Success Factors of Artificial Intelligence Implementation in Healthcare

Abstract

Background: Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare.Methods: A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare.Results: The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a “privacy by design” centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria.Conclusions: Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.

Artificial intelligence in healthcare: a comprehensive review of its ethical concerns

“…The last objective (4) consists of offering recommendations to the healthcare professionals to minimize the dilemmas and consider the implementation of AI, quite comprehensively. Previous research conducted by Wolff (2021) suggested 3 key success factors of AI in healthcare sector. The first factor recommends the setting of a risk adjusted policy frame that clarifies the limits of actions in term of precautions, permissions, accountability, liability and culpability.…”

“…The second success criteria suggest maintaining a centralized technology architecture that allows for practical and lawful metadata. Finally, Wolff (2021) highlights the necessity of having key performance indicators (KPI) that measure the medical and economic impact of AI in healthcare. Building on that, we suggest 4 key success factors of AI in healthcare sector that could potentially reduce the impact of the observed ethical dilemmas.…”

Machine learning models in clinical practice for the prediction of postoperative complications after major abdominal surgery

“…Independent Consultant in Computational Intelligence, Amsterdam, The Netherlands Table 1 summarizes the outcomes of the largest, most recent articles on postoperative complication prediction with ML in major abdominal surgery. The shift to clinical implementation will depend on five main improvement categories: technology, policy, medical and economic impact, transparency, and reporting [4,16,17].…”

Using artificial intelligence in diabetic retinopathy

“…Following factors need to be considered in implementation of AI in healthcare settings: Policy setting which involves a risk adjust policy, technological implementation, and medical and economic impact quantification. [48] There are several challenges that stand in the way of wider adoption of AI. These include: Workflow integration, enhanced explainability and interpretability, workforce education on how to use AI, appropriate regulatory mechanisms, problem identification and focusing on intervention drive AI, understanding the potential impact of AI on clinician and patient relationship and data quality, access, and sharing and compliance with privacy.…”