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 Table of Contents  
ORIGINAL ARTICLE
Year : 2018  |  Volume : 4  |  Issue : 2  |  Page : 112-118

Reducing telemetry use while improving patient outcomes: University health network experience with the implementation of oximetry-based monitoring system


1 Department of Surgery, Section of Pulmonology and Critical Care, Bethlehem, Pennsylvania, USA
2 Department of Anesthesia, Section of Pulmonology and Critical Care, Bethlehem, Pennsylvania, USA
3 Department of Quality Resources, Section of Pulmonology and Critical Care, Bethlehem, Pennsylvania, USA
4 Department of Family Medicine – Warren, St. Luke's University Health Network, Phillipsburg, New Jersey, USA
5 Department of Medicine, Section of Pulmonology and Critical Care, Bethlehem, Pennsylvania, USA
6 Department of Surgery, Section of Pulmonology and Critical Care; Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Date of Submission11-Jul-2018
Date of Acceptance28-Jul-2018
Date of Web Publication30-Aug-2018

Correspondence Address:
Dr. Stanislaw P Stawicki
Department of Research and Innovation, St. Luke's University Health Network, 801 Ostrum Street, Bethlehem, Pennsylvania 18015
USA
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/IJAM.IJAM_29_18

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  Abstract 


Introduction: Unrecognized clinical patient deterioration (CPD) is a precursor to preventable morbidity and mortality among hospitalized patients. The current standard of intermittent vital signs and physical assessments is inadequate for detecting early CPD and thus prevention of cardiopulmonary events. Continuous oximetry monitoring using a SafetyNet monitoring system (SNMS) may help facilitate early recognition of CPD and early intervention. However, some of the concerns regarding continuous monitoring systems include cost and alarm fatigue. We hypothesized that deployment of SNMS at our institution would result in improved detection of patient deterioration, fewer Intensive Care Unit (ICU) transfers, and reduced telemetry usage.
Methods: We conducted a post hoc analysis of data from a quasi-experimental quality improvement project that took place on medical-surgical units (MSUs) at a large, tertiary referral center between January 1, 2015, and December 31, 2016. The 24-month study period included a 12-month pre-SNMS period (January–December 2015) and a 12-month post-SNMS period (January–December 2016). Clinical data were collected on two adjacent MSUs (“P8” and “P9”) with “P8” serving as the control unit where SNMS was not deployed. The primary study outcome was rate of ICU transfers tracked as transfers per 1000 patient-days. Telemetry usage and nonclinical alarm burden were our secondary outcomes. Estimated cost-saving analysis was also performed based on the reduction of ICU transfers.
Results: The 24-month study period encompassed 21,189 patient-days on the P9 MSU (11,702 pre-SNMS and 9487 post-SNMS) and 23,388 patient-days on the P8 MSU (13,616 pre-SNMS and 9772 post-SNMS). The median case-mix index (a measure of patient acuity based on comorbidities) was higher for P9 than P8 during the duration of the study (2.08 [interquartile range (IQR) 1.98–2.17] vs. 1.67 [IQR 1.64–1.76], respectively). The rate of ICU transfers per 1000 patient-days on the P9 MSU declined from 11.7 during preintervention period to 8.8 post-SNMS implementation (P < 0.03), whereas the comparison unit demonstrated no change. Mean telemetry usage post-SNMS implementation significantly decreased on the P9 unit (21.6 to 16.5 per 1000 patient-days, P < 0.01). Based on the observed difference of 38 ICU transfers between pre- and post-SNMS periods, the estimated cost savings for our Network were $902,386.
Conclusions: The current standard of inpatient monitoring through intermittent vital sign sampling, physical examination assessments, and continuous telemetry for patients deemed to be “high-risk” is ineffective in detecting early CPD. This study suggests that implementation of SNMS may help reduce ICU transfers (and associated costs) while at the same time decreasing the reliance on telemetry monitoring.
The following core competencies are addressed in this article: Interpersonal and communication skills, Practice-based learning and improvement, Systems-based practice.

