Optimizing Decision Support Alerts to Reduce Telemetry Duration: A Multicenter Evaluation

Abstract

Background Telemetry monitoring is crucial for high-risk patients but excessive use beyond practice standards increases costs. Prior studies have shown that electronic health record (EHR) alerts reduce low-value telemetry monitoring. However, specific components of these alerts that contribute to effectiveness are unknown.

Objectives We aimed to revise previously implemented EHR Best Practice Advisories (BPAs) to optimize their effectiveness in reducing telemetry duration. The secondary objective was to assess the impact on clinicians' alert burden.

Methods A multicenter retrospective study was conducted at Johns Hopkins Hospital (JHH), Johns Hopkins Bayview Medical Center (JHBMC), and Howard County General Hospital (HCGH). An EHR alert in the form of a BPA was previously implemented at JHH/JHBMC, firing at 24, 48, or 72 hours based on order indication. HCGH used an alert firing every 24 hours. A revised BPA was implemented at all hospitals optimizing the prior JHH/JHBMC alert by including patient-specific telemetry indications, restricting alerts to daytime hours (8:00 a.m.–6:00 p.m.), and embedding the discontinuation order within the BPA alert. A retrospective analysis from October 2018 to December 2021 was performed. The primary outcome was telemetry duration. The secondary outcome was the mean monthly BPA alerts per patient-day.

Results Compared with the original BPA, the revised BPA reduced telemetry duration by a mean of 6.7 hours (95% CI: 5.2–9.1 hours, p < 0.001) at JHH/JHBMC, with a minimal increase of 0.06 mean monthly BPA alerts per patient-day (p < 0.001). The BPA acceptance rate increased from 7.8 to 31.3% postintervention at JHH/JHBMC (p < 0.0001). At HCGH, the intervention led to a mean monthly reduction of 20.2 hours in telemetry duration per hospitalization (95% CI: 19.1–22.8 hours, p < 0.0001).

Conclusion Optimizing EHR BPAs reduces unnecessary telemetry duration without substantially increasing clinician alert burden. This study highlights the importance of tailoring EHR alerts to enhance effectiveness and promote value-based care.

Keywords

telemetry - clinical decision support - electronic health record - value-based health care

Background and Significance

Continuous electrocardiographic monitoring of hospitalized patients, known as telemetry monitoring, is recommended for specific cardiac indications with suggested durations by the 2017 American Heart Association (AHA) practice standards.[1] Telemetry monitoring benefits high-risk patients by enabling timely management decisions when significant arrhythmias occur.[2] However, the yield of telemetry monitoring beyond AHA recommendations is low. Prior studies report a low incidence of clinically significant arrhythmias on nonindicated telemetry monitoring days (3.1 per 100 days) and only a 0.01% occurrence rate of potentially life-threatening arrhythmias among 7,200 telemetry alarms.[3] [4]

Telemetry monitoring for noncritically ill patients often does not align with AHA practice standards, with an estimated 35 to 76% of telemetry days deemed inappropriate.[3] [5] [6] This inappropriate use is associated with increased health care costs, estimated at $53 per patient per day in time–motion studies that assess nursing time spent on telemetry-related tasks and equipment costs.[3] [5] [6] [7]

Interventions to reduce unnecessary telemetry monitoring based on AHA practice standards have been shown not to compromise patient safety, as measured by the frequency of code and Rapid Response Team activations.[7] [8] [9] Therefore, the AHA, Choosing Wisely campaign, and Society of Hospital Medicine recommend the judicious use of telemetry based on AHA practice standards.[1] [10] Previous interventions to reduce unnecessary telemetry use included multipronged approaches incorporating stakeholder education, empowerment of nursing staff to discontinue telemetry, development of audit/feedback systems, and implementation of electronic health record (EHR) ordering systems with embedded practice standards and telemetry discontinuation alerts.[7] [11] [12] [13] [14] Specifically, EHR alerts used as a single-component intervention have been effective in reducing telemetry duration without associated adverse events.[9] [15] [16] [17] [18]

