Development and Evaluation of a Nurse-Driven Checklist to Reduce Continuous Cardiac

Monitoring Among Hospitalized Patients

by

Stefanie Marie Glenn

An evidence-based scholarly project submitted

in partial fulfillment of the requirements

for the degree of

Doctor of Nursing Practice

Wilmington University

College of Health Professions

November 2018

Development and Evaluation of a Nurse-Driven Checklist to Reduce Continuous Cardiac Among Hospitalized Patients by Stefanie Marie Glenn

I certify that I have read this Doctor of Nursing Practice scholarly project and that in my opinion it meets the academic and professional standards required by Wilmington University for the degree of Doctor of Nursing Practice.

Walton F. Reddish, DNP, FNP-BC Chairperson, DNP Project Committee

Srisuda Siera Gollan, PhD, RN-BC Member, DNP Project Committee

Aaron M. Sebach, DNP, MBA, AGACNP-BC, FNP-BC, CEN, CPEN, FHM Chair, Doctor of Nursing Practice Program

Denise Z. Westbrook, EdD, RN Dean, College of Health Professions

Date: ______November 29, 2018

ii

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 3

Table of Contents

Contents Page

Title Page ...... i

Signature Page ...... ii

Table of Contents ...... 3

List of Tables ...... 6

List of Figures ...... 7

List of Abbreviations ...... 8

Acknowledgments...... 9

Abstract ...... 10

Introduction ...... 12

Problem Description ...... 12

Available Knowledge...... 14

Rationale ...... 20

Specific Aims ...... 22

Methods...... 23

Context ...... 23

Population ...... 26

Intervention ...... 26

Measures ...... 31

Analysis...... 32

Ethical Considerations ...... 32

Results ...... 33

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 4

Checklist Data ...... 41

Discussion ...... 42

Summary ...... 42

Interpretation ...... 44

Limitations ...... 47

Implications for Advanced Nursing Practice ...... 48

Essential I. Scientific underpinnings for practice ...... 49

Essential II. Organizational and systems leadership for quality improvement and

systems thinking...... 49

Essential III. Clinical scholarship and analytical methods for evidence-based

practice ...... 49

Essential IV. Information systems/technology and patient care technology for the

improvement and transformation of health care ...... 50

Essential V. Health care policy for advocacy in health care ...... 50

Essential VI. Interprofessional collaboration for improving patient and population

health outcomes ...... 50

Essential VII. Clinical prevention and population health for improving the

nation’s health ...... 51

Essential VIII. Advanced nursing practice ...... 51

Conclusion ...... 51

References ...... 53

Appendices ...... 60

Appendix A: Wilmington Human Subject Review Committee Letter of Approval ...... 61

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 5

Appendix B: Approved Wilmington Human Subjects Review Committee Application ..62

Appendix C: Howard County General Hospital Letter of Approval ...... 76

Appendix D: Howard County General Hospital Quality Improvement Project Approval

Determination ...... 77

Appendix E: Print and Electronic Copyright Use Agreement ...... 78

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 6

List of Tables

Table Page

Table 1: Medical Records Administrative Variables ...... 31

Table 2: Discontinuation Checklist (TDC) Variables ...... 32

Table 3: Descriptive Statistics: Mean Length of Time Spent in Telemetry Hours by Month...... 34

Table 4: Independent samples t-Test Comparing Means of Hours on Telemetry Between

Baseline 2017 and Intervention 2018 Periods ...... 35

Table 5: Mann-Whitney U Non-parametric Test ...... 36

Table 6: Descriptive Statistics for Gender and Race in Patients Study Population (N=210) ...... 37

Table 7: Descriptive statistics for age in Patient study population (N=210) ...... 37

Table 8: Student’s Independent Samples t-test Comparing Means of Ages of Patient’s Telemetry

Between Baseline and Intervention Periods ...... 38

Table 9: Student’s Independent Samples t-test Comparing Means of Ages of Patient’s Telemetry

Between Baseline and Intervention Periods ...... 39

Table 10: Student’s Independent Samples t-test Comparing Means of Hours of Length of Stay of

Patients on Telemetry Between Baseline and Intervention Periods ...... 40

Table 11: Student’s Independent Samples t-test Comparing Gender Between Baseline and

Intervention Periods ...... 41

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 7

List of Figures

Figure Page

Figure 1: Applying classification of recommendations and level of evidence ...... 19

Figure 2: PDSA Cycle Template ...... 21

Figure 3: Front of the telemetry pocket card ...... 24

Figure 4: Back of the telemetry pocket card ...... 25

Figure 5: 4 South average time on telemetry January 2017–April 2018 ...... 25

Figure 6: Initial draft of the telemetry checklist, page 1 ...... 27

Figure 7: Initial draft of the telemetry checklist, page 2 ...... 28

Figure 8: Final version of the telemetry discontinuation checklist ...... 29

Figure 9: Telemetry Checklist Process ...... 30

Figure 10: Mean length of time spent on telemetry in hours; Comparison Group (2017) versus

Intervention Group (2018) ...... 45

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 8

List of Abbreviations

CTM Continuous Telemetry Monitoring

DNP Doctor of Nursing Practice

HCGH Howard County General Hospital

ICD-10-CM International Classification of Diseases, Tenth Revision, Clinical Modification

Codes

ICU Intensive Care Unit

JCAHO Joint Commission on Accreditation of Healthcare Organizations

NASA National Aeronautics and Space Administration

NP Nurse Practitioner

PA-C ’s Assistant

PDSA Plan, Do, Study, Act

PI Principal Investigator

PICOT Population, Intervention, Comparison, Outcome, and Time

QI Quality Improvement

RN Registered Nurse

TDC Telemetry Discontinuation Checklist

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 9

Acknowledgments

First and foremost, I would like to acknowledge my family who always sacrifices so I can achieve and better myself so that I may continue to serve our great nation. Rob, Karenna, Carlin and Casey…you are my oxygen supply and I could not do it without you. I would like to thank my Team Member, Siera Gollan PhD, RN, who kept me sane and on track even though we are on different continents and in different time zones. To say you are the true definition of a lifelong friend feels minimal but I fail to find words worthy enough to describe how much you mean to my family and me. To my Project Chair, Walton Reddish, DNP, your gentle guidance and honest feedback helped me remain calm when I had to change my entire direction just past the midway point of DNP 8000. That is not a small feat for a Type A doctoral student. I also have a mountain of gratitude for the Wilmington University Chairman of the DNP program, Aaron Sebach, DNP.

Your ongoing support since day one made all the issues that could have become major roadblocks be effortlessly demoted to non-issue status so I could focus on the immediate situation at hand. To my mentor and Hospitalist colleague Trushar Dungarani, DO, thank you for allowing me to work with you on this project. I look forward to partnering with you again in the near future on other QI initiatives. I would also like to recognize Tiffany Mast, the Lean

Facilitator at Howard County General Hospital for your kindness and indulging me with last- minute data requests. Finally, to the nurses on 4 South, your willingness to support this project and provide your insight was invaluable and infinitely appreciated.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 10

Abstract

BACKGROUND: Continuous telemetry monitoring (CTM) is frequently utilized in the hospital setting. While the benefits of CTM are well documented, there are significant issues related to overuse to include unnecessary cost, alarm fatigue, and increased risk of patient harm.

PROBLEM: The American Association and the Choosing Wisely initiative recommend guidelines for the use of CTM. However, the use of CTM in inpatient, non-ICU settings remains inconsistent. There is a paucity of literature describing successful strategies to reduce the duration of CTM monitoring in the non-ICU setting. METHODS: This Quality Improvement (QI) project is a retrospective chart review. Participants in this QI initiative were a convenience sample of all patients who were placed on CTM, >18 years old admitted to a 26-bed telemetry capable general medical/ floor in a suburban, community hospital from July 1, 2018 to September 30,

2018. Data were extracted from the electronic medical record and maintained in a computerized codebook that was password protected. A checklist was developed then utilized by the bedside registered nurse (RN) that assisted in determining the appropriate duration of CTM. The control group consisted of all patient charts admitted to the designated medical unit on CTM exactly one year before the checklist was implemented. The intervention group was all patient charts from the first three months the checklist was piloted. RESULTS: The mean duration of time spent on telemetry monitoring was statistically significant (p<.05) between two of the three comparison groups. In July, the mean hours on telemetry decreased from 45.35 to 29.93 with a 68% use of the checklist. The mean September telemetry hours decreased from 40.49 to 22.58 with a 54% use of checklist. In August 2018, there was an increase in mean hours on telemetry from 40.49 to 43.48 hours with a 50% use of the checklist. CONCLUSION: The use of a telemetry discontinuation checklist may have a positive effect on the duration of telemetry monitoring.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 11

Keywords: telemetry guidelines, continuous cardiac monitoring, electrocardiographic monitoring guidelines, clinical decision support, alarm fatigue, quality improvement, telemetry indications, telemetry discontinuation checklist, multi-disciplinary rounds checklist, guideline adherence

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 12

Introduction

Problem Description

Continuous telemetry monitoring (CTM) was first introduced by the National

Aeronautics and Space Administration (NASA) over 50 years ago to monitor and observe the physiology of astronauts while in space (Bohossain & An, 2017; "Space-proven Medical

Monitor," 2006). Although not originally intended for widespread use in the medical field, the significant growth in technology allowed CTM to be adapted and utilized throughout the inpatient hospital setting (Bohossain & An, 2017). While CTM (also known as telemetry) use has demonstrated significant value in patient care, the use of this technology when not indicated, has a negative impact on patients, health care providers, and hospital systems.