Keywords: Clinical outcomes, hemodynamic monitoring, pulse oximetry, quality improvement, telemetry monitoring


How to cite this article:
Cipriano A, Roscher C, Carmona A, Rowbotham J, Wojda TR, Guglielmello G, Stawicki SP. Reducing telemetry use while improving patient outcomes: University health network experience with the implementation of oximetry-based monitoring system. Int J Acad Med 2018;4:112-8

How to cite this URL:
Cipriano A, Roscher C, Carmona A, Rowbotham J, Wojda TR, Guglielmello G, Stawicki SP. Reducing telemetry use while improving patient outcomes: University health network experience with the implementation of oximetry-based monitoring system. Int J Acad Med [serial online] 2018 [cited 2018 Nov 13];4:112-8. Available from: http://www.ijam-web.org/text.asp?2018/4/2/112/240133




  Introduction Top


Effective detection of clinical patient deterioration (CPD) on medical-surgical units (MSUs) continues to pose a significant challenge.[1],[2],[3] In the evolving paradigm of value-driven care delivery, suboptimal utilization of hospital resources may negatively affect institutional quality, safety, and finances.[4],[5] The so-called “failure to rescue” phenomenon is well described and has been broadly defined as “hospital deaths after adverse occurrences.”[6] The goal of the current study was to evaluate a pilot implementation of pulse oximetry-based SafetyNet monitoring system (SNMS)[7] as a method of resource-efficient CPD detection on MSU at a large, tertiary referral center. We hypothesized that the deployment of SNMS will be associated with improved detection of CPD and reduced Intensive Care Unit (ICU) transfers. In addition, the SNMS was thought to be associated with reduced telemetry use, lower rates of nonclinical alarm burden (NCAB), and cost savings.


  Methods Top


This study is a post hoc analysis of a quality improvement pilot project conducted between January 1, 2015, and December 31, 2016, at St. Luke's University Hospital in Bethlehem, Pennsylvania. The initiative's aim was to increase CPD detection rate and to prevent unplanned ICU transfers on our trauma/orthopedic MSU through the use of SNMS (Masimo, Irvine, CA[8],[9]). Over the past 5 years, our institution noted persistent telemetry overutilization, including the associated “alarm fatigue” and inconsistent indications for monitoring. The above phenomena are well described in various health-care sources.[1],[10],[11],[12] The SNMS was specifically designed to help address these important problems through the use of algorithm-driven pulse oximetry technology featuring signal noise reduction that was found to reduce both false alarms and “alarm fatigue.”[13] The bedside equipment consists of a disposable finger oximetry probe that wirelessly communicates with monitoring device in each patient room. The system is centralized around a server that analyzes and issues paging alerts to nurses when alarm thresholds are triggered.

The overall study duration was 24 months, with pre-SNMS implementation period of 12 months (January–December 2015) and a 12-month post-SNMS implementation period (January–December 2016). Primary study outcome was the rate of patient transfers to ICU. Telemetry utilization and NCAB were our secondary outcomes. Clinical data used for subsequent group comparisons of primary and secondary outcomes were collected on two adjacent MSUs (Priscilla Payne Hurd Pavilion 8th Floor MSU or “P8” and Priscilla Payne Hurd Pavilion 9th Floor MSU or “P9”). The P8 unit served as “control” during the post-SNMS implementation period (e.g., the SNMS was exclusively deployed on the P9 MSU). Patient acuity was tracked using the aggregate measure of case-mix index (CMI).

Basic units of analysis were patient-days, with corresponding clinical event rates (e.g., ICU transfers per 1000 patient-days). Data analyses were performed using established quality reporting methodologies, including quality control charting.[14] Additional statistical testing consisted of Fisher's Exact and Mann–Whitney U-test for comparing outcome/process differences between pre- and postintervention periods. Statistical significance was set at α = 0.05. An institutional review board exemption was obtained before the current data analysis and publication.


  Results Top


During the entire 24-month study period, there were two distinct pre-SNMS (January–December 2015) and post-SNMS (January–December 2016) periods. The study encompassed 21,189 patient-days on the P9 MSU (11,702 pre-SNMS and 9487 post-SNMS) and 23,388 patient-days on the P8 MSU (13,616 pre-SNMS and 9772 post-SNMS). The median CMI was higher for P9 than P8 during the duration of the study (2.08 [IQR 1.98–2.17] vs. 1.67 [IQR 1.64–1.76], respectively). There were no significant differences in mortality between P8 (monthly range, 0.9–1.1 per 1000 patient-days) and P9 (monthly range, 0.5–0.8 per 1000 patient-days) MSUs, both during pre-SNMS and post-SNMS periods. Likewise, patient group characteristics were similar between the two MSUs during both study periods, with a fixed difference in the percentage of trauma patients [Table 1].
Table 1: Basic patient group characteristics for P8 and P9 medical-surgical units, organized by pre- and post-SafetyNet monitoring system implementation periods