As EHR alerts have variable features and configurations, their effectiveness may change based on individual components. In general, alert acceptance rates and effectiveness improve when iterative revisions based on user experience are incorporated, focusing on maximizing patient data incorporation and including features that reduce the need to “do work twice” by accepting an alert and separately placing a discontinuation order.[19] [20] [21] [22] Prior studies demonstrating the effectiveness of EHR alerts in reducing telemetry duration have used comparison groups without EHR alerts.[9] [15] [16] [17] To our knowledge, no prior studies have assessed the effectiveness of iterative changes to EHR alerts compared with previously implemented nonoptimized EHR alerts. As such, data on important technological features of EHR alerts that enhance effectiveness are lacking.

Objectives

This study aimed to fill the gap in the existing literature by evaluating the incremental benefit of optimizing specific features of EHR alerts compared with previously implemented, less optimized alerts. The primary objective of this study was to enhance previously implemented EHR alerts, specifically Best Practice Advisories (BPAs), to reduce telemetry monitoring duration for hospitalized patients. As a secondary outcome, we assessed the impact of targeted BPAs on the number of alerts encountered by providers, referred to hereafter as the clinicians' alert burden. We hypothesized that tailoring the BPA alerts to include patient-specific information on the indication-based duration of telemetry, to fire during typical daytime shifts, and to embed a discontinuation order in the alert, would reduce telemetry duration and alert burden.

Methods

The study was approved by the Institutional Review Boards at Johns Hopkins Hospital (JHH), Johns Hopkins Bayview Medical Center (JHBMC), and Howard County General Hospital (HCGH).

Setting

We included three hospitals in this study: JHH, a 942-bed academic medical center in Baltimore, MD; JHBMC, a 468-bed academic medical center in Baltimore, MD; and HCGH, a 244-bed community hospital in Columbia, MD. All hospitals used the same Epic (Verona, WI) EHR system. Patients at JHH and JHBMC are cared for by resident physicians, fellows, advanced practice providers (APPs), and attending physicians. Patients at HCGH are cared for by APPs and attending physicians. At each hospital, the provider assigned as the point of first contact for issues with a particular patient is given the role “First Call,” and the primary attending physician has the role “Attending” on the patient's “Treatment Team” in Epic.

Telemetry at all hospitals provided continuous monitoring of patients' heart rates and rhythms, with alarms set to trigger when abnormalities were detected. At JHBMC, dedicated monitor watchers review the data before contacting the unit nurses for actionable alerts.[23] At JHH and HCGH, telemetry data are reviewed by the unit staff, who are responsible for responding to these alarms on the medical and surgical floors.

Preintervention Telemetry Order and Best Practice Advisory Alert

Prior to October 2019, JHH and JHBMC utilized a telemetry order that required the selection of an order indication with the associated recommended duration of telemetry based on the AHA telemetry practice standards. After the prespecified 24, 48, or 72 hours of telemetry duration elapsed, the “Attending” and “First Call” provider for a patient received an interruptive telemetry alert, henceforth referred to as a BPA, after opening the patient chart. For example, if the indication selected on the telemetry order was acute decompensated heart failure, the BPA would fire 48 hours after the time of initial order placement for the “First call” and “Attending” providers ([Fig. 1A]). To dismiss the alert the provider would be required to select one of three “Acknowledge Reasons” to suppress the alert for varying durations: “Chart Review/Preserve Alert” (BPA not suppressed), “Will Discontinue Telemetry” (BPA not suppressed), or “Continued Telemetry Indicated” (BPA suppressed for that user for 12 hours). A hyperlink to the AHA telemetry practice standards was also included in the BPA as a point-of-care educational resource.

Fig. 1 Best Practice Advisory alerts used at JHH and JHBMC (A) and HCGH (B) before October 2019. HCGH, Howard County General Hospital; JHBMC, Johns Hopkins Bayview Medical Center; JHH, Johns Hopkins Hospital. © 2024 Epic Systems Corporation.

