Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions

Gyllensten, Illapha Cuba; Crundall-Goode, Amanda; Aarts, Ronald M.; Goode, Kevin M.

Abstract

© 2017 The Author(s). Background: Home telemonitoring (HTM) of chronic heart failure (HF) promises to improve care by timely indications when a patient's condition is worsening. Simple rules of sudden weight change have been demonstrated to generate many alerts with poor sensitivity. Trend alert algorithms and bio-impedance (a more sensitive marker of fluid change), should produce fewer false alerts and reduce workload. However, comparisons between such approaches on the decisions made and the time spent reviewing alerts has not been studied. Methods: Using HTM data from an observational trial of 91 HF patients, a simulated telemonitoring station was created and used to present virtual caseloads to clinicians experienced with HF HTM systems. Clinicians were randomised to either a simple (i.e. an increase of 2 kg in the past 3 days) or advanced alert method (either a moving average weight algorithm or bio-impedance cumulative sum algorithm). Results: In total 16 clinicians reviewed the caseloads, 8 randomised to a simple alert method and 8 to the advanced alert methods. Total time to review the caseloads was lower in the advanced arms than the simple arm (80 ± 42 vs. 149 ± 82 min) but agreements on actions between clinicians were low (Fleiss kappa 0.33 and 0.31) and despite having high sensitivity many alerts in the bio-impedance arm were not considered to need further action. Conclusion: Advanced alerting algorithms with higher specificity are likely to reduce the time spent by clinicians and increase the percentage of time spent on changes rated as most meaningful. Work is needed to present bio-impedance alerts in a manner which is intuitive for clinicians.

Citation

Gyllensten, I. C., Crundall-Goode, A., Aarts, R. M., & Goode, K. M. (2017). Simulated case management of home telemonitoring to assess the impact of different alert algorithms on work-load and clinical decisions. BMC Medical Informatics and Decision Making, 17(1), Article ARTN 11. https://doi.org/10.1186/s12911-016-0398-9

• Journal Article Type: Article
• Acceptance Date: Dec 9, 2016
• Online Publication Date: Jan 17, 2017
• Publication Date: Jan 17, 2017
• Deposit Date: Mar 10, 2017
• Publicly Available Date: Mar 10, 2017
• Journal: BMC medical informatics and decision making
• Electronic ISSN: 1472-6947
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 17
• Issue: 1
• Article Number: ARTN 11
• DOI: https://doi.org/10.1186/s12911-016-0398-9
• Keywords: Health Policy; Health Informatics
• Public URL: https://hull-repository.worktribe.com/output/449418
• Publisher URL: https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-016-0398-9
• Additional Information: This is a copy of an article published in BMC medical informatics and decision making, 2017, v.17 issue 11.
• Contract Date: Mar 10, 2017