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 Table of Contents  
CONFERENCE ABSTRACTS AND REPORTS
Year : 2022  |  Volume : 8  |  Issue : 1  |  Page : 47-70

Selected long abstracts from the St. Luke's university health network quality awards program (2019-2020)


1 Department of Quality Administration, St. Luke's University Health Network, Bethlehem, PA, USA
2 Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, PA, USA

Date of Web Publication30-Mar-2022

Correspondence Address:
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2455-5568.341187

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How to cite this article:
Tarone DM, Pellegrino AN. Selected long abstracts from the St. Luke's university health network quality awards program (2019-2020). Int J Acad Med 2022;8:47-70

How to cite this URL:
Tarone DM, Pellegrino AN. Selected long abstracts from the St. Luke's university health network quality awards program (2019-2020). Int J Acad Med [serial online] 2022 [cited 2022 May 18];8:47-70. Available from: https://www.ijam-web.org/text.asp?2022/8/1/47/341187



Background Information and Event Highlights: The Annual St. Luke's University Health Network (SLUHN) Quality Awards Program (QAP) was created in 2008 to celebrate quality improvement throughout the Network. The program recognizes the contributions made by staff every day to provide quality health care to our patients and community. This venture has been very successful in encouraging our staff to become involved and to embrace performance improvement (PI) projects. The QAP is open to all twelve SLUHN campuses and other entities, including inpatient and outpatient areas, and both clinical and nonclinical staff.

The PI Project Manager is responsible for planning, organizing, and managing the timeline for the event with the assistance of other Quality Resources Department (QRD) members. The QRD also coordinates the organization's PI activities. These activities include network-wide planning, project development, team facilitation, statistical analysis, information retrieval, and the preparation of project data for audio-visual presentations. This team also identifies benchmark data sources, monitors PI requirements of regulatory and accrediting agencies, and educates staff on the appropriate use of PI tools and terminology.

A quality resources member is assigned to each project and serves in consultative/advisory capacity, providing mentorship to participating teams and supporting the application process. Blinded applications are sent to competition judges for review using pre-defined, objective criteria. Scoring evaluates PI methodology, relevance of the project to SLUHN institutional mission and alignment with The Five Points of the Star Leadership Model (quality, service, people, finance, and growth) which defines focus for our organization.

The Annual St. Luke's University Health Network (SLUHN) Quality Awards Program (QAP) was held October 22, 2020 in Bethlehem, Pennsylvania. This special event marked the QAPs 12th year of recognizing network-wide contributions to improve the quality of care provided to our patients and community. Members from the Board of Trustees and Network Administrators participated in the ceremony, along with numerous care providers and other institutional contributors/supporters. There are five first-place winners and five second-place winners. The submissions are grouped according to the above-defined areas of quality, people, service, finance, and growth. From among the first-place winners, The President's Award for Quality is chosen. In 2020, The President's Award for Quality was awarded to a project titled: “High Utilizer Care Plan Project: A Network Initiative to Decrease Inappropriate Resource Utilization Among High-Risk Patients.” Team members: Beth Kern, Deborah Bartholomew, Rebecca Louden. Team leaders: Shawn Owens, Karen Dietz, Marisa Schwartz

Traditionally, our QAP has served as a springboard to external awards, other programs, as well as publications. We also take the opportunity to use this venue to acknowledge other achievements from around SLUHN. St. Luke's employees deeply value the QAP, and embrace the recognition and pride it brings to the Network. In addition, the QAP creates a sense of healthy competition within the organization. It is an excellent way to recognize teams that proactively engage in, and demonstrate, performance excellence. In that capacity, the QAP plays a critical role in our journey toward performance excellence as an organization.

In this issue of the International Journal of Academic Medicine, we will present selected long abstracts from the award years 2019-2020, focusing on the highest quality submissions and QAP winners. Each abstract listing features primary authors while also fully recognizing all scientific contributors and participating quality project team members. As in previous years, each long abstract is uniformly structured and consists of an introductory section, project aim/objective, methods, results, sourced/referenced discussion, and conclusions.

The following core competencies are addressed in this article: Interpersonal and communication skills, Medical knowledge, Patient care, Practice-based learning and improvement, Professionalism, Systems-based practice.

Keywords: Hospital and Health system association of Pennsylvania, performance improvement, quality awards, quality improvement, St. Luke's University Health Network

Ethical conduct of research: All of the projects published herein underwent required approval process by the St. Luke's University Health Network Institutional Review Board. For case presentations, patient consent documentation was mandatory. In addition, the authors/teams were required to follow applicable EQUATOR Network (http://www.equator-network.org/) guidelines during the conduct of research.


  Abstract #1: Top


Effect of EOS Calculator Risk Stratification on Antibiotic Usage in Infants Born to Mothers with Chorioamnionitis

K. Staub1, D. Jacobetz1

Scientific contributors (alphabetically): J. Ballesteros1, P. Burskii2 K. Costello1, J. Janco1, P. Philpot1, H. Rosen1, S. Sannoh1, M. Tibrewal1, C. Wheeler2

Departments of Pediatrics and Neonatal Intensive Care1, Quality Resources2, and Research and Innovation2, St. Luke's University Health Network, Bethlehem, PA, USA

Year of Submission: 2020

Introduction: Early-onset sepsis (EOS) in newborns is defined as a potentially fatal invasive infection occurring within 72 h of birth due to the vertical transmission of bacteria.[1] Contemporary measures to reduce EOS in newborns include intrapartum antibiotic prophylaxis for mothers colonized with group B Streptococcus (GBS) and mothers with chorioamnionitis. These approaches have been in use since the mid 1990's and have been shown to be effective.[2] However, accurate identification of newborns at risk still presents a clinical challenge.The American Academy of Pediatrics (AAP) and the Centers for Disease Control and Prevention (CDC) recommend treating asymptomatic infants born to mothers with clinically diagnosed chorioamnionitis with broad-spectrum antibiotics for at least 48 hours until infection can be ruled out.[2] As a result, a significant proportion of infants must be treated with empiric antibiotics to prevent a single case of EOS.

The reported ratio of newborns receiving antibiotic treatment per one case of culture-confirmed EOS ranges from 18 to nearly 120 in high-risk infants, and up to 1,400 in well-appearing infants born to mothers with chorioamnionitis.[3],[4],[5],[6],[7],[8],[9],[10],[11],[12] Maternal chorioamnionitis is a significant risk factor in developing EOS in the newborn population. Before 2019, St. Luke's University Health Network (SLUHN) administered broad-spectrum empiric ampicillin and gentamicin to all neonates born to mothers with clinical chorioamnionitis, regardless of other risk factors or neonatal exam findings. Neonates had blood cultures drawn and were placed on the empiric antibiotics for 48 hours or until blood cultures became negative.[13] Empiric antibiotic use has significant side effects, including the appearance of necrotizing enterocolitis, fungal infections, and preterm infant death.[14] Furthermore, it is associated with deleterious effects on the neonatal microbiome, impaired maternal/neonatal bonding and breastfeeding, gentamicin ototoxicity, poor antibiotic stewardship, the treatment of uninfected infants, and increasing additional healthcare costs.[4],[5] With many neonates ultimately having negative blood cultures, it is imperative to identify who should be placed on antibiotics to accurately capture all neonates who require treatment while sparing those who do not. Kaiser Permanente (Oakland, California, USA)developed an EOS calculator, which strives to risk-stratify neonates based upon gestational age, length of rupture of membranes, maternal highest intrapartum temperature, GBS status, and use of intrapartum antibiotics.[6],[7],[12] Paired with neonatal exam findings, the calculator offeres both a raw and exam-adjusted likelihood of a neonate having EOS. Before implementing the calculator, the use of antibiotics was empiric and anecdotally noted to be high, quantified at 98.8% after retrospective chart review. After a comprehensive literature review, followed by in-depth education of the neonatal unit staff, SLUHN decided to implement the calculator in its clinical practice.

Aims and Objectives: Data collected at large academic institutions showed a significant decrease in antibiotic use when utilizing the EOS calculator in all infants.[5],[6],[7],[8],[9],[12] Specific studies of neonates born to mothers with clinical chorioamnionitis, as well as experiences from smaller community hospitals are still limited.[9],[10] Our study's primary objective was to decrease antibiotic use by >50% in infants born to mothers with chorioamnionitis following the implementation of the EOS calculator. We also sought to determine whether the implementation of the EOS calculator would impact the length of stay for these infants, with a secondary objective to decrease the hospital length of stay (HLOS) by 10%.[15]

Methods: The current quality improvement project focuses on decreasing antibiotic use and reducing HLOS in the above-outlined target patient population. We conducted a retrospective, observational, single-center cohort study which evaluated the neonates born to mothers with chorioamnionitis and their antibiotic usage patterns in the calendar year of 2018 with the prior protocol of empiric antibiotics compared to the 2019 cohort with implemented EOS calculator [Figure 1] to guide clinical decision making.



