The appropriateness of cardiac telemetry based on real-time data

Introduction: Continuous cardiac monitoring, or telemetry, is a noninvasive method of closely monitoring a patient’s heart rate and rhythm in the healthcare setting. Figuring out the right patient to monitor remains imperative, especially as its costs increase while there is pressure to reign in costs for healthcare (1). Growth in the United States healthcare expenditures is astronomical to say the least. Paradoxically, higher cost regions in the country are not associated with improved quality or outcomes. In many cases, the reverse is true (1). Variations in healthcare likely explain this discrepancy (2). An example of necessary variation is a patient who has an allergy to aspirin or chooses not to proceed with an intervention. Contrarily, unnecessary variation does not result in benefit to the patient and is associated with increased cost. For instance, a patient presents with stable atrial fibrillation and gets admitted for cardiac telemetry, the cost is probably 6000-8000 dollars, alternate to a patient who is started on a beta blocker and gets referred for an outpatient consult, the cost is under a thousand dollars. If unnecessary variations were eliminated, this pattern would diminish. As Michael Porter, a PhD graduate from Harvard, puts it, “Quality improvement is the most powerful driver of cost containment”.

The proposed intervention in this direct observational study is to render telemetry cost-effective by reviewing the electrocardiographic (ECG) monitoring standards for hospital settings as updated by the American Heart Association (AHA) in 2017 (3) and incorporating them into order sets. The fastest way is to incorporate them into telemetry order sets that are electronic so that a provider admits a patient based on a predetermined indication alongside guidelines on duration. Besides the cost savings of it, there is a pressing need for physicians to allocate telemetry to the patients who need it the most, and defer the rest because they are going to generate a large number of clinically irrelevant alarms (4). Materials and Methods: A prospective observational study was performed on patients from January 1st, 2017 to July 1st, 2017 who were placed on continuous ECG monitoring, aged 18-105. A total of 8,894 patients were found to meet this criterion. Based on AHA guidelines, appropriateness of telemetry usage was determined. We found 4,004 out of 8,894 patients were correctly placed on telemetry monitoring. Based on the average cost of telemetry, the hospital lost at least $264,060 if the incorrect telemetry patients were only on telemetry for one day. On March 18th, 2019, an update was done on the hospital's electronic medical records (EMR): 1. Every order of cardiac telemetry had an indication added. Every indication was added to the problem list 2. The duration of cardiac telemetry was also added. Proposed interventions: 1. To utilize the date from 3/18 to 5/18 based on appropriate indication and duration of telemetry and to compare this to the previous study 2. We went one step further and analyzed the number of alarms that were made pre- and post- intervention to see if our intervention worked.

Discussion:

Clinical benefits of continuous cardiac monitoring are well-recognized and obvious, but the cost-saving benefits are not so easy to recognize. Interventions that promise savings are commonplace, but financial incentives are often flipped and need to be considered differentially.

Studies focusing on increased implementation of AHA practice standards for ECG monitoring have shown reductions in telemetry use with no adverse effects on cardiac monitoring. Benjamin et al. examined telemetry bed use for one week in a retrospective, four-hospital study. On 35% of those days, ECG monitoring was not supported by clinical indications. Implementation of proper monitoring was associated with a minimum saving of $53 per patient/day (5). Dressler et al. revised telemetry order sets based on the AHA practice standards where prescribers were required to select from a list of clinical indications, each with a predetermined duration of monitoring. Mean daily number of patients decreased by 70%, with cost savings noted (6).

In a more recent publication by Stoltzfus et al. (1), the authors describe how they initially developed a conservative huddle intervention for their institution applying telepathy in its progressive care units. There was slight reduction in telemetry utilization after this intervention and it did not have a lasting impact. The other targeted intervention employed thereafter was the hard-stop intervention. By instituting this intervention, the hospital saw a relative reduction in telemetry utilization by 17.8% across all hospital units. Its effects were consistent. This study did not include measures of baseline costs incurred and later saved due to the intervention.

The hospital that we picked is a midrange hospital, about 9000 telemetry beds with a typical occupancy rate of 99%. In the preliminary work that we did for the year 2017, we found that 55% of those did not meet AHA guidelines for telemetry. Continually leveraging data enabled us to better predict outcomes and ultimately save costs. An innovative way to execute telepathy as we did the following year was to identify high-risk patients and reduce length of stay on telemetry. Risk-stratification meant stratifying patients who need to be watched every second in the hospital from those who might need just SpO2 monitoring instead based on the AHA guidelines e.g., for patients admitted with post-cardiac catheterization, the first 48 to 72 hours are very important. Importantly, we identified admission as a starting point where they might be excluded. Measurable outcomes included the number of alarms. There was an immediate turnaround as the hospital was able to save $264,060/day that was being wasted on unnecessary telemetry including the cost of alarms. Although alarm fatigue has been documented widely and specifically in a prospective study by Rayo et al. as a decrease in the percentage of false alarms from 18.8% to 9.6% following a strict adherence to guidelines (4,7), there is limited research examining interventions to address its financial repercussions. The present study examines financial impact as a component of a larger quality improvement project; it provides moderately strong evidence supporting the use of a hard-stop intervention to reduce costs associated with unindicated orders of cardiac monitoring alarms. Even more than the financial impact is the clinical outcomes, such as the ability to reduce monitored days for patients with low-risk STEMI.

Our study has some limitations including its retrospective design. We could not account for the re-hospitalizations of patients once discharged. Also, we did not have access to the cost of the procedure for the patients but only for the hospital. Despite its limitations, we believe that the driven quality improvements are sufficient. Implementing was safe to the extent that neither the length of stay nor mortality increased significantly with the practice change. Moreover, the size of our cohort is sufficient for the generalizability of the results. Our future goals include incorporating telemetry monitoring into daily huddles, including goals surrounding alarm fatigue, and further reducing telemetry utilization through the identification and conversion of ‘’sleeper’’ telepathy patients to inpatient.

Works cited:
1. Stoltzfus KB, Bhakta M, Shankweiler C, et al. Appropriate utilisation of cardiac telemetry monitoring: a quality improvement project. BMJ Open Quality 2019;8:e000560. doi: 10.1136/bmjoq-2018-000560
2. Atsma F, Elwyn G, Westert G. Understanding unwarranted variation in clinical practice: a focus on network effects, reflective medicine and learning health systems. Int J Qual Health Care. 2020;32:271-274. doi:10.1093/intqhc/mzaa023
3. Sandau KE, Funk M, Auerbach A, et al. Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. Circulation. 2017;136:e273-e344. doi:10.1161/CIR.0000000000000527
4. Wilken M, Hüske-Kraus D, Klausen A, Koch C, Schlauch W, Röhrig R. Alarm Fatigue: Causes and Effects. Stud Health Technol Inform. 2017;243:107-111
5. 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:e225-e232
6. 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;174:1852-1854. doi:10.1001/jamainternmed.2014.4491
7. Rayo M, Mansfield J, Eiferman D, et al. Implementing an institution-wide quality improvement policy to ensure appropriate use of continuous cardiac monitoring: A mixed-methods retrospective data analysis and direct observation study. BMJ Qual Saf. 2016;25:796–802. doi: 10.1136/bmjqs-2015-004137