A Closed-Loop Falls Monitoring and Prevention App for Multiple Sclerosis Clinical Practice: Human-Centered Design of the Multiple Sclerosis Falls InsightTrack.
Valerie J BlockKanishka KoshalJaeleene WijangcoNicolette A MillerNarender SaraKyra HendersonJennifer R PearceArpita GopalSonam D MohanJeffrey Marc GelfandChu-Yueh GuoLauren OommenAlyssa N NylanderJames A RowsonEthan G BrownStephen SandersKatherine P RankinCourtney Rees LylesIda SimRiley M BovePublished in: JMIR human factors (2024)
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.
Keyphrases
- electronic health record
- multiple sclerosis
- healthcare
- mass spectrometry
- palliative care
- endothelial cells
- big data
- ms ms
- community dwelling
- clinical decision support
- clinical practice
- end stage renal disease
- white matter
- machine learning
- quality improvement
- ejection fraction
- induced pluripotent stem cells
- chronic kidney disease
- newly diagnosed
- mental health
- data analysis
- deep learning
- case report
- pain management
- artificial intelligence
- patient reported outcomes
- peritoneal dialysis
- patient reported