In comparison, prior to the intervention, HCGH utilized a telemetry order that did not include the option to choose an indication or duration for telemetry monitoring. The BPA at HCGH fired at 24-hour intervals after order placement for the attending physician and did not include a hyperlink to AHA telemetry practice standards or alert acknowledgment reasons ([Fig. 1B]).

At all three hospitals, the clinician had to order telemetry discontinuation separately after acknowledging the BPA.

Postintervention Telemetry Order and Best Practice Advisory Alert

Beginning in October 2019, we implemented the telemetry order from JHH/JHBMC that required the selection of an indication corresponding to a 24-, 48-, and 72-hour telemetry duration at all three hospitals. Furthermore, the preintervention BPA at JHH/JHBMC was revised and implemented at all three hospitals. The revised BPA included the following additional features ([Fig. 2]).

Fig. 2 Revised 24-hour telemetry Best Practice Advisory alert implemented at JHH, JHBMC, and HCGH in October 2019. AHA, American Heart Association; HCGH, Howard County General Hospital; NHO, Notify house officer; JHBMC, Johns Hopkins Bayview Medical Center; JHH, Johns Hopkins Hospital. © 2024 Epic Systems Corporation.

1. Displayed patient-specific information initially entered in the telemetry order about telemetry indication and indication-based duration.

2. Restricted firing from 8:00 a.m. to 6:00 p.m.

3. Added feature to queue up an order to discontinue telemetry from the BPA. If the user clicked “Accept” in response to the new BPA, they then only had to sign the queued “discontinue telemetry” order. The new discontinue telemetry order auto-discontinued the preexisting telemetry order. If “Do Not Order” was selected next to “Discontinue Telemetry” in the BPA, they were required to choose an “Acknowledge Reason.”

4. Redesigned BPA “Acknowledge Reasons” and functionality.

   • “Chart Review, Preserve Alert” and “Will Discontinue Telemetry” were consolidated to “Will Readdress in 1 Hour,” which suppressed the BPA for that user for 1 hour.

   • “Continue Telemetry” suppressed the BPA for that user for an additional 24, 48, or 72 hours, depending on the initial indication.

Measures and Statistical Analysis

The study's primary outcome was the duration of time on telemetry and the secondary outcome was the number of BPA alerts encountered by clinicians. We performed a retrospective analysis from October 2018 through December 2021 for adult patients on telemetry-capable medical and surgical floors at all hospitals. Patients who were in the intensive care or step-down units were excluded. BPA acceptance rate to discontinue telemetry was used as an intervention process measure.

Telemetry Duration Analysis

The primary outcome was monthly mean time to telemetry discontinuation per patient hospitalization. Telemetry duration data were available for all three hospitals and included in the analysis. Prior to the intervention in October 2019, the EHR allowed for multiple telemetry orders to be placed for an individual patient at the same time. In such cases, only the longest telemetry duration was included. Monthly mean time to telemetry discontinuation per patient hospitalization was analyzed for each individual hospital using a two-tailed t-test and interrupted time series (ITS) analysis. Data from JHH and JHBMC were also evaluated in aggregate in comparison with HCGH as they shared the same preintervention telemetry order and BPA. Python was utilized for data wrangling, formatting, and exploratory analysis. The R programming language was used for statistical analysis.

As the coronavirus disease 2019 (COVID-19) pandemic occurred during the postintervention period and may have impacted telemetry use, we conducted a sensitivity analysis to evaluate the intervention effect prior to the pandemic. This analysis included the entire preintervention period from October 2018 to September 2019 and the 5 months following the intervention, from October 2019 to February 2020, before the state of emergency for the pandemic was declared in March 2020. The same statistical methods as described above were applied to this subset of data.

