Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).

OBJECTIVES: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. METHODS: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. RESULTS: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. CONCLUSIONS: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

Measuring quality of life of adults with intellectual disabilities: Psychometric evaluation of the personal outcomes scale in the United Kingdom.

BACKGROUND: The Personal Outcomes Scale (POS) is a scale developed to measure quality of life of adults (18+) with intellectual disability. Previous studies have reported good fit for Spanish and Portuguese language versions of POS. AIMS: This study aimed to evaluate the factor structure of the English language version of POS when used to measure the quality of life of adults (18+) with intellectual disability in the UK. MATERIALS AND METHODS: Analysis was conducted on POS data from 310 adults with an intellectual disability. First and second order factor models and multi-level models were used to assess fit. RESULTS: There was poor fit to the data for all tested models. We estimated that 23% of variance in POS scores was accounted for by interviewer cluster. DISCUSSION: This was the first UK-based evaluation of POS and our data did not confirm the factor structure of the POS measure. The identification of systematic variability within the dataset indicates that inter-rater reliability is a potential limitation of the POS tool. CONCLUSION: Further research is needed to investigate inter-rater reliability of POS interviewers and to explore factor structure.

Acceptability of Using Real-World Data to Estimate Relative Treatment Effects in Health Technology Assessments: Barriers and Future Steps.

OBJECTIVES: Evidence about the comparative effects of new treatments is typically collected in randomized controlled trials (RCTs). In some instances, RCTs are not possible, or their value is limited by an inability to capture treatment effects over the longer term or in all relevant population subgroups. In these cases, nonrandomized studies (NRS) using real-world data (RWD) are increasingly used to complement trial evidence on treatment effects for health technology assessment (HTA). However, there have been concerns over a lack of acceptability of this evidence by HTA agencies. This article aims to identify the barriers to the acceptance of NRS and steps that may facilitate increases in the acceptability of NRS in the future. METHODS: Opinions of the authorship team based on their experience in real-world evidence research in academic, HTA, and industry settings, supported by a critical assessment of existing studies. RESULTS: Barriers were identified that are applicable to key stakeholder groups, including HTA agencies (eg, the lack of comprehensive methodological guidelines for using RWD), evidence generators (eg, avoidable deviations from best practices), and external stakeholders (eg, data controllers providing timely access to high-quality RWD). Future steps that may facilitate future acceptability of NRS include improvements in the quality, integration, and accessibility of RWD, wider use of demonstration projects to highlight the value and applicability of nonrandomized designs, living, and more detailed HTA guidelines, and improvements in HTA infrastructure relating to RWD. CONCLUSION: NRS can represent a crucial source of evidence on treatment effects for use in HTA when RCT evidence is limited.