On January 1, 2019, all SLUHN neonatal intensive care unit (NICU) and newborn nurseries incorporated the EOS calculator in clinical decision making to guide antibiotic usage in neonates [[Figure 1] demonstrates calculator setup and sample results obtained]. The calculator was made available via a smart phone app to all attendings and later incorporated into EPIC (EPIC, Verona, Wisconsin) Electronic Health Record (EHR) system.[11] All providers writing intake notes for neonates born to mothers with clinical chorioamnionitis used the calculator and reported calculated risk both as a raw score and when corrected for an exam. Clinical decision-making was also documented. A chart review was conducted, including all neonates born ≥37 weeks gestation to mothers with clinical chorioamnionitis.

Data collection included the components of the neonatal sepsis calculator (gestational age, length of rupture of membranes, maternal highest intrapartum temperature, group B streptococcus [GBS] status, and use of intrapartum antibiotics), neonatal physical exam, EOS calculator scores (both raw and adjusted for the physical exam), NICU admission status, blood culture results, antibiotics used, and length of stay in both days and hours. The EOS calculator was also retroactively run on the 2018 cohort to determine scores implemented with that group. A subsequent statistical analysis was conducted to determine whether a significant decrease in antibiotic use occurred in the 2019 cohort, whether a significant decrease in antibiotics would have occurred in the 2018 cohort had they used the calculator, and if there was a significant decrease in HLOS. An improvement was defined as a statistically significant decrease in antibiotic usage, significantly decreased HLOS, and later significantly decreased healthcare costs.[16] A >50% decrease in antibiotic usage would be expected using the calculator based on a review of the current literature.[6],[9],[10],[12]

Results: Study cohorts consisted of 143 infants born to mothers diagnosed with clinical chorioamnionitis in 2018 (n = 84) and 2019 (n = 59). Of those neonates, 57 (39.9%) were admitted to the NICU, 42 (50.0%) in 2018 and 15 (25.4%) in 2019. A larger percentage of neonates received antibiotics in the 2018 cohort without the use of the EOS calculator (83/84, 98.8%) when compared to post-calculator implementation in 2019 (21/59, 35.6%, p <0.0001). It was determined that there was a 64.0% overall decrease in antibiotic usage since adopting the EOS calculator. Length of stay (measured in hospital days) was not significantly different between the 2018 and 2019 cohort (p =0.09), but the HLOS in hours was significantly lower (p =0.01), with an average of 66 hours in 2018 and 61 hours in 2019 (7.6% reduction).

Our overall results are presented in [Figure 2] and [Figure 3]. Statistical analysis revealed that, before adopting the calculator, 98.8% of neonates born to mothers with chorioamnionitis received empiric ampicillin and gentamicin, while only one neonate in the cohort developed culture-proven sepsis. After implementing the calculator, antibiotic usage dropped to 35.6%, a decrease of 64.0% (p <0.001). These results surpassed the aim of 50% antibiotic use reduction. The 2019 cohort also had one confirmed culture-positive sepsis case for which the calculator recommended antibiotic treatment. This revealed a substantial and successful decrease in antibiotics with the implementation of the calculator.



Additionally, data analysis revealed a statistically significant decrease in HLOS in hours after implementing the calculator. Length of stay decreased in hours from 66 to 61 hours, or by 7.6%. Although this degree of HLOS reduction did not reach our stated aim of 10%, it does represent a significant ongoing trend in the HLOS, directly related to healthcare costs.[16] Figure 2 compares our actual clinical decision-making with the calculator-recommended course of action for each cohort. The calculator was retrospectively run on 2018 neonates and compared to the clinician's actual decisions during hospitalization. When comparing our actions taken in 2019 to the calculator recommendations, 79.7% of recommendations were followed in the clinical setting. The calculator prompted the provider to “consider” antibiotic usage in 18.6% cases, which allowed for clinical acumen and discretion to guide antibiotic treatment. In only one case, did the provider not follow the calculator guidelines (1.7% of the time).

Data from our study highlight that our clinical decision-making process correlated highly with calculator guidelines. With the hypothetical implementation of the EOS calculator in 2018, our clinical decision-making would match app recommendations 42.9% of the time. Approximately 33% of the time, the clinician's decision would have been contrary to the calculator recommendations. Importantly, this cohort, too, would flag 23.8% of neonates as “consider” antibiotics, once again allowing for clinical discretion. This suggests that had we implemented the calculator in 2018, we would have expected a decrease in antibiotic usage within that cohort itself.

Discussion: Overall, the 2019 implementation of the EOS calculator in neonates born to mothers with chorioamnionitis was a success at our institution. Anecdotally there were only a few times upon retrospective chart review for 2019 in which EOS calculator use was not documented, although clinical decision making often aligned with estimated calculator-based recommendations. Most providers were familiar with the calculator and quickly adopted its use in clinical practice. The EOS calculator was also introduced to all medical students and residents rotating through the newborn nursery.

Empiric therapy of infants at high risk or with already confirmed EOS is among key factors associated with antibiotic use early in life. Therefore, it is a matter of primary importance to avoid overtreatment and possible adverse effects. The EOS calculator algorithm is based on a selected US population, and differences between healthcare settings can limit its generalizability.[17] Moreover, from the standpoint of safety, even well-appearing neonates without obvious risk factors can develop EOS. In our study, considering a statistically significant decrease in antibiotic usage in neonates born to mothers with chorioamnionitis in 2019, we achieved our initial aim of decreasing antibiotic usage by >50%. The observed decrease in HLOS (expressed in hours) was also significant at 7.5%, but did not achieve our stated goal of 10%. A significant decrease in antibiotic usage and HLOS relates to cost-effectiveness, improved family bonding, and better antibiotic stewardship.

In terms of sustaining our results in the long-term, we noted improved compliance with the established protocol after the calculator became available within our EPIC EHR platform. The EHR integration facilitates automatic EOS risk calculation and eliminates the need for a provider to manually consult their smart phone app and plug-in data. The cost of implementing EOS calculator usage into everyday practice was negligible, as “EOSCalc” is a free application and is also integrated into our existing EPIC EHR platform software. Although no formal cost analysis has been performed to date, a decrease in antibiotic usage and HLOS is traditionally associated with decreased cost.[16] The 2019 data set revealed 12 months of sustained antibiotic decrease in infants born to mothers with chorioamnionitis, as described in Table 1 and Figure 3. The EOS calculator is still being employed across all of our well-baby nurseries and NICUs.

Conclusion: The use of a neonatal EOS calculator is associated with a statistically significant reduction in empirical antibiotic use for suspected EOS. Further investigation evaluating the cost-effectiveness of the EOS calculator is needed. With decreased antibiotic usage, shorter NICU admission time, and decreased HLOS, further investigation is warranted regarding not only the effects of our increased antibiotic stewardship but also how our interventions directly (and indirectly) impacted healthcare costs associated with caring for this vulnerable population. Based on these preliminary results, continued encouragement of the EOS calculator usage is warranted in both the NICU and newborn nursery settings.





