Predictive performance and impact of algorithms in remote monitoring of chronic conditions: a systematic review and meta-analysis (Preprint)

Abstract

UNSTRUCTURED Objectives: 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 and mortality, but there was a positive effect on generic health status and diabetes control (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, most performance studies (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 had superior performance to those involved in predicting a future event. Detection of arrythmia and ischaemia utilising ECG data showed particularly promising results. Discussion/Conclusion: The performance of some data processing algorithms is promising. However, most of these algorithms have not been tested in experimental impact studies. Thus, there is currently limited evidence of the effect of integrating advanced inference algorithms in remote monitoring interventions.