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Table of Contents
Year : 2019  |  Volume : 9  |  Issue : 4  |  Page : 206-231

Selected long abstracts from the St. Luke's university health network quality awards program (2018)

Department of Quality Resources, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Date of Submission24-Nov-2019
Date of Decision01-Dec-2019
Date of Acceptance04-Dec-2019
Date of Web Publication11-Dec-2019

Correspondence Address:
Ms. Diana M Tarone
Department of Quality Resources, St. Luke's University Health Network, 801 Ostrum Street, Bethlehem, Pennsylvania
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/IJCIIS.IJCIIS_105_19

Rights and Permissions

The St. Luke's Annual Quality Awards Program was created in 2008 to promote innovation and quality improvement throughout the network. The awards ceremony is held annually in conjunction with National Healthcare Quality Week in October. The program is open to all nine campuses in our network and other entities including inpatient and outpatient units, and both clinical and nonclinical areas that contribute to our high-quality care and excellent patient outcomes. Here we include selected long abstracts representing some of the most meritorious quality project submissions for the past two academic years.
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 Healthsystem Association of Pennsylvania, performance improvement, quality awards, quality improvement, St. Luke's University Health Network

How to cite this article:
Tarone DM, Sabol D. Selected long abstracts from the St. Luke's university health network quality awards program (2018). Int J Crit Illn Inj Sci 2019;9:206-31

How to cite this URL:
Tarone DM, Sabol D. Selected long abstracts from the St. Luke's university health network quality awards program (2018). Int J Crit Illn Inj Sci [serial online] 2019 [cited 2020 Jan 25];9:206-31. Available from: http://www.ijciis.org/text.asp?2019/9/4/206/272764

Background Information and Event Highlights: The Annual St. Luke's University Health Network (SLUHN) Quality Awards Program (QAP) was held October 25, 2018 at the University Hospital in Bethlehem, Pennsylvania. This special event marked the QAPs 11th year of recognizing network-wide contributions to improve the quality of care provided to our patients and community.

The program recognizes various innovative ideas and contributions made by staff, primarily intended to increase efficiencies, raise standards, and improve care and services provided to our patients and the regional community. The QAP criteria align with SLUHN's mission, unwavering commitment to excellence, the creation of superior value, and making the patient our highest priority.

This year's program received 29 submissions. One of the most important aspects of the QAP is the focus on post-implementation sustainability. As a testament to this, many of the projects saw expanded implementations across the network, additional institutional investment, national recognition, and scholarly publications. Furthermore, the dissemination of quality initiatives throughout the network resulted in numerous innovations, process efficiencies, best practices, improvements in patient care, and better overall organizational performance.

Many of the SLUHN quality projects are also submitted to other award programs. The Hospital and Healthsystem Association of Pennsylvania (HAP) honors hospitals and health systems for their innovation, creativity, and commitment to patient care through an annual Achievement Awards program. There were 127 entries competing in the 2019 program, with 14 winners recognized across four categories. One of those awards was presented to SLUHN. The winning team was recognized at HAP's Patient Safety, Quality and Sepsis Symposium on September 23, 2019. The “In Safe Hands Award” was presented to St. Luke's University Health Network, Bethlehem Campus for the project: “Developing an Industry-Leading Vaccine management Program.”

In this issue of the International Journal of Critical Illness and Injury Science, we will present selected long abstracts from the past two award years, 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.

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. In addition, the authors/teams were required to follow applicable EQUATOR Network (http://www.equator-network.org/) guidelines during the conduct of research.

   Abstract Number 1 Top

   Improving Sepsis Care: A University Health Network Initiative Top

V. Yellapu, D. Tarone, S. N. DeTurk, A. Alam, C. Sonday, J. Axelband, B. Wilson, M. Irick

Scientific contributors (alphabetically): T. Binstead, M. Bowen, S. Brown, M. Buschemi, K. C. Schlegel, S. Casey, J. Concilio, J. Davis, S. Depcinski, R. DeQuevedo, J. Gillard, S. Heffner, R. Hummel, A. Kolbe, D. Martins, M. McCreery, R. Morcrette, K. Nunemacher, V. Paulson, B. Pequeno, D. Rinker, D. Rowe, C. Ruggeri, T. Samson, D. Schroettner, S. P. Stawicki, C. Stromski

