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
Complete and timely communication between health care providers is integral to seamless transitions in care [- ]. The transfer of critically ill patients from the intensive care unit (ICU) to another patient care unit (PCU) is a particularly vulnerable period in patient care, due to the high acuity of patients [ - ] as well as the number of health care providers involved and their professional diversity [ - ]. The movement of patients between units requires a high degree of collaboration, with verbal and written communication between health care providers [ - ] as well as patients and families [ , - ]. Suboptimal communication during transitions can have profound implications for patients, families, and the health care system [ - ], including increased risk of preventable medical errors, adverse events, redundant testing, readmissions, and dissatisfaction with the quality of care [ - ].
An ICU transfer summary is a clinical document that ICU physicians and nurse practitioners (NPs) often prepare to describe a patient’s stay in the ICU, including active and resolved health issues and the current care plan. The transfer summary is intended to support verbal communication between transferring and accepting medical teams and should provide sufficient detail to serve as a stand-alone communication . Complete and timely exchanges of patient care information during transitions in care are critical, not only for immediate continuity of care but also for efficient coordination of future care [ , ]. As such, the transfer summary should be easily accessible to inpatient and outpatient health care providers as part of the patient’s permanent health care record.
Standardized transfer protocols that structure documentation are integral for preventing failures in patient care due to incomplete and delayed exchange of information [, - ]. However, their value can be limited by the very methods used to produce the document. While quick for the clinician to prepare, traditional methods like dictation or handwritten notes in the patient chart have been associated with inaccurate, incomplete, and lengthy delays in communication [ , , , ], particularly in comparison to transfer summaries prepared using electronic standardized tools [ , - ]. The advancements of clinical information systems (CISs) and integrated electronic medical records (EMRs) provide a prime opportunity to optimize text-based communication. Structured templates can facilitate completeness of important patient information as well as substantiate and prompt verbal communication between health care providers at the point of care. They can also provide flexibility, permitting physicians to create a “living” document that can be edited over the course of stay and finalized at the point of patient transfer, effectively facilitating clinical workflow in complex settings. Despite the potential for optimizing efficient interprovider communication, the use of standardized tools to prepare ICU transfer summaries has not been widespread, with factors such as usability [ ], cost, and workload [ ] being barriers to adoption.
Local Initiative to Standardize Transfer Summaries: The Electronic ICU Transfer Tool
In 2017, we began designing an evidence-informed, computer-enabled, ICU-specific communication tool in the primary, integrated patient care CIS—Sunrise Clinical Manager (Eclipsys Corporation)—used in four acute care hospitals in a single Canadian city. This work was initiated as a quality improvement project to improve upon the conventional system of dictation that physicians and NPs—herein called ICU clinicians—use to prepare medical transfer summaries for ICU patients . To dictate a summary, ICU clinicians use eScription, 2010 release (Nuance Communications), a health information management dictation, speech recognition, and transcription (DST) platform. The clinician verbalizes relevant patient transfer information to create a voice file that is run through speech recognition software to create a text report. The report is then edited by a transcriptionist and sent to the designated ICU clinician for approval before being uploaded for electronic viewing in the CIS as well as to a provincial, web-based health data repository accessible by community-based physicians (Alberta Netcare).
The content and structure of the ICU electronic transfer (e-transfer) tool was based on a national survey of existing transfer summary tools , subsequent consensus-based recommendations of two independent multidisciplinary groups of health care providers [ , ], and a heuristic evaluation [ ]. The e-transfer tool consists of 10 overarching document sections: visit data, goals of care, allergy and intolerances, diagnoses and visit issues, course in ICU, investigations, medications, discharge to home or community, send copies to, and completion. These sections are designed with a user interface combination of structured fields (eg, checkboxes); automated fields, which pull in relevant patient data from other CIS locations; and free-text fields (see ). The tool permits ICU clinicians to open an ICU summary as a clinical document directly in the patient’s EMR and edit the summary over the course of the patient’s ICU stay. As with the DST system, the designated ICU clinician must approve transfer summaries. The summaries remain in the CIS and are uploaded to the provincial repository.
In a small pilot test of the e-transfer tool in one ICU , electronic summaries had a significantly greater proportion of essential information fields completed overall (median 87.5%) than those prepared by dictation (median 62.5%) and were available to receiving teams closer to patient release (2.3 versus 45.0 hours). Primary users of the e-transfer tool also responded positively to its use, establishing favorable evidence to scale up implementation across additional municipal hospitals.
In this study, we will evaluate the effectiveness of the ICU e-transfer tool for improved completeness and timeliness of transfer summaries and reduced adverse patient outcomes by comparing transfer summaries produced postimplementation to those produced preimplementation.
