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Variations in provider practices in remote patient monitoring on peritoneal dialysis in the USA and Canada

医学 协议(科学) 腹膜透析 医疗急救 数据共享 外科 替代医学 病理
作者
Osama El Shamy,Raef Fadel,ED Weinhandl,Graham E. Abra,Megha Salani,JI Shen,Jeffrey Perl,TS Malavade,Dinesh K. Chatoth,MV Naljayan,KB Meyer,SQ Lew,MJ Oliver,TA Golper,Jaime Uribarri,RR Quinn
出处
期刊:Peritoneal Dialysis International [SAGE Publishing]
卷期号:45 (3): 190-194 被引量:6
标识
DOI:10.1177/08968608241270294
摘要

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.
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