Abstract
Wearable biometric monitoring devices (BMDs) and artificial intelligence (AI) enable the remote measurement and analysis of patient data in real time. These technologies have generated a lot of “hype,” but their real-world effectiveness will depend on patients’ uptake. Our objective was to describe patients’ perceptions of the use of BMDs and AI in healthcare. We recruited adult patients with chronic conditions in France from the “Community of Patients for Research” (ComPaRe). Participants (1) answered quantitative and open-ended questions about the potential benefits and dangers of using of these new technologies and (2) participated in a case-vignette experiment to assess their readiness for using BMDs and AI in healthcare. Vignettes covered the use of AI to screen for skin cancer, remote monitoring of chronic conditions to predict exacerbations, smart clothes to guide physical therapy, and AI chatbots to answer emergency calls. A total of 1183 patients (51% response rate) were enrolled between May and June 2018. Overall, 20% considered that the benefits of technology (e.g., improving the reactivity in care and reducing the burden of treatment) greatly outweighed the dangers. Only 3% of participants felt that negative aspects (inadequate replacement of human intelligence, risks of hacking and misuse of private patient data) greatly outweighed potential benefits. We found that 35% of patients would refuse to integrate at least one existing or soon-to-be available intervention using BMDs and AI-based tools in their care. Accounting for patients’ perspectives will help make the most of technology without impairing the human aspects of care, generating a burden or intruding on patients’ lives.
Highlights
Coupled with the progress of artificial intelligence (AI), the thousands of data points collected from biometric monitoring devices (BMDs) may help in informing diagnosis, predicting patient outcomes, and helping care professionals select the best treatment for their patients.[4,5]
We report two experiments to document the perception of patients with chronic conditions on the use of BMDs and AI in care
50% of patients felt that the development of digital tools and AI in healthcare was an important opportunity and 11% considered it a danger
Summary
The development of wearable biometric monitoring devices (BMDs) (i.e., sensors embedded in smartphones or wearable equipment to collect physiological, biological, environmental or behavioral information) allows for remote, high-frequency and high-resolution monitoring of patients’ health outside the hospital.[1,2,3] Coupled with the progress of artificial intelligence (AI), the thousands of data points collected from BMDs may help in informing diagnosis, predicting patient outcomes, and helping care professionals select the best treatment for their patients.[4,5]These two technical revolutions have generated a lot of hope and “hype,” and myriad digital and AI-based tools for healthcare have been developed.[6,7,8]Today, AI can outperform medical practitioners in the analysis of skin lesions, pathology slides, electrocardiograms or medical imaging data.[9,10,11,12] Continuous glucose monitoring systems combined with closed-loop insulin delivery systems can improve type 2 diabetes mellitus control.[13]. The development of wearable biometric monitoring devices (BMDs) (i.e., sensors embedded in smartphones or wearable equipment to collect physiological, biological, environmental or behavioral information) allows for remote, high-frequency and high-resolution monitoring of patients’ health outside the hospital.[1,2,3] Coupled with the progress of artificial intelligence (AI), the thousands of data points collected from BMDs may help in informing diagnosis, predicting patient outcomes, and helping care professionals select the best treatment for their patients.[4,5]. Multiple AI algorithms using data from BMDs are being tested to detect unknown disease, predict patient outcomes and provide reactive guidance or proactive interventions.[14,15,16,17,18] Despite these good preliminary results, the real-world effectiveness of such interventions that occur outside of hospitals is still uncertain and will depend on patients’ engagement, uptake and adherence to these interventions.[19]
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