Enhancing clinical information display to improve patient encounters: human-centered design and evaluation of the Parkinson’s Disease-BRIDGE platform (Preprint)

How Information Affects Patients with Parkinson's Disease: A Scoping Review of the Literature

Background

Patients with Parkinson's disease (PD) need to receive adequate information to manage their disease. However, little is known about how information provision affects patients.

Objective

To conduct a scoping review of the literature on the relationship between content, timing, manner of delivery, and source of PD-specific information on the one hand, and patient outcomes on the other.

Methods

All literature reporting about original data and published until April 2024 in peer-reviewed journals was searched in MEDLINE (Ovid), Embase (Ovid) and PsychInfo (Ovid). Subsequently, data were extracted and synthesized.

Results

40 publications describing the effects of information provision or patients' evaluation thereof were retrieved. Four categories of patient outcomes were described, namely 1) evaluation and experience of information provision; 2) physical functioning; 3) psychosocial well-being; and 4) quality of life. In intervention studies, patients generally valued the provided information. Findings from cross-sectional and qualitative studies showed the importance of tailoring information to individuals' needs and capabilities. Due to variation in study designs and outcomes, no unambiguous conclusions could be drawn regarding the relationship between information and outcomes.

Conclusions

This scoping review identified how PD patients acquire information and revealed a lack of systematic research into the effect of information on patient outcomes. Future studies should assess 1) what information is currently provided by clinicians; 2) what additional information might be beneficial to provide; and 3) how information can be effectively aligned to benefit patients. This will eventually yield insight into how information might optimally empower PD patients.

Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data

BACKGROUND AND PURPOSE

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system, with numerous therapeutic options, but a lack of biomarkers to support a mechanistic approach to precision medicine. A computational approach to precision medicine could proceed from clinical decision support systems (CDSSs). They are digital tools aiming to empower physicians through the clinical applications of information technology and massive data. However, the process of their clinical development is still maturing; we aimed to review it in the field of MS.

METHODS

For this scoping review, we screened systematically the PubMed database. We identified 24 articles reporting 14 CDSS projects and compared their technical and software development aspects.

RESULTS

The projects position themselves in various contexts of usage with various algorithmic approaches: expert systems, CDSSs based on similar patients' data visualization, and model-based CDSSs implementing mathematical predictive models. So far, no project has completed its clinical development up to certification for clinical use with global release. Some CDSSs have been replaced at subsequent project iterations. The most advanced projects did not necessarily report every step of clinical development in a dedicated article (proof of concept, offline validation, refined prototype, live clinical evaluation, comparative prospective evaluation). They seek different software distribution options to integrate into health care: internal usage, "peer-to-peer," and marketing distribution.

CONCLUSIONS

This review illustrates the potential of clinical applications of information technology and massive data to support MS management and helps clarify the roadmap for future projects as a multidisciplinary and multistep process.

A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack

BACKGROUND

Falls are common in people with multiple sclerosis (MS), causing injuries, fear of falling, and loss of independence. Although targeted interventions (physical therapy) can help, patients underreport and clinicians undertreat this issue. Patient-generated data, combined with clinical data, can support the prediction of falls and lead to timely intervention (including referral to specialized physical therapy). To be actionable, such data must be efficiently delivered to clinicians, with care customized to the patient's specific context.

OBJECTIVE

This study aims to describe the iterative process of the design and development of Multiple Sclerosis Falls InsightTrack (MS-FIT), identifying the clinical and technological features of this closed-loop app designed to support streamlined falls reporting, timely falls evaluation, and comprehensive and sustained falls prevention efforts.

METHODS

Stakeholders were engaged in a double diamond process of human-centered design to ensure that technological features aligned with users' needs. Patient and clinician interviews were designed to elicit insight around ability blockers and boosters using the capability, opportunity, motivation, and behavior (COM-B) framework to facilitate subsequent mapping to the Behavior Change Wheel. To support generalizability, patients and experts from other clinical conditions associated with falls (geriatrics, orthopedics, and Parkinson disease) were also engaged. Designs were iterated based on each round of feedback, and final mock-ups were tested during routine clinical visits.

RESULTS

A sample of 30 patients and 14 clinicians provided at least 1 round of feedback. To support falls reporting, patients favored a simple biweekly survey built using REDCap (Research Electronic Data Capture; Vanderbilt University) to support bring-your-own-device accessibility-with optional additional context (the severity and location of falls). To support the evaluation and prevention of falls, clinicians favored a clinical dashboard featuring several key visualization widgets: a longitudinal falls display coded by the time of data capture, severity, and context; a comprehensive, multidisciplinary, and evidence-based checklist of actions intended to evaluate and prevent falls; and MS resources local to a patient's community. In-basket messaging alerts clinicians of severe falls. The tool scored highly for usability, likability, usefulness, and perceived effectiveness (based on the Health IT Usability Evaluation Model scoring).

CONCLUSIONS

To our knowledge, this is the first falls app designed using human-centered design to prioritize behavior change and, while being accessible at home for patients, to deliver actionable data to clinicians at the point of care. MS-FIT streamlines data delivery to clinicians via an electronic health record-embedded window, aligning with the 5 rights approach. Leveraging MS-FIT for data processing and algorithms minimizes clinician load while boosting care quality. Our innovation seamlessly integrates real-world patient-generated data as well as clinical and community-level factors, empowering self-care and addressing the impact of falls in people with MS. Preliminary findings indicate wider relevance, extending to other neurological conditions associated with falls and their consequences.