We will apply the Donabedian three-pronged model of health care quality (ie, structure, process, and outcome)  and the National Health Service Sustainability Model [ ] to frame our evaluation of the e-transfer tool. The Donabedian model has been successfully used in multiple contexts to support quality improvement initiatives related to structures (ie, health care context), processes (ie, actions and events in health care), patient outcomes (ie, effects on health status, quality, knowledge, or behavior), and use of resources [ , ]. Similarly, the National Health Service Sustainability Model has been successfully used to predict the likelihood of sustainability for improvement initiatives [ ]. In drawing from each of these models, we will ensure that we identify areas that need strengthening and that we are well positioned for sustainability and continual improvement.
This evaluation study takes place in four acute care hospitals servicing a single city, Calgary, Alberta, Canada, which has a referral population of approximately 1.7 million. Three of the four hospitals are academic hospitals operating a combined 56 adult medical-surgical ICU beds; the fourth is a nonacademic, community-based hospital operating 10 ICU beds. The annual ICU admission rate across the city approximates 3000 patients. In addition to the CIS hosting the e-transfer tool (ie, Sunrise Clinical Manager), all ICUs also use a dedicated provincial critical care CIS (ie, eCritical MetaVision) and clinical analytics system (ie, eCritical TRACER) that capture key demographic, clinical, health care service, and outcome data for all ICU patients . ICUs are staffed by multidisciplinary teams; those in academic-based hospitals operate with a clinical fellow and 4 to 10 residents working under the supervision of an attending physician. One ICU has an NP. Critical care resident rotation blocks are 4 weeks in duration. The community-based ICU functions with an attending physician and 4 NPs.
This study uses a cluster-specific pre-post trial design with randomized and staggered implementation of the e-transfer tool across four hospitals.
E-Transfer Tool Implementation
The e-transfer tool has been sequentially implemented into the four study hospitals at a new site every 2 months. This occurred between July 2018 and January 2019. The study biostatistician (AS), who was not involved with clinical practice in the ICUs, randomized the order of hospitals for implementation. Dictation remained available after implementation, but the ICU e-transfer tool was endorsed as the primary method to prepare ICU transfer summaries; as well, use of the tool was supported with strategies that have been successfully used in previous local initiatives, including in-person and web-based education, point-of-care support, and electronic audit and feedback .
ICU patients from the four participating hospitals were eligible for inclusion in the study if the patient (1) was admitted to the ICU during the defined pre-post periods; (2) was 18 years of age or older; (3) had an ICU stay equal to, or longer than, 24 hours; and (4) was transferred from the ICU to an in-hospital PCU. Patient admission lists were extracted retrospectively by a data analyst with the critical care CIS repository (ie, eCritical TRACER). As the primary creators of most ICU transfer summaries , NPs and residents were invited to participate in a brief survey soliciting their experience creating transfer summaries.
We set pre- and postimplementation data collection periods to extend for 6 months each, based on the staggered dates when the ICU e-transfer tool was implemented at each hospital. Patients transferred from the ICU prior to the intervention implementation date of their hospital are considered in the preimplementation period, while patients transferred from the ICU on or after the intervention implementation date of their hospital are considered in the postimplementation period.
Data collection involves (1) electronic extraction from provincial system repositories and a local critical care database, (2) manual abstraction from the patient’s electronic and paper medical record by trained researchers, and (3) manual rating of sampled transfer summaries by clinicians. Survey data of ICU clinician perspectives was collected pre- and postimplementation of the e-transfer tool. The flow of data collection is shown in. Where feasible, we are deidentifying hospital name, dates, and clinician and patient identifiers from clinical documents (eg, transfer summaries and clinician progress notes) secured for manual data abstraction. All data will be encrypted and retained in a secured office.
Patient demographic data includes the following: age; sex; ICU and hospital admission and discharge dates, times, and locations; hospital mortality; comorbidities; ICU interventions (ie, intubation, ventilation, vasoactive medications, and dialysis); and severity of illness measures, including the Acute Physiology and Chronic Health Evaluation II (APACHE II) score , the Glasgow Coma Scale (GCS) score [ ], and the Sequential Organ Failure Assessment (SOFA) score [ ].
The primary outcome of interest is a binary composite measure of two conditions: information presence and availability (see). In the first condition, the presence of four essential information elements in the transfer summary—goals of care designation, diagnosis, list of active issues on transfer, and medications to continue on transfer—will be assessed and recorded as yes or no. All four elements must be present to be recorded as yes. In the second condition, the availability of the transfer summary to the accepting clinicians at the time of patient transfer from the ICU will be recorded as yes or no. Transfer summaries that meet these two conditions will be coded as present; those that do not will be coded as absent.