Departments of Critical Care, Quality Resources, Research and Innovation, Emergency, Patient Services, Pharmacy, Nursing, Anesthesia, Internal Medicine, Clinical Informatics, St. Luke's University Health Network. Participating campuses: Allentown, Anderson, Bethlehem, Miners, Quakertown Campuses, Pennsylvania, Warren Campus, New Jersey, USA

Year of Submission: 2018

Introduction: The incidence of sepsis is increasing and it continues to be a major healthcare challenge world-wide, with roughly 1.5 million cases per year.[1],[2],[3] In fact, documented annual cases of sepsis may be increasing by as much as 9%-13%.[1],[3],[4],[5],[6] Mortality associated with severe sepsis remains high at 30-50%, and may approach 60% when septic shock develops.[7],[8] The “Surviving Sepsis Campaign,” a global initiative of the Society of Critical Care Medicine and European Society of Intensive Care Medicine, sought to create awareness, improve recognition and enhance treatment of sepsis, with the goal of reducing mortality rates associated with the condition through the use of a protocolized “Sepsis Bundle”.[9],[10],[11] This protocol, now a part of the Centers for Medicare and Medicaid Services (CMS) compliance metrics, referred to as SEP-1, has been shown to reduce overall hospital mortality in sepsis patients.[2]

In addition to the above considerations, an aging population, complex co-morbidities, and the spread of antibiotic-resistant organisms all contribute to the increased incidence and severity sepsis.[8],[12] Furthermore, from economic perspective sepsis is considered to be among the most expensive medical conditions. It has been estimated that sepsis creates a financial burden of $20-$24 billion per year at the national level.[4],[13],[14]

Aims and Objectives: The primary objective of this quality improvement initiative was to increase the rates of early initiation of goal-directed therapy (GDT). This entailed prompt identification and treatment of patients suspected of having severe sepsis or septic shock. Our specific aim was to improve our CMS sepsis bundle compliance from approximately 50% to >70% within a 1-year timeframe. The following key measures were implemented to help achieve our goals:

  1. The creation of a multidisciplinary Electronic Medical Record (EMR) order set that could be used for quick sepsis screening.
  2. The adjustment of our Network's critical lab value for lactate to >2 mmol/L
  3. The development of a formal, protocolized “Sepsis Alert” process [[Figure 1] and [Figure 2].
Figure 1: Screening and management tools that were eventually implemented into Electronic Medical Record

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Figure 2: Early identification and management algorithm

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Methods: Multiple strategies and interventions were implemented during the process in order to improve early identification and treatment of septic patients [Figure 2]. Initial strategies focused on translating an existing paper-based processes and tools into the EPIC™ EMR, as well as the creation of new tools such as Best Practice Alerts/Advisories (BPAs). The initial EMR tools included “Sepsis Order Set” and a screening tool for nurses. The sepsis order set featured items required to meet the CMS guidelines including baseline lactate level, repeat lactate after 2 hours, and the administration of a weight-based fluid bolus. A “Sepsis Navigator” was created to provide one convenient reference point for all “Sepsis Tools”, including links to order sets, documentation forms, flow sheets for providers, and summary of sepsis care. An algorithm explaining the process was developed to guide the evidence-based care and ensure the sepsis bundle was followed [[Figure 1] and [Figure 2]. Corresponding materials were distributed as posters and pocket cards across our Health Network. The CMS list of approved antibiotics was also simplified to align with our Network's formulary.

Following these implementations, a Grand Rounds presentation was provided to the Network and recorded for later viewing by those unable to attend. Providers were given continuing medical education (CME) credits to further incentivize participation. Key educational points included the lowering of “critical lab value” for lactate (to 2.0 mmol/L) to help identify early sepsis. Our laboratory has a mandatory provider notification mechanism that communicates all “critical lab values” and sends a reminder to repeat lactate when necessary.

A “sepsis alert” (SA) process was piloted at one of our Network's emergency departments (ED) and then implemented in all EDs as well as acute care areas. The SA activates “rapid response” teams (RRT) to help facilitate the implementation of the “sepsis alert” protocol (SAP) by bringing additional resources to the bedside if needed. The alert system requires collaboration with telecommunication services, RRT, hospital supervisors, laboratory services, radiology services, pharmacists and providers at all levels, both in the inpatient and ED settings. In addition, EPIC™ EMR order sets were appropriately updated to optimize weight-based fluid orders.