Best Practice Advisory Alert Analysis

The secondary outcome was the mean monthly BPA alerts per patient-day. Preintervention BPA firing data were not available for HCGH; therefore, analysis was limited to JHH and JHBMC. Patients on telemetry with zero BPA alerts during hospitalization were excluded as the telemetry order was discontinued before an alert was fired. Mean BPA alerts per patient-day were analyzed with a two-tailed t-test. ITS analysis was conducted using an adjusted mixed multilevel Poisson regression model. The Poisson regression was chosen due to the skewed, yet not highly dispersed nature of the BPA patient-day outcome measure. The multilevel mixed model allowed for treatment of the patient and the location as nested levels, thus accounting for the clustering of the outcomes by patient and location (JHH or JHBMC). Location was also controlled within the adjusted model and a term for date of patient-day was used to assess secular trends in BPA alerts over time. A term representing whether the patient-day occurred before or after the intervention was used to isolate the change in the outcome after the intervention, while accounting for the underlying time trend. Data analysis was performed using Stata software.

Clinician responses to the BPA were used as a process measure. Responses were categorized into “intent to continue telemetry” and “intent to discontinue telemetry.” BPA responses of “Continue Telemetry,” “Will Re-address BPA in 1 Hour” without selecting the discontinue telemetry order in the BPA, and “Chart Review, Preserve Alert” were categorized as “intent to continue telemetry.” BPA responses of “Discontinue Telemetry” and “Will Re-address BPA in 1 Hour” followed by selecting the discontinue telemetry order were categorized as “intent to discontinue telemetry.”

Results

Telemetry Duration Data

There were 35,193 unique patient hospitalizations analyzed for duration of time on telemetry corresponding to 4,014, 3,707, and 3,409 hospitalizations in the preintervention cohort and 7,818, 6,362, and 9,883 hospitalizations in the postintervention cohort at JHH, JHBMC, and HCGH, respectively. The preintervention monthly mean time to telemetry discontinuation per patient hospitalization at JHH/JHBMC was 70.9 hours with a decrease to 64.2 hours after the intervention, corresponding to a mean 6.7-hour reduction in telemetry duration (95% CI 5.2–9.1 hours, p < 0.001). The statistically significant decrease in telemetry duration persisted in subgroup analysis of each individual hospital. At HCGH, a greater reduction in telemetry duration was noted, decreasing from a mean of 69 hours preintervention to 48.9 hours postintervention for a mean 20.2-hour reduction in telemetry duration (95% CI 19.1–22.8, p < 00001).

ITS analysis ([Fig. 3]) at JHH/JHBMC, showed a nonstatistically significant decrease in telemetry duration per month prior to the intervention (slope = − 0.58, p = 0.12) with a 5.96-hour decrease in telemetry duration immediately after the intervention (p = 0.048) and a slope change of 0.76 hours/month (p = 0.053). At HCGH, the immediate intervention effect was stronger, with a reduction in telemetry duration by 19.95 hours (p < 0.0001) with a slope change of 0.63 hours/month (p = 0.18).

Fig. 3 Telemetry monitoring duration using interrupted time series analysis for the entire pre- and postintervention period up to December 2021. Results for both Johns Hopkins Hospital (JHH) and Johns Hopkins Bayview Medical Center (JHBMC) are shown in panel (A), and for Howard County General Hospital (HCGH) in panel (B). The dotted line corresponds to the intervention at the respective hospitals in October 2019. *Corresponds to significant values with p < 0.05. ^†Mean difference without accounting for time trend.

The sensitivity analysis, excluding postintervention data after the COVID-19 emergency declaration in March 2020, revealed a mean decrease in telemetry duration of 12.36 hours (p < 0.001) at JHH/JHBMC and 22.41 hours (p < 0.001) at HCGH following the intervention ([Fig. 4]). The ITS analysis indicated that the intervention led to an immediate reduction in telemetry duration by 7.68 hours (p = 0.14) at JHH/JHBMC and 2.66 hours (p = 0.70) at HCGH. However, the slope change at JHH/JHBMC was positive (slope = 0.65, p = 0.64), while a significant negative slope was observed at HCGH (slope = − 4.11, p = 0.046).