  1. Polin RA; Committee on Fetus and Newborn. Management of neonates with suspected or proven early-onset bacterial sepsis. Pediatrics 2012;129:1006-15.
  2. Benitz WE, Wynn JL, Polin RA. Reappraisal of guidelines for management of neonates with suspected early-onset sepsis. J Pediatr 2015;166:1070-4.
  3. Wortham JM, Hansen NI, Schrag SJ, Hale E, Van Meurs K, Sánchez PJ, et al. Chorioamnionitis and culture-confirmed, early-onset neonatal infections. Pediatrics 2016;137:e20152323.
  4. Cotten CM. Adverse consequences of neonatal antibiotic exposure. Curr Opin Pediatr 2016;28:141-9.
  5. Money N, Newman J, Demissie S, Roth P, Blau J. Anti-microbial stewardship: Antibiotic use in well-appearing term neonates born to mothers with chorioamnionitis. J Perinatol 2017;37:1304-9.
  6. Escobar GJ, Puopolo KM, Wi S, Turk BJ, Kuzniewicz MW, Walsh EM, et al. Stratification of risk of early-onset sepsis in newborns ≥ 34 weeks' gestation. Pediatrics 2014;133:30-6.
  7. Kuzniewicz MW, Puopolo KM, Fischer A, Walsh EM, Li S, Newman TB, et al. A quantitative, risk-based approach to the management of neonatal early-onset sepsis. JAMA Pediatr 2017;171:365-71.
  8. Puopolo KM, Draper D, Wi S, Newman TB, Zupancic J, Lieberman E, et al. Estimating the probability of neonatal early-onset infection on the basis of maternal risk factors. Pediatrics 2011;128:e1155-63.
  9. Akangire G, Simpson E, Weiner J, Noel-MacDonnell J, Petrikin J, Sheehan M. Implementation of the neonatal sepsis calculator in early-onset sepsis and maternal chorioamnionitis. Adv Neonatal Care 2020;20:25-32.
  10. Perez EM, Taylor M, Swanson K, Laferney JD. Implementation of an antibiotic stewardship quality improvement initiative in a community hospital for infants born at ≥35 weeks. Proc (Bayl Univ Med Cent) 2020;33:188-90.
  11. Stipelman CH, Smith ER, Diaz-Ochu M, Spackman J, Stoddard G, Kawamoto K, et al. Early-onset sepsis risk calculator integration into an electronic health record in the nursery. Pediatrics 2019;144:e20183464.
  12. Kuzniewicz MW, Walsh EM, Li S, Fischer A, Escobar GJ. Development and implementation of an early-onset sepsis calculator to guide antibiotic management in late preterm and term neonates. Jt Comm J Qual Patient Saf 2016;42:232-9.
  13. Ottolini MC, Lundgren K, Mirkinson LJ, Cason S, Ottolin MG. Utility of complete blood count and blood culture screening to diagnose neonatal sepsis in the asymptomatic at risk newborn. Pediatr Infect Dis J 2003;22:430-4.
  14. Esaiassen E, Fjalstad JW, Juvet LK, van den Anker JN, Klingenberg C. Antibiotic exposure in neonates and early adverse outcomes: A systematic review and meta-analysis. J Antimicrob Chemother 2017;72:1858-70.
  15. Cussen A, Guinness L. Cost savings from use of a neonatal sepsis calculator in Australia: A modelled economic analysis. J Paediatr Child Health 2021;57:1037-43.
  16. Gong CL, Dasgupta-Tsinikas S, Zangwill KM, Bolaris M, Hay JW. Early onset sepsis calculator-based management of newborns exposed to maternal intrapartum fever: A cost benefit analysis. J Perinatol 2019;39:571-80.
  17. Achten NB, Klingenberg C, Benitz WE, Stocker M, Schlapbach LJ, Giannoni E, et al. Association of use of the neonatal early-onset sepsis calculator with reduction in antibiotic therapy and safety: A systematic review and meta-analysis. JAMA Pediatr 2019;173:1032-40.



  Abstract # 2 Top


Hypertension: Improving Patient Care in an Urban Environment

V. Allotey1,2, G, Augustine1,2, N. Veeraraghavan1,2

Scientific contributors (alphabetically): A.Alam1,2, H. Bharwhani1,2, K. Joseph1,2, S. Lukose1,2, I. Atieno Olonde1,2

1Department of Family Medicine, Sacred Heart Hospital, St. Luke's University Health Network, Allentown, and 2Department of Research and Innovation, St. Luke's University Hospital, Bethlehem, PA USA

Year of Submission: 2020

Introduction: Hypertension is one of the most common medical conditions encountered in the primary care setting. According to the Centers for Disease Control and Prevention (CDC), approximately 108 million adults living in the United States have hypertension, defined as systolic blood pressure ≥130 mm Hg or diastolic blood pressure ≥80 mm Hg (per American Heart Association guidelines) or are taking medication for hypertension. However, it is estimated that only 24% of those with hypertension have attained sufficient medical control.[1] The economic burden of uncontrolled hypertension on our healthcare system is substantial, with the CDC estimating that on average the United Sates spends $131 billion a year on this issue.[2]

Ever since the 1920's cardiovascular disease was consistently ranked as the leading cause of death in the United States.[3],[4] Fueled by the need to further understand cardiovascular disease, the ground-breaking Framingham Heart Study was initiated in 1948. Not only did this important study help expand our knowledge of heart disease and stroke but it also was critical in the identification of key causative factors, with hypertension being the leading risk factor for cardiovascular disease.[5] In addition, there is an association between hypertension and socioeconomic status, highlighting the heterogeneity of modulating factors.[6] Our primary care clinic, 'Star Community Health - Sigal' is located in downtown Allentown, Pennsylvania. Based on our institution's Community Needs Health Assessment in 2019, 33% of the Allentown service area residents live at or below 200% of the Federal Poverty Level ($24,600 for a family of four in 2017) which is higher than the state poverty line of 12.5%. This places our population at increased risk for developing hypertension and subsequent cardiovascular disease, with significant proportion of our patients already diagnosed with hypertension.

Aims and Objectives: The aim of this Quality Improvement Project was to improve patient care through 3 different initiatives: (1) Educating physicians within our practice; (2) Identifying risk factors for hypertension; and (3) Implementing psychoeducational counseling for our patients (An evidence-based intervention that involves patient education of an illness and its treatment and integrating it with counselling techniques to improve adherence to treatment).

Methods: Institutional approval for the project was obtained. Participants were chosen using a convenience sampling technique. In total, 175 participants were randomly selected. Participants had to be 18 years of age or older with an established diagnosis of hypertension. The study sought to enroll participants who speak either English or Spanish, with no exclusions based on gender or ethnicity. Physicians within the practice were educated through a series of two dedicated lectures on hypertension given by the faculty. In addition, study-specific pocket guides were provided, featuring the Eight Joint National Committee (JNC 8) dvidence-based hypertension treatment guidelines.[7]

Data were gathered regarding known risk factors for hypertension. This included specific items such as tobacco smoking status, alcohol use, nutrition (e.g., adherence to a low salt diet), physical activity, whether patients were at the target blood pressure based on JNC 8 guidelines (as endorsed by the American Academy of Family Physicians, AAFP) as well as recommended screening such as urine microalbumin/creatinine ratio and lipid panel. Data were captured using specially designed macros built into our EMR system (EPIC Systems, Verona, Wisconsin) [Figure 1].



Participants were provided with handouts on the basic principles of the Dietary Approaches to Stop Hypertension (DASH) diet and exercise recommendations (both in English and Spanish) at the end of their visits.[8] This process was developed by the team members based on resources from the AAFP, Mayo Clinic and the National Heart Lung and Blood Institute. This was also incorporated into a standardized EPIC EMR phrase for compliance tracking. Physician resident trainees were responsible for providing psychoeducation and counseling for participating patients. This helped resident trainees to identify any barriers to lifestyle change (e.g. alcohol use, lack of physical activity, cigarette smoking) which could be addressed during the office visit. Jointly, the patient and physician trainee could come up with a plan of action which in some circumstances involved making the appropriate referral to interdisciplinary team members such as social work and behavioral health. It was decided to use EMR templated language because it provided an easy, convenient, and standardized method of implementing the above steps within our clinic environment. Templated EMR language was incorporated into the office visit workflow, providing multi-functional utility, from prompting physician resident trainees to facilitating standardized counseling. These interventions were employed over a period of 6 months.

To demonstrate sustainability of the overall system, these interventions were then continued for another 6 months. In terms of limitations, due to the covid-19 pandemic, we only had 60 participants included in second phase of the study. Study team leaders were in constant communication throughout the whole project and regular meetings were conducted to ensure compliance. Descriptive results were gathered and tabulated to help quantify intervention-specific outcomes.

Results: Overall, we saw a significant increase in the 3 key parameters examined in this study. More specifically, these included blood pressure control, DASH diet counseling and microalbumin/creatinine urine tests as shown in [Figure 2], [Figure 3], [Figure 4]. The patient handouts allowed our practice to tailor our counseling to lifestyle modification recommendations for patients with hypertension, which is reflected in our post-intervention data.



Pre-intervention data showed that only 60% of patients had a blood pressure that was at goal per JNC 8 guidelines. However, following our intervention the proportion of patients that had blood pressure within goal parameters increased significantly to 99% after 6 months, and at 1 year post-intervention the proportion of patients with blood pressure at goal was at 82%.

DASH diet counseling was another area in which we saw improvement. More specifically, 64% of patients received DASH diet counseling prior to our intervention. This improved to 85% at 6 months post-intervention, with a decline to 74% after one year.

At the start of our project approximately 54% of patients had microalbumin testing performed. This improved to 81% at 6 months but did decrease to 72% at 12-months.

Discussion: Significant progress has been made since the publication of the Framingham study.[5] Death rates from cardiovascular disease and stroke have dropped significantly since the 1960's. This is mainly attributed to a reduction in risk factors such as high blood pressure through effective medications, lifestyle modifications and disease-specific counseling.[9] Despite this, cardiovascular disease still remains the leading cause of death in the United States and globally.[10] According to the CDC, high blood pressure was cited as a primary or contributing cause of death for nearly 500,000 individuals in the United States in 2018.[11] Hypertension is a condition that can be successfully treated. However, one of the major barriers faced by primary care providers is the absence of a comprehensive, standardized, population healthcare-based approach.[12] Prior to the current intervention, the way we sought to improve hypertension care in our practice was to focus on problems primarily as they emerge, rather than having standardized treatment strategies in place. Our quality improvement project showed that through standardized, evidence-based treatment protocols and by using a comprehensive treatment approach we can more effectively improve hypertension care. Ultimately, the goal is to decrease the risk of premature death from cardiovascular disease, and hypertension management is an important component of a more comprehensive approach. One of the shortcomings of the current project however is that participating residents did not receive standardized training on psychoeducation counseling techniques. Such training, if universally implemented, may have contributed to further improvements in our post intervention results, especially when looking at longer-term (12-month) outcomes.

