Background The digitization and automation of diagnostics and treatments promise to alter the quality of health care and improve patient outcomes, whereas the undersupply of medical personnel, high workload on medical professionals, and medical case complexity increase. Clinical decision support systems (CDSSs) have been proven to help medical professionals in their everyday work through their ability to process vast amounts of patient information. However, comprehensive adoption is partially disrupted by specific technological and personal characteristics. With the rise of artificial intelligence (AI), CDSSs have become an adaptive technology with human-like capabilities and are able to learn and change their characteristics over time. However, research has not reflected on the characteristics and factors essential for effective collaboration between human actors and AI-enabled CDSSs. Objective Our study aims to summarize the factors influencing effective collaboration between medical professionals and AI-enabled CDSSs. These factors are essential for medical professionals, management, and technology designers to reflect on the adoption, implementation, and development of an AI-enabled CDSS. Methods We conducted a literature review including 3 different meta-databases, screening over 1000 articles and including 101 articles for full-text assessment. Of the 101 articles, 7 (6.9%) met our inclusion criteria and were analyzed for our synthesis. Results We identified the technological characteristics and human factors that appear to have an essential effect on the collaboration of medical professionals and AI-enabled CDSSs in accordance with our research objective, namely, training data quality, performance, explainability, adaptability, medical expertise, technological expertise, personality, cognitive biases, and trust. Comparing our results with those from research on non-AI CDSSs, some characteristics and factors retain their importance, whereas others gain or lose relevance owing to the uniqueness of human-AI interactions. However, only a few (1/7, 14%) studies have mentioned the theoretical foundations and patient outcomes related to AI-enabled CDSSs. Conclusions Our study provides a comprehensive overview of the relevant characteristics and factors that influence the interaction and collaboration between medical professionals and AI-enabled CDSSs. Rather limited theoretical foundations currently hinder the possibility of creating adequate concepts and models to explain and predict the interrelations between these characteristics and factors. For an appropriate evaluation of the human-AI collaboration, patient outcomes and the role of patients in the decision-making process should be considered.
The digitization of health care promises an improvement of medical care through the adoption of virtual reality (VR) related technologies. Although most undergoing mechanisms of clinical effectiveness are yet not defined theoretically, research approaches have already taken place in several empirical settings. To structure current and upcoming scientific work in this field, we conducted a literature review with regard to theoretical implications of both IS-related and healthcare-related research. We found several theoretical bases to build upon in the field of psychology, but expressed a need for enrichment of theoretical foundations in the field of IS research. We therefore plead for a theoretical foundation enriched by synergetic concepts of clinically effective VR related technologies. Finally, we conclude that VR related technologies appear as a promising approach worth further theoretical and empirical research in order to improve medical care.
of novel drugs to patients and fosters divergence in reimbursement status. The objectives were to identify differences in REA by EU-HTA agencies, discuss the impact of this variation on time to patient access and assess the potential benefits of a centralized REA. Methods: Differences in clinical assessment across EU-HTA bodies were analysed for selected EMA approved Novartis drugs (internal data and structured telephonic interviews with Novartis Country Pharma Organisations (CPOs) and drugs marketed by other companies (literature review). Results: Differences in acceptance of comparators, subgroups and end-points were seen in REA across countries. Although fingolimod, secured wide reimbursement in EU for Relapsing-Remitting Multiple Sclerosis (RRMS), HTA bodies differed in their end-points' acceptance. Example, UK accepted annual relapse rate, disability progression and MRI-lesions, while Germany didn't accept the latter as end-point. Variations were seen in countries' requirements for additional analysis to support REA. Example, Germany granted a 'small additional benefit' rating for Rapidly Evolving Severe RRMS subgroup based on indirect comparison, while, even an additional mixed treatment comparison couldn't support REA for this subgroup in UK. Amongst non-Novartis drugs; pertuzumab review by IQWIG and GBA resulted in different end-point acceptance, with negative and restricted access recommendation, respectively. For sofosbuvir, HTA bodies differed from EMA label and saw clinical value only in some patient subgroups. Example, IQWIG recommended the drug for only HCV genotype-2 whereas ZIN (Netherlands) recommended it for genotype-1 and 4. CPOs' survey indicated that centralized EU-REA could reduce time to patient access (by ~3-4 months), through avoiding repetitive clinical evaluation and saving time on pricing/reimbursement pathway. ConClusions: Harmonized REA has the potential to reduce delay in patient access, strengthen the equity of care and increase predictability of expectations from pharmaceutical companies' research programs.
Autonomy is a pivotal concept that allows researchers to investigate important aspects such as job-related outcomes in Information Systems (IS) research. With the increase of mobile technologies, autonomy is increasingly gaining importance. Given the growing body of research in this area, this research presents the results of a systematic literature review. Our results show in detail how autonomy has been used and identifies fruitful avenues for future research. Specifically, we suggest that future research should contextualize autonomy to give it a central theoretical significance for IS research. Moreover, future research should also acknowledge the multidimensional facets of autonomy to enhance its explanatory power.
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