Secondary outcomes of interest fall into three main domains (see): (1) transfer summary quality (ie, completeness, timeliness, and clinician ratings), (2) patient outcomes (ie, post-ICU rapid response activation, cardiac arrest, adverse events, and ICU readmission), and (3) clinician perceptions. The rate of use of the e-transfer tool will also be measured by extracting the type of method—dictation or tool—used to prepare the medical summary for each patient transferred from the ICU during the study period.
|Domain and outcome||Outcome description|
|Primary outcome—binary (present or absent) composite measure of two conditions: information presence and availability (both conditions need to be met)||Presence of four essential information elements:|
|Transfer summary quality|
|Overall completeness: proportion of eight requisite information elements present in transfer summary (%) and presence or absence of each essential information element in transfer summary||Summative score of the presence (score=1) of eight essential information elements—the four elements listed above and the following elements:|
|Timeliness: availability of summary relative to the date and time of patient transfer (in hours)||Difference between the following:|
|Clinician ratings: clinician ratings of perceived general quality of transfer summary (median, IQR)||Rate five criteria on a 7-point Likert scale:|
|Occurrence of negative patient outcomes within 3 days post–ICU transfer (%)||Patient events occurring within 3 days post-ICU:|
|Hospital and ICU total length of stay (in days)||Time between admission and discharge|
|Mortality (in hours)||Time from ICU transfer to hospital mortality|
|ICU clinicians’ perceptions of their last transfer summary||Rate seven criteria on a 7-point Likert scale:|
Transfer Summary Quality
Completeness of Information
Trained researchers will manually abstract overall completeness of information in the summary. Completeness will be calculated as the sum of the individual binary scores (1=present; 0=absent) that the researchers will record for eight prospectively identified information elements prioritized as requisite from a list of 63 essential elements identified as important in ICU transfer summaries . The eight information elements are as follows: goals of care designation, patient medical history, diagnosis, ICU active problem list, patient supportive care needs, patient attachments (ie, lines and tubes), active medications, and medication reconciliation. We designed a chart review form in REDCap (Research Electronic Data Capture) [ ] (see ). As the researchers will need to access relevant clinical documents in study patients’ medical records, they will not be blinded to the study period or hospital.
Timeliness of Information
Timeliness of the summary is defined as the difference in hours between the date and time the patient transferred out of the ICU and the date and time the transfer summary was either transcribed, in the case of dictated documents, or last updated in the CIS, in the case of e-transfer tool documents.
We will recruit ICU and PCU clinicians as volunteers to review and rate the general quality of a subsample of ICU transfer summaries randomly sampled from the larger pool of sampled summaries. Clinicians will use a 7-point scale to assess five criteria adapted from a previous study evaluating a similar tool [, ]: organization (ie, presentation was logical and clear), completeness (ie, no information gaps or omissions), pertinence (ie, all content was relevant to patient care), overall satisfaction with the quality of the summary, and degree of confidence that the accepting clinician will understand the patient care plan after reading the transfer summary (see ). Clinicians will be blinded to both the study period and hospital.
Incidents of ICU readmissions and rapid response team activations occurring within 3 days of ICU transfer were extracted from the critical care system repository; cardiac events within 3 days of ICU transfer were extracted from the Code Blue database maintained within the Department of Critical Care Medicine (see). Patients who were readmitted to the ICU within 3 days of their first ICU transfer will be further evaluated by a clinician (see ) to determine if the reason for their readmission was related to a health issue documented in the transfer summary of their first ICU admission; this will be recorded as yes, no, or unclear.
Adverse events within 3 days of ICU transfer will be abstracted using a two-stage manual abstraction process based on the Institute for Healthcare Improvement Global Trigger Tool (GTT) method of chart review . The GTT definition of an adverse event, as described on page 5 of the Institute for Healthcare Improvement white paper [ ], is “any unintended physical injury resulting from or contributed to by medical care that requires additional monitoring, treatment or hospitalization, or that results in death.” In Stage 1, two trained researchers will independently review the daily clinician progress notes charted in each patient’s paper medical record 3 days post–ICU transfer. Using a list of 19 patient safety indicators, they will identify and record yes, no, or unsure for each incident of a suspected adverse event. The patient safety indicators are based on Southern and colleagues’ [ ] list of 18 triggers adapted to a Canadian context with newer iterations of health coding data, with the addition of “patient falls.” In order to ensure good interrater reliability, Stage 1 reviewers will appraise a small sample of charts, compare results, and resolve any discrepancies before moving forward to evaluate the full sample. In Stage 2, any suspected adverse event recorded as yes or unsure during Stage 1 will be flagged for review by a third reviewer who will be a clinician. The clinician will review the notes and evaluate each suspected adverse event to confirm or reject the occurrence of the event using the GTT definition. In cases with a confirmed adverse event, the clinician reviewer will determine if the adverse event was preventable (ie, yes, no, or unsure), as well as designate the severity of the adverse event using the GTT categories of harm [ ].