Conclusion: Overall the current quality improvement project showed that standardized, evidence-based treatment approach is effective in achieving blood pressure control. However, further longitudinal studies are needed to determine how effective our interventions are in the long-term. It may also be worthwhile to look at the role mental health plays in negative coping skills such as alcohol abuse and tobacco smoking, which are risk factors for hypertension. Our dot phrases are currently available to use in the St. Luke's EPIC EMR system and can be shared and utilized by other family medicine residencies/ primary care offices within the St. Luke's University Health Network. Our residents continue to be educated regarding clinical guidelines on hypertension and its numerous risk factors and how standardized treatment techniques using dot phrases can be an effective tool in healthcare.


  References Top


  1. Centers for Disease Control and Prevention (CDC). Hypertension Cascade: Hypertension Prevalence, Treatment and Control Estimates among US Adults Aged 18 Years and Older Applying the Criteria From the American College of Cardiology and American Heart Association's 2017 Hypertension Guideline – NHANES 2013-2016 External Icon. Atlanta, GA: US Department of Health and Human Services; 2019.
  2. Kirkland EB, Heincelman M, Bishu KG, Schumann SO, Schreiner A, Axon RN, et al. Trends in healthcare expenditures among US adults with hypertension: National estimates, 2003-2014. J Am Heart Assoc 2018;7:e008731.
  3. Centers for Disease Control and Prevention. Leading causes of death, 1900-1998. Natl Vital Stat Rep 1998;48:1-67.
  4. Mahmood SS, Levy D, Vasan RS, Wang TJ. The Framingham heart study and the epidemiology of cardiovascular disease: A historical perspective. Lancet 2014;383:999-1008.
  5. Dawber TR. The Framingham Study: The Epidemiology of Atherosclerotic Disease. Cambridge, MA: Harvard University Press; 1980.
  6. Anstey DE, Christian J, Shimbo D. Income inequality and hypertension control. J Am Heart Assoc 2019;8:e013636.
  7. Armstrong C; Joint National Committee. JNC8 guidelines for the management of hypertension in adults. Am Fam Physician 2014;90:503-4.
  8. Steinberg D, Bennett GG, Svetkey L. The DASH diet, 20 years later. JAMA 2017;317:1529-30.
  9. Ford ES, Ajani UA, Croft JB, Critchley JA, Labarthe DR, Kottke TE, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980-2000. N Engl J Med 2007;356:2388-98.
  10. Writing Group Members; Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, et al. Heart disease and stroke statistics-2016 update: A report from the American heart association. Circulation 2016;133:e38-360.
  11. Centers for Disease Control and Prevention. Underlying Cause of Death, 1999-2018. CDC WONDER Online Database. Atlanta, GA: Centers for Disease Control and Prevention; 2018. Available from: http://wonder.cdc.gov/ucd-icd10.html. [Last accessed on 2020 Mar 12].
  12. DiPette DJ, Goughnour K, Zuniga E, Skeete J, Ridley E, Angell S, et al. Standardized treatment to improve hypertension control in primary health care: The HEARTS in the Americas initiative. J Clin Hypertens (Greenwich) 2020;22:2285-95.



  Abstract # 3 Top


I've Fallen and I Can't Get up… Preventing Inpatient Falls

B. Kern-Skrapits1,2, J. Alessandro1,2

Scientific contributors (alphabetically): D. Brybag1,2, J. Eggstein1,2, P. Fontenelle1,2, L. Gregg1,2, L. Gutierrez1,2, K. Martonik1,2, J. Meckes1,2, M. Morales1,2, D. Morris1,2, F. Ndam1,2, O. Olaniyan1,2, S. Schwarz1,2, T. Venuto1,2

Departments of 1Quality Administration and 2Research and Innovation, St. Luke's University Health Network, Bethlehem, PA, USA

Year of Submission: 2020

Introduction: Patient falls continue to be a challenge across healthcare facilities [1],[2]. At Monroe Campus (MC) of St Luke's University Health Network (SLUHN) an increase was noticed in fall rates above the Pennsylvania Patient Safety Authority (PPSA) benchmark value of 3.21 per 1000 patient-days that is used as a standard measurement. The 2017 baseline rate for the campus was 3.34 per 1000 patient days. The MC leadership supported the formation of a dedicated team to address and implement improvements for this patient safety care focus.

Based on our primary findings, an “aim statement” was developed of reducing inpatient falls to a rate lower than the PPSA Benchmark of 3.20 per 1,000 patient-days by June 2018. Data review (retrospective and then ongoing) helped to focus on trends while concurrent input from bedside staff helped to focus on improvement actions. Actions were divided in 'quick fixes' (e.g., implemented quickly with minimal process change) and 'ongoing fixes' (e.g., items that required planning, education, or additional approvals). General principles of healthcare “change leadership” were utilized in the process [3],[4],[5].

Best practice information was consistently reviewed from external sources (Joint Commission, PPSA, and other authoritative sources) to maintain all possible processes under sufficient control and to incorporate any applicable improvement(s) at the facility level [6],[7]. Utilizing hardwired periodic process review (rapid cycle format) we noticed a favorable trend in our primary outcome (decreasing fall rate). In addition, the change was durable, with an 8-month run of event rates below the pre-deterimend benchmark during 2018 (e.g., observed rates decreased to 1.97 per 1,000 patient-days). We did note a challenging stretch at the beginning of 2019 as our fall rate increased (up to 2.79 per 1,000 patient days for January-June). This prompted a re-evaluation and re-implementation of the program, resulting in another period of sustained, 8-month decrease in fall rates below our pre-determined benchmark (e.g., fall rate of 1.47 per 1,000 patient-days).

Aims and Objectives: The primary goal of this quality improvement project was to successfully reduce inpatient falls to a rate lower than those reported by the Pennsylvania Patient Safety Authority (e.g., benchmark of 3.20 per 1,000 patient days) within a 4-month period.

Methods: Patient falls are tracked and trended monthly using our established campus-wide patient safety process. The project team identified a spike in the campus fall rate in March and April of 2017 that lead to a dedicated campus performance improvement (PI) team being formed in May 2017 [Figure 1].



Project Start-Up: A dedicated PI team embarked on a data drilldown, specifically looking at factors potentially associated with falls. Variables examined included the clinical unit/physical location, day of the week, time of day, patient age and any known reason(s) for falls. Retrospective data were initially utilized, with a transition to concurrent monthly review upon the formal initiation of the project. Temporal trends were identified based on the clinical areas of occurrence (Medical-Surgical unit).

When attempting to define specific root causes, the PI team found that more than one causative factor was involved in majority of cases, dictating that vast majority of circumstances of fall-related patient care are considered on case-by-case basis. The existing “Patient Assistant - Call Don't Fall Agreement” in place at the time of the initial review, along with campus policy on falls (including related interventions) was reviewed carefully. Benchmarking was started by using available PPSA data, then incorporating the Joint Commission Sentinel Event Alert #55 on “Preventing Falls and Fall-related Injuries” to ensure all of their recommendations were addressed. Additionally, Premier (Charlotte, North Carolina, USA) “Best Practice” educational offerings were also reviewed, and the team reviewed strategies that Hospital Improvement Innovation Network (HIIN) and QUEST found beneficial to determine if there were any applicable new interventions that had not yet been considered (e.g., “Head Over Heels for falls Prevention”) [6],[7],[8]. Decreasing falls (with and without harm) is both a top priority for our campus as well as the entire hospital network, and has been incorporated into key quality outcome metrics accordingly.

Results: Fall rates were (and continue to be) monitored monthly, and reported out our hospital campus Patient Safety Committee and Clinical Operations meetings. Results for each of the reporting periods were color coded, with green indicating “favorable trend,” yellow indicating “concerning trend,” and red indicating “worsening” trend. This color coding provides a quick, standardized “stoplight” format to help communicate the current status and the overall progress of the intervention.

After a few months of alternating up-and-down trends (fall and winter of 2017), the team was able to achieve 8 consistent months of patient fall rates that fell under the reference point of the PPSA benchmark (May 2018 - December 2018). There was an increase in fall rate during January 2019, which resulted in prompt review of existing policies and procedures, re-education of bedside staff, and a subsequent decrease over the following months.

In July 2019, we published “Patient Safety Pearls” and distributed this information across our clinical units, with specific focus on patient falls. This helped increase the awareness of the critical importance of reducing patient falls as an essential component of the overall quality and safety equation [Exhibit 2].