Finally, a predictive analytics model was developed using an in-depth review of medical record data from our acute care floors and EDs. The predictive score was based on >80 attributes and developed using data gathered between January-June 2017. Subsequent analyses identified appropriate trigger levels for the predictive model, with a score of 7% used to identify the need for a nurse to screen the patient for sepsis and notify the provider with any positive screens. A score of 9% prompted for a screen as well, with providers notified in cases of both positive and negative screens. The model was then implemented across the entire Network in January 2018, following mandatory provider education.

During the baseline period and prior to our interventions, between October 2015 and September 2016, compliance with the protocol was inconsistent. The overall network compliance with the “Sepsis Bundle” (SB) was only 46.4%. As true benchmark was not available, the target of ≥70% SB compliance was identified as a reasonable goal based on comparison data originating from publically available sources. Additional descriptive analyses, as appropriate, were conducted using the International Statistical Classification of Diseases and Related Health Problems (ICD) list by the World Health Organization (Geneva, Switzerland).

Results: Bundle compliance for the Network surpassed the 70% mark in July of 2017 (a 50% increase from baseline) and has since been sustained. The CMS-defined “top decile” target of 76% was subsequently attained in November 2017 (a 63% increase from baseline) and has also since been sustained [Figure 3]. Since this is a CMS mandatory reported and publicly displayed measure, abstraction for bundle compliance continues and is shared throughout the Network on a monthly basis. Simultaneously, the team continues to monitor our sepsis mortality. We were able to maintain our strong performance around the top decile (top decile is about 5.42%, top quartile is about 7.19%) in the Premier (Charlotte, North Carolina, USA) database [Figure 4].
Figure 3: Network Centers for Medicare and Medicaid Services September 1 bundle compliance

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Figure 4: Network mortality performance – Premier top decile is about 5.42%

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We also monitored patient volumes for severe sepsis with shock (ICD-10 code R65.21) and severe sepsis without shock (ICD-10 code R65.20). We found that over time fewer patients were progressing along the sepsis continuum to septic shock (severe sepsis with shock). After annualizing data for clinical year 2018, we expect to see 5.2% fewer septic shock cases which equates to an absolute 50 case reduction. The estimated total cost for a patient with septic shock is about $29,243. The estimated added cost for a patient with severe sepsis (without shock) is about $15,525. The resultant difference in cost (or $13,718) multiplied by approximately 50 patients contributed to an estimated $685,900 reduction in total costs.

Discussion: Healthcare institutions continue to face challenges regarding early identification and timely management of sepsis. With more hospitals switching to EMRs, the opportunity emerged of practical application of EMR tools to help screen and quickly identify high-risk patients.[15],[16] At the same time, it is important to acknowledge that early detection of sepsis though EMR-based tools may not be sufficient by itself.[17] Early goal directed management is the key to successful treatment and containment of active sepsis, and is the most critical component of stopping the progression to septic shock and multi-organ failure.[18] Given that the current project was successful in decreasing the rate of sepsis progression in patients and helped increase our 3-hour SB compliance, we consider this intervention a success. Using this as a springboard for further quality improvement initiatives, we embraced an expanded program that utilizes a robust EMR-based predictive analytics model. We anticipate that the continued re-evaluation and refinement of the predictive analytics process, along with ongoing efforts to improve our SAP, will enable us to expand its use to other areas of care, well beyond sepsis.

In the meanwhile, our team continues to focus on documentation and workflow improvements to incorporate within the existing EMR framework. We hope this will make the overall process easier for our care providers, further enhancing protocol compliance. Future work includes focus on aftercare for the sepsis patients, including readmission reduction for this high acuity patient population (as well as additional financial/resource utilization considerations). The SB/SAP and predictive analytics models have been used in other studies and have been shown to be effective in improving outcomes.[19],[20]

There are important limitations of this report, including its retrospective nature and single-institution implementation. This makes applicability and reproducibility of our data across other acute care settings and/or hospitals difficult. In addition, we did not fully account for some of the common physiological indices when performing analyses. Thus, our data may contain biases inherently associated with such approaches. Additionally, other studies have shown similar low performance at rollout of SEP-1 with subsequent improvement after the first year of the metric, similar to our data.[21] Given the retrospective nature of this project, we are unable to assess if our improvement aligns or surpasses that of other early adopters of SB/SAP. Ongoing monitoring is necessary to ensure these efforts sustain compliance. Finally, it is important to acknowledge that with our data representing approximately 3 years of results, temporal bias may be present.

