Effectiveness of an Electronic Communication Tool on Transitions in Care From the Intensive Care Unit: Protocol for a Cluster-Specific Pre-Post Trial

Jeanna Parsons Leigh, Rebecca Brundin-Mather, Liam Whalen-Browne, Devika Kashyap, Khara Sauro, Andrea Soo, Jennie Petersen, Monica Taljaard, Henry T Stelfox, Jeanna Parsons Leigh, Rebecca Brundin-Mather, Liam Whalen-Browne, Devika Kashyap, Khara Sauro, Andrea Soo, Jennie Petersen, Monica Taljaard, Henry T Stelfox

Abstract

Background: Transitions in care are vulnerable periods in health care that can expose patients to preventable errors due to incomplete or delayed communication between health care providers. Transitioning critically ill patients from intensive care units (ICUs) to other patient care units (PCUs) is particularly risky, due to the high acuity of the patients and the diversity of health care providers involved in their care. Instituting structured documentation to standardize written communication between health care providers during transitions has been identified as a promising means to reduce communication breakdowns. We developed an evidence-informed, computer-enabled, ICU-specific structured tool-an electronic transfer (e-transfer) tool-to facilitate and standardize the composition of written transfer summaries in the ICUs of one Canadian city. The tool consisted of 10 primary sections with a user interface combination of structured, automated, and free-text fields.

Objective: Our overarching goal is to evaluate whether implementation of our e-transfer tool will improve the completeness and timeliness of transfer summaries and streamline communications between health care providers during high-risk transitions.

Methods: This study is a cluster-specific pre-post trial, with randomized and staggered implementation of the e-transfer tool in four hospitals in Calgary, Alberta. Hospitals (ie, clusters) were allocated randomly to cross over every 2 months from control (ie, dictation only) to intervention (ie, e-transfer tool). Implementation at each site was facilitated with user education, point-of-care support, and audit and feedback. We will compare transfer summaries randomly sampled over 6 months postimplementation to summaries randomly sampled over 6 months preimplementation. The primary outcome will be a binary composite measure of the timeliness and completeness of transfer summaries. Secondary measures will include overall completeness, timeliness, and provider ratings of transfer summaries; hospital and ICU lengths of stay; and post-ICU patient outcomes, including ICU readmission, adverse events, cardiac arrest, rapid response team activation, and mortality. We will use descriptive statistics (ie, medians and means) to describe demographic characteristics. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for patient characteristics.

Results: Participating hospitals were cluster randomized to the intervention between July 2018 and January 2019. Preliminary extraction of ICU patient admission lists was completed in September 2019. We anticipate that evaluation data collection will be completed by early 2021, with first results ready for publication in spring or summer 2021.

Conclusions: This study will report the impact of implementing an evidence-informed, computer-enabled, ICU-specific structured transfer tool on communication and preventable medical errors among patients transferred from the ICU to other hospital care units.

Trial registration: ClinicalTrials.gov NCT03590002; https://www.clinicaltrials.gov/ct2/show/NCT03590002.

International registered report identifier (irrid): DERR1-10.2196/18675.

Keywords: ICU; clinical documentation; communication; discharge tools; effective; electronic charting; electronic tool; electronic transfer summaries; intensive care unit; interprovider communication; patient; patient discharge summaries; patient transfers; protocol; transfer; transition; transitions in care.

Conflict of interest statement

Conflicts of Interest: None declared.

©Jeanna Parsons Leigh, Rebecca Brundin-Mather, Liam Whalen-Browne, Devika Kashyap, Khara Sauro, Andrea Soo, Jennie Petersen, Monica Taljaard, Henry T Stelfox. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 08.01.2021.

Figures

Figure 1
Figure 1
Electronic transfer tool sections and screenshots. ICU: intensive care unit.
Figure 2
Figure 2
Data collection flow. B: critical care Code Blue database (data source); CIS: clinical information system; E: electronic extraction by CIS analyst (data collection method); ICU: intensive care unit; LOS: length of stay; M: manual extraction by study researcher (data collection method); P: paper chart (ie, medical doctor or nurse practitioner daily progress notes; data source); S: Hospital CIS (ie, Sunrise Clinical Manager; data source); T: critical care CIS analytics (ie, eCritical TRACER; data source).

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