In November 2019, a pilot program called “Blue Star” was initiated on our Medical-Surgical unit in response to identifying that there were patients with cognitive impairments that might not be readily identifiable but may contribute to a significantly higher fall risk. Corresponding education and posters explaining this process were designed and disseminated accordingly [Exhibit 2]. Subsequent to this latest round of program enhancements, results were positive upon the January 2020 re-assessment. To further assess the effectiveness of the pilot program, patient care services survey was completed, and the PI team reviewed fall rates for the Medical-Surgical unit. Survey results indicated that the majority of nurses and PCAs (Patient Care Attendants) on the Medical-Surgical unit reported that they used the “Blue Star” program to identify patients and change their practice accordingly [Exhibit 3]. The reported unit fall rate decreased from 1.48 per 1,000 patient days for the 4 months prior to the pilot to 0 for the 2 months since the pilot. Due to its success, the pilot has now been expanded to a second Medical-Surgical unit.



Because falls are linked to financial implications at the systemic level, there is ongoing focus on these never-events, and especially those that involve harm. Estimated cost savings can be substantial when significant reductions in falls (both with and without harm) can be achieved. Our program shows that it is possible to attain minimal event levels using a well-coordinated effort [Figure 2].



Discussion: Despite their designation as “never events,” as well as our best efforts to prevent them, healthcare-associated falls continue to occur [1],[2],[9]. Beyond our continued focus on reducing preventable patient harm, we must consider the importance of designing safe systems that are characterized by long-term sustainability and built-in quality improvement cycles.

Within the context of the current study, we identified two potential actions that could be implemented to effectively assist with the key issues identified during the exploratory phase. Broadly, these actions were categorized as either “quick fixes” that can be addressed immediately or “ongoing fixes” that are more likely to require longer periods of time to implement.

Quick Fixes: Our PI team identified the following issues that were deemed amenable to rapidly implementable actions: (A). Adding nutritional services and environmental services staff to the team due to the integral nature of their interactions with patients; (B) Mandatory use of post-fall huddle sheets to proactively capture all potentially involved risk factors; (C) Focus on bed placement for high-risk patients on each Medical-Surgical unit; (D) Enforcement of safety huddle completion with focus on identifying high-risk patients and reviewing their specific risk factors; (E) Completion of “patient fall” alarm check audits and ensuring high alarm use / compliance.

Ongoing Fixes: These issues were identified as requiring a more prolonged and sustained interventions to address. Among the “ongoing fixes” were: (A) Re-evaluation of signage to support identifying high-risk patients; (B) Assessment of, and attention to, hourly rounding process to ensure properly completed; (C) Revisiting of the patient & family education process and agreement signature; [D] Expanded use of the in-room white board to capture fall risk; and (E) PCA knowledge level assessment related to experience and accountability for training needs.

A number of the above “ongoing fixes” required the development of a brief (one-page, color-coded) education sheet on how to correctly connect/deploy the bed/chair alarm. There was also a new process implemented regarding the standardized documentation of patient refusal of bed and chair alarm utilization.

Using a rapid “plan-do-check-act” (PDCA) cyclic process at each PI team meeting [10], individual action items were reviewed methodically. This allowed for tracking and assessment of what was working and what was not working, in addition to providing a forum for status updates on the fixes that required more work.

Biomedical engineering team was added as an ad hoc member of the overall PI team, primarily to address various sound alarms whenever this was identified as a concern. Other interventions were added to the 'fix list' as issues came up – for example, the availability of yellow bands/socks (indicators of “fall risk”) for patients was reported as a concern on a post-fall huddle sheet. To quickly address this important issue, the process was changed to have the yellow bands/socks stocked within each room prior to the patient arrival. In addition, we also established a close collaboration with campus NICHE (Nurses Improving Care for Health system Elderly) team to support the implementation of activities from the NICHE activity baskets, which tend to be effective for elderly patiens with dementia and/or encephalopathy. Specific items available for use are intended to encourage creativity, stimulate individual curiosity, and include word searches, coloring books, crafts (crocheting, simple wood projects, window hangers), card games, etc.

Proactive leadership is critical to successful implementaitons of quality improvement endeavors [11],[12]. Our team learned a great deal about fall risk reduction while conducting this PI project. Lessons learned were then applied to various other clinical units, including the Emergency Department and Surgical Services. Overall, we were able to build upon each implemented action, capitalize on small wins, and eventually attain a noticeable decrease in falls. Some degree of flexibility was required during the implementation phase. For example, we encountered administrative challenges when addressing signage for high-risk patients. The standardized approach toward this at our campus was to use door signage that is configured flat against the wall. Although aesthetically pleasing, sich signage was difficult for the staff to notice. Consequently, a decision was made to develop signage that could be both reused and would by design protrude out from the doorway to more effectively identify at-risk patients. Criteria were developed for such supplemental, more prominent signs, followed by a pilot implementation between February 5th and April 11th, 2018 on our Medical-Surgical unit. After a highly successful pilot, expansion across all campus clinical units was completed in August 2018 and remains in effect at this time. The development (and support) of patient safety champions was instrumental to the success of our PI initiatives [13],[14].

To highlight and disseminate decreases in fall rates across our institution, each clinical unit was tasked with capturing the “number of days without a patient fall” on their safety huddle boards. Feedback regarding this initiative was anecdotally very positive. Further, in response to information learned from project-derived data, specialized fall mats (two sets per clinical unit) were ordered in July 2017. Benchmarking was again revisited in February 2018 when Joint Commission “best practice” information was re-assessed, focusing on potential new interventions which previously have not been considered. There was a subsequent decision to promote fall prevention with a “No Falls February” initiative (2018). This was accompanied by a number of reward-based, positive reinforcement activities [Exhibit 1]. These activities were generally well received by clinical staff.



Conclusion: In addition to representing a form of preventable physical harm, healthcare-reated falls also reflect on the overall quality of patient care. Thus, fall rates are a very important metric of healthcare quality and value. As demonstrated by our PI project, fall rates can be reduced via a combination of patient and bedside staff education, as well as greater awareness by all involved stakeholders. The current project also highlights the importance of critical information sharing across various clinical units, the benefits of incrementally implementing experiences and lessons learned, and concerted efforts to maintain high levels of awareness and education. Our team was able to maintain the momentum of this important initiative, and our hope is to share our experiences with others who are implementing similar programs. As a form of recognition, our fall prevention program became a standard at other campuses within our health network.


  References Top


  1. Bouldin EL, Andresen EM, Dunton NE, Simon M, Waters TM, Liu M, et al. Falls among adult patients hospitalized in the United States: Prevalence and trends. J Patient Saf 2013;9:13-7.
  2. Kern-Skrapits B. Inpatient Fall Rates [Unpublished Raw Data]. Hospital Incident Reports; 2016 – 2020.
  3. Kotter J (1996). Leading Change. Harvard Business School Press, Boston. Available from: https://www.kotterinc.com/wp-content/uploads/2019/04/8-Steps-eBook-Kotter-2018.pdf. [Last accessed on 2022 March 10].
  4. Kotter's 8-step change Model: implementing Change powerfully and successfully. [online] available at: <http//www.mindtools.com/pages/article/newPPM_82.htm> [accessed 12 february 2015]
  5. Scholtes, P.R., Joiner BL, Streibel BJ,(1996) The Team Handbook. Madison, WI, USA: Joiner
  6. Gardner L, Finley E. Falls, a Hospital-Acquired Condition: The Pennsylvania Patient Safety Authority's Enhanced Reporting Program. Pennsylvania Patient Safety Authority; 2012. Available from: http://patientsafety.pa.gov/ADVISORIES/Documents/201206_47.pdf. [Last accessed on 2022 March 10].
  7. Hospital-Acquired Conditions. CMS (Centers for Medicare & Medicaid); 2020. Available from: https://www.cms.gov/medicare/medicare-fee-for-service-payment/hospitalacqcond/hospital-acquired_conditions. [Last accessed on 2022 March 10].
  8. Sentinel Event Alert: Preventing Falls and Fall-Related Injuries in Health Care Facilities. The Joint Commission; September 28, 2015. Available from: https://www.jointcommission.org/-/media/deprecated-unorganized/imported-assets/tjc/system-folders/topics-library/sea_55pdf.pdf?db=web&hash=53EE3CDCBD00C29C89B781C4F4CFA1D7/. [Last accessed on 2022 March 10].
  9. Staggs VS, Mion LC, Shorr RI. Assisted and unassisted falls: Different events, different outcomes, different implications for quality of hospital care. Jt Comm J Qual Patient Saf 2014;40:358-64.
  10. Saeed M, Swaroop M, Ackerman D, Tarone D, Rowbotham J, Stawicki SP. Fact versus conjecture: Exploring levels of evidence in the context of patient safety and care quality. IntechOpen; 2018 Sep 5.
  11. Marquis BL, Huston CJ. (2017). Leadership Roles and Management Functions in Nursing. 9thed.Philadelphia, PA:Lippincott Williams and Wilkins; 2000
  12. Patterson K, Grenny J, Maxfield D, McMillan R, Switzler A. Crucial Accountability. 2nd ed. New York: McGraw Hill Education (2013).
  13. Tolentino JC, Martins N, Dweeney J, Maarchionni C, Valenza P, McGinely TC. Introductory chapter: Developing patient safety champions. Vignettes in Patient Safety. 2018 Jan 10;2:1-23.
  14. Stawicki SP, Firstenberg MS. Introductory chapter: The decades long quest continues toward better, safer healthcare systems. Vignettes in Patient Safety. 2017 Sep 13;1:1.



