Variations in provider practices in remote patient monitoring on peritoneal dialysis in the USA and Canada.
Osama El ShamyR FadelE D WeinhandlG AbraM SalaniJenny I ShenJ PerlT S MalavadeD ChatothM V NaljayanK B MeyerSusie Q LewMatthew J OliverT A GolperJaime UribarriRobert R QuinnPublished in: Peritoneal dialysis international : journal of the International Society for Peritoneal Dialysis (2024)
Automation has allowed clinicians to program PD treatment parameters, all while obtaining extensive individual treatment data. This data populates in a centralized online platform shortly after PD treatment completion. Individual treatment data available to providers includes patients' vital signs, alarms, bypasses, prescribed PD treatment, actual treatment length, individual cycle fill volumes, ultrafiltration volumes, as well as fill, dwell, and drain times. However, there is no guidance about how often or if this data should be assessed by the clinical team members. We set out to determine current practice patterns by surveying members of the home dialysis team managing PD patients across the United States and Canada. A total of 127 providers completed the survey. While 91% of respondents reported having access to a remote monitoring platform, only 31% reported having a standardized protocol for data monitoring. Rating their perceived importance of having a standard protocol for remote data monitoring, on a scale of 0 (not important at all) to 10 (extremely important), the average response was 8 (physicians 7; nurses 9). Most nurses reported reviewing the data multiple times per week, whereas most physicians reported viewing the data only during regular/monthly visits. Although most of the providers who responded have access to remote monitoring data and feel that regular review is important, the degree of its utilization is variable, and the way in which the information is used is not commonly protocolized. Working to standardize data interpretation, testing algorithms, and educating providers to help process and present the data are important next steps.
Keyphrases
- end stage renal disease
- electronic health record
- big data
- peritoneal dialysis
- healthcare
- chronic kidney disease
- primary care
- mental health
- machine learning
- clinical trial
- palliative care
- ejection fraction
- randomized controlled trial
- deep learning
- artificial intelligence
- prognostic factors
- high throughput
- social support
- case report
- cross sectional
- combination therapy
- study protocol
- replacement therapy