ICU and hospital length of stay will be captured using ICU and hospital admission and discharge dates and times. In-hospital mortality will be captured as the time from ICU discharge to hospital mortality, with censoring at hospital discharge for those who survived hospital.
Clinician Perceptions of Practice
To obtain ICU clinician feedback on preparing transfer summaries (see), we will analyze survey data collected pre- and postimplementation of the e-transfer tool. Our survey was adapted from a validated survey used to assess physician perceptions using a similar transfer tool [ , ]. We disseminated it via paper and online to ICU NPs and residents. The time between the two dissemination periods was over a year, making response bias unlikely. Participants were asked to rate their experience completing their last transfer summary on seven criteria: process (ie, understood what to include and how to accomplish this), workload (ie, completing was manageable within routine ICU workflow), effectiveness (ie, able to communicate all relevant information clearly and logically), revision (ie, able to easily edit and update the transfer summary with new information), timeliness (ie, able to complete by the time the patient is transferred from ICU), satisfaction (ie, summary was of high quality), and confidence that the accepting medical team will understand the patient care plan. Participants were also asked to estimate how long it took them, in minutes, to complete their last ICU transfer summary.
Sample Size Calculations
Sample size calculations were based on the cluster-specific pre-post study design. Based on our pilot [, ], we calculated a required sample size of 144 pre- and 144 postimplementation ICU transfer summaries from each hospital to assess our primary outcome. This will be sufficient to detect an absolute difference in our primary outcome of 15% for each hospital with 82% power and an α value of 5% based on a baseline proportion of 20%; we observed a change in our pilot from 23% to 83%. A random sample of 24 ICU patients per hospital per month, over 6 months pre- and 6 months postimplementation, will facilitate secondary analyses, which accommodate the possibility of secular trends. The study biostatistician (AS) determined the random sample by assigning computer-generated random numbers to the complete list of patients transferred from each ICU within the study period, which was extracted by a data analyst with the critical care analytics system (see ). The study biostatistician was blinded to the method used to create the summary at the time of randomization.
To collect clinician ratings of the transfer summary quality, we calculated requiring 64 summaries preimplementation (ie, 16 per hospital × 4 hospitals = 64) and 64 summaries postimplementation (ie, 16 per hospital × 4 hospitals = 64), which will be sampled from aforementioned summaries, to detect an absolute difference in means as small as 0.5, assuming an SD of 1, with 80% power and an α value of 5%. The same patient cases will be used to assess for suspected post-ICU adverse events.
Demographic characteristics pre- and postimplementation for each hospital will be described using medians with IQRs, means with SDs, and frequencies with percentages, as appropriate. The primary outcome will be compared within each hospital pre- and postimplementation using separate logistic regression models for each hospital, with adjustment for the following patient characteristics: age, sex, reason for ICU admission, status on ICU admission (ie, Charlson Comorbidity Index, APACHE II, GCS, and SOFA), therapies received while in ICU (ie, ventilation, vasoactive medications, intermittent hemodialysis, and continuous renal replacement therapy), status on transfer (ie, transfer delay time, transfer decision cancellations, and ICU occupancy), and ICU length of stay. Pooled analyses across all four hospitals will use mixed-effects logistic regression models with a fixed effect for intervention and a fixed effect for time in months, in order to model the underlying secular trend. A fixed effect for patient characteristics will also be used, as noted above, and random effects will be used for hospital and hospital by time to account for intracluster and interperiod correlation. In case of poor model fit or convergence issues due to a limited number of clusters, hospital-level analyses will be considered by aggregating the primary outcome over all summaries in each hospital during each month and using linear regression of the aggregated cluster-period proportions of complete and timely summaries with fixed effects for hospital and time in months. Secondary outcomes will be analyzed as described for the primary outcome, using within-hospital and pooled analyses. Wilcoxon rank-sum tests will be used to compare ICU and hospital length of stay, and log-rank tests will be used to compare time from ICU discharge to hospital mortality.
Open-ended comments collected through clinician surveys will be analyzed according to standard practices of qualitative textual analysis.