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The implementation of the SNMS was associated with significant reduction of ICU transfers form P9, as compared to both pre-SNMS P9 and total P8 MSU transfers to ICU. Overall, the rate of ICU transfers per 1000 patient-days declined from 11.7 during preintervention period to 8.8 post-SNMS implementation [Figure 1], P < 0.03]. During the same periods, ICU transfers from P8 remained unchanged [Figure 2]. The corresponding total pre-SNMS and post-SNMS implementation ICU transfers for P9 were 147 versus 109, and for P8, they were 98 versus 96. Based on the absolute difference of 38 ICU transfers between pre- and post-SNMS periods, the associated estimated cost savings for our institution were $902,386. Summary of factors utilized in the calculation of corresponding cost savings is provided in [Table 2].
Figure 1: Quality control chart for the number of P9 medical-surgical unit transfers to Intensive Care Unit. Data are shown on quarterly basis, with median transfer rates (per 1000 patient-days) and the corresponding upper and lower control limits shown for pre-SafetyNet monitoring system (gray) and post-SafetyNet monitoring system (green) implementation periods

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Figure 2: Line graph showing no change in Intensive Care Unit transfers (per 1000 patient-days) on the “control” P8 medical-surgical unit. Color coding indicates pre- and post-SafetyNet monitoring system periods

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Table 2: Estimated cost savings associated with reduction in Intensive Care Unit transfers

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In terms of secondary outcomes, mean telemetry utilization on the P9 MSU declined from 21.6 pre-SNMS to 16.5 post-SNMS (P < 0.01). The reverse was observed on the P8 MSU, with markedly increased telemetry utilization during the same periods (18.9 and 25.8, respectively). [Figure 3] summarizes the above results. Finally, targeted staff training and “sensor off” delay implementation (e.g., allowing slightly longer periods of “out-of-range” measurements before triggering alarm) resulted in significant reduction of weekly “nonclinical” device notifications, from 72.3 to 36.5 pages/device [Figure 4], P < 0.01]. [Figure 5] shows the timeline of the above interventions.
Figure 3: Mean telemetry utilization on the P9 medical-surgical unit (yellow) declined significantly during the post-SafetyNet monitoring system period. The opposite was observed on the P8 “control” medical-surgical unit (blue)

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Figure 4: Incidence of pre- and post-intervention “nonclinical” pager notifications. Specific interventions, including their implementation timeline, are presented in the subsequent figure

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Figure 5: Timeline of specific interventions implemented to reduce the incidence of “nonclinical” nursing pager notification. The most common type of “nonclinical” event and at the same time the one most amenable to intervention was the “sensor off” notification (red line)

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  Discussion Top


Identification of CPD continues to pose a formidable challenge in the acute inpatient hospital setting. Additional complexity is being introduced through the concurrent increase in age among medical-surgical patients.[3] Reflexively, hospitals responded to the above sociomedical realities by emphasizing clinical vigilance and deploying increasingly complex telemetry monitoring systems.[15],[16] Although certainly not without benefits, the “information overload” associated with pervasive electronic notifications can result in the so-called “alarm fatigue” – a very real and potentially dangerous phenomenon across modern health-care environments.[1],[17] Moreover, unnecessary use of telemetry has resulted in escalating costs of care[10],[18] and declining clinical employee job satisfaction.[19] The current study represents an attempt at tackling this highly complex set of interrelated problems. As with many domains within health care, potential solutions involve the implementation of more accurate hardware and software systems, better provider education regarding telemetry use, appropriately implemented clinical integration, as well as greater involvement of patient/family as stakeholders in acuity escalation scenarios.[20],[21]

Our data show that thoughtful implementation of SNMS on a busy, high-volume MSU can produce tangible benefits, including more effective identification of CPD, fewer escalations of care, lower utilization of telemetry, decreasing “alarm fatigue” among health-care personnel, and nontrivial estimated cost savings. The demonstration of significantly fewer critical care transfers during this limited-duration trial involving only one MSU at our university hospital campus – especially given the relatively small scope of the intervention – highlights the potential for synergistically enhancing patient safety and quality of care, staff satisfaction, all while improving hospital financial performance. Similar benefits have been described by others, including reports of more effective use of clinical data to drive constructive systemic changes,[22] setting appropriate alarm parameters and limits,[23],[24] as well as various “alarm escalation systems,” and off-site “central monitoring unit” implementations.[25],[26] Ultimately, it is through this and other similar efforts that meaningful advances can be made in the overall health-care value and quality equation, with benefits registered across the entire community of stakeholders.