Telemetry’s intended use is to alert staff when the monitor detects a potentially fatal arrhythmia, change in QT interval, and myocardial ischemia (Chen et al., 2016). The overuse or misuse of CTM in the non-intensive care unit (ICU) setting has been shown to contribute an increased opportunity for patient harm, increased burden among the nursing staff, and negative economic implications (Benjamin, Klugman, Luckman, Fairchild, & Abookire, 2013; Bayley et al., 2005; Chen et al., 2016; Perrin, Nelson, Sawyer, & Pfoh, 2016; Solet & Barach, 2012;

Wallis, 2010).

Although CTM is non-invasive, it is not without risk. Inappropriate use of telemetry can lead to patient injury (Benjamin et al., 2013; Paine et al., 2016). It is estimated that between 85% and 99% of alarms triggered require no clinical response because the underlying cause is unrelated to an arrhythmia (Drew et al., 2014; "The Joint Commission Sentinel Event Alert”

2013). When nurses are consistently inundated with alarms during a busy twelve-hour shift, desensitization and alarm fatigue occur (Feder & Funk, 2013; Paine et al., 2016; Ross, 2015).

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 13

Additionally, telemetry is often used for non-cardiac and/or non-indicated reasons (Chen &

Zakaria, 2015; Yeow et al., 2018). This has been shown to cause a false sense of security for clinicians and can lead to less frequent face-to-face encounters with patients (Chen & Zakaria,

2015; Yeow et al., 2018). The literature describes several cases where alarms have gone unnoticed resulting in sentinel events prompting a dedicated issue of the Joint Commission on

Accreditation of Healthcare Organizations (JCAHO) Sentinel Event Alert in 2013 ("JCAHO

Sentinel Event Alert," 2013; Chen & Zakaria, 2015). Additionally, the Society of Hospital

Medicine and the Choosing Wisely® Initiative have also identified inappropriate use of CTM as an issue that needs to be addressed (Benjamin et al., 2013; Five things and patients should question, 2013; Svec et al,.2015).

Another challenge related to the inappropriate use of telemetry is patients being subjected to unnecessary diagnostic tests or procedures as the result of an alarm being triggered for a clinically insignificant reason (Dhillon et al., 2009; Henriques-Forsythe et al., 2009). This not only exposes patients to further tests with potential complications, it increases the cost of care

(Benjamin et al., 2013; Chong, Bennet, Milani & Morin, 2016). The cost associated with the inappropriate use of CTM is caused by an increased length of inpatient stay; inappropriate boarding of patients in the emergency room due to unavailable telemetry beds; ambulance diversion because of overcrowded emergency departments; and decreased hospital through-put

(Bulger et al., 2013; Chen & Hollander, (2007); Dawson et al., 2017; Dressler & Doorey, 2015;

Edholm et al., 2018; Snipelisky et al., 2016; Svec et al., 2015).

The American Heart Association (AHA) and the Choosing Wisely® initiative recommend guidelines for the use of CTM (Bulger et al., 2013; Sandau et al., 2017). However, using CTM in inpatient, non-ICU settings remains inconsistent (Funk et al., 2017). There is a dearth of

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 14

literature describing successful strategies to reduce the duration of telemetry monitoring in the

non-ICU setting (Sandau et al., 2017).

Available Knowledge

A comprehensive electronic database search was executed to gather existing literature

and identify gaps regarding decreasing the duration of CTM in the non-ICU setting. Several

databases were utilized to identify relevant literature to include the Medical Literature Analysis

and Retrieval System Online (MEDLINE) with Full Text, Google Scholar, Cumulative Index to

Nursing and Allied Health Literature (CINAHL) Plus with Full Text, the Cochrane Database of

Systematic Reviews, and the PubMed U.S. National Library of were utilized. The

databases were searched for relevant, peer-reviewed literature from January 1960 to August

2018. Key search terms were selected due to their direct relation to the stated Population,

Intervention, Comparison, Outcome, and Time (PICOT) question and included telemetry

guidelines, continuous cardiac monitoring, electrocardiographic monitoring guidelines, clinical

decision support, alarm fatigue, quality improvement, telemetry indications, telemetry

discontinuation checklist, multi-disciplinary rounds checklist, guideline adherence. Studies that

did not adequately address elements of the PICOT question, deemed to be of low quality, or

contained outdated clinical evidence were excluded.

The first use of CTM was not intended for use in the medical field (Bohossain & An,

2017; "Space-proven Medical Monitor," 2006). In the early 1960s, the National Aeronautics and

Space Administration utilized this technology to monitor the physiologic response of astronauts

to prolonged space travel and spacewalks outside of the spacecraft ("Space-proven Medical

Monitor," 2006). To date, CTM technology has not been extensively studied and tested as other

medical devices have been using randomized trials (Bohossain & An, 2017; Najafi, 2014;

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 15

Perkins et al., 2017)). Despite this, telemetry rapidly found a place initially in the ICU setting and then on regular medical/surgical units (Bohossain & An, 2017).

Despite 30 years of use, there were no guidelines established until 1991 for appropriately using inpatient CTM (Jaffe et al., 1991). In November of 1991, the American College of

Cardiology published the first recommended guidelines for in-hospital use of telemetry for adults and placed them into three classes:

 Class I: For all patients in this group, monitoring is indicated. All patients at significant

risk for a life-threatening arrhythmia are classified as Class I. Examples of this are

patients who have suffered a cardiac arrest, early phase of acute coronary syndrome,

patients who have had a cardiac or procedure, patients with a life-threatening

arrhythmia, and patients requiring admission to the ICU.

 Class II: Cardiac monitoring might be beneficial in some patients but not essential for all.

For example, day three, after myocardial infarction, patients with potentially lethal

arrhythmias several days after the initial presentation of a lethal arrhythmia, patients with

a clinically significant arrhythmia () and an underlying disease that puts

them at increased risk for cardiac arrest or a life-threatening arrhythmia, initial treatment

with a Type I or Type II antiarrhythmic agent, patients with suspected acute phase of

pericarditis, unexplained syncope, and patients in the first 48-72 hours after pacemaker

placement.

 Class III telemetry is not indicated because the risk of a serious arrhythmia is negligible

and there is no benefit to monitoring. For example, patients who had a simple,

uncomplicated surgery, obstetric patients, patients with terminal illness, stable chronic

atrial fibrillation and stable underlying cardiac disease (Jaffe et al., 1991).

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 16

In 1998, Sivaram, Summers, and Ahmed evaluated the utility of these guidelines in practice. In this observational study lasting four weeks, 61 male patients (age 40-61 years) who were directly admitted to the telemetry unit or transferred from the ICU were followed for the duration the CTM order was active (Sivaram et al., 1998). The indication for CTM and the value added to the clinical management of these patients was evaluated (Sivaram et al., 1998). The average duration of telemetry monitoring was 6.2 days, with 279 events in the study (Sivaram et al., 1998). The patients were classified according to the American College of recommendations: 14 patients (22%) had Class I indication, with 18.2% of all telemetry events;

21 patients (34.4%) had Class II indication, with 39.7% of all telemetry events; and 26 patients

(42.6%) were determined to have a Class III indication, and had 42% of all telemetry events

(Jaffe et al., 1991; Sivaram & Summers, 1998). Of all 279 telemetry events, 12 (4%) resulted in a change in clinical management and none of those patients were in the Class III indication group

(Sivaram et al., 1998). A similar study was performed by Snider et al. (2002) evaluating using

CTM in 414 patients with low- and high-risk acute at a large tertiary care facility during a 3-month period. Compared to patients with symptoms associated with acute coronary syndrome and electrocardiogram changes, there were 45 events resulting in 23 interventions versus 12 events and four interventions in the low-risk group (atypical chest pain and a normal electrocardiogram) concluding that patients in the low-risk group did not have significant risk of having a life-threatening arrhythmia (Snider et al., 2002).

Telemetry technology continued to evolve, become more sophisticated, and widespread in use throughout hospitals in the non-ICU setting (Drew et al., 2004). Although no randomized clinical trials validated the indications for CTM, in 2004 the AHA published a scientific statement with recommendations for the use of CTM throughout the hospital in various levels of

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 17 acuity (Drew et al., 2004). These recommendations classified indications into a rating system of three categories:

 Class I: For most, if not all patients in this group, monitoring is indicated. All patients at

significant risk for a life-threatening arrhythmia are classified as Class I. Examples of this

are patients who have suffered a cardiac arrest, early phase of acute coronary syndrome,

patients who have had a or procedure, patients with a life-threatening

arrhythmia, and patients requiring admission to the ICU.

 Class II: Cardiac monitoring may be beneficial but is not expected to save lives and is not

considered essential in the clinical management of the patient. The setting is often in an

ICU step-down unit or a medical-surgical floor. Examples of these cases include 24-48

hours post-myocardial infarction, patients with chest pain syndromes, patients who are

status post-percutaneous coronary intervention, uncomplicated arrhythmia ablation, had

routine angiography, sub-acute heart failure, syncope evaluation, and patients with do not

resuscitate orders who have arrhythmias that cause pain.

 Class III: Patients at low risk for cardiac arrhythmias (Drew et al., 2004).

In 2017, the AHA published an update to the original practice standards published in

2004 by Drew and colleagues. As was the case in 2004 when the first AHA standards were published, there is still a paucity of literature and randomized clinical trials regarding this topic

(Sandau et al., 2017). Therefore, this update is based on author expertise, a literature review of publications based on the previous practice standards, and the AHA Level of Evidence grading algorithm that was already in existence at the time the new standards were commissioned

(Sandau et al., 2017).