  Abstract # 4 Top


Telemetry Utilization: A Comprehensive Intervention

S. Aquilina1, J. Diasio2, P. Koch3, T.Kress3, K. Maggipinto3, K. N. Kumar4,

Scientific contributors (alphabetically):M. Antonioli5, B. Apgar5, D.Coleman5, D. Frack3, J. Hillegass2, A. Ivankovits6, D. Napoleon5, S. Owens2, B. Rumble5, J. Tanhauser6

Department of 1Professional Development, 2Department of Internal Medicine, 3Department of Patient Care Services, 4Research and Innovations, 5Department of Nursing, 6Department of Clinical Analytics, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Year of Submission: 2020

Introduction: Clinical alarm systems are used in hospitals to alert caregivers of potential patient issues.[1] However, the overuse of alarm systems may paradoxically become a barrier to patient safety, as clinicians become desensitized to the repeated alarms throughout the day – a concept referred to as “alarm fatigue”.[2] As the parameters for alarm systems are not set for patient-specific conditions but rather geared toward a generalized population, records suggest that as many as 80-99% of alarms may be false. This, in turn, may contribute to unsafe actions and thus endanger patient wellbeing and satisfaction.[2] For example, 566 patient deaths have been reported by the US Food and Drug Administration (FDA) relating to monitoring device alarms from 2005 to 2008.[2]

The Joint Commission Resources include the publication of the National Patient Safety Goals (Standard NPSG 06.01.01) to improve the safety of clinical alarm systems.[1],[3] The rationale of NPSG 06.01.01 led St. Luke's University Health Network (SLUHN) - Anderson campus Patient Safety Monitoring Committee to develop a systematic, coordinated approach to safe alarm management that reflects established best practices. This was done by setting a network standard in 2017 for each entity to attain ≤15% telemetry utilization. The SLUHN- Anderson campus utilization was as high as 37.4% with a national benchmark of 12.64%.[7] NPSG 06.01.01 for Alarm Safety suggests that improvements can be made to decrease alarm fatigue and at the same time optimize responsiveness.[1] In an effort to address the high telemetry utilization rate and associated factors, the SLUHN-Anderson Telemetry Workgroup was established. The team recognized a significant need to reduce unnecessary telemetry to meet the above-mentioned national and network goals.

Project Aims/Objectives: The aim of the current project was to achieve 15% or less telemetry utilization rate for SLUHN-Anderson Campus over the course of 3 years (2017-2020). Eventually, the goal extended to managing telemetry utilization comprehensively and collaboratively across service lines, with specific focus on appropriate order placement and discontinuation, updating accommodation codes promptly, ensuring complete documentation of cardiac rhythm strips, and strengthening the overall process for alarm management.

METHODS: In 2017, the Anderson Telemetry Workgroup (ATW) was established as a cross-functional team to evaluate utilization and the multifaceted components of telemetry management. The workgroup met monthly to assess and respond to the multidimensional aspects of telemetry utilization, including creative and strategic approaches to safely reduce the use, consistently update bed accommodation codes, limit “white noise” (unnecessary or redundant alarms), and assure timely response to valid alarms. Each member of the team was responsible for disseminating the strategies within their department(s) and for collaborating with appropriate staff to achieve desired changes. Staff members in each department were educated regarding the institutional goal of ≤ 15% for telemetry utilization.

At the beginning of the process, a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis was performed to assess the environment at the SLUHN - Anderson campus for the readiness to change.[5] The strengths included a strong stakeholder support of the project and a structured workgroup with commitment for change. Anticipated weaknesses involved the presence of concerns regarding the required behavioral changes and the need to generate sufficient buy-in from each participating stakeholder/discipline.

The ATW devised a multi-disciplinary plan to improve telemetry utilization [Figure 1]. Monthly interventions included monitoring telemetry utilization rates on Tableau (Mountain View, California, USA) reports [Exhibit 1], reviewing appropriateness of telemetry orders, updating bed accommodation codes in Epic, auditing compliance of rhythm strip documentation [Exhibit 2], and developing an Insights report designed to evaluate responsiveness to ASCOM (Morrisville, North Carolina, USA) telemetry alarms [Exhibit 3].



Multidisciplinary commitment and adherence to follow network approved telemetry inclusion and exclusion criteria [Exhibit 4] was the backbone to managing appropriate orders. Internal medicine practitioners reviewed all current telemetry orders on a daily basis with the primary provider to determine appropriateness [Exhibit 5]. Patient Care Managers and Clinical Coordinators collaborated with the care team regarding the continued need for telemetry on an individual patient basis according to the inclusion/exclusion criteria. Huddle boards were updated daily for expanded communication. Unit clerks updated the bed accommodation status using a workgroup designed dashboard report to make accurate and real-time changes. Practitioners were notified via AMiON and TigerText with inappropriate tele orders using the daily audit tool [Exhibit 6] for real-time collaboration with bedside nursing staff. Staff nurses also implemented an ink stamp that included a blank for date, time, interpretation and full signature to facilitate complete documentation of rhythm strip interpretation as written in policy [Exhibit 7]. Unit clerks updated the bed accommodation status using a project designed dashboard report in Epic [Exhibit 8] to make accurate and timely changes. A dysrhythmia competency was created and deployed in My e-Learning (MEL) to prepare nurses to use the Epic telemetry needs assessment/screening tool as a validation to remove unnecessary telemetry upon expiration of the order. In addition, the MEL included a review of rhythm strip documentation, alarm customization, and electrode etiquette to minimize alarm fatigue. Alarm management was monitored using a uniquely designed Insights report with the intention of addressing leads fail and crisis alarms at the department and shift levels.



Barriers and Mitigation along the Way:

  • Inconsistent use of the nursing EPIC screening tool to remove expired telemetry. Similar concerns were acknowledged throughout the network at the Patient Safety Monitoring committee. A dysrhythmia competency was written in MEL with instruction for nurses to document and utilize the needs assessment/screening tool in Epic. In addition, a nurse residency evidence-based project was completed on SMS3, the department with the greatest utilization at SLUHN-Anderson, which educated and empowered the nurses to use the tool, resulting in a 37% increase in staff utilization of the tool and contributed to a unit-based reduction of telemetry by 18%.
  • Inconsistent discontinuation of telemetry by providers when orders were inappropriate in a timely fashion, especially in regard to Congestive Heart Failure (CHF), Cerebrovascular Accident (CVA), and respiratory failure patients. The St. Luke's Internal Medicine (SLIM) team developed a system whereby their middle-shift advanced practitioner would evaluate all current telemetry orders in the afternoon, determine if indicated to keep telemetry on, and discuss with primary providers if they felt it may be removed [Exhibit 5]. Daily removal of unnecessary telemetry averaged 22.1% from February to June of 2020. The SLIM telemetry log was reviewed at department meetings.
  • Accommodation codes were not updated in a timely manner when tele orders were added or discontinued, sometimes more than 24 hours afterwards. The dashboard report [Exhibit 8], designed with unit clerk input helped to simplify the process of updating bed accommodations when telemetry statuses changed. With the report, unit clerks made a plan to update bed accommodation codes twice a day on the day and evening shifts reflective of real-time order changes. Accurate updates to changes in bed status contributed to the overall reduction in telemetry utilization.


Alarm response time measurements were not reported in a meaningful way for the staff responsible for answering telemetry alarms. Therefore, an Insights report [supplementary Figure 3] was developed to break down response times by “Crisis/High” and “Leads Fail” alarms in seconds and minutes per shift and department. Included in the dysrhythmia competency written in MEL, nurses were instructed on electrode etiquette to reduce leads-fail alarms and the steps to customize alarms to limit nonactionable alarms and white noise and thus ease alarm fatigue. The workgroup continues to review and disseminate response times with the bedside nurses, focusing on the management of alarms to acknowledge the need to understand the priority of maximizing patient safety. Nurses are required to complete the annual dysrhythmia competency in MEL to review customization and alarm management.

Results: Campus telemetry utilization reduced by 10% in the first two years of our workgroup (2017 – 2019) and another 10% in 2020 [Figure 2]. The SLUHN - Anderson campus telemetry utilization at the end of March 2020 reached an all-time low of 14.5%, 0.5% below the network goal and an overall reduction by 22.9% since the start of our project in July 2017. Emphasis was made on each of the initiatives across all disciplines because all components overlap in meeting the NPSG.



