Toward the Design of Sensing-Based Medication Adherence Aids That Support Individualized Activities of Daily Living: Survey and Interviews With Patients and Providers.
Jacob T BiehlRavi PatelAdam J LeePublished in: JMIR human factors (2023)
There is considerable potential to improve individual medication adherence by creating behavior-focused interventions that leverage emerging artificial intelligence (AI), machine learning (ML), and in-home Internet of Things (IoT) sensing technologies. However, success will be dependent on the technology's ability to learn effectively and accurately from individual behaviors, needs, and routines and tailor interventions accordingly. Patient routines and attitudes toward adherence will likely affect the use of proactive (eg, AI-assistant routine modification) versus reactive (eg, notification of associated behaviors with missed dosages) intervention strategies. Successful technological interventions must support the detection and tracking of patient routines that can adjust to variations in patient location, schedule, independence, and habituation.
Keyphrases
- artificial intelligence
- machine learning
- big data
- case report
- deep learning
- physical activity
- end stage renal disease
- randomized controlled trial
- healthcare
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- clinical practice
- prognostic factors
- health information
- social media
- weight loss
- label free
- loop mediated isothermal amplification
- patient reported