Remote Monitoring via IoT Medical Devices

Abstract

Internet-of-Things medical devices promise to shift chronic disease management from episodic clinic visits to continuous, data-rich remote surveillance, yet adoption rates vary widely across clinical domains and device categories. We evaluated 208 remote patient monitoring programmes that deployed IoT medical devices across hospitals and outpatient networks affiliated with the Swiss Institute of Machine Intelligence between 2016 and 2024, covering five device categories: wearable cardiac monitors, connected respiratory sensors, smart glucometers and continuous glucose monitors, implantable haemodynamic sensors, and multi-parameter home telemetry hubs. A Remote Monitoring Effectiveness Index (RMEI) was constructed from five sub-scores -- clinical alert accuracy, patient adherence rate, data transmission reliability, clinician workflow integration, and reduction in unplanned hospitalisations -- with weights from regression against sustained programme retention beyond 12 months. RMEI correlated with programme retention at r = +0.82 and discriminated retained from discontinued programmes with an AUC of 0.876. Wearable cardiac monitors scored highest (mean RMEI 0.818), while multi-parameter home hubs trailed at 0.596. Only 33.7 percent of programmes exceeded the 0.75 RMEI threshold. Clinical alert accuracy carried the largest regression weight (beta = +0.282), followed by patient adherence rate (beta = +0.226). These findings indicate that remote monitoring programmes should prioritise reducing false-positive alerts and simplifying patient-facing device interfaces to improve long-term adoption.