Fig. 4 Sensitivity analysis of telemetry monitoring duration using interrupted time series with the entire preintervention period and the postintervention data up to February 2020, before the declaration of COVID-19 state of emergency in March 2020. Results for both Johns Hopkins Hospital (JHH) and Johns Hopkins Bayview Medical Center (JHBMC) are shown in panel (A), and for Howard County General Hospital (HCGH) in panel (B). The dotted line corresponds to the intervention at the respective hospitals in October 2019. * Corresponds to significant values with p < 0.05. ^†Mean difference without accounting for time trend.

Best Practice Advisory Alerts Data

We analyzed data from 58,339 total BPA alerts corresponding to 10,491 unique patient hospitalizations at JHH/JHBMC. Of those, 4,412 hospitalizations were at JHBMC, and 6,079 hospitalizations were at JHH for a total of 34,594 patient-days. Telemetry orders for patients in the initial study population who had telemetry discontinued before the first BPA were not included (n = 8,403). The mean monthly alerts per patient-days were 1.65 before the implementation of the revised BPA with an increase to 1.71 after the intervention, corresponding to a monthly mean increase of 0.06 BPA alerts per patient-day (95% CI 0.04–0.08, p < 0.001; [Fig. 5]). ITS analysis showed a mean monthly net slope difference of 0.02 per patient-day after adjusting for time trends and location of care (p = 0.05).

Fig. 5 Interrupted time series analysis of monthly mean Best Practice Advisory (BPA) alerts per patient-day at JHH and JHBMC. ^†Mean monthly BPA alerts per patient-day prior to intervention is 1.6 (95% CI 1.63–1.66) and 1.7 (95% CI 1.69–1.72) postintervention with mean difference of 0.06 without accounting for time trend. *Corresponds to significant values with p < 0.05. JHBMC, Johns Hopkins Bayview Medical Center; JHH, Johns Hopkins Hospital.

Composite process measure data from JHH and JHBMC showed that the “intent to continue telemetry order” from the BPA decreased from 92.2 to 68.7% with a corresponding increase in “intent to discontinue telemetry” from 7.8% prior to the intervention to 31.3% postintervention (p < 0.0001).

Discussion

Implementing targeted revisions to an existing telemetry BPA significantly reduced the mean monthly telemetry duration by 6.7 hours per hospitalization at JHH/JHBMC. At HCGH, a greater monthly mean reduction in telemetry duration by 20.2 hours was observed after optimizing both the BPA alert and the telemetry order. Specifically, enhancing BPA capabilities by including patient-specific indication and AHA-recommended monitoring durations, restricting firing to daytime hours for the primary team, and embedding discontinuation orders within the BPA led to an immediate reduction in telemetry duration.

The greater magnitude of the intervention effect at HCGH compared with JHH/JHBMC may be due to differences in the preintervention telemetry order and BPA, as well as inherent differences in the hospital settings. Prior to the intervention, HCGH used a “one-size-fits-all” approach for telemetry orders and alerts with the preintervention BPA firing every 24 hours for all monitored patients. Following the intervention, the new indication-based telemetry order with associated AHA-recommended duration enabled the BPA to fire after the patient-specific indication-based time interval. Additionally, as a community hospital, HCGH serves a less medically complex patient population and does not have trainees in the “first call” role, unlike JHH/JHBMC. These differences may result in variability in the quantity and indications of telemetry monitoring and a greater provider willingness to act on BPA alerts without needing to consult supervising physicians.

A recent study by Krouss et al showed a mean reduction of 11.7 hours in telemetry duration after implementation of an EHR BPA alert.[17] In our study, the telemetry orders used before and after the intervention at the JHH/JHBMC sites were the same as those used in the intervention arm of Krouss et al's study. The technological features of the postintervention BPA alert in our study also closely mirrored those in Krouss et al's study. However, while Krouss et al's study demonstrated a reduction in telemetry duration by comparing the results to a control group without any EHR alerts, our study further clarified the incremental benefit of optimizing just the BPA alert component. In other words, our study builds upon Krouss et al's study by evaluating the effectiveness of specific features of an EHR alert, where our comparison group utilized the same telemetry orders and a more rudimentary EHR alert.

