Managing Risk and Quality of AI in Healthcare: Are Hospitals Ready for Implementation?

Artificial intelligence (AI) provides a unique opportunity to help meet the demands of the future healthcare system. However, hospitals may not be well equipped to handle safe and effective development and/or procurement of AI systems. Furthermore, upcoming regulations such as the EU AI Act may enforce the need to establish new management systems, quality assurance and control mechanisms, novel to healthcare organizations. This paper discusses challenges in AI implementation, particularly potential gaps in current management systems (MS), by reviewing the harmonized standard for AI MS, ISO 42001, as part of a gap analysis of a tertiary acute hospital with ongoing AI activities. Examination of the industry agnostic ISO 42001 reveals a technical debt within healthcare, aligning with previous research on digitalization and AI implementation. To successfully implement AI with quality assurance in mind, emphasis should be put on the foundation and structure of the healthcare organizations, including both workforce and data infrastructure.

Designing an Efficient Implementation Process for Large Scale Adoption of Telehealth.

Telehealth may be one of the solutions to the increasing demand of healthcare. However, implementation of such systems is a considerable effort, and requires an efficient and systemized process for large scale adoption. This study provides a process for telehealth implementation. Although quantitative studies of implementation processes introduce significant challenges, the study provide initial indications of increasing effectiveness, specifically in time to deployment.

Data Quality in Healthcare for the Purpose of Artificial Intelligence: A Case Study on ECG Digitalization.

The quantity of data generated within healthcare is increasing exponentially. Following this development, the interest of using data driven methodologies such as machine learning is on a steady rise. However, the quality of the data also needs to be considered, since information generated for human interpretation may not be optimal for quantitative computer-based analysis. This work investigates dimensions of data quality for the purpose of artificial intelligence applications in healthcare. Particularly, ECG is studied which traditionally rely on analog prints for initial examination. A digitalization process for ECG is implemented, together with a machine learning model for heart failure prediction, to quantitatively compare results based on data quality. The digital time series data provide a significant accuracy increase, compared to scans of analog plots.

Enabling Clinical Trials of Artificial Intelligence: Infrastructure for Heart Failure Predictions.

The last decade has seen a large increase in artificial intelligence research within healthcare. However, relatively few attempts of clinical trials have been made for such configurations. One of the main challenges arise in the extensive infrastructure necessary, both for development, but particularly to run prospective studies. In this paper, infrastructural requirements are first presented, together with constraints due to underlying production systems. Then, an architectural solution is presented, with the aim of both enabling clinical trials and streamline model development. Specifically, the suggested design is intended for research of heart failure prediction from ECG, but is generalizable to projects using similar data protocols and installed base.

Fairness in Artificial Intelligence: Regulatory Sanbox Evaluation of Bias Prevention for ECG Classification.

As the use of artificial intelligence within healthcare is on the rise, an increased attention has been directed towards ethical considerations. Defining fairness in machine learning is a well explored topic with an extensive literature. However, such definitions often rely on the existence of metrics on the input data and well-defined outcome measurements, while regulatory definitions use general terminology. This work aims to study fairness within AI, particularly bringing regulation and theoretical knowledge closer. The study is done via a regulatory sandbox implemented on a healthcare case, specifically ECG classification.