BACKGROUND
The use of telehealth interventions, such as the remote monitoring of patient clinical data (e.g. blood pressure, blood glucose, heart rate, medication use), has been proposed as a strategy to better manage chronic conditions and to reduce the impact on patients and healthcare systems. The use of algorithms for data acquisition, analysis, transmission, communication and visualisation are now common in remote patient monitoring. However, their use and impact on chronic disease management has not been systematically investigated.
OBJECTIVE
To investigate the use, impact, and performance of remote monitoring algorithms across various types of chronic conditions.
METHODS
A literature search of MEDLINE complete, CINHAL complete, and EMBASE was performed using search terms relating to the concepts of remote monitoring, chronic conditions, and data processing algorithms. Comparable outcomes from studies describing the impact on process measures and clinical and patient-reported outcomes were pooled for a summary effect and meta-analyses. A comparison of studies reporting the predictive performance of algorithms was also conducted using the Youden Index.
RESULTS
A total of 89 articles were included in the review. There was no evidence of a positive impact on healthcare utilisation [OR 1.09 (0.90 to 1.31); P=.35] and mortality [OR 0.83 (0.63 to 1.10); P=.208], but there was a positive effect on generic health status [SDM 0.29 (0.06 to 0.51); P=.010] and diabetes control [SDM -0.53 (-0.74 to -0.33); P<.001; I2 = 15.71] (with two of the three diabetes studies being identified as having a high risk of bias). While the majority of impact studies made use of heuristic threshold-based algorithms (n=27 ,87%), most performance studies (n=36, 62%) analysed non-sequential machine learning methods. There was considerable variance in the quality, sample size and performance amongst these studies. Overall, algorithms involved in diagnosis (n=22, 47%) had superior performance to those involved in predicting a future event (n=25, 53%). Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results.
CONCLUSIONS
The performance of data processing algorithms involved with the diagnosis of a current condition, particularly those related to the detection of arrythmia and ischaemia, is promising. However, there appears to minimal testing in experimental studies, with only two included impact studies citing a performance study as support for the intervention algorithm used. Thus, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions.
CLINICALTRIAL