The secondary outcome of mean BPA alerts per patient-day was selected as we hypothesized that a targeted, more effective BPA would reduce the alert burden on clinicians. EHR-based interventions have the benefit of enduring presence without the need for ongoing educational efforts or clinician-specific feedback systems; however, their effectiveness is often attenuated by clinician EHR alert burden.[20] [22] In our study, there was a mean monthly increase of 0.06 BPA alerts per patient-day. Though this was statistically significant, the magnitude of difference would not have a practical significance on daily clinician alert burden. Of note, patients who had telemetry discontinued before an alert was fired were excluded from this analysis. As such, the exclusion of the value of “zero” for BPA alert numbers may affect the true mean monthly alert per patient-day.

There are several limitations to the results of our study. The retrospective nature of the analysis allows for unmeasured confounders that could influence the reduction in telemetry duration. An important confounder in this study is the COVID-19 pandemic that occurred in the postintervention period and has been associated with increased telemetry use.[24] To assess the role of this confounder in our results, we performed a sensitivity analysis to exclude the pandemic period after March 2020. This analysis revealed a more substantial mean reduction in telemetry duration at JHH/JHBMC following the intervention. The ITS analysis indicated an immediate reduction in telemetry duration at all hospital systems postintervention, though the results did not reach statistical significance, possibly due to the limited number of postintervention data points. Although the postintervention rate of change at JHH/JHBMC remained positive, HCGH showed a statistically significant negative rate of change at 4.11 in the sensitivity analysis. This significantly negative slope of decline in telemetry duration was not observed in the analysis that included the pandemic. Overall, the sensitivity analysis indicates a substantial reduction in telemetry duration after the intervention at all hospitals. However, the impact of the pandemic on long-term intervention outcomes varied: JHH/JHBMC experienced a delayed but significant long-term reduction in telemetry duration, while HCGH saw an immediate and sustained reduction. We hypothesize that the pressures for resource stewardship during the pandemic, with limited telemetry monitoring capacity, may have contributed to the sustained reduction in telemetry duration following the pandemic despite an overall increase in telemetry monitoring.

Another limitation of our study is that the preintervention BPA was introduced in 2016 at JHH/JHBMC and itself was previously shown to lead to a significant reduction in telemetry orders.[13] This may account for the nonstatistically significant downward trajectory of telemetry duration prior to the intervention. However, no additional educational efforts or interventions were implemented prior to our intervention to account for the preintervention decline.

Additionally, our study did not assess patient outcomes associated with reducing telemetry monitoring. However, prior studies have shown telemetry alarms on unindicated days of telemetry use rarely lead to clinically meaningful changes in outcomes, and reducing telemetry duration does not reduce patient safety.[7] [8] [9] Specifically, a prior randomized control trial with an intervention telemetry order and BPA arm that closely mirrors one used in our study did not demonstrate a significant difference in rapid-response calls or medical emergency events.[9]

Lastly, in assessing the process measure of BPA acceptance for telemetry discontinuation, final signed discontinuation order data were not available and intent for telemetry discontinuation was used as a process measure based on response to the BPA. It is also important to note that not all electronic medical record systems can support the features of our BPA, and we were unable to assess which specific parts of the changes to the BPA specifically contributed to the effects seen at the sites.

Conclusion

Well-crafted EHR alerts enhance effectiveness and reduce unnecessary care. Our results underscore the vital role of tailoring decision support alerts to the right clinician, at the right time, with the right information to enhance the effectiveness and promotion of value-based care.