Ethical Oversight and Trial Registration
The University of Calgary Conjoint Health Research Ethics Board reviewed this study (No. 17-2317) and granted a waiver of consent to collect retrospective data from relevant sections of patients’ paper medical records and EMRs. ICU clinicians who submit a survey will have implied their consent. Operational approvals and a data disclosure agreement was established with the provincial health custodian, Alberta Health Services. All protocol modifications will be reviewed by our research ethics board before being implemented. The trial was registered at ClinicalTrials.gov (NCT03590002).
Based on our study design, in fall 2019, the eCritical data analyst completed preliminary extraction of the list of patients transferred from the ICU within the 18-month range: February 12, 2018, to June 30, 2019. We have randomly sampled eligible patients from each ICU, restricting sampling to 6 months before and 6 months after the date the e-transfer tool was implemented in the hospital. Abstraction of primary and secondary outcomes is underway. We anticipate all data to be collected by early 2021, with data cleaning and analyses conducted and first results ready for publication in spring or summer 2021.
The ICU e-transfer tool was designed to improve and standardize textual communication between clinicians during transitions in care from the ICU to other PCUs. The number of individuals who experience and recover from critical illness in their lifetime is steadily increasing. The proliferation of life-sustaining technologies has resulted in new challenges with transitions in care of newly vulnerable critically ill patients. We have documented significant gaps in continuity of care for ICU patients, one of the most clinically high-risk groups in the health care system [, ]. The evidence-informed ICU e-transfer tool that we have developed and will evaluate can potentially optimize care across the health care continuum by mitigating communication errors and adverse events and contributing to improved experiences and outcomes for critically ill patients. Our evaluation will identify how the tool performs, what elements are effective, and what elements are ineffective and need to be refined or eliminated.
This research will build a foundation for addressing an identified priority gap in patient care by rigorously evaluating a standardized electronic tool that will be adaptable to individual settings and scalable across health care jurisdictions. The study findings will add to the current literature on the effect of computerized tools on reducing communication breaks between the ICU and other PCUs during transitions in care and to ultimately improve patient safety.
JPL and HS conceived of the study. All authors contributed to the study design. AS and MT provided statistical expertise. HS, DK, LWB, and RBM were involved in the implementation of the ICU e-transfer tool. JPL, JP, and RBM prepared the initial manuscript draft. All authors substantively revised the manuscript, and all authors have read and approved the submitted version. This work is supported by the Canadian Frailty Network (grant No. KT2017-15-Grant) and the Canadian Institutes of Health Research (grant No. RN381460-420324). The funders had no role in the study design or in the submission of this manuscript; they will not take part in the collection or analysis of data or in the assessment of outcomes.
Conflicts of Interest
Intensive care unit (ICU) transfer summary data abstraction form in REDCap (Research Electronic Data Capture).PDF File (Adobe PDF File), 438 KB
Grant funding agency peer reviewer comments.PDF File (Adobe PDF File), 167 KB
- Li P, Stelfox HT, Ghali WA. A prospective observational study of physician handoff for intensive-care-unit-to-ward patient transfers. Am J Med 2011 Sep;124(9):860-867. [CrossRef] [Medline]
- Lin F, Chaboyer W, Wallis M. A literature review of organisational, individual and teamwork factors contributing to the ICU discharge process. Aust Crit Care 2009 Feb;22(1):29-43. [CrossRef] [Medline]
- Audit Commission. Setting the Record Straight: A Review of the Progress in Health Records Services. London, UK: HMSO; 1999.
- The role of the ICU: What to expect. Canadian Critical Care Scociety. URL: https://www.canadiancriticalcare.org/Patients-&-Families [accessed 2020-12-12]
- Prendergast TJ, Luce JM. Increasing incidence of withholding and withdrawal of life support from the critically ill. Am J Respir Crit Care Med 1997 Jan;155(1):15-20. [CrossRef] [Medline]
- Tonelli MR, Misak CJ. Compromised autonomy and the seriously ill patient. Chest 2010 Apr;137(4):926-931. [CrossRef] [Medline]
- Apker J, Mallak LA, Gibson SC. Communicating in the "gray zone": Perceptions about emergency physician hospitalist handoffs and patient safety. Acad Emerg Med 2007 Oct;14(10):884-894 [FREE Full text] [CrossRef] [Medline]
- Riesenberg LA, Leitzsch J, Massucci JL, Jaeger J, Rosenfeld JC, Patow C, et al. Residents' and attending physicians' handoffs: A systematic review of the literature. Acad Med 2009 Dec;84(12):1775-1787. [CrossRef] [Medline]
- Horwitz LI, Meredith T, Schuur JD, Shah NR, Kulkarni RG, Jenq GY. Dropping the baton: A qualitative analysis of failures during the transition from emergency department to inpatient care. Ann Emerg Med 2009 Jun;53(6):701-710.e4. [CrossRef] [Medline]
- Pronovost P, Vohr E. Safe Patients, Smart Hospitals: How One Doctor's Checklist Can Help Us Change Health Care from the Inside Out. New York, NY: Hudson Street Press; 2010.