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 18

The 2017 update addresses new issues that have emerged to include the overuse of continuous cardiac monitoring in several populations, alarm management, appropriate use of

QT-interval monitoring, and electronic health record documentation (Sandau et al., 2017). This updated statement not only discusses clear clinical recommendations, it also touches on alarm hazards. Sandau et al. (2017) state alarm hazards, “Include an array of problems that may occur with alarm• equipped medical devices such as electrocardiographic monitors. These interrelated hazards include alarm fatigue, inadequate alarm response, and lack of a reliable alarm notification system.” When faced by any or all of these issues, the increase in the potential for patient harm and cost of care substantially replicates (Drew et al., 2014; Funk, Clark, Bauld, Ott,

& Coss, 2014; "The Joint Commission Sentinel Event Alert” 2013). In building on the three- level classification system published in 2004, this publication expands this to include the application of recommendations and level of evidence. In addition to the existing designation of no benefit, these updates include a designation for harm in the Class III recommendation. (see

Figure 1).

Including the harm designation in cardiac monitoring underscores the emergence of this issue in the literature. An example of an indication receiving a Class III (harm) label is noted in the recommendations for Continuous ST-Segment Monitoring of Hospitalized Adult Patients.

Per Sandau et al. (2017),

Continuous ST•-segment monitoring is potentially harmful because it will likely trigger

false or non-actionable alarms that may disturb patients, distract nurses, or lead to

unnecessary treatment for condition• specific changes in repolarization, myopericarditis,

chronic ‘scooped’ ST segment caused by prolonged digitalis use, left bundle branch

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 19

block, right bundle branch block (unless advanced interpretation skills are present), and

paced rhythms. (p. e281, Table 3)

Figure 1. Applying Classification of Recommendations and Level of Evidence. From Update to practice standards for electrocardiographic monitoring in hospital settings: A scientific statement from the American Heart Association. Circulation, 136(19), by K. E. Sandau et al. 2017. Copyright, 2017. Reprinted with permission Circulation.2017;136:e273-e344 ©2017 American Heart Association, Inc.

Regarding the increased cost of care, in 2005, Bayley et al. noted there is a direct relationship between (ED) overcrowding and financial burden associated with patients being boarded in the ED while awaiting admission to a telemetry bed. Out of 904

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 20 admitted patients >24 years old presenting to the ED over a 12-month period, 825 waited over 3 hours for a telemetry bed (Bayley et al., 2005). This delay translated into $204.00/patient of lost revenue ($168,300.00) (Bayley et al., 2005). This dollar amount is likely an underestimate as it does not consider ambulance diversion when the ED is full (there are no beds in the telemetry unit for patients to be placed in), patients leaving without being seen and current cost of care

(Bayley et al., 2005; Larson & Brady, 2008). In the Benjamin et al. (2013) report over the duration of a week in January 2008, the total number of patient days of monitoring was 1,559;

548 of those days (35%) were found to be non-indicated. This translated into at least $53.00-

$83.00/day in unnecessary costs (Benjamin et al., 2013). Inappropriate use of telemetry has been associated with unnecessary testing and that directly translates to an increase in the cost of care

(Henriques-Forsythe et al., 2009; Yeow et al., 2018). Artifacts recorded on telemetry have led to unnecessary, invasive procedures that have increased cost (Henriques-Forsythe et al., 2009).

Rationale

This is a quality improvement (QI) project. One of the most frequently utilized theoretical frameworks in the QI space is the Plan-Do-Study-Act (PDSA) cycle (Taylor, McNicholas,

Nicolay, Darzi, & Reed, 2014). It is a component of the Model of Improvement that asks three questions, “What are we trying to accomplish, how will we know that a change is an improvement, and what change can we make that will result in an improvement?” (Langley et al., 2009). To begin using the PDSA cycle, the first step is setting an aim. Setting an aim entails selecting a goal that is measurable, defines the system or population affected and the amount of time to test the aim should be specified (Langley et al., 2009). The second step is establishing measures. The analysis of the measures is how it will be determined if a change has occurred and if it was an improvement (Moule, Evans, & Pollard, 2013). The third step is to select the actual

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 21 change to be tested. The designated change comes from within the system, facility, department, unit etc. (Langley et al., 2009). The actual test of change happens in step four when the PDSA cycle is implemented (see Figure 2). The test of change is planned (plan), tried (do), observed

(study), and the results are acted on (act) (Langley et al., 2009). Sometimes there needs to be several PDSA cycles to refine the actual test of change until the process advances to the fifth step: implementation. Implementation is the process where the change is piloted on a small scale.

If successful and the change has the potential to benefit on a larger scale, then the project moves to the final step; spreading the change (Langley et al., 2009).

• What changes •What exactly are we going to are we going to make based on our do? findings? Act Plan

Study Do

• What were the • When and how results? did we do it?

Figure 2. From “PDSA Cycle Template,” by U.S. Departments of Health and Human Services, Center for Medicare Services. 2018. https://www.cms.gov/medicare/provider-enrollment-and- certification/qapi/downloads/pdsacycledebedits.pdf. In the public domain.

The PDSA cycle best suits this project because the design is a quality improvement test of change. PDSA cycles are usually short-lived experiments (this endeavor will be a three-month trial), and is frequently used in the quality improvement space. The project will be piloted on a designated medical-surgical/oncology unit covered by the Hospitalist service so it is contained to one area. If there are any new PDSA cycles to be implemented based upon what is learned from

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 22 the first test of change, the staff will already be versed in the process. Additionally, this paper has been authored utilizing the Standards for Quality Improvement Reporting Excellence (SQUIRE)

2.0 guidelines (Ogrinc et al., 2016).

Specific Aims

Evidence–based guidelines exist to improve patient outcomes and facilitate congruency of practice among clinicians (Keiffer, 2015). Providers should follow established clinical guidelines and not place patients on telemetry who do not meet criteria. However, this is frequently not the case. Limited evidence exists to address interventions that focus on decreasing the duration of telemetry monitoring once it has been initiated. Additionally, nurses can play a key role in determining if a patient meets criteria for telemetry monitoring (Crawford & Halm,

2015). This underscores the continued expectation that nurses practice in accordance with the evidence base (Fisher et al., 2016). The objective of this project is to determine if introducing a telemetry checklist completed by the patient’s registered nurse (RN) will decrease the duration of telemetry monitoring. The research question is as follows:

In adults over the age of 18 admitted to the Hospitalist Service at Howard County

General Hospital to a medical/oncology unit requiring continuous cardiac monitoring,

does the utilization of a telemetry checklist by the RN during multi-disciplinary rounds

reduce the duration of telemetry monitoring over a three-month period?

Sustainability, limitations, and suggestions for further research will be addressed as part of the project evaluation. This paper describes a scholarly project designed to further investigate the impact of utilizing a telemetry checklist during multi-disciplinary rounds to decrease the duration of telemetry monitoring on an inpatient adult medical-surgical unit and was the

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 23 culminating assignment in partial fulfillment of the requirements for the Doctorate in Nursing

Practice (DNP) program at Wilmington University in New Castle, Delaware.

Methods Context

This quality improvement project was a retrospective chart review comparing length of telemetry use in the baseline period to length of telemetry use after implementation of a telemetry discontinuation checklist (TDC). This study was conducted at Howard County General

Hospital (HCGH) in Maryland. HCGH is an acute care medical facility in the Johns Hopkins

Health System and is located in the suburbs of Baltimore, Maryland, and Washington, DC. With

267 licensed beds, HCGH offers a full range of services to include , orthopedics, surgery, oncology, cardiology, labor and delivery, , ICU level care, and many community health and outreach programs.

As part of continuous quality improvement, multi-disciplinary teams are assembled to identify issues that may benefit from focused improvement efforts. The overuse use of CTM has been identified in the literature and at HCGH as an ongoing concern. There are five telemetry capable, non-ICU units in HCGH. The average time on telemetry per unit ranged between 31 to

70 hours per patient.

In August of 2017, HCGH initiated the first telemetry QI project on 4 South (a general medicine/oncology unit) with the goal of decreasing the duration of CTM to an average of 29 hours per patient. Previously, from January through July 2017, the 4 South patients on CTM had an average time of 42.4 hours per patient (median time of 38.8 hours). This initial project consisted of a simple pocket card adopted from the 2004 AHA Guidelines that was distributed to all the RNs and Hospitalists to utilize as a reference (see Figures 3 & 4). The primary outcome measure for CTM was the average length of time on telemetry (hours). Between August 2017

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 24 and April 2018, the average time on telemetry for 4 South was 39.8 hours per patient with an average median time of 38.4 (see Figure 5).

This study was developed to augment the previously implemented efforts to reduce unnecessary telemetry use. The addition of another intervention, the TDC, may be beneficial in obtaining the stated facility goal of decreasing total telemetry hours to an average of 29 hours per patient. While reinforcing the AHA guidelines for appropriate telemetry use, this study specifically aimed to pilot a nurse led checklist to assess the continued need for telemetry monitoring on 4 South. This QI project was a retrospective chart review comparing length of telemetry use in the baseline period to length of telemetry use after implementation of a TDC to determine if using a checklist reduces duration of telemetry monitoring.

Figure 3. Front of the telemetry pocket card. From “Telemetry Guidelines,” by Johns Hopkins Bayview Medical Center, Providers for Responsible Ordering. 2015. http://www.bayviewpro.org/project-toolbox. In the public domain.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 25

Figure 4. Back of the telemetry pocket card. From “Telemetry Guidelines,” by Johns Hopkins Bayview Medical Center, Providers for Responsible Ordering. 2015. http://www.bayviewpro.org/project-toolbox. In the public domain.

Figure 5. 4 South average time on telemetry January 2017 – June 2018

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 26

Population

The study population included all adults aged 18 or greater, admitted to the Hospitalist service, admitted to 4 South, on CTM, and admitted from July 1 through September 30, 2018.