Reduction of patient transfers from MSUs to ICU was the primary outcome in the current study. Our data support the hypothesis that SNMS implementation in the setting of a busy MSU is associated with fewer ICU transfers. Similar results have been observed by Taenzer et al.,[7] who noted that the number of clinical “rescue events” per 1000 patient discharges following the implementation of SNMS was reduced by approximately two-thirds. Moreover, they noted that ICU transfers were reduced by about 48%.[7] Although the observed reduction in ICU transfers was not as dramatic in our study (e.g., approximately 24%), it was nevertheless significant. Similar to our findings, Taenzer et al. noted no differences between ICU transfer rates among two “control” clinical units.[7] A similar post hoc analysis comparing continuous monitored pulse oximetry (CPOX) patients with unmonitored patients on a postcardiothoracic surgery care floor discovered that CPOX was associated with reduced postoperative ICU admissions for pulmonary complications, decreased costs of care from study enrollment to sensor removal, and lower costs during ICU stay after transfer from the floor; however, CPOX monitoring was not associated with an overall decrease in ICU transfers, mortality, or total estimated costs of hospitalization.[27] One difference between our report was staff education, which their study did not incorporate.

Telemetry utilization was an important secondary outcome measure in our study. We found that the approximate 24% reduction in telemetry use on the P9 MSU was contrasted with an approximately 37% increase in telemetry use on the P8 “control” MSU during the same period. This is a significant difference, especially when considering the combined financial and nursing implications associated with telemetry monitoring. It has been shown that nursing staff spent nearly 20 min per patient on telemetry-related tasks daily, with the associated daily cost of approximately $53 for each patient.[28] While Dressler et al.[28] emphasize the importance of hardwiring and adherence to the American Heart Association (AHA) guidelines to reduce telemetry overuse, our study suggests that SNMS implementation can have similar effect. It will certainly be important to determine simultaneous effect of SNMS and AHA guideline enforcement, with focus on finding potential synergies between those two interventions.

Nursing is the cornerstone of health care, with nurses constituting an essential component of the overall patient safety and care quality matrix. Nursing job satisfaction and retention is an important determinant of institutional ability to function optimally and to attract top nursing talent to join its workforce.[29] There is also a strong correlation between nursing job satisfaction, the perceived quality of care delivery, and the presence of nonvalue-added work.[30],[31] Our data indirectly show that the implementation and subsequent institution-specific customization of SNMS workflow and algorithms can result in the retention of beneficial aspects of the SNMS system while reducing the amount of nonvalue-added work. For example, we were able to reduce nonproductive “sensor off” pager notifications to nursing by >60% through targeted staff education and the implementation of “sensor off” delay [Figure 5]. Another study concerning surveillance management monitoring showed a reduction of unplanned patient transfers by 50%, decrease in rescue events by more than 60%, as well as a sustainability of two alarms per patient per 12-hour nursing shift despite increasing patient acuity and unit occupancy.[32] In addition, a sample analysis of pager notifications discovered that greater than 85% of all alarm conditions were resolved within 30 s and over 99% before escalation was triggered.[32] Therefore, an emphasis on a systematic approach to design and implementation of a monitoring system may enhance nursing resource utilization and optimize care even in the face of increased external pressures.

Estimated financial implications of our short-duration, limited-scope SNMS trial are substantial. If translated across all of our care units at the university hospital, as well as across our entire 10-hospital network, the number of ICU transfers prevented and the total cost savings would be easily noticeable from operational perspective. Although not directly factored into our analysis, the cost of nursing turnover secondary to low job satisfaction constitutes yet another compelling economic reason for thoughtful and nurse-centric implementation of clinical monitoring equipment.[33] Given the results of the current study, our organization is planning to actively scale the implementation of this technology across various MSUs throughout other hospitals within our Network.