- Leith BA. Patients' and family members' perceptions of transfer from intensive care. Heart Lung 1999;28(3):210-218. [CrossRef] [Medline]
- Saarmann L. Transfer out of critical care: Freedom or fear? Crit Care Nurs Q 1993 May;16(1):78-85. [CrossRef] [Medline]
- Odell M. The patient's thoughts and feelings about their transfer from intensive care to the general ward. J Adv Nurs 2000 Feb;31(2):322-329. [CrossRef] [Medline]
- Jacobs P, Noseworthy TW. National estimates of intensive care utilization and costs: Canada and the United States. Crit Care Med 1990 Nov;18(11):1282-1286. [CrossRef] [Medline]
- Needham DM, Bronskill SE, Calinawan JR, Sibbald WJ, Pronovost PJ, Laupacis A. Projected incidence of mechanical ventilation in Ontario to 2026: Preparing for the aging baby boomers. Crit Care Med 2005 Mar;33(3):574-579. [CrossRef] [Medline]
- Finfer S, Vincent J. Critical care--An all-encompassing specialty. N Engl J Med 2013 Aug 15;369(7):669-670. [CrossRef] [Medline]
- Stelfox HT, Soo A, Niven DJ, Fiest KM, Wunsch H, Rowan KM, et al. Assessment of the safety of discharging select patients directly home from the intensive care unit: A multicenter population-based cohort study. JAMA Intern Med 2018 Oct 01;178(10):1390-1399 [FREE Full text] [CrossRef] [Medline]
- Li P, Boyd JM, Ghali WA, Stelfox HT. Stakeholder views regarding patient discharge from intensive care: Suboptimal quality and opportunities for improvement. Can Respir J 2015;22(2):109-118 [FREE Full text] [CrossRef] [Medline]
- Bell CM, Brener SS, Gunraj N, Huo C, Bierman AS, Scales DC, et al. Association of ICU or hospital admission with unintentional discontinuation of medications for chronic diseases. JAMA 2011 Aug 24;306(8):840-847. [CrossRef] [Medline]
- Stelfox HT, Lane D, Boyd JM, Taylor S, Perrier L, Straus S, et al. A scoping review of patient discharge from intensive care: Opportunities and tools to improve care. Chest 2015 Feb;147(2):317-327 [FREE Full text] [CrossRef] [Medline]
- Brooke J, Hasan N, Slark J, Sharma P. Efficacy of information interventions in reducing transfer anxiety from a critical care setting to a general ward: A systematic review and meta-analysis. J Crit Care 2012 Aug;27(4):425.e9-425.e15. [CrossRef] [Medline]
- Camiré E, Moyen E, Stelfox HT. Medication errors in critical care: Risk factors, prevention and disclosure. CMAJ 2009 Apr 28;180(9):936-943 [FREE Full text] [CrossRef] [Medline]
- Lyons PG, Arora VM, Farnan JM. Adverse events and near-misses relating to intensive care unit-ward transfer: A qualitative analysis of resident perceptions. Ann Am Thorac Soc 2016 Apr;13(4):570-572. [CrossRef] [Medline]
- Santhosh L, Lyons PG, Rojas JC, Ciesielski TM, Beach S, Farnan JM, et al. Characterising ICU-ward handoffs at three academic medical centres: Process and perceptions. BMJ Qual Saf 2019 Aug;28(8):627-634. [CrossRef] [Medline]
- de Grood C, Leigh JP, Bagshaw SM, Dodek PM, Fowler RA, Forster AJ, et al. Patient, family and provider experiences with transfers from intensive care unit to hospital ward: A multicentre qualitative study. CMAJ 2018 Jun 04;190(22):E669-E676 [FREE Full text] [CrossRef] [Medline]
- Santana MJ, Holroyd-Leduc J, Southern DA, Flemons WW, O'Beirne M, Hill MD, e-DCT Team. A randomised controlled trial assessing the efficacy of an electronic discharge communication tool for preventing death or hospital readmission. BMJ Qual Saf 2017 Dec;26(12):993-1003. [CrossRef] [Medline]
- Mathioudakis A, Rousalova I, Gagnat AA, Saad N, Hardavella G. How to keep good clinical records. Breathe (Sheff) 2016 Dec;12(4):369-373 [FREE Full text] [CrossRef] [Medline]
- Pilcher DV, Duke GJ, George C, Bailey MJ, Hart G. After-hours discharge from intensive care increases the risk of readmission and death. Anaesth Intensive Care 2007 Aug;35(4):477-485 [FREE Full text] [CrossRef] [Medline]