Patients who met the criteria were excluded if they had been admitted to the ICU or the special care unit (ICU step-down) prior to moving to 4 South or if they required transfer to either of those units due to a decline in their condition. An admission to either unit may indicate a level of complexity and illness that increases the length of time on telemetry and may differ from those not requiring that increased level of care.

Intervention

The HCGH telemetry discontinuation checklist (TDC) focused on evidence-based criteria for telemetry and was piloted for use by the RN during multi-disciplinary rounds. The initial document was drafted from a checklist already in use in the medical system at the Johns Hopkins

Hospital in Baltimore, Maryland (see Figure 6 and Figure 7). The Johns Hopkins Hospital is a large, multi-specialty, tertiary, academic medical center. In determining acceptability, copies of the form were provided to the nurse manager of 4 South and the staff RNs. Most felt that the form was too long, complicated, and would not be feasible for implementation. Besides decreasing the duration patients are in CTM, one of the primary goals was to produce a tool that was not cumbersome to complete and endorsed by the RN staff to ensure sustainability.

Based on feedback and the goals of this study, a revised checklist was developed. (see

Figure 8). Nurses stated that the forms must be easy to use and quick to complete. The checklist was reviewed for nursing feedback and those comments were considered in the final version.

Several checklist champions were identified among the staff and processes were developed to facilitate ease of use.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 27

Figure 6. Initial draft of the telemetry checklist, page 1. Adapted from: Effect of a nurse- managed telemetry discontinuation protocol on monitoring duration, alarm frequency, and adverse patient events. Journal of Nursing Care Quality, 32(2), 126-133. By K. Perrin et al. 2017. Copyright, 2017.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 28

Figure 7. Initial draft of the telemetry checklist, page 2. Adapted from: Effect of a nurse- managed telemetry discontinuation protocol on monitoring duration, alarm frequency, and adverse patient events. Journal of Nursing Care Quality, 32(2), 126-133. By K. Perrin et al. 2017. Copyright, 2017.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 29

Prior to implementing the project, short educational sessions of no more than 5 minutes duration were held with the RN staff. The sessions focused on how to complete the checklist, awareness of the AHA guidelines, the previously disseminated pocket cards, and the role of nurses in facilitating best practices and patient safety. The process and procedures for completing the TDC were developed in conjunction with the nursing staff and designated checklist champions.

Figure 8. Final version of the telemetry discontinuation checklist.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 30

The process for completing the TDC is as follows (see Figure 9):

1. Monday through Friday, on every night shift, the RN initiates a new TDC checklist for all

their patients on telemetry

2. The night shift RN completes as much of the checklist as possible

3. The night shift RN provides the day shift RN the initiated form during morning report

4. The dayshift RN brings the checklist to daily multi-disciplinary rounds (held Monday

through Friday) and discusses discontinuing or continuing CTM with the provider

5. The dayshift RN completes the remaining fields on the form

6. The dayshift RN places the completed form in a designated envelope in the staff multi-

disciplinary room to ensure compliance with confidentiality

Figure 9. Telemetry checklist process.

To assess use of the checklist, the completed checklists will be compared to the actual number of telemetry patients to determine response rates. Follow up meetings will be held with

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 31 the unit to evaluate their perception of the tool including usability, sustainability, and value to their individual practice.

Measures

The primary measure for this study focused on the outcomes of care as measured by duration of time spent on telemetry monitoring. Data from the medical chart were extracted to ascertain length of time on telemetry. Data were obtained from the hospital based electronic medical record utilizing the EPIC electronic health record platform.

The variables for this study were obtained from two sources: medical record administrative data queries and the TDC (see Table 1 and Table 2). For the administrative variables of interest, missing data were manually abstracted from the medical record. If the data was not available, the record was excluded.

Table 1

Medical Records Administrative Variables

Hospital Admin Code Study Recode Definition MRN SPSS_ID Study identification code HOSP_ADMSN_TIME Hosp_Admsn Hospital admission: date and time HOSP_DISCH_TIME Hosp_Disch Hospital discharge: date and time N/A Los_Hours Total length of stay (subtract admsn from disch) PROC_Start_Time Tele_Start Start time of the telemonitor procedure D/C Order TM Tele_Stop Time that provider electronically ordered discontinuation of CTM D/C Order Tm - Proc Start Tm Tele_Hours Formula (subtract start time from stop (hrs) time) in hours Prin Dx Prin_Dx Descriptive of principal diagnosis

The second source of data was from the checklist. The checklist data were documented by the RN’s and were compared to administrative data for congruence. The additional narrative

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 32 data may provide further insights to clinical decision-making and trends that may not be easily captured through queries of administrative data.

Table 2

Telemetry Discontinuation Checklist (TDC) Variables

Variable Study Code Definition MRN Study_IDC Study ID Age Age Patient age in years Sex Gender Male or Female Admit Dx Admit_Dx Admitting diagnosis Reason placed on continuous Why CTM Reason_On_Tele telemetry monitoring If not discontinued. reason for If CTM Not D/C'd Why Tele_No_Dc continuing continuous telemetry monitoring Other comments/ observations made Oth_Comments Misc by nursing staff

Analysis

Descriptive statistics were used to analyze demographic variables: age, race and gender.

To determine if the intervention had an effect on patient hours spent on telemetry, statistical analysis consisted of independent samples student’s t-test for comparison of data between baseline and intervention periods. The student’s t-test is an appropriate statistical method to employ when comparing the means of two independent groups using time data (Field, 2013).

The test of significance was set at p<.05 and the IBM SPSS (Statistical Package for the Social

Sciences), version 25, was utilized for data analysis.

Ethical Considerations

Institutional Review Board approval for this quantitative study was granted from

Wilmington University (see Appendix A and Appendix B). Permission was granted by HCGH to implement this DNP project at the facility (Appendix C). This study meets the criteria for a QI project set forth by the HCGH institutional review board (See Appendix D). Therefore,

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 33 additional approval at the facility was not required. Permission to utilize Figure 1 for purposes of this paper was granted by the publisher (Appendix E). This analysis of previously collected data is considered appropriate for an expedited review with a request for a waiver of the Health

Insurance Portability and Accountability Act (HIPPAA) and a waiver of consent. An expedited review allows for research that involves materials, such as data, documents, or records, which have been collected for non-research purposes. Individual consent from each subject was not required in this study as the data were previously collected for health care purposes. No study subjects were contacted nor remunerated. The study was implemented after all appropriate approvals were obtained.

As this study contains private health information, release of this information would violate HIPAA and may have a negative social, financial, or legal impact on the subject. To minimize risk, all data were maintained on an encrypted, secured server. Each subject was assigned a study identifier code, and the code list of study subjects was kept separately in a secure location. Data abstraction and analysis were conducted in a private, secured space.

Patients’ names and other identifying information will not be published.

Results

The primary outcome for this study focused on the duration of time spent on telemetry monitoring. A telemetry discontinuation checklist was implemented in 2018 to facilitate appropriate termination of continuous cardiac monitoring if not clinically indicated. Students’ independent samples t-tests were conducted to compare the mean number of hours spent on telemetry monitoring between the 2017 baseline and 2018 intervention periods. Each episode of telemetry monitoring (N = 219) was examined for the baseline and intervention timeframes. The comparison time frames were: July, August, and September of 2017 to July, August, and

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 34

September of 2018. All eligible telemetry episodes (N = 219) were included in the population without sampling. Sometimes, a single patient could have more than one episode of telemetry monitoring during an admission. If the episodes overlapped in time, they were combined and a new total hour value was calculated. If the episodes of telemetry did not overlap even though they occurred during the same admission period, the episodes remained separate. The data were examined to determine if assumptions for t-tests were met, including distribution and equality of variances.

The goal of measuring the duration of telemetry hours was to determine if using the TDC impacted the length of time a patient was on telemetry. The mean length of time spent in telemetry hours is presented for the comparison groups in Table 3.

Table 3

Descriptive Statistics: Mean Length of Time Spent in Telemetry Hours by Month

Date Group by Month n Mean SD SEM July 2017 34 45.35 32.84 5.63 July 2018 44 29.93 19.47 2.93 August 2017 37 41.03 44.04 7.24 August 2018 39 43.38 36.97 5.92 September 2017 41 40.49 26.93 4.20 September 2018 24 22.58 19.23 3.92

There were significant differences in the mean hours spent on telemetry for the July and

September comparison groups, but no difference in means for August (see Table 4). The number of hours spent on telemetry during July 2017 (M = 45.35, SD = 32.84) was significantly higher, t(50.51) = 2.428, p = .011, than in July 2018 (M = 29.93, SD = 19.475). However, Levene’s F test, F(76) = 6.84, p = 0.01, indicates the assumption of equal variances was violated. Therefore, the non-parametric Mann-Whitney test was applied to compare means of hours spent on telemetry (see Table 5). The results confirm a statistically significant (p = 0.025) difference in

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 35 means between July 2017 and 2018. Those patients admitted in July 2017 were associated with a statistically significant greater mean number of hours spent on telemetry than those admitted in

July 2018.

Table 4

Independent samples t-Test Comparing Means of Hours on Telemetry Between Baseline 2017 and Intervention 2018 Periods

Levene’s Test for Equality of Equal t -test for Equality of Means Variances 95% Confidence Interval of the Sig Mean Std. Error F Sig. t df Difference (2-tailed) Difference Difference Number of Hours on Lower Upper Telemetry Equal Variances 6.84 0.01 -2.58 76 0.012 -15.421 5.967 -27.31 -3.537 Assumed Equal Variances July -2.43 50.5 0.019 -15.421 6.351 -28.18 -2.667 Not Assumed Equal Variances 0.01 0.95 0.253 74 0.801 2.358 9.311 -16.2 20.91 Assumed August Equal Variances 0.252 70.4 0.802 2.358 9.354 -16.3 21.01 Not Assumed Equal Variances 2.22 0.14 -2.85 63 0.006 -17.904 6.273 -30.44 -5.368 Assumed September Equal Variances -3.11 60.4 0.003 -17.904 5.754 -29.41 -6.396 Not Assumed The mean number of hours on telemetry was also significantly different between

September 2017 (M = 40.49, SD = 26.937) and September 2018 (M = 22.58, SD = 19.233), t(63)

=2.854, p =.006. The patients admitted in September 2017 were associated with a statistically

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 36 significant greater mean number of hours on telemetry than those admitted in September 2018.

