Preferences and willingness to pay for personalized nutrition interventions:Discrete choice experiments in Europe and the United States

This study gives insight into what intervention-related factors are crucial for using personalized nutrition (PN) interventions, as well as what the general population is willing to pay for PN. This was done by focusing on two different types of PN (i.e., PN advice and personalized meals) in two discrete choice experiments (DCEs). The DCEs were conducted in four European countries and the United States, including at least 500 respondents per country aged 18–65 years. Panel mixed multinomial logit models were used to evaluate the preferences. Results show that for both types of PN in all countries, the total expenditure on nutrition was the most crucial factor when choosing a PN intervention. The participation rate for specific hypothetical scenario's varied but was considered high overall (maximum 81 % for ‘PN advice’ and 87 % for ‘personalized meals’ in Spain). Moreover, highest willingness to pay estimates were found for six kilograms of weight loss. For example, Polish respondents were willing to spend an extra 25.78 euros per week for ‘personalized meals’ for a 4-month period to lose six kilograms. Our models showed preference heterogeneity between, but also within, the different countries. In conclusion, this study showed that people seem willing to pay for and participate in PN interventions. Since PN interventions may improve health outcomes, policymakers should consider subsidizing some of the costs, financially incentivizing PN interventions or introducing commitment lotteries to encourage uptake. More research is needed to study heterogeneity in preferences.</p

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

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 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

Outpatient costs in pharmaceutically treated diabetes patients with and without a diagnosis of depression in a Dutch primary care setting

<p>Abstract</p> <p>Background</p> <p>To assess differences in outpatient costs among pharmaceutically treated diabetes patients with and without a diagnosis of depression in a Dutch primary care setting.</p> <p>Methods</p> <p>A retrospective case control study over 3 years (2002-2004). Data on 7128 depressed patients and 23772 non-depressed matched controls were available from the electronic medical record system of 20 general practices organized in one large primary care organization in the Netherlands. A total of 393 depressed patients with diabetes and 494 non-depressed patients with diabetes were identified in these records. The data that were extracted from the medical record system concerned only outpatient costs, which included GP care, referrals, and medication.</p> <p>Results</p> <p>Mean total outpatient costs per year in depressed diabetes patients were €1039 (SD 743) in the period 2002-2004, which was more than two times as high as in non-depressed diabetes patients (€492, SD 434). After correction for age, sex, type of insurance, diabetes treatment, and comorbidity, the difference in total annual costs between depressed and non-depressed diabetes patients changed from €408 (uncorrected) to €463 (corrected) in multilevel analyses. Correction for comorbidity had the largest impact on the difference in costs between both groups.</p> <p>Conclusions</p> <p>Outpatient costs in depressed patients with diabetes are substantially higher than in non-depressed patients with diabetes even after adjusting for confounders. Future research should investigate whether effective treatment of depression among diabetes patients can reduce health care costs in the long term.</p

Short- and long-term effects of a quality improvement collaborative on diabetes management

Introduction: This study examined the short- and long-term effects of a quality improvement collaborative on patient outcomes, professional performance, and structural aspects of chronic care management of type 2 diabetes in an integrated care setting.Methods: Controlled pre- and post-intervention study assessing patient outcomes (hemoglobin A1c, cholesterol, blood pressure, weight, blood lipid levels, and smoking status), professional performance (guideline adherence), and structural aspects of chronic care management from baseline up to 24 months. Analyses were based on 1,861 patients with diabetes in six intervention and nine control regions representing 37 general practices and 13 outpatient clinics.Results: Modest but significant improvement was seen in mean systolic blood pressure (decrease by 4.0 mm Hg versus 1.6 mm Hg) and mean high density lipoprotein levels (increase by 0.12 versus 0.03 points) at two-year follow up. Positive but insignificant differences were found in hemoglobin A1c (0.3%), cholesterol, and blood lipid levels. The intervention group showed significant improvement in the percentage of patients receiving advice and instruction to examine feet, and smaller reductions in the percentage of patients receiving instruction to monitor blood glucose and visiting a dietician annually. Structural aspects of self-management and decision support also improved significantly.Conclusions: At a time of heightened national attention toward diabetes care, our results demonstrate a modest benefit of participation in a multi-institutional quality improvement collaborative focusing on integrated, patient-centered care. The effects persisted for at least 12 months after the intervention was completed.Trial number: http://clinicaltrials.gov Identifier: NCT 00160017