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
- endothelial cells
- mass spectrometry
- palliative care
- community dwelling
- clinical decision support
- ms ms
- end stage renal disease
- big data
- clinical practice
- machine learning
- adverse drug
- mental health
- ejection fraction
- newly diagnosed
- chronic kidney disease
- induced pluripotent stem cells
- prognostic factors
- peritoneal dialysis
- deep learning
- chronic pain