The means of the hours spent on telemetry were not significantly different between August 2017

(M = 41.03, SD = 44.049) and August 2018 (M = 43.38, SD = 39.76), t(74) = -.253, p = .801.

Table 5

Mann-Whitney U Non-parametric Test

Sum of Number of Hours on Telemetry n Mean Rank Ranks July 2018 44 34.45 1516 July 2017 34 36.03 1565 Total 78

Mann-Whitney U 526

Wilcoxon W 1516

Z -2.238 Asymp. Sig.(2-tailed) 0.025

Secondary measures included examination of patient age in years, gender, and patient length of stay in hours. Nine patients were excluded from the study population (N = 219) examining episodes of telemetry hours, as they were represented more than once in that data set.

The second study population, Patients (N =210), was patient specific, meaning each patient was only represented once in the population. These variables were assessed to further determine comparability of baseline and intervention periods using student’s t-test. The data were examined to ensure assumptions for t-tests were met, including distribution and equality of variances. The patients study population included all eligible subjects without sampling and descriptive statistics are presented in Tables 6 and 7.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 37

Table 6

Descriptive Statistics for Gender and Race in Patients Study Population (N=210)

Timeframe n Gender Female Male July 2017 32 19 13 July 2018 44 26 18 August 2017 35 26 9 August 2018 36 13 23 September 2017 39 21 18 September 2018 24 10 14 Total 210 115 95

Race n % Gender Female Male Asian 15 (7.1%) 6 9 Black 61 (29.0%) 34 27 Hispanic 10 (1.4%) 1 2 White 121 (57.6%) 67 54 Other 3 (1.4%) 1 2 Total 210 115 95

Table 7

Descriptive statistics for age in Patient study population (N = 210)

Minimum Maximum M SD Percentile Percentile Percentile 25 50 75 18 102 64.6 19.5 53 67 80

Patient means were examined for age in years, gender, and length of stay in hours to determine differences between baseline and intervention groups. Table 8 presents the means and standard deviations for each variable by timeframes.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 38

Table 8

Student’s Independent Samples t-test Comparing Means of Ages of Patient’s Telemetry Between

Baseline and Intervention Periods

Baseline/Intervention n Mean SD SEM Age in Years July 2017 32 59.25 20.911 3.69 July 2018 44 62.75 20.768 3.131 Age in Years August 2017 35 63.51 20.159 3.407 August 2018 36 68.92 14.042 2.34 Age in Years September 2017 39 63.44 20.474 3.29 September 2018 24 72.33 18.422 3.76 Length of Stay in Hours July 2017 32 4.05 4.136 0.731 July 2018 44 4.22 5.979 0.901 Length of Stay in Hours August 2017 35 4.39 4.154 0.702 August 2018 36 4.62 3.563 0.594 Length of Stay in Hours September 2017 39 7.46 19.108 3.06 September 2018 24 3.57 2.145 0.438 Gender July 2017 32 0.41 0.499 0.088 July 2018 44 0.41 0.497 0.075 Gender August 2017 35 0.26 0.443 0.075 August 2018 36 0.64 0.487 0.081 Gender September 2017 39 0.46 0.505 0.081 September 2018 24 0.58 0.504 0.103

The t-test indicated no significant differences among the three comparison groups concerning patient age in years or length of stay in hours (see Table 9 and Table 10). However, one significant difference was determined in the gender category (see Table 11). The means for gender were significantly different between August 2017 (M = .26, SD = .443) and August 2018

(M = .64, SD = .487), t(69) = .066, p = .001). Although both months had similar total sample sizes, the gender composition of August 2017 included 26 females and nine males, while August

2018 included 13 females and 23 males.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 39

Table 9

Student’s independent samples t-test comparing means of ages of patient’s telemetry between baseline and intervention periods

Levene’s Test for Equality of Equal t -test for Equality of Means Variances 95% Confidence Age in Sig Mean Std. Error Interval of the F Sig. t df Years (2-tailed) Difference Difference Difference Lower Upper Equal Variances 0.055 0.816 -0.723 74 0.472 -15.421 4.839 -13.42 6.142 Assumed Equal Variances July -0.722 66.687 0.473 -3.5 4.844 -13.17 6.17 Not Assumed Equal Variances 3.524 0.065 -1.313 69 0.913 -5.402 4.113 -13.608 2.803 Assumed August Equal Variances -1.307 60.533 0.196 -5.402 4.134 -13.669 2.865 Not Assumed Equal Variances 0.664 0.418 -1.739 61 0.087 -8.897 5.118 -19.131 1.336 Assumed September Equal Variances -1.783 52.793 0.08 -8.897 4.989 -18.905 1.11 Not Assumed

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 40

Table 10

Student’s independent samples t-test comparing means of hours of length of stay of patients on telemetry between baseline and intervention periods

Levene’s Test for Equality of Equal t -test for Equality of Means Variances 95% Confidence Length of Sig Mean Std. Error Interval of the Stay in F Sig. t df (2-tailed) Difference Difference Difference Hours Lower Upper Equal Variances 0.794 0.376 -0.139 74 0.89 -0.17 1.228 -2.617 2.277 Assumed Equal Variances July -0.147 73.853 0.884 -0.17 1.161 -2.483 2.143 Not Assumed Equal Variances 0.304 0.583 -0.248 69 0.805 -0.227 0.0918 -2.058 1.603 Assumed August Equal Variances -0.247 66.821 0.805 -0.227 0.92 -2.063 1.608 Not Assumed Equal Variances 2.45 0.123 0.99 61 0.326 3.889 3.928 -3.964 11.743 Assumed September Equal Variances 1.258 39.545 0.216 3.889 3.091 -2.36 10.139 Not Assumed

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 41

Table 11

Student’s independent samples t-test comparing gender between baseline and intervention periods

Levene’s Test for Equality of Equal t -test for Equality of Means Variances 95% Confidence Sig Mean Std. Error Interval of the Gender F Sig. t df (2-tailed) Difference Difference Difference Lower Upper Equal Variances 0.002 0.961 -0.025 74 0.98 -0.003 0.116 -0.233 0.228 Assumed Equal Variances July -0.025 66.828 0.98 -0.003 0.116 -0.234 0.228 Not Assumed Equal Variances 3.478 0.066 -3.45 69 0.001 -0.382 0.11 -0.602 -0.161 Assumed August Equal Variances -3.455 67.708 0.001 -0.382 0.11 -0.602 -0.161 Not Assumed Equal Variances 0.449 0.485 -0.931 61 0.356 -0.122 0.131 -0.384 0.14 Assumed September Equal Variances 0.931 48.934 0.356 -0.122 0.131 -0.385 0.141 Not Assumed Checklist Data

During the 2018 intervention period, a total of 61 eligible patients had a TDC completed.

TDCs were completed for 30 of 44 eligible patients (68%) in July, 18 of 36 eligible patients

(50%) in August, and 13 of 24 eligible patients (54%) in September. The TDC was filled out by the nursing staff and then compared against hospital administrative data for confirmation of eligibility and documentation. The admitting diagnoses, as reported by the nurses, were consistent with the principal diagnosis listed in the administrative data as delineated by the

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 42

International Classification of Diseases, Tenth Revision, Clinical Modification Codes (ICD-10-

CM). Of the 61 eligible patients, only 31 patients' admitting diagnosis was consistent with or appeared to be related to the indication for CTM represented by question two on the checklist,

“Why was the patient placed on telemetry?” Examples of non-matched admitting diagnosis and reason for CTM include 1. Admitting diagnosis: vomiting/ reason for CTM: bradycardia; 2.

Admitting diagnosis: diarrhea/ reason for CTM: atrial fibrillation. In these cases, it was the nurses’ response to the reason for CTM that provided insight as to the indication for monitoring.

The most commonly cited reasons for patients being placed on CTM were for cardiac related issues (medication, atrial fibrillation, chest pain, congestive heart failure, hypo/hypertension, patient had a pacemaker/internal defibrillator, syncope, brady/tachycardia), and electrolyte imbalances (hyper/hypokalemia, hyponatremia). Other diagnoses represented were neurologic

(dizziness, altered mental status), substance abuse, anemia, respiratory status, and sepsis.

Of those placed on CTM, only 12 of the 61 (20%) patients remained on CTM after the first checklist was completed (more than one day). The reasons nurses cited for continued monitoring included as a precaution, (i.e., the provider thought the patient needed one more day), continued electrolyte imbalance, provider not available or did not attend multi-disciplinary rounds, shortness of breath, sepsis, drug protocol, and no specific reason provided by the provider.

Discussion

Summary

The objective of this project was to evaluate if a simple checklist initiated by the RNs and then reviewed during multidisciplinary rounds on a busy general medicine/oncology unit would decrease the duration patients were on CTM. Although on average over the three months, 57% of

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 43 the time checklists were completed on eligible cases, in July and September 2018 there was a statistically significant decrease in the duration patients were monitored on telemetry in comparison to the same timeframe the previous year.

