What are the implications for patient safety and experience of a major healthcare IT breakdown? A qualitative study

The use of artificial intelligence applications in medicine and the standard required for healthcare provider-patient briefings-an exploratory study

Introduction

Digital Health Technologies (DHTs) are currently being funneled through legacy regulatory processes that are not adapted to the unique particularities of this new technology class. In the absence of adequate regulation of DHTs, the briefing of a patient by their healthcare provider (HCP) as a component of informed consent can present the last line of defense before potentially harmful technologies are employed on a patient.

Methods

This exploratory study utilizes a case vignette of a machine learning-based technology for the diagnosis of ischemic heart disease that is presented to a group of medical students, physicians, and bioethicists. What constitutes the necessary standard and content of the HCP-patient briefings is explored using a survey (N = 34). Whether participants actually provide a sufficient HCP-patient briefing is evaluated based on audio recordings.

Results and Conclusions

We find that participants deem artificial intelligence use in medical context should be declared to patients and argue that the explanation should currently follow the standard required of other experimental procedures. Further, since our study provides indications that implementation of HCP-patient briefings lacks behind the identified standard, opportunities for incorporation of training on the use of DHTs into medical curricula and continuous training schedules should be considered.

Performability Evaluation of Load Balancing and Fail-over Strategies for Medical Information Systems with Edge/Fog Computing Using Stochastic Reward Nets

The aggressive waves of ongoing world-wide virus pandemics urge us to conduct further studies on the performability of local computing infrastructures at hospitals/medical centers to provide a high level of assurance and trustworthiness of medical services and treatment to patients, and to help diminish the burden and chaos of medical management and operations. Previous studies contributed tremendous progress on the dependability quantification of existing computing paradigms (e.g., cloud, grid computing) at remote data centers, while a few works investigated the performance of provided medical services under the constraints of operational availability of devices and systems at local medical centers. Therefore, it is critical to rapidly develop appropriate models to quantify the operational metrics of medical services provided and sustained by medical information systems (MIS) even before practical implementation. In this paper, we propose a comprehensive performability SRN model of an edge/fog based MIS for the performability quantification of medical data transaction and services in local hospitals or medical centers. The model elaborates different failure modes of fog nodes and their VMs under the implementation of fail-over mechanisms. Sophisticated behaviors and dependencies between the performance and availability of data transactions are elaborated in a comprehensive manner when adopting three main load-balancing techniques including: (i) probability-based, (ii) random-based and (iii) shortest queue-based approaches for medical data distribution from edge to fog layers along with/without fail-over mechanisms in the cases of component failures at two levels of fog nodes and fog virtual machines (VMs). Different performability metrics of interest are analyzed including (i) recover token rate, (ii) mean response time, (iii) drop probability, (iv) throughput, (v) queue utilization of network devices and fog nodes to assimilate the impact of load-balancing techniques and fail-over mechanisms. Discrete-event simulation results highlight the effectiveness of the combination of these for enhancing the performability of medical services provided by an MIS. Particularly, performability metrics of medical service continuity and quality are improved with fail-over mechanisms in the MIS while load balancing techniques help to enhance system performance metrics. The implementation of both load balancing techniques along with fail-over mechanisms provide better performability metrics compared to the separate cases. The harmony of the integrated strategies eventually provides the trustworthiness of medical services at a high level of performability. This study can help improve the design of MIS systems integrated with different load-balancing techniques and fail-over mechanisms to maintain continuous performance under the availability constraints of medical services with heavy computing workloads in local hospitals/medical centers, to combat with new waves of virus pandemics.