An automated personalised intervention algorithm for remote patient monitoring.

An automated personalised intervention algorithm was developed to determine when and if patients with chronic disease in a remote monitoring programme required intervention for management of their condition. The effectiveness of the algorithm has so far been evaluated on 29 patients. It was found to be particularly effective in monitoring newly diagnosed patients, patients requiring a change in medication as well as highlighting those that were not conforming to their medication. Our approach indicates that RPM used with the intervention algorithm and a clinical protocol can be effective in a primary care setting for targeting those patients that would most benefit from monitoring.

Early experience in using telemonitoring for the management of chronic disease in primary care.

We have investigated the use of telemonitoring in three long-term conditions: chronic heart failure (CHF), type 2 diabetes and essential hypertension. Participants were provided with a home telemonitoring unit for a 12-week period and entered physiological data each day. The data were sent automatically via the participant's telephone line to a server and could be viewed via a web browser. An intervention algorithm was developed to improve the accuracy with which patients requiring intervention were recognized compared to existing systems based on a simple threshold. Thirty patients completed the 12-week trial. One patient dropped out, giving data on 29 patients (mean age 70 years, 17 women). The algorithm prompted a clinical intervention in 11 patients (38%). The average time that elapsed before the first intervention was 47 days (SD 21). Primarily the interventions (72%) resulted in changes to medication and health advice. The results suggest that four weeks is sufficient time in which to recognize the need to intervene clinically and that in 12 weeks it is possible to effect a change towards a target.