A, Number of telemetry orders per week. B, Number of hours of telemetry use per patient. AHA indicates American Heart Association; brown lines, mean values; and horizontal shaded bars, upper and lower control limits (corresponding to 3 SDs).
Dressler R, Dryer MM, Coletti C, Mahoney D, Doorey AJ. Altering Overuse of Cardiac Telemetry in Non–Intensive Care Unit Settings by Hardwiring the Use of American Heart Association Guidelines. JAMA Intern Med. 2014;174(11):1852-1854. doi:10.1001/jamainternmed.2014.4491
Arrhythmia detection is reported to affect the clinical management of care in 3.4% to 12.7% of patients.1 The American Heart Association’s (AHA’s)2 published recommendations addressing the use of non–intensive care unit (non-ICU) cardiac telemetry stratify patients into 3 categories: cardiac telemetry is indicated, may provide benefit, or is unlikely to provide benefit. Clinical-effectiveness studies of implementing these guidelines have either reported the use of labor-intensive strategies3 or nonsustained decreases in non-ICU cardiac telemetry use.4 Various efforts to reduce the perceived overuse of cardiac telemetry at Christiana Care Health System, a 1100-bed tertiary care system, were unsuccessful. In August 2012 we convened a team to increase the appropriate use of non-ICU cardiac telemetry through the integration of AHA guidelines into our electronic ordering system (EOS). This effort was validated in March 2013 when non-ICU use of cardiac telemetry appeared on the Society of Hospital Medicine’s top 5 list for the Choosing Wisely campaign.5
Approval for this study was received from the institutional review board of Christiana Healthcare System; need for patient consent was waived. Our interdisciplinary team redesigned and standardized all cardiac telemetry orders within our EOS. Cardiac telemetry orders were removed from order sets for clinical conditions for which monitoring was not supported by the AHA guidelines.2 The remaining orders for cardiac telemetry required providers to select from a list of clinical indications, each with its AHA guideline–based predetermined telemetry duration (Box). Bedside nurse assessment guidelines were embedded in the EOS to facilitate safe, timely, and automatic discontinuation of cardiac telemetry. When telemetry discontinuation was believed to be unsafe, such as in a patient with unstable blood pressure, the nurse was required to contact the physician, and telemetry could be reordered when appropriate.
Chest pain, rule out MI
Nonurgent percutaneous coronary interventions
Implantation of an automatic defibrillator lead or a pacemaker lead
Uncomplicated ablation of an arrhythmia
Syncope of truly unknown origin
CHF, acute and subacute
Syncope with suspected arrhythmia
Thoracic (noncardiac) surgery
Complex major surgery
Cardiac surgery during this admission
Use of a wearable personable automatic defibrillator (LifeVest; ZOLL Medical Corp)
Complex cardiac disorders (eg, ventricular tachycardia storm)
Abbreviations: CHF, congestive heart failure; MI, myocardial infarction.
Adapted from Drew et al.2
We calculated total costs (direct and indirect) for the delivery of non-ICU telemetry. Time-motion studies were conducted to measure the nondirect patient care nursing time spent on telemetry-related tasks.
The study period began December 31, 2012, and ended August 12, 1013. The redesigned telemetry orders went into effect on March 18, 2013; there were 11 and 22 weeks in the preimplementation and postimplementation periods, respectively. In non-ICU patients 18 years or older, we measured the mean weekly number of patients with telemetry orders, the mean duration of telemetry, and the numbers of rapid response activations, codes, and deaths.
Implementation of the revised telemetry order sets resulted in an immediate and sustained reduction in the mean (SD) weekly number of telemetry orders from 1032.3 (32.1) to 593.2 (21.3), and the mean duration of telemetry fell from 57.8 (2.4) to 30.9 (0.9) hours (reductions of 43% and 47%, respectively; P < .001) (Figure). The mean daily number of patients monitored with telemetry decreased 70%, from 357.5 (20.6) to 109.1 (4.3). Hospital census, code blue, mortality, and rapid response team activation rates were stable throughout the observation period. Nurses spent a mean of 19.75 minutes per patient on telemetry-related tasks daily (>115 hours system wide). The estimated total daily cost to deliver telemetry was $53.44 per telemetry patient; thus, our mean daily cost for non-ICU cardiac telemetry decreased from $18 971 to $5772.
Although overuse of cardiac telemetry in non-ICU settings is widely recognized, there is a paucity of literature outlining successful and safe strategies addressing this concern. Our project led to a sustained 70% reduction in telemetry use without adversely affecting patient safety. In fact, patient safety may be enhanced by reducing the potential for alarm fatigue and provider workflow interruption.6 This initiative’s key success factors included the algorithm’s simplicity and focus on appropriateness, an interprofessional frontline team creating improvements for relevant disciplines, and “hardwiring” national guidelines into our EOS. This intervention is estimated to save our organization $4.8 million annually, suggesting that efforts addressing opportunities listed in the Choosing Wisely campaign can be an effective strategy to enhance value-added health care.
Corresponding Author: Robert Dressler, MD, MBA, Department of Medicine, Christiana Care Health System, Room 2C50, 4755 Ogletown-Stanton Rd, PO Box 6001, Newark, DE 19718 (firstname.lastname@example.org).
Published Online: September 22, 2014. doi:10.1001/jamainternmed.2014.4491.
Author Contributions: Ms Mahoney had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
Study concept and design: Dressler, Dryer, Coletti, Doorey.
Acquisition, analysis, or interpretation of data: All authors.
Drafting of the manuscript: Dressler, Dryer.
Critical revision of the manuscript for important intellectual content: All authors.
Statistical analysis: Dressler, Dryer, Mahoney.
Administrative, technical, or material support: Dressler, Dryer, Coletti, Mahoney.
Study supervision: Dressler, Doorey.
Conflict of Interest Disclosures: None reported.
Funding/Support: Participation of the students (Vasanth Chandrasekhar and Reiss Dhillon) was made possible by the Delaware IDeA Networks of Biomedical Research Excellence (INBRE) program, supported by grant National Institute of General Medical Sciences grant 8 P20 GM103446-13 from the National Institutes of Health.
Role of the Funder/Sponsor: The funding organization had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
Additional Contributions: Meredith Hollinger, PharmD (Christiana Care Health System), led the formulary leveling and policy adjustments, Sharon Kleban, MA, and Joyce Witkowski, RN (Christiana Care Health System), coded the computer redesign components and Tameka Thomas, RN, Brittney N. Henning, RN, and Paige Hilberg, RN (Christiana Care Health System), led the revisions of nursing protocols. Messrs Chandrasekhar (University of Delaware) and Dhillon (George Washington University), INBRE summer students (Christiana Care Health System), were instrumental in the financial analysis. Financial compensation was received only by Messrs Chandrasekhar and Dhillon.