Discussion: SLUHN - Anderson Campus succeeded in reaching a 15% telemetry utilization rate at by the end of 2020 [Figure 2]. In 2017, the scientific journal, Circulation, published The American Heart Association (AHA) Scientific Statement: Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings as a national recommendation for the indication and duration of electrocardiographic monitoring, as well as organizational aspects of alarm management, education of staff, and documentation.[4] SLUHN medical stakeholders reviewed the scientific statement to update the network's Telemetry Inclusion and Exclusion Criteria to delineate 24 and 48-hour monitoring standards [Exhibit 4]. The approved criteria became the foundation for determining appropriate telemetry utilization, the length of inclusion, basis for nurse driven removal upon order expiration using the Epic needs assessment, and collaboration between providers and nursing staff regarding continuation. Section 3 of the AHA statement summarized evidence-based literature regarding alarm fatigue from false or nonactionable alarm signals.[4]

Prior to the workgroup initiatives, telemetry alarm response times were not clearly reported or evaluated to the bedside nurse. Review of the literature also inspired the workgroup to improve education for the staff in regards to electrode etiquette, customization of alarms, and rhythm strip documentation.

In addition, Epic Clinical Programs referenced the work of the University of California San Diego, Hawaii Pacific Health and Nebraska Medicine for their programs to reduce unnecessary use of cardiac telemetry.[6] The core of the program was to standardize telemetry orders by expiration date, and then build patient list reports, best practice advisories, and work-list tasks to streamline the workflow. Each component encouraged clinicians to contemplate the need for starting and continuing telemetry monitoring. Many of the Epic features were customized for SLUHN throughout the project.

Financial implications of the reduction in telemetry utilization were substantial [Figure 3]. The finance department reported the average direct cost of telemetry at the Anderson Campus as $430, $428, $457 and $478 per day for FY 17, 18, 19, and 20 respectively. Total telemetry days for the campus declined over the same period of time from 8,259 to 5,102. Annual costs steadily declined from $3,551,370.00 in FY2017 to $2,438,756 for FY20. Even with the annual rising cost of telemetry, $1,112,614.00 was saved based on the reduction in utilization since FY17.



COVID-19 Impact: Utilization of telemetry rose again starting in April of 2020 with the unexpected influx of COVID-19 patient admissions. SMS4, the department that received these admissions, the majority of whom required QTc monitoring using telemetry as the standard of care for COVID patients while on hydroxychloroquine and/or azithromycin. Typically, the department's mean utilization rate was 6.8% from July 2019 to February 2020 prior to the COVID crisis. Utilization on SMS4 in April saw an unprecedented increase as high as 24.5%, nearly 18 percentage points higher than average, during the peak of the COVID pandemic. If the utilization rate remained consistent at 6.8% in March through June, the campus utilization would likely have been 13.3, 12.7, 13.0, and 16.9% respectively. These projections were estimated by imputing the average pre-COVID FY2020 average utilization rate for SMS4 of 6.8% in place of its COVID-era peaks. It is important to recognize the potential for rate variability related to unpredictable patient care needs and thus the Patient Safety Monitoring Committee may want to consider an adjustable goal depending on the circumstances.

Future Directions: The SLUHN - Anderson Telemetry Workgroup will continue to meet on a monthly basis to review data, address impacting concerns and develop new strategies to maintain a ≤15% utilization rate. The group will continue to collaborate with the medical staff with daily reviews of all telemetry orders. It will be imperative for the multi-disciplinary staff to maintain a commitment to review orders, assure rhythm strips are documented as required by policy, and that alarms are attended to promptly while staff take a proactive approach to minimize alarms using electrode etiquette and customization. We plan to continue sharing our progress with members of the Network Patient Safety Monitoring Committee to carry forward ways that each entity can improve their campus telemetry utilization as well.

Conclusion: Our goal to reach a 15% telemetry utilization rate at the SLUHN-Anderson campus by the end of Fiscal Year 2020 was accomplished. Telemetry utilization declined steadily from 37.4% in 2017 to 14.5% in March of 2020, nearly a 23% reduction. The workgroup plans to continue to meet monthly to review utilization, rhythm strip documentation and alarm management. We will continue to have representation on the Patient Safety Monitoring Committee to share ways of improving and sustaining campus and network goals.


  References Top


  1. National Patient Safety Goals Effective January 2019. Goal 6: NPSG.06.01.01. Hospital Accreditation Program. The Joint Commission. 2019. https://www.jointcommission.org/-/media/tjc/documents/standards/national-patient-safety-goals/historical/npsg_chapter_hap_jan2019.pdf?db=web&hash=D7E1C2DA08C73CE3F9C8120305A3A8AB. [Last accessed on 2021 Apr 16].
  2. Bach TA, Berglund LM, Turk E. Managing alarm systems for quality and safety in the hospital setting. BMJ open quality, 2018;7:e000202. https://doi.org/10.1136/bmjoq-2017-000202.
  3. Hospital National Patient Safety Goals. NPSG.06.01.01. The Joint Commission 2017. https://www.jointcommission.org/-/media/tjc/documents/standards/national-patient-safety-goals/historical/2017_npsg_hap_erpdf.pdf?db=web&hash=08B0E173E6DB13140B6F719E5FB069FE. [Last accessed on 2021 Apr 16].
  4. Sandau KE, Funk M, Auerbach A, Barsness GW, Blum K, Cvach M, et al. American Heart Association Council on Cardiovascular and Stroke Nursing; Council on Clinical Cardiology; and Council on Cardiovascular Disease in the Young. Update to Practice Standards for Electrocardiographic Monitoring in Hospital Settings: A Scientific Statement From the American Heart Association. Circulation, 2017;136:e273–e344. https://doi.org/10.1161/CIR.0000000000000527.
  5. SWOT Analysis. MindTools. https://www.mindtools.com/pages/article/newTMC_05.htm.
  6. Epic clinical programs: working together to improve quality of care. Reducing unnecessary use of cardiac telemetry. UC San Diego Health, Hawaii Pacific Health, and Nebraska Medicine. 2016;3-20
  7. Quality Advisor. Telemetry Peer. St. Luke's Anderson Campus (691630). Facility: 07-01-2013 to 05-26-2019; Peer: 07-01-2013 to 03-31-2019. Clinician Performance Management. HYPERLINK “http://www.premiereinc.com” www.premiereinc.com.



  Abstract # 5 Top


Tolerance Not Avoidance: Improving Outcomes for the Pediatric Patient with Mild Traumatic Brain Injury (mTBI)

S. Campf1, N. Stewart2,3, K. Appiotti4, L. Erickson-Parsons1, P. Gubich4,5, J. Knipe3

Department of 1Pediatrics, 2Research and Innovations, 3Trauma, 4Pediatrics and Critical Care,

Submission Year: 2020

Background: 75% of traumatic brain injuries within the United States are categorized as mild, concussive, or minor (mTBI), however the exact number that occur each year is grossly underestimated as they are either not reported, or are dealt with somewhere other than a hospital setting.[1] Taking into account the large quantity of mTBI cases involving children that are managed in the absents of a trained trauma physician, the Center of Disease Control (CDC) has taken the stance of emphasizing the importance of patient and family health literacy and behavior modification in mTBI to optimize health outcomes.[2]

There is general consensus amongst the present TBI literature that there is an overall lack of public understanding and perception regarding TBI's across all age ranges. In fact, having recognized this knowledge deficit across the populace, nationwide legislation has been passed in an effort to provide education about TBI's to parents and coaches of young athletes.[3]

In May 2019, the inpatient pediatric unit and trauma department at St Luke's University Hospital (SLUH) identified a need for improved continuity of care for pediatric patients diagnosed with mTBI. In response to new guidelines published by the American Academy of Pediatrics (AAP)/CDC we found the need to upgrade our current mTBI education guidelines from admission through post hospital recovery. The purpose of our intervention was to enhance our educational protocols to achieve a 25% reduction in the length of time necessary before a child could return to baseline activities following a mTBI. In addition, the inpatient pediatric unit sought to sustain a 30% reduction in specialty referrals and to retain 60% of these pediatric mTBI patients within the SLUH network.

Methodology: In August 2019 the inpatient pediatric unit established the Tolerance Not Avoidance Project, which focused on consistent messaging from admission, through hospitalization, continuing to discharge and follow up. For patients to be included in the study, a diagnosis of mild traumatic brain injury, concussion with loss of consciousness (LOC), or concussion without LOC was required. Pre-data was collected from the Epic electronic medical record (EMR) for 6 months prior to initiation of the project.

Using recommendations from the CDC and AAP, a multidisciplinary team developed a treatment plan that focused on patient education, awareness and management of worsening symptoms, and improving patient outcomes while enhancing the medical treatment being provided. The team consisted of professionals from the trauma department, medicine, nursing, physical and occupational therapy (PT/OT), all of whom took part in patient and family education.