There are important limitations of the current analyses, resulting in potential biases. First, our study represents a retrospective “snapshot” of aggregate data collected over a relatively short period. Second, our data lack sufficient granularity and physiologic acuity information to adequately risk-adjust our outcomes. Third, the clinical utility of SNMS technology may vary across different patient populations (e.g., elective surgical population is much different from patients with chronic pulmonary or cardiac conditions). Fourth, our economic analysis does not include important considerations, such as medico-legal costs, employee turnover expenditures, and/or cost savings when comparing patients treated with and without SNMS. Fifth, ours was a limited duration trial of SNMS, with the potential for bias related to temporal factors and other considerations out of our direct control. Strengths of the current study include the “real life” deployment of new technology in a clinically relevant fashion, the simplicity of the quality project design, and the compelling effect size of observed outcomes.


  Conclusions Top


Implementation of SNMS was associated with 25% reduction in ICU transfers per 1000 patient-days on our P9 MSU. At the same time, median telemetry utilization on P9 MSU was reduced by 24%. In addition, we were able to reduce the number of nonclinical nursing notifications by 50% through the combined use of targeted staff education and “sensor off” delay implementation. Estimated cost savings are substantial and likely scalable through further SNMS deployments. Due to its success, this pilot program is being expanded to other MSUs within our network.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no direct conflicts of interest. However, the study describes outcomes attributable to equipment that was provided free of charge by Masimo, Irvine, California. Neither Masimo nor its representatives participated in data collection, analysis, or the scientific writing/publication process.

Ethical conduct of research

The authors attest that this scholarly work was conducted in accordance with the recommendations of The International Committee of Medical Journal Editors. Institutional Review Board approval was obtained prior to the submission of this manuscript for publication in the International Journal of Academic Medicine.



 
  References Top

1.
Cvach M. Monitor alarm fatigue: An integrative review. Biomed Instrum Technol 2012;46:268-77.  Back to cited text no. 1
    
2.
Flanagan EM. The Implementation of an Early Warning System to a Sub-Acute Unit; 2013.  Back to cited text no. 2
    
3.
McAdams DJ. Acute hospitalist medicine and the rapid response system. In: Textbook of Rapid Response Systems. New York, NY: Springer; 2011. p. 47-53.  Back to cited text no. 3
    
4.
Swensen SJ, Dilling JA, Mc Carty PM, Bolton JW, Harper CM Jr. The business case for health-care quality improvement. J Patient Saf 2013;9:44-52.  Back to cited text no. 4
    
5.
Tolentino JC, Martins N, Sweeney J, Marchionni C, Valenza P, McGinely TC, et al. Introductory chapter: Developing patient safety champions. In: Vignettes in Patient Safety. Vol. 2. Rijeka, Croatia: In Tech; 2018.  Back to cited text no. 5
    
6.
Taenzer AH, Pyke JB, McGrath SP. A review of current and emerging approaches to address failure-to-rescue. Anesthesiology 2011;115:421-31.  Back to cited text no. 6
    
7.
Taenzer AH, Pyke JB, McGrath SP, Blike GT. Impact of pulse oximetry surveillance on rescue events and Intensive Care Unit transfers: A before-and-after concurrence study. Anesthesiology 2010;112:282-7.  Back to cited text no. 7
    
8.
Goldman JM, Petterson MT, Kopotic RJ, Barker SJ. Masimo signal extraction pulse oximetry. J Clin Monit Comput 2000;16:475-83.  Back to cited text no. 8
    
9.
Hay WW Jr., Rodden DJ, Collins SM, Melara DL, Hale KA, Fashaw LM, et al. Reliability of conventional and new pulse oximetry in neonatal patients. J Perinatol 2002;22:360-6.  Back to cited text no. 9
    
10.
Sharma P, Tesson A, Wachter A, Thomas S, Bae JG. Physician awareness of patient cardiac telemetry monitoring. J Hosp Adm 2016;5:76.  Back to cited text no. 10
    
11.
Gazarian PK, Carrier N, Cohen R, Schram H, Shiromani S. A description of nurses' decision-making in managing electrocardiographic monitor alarms. J Clin Nurs 2015;24:151-9.  Back to cited text no. 11
    
12.
Rayo MF, Mansfield J, Eiferman D, Mignery T, White S, Moffatt-Bruce SD, 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.  Back to cited text no. 12
    
13.
Welch J. An evidence-based approach to reduce nuisance alarms and alarm fatigue. Biomed Instrum Technol 2011;Suppl: 46-52.  Back to cited text no. 13
    
