OBJECTIVES:

There is great hope that remote patient monitoring (RPM) combined with artificial intelligence (AI) will help clinicians to better detect and manage the progression of Parkinson’s disease (PD). Although stakeholder involvement in the developing process is crucial for successful implementation, limited evidence exists on patients and physicians’ preferences for these novel technologies. Therefore, we explored patients and neurologists’ preferences for AI-enhanced remote monitoring in PD care.

METHODS:

Using a qualitative approach, preferences of neurologists and PD patients were elicited in Finland and Italy. First, individual semi-structured interviews were conducted with patients (N=7) and neurologists (N=6). Hereafter, 5 focus groups involving a total of 22 patients were conducted. Three topics were addressed: disease progression detection, current disease monitoring process and perceptions about an AI and RPM. Data were pooled across both countries and analyzed using template analysis.

RESULTS:

Constant uncertainty about the actual disease stage and suboptimal therapeutic choices due to the subjectivity of the current monitoring techniques were reported consistently. In general, persons with PD and neurologists were positive towards AI-enhanced remote monitoring to address these issues. Doctors described preferences for tools that generate objective and meaningful clinical data. Patients emphasized a desire for devices that produce feedback to raise awareness of the disease and help their physicians to gain meaningful insights. Patients expressed concerns about data privacy, stigmatization, costs, and intrusiveness of the devices. However, potential benefits in controlling symptoms or disease progression seem to outweigh most of the inconveniences brought by the technology.

CONCLUSIONS:

PD patients and clinicians want to increase their ability to monitor disease progression in a timely fashion, amplifying their control over the condition. Patients acknowledge trade-offs between potential benefits and undesirable aspects of AI-enhanced remote monitoring. Data from this study will be used to design a discrete choice experiment to assess the trade-offs quantitatively.