Possible barriers related to the 57% participation rate of the nursing staff are; burden of completing the checklist, demanding patient load, failure to clearly delineate a champion (s) for the project to provide consistent reminders to use the tool, and the inability for the Principal

Investigator (PI) to be onsite every day to monitor the progress of the project. However, when the PI verbally surveyed the nursing staff, it was reported they liked how “user friendly” and

“short” the checklist is. Additionally, the night shift nurses appreciated the opportunity to “have a voice” in multi-disciplinary rounds where prior to this project, that opportunity did not exist.

When asked if they thought that overall, this intervention was useful, those queried stated that in totality, it was a beneficial process. When asked verbally and via email about potential areas for improvement, no feedback was provided.

Despite the nursing staff participating 57% of the time, there was support for this project and they easily articulated the importance and ease of this intervention. They verbalized a longstanding desire for a method to decrease the duration of CTM for their patients who do not meet clinical criteria. Additionally, the Director of Nursing, the nurse manager, and the

Physician Lead for the Johns Hopkins Health System High Value Care Committee at HCGH were all in support and facilitated the process of implementing this QI project.

Throughout the operational phase of this endeavor, plans to maintain sustainability were created. An attribute of this checklist is that it is a fluid document and can be revised to suit the needs of a particular unit or scaled in its current version to be utilized hospital wide. Currently, there are plans for the PI to facilitate a work group with the nurses on 4 South and 4 Pavilion

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 44

(both managed by the same individual) to explore how the checklist can be revised. Items to be considered are other potentially harmful practices to include prolonged Foley catheter and central line use, and additional questions or issues that should be regularly addressed at multi- disciplinary rounds that can be frequently overlooked. Additionally, there are other possible opportunities to spread this checklist to other health systems.

Interpretation

The statistically significant difference in means of telemetry hours between July 2017

(45.35) and 2018 (29.93) may be associated with the increased use of the TDC and increased awareness of the telemetry guidelines. In July 2018, 68% of all eligible patients had a checklist completed, which was the highest percentage of participation. There was also an overall heightened awareness of this new project and it may have prompted conversations about telemetry removal regardless of checklist use. The July 2017 and July 2018 groups were comparable for gender, age, and length of stay. Assumptions for homogeneity were addressed through additional statistical tests. The mean hours on telemetry for July 2018 were in line with the larger organizational goal of decreasing time on telemetry to 29 hours per episode.

The significant difference in mean hours was determined between September 2017

(40.49) and 2018 (22.58) (Figure 10). Although no patient specific differences were found in relation to age, gender and length of stay, it is important to note that the samples size for

September 2017 (n = 39, 62%) and September 2018 (n = 24, 38%) were not similarly numerically distributed. Even if the assumptions of homogeneity were violated (which it was not), t-test results continue to indicate a significant difference in means. In relation to the smaller population, September 2018 had a lower census count across all units, with one unit closed intermittently throughout the month. The reason for overall lower facility census was unknown.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 45

Checklist completion rates were 54%. Although no significant differences were found between

August 2017 and 2018, there was a slight increase in the mean number of telemetry hours (41.03 versus. 43.38 hours). Visual inspection of the data identified 12 episodes of >50 consecutive telemetry hours for August 2018. There were four episodes >50 hours in July 2018 and 3 such episodes in September 2018. There were no obvious changes in providers or nursing staff and there were no diagnoses out of the ordinary, therefore, it may be likely that patients were of higher acuity and might have benefited from a higher level of care.

= 2017 Mean 50 45.35 44 Duration of 43.38 Telemetry 45 37 41.03 39 41 40.49 40 = 2018 Mean 34 Duration of 35 29.93 Telemetry 30 24 25 22.58 20 15 10 Mean Time on Telemetry on Time Mean 5 0 Jul-17 Jul-18 Aug-17 Aug-18 Sep-17 Sep-18 n 34 44 37 39 41 24 Mean 45.35 29.93 41.03 43.38 40.49 22.58 Figure 10. Mean length of time spent on telemetry in hours; Comparison Group (2017) versus Intervention Group (2018)

Individual patient means were examined for age in years, gender, and length of stay in hours to determine if the baseline and intervention groups were similar or comparable. Gender was associated with a statistically significant difference in means for August 2017 and 2018.

During this timeframe, the gender counts were reversed while total sample size, age in years, and length of stay remained constant. This finding does not have clinical impact on patient care or outcomes.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 46

The primary admitting diagnosis and the reason why patients were placed on the TDC as noted by the nurses were often not reflective of a clinical indication for CTM. Examination of data from the TDC highlighted the lack of continuity between the admitting or principal diagnosis and its’ relationship to the reason the patient was placed on CTM. However, moving further into the form, the data collected under the “Why CTM?” provided the needed insight.

The answers the nurses provided were based on both subjective and objective information.

An ad-hoc to determine usefulness of the indication for CTM was performed.

Each checklist primary admitting diagnosis was coded based upon the three classes of indication for CTM. The principal diagnosis was compared to the admitting diagnosis the nurses recorded on the checklist to determine consistency. In 10 instances (16%), the admitting diagnosis recorded on the checklist did not match the official principal diagnosis based upon administrative data. Of those 10 cases, two were re-classified to a higher level AHA category level based on additional information provided as to the reason for “Why CTM?” Out of all eligible patients with checklists completed, 70% had a matched primary diagnosis with a nurse reported diagnosis that designated them as a Class III recommendation; no benefit from cardiac monitoring.

However, when the reasons for CTM, as documented by the RNs, were considered in determining AHA category level, the number of those meeting AHA category III decreased from

70% to 52% thereby increasing the number of AHA Categories I and II from 30% to 48%. These findings were further validated by random chart review. Based on clinical understanding of the

AHA category levels, the narrative data collected from this checklist provided additional evidence to support the appropriate use of TCM.

When considering hour and cost reduction, the actual time patients were on telemetry in

2017 (n=106) was 4,720 hours or 44.53 hours per patient (average for July, August, and

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 47

September 2017 = 42.29 hours per episode). In 2018, the actual time patients (n=104) were on telemetry was 3,551 hours or 34.14 hours per patient (average for July, August, September 2018

= 31.96 hours per episode). Actual hours of telemetry saved per episode during the intervention period were 10.39. When this is applied to the intervention group, a total of 1,080.56 hours of

TCM was saved in 2018 (10.39 hours x 104 patients). There were a total of 49 fewer days on

TCM in 2018 (4720/24 hours = 197 days on TCM in 2017; 3551/24 hours = 148 days on TCM in

2018; 197-148 = 49 fewer days on TCM in 2018). As stated by Benjamin et al. (2013), the average daily cost of delivering TCM in a non-ICU setting ranges from $53.00 - $83.00 thus resulting in a total three-month savings of $2,597.00 - $4,067.00. Additionally, Benjamin and colleagues (2013) estimated that nurses spend 45 - 90 minutes more per shift caring for a patient on CTM. This translates to a range of approximately 2,205 minutes (2205/60 minutes = 33.75 days) to 4,410 (4410/60 minutes = 73.5 days) of nursing care saved during this QI project. When randomly verbally queried by the PI, the nurses reported an increased in job satisfaction with fewer patients on telemetry.

Limitations

This study was conducted as a quality improvement project in a single nursing unit, at one community hospital facility in a suburban area in Maryland. The generalizability of this study may be limited to similar institutions within similar geographic settings. The study population size was small and power was not calculated, as sampling was not used. To strengthen the study design and minimize variability, all eligible subjects were included in the study population without sampling and homogeneity was ensured through applying eligibility criteria and using statistical tests. Disproportionate sample sizes may have an effect on accuracy

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 48 of calculated p-values regardless of homogeneity. Data collection for the intervention period was limited to a three-month time frame. A longer data collection period may yield different results.

The checklist used in this project was specifically designed for this pilot. Therefore, it is not a validated tool. The intention was to devise an easy to use tool that does not cause undue burden on the nursing staff yet will yield a positive clinical impact. Use of the checklist decreased to approximately 50% over the study period. However, when the checklist was completed, the data provided by the nurses was reliable and consistent with the administrative data. The absence of consecutively completed checklists inhibited the ability to understand why patients remain on telemetry for prolonged periods of time.

Finally, especially among QI projects within dynamic practice settings, other factors that may not have been considered could have an impact on the study population and results.

Although significant associations were found, reasons other than the study intervention may have contributed to the association.

Implications for Advanced Nursing Practice

Quality Improvement has become a crucial part of success for a health-related organization and advanced practice nurses are a huge asset to this field. From the first classes in an undergraduate program, nurses are taught and conditioned to look at patients holistically.

Nurses examine how patients fit in and function within their lives after the acute phase of an illness, or how their lives can affect or facilitate ongoing wellness. The Centers for Medicare and

Medicaid are transitioning from fee for service reimbursement to a value-based payment model.

Quality of care will be the benchmark for payment as opposed to the quantity of patients a provider can see in a day. With the education and experience DNP prepared advanced practice

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 49 nurses have, they are poised to be a high in demand, essential component in the rapidly expanding quality improvement arena.

The foundation of a DNP prepared advanced practice nurse are the eight Essentials.

These concepts were incorporated into this project in the following ways:

Essential I: Scientific Underpinnings for Practice. As work began on this project, understanding the science and available evidence-based recommendations related to cardiac telemetry monitoring was imperative. Identifying gaps in the literature and understanding their clinical implications was important to writing a solid PICOT question on which to base this work. For this project, the role of the DNP prepared advanced practice nurse was to take the science and known evidence to devise a feasible process and put it into practice.

Essential II: Organizational and Systems Leadership for Quality Improvement and

Systems Thinking. While this project was being designed, the end goal was to have a document and process that is useful, fluent, and easily scaled to the entire facility and possibly beyond to other hospitals in the health system. To do this, it is important to have a strong aim statement and, in this case, the PICOT question served in that role. Achieving an aim will build results and results builds systems. In the QI arena, it is important to consider how one small intervention like adding a checklist to improve a process can be built upon and eventually contribute to a systems wide improvement. After the three-month PDSA cycle, the results were favorable enough for

HCGH leadership to consider scaling the checklist.