Based on the treatment plan, the Pediatric Concussion Focused order set was built into the Epic EMR. All members of the team were instructed on the process regarding patient education prior to initiation of the project. The order set, which was created following the AAP guidelines and an extensive literature review, contained symptomatic support medications such as Zofran and Tylenol. Self-awareness of symptoms was a key factor in the child's care, therefore on admission, the child would be given a “Brain Rest” sign to place on the door when he/she was having worsening symptoms requiring less stimulation and the need for more rest. Additional aspects of the concussion protocol included PT and OT assessment, and neuro checks every 4 hours.

Our primary objective as a team was to be in one accord and consistently communicate the same information in order to avoid the problems which often occur secondary to the confusion of mixed messages to patients and family. For this reason, the team developed a patient education handout, Pediatric Concussion Care: Guidelines for Patients and Families. The trauma team members developed discharge instructions, while the nursing team members developed patient education materials. This handout was given on admission and was reinforced throughout hospitalization with the patient and family. In doing this, the patient remained aware he/she may resume activity when experiencing minimal symptoms or when asymptomatic. The patients were also instructed on the importance of stopping or limiting activity and resting when symptoms worsened. The guidelines detailed what activities the child could perform and what alternative activities were beneficial if having symptoms.

Nursing communication were put in place to call the trauma staff if any worsening symptoms were observed during the time as an inpatient. The education provided during admission was followed up by the Pediatric Concussion Discharge Instructions, which was added by the provider to the After Visit Summary (AVS). Here the parent was given information regarding scheduling a follow up appointment including the physician's name, phone number, and when the child should be seen in the office.

Our secondary objective was to collect data to determine the number of patients retained within the St. Luke's network post discharge. We set realistic goals as dictated by our Specific Measurable Achievable Reasonable Time-bound (SMART) aim. Our intentions were to accomplish a 30% reduction in the number of pediatric mTBI patients requiring a specialist referral, to decrease the length of time until the child could return to baseline activity to less than one-month post-discharge by 25%, and to retain 60% of these patients within the SLUH network. We believed that by providing consistent information, these measures could be realized within 12 months' time. Two fish bone diagrams were developed to identify the various physiological, financial, educational, and environmental factors affecting these concerns [Figure 1] and [Figure 2].



For continued data collection throughout the study, an Epic report was sent to the team monthly. Information gathered from the report included the patient's name, medical record number, diagnosis, and dates of admission and discharge. This information was later collated into one report containing completion of patient education by nursing, AVS discharge instructions, completion of follow up visit with trauma, a specialist referral if needed, and the date the child was released from medical care and cleared to return to baseline activities.

Results: Initial baseline data, collected from Epic from January 2019 to July 2019, showed 22.2% of children who experienced a mTBI required specialized care referrals to see a neurologist or a sports medicine physician after initial follow up with the trauma team due to continued symptoms after 2 weeks. There was a 30.6% reduction in the number of children with mTBI requiring a referral to specialized care (22.2% preintervention to 15.4% post intervention) [Figure 3]. On average, the data also showed, these children were returning to baseline activity at a median timeframe of approximately 31 days post injury. Our investigation revealed a 54.8% reduction in the median length of time before the child was able to return to baseline activity (31 days preintervention to 14 days post intervention) [Figure 4]. Additionally, we were successful in raising our patient in network retention from 38.9% to 73.1% accounting for nearly an 88% increase in patients kept within St. Luke's University Health Network [Figure 5].



Discussion: Data analysis shows the project was able to meet and exceed all metrics set forth in our objectives. One of the most contributory strategies implemented in our study was the use of the “Brain Rest” sign on admission. This subtle change played a pivotal role in alerting both families and healthcare staff of the degree of rest that was necessary to control each individual patient's symptoms. It is important to note that while early rest may be beneficial, prolonged rest periods beyond three to seven days show no documented advantages, and can even yield the counter effect of prolonging post-concussive symptoms.[4] Evidence suggests patients can return to baseline activity once symptoms are minimal; however, rest is suggested if the child begins to experience new or worsening post-concussive symptoms.[5],[6] Upon implementing the Pediatric Concussion Care: Guidelines for Patients and Families handout, families had a clearer understanding that mTBI symptoms could range from a variety of presentations including balance issues, blurred vision, visual fatigue, tinnitus, sleep disturbances, and cognitive and emotional changes that could potentially last as much as a year beyond the initial incident.[7],[8]

The impetus for the interventions carried out in this study was brought on by the inconsistencies noticed in our systems education surrounding management of mTBI. Irregularities in the information being delivered to parents and caregivers exist as a classic source of confusion in the general care of children following mTBI, and can delay a patient's return to normalcy. Poor communication can lead to a more protracted recovery phase secondary to an unawareness of presenting post concussive symptoms, which subsequently cause a loss of educational and leisure time, thereby raising the need for extra follow-up.[9] This domino effect often ends up involving resources outside of the SLUH network.

Prior to instituting the Tolerance Not Avoidance project, only 38.9% of pediatric patients with mTBI returned for a follow-up visit. At completion of the study, the patient follow-up rate increased to 73.1%. With Trauma visits charged at $119/visit, this shows a significant increase in revenue or the network.

Throughout this project, the team met various obstacles which required further actions to improve the project and ultimately patient outcomes. The team worked with several other departments which as a result delayed the initial start date. Due to varying residents frequently rotating through the trauma service, not all residents were able to receive education regarding the treatment plan and discharge instructions. To resolve this, nursing staff was asked to verify the discharge information was added to the After Visit Summary. The trauma team members worked with Epic to build the order set and discharge instructions to ensure the discharge instruction would automatically populate into the AVS for patients with specific ICD codes relating to mTBI.

Nursing required approval from the Patient Education Committee prior to use of the patient education materials. As the project progressed, it was noted documentation of education was not consistent. In December 2019, nursing staff was re-educated on the patient education materials and Brain Rest sign. At this time, it was made apparent that staff were providing the education, however were unaware of the correct documentation procedure. To resolve this issue Epic was contacted to assist with building the Pediatric Concussion Guidelines into the EMR. Once the guidelines were complete, the nursing staff was oriented, resulting in more consistent documentation of the education.

Before initiation of the plan, mild TBI patients were inconsistently receiving cognitive evaluations by PT/OT prior to discharge. Since the implementation of the new protocol, members of the PT/OT department have researched various assessment tools, allowing for the development of a standardized cognitive evaluation of mTBI patients. The PT/OT evaluations have since become standard in the mTBI admission order set.

Language barriers also served as an obstacle in carrying out this investigation. In the future, the team will have all printed educational materials adequately translated, and appropriate deployment of the Cyracom phone will continue to be utilized during patient interactions in order to enhance communication and understanding.

Conclusion: The results of the Tolerance Not Avoidance project have been extremely positive. We were able to either meet or exceed all metrics we set forth including educating patients and families, decreasing days to return to baseline activity, minimizing specialist referrals, and increasing patient retention rates. It is our desire to see this protocol expanded on a broader scale throughout the network, and perhaps even used in the adult population


  References Top


  1. Lumba-Brown, A., et al., Diagnosis and Management of Mild Traumatic Brain Injury in Children: A Systematic Review. JAMA Pediatrics, 2018. 172(11): p. e182847-e182847.
  2. Lumba-Brown, A., et al., Centers for Disease Control and Prevention Guideline on the Diagnosis and Management of Mild Traumatic Brain Injury Among Children. JAMA Pediatrics, 2018. 172(11): p. e182853.
  3. Schellinger, S.K., B. Munson, and M.R. Kennedy, Public perceptions of traumatic brain injury: predictors of knowledge and the effects of education. Brain injury, 2018. 32(11): p. 1377-1385.
  4. Polinder, S., et al., A Multidimensional Approach to Post-concussion Symptoms in Mild Traumatic Brain Injury. Frontiers in Neurology, 2018. 9.
  5. Thomas, D.G., et al., Benefits of strict rest after acute concussion: a randomized controlled trial. Pediatrics, 2015. 135(2): p. 213-23.
  6. Halstead, M.E., K.D. Walter, and K. Moffatt, Sport-related concussion in children and adolescents. Pediatrics, 2018. 142(6).
  7. Armstrong, R.A., Visual problems associated with traumatic brain injury. Clinical and Experimental Optometry, 2018. 101(6): p. 716-726.
  8. Martini, D.N. and S.P. Broglio, Long-term effects of sport concussion on cognitive and motor performance: a review. International journal of psychophysiology, 2018. 132: p. 25-30.
  9. Cook, R.S., et al., Effect of an Educational Intervention on Nursing Staff Knowledge, Confidence, and Practice in the Care of Children With Mild Traumatic Brain Injury. Journal of Neuroscience Nursing, 2013. 45(2): p. 108-118.







 

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