Clinical Relevance Statement

The effectiveness of EMR alerts in changing practice is significantly impacted by their particular features. Prior studies have not assessed specific features of EMR alerts on their effectiveness in reducing telemetry monitoring. Our study suggests that incorporating patient-specific monitoring indications, delivered to daytime providers, with embedded features to complete the suggested task of telemetry discontinuation significantly increases the effectiveness of EMR alerts in reducing low-value telemetry monitoring compared with EMR alerts without these features.

Multiple-Choice Questions

1. Which of the following features of a BPA alert about unnecessary telemetry monitoring can contribute to increasing its effectiveness in reducing monitoring duration?

   • BPA alerts agnostic to patient indication for monitoring firing every 4 hours.

   • BPA alerts agnostic to patient indication for monitoring firing every 24 hours after admission.

   • BPA alerts based on patient indication for monitoring firing at 24, 48, or 72 hours after admission.

   • BPA alerts based on patient indication for monitoring firing at 24, 48, or 72 hours after admission between 8:00 a.m. and 6:00 p.m.

   Correct Answer: The correct answer is option d. The results of our study showed that at JHH/JHBMC with a previously implemented BPA alert that fired at 24, 48, or 72 hours after admission based on patient indication (choice c.), a decrease in duration of telemetry monitoring was seen when alerts were restricted to 8:00 a.m. to 6:00 p.m. as a part of iterative optimization (choice d.). At HCGH, the BPA alerts fired at 24-hour intervals without patient indication (choice b.) with a more significant decline in telemetry duration after incorporating features of choice d. in the postintervention BPA alert. Choice a. risks clinician alert burden and is not concordant with AHA recommendations for telemetry monitoring duration.

2. Which of the following features generally increase alert acceptance rates and effectiveness?

   • Iterative revisions based on user experience

   • Incorporation of patient-specific data

   • Embedding features that reduce need for additional steps outside the alert

   • All of the above.

   Correct Answer: The correct answer is option d. Prior studies have shown that alert acceptance rates and effectiveness are improved when iterative revisions of the alert based on user experience are incorporated and there is a focus on maximizing incorporation of patient data and including features that reduce the need to “do work twice” by accepting an alert and separately placing a discontinuation order.[15] [16] [17] [18] Our results confirm the importance of iterative revisions to an EHR alert to incorporate these features.

Conflict of Interest

None declared.

Acknowledgments

We would like to acknowledge the assistance of Chat Generative Pretrained Transformer (ChatGPT), a language model developed by OpenAI, for aiding in the preparation of this manuscript by enhancing the overall clarity and coherence of the text. The integrity of the content generated with the assistance of ChatGPT has been confirmed by the authors.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Institutional Review Boards at JHH, JHBMC, and HCGH.

