Overcoming Barriers in Hospital-Based Health Technology Assessment (HB-HTA): International Expert Panel Consensus.

The purpose of this article is to investigate the common facilitators and barriers associated with the implementation of hospital-based health technology assessment (HB-HTA) across diverse hospital settings in seven countries. Through a two-round Delphi study, insights were gathered from a panel of 15 HTA specialists from France, Hungary, Italy, Kazakhstan, Poland, Switzerland, and Ukraine. Experts initially conducted a comprehensive review of the HB-HTA implementation in their respective countries, identifying the barriers and facilitators through descriptive analysis. Subsequently, panel experts ranked these identified barriers and facilitators on a seven-point Likert scale. A median agreement score ≥ 6 and interquartile range (IQR) ≤ 1 was accepted as reaching a consensus. Out of the 12 statements categorized as external and internal barriers and facilitators, the expert panel reached consensus on six statements (two barriers and four facilitators). The external barrier, which achieved consensus, was the lack of the formal recognition of the role of HB-HTA in national or regional legislations. The internal barrier reaching consensus was the limited availability of human resources dedicated to HB-HTA. This qualitative study indicates that HB-HTA still has progress to make before being formally accepted and integrated across most countries, although by building on the facilitating factors we identified there may be an opportunity for the implementation of internationally developed strategies to strengthen HB-HTA practices.

The PICOTS-ComTeC Framework for Defining Digital Health Interventions: An ISPOR Special Interest Group Report.

OBJECTIVES: Digital health definitions are abundant, but often lack clarity and precision. We aimed to develop a minimum information framework to define patient-facing digital health interventions (DHIs) for outcomes research. METHODS: Definitions of digital-health-related terms (DHTs) were systematically reviewed, followed by a content analysis using frameworks, including PICOTS (population, intervention, comparator, outcome, timing, and setting), Shannon-Weaver Model of Communication, Agency for Healthcare Research and Quality Measures, and the World Health Organization's Classification of Digital Health Interventions. Subsequently, we conducted an online Delphi study to establish a minimum information framework, which was pilot tested by 5 experts using hypothetical examples. RESULTS: After screening 2610 records and 545 full-text articles, we identified 101 unique definitions of 67 secondary DHTs in 76 articles, resulting in 95 different patterns of concepts among the definitions. World Health Organization system (84.5%), message (75.7%), intervention (58.3%), and technology (52.4%) were the most frequently covered concepts. For the Delphi survey, we invited 47 members of the ISPOR Digital Health Special Interest Group, 18 of whom became the Delphi panel. The first, second, and third survey rounds were completed by 18, 11, and 10 respondents, respectively. After consolidating results, the PICOTS-ComTeC acronym emerged, involving 9 domains (population, intervention, comparator, outcome, timing, setting, communication, technology, and context) and 32 optional subcategories. CONCLUSIONS: Patient-facing DHIs can be specified using PICOTS-ComTeC that facilitates identification of appropriate interventions and comparators for a given decision. PICOTS-ComTeC is a flexible and versatile tool, intended to assist authors in designing and reporting primary studies and evidence syntheses, yielding actionable results for clinicians and other decision makers.

Intelligent digital tools for screening of brain connectivity and dementia risk estimation in people affected by mild cognitive impairment: the AI-Mind clinical study protocol.

More than 10 million Europeans show signs of mild cognitive impairment (MCI), a transitional stage between normal brain aging and dementia stage memory disorder. The path MCI takes can be divergent; while some maintain stability or even revert to cognitive norms, alarmingly, up to half of the cases progress to dementia within 5 years. Current diagnostic practice lacks the necessary screening tools to identify those at risk of progression. The European patient experience often involves a long journey from the initial signs of MCI to the eventual diagnosis of dementia. The trajectory is far from ideal. Here, we introduce the AI-Mind project, a pioneering initiative with an innovative approach to early risk assessment through the implementation of advanced artificial intelligence (AI) on multimodal data. The cutting-edge AI-based tools developed in the project aim not only to accelerate the diagnostic process but also to deliver highly accurate predictions regarding an individual's risk of developing dementia when prevention and intervention may still be possible. AI-Mind is a European Research and Innovation Action (RIA H2020-SC1-BHC-06-2020, No. 964220) financed between 2021 and 2026. First, the AI-Mind Connector identifies dysfunctional brain networks based on high-density magneto- and electroencephalography (M/EEG) recordings. Second, the AI-Mind Predictor predicts dementia risk using data from the Connector, enriched with computerized cognitive tests, genetic and protein biomarkers, as well as sociodemographic and clinical variables. AI-Mind is integrated within a network of major European initiatives, including The Virtual Brain, The Virtual Epileptic Patient, and EBRAINS AISBL service for sensitive data, HealthDataCloud, where big patient data are generated for advancing digital and virtual twin technology development. AI-Mind's innovation lies not only in its early prediction of dementia risk, but it also enables a virtual laboratory scenario for hypothesis-driven personalized intervention research. This article introduces the background of the AI-Mind project and its clinical study protocol, setting the stage for future scientific contributions.

Personalized Predictive Hemodynamic Management for Gynecologic Oncologic Surgery: Feasibility of Cost-Benefit Derivatives of Digital Medical Devices.

BACKGROUND: Intraoperative hypotension is associated with increased perioperative complications, hospital length of stay (LOS) and healthcare expenditure in gynecologic surgery. We tested the hypothesis that the adoption of a machine learning-based warning algorithm (hypotension prediction index-HPI) might yield an economic advantage, with a reduction in adverse outcomes that outweighs the costs for its implementation as a medical device. METHODS: A retrospective-matched cohort cost-benefit Italian study in gynecologic surgery was conducted. Sixty-six female patients treated with standard goal-directed therapy (GDT) were matched in a 2:1 ratio with thirty-three patients treated with HPI based on ASA status, diagnosis, procedure, surgical duration and age. RESULTS: The most relevant contributor to medical costs was operating room occupation (46%), followed by hospital stay (30%) and medical devices (15%). Patients in the HPI group had EURO 300 greater outlay for medical devices without major differences in total costs (GDT 5425 (3505, 8127), HPI 5227 (4201, 7023) p = 0.697). A pre-specified subgroup analysis of 50% of patients undergoing laparotomic surgery showed similar medical device costs and total costs, with a non-significant saving of EUR 1000 in the HPI group (GDT 8005 (5961, 9679), HPI 7023 (5227, 11,438), p = 0.945). The hospital LOS and intensive care unit stay were similar in the cohorts and subgroups. CONCLUSIONS: Implementation of HPI is associated with a scenario of cost neutrality, with possible economic advantage in high-risk settings.