Essential III: Clinical Scholarship and Analytical Methods for Evidence–Based

Practice. Throughout the design and implementation of this project, it was important to maintain an inquisitive posture and continually analyze the process and assess if there was a better way to accomplish the aim. While it is imperative to complete the full PDSA cycle, gathering data and

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 50 anecdotes along the way while continuing to question and learn is scholarly and analytical. At the end of the project, there are already data obtained on how to improve upon the next iteration.

Essential IV: Information Systems/Technology and Patient Care Technology for the

Improvement and Transformation of Health Care. There is a lot of technology used in healthcare that improves the care and lives of patients. For the purpose of this paper, there are too many examples to begin a list. Conversely, the plethora of technological advances can be too much or overused; especially when evidence-based clinical guidelines are, for whatever reason, not adhered to. For this project, when the clinical recommendations were followed, the desired outcome was less use of telemetry (technology). Decreasing the duration a patient is on telemetry using a simple checklist is transformational and the kind of project that can be built upon to improve patient care and transform health systems.

Essential V: Health Care Policy for Advocacy in Health Care. This project does not directly translate into the traditional sense of health care policy and advocacy. However, it did encourage and increase patient advocacy on the unit level. By introducing the checklist, the nursing staff had clear evidence-based facts to advocate for their patients at multi-disciplinary rounds. The night shift nurses felt they had a voice in multi-disciplinary rounds because the checklist was started by the RN staff on night shift. Ensuring your voice is heard is a crucial aspect of advocacy. Small successful tests of change like this project can lead to a change in policy and practice at the unit level or facility wide.

Essential VI: Interprofessional Collaboration for Improving Patient and Population

Health Outcomes. Healthcare transformation and QI initiatives in healthcare facilities could not be successful without collaboration among the various disciplines. For this project, nursing leadership, provider leadership, and staff nurses collaborated to improve the care of patients on

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 51

CTM. As a result of this team work, the care of the population of patients on CTM was favorably influenced by implementing the checklist.

Essential VII: Clinical Prevention and Population Health for Improving the

Nation’s Health. In thinking about the seventh essential, outpatient primary care comes to mind.

Practicing preventative medicine and maintaining wellness is one way to improve the health of the population and the nation. Unfortunately, our healthcare system is far from this sort of attribute being a universal finding. For the population of patients admitted to the hospital requiring CTM, it is possible to practice preventative medicine. Following clinical guidelines and discontinuing telemetry when a patient’s condition no longer requires it is preventing potential harm for this particular subset of the inpatient population. As previously stated, this is not a process immediately recognized as a modality that will improve the nation’s health. However, over the course of time, continuing to achieve designated aims will lead to transforming a system. Transforming a system can potentially have an effect on the landscape of the nation’s healthcare system.

Essential VIII: Advanced Nursing Practice. It is difficult to distinguish how competence of this DNP essential was achieved during this project. Advanced nursing practice encompasses all of the other seven Essentials. When proficiency can be demonstrated as an advanced practice nurse in a chosen specialty in tandem with the DNP Essentials, this is advanced nursing practice.

Conclusion

The use of a telemetry discontinuation checklist may have a positive effect on the duration of telemetry monitoring. Two (July/September 2018) out of the three months during the pilot had statistically significant results. Additionally, in those two months the goal of decreasing

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 52 the duration of telemetry to < 29 hours was achieved, thereby decreasing the potential of exposing patients to harm caused by prolonged monitoring that was not indicated. Despite the short duration of the pilot, leadership at HCGH is pursuing the option to scale the checklist to other units in the hospital.

Achieving successful results and spreading the checklist to other units within the facility has been a part of the dissemination plan since the inception of the project. Prior to successful completion of the program, the manuscript was uploaded to ProQuest. Dissemination strategy includes the submission of papers generated from this manuscript to peer-reviewed journals and the submission of abstracts and posters to be presented at national conferences are planned.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 53

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Appendices

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 61

Appendix A: Wilmington Human Subject Review Committee Letter of Approval

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 62

Appendix B: Approved Wilmington Human Subjects Review Committee Application

HUMAN SUBJECTS REVIEW COMMITTEE (HSRC) PROTOCOL FORM

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 63

Table of Contents

HSRC Protocol Form Completion Overview ...... 64 Academic Level ...... 65 Forms Check List ...... 65 RECORD AND REVIEW OF RESEARCH PROTOCOL ...... 66 Contact Information ...... 66 Project Status ...... 66 Project Information ...... 66 External Research ...... 67 Population Information ...... 69 Confidentiality and Security ...... 71 Research Protocol ...... 72 Consent Forms ...... 73 Obligations of Principal Investigator ...... 74 PROTOCOL REVIEW ...... 75

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 64

HSRC Protocol Form Completion Overview

The information and question responses provided by the person or persons submitting this form must be accurate and complete. Be sure to review the Protection of Human Subjects Policies and Procedures document on the University’s webpage for additional information (http://www.wilmu.edu/academics/humansubjects/materials.aspx) prior to submitting this document.

This HSRC Protocol form must be used when the research does not conform to one of the U.S. Department of Human Services, Office for Human Research Protections (OHRP) Exempt Categories in 45 CFR 46.101(B) - (HRP-312) (see https://www.hhs.gov/ohrp/regulations-and- policy/regulations/45-cfr-46/index.html#46.101 for the categories).

This document is set up as a fill-in form. Your mouse pointer and a “left click” will select fields within the document or you can press the “tab” key to advance the cursor between fields in the form. All fields requiring lengthy responses (paragraphs v. sentences) will automatically expand to accept your information along with adjusting the document pagination. Please note, information can be copied (cut and pasted) into any field of the document and the instructions shown in red text will not appear on printed pages.

Information added to this form must be typed, with the exception of signatures. Typed signatures are not acceptable. In addition, the information should be thoroughly reviewed for correct grammar, spelling, and punctuation prior to submitting the document to the Human Subjects Review Committee.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 65

Academic Level

☒ 1. Doctoral Dissertation/Capstone

☐ 2. Master’s Thesis/Capstone

☐ 3. Undergraduate

☐ 4. Faculty 5. Other ☐

Forms Check List Assemble materials in the order shown below

☐ 1. Human Subjects Protocol

☐ 2. Human Subject Certificate

☐ 3. Consent Forms

☐ 4. Instruments

☐ 5. Other

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 66

RECORD AND REVIEW OF RESEARCH PROTOCOL

Contact Information (Type or print the information into the appropriate) areas)

Principal Investigator: Glenn Stefanie M.

(Last) (First) (Middle) Student ID: W W00140943

Project Status

New ☒ Renewal ☐ Re-evaluation ☐

Instructor or assigned faculty sponsor: Walton Reddish, DNP

Project Information

Title of study (12 to 15 words max):

A Telemetry Centric Checklist on a Medical Surgical Unit to Reduce the Duration of Monitoring

Research purpose or issue: In 2004, the American Heart Association (AHA) published a Scientific Statement that discussed standards for electrocardiographic monitoring in the hospital setting (Drew et al., 2004). However, this study did not establish evidence-based guidelines for use of electrocardiography (from henceforth referred to as telemetry or cardiac monitoring) in the non-intensive care (ICU) setting (Najafi, 2014). As a result, the use of telemetry in inpatient, non-ICU settings is inconsistent (Funk et al., 2017). Cardiac monitoring is frequently utilized in the hospital setting (Funk et al., 2010). This has led to significant overuse, unnecessary cost, and increased risk of patient harm (Benjamin, Klugman, Luckman, Fairchild, & Abookire, 2013; Chen et al., 2016). This is a quality improvement project that will evaluate the utilization of a telemetry checklist by the patient’s nurse during multidisciplinary rounds to decrease the duration of telemetry monitoring on a medical/surgical unit in a suburban, community hospital. The goal of this project is to decrease the opportunity for patients to be harmed by unnecessary, prolonged cardiac monitoring, decrease the cost of care, and achieve cost savings.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 67

External Research If the research will involve other organizations, it is necessary to obtain permission from these organizations prior to collecting data. Some organizations have Institutional Review Boards (IRBs), and it may be necessary to obtain formal approvals from these IRBs. In other cases, a document from an appropriate organizational executive specifically approving the research would be sufficient. The researcher is responsible for determining what type of approval is required and obtaining the approval.

In cases where approval from Wilmington University’s HSRC is required as a precondition to obtaining approval from another organization, the HRSC’s approval will be provisional, requiring the additional step of obtaining research approval documents from other organizations before receiving full approval from Wilmington University’s HSRC.

If the research involves other organizations, please fill out this section.

YES NO

☐ ☒ Do these organizations require approval by their IRBs?

☐ ☒ Has IRB approval been obtained? If YES, please attach the approval to this submission

Have other permission documents been obtained? If YES, please attach the ☐ ☒ approvals to this submission.

Other relevant information or comments:

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 68

This is a quality improvement project. The organization’s research program administrator provided the attached algorithm utilized to determine if this quality improvement project requires IRB approval at the facility where the project will be implemented. Trushar Dungarani, DO is my project sponsor and is the physician lead for the Johns Hopkins High Value Care Committee. This project falls within his purview. In the attached algorithm, the project is required to fall into one of the following categories, “Initiated by a Johns Hopkins Health System hospital or safety committee and concern that entity’s own operations, or the activities address a question that is within the project leader’s job description to answer for a Johns Hopkins Health System entity’s safety or quality purposes.” Additionally, this project is not receiving funding from any source.