• References

• 1 Sandau KE, Funk M, Auerbach A. et al; American Heart Association Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Council on Cardiovascular Disease in the Young. Update to practice standards for electrocardiographic monitoring in hospital settings: a scientific statement from the American Heart Association. Circulation 2017; 136 (19) e273-e344
• 2 Cantillon DJ, Burkle A, Kirkwood D. et al. Indication-specific event rates among hospitalized patients undergoing continuous cardiac monitoring. Clin Cardiol 2019; 42 (10) 952-957
• 3 Benjamin EM, Klugman RA, Luckmann R, Fairchild DG, Abookire SA. Impact of cardiac telemetry on patient safety and cost. Am J Manag Care 2013; 19 (06) e225-e232
• 4 Kansara P, Jackson K, Dressler R. et al. Potential of missing life-threatening arrhythmias after limiting the use of cardiac telemetry. JAMA Intern Med 2015; 175 (08) 1416-1418
• 5 Knees M, Mastalerz K, Simonetti J, Berry A. Decreasing inappropriate telemetry use via nursing-driven checklist and electronic health record order set. Cureus 2022; 14 (09) e28999
• 6 Chong-Yik R, Bennett AL, Milani RV, Morin DP. Cost-saving opportunities with appropriate utilization of cardiac telemetry. Am J Cardiol 2018; 122 (09) 1570-1573
• 7 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
• 8 Xie L, Garg T, Svec D. et al. Reducing telemetry use is safe: a retrospective analysis of rapid response team and code events after a successful intervention to reduce telemetry use. Am J Med Qual 2019; 34 (04) 398-401
• 9 Najafi N, Cucina R, Pierre B, Khanna R. Assessment of a targeted electronic health record intervention to reduce telemetry duration: a cluster-randomized clinical trial. JAMA Intern Med 2019; 179 (01) 11-15
• 10 Bulger J, Nickel W, Messler J. et al. Choosing wisely in adult hospital medicine: five opportunities for improved healthcare value. J Hosp Med 2013; 8 (09) 486-492
• 11 Yeow RY, Strohbehn GW, Kagan CM. et al. Eliminating inappropriate telemetry monitoring: an evidence-based implementation guide. JAMA Intern Med 2018; 178 (07) 971-978
• 12 Edholm K, Kukhareva P, Ciarkowski C. et al. Decrease in inpatient telemetry utilization through a system-wide electronic health record change and a multifaceted hospitalist intervention. J Hosp Med 2018; 13 (08) 531-536
• 13 Duffy E, Niessen T, Perrin K. et al. Empowering nurses and residents to improve telemetry stewardship in the academic care setting. J Eval Clin Pract 2021; 27 (05) 1154-1158
• 14 Narayanan M, Starks H, Tanenbaum E, Robinson E, Sutton PR, Schleyer AM. Harnessing the electronic health record to actively support providers with guideline-directed telemetry use. Appl Clin Inform 2021; 12 (05) 996-1001
• 15 Chin KK, Svec D, Leung B, Sharp C, Shieh L. E-HeaRT BPA: electronic health record telemetry BPA. Postgrad Med J 2020; 96 (1139) 556-559
• 16 Rizvi W, Munguti CM, Mehta J, Kallail KJ, Youngman D, Antonios S. Reducing over-utilization of cardiac telemetry with pop-ups in an electronic medical record system. Cureus 2017; 9 (05) e1282
• 17 Krouss M, Israilov S, Alaiev D. et al. Tell-a provider about tele: Reducing overuse of telemetry across 10 hospitals in a safety net system. J Hosp Med 2023; 18 (02) 147-153
• 18 Bergstedt A, Hilliard B, Alabsi S. et al. Evaluation of a clinical decision support tool to guide adoption of the American Heart Association Telemetry Monitoring Practice Standards. J Am Heart Assoc 2024; 13 (09) e031523
• 19 Jankovic I, Chen JH. Clinical decision support and implications for the clinician burnout crisis. Yearb Med Inform 2020; 29 (01) 145-154
• 20 Arts DL, Medlock SK, van Weert HCPM, Wyatt JC, Abu-Hanna A. Acceptance and barriers pertaining to a general practice decision support system for multiple clinical conditions: a mixed methods evaluation. PLoS ONE 2018; 13 (04) e0193187
• 21 Tolley CL, Slight SP, Husband AK, Watson N, Bates DW. Improving medication-related clinical decision support. Am J Health Syst Pharm 2018; 75 (04) 239-246
• 22 Khan S, Richardson S, Liu A. et al. Improving provider adoption with adaptive clinical decision support surveillance: an observational study. JMIR Hum Factors 2019; 6 (01) e10245
• 23 Palchaudhuri S, Chen S, Clayton E, Accurso A, Zakaria S. Telemetry monitor watchers reduce bedside nurses' exposure to alarms by intercepting a high number of nonactionable alarms. J Hosp Med 2017; 12 (06) 447-449
• 24 Kim J, Miyazaki K, Nishimura Y, Honda R. Inappropriate telemetry use is increased during the COVID-19 era. Healthcare (Basel) 2021; 9 (12) 1610

Publication History

Received: 02 April 2024

Accepted: 27 July 2024

Article published online:
23 October 2024