Conclusion: Patient outcomes in the area of sepsis management improved following the implementation of SB/SAP at our institution. As we continue to refine the predictive analytics process, we hope that the SAP will continue to reduce the number of escalations from sepsis to septic shock, and thus help improve overall patient mortality. We also hope to apply our experiences with the SB/SAP model in other clinical areas.

  1. Angus DC, Linde-Zwirble WT, Lidicker J, Clermont G, Carcillo J, Pinsky MR. Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care. Crit Care Med 2001;29:1303-10.
  2. Seymour CW, Gesten F, Prescott HC, Friedrich ME, Iwashyna TJ, Phillips GS, et al. Time to Treatment and Mortality during Mandated Emergency Care for Sepsis. N Engl J Med 2017;376:2235-44.
  3. Gaieski DF, Edwards JM, Kallan MJ, Carr BG. Benchmarking the incidence and mortality of severe sepsis in the United States. Crit Care Med 2013;41:1167-74.
  4. Paoli CJ, Reynolds MA, Sinha M, Gitlin M, Crouser E. Epidemiology and costs of sepsis in the United States-an analysis based on timing of diagnosis and severity level. Crit Care Med 2018;46:1889-97.
  5. Dombrovskiy VY, Martin AA, Sunderram J, Paz HL. Rapid increase in hospitalization and mortality rates for severe sepsis in the United States: A trend analysis from 1993 to 2003. Crit Care Med 2007;35:1244-50.
  6. Wang HE, Shapiro NI, Angus DC, Yealy DM. National estimates of severe sepsis in United States emergency departments. Crit Care Med 2007;35:1928-36.
  7. Gerber K. Surviving sepsis: A trust-wide approach. A multi-disciplinary team approach to implementing evidence-based guidelines. Nurs Crit Care 2010;15:141-51.
  8. Freeman BD, Natanson CJ. Anti-inflammatory therapies in sepsis and septic shock. Expert opinion on investigational drugs 2000;9:1651-63.
  9. Dellinger RP, Vincent JL. The Surviving Sepsis Campaign sepsis change bundles and clinical practice. Critical care 2005;9:653.
  10. Chamberlain DJ, Willis EM, Bersten AB. The severe sepsis bundles as processes of care: A meta-analysis. Aust Crit Care 2011;24:229-43.
  11. Levy MM, Evans LE, Rhodes A. The Surviving Sepsis Campaign Bundle: 2018 update. Intensive Care Med 2018;44:925-8.
  12. Martín S, Pérez A, Aldecoa C. Sepsis and Immunosenescence in the elderly patient: A review. Front Med (Lausanne) 2017;4:20.
  13. Arefian H, Heublein S, Scherag A, Brunkhorst FM, Younis MZ, Moerer O, et al. Hospital-related cost of sepsis: A systematic review. J Infect 2017;74:107-17.
  14. Torio CM, Moore BJ. National Inpatient Hospital Costs: The Most Expensive Conditions by Payer, 2013: Statistical Brief #204. Agency for Healthcare Research and Quality (US), Rockville (MD): 2006.
  15. McRee L, Thanavaro JL, Moore K, Goldsmith M, Pasvogel A. The impact of an electronic medical record surveillance program on outcomes for patients with sepsis. Heart Lung 2014;43:546-9.
  16. Gatewood MO, Wemple M, Greco S, Kritek PA, Durvasula R. A quality improvement project to improve early sepsis care in the emergency department. BMJ Qual Saf 2015;24:787-95.
  17. Despins LA. Automated detection of sepsis using electronic medical record data: A systematic review. J Healthc Qual 2017;39:322-33.
  18. Gaieski DF, Mikkelsen ME, Band RA, Pines JM, Massone R, Furia FF, et al. Impact of time to antibiotics on survival in patients with severe sepsis or septic shock in whom early goal-directed therapy was initiated in the emergency department. Crit Care Med 2010;38:1045-53.
  19. Parikh RB, Kakad M, Bates DW. Integrating predictive analytics into high-value care: The dawn of precision delivery. JAMA 2016;315:651-2.
  20. Kamaleswaran R, Akbilgic O, Hallman MA, West AN, Davis RL, Shah SH. Applying artificial intelligence to identify physiomarkers predicting severe sepsis in the PICU. Pediatr Crit Care Med 2018;19:e495-503.
  21. Venkatesh AK, Slesinger T, Whittle J, Osborn T, Aaronson E, Rothenberg C, et al. Preliminary Performance on the New CMS Sepsis-1 National Quality Measure: Early Insights From the Emergency Quality Network (E-QUAL). Ann Emerg Med 2018;71:10-50.

