quality dimensions in the CLIQ framework. Responses on the relevance of the dimensions were summarized to inform decisions on retention of the dimensions according to prespecified rules. Thematic analysis of the free-text responses was used to revise definitions and the assessment of dimensions. RESULTS: Thirty-five clinicians from 10 countries participated in the study, which was concluded after the second round. Consensus was reached on all dimensions and categories in the CLIQ framework: informativeness (accuracy, completeness, interpretability, plausibility, provenance, and relevance), availability (accessibility, portability, security, and timeliness), and usability (conformance, consistency, and maintainability). A new dimension, searchability, was introduced in the availability category to account for the ease of finding needed information in the DHTs. Certain dimensions were renamed, and some definitions were rephrased to improve clarity. CONCLUSIONS: The CLIQ framework reached a high expert consensus and clarity of language relating to the information quality dimensions. The framework can be used by health care managers and institutions as a pragmatic tool for identifying and forestalling information quality problems that could compromise patient safety and quality of care. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-057430.
Assuntos
Tecnologia Digital , HumanosRESUMO
Objective: Sleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students' sleep quality. Methods: In a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention. Results: Analysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = -0.51, 95% CI: [-1.55-0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9-59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7-61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep. Conclusions: The proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.
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BACKGROUND: Extended reality, which encompasses virtual reality (VR), augmented reality (AR), and mixed reality (MR), is increasingly used in medical education. Studies assessing the effectiveness of these new educational modalities should measure relevant outcomes using outcome measurement tools with validity evidence. OBJECTIVE: Our aim is to determine the choice of outcomes, measurement instruments, and the use of measurement instruments with validity evidence in randomized controlled trials (RCTs) on the effectiveness of VR, AR, and MR in medical student education. METHODS: We conducted a systematic mapping review. We searched 7 major bibliographic databases from January 1990 to April 2020, and 2 reviewers screened the citations and extracted data independently from the included studies. We report our findings in line with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. RESULTS: Of the 126 retrieved RCTs, 115 (91.3%) were on VR and 11 (8.7%) were on AR. No RCT on MR in medical student education was found. Of the 115 studies on VR, 64 (55.6%) were on VR simulators, 30 (26.1%) on screen-based VR, 9 (7.8%) on VR patient simulations, and 12 (10.4%) on VR serious games. Most studies reported only a single outcome and immediate postintervention assessment data. Skills outcome was the most common outcome reported in studies on VR simulators (97%), VR patient simulations (100%), and AR (73%). Knowledge was the most common outcome reported in studies on screen-based VR (80%) and VR serious games (58%). Less common outcomes included participants' attitudes, satisfaction, cognitive or mental load, learning efficacy, engagement or self-efficacy beliefs, emotional state, competency developed, and patient outcomes. At least one form of validity evidence was found in approximately half of the studies on VR simulators (55%), VR patient simulations (56%), VR serious games (58%), and AR (55%) and in a quarter of the studies on screen-based VR (27%). Most studies used assessment methods that were implemented in a nondigital format, such as paper-based written exercises or in-person assessments where examiners observed performance (72%). CONCLUSIONS: RCTs on VR and AR in medical education report a restricted range of outcomes, mostly skills and knowledge. The studies largely report immediate postintervention outcome data and use assessment methods that are in a nondigital format. Future RCTs should include a broader set of outcomes, report on the validity evidence of the measurement instruments used, and explore the use of assessments that are implemented digitally.