I have attached a letter signed by Dr. Dungarani that grants me permission to do this DNP project at Howard County General Hospital in Columbia, MD. Additionally, I practice at this facility one to two weekends a month as a hospitalist with Dr. Dungarani. Therefore, I am required to maintain all training as it relates to patient privacy and cyber security so I can access the electronic health record from home on a computer that meets the security requirements of the health system.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 69

Population Information Population to be studied: Gender M/F Age >18 Race/ethnicity All

Number of groups and number of participants in each group: There will be two groups. All charts of patients admitted to the designated medical surgical unit who were on telemetry three months before the checklist is implemented will be reviewed. This will serve as the control group. The intervention group will be all patient charts from the first three months the checklist is piloted. The average number of patients on telemetry per day is six. There will be approximately 540 telemetry patient charts in both the control and intervention groups to be evaluated (total = 1,080). This number was arrived at via the following calculation: 6 telemetry patients/day x 30 days/month x 3 months = 540. 540 (control group) + 540 (intervention group) = 1,080 total charts to be reviewed. The number of telemetry patients per day was obtained directly from the nurse manager of the unit the project will be implemented. Based upon how many patients are admitted to this unit that require telemetry, this number could fluctuate a bit.

How participants will be selected: This will be a convenience sample of all charts of patients on telemetry who were admitted to the designated medical/surgical unit during a specific timeframe.

What qualification criteria will be used to include participants in the sample? Adults age 18 or older, admitted to the hospitalist service, admitted to a non-intensive care unit (ICU) unit, telemetry monitoring ordered

What criteria will be used to exclude potential participants in the sample? Charts of patients who are under the age of 18, charts of patients not admitted to the designated medical surgical unit for this project, and charts of patients admitted to a non-medical surgical unit to include the ICU, ICU stepdown unit, or the post-anesthesia care unit (PACU).

How subjects will be recruited? The patient charts to be utilized for this project will be selected via the following criteria:- Only patient charts from people who were admitted to the designated medical surgical unit for this project will be utilized- All charts of patients who were placed on telemetry monitoring during the designated timeframe (three months pre-intervention and the first three months of the intervention) regardless of the admitting diagnosis will be utilized

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 70

Describe the procedures that the participants will undergo in the proposed research project including the physical location and duration of subject participation. Attach a copy of all research instruments, e.g., surveys, questionnaires, interview questions, etc.: This project will not include contact of any kind with actual patients. All data will be collected via chart review and the telemetry checklist that will be utilized by the patient’s nurse during multidisciplinary rounds (this checklist will only be used for patients who are on telemetry and admitted to the designated medical surgical unit for this project). Multidisciplinary rounds are a daily meeting between all disciplines involved the patient’s care (nurse, provider, social worker, nurse case manager, physical/occupational therapist, pharmacist etc.). The purpose of these rounds is to discuss the needs/treatment/condition of the patient while they are in the hospital, when they could be potentially well enough to be discharged from the hospital and what, if any needs the patient will have regarding follow up or care that has to be arranged when they leave the hospital.

The attached checklist will be utilized by the registered nurse during multidisciplinary rounds to help determine the appropriate duration of telemetry monitoring. Once this checklist is completed, it will be placed in an envelope with the project PI’s name on it. The location of this envelope is in a locked office on the floor where the designated medical surgical unit is located. Only personnel who are in direct patient care will have access to the checklist. The PI will extract information from these completed checklists in the locked office where the collection envelope will be located. Once the data are extracted from the checklists, they will be shredded.

The patient charts are all electronic. At the hospital, access to the chart is gained via use of the employee’s personal ID badge. If the patient charts are to be accessed remotely by the PI, it will be done on a computer that is up to date on all security software, is connected to the internet via Ethernet (i.e. not WIFI) and has met all the security regulations required of the Johns Hopkins Health System. Once all these requirements are met, regular access to patient charts at home is only achieved via double authentication.

All data extracted will be maintained electronically in a code book, on a computer that is up to date on all security requirements, and access is password protected.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 71

Confidentiality and Security Please answer yes or no to the following questions:

YES NO Procedures have been taken to ensure that individuals cannot be identified via names, ☒ ☐ digital identifiers (e.g., email address, IP address), images or detailed demographic information.

Code to name association data/information is securely and separately stored. ☒ ☐ (Participants are given codes and the codes are securely stored separately from their answers.)

☒ ☐ All data is maintained in encrypted and/or password protected digital/electronic files.

Individually identifiable information will be securely maintained for three years past the ☒ ☐ completion of the research, and then destroyed rendering the data unusable and unrecoverable.

Please provide further information concerning any “NO” answers given above (including cases where a procedure is not applicable). Describe any other procedures you are taking to maintain anonymity, confidentiality, or information security.

DEVELOPMENT AND EVALUATION OF A NURSE-DRIVEN 72

Research Protocol Please answer yes or no to all questions below.

Does this research involve:

YES NO prisoners, probationers, pregnant women (if there is a medical procedure or ☐ ☒ special risk relating to pregnancy), fetuses, the seriously ill or mentally or cognitively compromised adults, or minors (under 18 yrs) as participants

the collection of information regarding sensitive aspects of the participants behavior ☐ ☒ (e.g., drug, or alcohol use, illegal conduct, sexual behavior)

the collection or recording of behavior which, if known outside the research, could place ☐ ☒ the participants at risk of criminal or civil liability or could be damaging to the participant’s financial standing, employability, insurability, or reputation

procedures to be employed that present more than minimal risk1 to ☐ ☒ participants

☐ ☒ deception or coercion benefits or compensation to participants (beyond the general benefits of the ☐ ☒ knowledge to be gained or small gifts/lottery prizes)

a conflict of interest (e.g., teacher/student, employer/employee; could there be ☐ ☒ perceived coercion to participate; is there any financial interest in this research)

If you answered “NO” to all of the questions please proceed to the next page.

If you answered “YES” to any of the questions your proposal must clearly indicate why the use of participants in any of these categories is scientifically necessary and what safeguards will be employed to preserve the participants’ anonymity/confidentiality. The proposal must identify all risks (physical, psychological, financial, social, other) connected to the proposed procedures, indicate clearly how such risks to participants are reasonable in relation to anticipated benefits, describe methods to protect or minimize such risks1, and access their likely effectiveness. Consent/assent forms must be included for research involving minors.

1 Minimal risk means that the probability and magnitude of harm or discomfort anticipated in the proposed research are not greater than those ordinarily encountered in everyday life or during the performance of routine physical or psychological examinations or tests

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Consent Forms

YES ☐ NO ☒ Is a consent form included with this study? If so, attach a copy.

YES ☐ NO ☒ Are child assent forms included with this study? If so, attach a copy.

Minors must provide an affirmative consent to participate by signing a simplified form, unless the principal investigator can provide evidence that the minors are not capable of assenting because of age, maturity, psychological state, or other factors.

Please refer to the informed consent outline and checklist and the assent outline, which can be found in the Human Subjects Review Committee section of the Wilmington University website.

Implied consent–For some exempt or expedited research, it is not necessary to have a signed consent form. For example, a relatively short survey of competent adults which is anonymous and deals with noncontroversial topics could use a less formal means of providing information. In such cases, the person’s voluntary participation indicates implied consent. Typically, the invitation to participate would be less legal in tone than a consent form but would provide information about the principal investigator, study purpose, voluntary participation, nature/duration of participation, and anonymity/confidentiality.

If implied consent is being used, attach a copy of the invitation

Who is obtaining consent? Check all that apply:

Principal Investigator Research Assistant Other (specify) ☐ ☐ ☐

How is consent being obtained? This is not a human subject study so consent is not required for this project.

What steps are being taken to determine that potential subjects are competent to participate in the decision-making process?

This is a Quality Improvement study.

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Obligations of Principal Investigator:

The HSRC meets on the second Thursday of each month September to May and as needed during the summer months. Protocol must be received two weeks before that date.

Any substantive changes made to the research protocol must be reported to college representatives of the HSRC for review prior to implementation of such change. Any complications, adverse reactions, or changes in the original estimates of risks must be reported at once to the HRSC chairperson before continuing the project.

According to federal regulation all data, including signed consent form documents must be retained for a minimum of three years past the completion of the research.

I have read and understand my obligations as an investigator. I certify that the research proposal is accurate and complete.

Print name: Stefanie M. Glenn

Signature: Date: 4/22/2018

Instructor or Assigned Faculty Sponsor:

Print name: Walton F. Reddish

Signature: Walton F.Reddish Date: 4/23/2018

This form must be signed by an appropriate Wilmington University dean or executive prior to being submitted to the HSRC if any of the following describes your situation:  Wilmington University faculty who wish to conduct research that involves human subjects  Wilmington University employees who are students at other schools and wish to collect data from the University, its students, or employees  Outside researchers who wish to collect data from the University, its students, or employees The executive signing this form is responsible for conferring with institutional research or other parts of the university which would need to support the research.

(If needed) Dean or Executive:

Print name:

Signature: Date:

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PROTOCOL REVIEW

This section is to be completed by the HSR Committee Person.

Principal Investigator:

Date Submitted:

The protocol and attachments were reviewed:

The proposed research is approved as:

☐ Exempt ☒ Expedited ☐ Full Committee ☐ Provisional (see External Research section)

The proposed research was approved pending the following changes: ☐ See attached letter ☐ Resubmit changes to the HSRC chairperson

The proposed research was disapproved:

☐ See attached letter for more information.

HSRC Chair or Representative Barbara H Sartell EdD, ANP-BC, WCC Printed Name

Signature Date 4/22/2018

HSRC Chair or Representative Printed Name

Signature Date

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Appendix C: A Howard County General Hospital Letter of Approval

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Appendix D: Howard County General Hospital Quality Improvement Project Approval

Determination

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Appendix E: Print and Electronic Copyright Use Agreement

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