   Abstract Number 2 Top

   Reducing Surgical Site Infections in the Open Heart Surgery Population Top

S. Olenchock, A. Rutkowski, L. McSorley, A. Buono, M. Mohler, N. Stewart, A. White

Scientific contributors (alphabetically): J. Irick, K. Mascitti, J. Sgro, T. Shine, S. P. Stawicki, C. Zelko-Bennick

Departments of Cardiothoracic Surgery, Infection Control and Prevention, Infectious Diseases, Patient Care Services, Quality Resources, Surgical Services, and Research and Innovation, St. Luke's University Health Network, Bethlehem, Department of Emergency Medicine, St. Luke's Richard A. Anderson Campus, Easton, Pennsylvania, USA

Year of Submission: 2018

Introduction: Sternal wound infections (SWIs) after cardiac surgery [coronary artery bypass graft (CABG) or valve replacement/repair (VR)] are associated with increased morbidity and mortality, prolonged lengths of stay, and increased hospital costs.[1],[2] Rates of deep SWIs are publicly reported through the STS (Society of Thoracic Surgeons, Chicago, Illinois, USA) registry and are shared with the NHSN (National Healthcare Safety Network, Atlanta, Georgia, USA). These metrics are intended to be utilized as surrogate markers of the quality of surgical services provided to patients. The incidence of superficial SWIs is 0.5-8% with associated morbidity and mortality of 0.5-9%, whereas the incidence of deep SWIs is ranges between 0.5–6.8% with mortality rates from 7–47%.[3]

Postoperative management strategies range from simple incision and drainage of superficial infections, to the complex wound debridement and reconstruction required for cases of severe mediastinitis.[3],[4] The development of post-sternotomy SWIs is a major area of concern due to the impact it plays on patient recovery and the financial burden that may be associated with it. For this reason, multiple studies have sought to minimize the factors contributing to SWIs, with a goal of reducing the incidence of these infections.[5],[6],[7] Although it is not possible to isolate a singular cause, the literature supports a number of common perioperative factors that influence the development of SWIs, each with varied degrees of modifiability. These factors include male gender, obesity, diabetes mellitus (DM), chronic obstructive pulmonary disease (COPD), prolonged ventilation, previous cardiac surgery, and use of bilateral mammary arteries.[8]

In August 2016, the cardiothoracic surgery (CTS) team at St. Luke's University Hospital in Bethlehem, Pennsylvania (SLUHN) identified an early indication that wound complications following open heart surgery (OHS) procedures may be increasing. This was the first time since 2012 that such complications occurred at the University Hospital. As a result, a multidisciplinary workgroup was formed to focus on quality of care and patient safety (PS) concerns surrounding this issue. This quality improvement project focused on identifying and proactively modifying specific risk factors for wound complications. The ultimate goal of the initiative was to achieve zero incidence of SWI in post-cardiac surgery patients before the start of the 2018 fiscal year.

Methods: In November 2016 a multidisciplinary workgroup was formed to implement evidenced based strategies to improve the SWI rate for OHS patients. A series of interventions were implemented and included: (a) enhanced education of high-risk patients and the staff responsible for their perioperative care; (b) postoperative wound management of chest tube sites; (c) standardizing operative room (OR) cleaning schedules; and (d) direct observations by OR management to ensure practice compliance.

The Infection Control team and members of the Quality Resources Department were responsible for identifying and trending cardiac surgical site infections (SSIs). SWI rates were reported on a bi-monthly basis to the Heart Surgery Taskforce as part of the Heart and Vascular Balanced Scorecard. A summary of identified opportunities for improvement and project updates were regularly provided to the group.

Preoperative cardiac surgical patients were identified throughout the Health Network and were prioritized to be centrally placed on the Cardiac Nursing Unit (CNU). This allowed for further standardization of preoperative SWI reduction practices. Patient care assistants were trained and instructed to perform appropriate hair clipping and preoperative bathing technique on patients scheduled for OHS. According to our new protocol, hair removal was to be completed outside of the OR, with the arms of the staff performing the surgical site preparation being fully covered. Bed linens were changed daily and use of disposable ECG leads and blood pressure cuffs was mandated.

An audit of OR practices related to room cleaning and equipment sterilization was performed. Cycle cleaning practices were revised so that wall clutter was minimized during the cleaning process. OR traffic patterns were revised to limit the number of people entering the operating room. Finally, OR temperature/humidity were monitored to ensure strict and appropriate environmental levels/ranges.

