Future of Data Analytics in the Era of the General Data Protection Regulation in Europe

The development of evidence to demonstrate ‘value for money’ is regarded as an important step in facilitating the search for the optimal allocation of limited resources and has become an essential component in healthcare decision making. Real-world evidence collected from de-identified individuals throughout the continuum of healthcare represents the most valuable source in technology evaluation. However, in the European Union, the value assessment based on real-world data has become challenging as individuals have recently been given the right to have their personal data erased in the case of consent withdrawal or when the data are regarded as being no longer necessary. This act may limit the usefulness of data in the future as it may introduce information bias. Among healthcare stakeholders, this has become an important topic of discussion because it relates to the importance of data on one side and to the need for personal data protection on the ot

A Systematic Review of Cost-Effectiveness Studies of Interventions with a Personalized Nutrition Component in Adults

Objectives: Important links between dietary patterns and diseases have been widely applied to establish nutrition interventions. However, knowledge about between-person heterogeneity regarding the benefits of nutrition intervention can be used to personalize the intervention and thereby

Radiology AI Deployment and Assessment Rubric (RADAR) to bring value-based AI into radiological practice

Objective: To provide a comprehensive framework for value assessment of artificial intelligence (AI) in radiology. Methods: This paper presents the RADAR framework, which has been adapted from Fryback and Thornbury’s imaging efficacy framework to facilitate the valuation of radiology AI from conception to local implementation. Local efficacy has been newly introduced to underscore the importance of appraising an AI technology within its local environment. Furthermore, the RADAR framework is illustrated through a myriad of study designs that help assess value. Results: RADAR presents a seven-level hierarchy, providing radiologists, researchers, and policymakers with a structured approach to the comprehensive assessment of value in radiology AI. RADAR is designed to be dynamic and meet the different valuation needs throughout the AI’s lifecycle. Initial phases like technical and diagnostic efficacy (RADAR-1 and RADAR-2) are assessed pre-clinical deployment via in silico clinical trials and cross-sectional studies. Subsequent stages, spanning from diagnostic thinking to patient outcome efficacy (RADAR-3 to RADAR-5), require clinical integration and are explored via randomized controlled trials and cohort studies. Cost-effectiveness efficacy (RADAR-6) takes a societal perspective on financial feasibility, addressed via health-economic evaluations. The final level, RADAR-7, determines how prior valuations translate locally, evaluated through budget impact analysis, multi-criteria decision analyses, and prospective monitoring.Conclusion: The RADAR framework offers a comprehensive framework for valuing radiology AI. Its layered, hierarchical structure, combined with a focus on local relevance, aligns RADAR seamlessly with the principles of value-based radiology. Critical relevance statement: The RADAR framework advances artificial intelligence in radiology by delineating a much-needed framework for comprehensive valuation. </p

Broadening the HTA of medical AI:A review of the literature to inform a tailored approach

Objectives: As current health technology assessment (HTA) frameworks do not provide specific guidance on the assessment of medical artificial intelligence (AI), this study aimed to propose a conceptual framework for a broad HTA of medical AI. Methods: A systematic literature review and a targeted search of policy documents was conducted to distill the relevant medical AI assessment elements. Three exemplary cases were selected to illustrate various elements: (1) An application supporting radiologists in stroke-care (2) A natural language processing application for clinical data abstraction (3) An ICU-discharge decision-making application. Results: A total of 31 policy documents and 9 academic publications were selected, from which a list of 29 issues was distilled. The issues were grouped by four focus areas: (1) Technology &amp; Performance, (2) Human &amp; Organizational, (3) Legal &amp; Ethical and (4) Transparency &amp; Usability. Each assessment element was extensively discussed in the test, and the elements clinical effectiveness, clinical workflow, workforce, interoperability, fairness and explainability were further highlighted through the exemplary cases. Conclusion: The current methodology of HTA requires extension to make it suitable for a broad evaluation of medical AI technologies. The 29-item assessment list that we propose needs a tailored approach for distinct types of medical AI, since the conceptualisation of the issues differs across applications.</p

Broadening the HTA of medical AI:A review of the literature to inform a tailored approach

Objectives: As current health technology assessment (HTA) frameworks do not provide specific guidance on the assessment of medical artificial intelligence (AI), this study aimed to propose a conceptual framework for a broad HTA of medical AI. Methods: A systematic literature review and a targeted search of policy documents was conducted to distill the relevant medical AI assessment elements. Three exemplary cases were selected to illustrate various elements: (1) An application supporting radiologists in stroke-care (2) A natural language processing application for clinical data abstraction (3) An ICU-discharge decision-making application. Results: A total of 31 policy documents and 9 academic publications were selected, from which a list of 29 issues was distilled. The issues were grouped by four focus areas: (1) Technology &amp; Performance, (2) Human &amp; Organizational, (3) Legal &amp; Ethical and (4) Transparency &amp; Usability. Each assessment element was extensively discussed in the test, and the elements clinical effectiveness, clinical workflow, workforce, interoperability, fairness and explainability were further highlighted through the exemplary cases. Conclusion: The current methodology of HTA requires extension to make it suitable for a broad evaluation of medical AI technologies. The 29-item assessment list that we propose needs a tailored approach for distinct types of medical AI, since the conceptualisation of the issues differs across applications.</p

A new test of the construct validity of the CarerQol instrument: measuring the impact of informal care giving

Purpose: Most economic evaluations of health care programmes do not consider the effects of informal care, while this could lead to suboptimal policy decisions. This study investigates the construct validity of the CarerQol instrument, which measures and values carer effects, in a new population of informal caregivers. Methods: A questionnaire was distributed by mail (n = 1,100, net response rate = 21%) to regional informal care support centers throughout the Netherlands. Two types of construct validity, i.e., convergent and clinical validity, have been analyzed. Convergent validity was assessed with Spearman's correlation coefficients and multivariate correlation between the burden dimensions (CarerQol-7D) and the valuation component (CarerQol-VAS) of the CarerQol. Additionally, convergent validity was analyzed with Spearman's correlation coefficients between the CarerQol and other measures of subjective caregiver burden (SRB, PU). Clinical validity was evaluated with multivariate correlation between CarerQol-VAS and CarerQol-7D, characteristics of caregivers, care recipients and care situation among the whole sample of caregivers and subgroups. Results: The positive (negative) dimensions of CarerQol-7D were positively (negatively) related to CarerQol-VAS, and almost all had moderate strength of convergent validity. CarerQol-VAS was positively associated with PU and negatively with SRB. The CarerQol-VAS reflects differences in important background characteristics of informal care: type of relationship, age of the care recipient and duration of care giving were associated with higher CarerQol-VAS scores. These results confirmed earlier tests of the construct validity of the CarerQol. Furthermore, the dimensions of CarerQol-7D significantly explained differences in CarerQol-VAS scores among subgroups of carers. Conclusion: Notwithstanding the limitations of our study, such as the low response rate, this study shows that the CarerQol provides a valid means to measure carer effects for use in economic evaluations. Future research should derive a valuation set for the CarerQol and further address the instrument's content validity, sensitivity and reliability