14.
John PW. Quality control charts. Statistical Methods in Engineering and Quality Assurance. Somerset, NJ: John Wiley & Sons; 1990. p. 144-63.  Back to cited text no. 14
    
15.
Henriques-Forsythe MN, Ivonye CC, Jamched U, Kamuguisha LK, Olejeme KA, Onwuanyi AE, et al. Is telemetry overused? Is it as helpful as thought? Cleve Clin J Med 2009;76:368-72.  Back to cited text no. 15
    
16.
Bulger J, Nickel W, Messler J, Goldstein J, O'Callaghan J, Auron M, et al. Choosing wisely in adult hospital medicine: Five opportunities for improved healthcare value. J Hosp Med 2013;8:486-92.  Back to cited text no. 16
    
17.
Keller JP Jr. Clinical alarm hazards: A “top ten” health technology safety concern. J Electrocardiol 2012;45:588-91.  Back to cited text no. 17
    
18.
Spivack D. The high cost of acute health care: A review of escalating costs and limitations of such exposure in Intensive Care Units. Am Rev Respir Dis 1987;136:1007-11.  Back to cited text no. 18
    
19.
Whalen DA, Covelle PM, Piepenbrink JC, Villanova KL, Cuneo CL, Awtry EH, et al. Novel approach to cardiac alarm management on telemetry units. J Cardiovasc Nurs 2014;29:E13-22.  Back to cited text no. 19
    
20.
Albutt AK, O'Hara JK, Conner MT, Fletcher SJ, Lawton RJ. Is there a role for patients and their relatives in escalating clinical deterioration in hospital? A systematic review. Health Expect 2017;20:818-25.  Back to cited text no. 20
    
21.
McAdams DJ. Overview of hospital medicine. In: Medical Emergency Teams. New York, NY: Springer; 2006. p. 49-54.  Back to cited text no. 21
    
22.
Cvach MM, Currie A, Sapirstein A, Doyle PA, Pronovost P. Managing clinical alarms: Using data to drive change. Nurs Manage 2013;44:8-12.  Back to cited text no. 22
    
23.
Burgess LP, Herdman TH, Berg BW, Feaster WW, Hebsur S. Alarm limit settings for early warning systems to identify at-risk patients. J Adv Nurs 2009;65:1844-52.  Back to cited text no. 23
    
24.
Paine CW, Goel VV, Ely E, Stave CD, Stemler S, Zander M, et al. Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency. J Hosp Med 2016;11:136-44.  Back to cited text no. 24
    
25.
Cvach MM, Frank RJ, Doyle P, Stevens ZK. Use of pagers with an alarm escalation system to reduce cardiac monitor alarm signals. J Nurs Care Qual 2014;29:9-18.  Back to cited text no. 25
    
26.
Cantillon DJ, Loy M, Burkle A, Pengel S, Brosovich D, Hamilton A, et al. Association between off-site central monitoring using standardized cardiac telemetry and clinical outcomes among non-critically ill patients. JAMA 2016;316:519-24.  Back to cited text no. 26
    
27.
Ochroch EA, Russell MW, Hanson WC 3rd, Devine GA, Cucchiara AJ, Weiner MG, et al. The impact of continuous pulse oximetry monitoring on Intensive Care Unit admissions from a postsurgical care floor. Anesth Analg 2006;102:868-75.  Back to cited text no. 27
    
28.
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;174:1852-4.  Back to cited text no. 28
    
29.
Hatler C, Stoffers P, Kelly L, Redding K, Carr LL. Work unit transformation to welcome new graduate nurses: Using nurses' wisdom. Nurs Econ 2011;29:88-93.  Back to cited text no. 29
    
30.
Aron S. Relationship Between Nurses' Job Satisfaction and Quality of Healthcare they Deliver; 2015.  Back to cited text no. 30
    
31.
Capuano T, Bokovoy J, Halkins D, Hitchings K. Work flow analysis: Eliminating non-value-added work. J Nurs Adm 2004;34:246-56.  Back to cited text no. 31
    
32.
McGrath SP, Taenzer AH, Karon N, Blike G. Surveillance monitoring management for general care units: Strategy, design, and implementation. Jt Comm J Qual Patient Saf 2016;42:293-302.  Back to cited text no. 32
    
33.
Waldman JD, Kelly F, Arora S, Smith HL. The shocking cost of turnover in health care. Health Care Manage Rev 2004;29:2-7.  Back to cited text no. 33
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
 
 
    Tables

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