Health technology assessment (HTA) refers to the systematic evaluation of the properties, effects, and/or impacts of health technology. The main purpose of the assessment is to inform decisionmakers in order to better support the introduction of new health technologies. New digital healthcare solutions like mHealth, artificial intelligence (AI), and robotics have brought with them a great potential to further develop healthcare services, but their introduction should follow the same criteria as that of other healthcare methods. They must provide evidence-based benefits and be safe to use, and their impacts on patients and organizations need to be clarified. The first objective of this study was to describe the state-of-the-art HTA methods for mHealth, AI, and robotics. The second objective of this study was to evaluate the domains needed in the assessment. The final aim was to develop an HTA framework for digital healthcare services to support the introduction of novel technologies into Finnish healthcare. In this study, the state-of-the-art HTA methods were evaluated using a literature review and interviews. It was noted that some good practices already existed, but the overall picture showed that further development is still needed, especially in the AI and robotics fields. With the cooperation of professionals, key aspects and domains that should be taken into account to make fast but comprehensive assessments were identified. Based on this information, we created a new framework which supports the HTA process for digital healthcare services. The framework was named Digi-HTA.

Background Healthcare costs are rising, and a substantial proportion of medical care is of little value. De-implementation of low-value practices is important for improving overall health outcomes and reducing costs. We aimed to identify and synthesize randomized controlled trials (RCTs) on de-implementation interventions and to provide guidance to improve future research. Methods MEDLINE and Scopus up to May 24, 2021, for individual and cluster RCTs comparing de-implementation interventions to usual care, another intervention, or placebo. We applied independent duplicate assessment of eligibility, study characteristics, outcomes, intervention categories, implementation theories, and risk of bias. Results Of the 227 eligible trials, 145 (64%) were cluster randomized trials (median 24 clusters; median follow-up time 305 days), and 82 (36%) were individually randomized trials (median follow-up time 274 days). Of the trials, 118 (52%) were published after 2010, 149 (66%) were conducted in a primary care setting, 163 (72%) aimed to reduce the use of drug treatment, 194 (85%) measured the total volume of care, and 64 (28%) low-value care use as outcomes. Of the trials, 48 (21%) described a theoretical basis for the intervention, and 40 (18%) had the study tailored by context-specific factors. Of the de-implementation interventions, 193 (85%) were targeted at physicians, 115 (51%) tested educational sessions, and 152 (67%) multicomponent interventions. Missing data led to high risk of bias in 137 (60%) trials, followed by baseline imbalances in 99 (44%), and deficiencies in allocation concealment in 56 (25%). Conclusions De-implementation trials were mainly conducted in primary care and typically aimed to reduce low-value drug treatments. Limitations of current de-implementation research may have led to unreliable effect estimates and decreased clinical applicability of studied de-implementation strategies. We identified potential research gaps, including de-implementation in secondary and tertiary care settings, and interventions targeted at other than physicians. Future trials could be improved by favoring simpler intervention designs, better control of potential confounders, larger number of clusters in cluster trials, considering context-specific factors when planning the intervention (tailoring), and using a theoretical basis in intervention design. Registration OSF Open Science Framework hk4b2

Objectives There has been a lack of health technology assessment (HTA) methods for novel digital health technologies (DHTs) such as mHealth, artificial intelligence, and robotics in Finland. The Digi-HTA method has been developed for this purpose. The aim of this study is to determine whether it would be possible to use Digi-HTA recommendations to support healthcare decision-makers. Secondly, from the perspective of companies offering different types of DHT products, this study assesses the suitability of using the Digi-HTA framework to perform HTAs for their products. Methods Feedback about Digi-HTA recommendations was collected from healthcare professionals. DHT companies provided input about the Digi-HTA framework. Data were collected via a web-based survey and were analyzed using qualitative methods. Results Of the twenty-four healthcare professional respondents, twenty said that the Digi-HTA recommendations contained all the necessary information, and twenty-one found them useful for their work. Respondents hoped that the Digi-HTA recommendations would be better integrated into the decision-making processes and healthcare professionals would be more informed about this new HTA process. The questions of the Digi-HTA framework were applicable for different DHT products based on the responses from DHT companies (n = 8). Conclusions According to the study participants, although the Digi-HTA recommendations include clear and beneficial information, their integration into healthcare decision-making processes should be improved. Responses from DHT companies indicate that the Digi-HTA framework would be an appropriate tool for performing assessments for their products. To generalize the findings of this study, more comprehensive studies will be needed.