- Pronovost P, Weast B, Schwarz M, Wyskiel RM, Prow D, Milanovich SN, et al. Medication reconciliation: A practical tool to reduce the risk of medication errors. J Crit Care 2003 Dec;18(4):201-205. [CrossRef] [Medline]
- Graham AJ, Ocampo W, Southern DA, Falvi A, Sotiropoulos D, Wang B, et al. Evaluation of an electronic health record structured discharge summary to provide real time adverse event reporting in thoracic surgery. BMJ Qual Saf 2019 Apr;28(4):310-316. [CrossRef] [Medline]
- Stelfox HT, Leigh JP, Dodek PM, Turgeon AF, Forster AJ, Lamontagne F, et al. A multi-center prospective cohort study of patient transfers from the intensive care unit to the hospital ward. Intensive Care Med 2017 Oct;43(10):1485-1494. [CrossRef] [Medline]
- Brown KN, Leigh JP, Kamran H, Bagshaw SM, Fowler RA, Dodek PM, et al. Transfers from intensive care unit to hospital ward: A multicentre textual analysis of physician progress notes. Crit Care 2018 Jan 28;22(1):19 [FREE Full text] [CrossRef] [Medline]
- Mehta RL, Baxendale B, Roth K, Caswell V, Le Jeune I, Hawkins J, et al. Assessing the impact of the introduction of an electronic hospital discharge system on the completeness and timeliness of discharge communication: A before and after study. BMC Health Serv Res 2017 Sep 05;17(1):624 [FREE Full text] [CrossRef] [Medline]
- Motamedi SM, Posadas-Calleja J, Straus S, Bates DW, Lorenzetti DL, Baylis B, et al. The efficacy of computer-enabled discharge communication interventions: A systematic review. BMJ Qual Saf 2011 May;20(5):403-415. [CrossRef] [Medline]
- O'Leary KJ, Liebovitz DM, Feinglass J, Liss DT, Evans DB, Kulkarni N, et al. Creating a better discharge summary: Improvement in quality and timeliness using an electronic discharge summary. J Hosp Med 2009 Apr;4(4):219-225. [CrossRef] [Medline]
- Palma JP, Sharek PJ, Longhurst CA. Impact of electronic medical record integration of a handoff tool on sign-out in a newborn intensive care unit. J Perinatol 2011 May;31(5):311-317 [FREE Full text] [CrossRef] [Medline]
- Reinke CE, Kelz RR, Baillie CA, Norris A, Schmidt S, Wingate N, et al. Timeliness and quality of surgical discharge summaries after the implementation of an electronic format. Am J Surg 2014 Jan;207(1):7-16. [CrossRef] [Medline]
- Kripalani S, LeFevre F, Phillips CO, Williams MV, Basaviah P, Baker DW. Deficits in communication and information transfer between hospital-based and primary care physicians: Implications for patient safety and continuity of care. JAMA 2007 Feb 28;297(8):831-841. [CrossRef] [Medline]
- de Grood C, Eso K, Santana MJ. Physicians' experience adopting the electronic transfer of care communication tool: Barriers and opportunities. J Multidiscip Healthc 2015;8:21-31 [FREE Full text] [CrossRef] [Medline]
- Granja C, Janssen W, Johansen MA. Factors determining the success and failure of eHealth interventions: Systematic review of the literature. J Med Internet Res 2018 May 01;20(5):e10235 [FREE Full text] [CrossRef] [Medline]
- Parsons Leigh J, Brundin-Mather R, Zjadewicz K, Soo A, Stelfox HT. Improving transitions in care from intensive care units: Development and pilot testing of an electronic communication tool for healthcare providers. J Crit Care 2020 Apr;56:265-272. [CrossRef] [Medline]
- Boyd JM, Roberts DJ, Parsons Leigh J, Stelfox HT. Administrator perspectives on ICU-to-ward transfers and content contained in existing transfer tools: A cross-sectional survey. J Gen Intern Med 2018 Oct;33(10):1738-1745 [FREE Full text] [CrossRef] [Medline]
- ICU Transfer Summary Consensus Panel, de Grood C, Job McIntosh C, Boyd JM, Zjadewicz K, Parsons Leigh J, et al. Identifying essential elements to include in intensive care unit to hospital ward transfer summaries: A consensus methodology. J Crit Care 2019 Feb;49:27-32. [CrossRef] [Medline]
- Donabedian A. Evaluating the quality of medical care. Milbank Mem Fund Q 1966 Jul;44(3):Suppl:166-Suppl:206. [Medline]
