Artificial Intelligence and the Implementation Challenge

Abstract

Background Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. Objective In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). Methods After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. Results Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. Conclusions If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

A Path for Translation of Machine Learning Products into Healthcare Delivery

“…The "nonadoption, abandomnent, and challenges to the scale-up, spread, and sustainability of health and care technologies" is an example of a technology adoption framework that covers seven domains and has been recently applied to machine learning products. 92,93 To date, no machine learning product ingesting EHR data has successfully diffused and scaled across healthcare. The products listed in Table 2 are as far along as any and will be closely watched over the coming years.…”

Beyond the Randomized Clinical Trial: Innovative Data Science to Close the Pediatric Evidence Gap

“…These benefits need to be balanced with the right to privacy of the patients whose data are used in this research. Maintaining and further developing ethical and data security standards are crucial to ensure ongoing support by patients and their parents of data collection for research purposes . Maintaining data security is particularly challenging for patient‐centric data collection where sensitive data are collected on a mobile phone or wearable, as data leaks could occur when the device is lost or during data transfer from the device to the central database.…”

“…An important aspect of this is to engage in activities that have been shown to successfully support implementation, such as educational outreach and meetings, use of local opinion leaders, computerized reminders, audit, and feedback . For the implementation of machine learning as a medical decision support tool in pediatrics, there are additional challenges to overcome ( Figure , right column) …”

“…71 For the implementation of machine learning as a medical decision support tool in pediatrics, there are additional challenges to overcome (Figure 3, right column). 36 Because the predictions or classifications of machine-learning tools can incorporate information of multiple variables, they are not as readily integrated in clinical guidelines as knowledge that relies on a single variable (e.g., age or bodyweight) for decision making. Therefore, software packages might be needed so that physicians can easily use models in medical decision making (Figure 3, right column).…”

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

“…We aimed to conduct a detailed analysis of barriers to utilization with the perspectives of a framework focused on ML implementation in health care developed by Shaw et al 15 and by the Nonadoption, Abandonment, and Challenges to the Scale-up, Spread, and Sustainability of Health and Care Technologies (NASSS) framework by Greenhalgh et al 16 Because both of these frameworks, as well as the more general framework of diffusion of innovations by Everett Rogers 17 include a strong focus on end-users/adopters, we focused our analyses in this domain.…”