- Maher L, Gustafson D, Evans A. NHS Sustainability Model and Guide. Coventry, UK: NHS Institute for Innovation and Improvement; 2010. URL: https://webarchive.nationalarchives.gov.uk/20160805122935/http://www.nhsiq.nhs.uk/media/2757778/nhs_sustainability_model_-_february_2010_1_.pdf [accessed 2020-12-13]
- Stelfox HT, Brundin-Mather R, Soo A, Parsons Leigh J, Niven DJ, Fiest KM, et al. A multicentre controlled pre-post trial of an implementation science intervention to improve venous thromboembolism prophylaxis in critically ill patients. Intensive Care Med 2019 Feb;45(2):211-222. [CrossRef] [Medline]
- Stelfox HT, Bobranska-Artiuch B, Nathens A, Straus SE. Quality indicators for evaluating trauma care: A scoping review. Arch Surg 2010 Mar;145(3):286-295. [CrossRef] [Medline]
- Doyle C, Howe C, Woodcock T, Myron R, Phekoo K, McNicholas C, et al. Making change last: Applying the NHS institute for innovation and improvement sustainability model to healthcare improvement. Implement Sci 2013 Oct 26;8:127 [FREE Full text] [CrossRef] [Medline]
- Brundin-Mather R, Soo A, Zuege DJ, Niven DJ, Fiest K, Doig CJ, et al. Secondary EMR data for quality improvement and research: A comparison of manual and electronic data collection from an integrated critical care electronic medical record system. J Crit Care 2018 Oct;47:295-301. [CrossRef] [Medline]
- Sauro KM, Brundin-Mather R, Parsons Leigh J, Niven DJ, Kushner B, Soo A, et al. Improving the adoption of optimal venous thromboembolism prophylaxis in critically ill patients: A process evaluation of a complex quality improvement initiative. J Crit Care 2019 Apr;50:111-117. [CrossRef] [Medline]
- Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: A severity of disease classification system. Crit Care Med 1985 Oct;13(10):818-829. [Medline]
- Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet 1974 Jul 13;2(7872):81-84. [CrossRef] [Medline]
- Vincent JL, Moreno R, Takala J, Willatts S, De Mendonça A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996 Jul;22(7):707-710. [CrossRef] [Medline]
- Harris PA, Taylor R, Minor BL, Elliott V, Fernandez M, O'Neal L, REDCap Consortium. The REDCap consortium: Building an international community of software platform partners. J Biomed Inform 2019 Jul;95:103208 [FREE Full text] [CrossRef] [Medline]
- Santana MJ, Holroyd-Leduc J, Flemons WW, O'Beirne M, White D, Clayden N, et al. The seamless transfer of care: A pilot study assessing the usability of an electronic transfer of care communication tool. Am J Med Qual 2014;29(6):476-483. [CrossRef] [Medline]
- Griffin FA, Resar RK. IHI Global Trigger Tool for Measuring Adverse Events (Second Edition). IHI Innovation Series white paper. Cambridge, MA: Institute for Healthcare Improvement; 2009. URL: http://app.ihi.org/webex/gtt/ihiglobaltriggertoolwhitepaper2009.pdf [accessed 2020-12-10]
- Southern DA, Burnand B, Droesler SE, Flemons W, Forster AJ, Gurevich Y, et al. Deriving ICD-10 codes for patient safety indicators for large-scale surveillance using administrative hospital data. Med Care 2017 Mar;55(3):252-260. [CrossRef] [Medline]
|APACHE II: Acute Physiology and Chronic Health Evaluation II|
|CIS: clinical information system|
|DST: dictation, speech recognition, and transcription|
|EMR: electronic medical record|
|e-transfer: electronic transfer|
|GCS: Glasgow Coma Scale|
|GTT: Global Trigger Tool|
|ICU: intensive care unit|
|NP: nurse practitioner|
|PCU: patient care unit|
|REDCap: Research Electronic Data Capture|
|SOFA: Sequential Organ Failure Assessment|
Edited by G Eysenbach; submitted 18.03.20; peer-reviewed by S Tajalli; comments to author 03.11.20; revised version received 16.11.20; accepted 17.11.20; published 08.01.21Copyright
©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.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org, as well as this copyright and license information must be included.