Use of Acticoat™ (Smith and Nephew, Andover, Massachusetts, USA) dressings was stopped after the product was discontinued by the manufacturer. Mepilex™ (Mölnlycke Health Care, Gothenburg, Sweden) dressings were briefly used as a replacement; however, they were also abandoned due to adherence issues. Ultimately the decision was made to use a silver impregnated dressing on surgical sites.

Outpatient practitioners were tasked with identifying wound complications associated with tube thoracostomy (TT) removal sites early when patients presented to the outpatient CTS office for their scheduled follow-up visits. To minimize the risk of infection, silver impregnated dressings were also applied to the TT incisions and patients were discharged from the hospital with a care package including the Hibliclens CHG (chlorhexidine) soap, disposable wash cloths, as well as instructions on completing the Hibliclens bath. Post-discharge phone calls were completed to assess individual patient progress. Nursing staff inquired about the appearance of all surgical sites, and reviewed proper utilization of the CHG soap provided at discharge. Post-discharge calls also addressed blood glucose management and nutrition at home. Patients who contacted the office regarding any type of surgical site/incision-related issue were brought into the office for a visual inspection of incisions.

Strategies were deployed to ensure diabetic OHS patients had access to appropriate glucose control tools and education. Endocrinology consultations were obtained postoperatively and diabetic diet orders were placed in the electronic medical record (EMR) for all diabetic patients. Patients were educated in the importance of remaining on insulin therapy after discharge to help reduce their risk of developing a SSI. The case management and pharmacy teams ensured that patients had appropriate insulin therapy supplies and ability to readily obtain prescriptions.

Results: In July 2017, 1 sternal wound infection was identified for the National Healthcare Safety Network (NHSN) – CABG procedure category. The CABG SIR (Standardized Infection Ratio) from July 2017 to December 2017 was 0.483 [Figure 1]. From December 2016 to December 2017, there was an overall reduction in the CABG SIR Rate of 70%. There were no further sternal wound infections noted between August 2017 and May 2018 for the NHSN – CABG Rate. Additionally, no SWIs were noted for the valve procedures between September 2017 and May 2018. The team reached their goal of zero reported CABG and valve procedure SSIs in August 2017 and September 2017, respectively [Figure 2].
Figure 1: Coronary artery bypass graft Standardized Infection Ratio performance pre and post intervention

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Figure 2: Open heart surgery National Healthcare Safety Network surgical site infection data

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Discussion: Superficial and deep surgical wound infections following cardiac surgery are potentially serious complications and are associated with increased morbidity and mortality.[1],[2],[3] Pre-operative, intraoperative, and post-operative strategies have been utilized to significantly decrease the associated risk of these infections.[9] At the time of this quality improvement initiative, SLUHN's OHS-specific financial data could not be obtained. A review of the literature was completed to determine the national average of additional per-case costs associated with the development of mediastinitis following OHS.

Among key drivers of quality for national healthcare networks is the phasing out of reimbursement for expenses accumulated secondary to the treatment of deep SWIs, including post-CABG events, by the US Centers for Medicare and Medicaid Services.[9] These costs can be substantial, with one study reporting that the additional financial burden associated with mediastinitis ranged from $19,000 to $56,000 per case.[10] Extrapolating from these data, the estimated cost savings associated with the 7 identified mediastinitis cases at SLUHN may range between $133,000 and $392,000. A similar quality improvement study from Georgia demonstrated the elimination of post-CABG deep SWI after implementing strict perioperative measures.[5] The same report also noted a decrease in saphenous vein donor SSI incidence from 3.74 to 0.7 per 100 procedures, with an eventual reduction to “zero incidence.” After completion of 590 surgeries, it is estimated that these changes in clinical practice led to $600,000 in savings over a 30 month period.[5]

Our institution (SLUHN) participates in the STS (Society of Thoracic Surgeons) clinical outcomes registry. The registry tracks nationwide quality and performance data for all open-heart procedures. Hospitals have the ability to benchmark their data against other participating facilities in the nation. STS members voluntarily agree to publicly report their data and in exchange, they are given a star rating assigned biannually. The highest possible score for a hospital is the “3 stars” rating. SWI rates following OHS are a key marker of quality and help determine the star rating. As a result of the current initiative, the CTS team was awarded 3 stars for CABG, and 2 stars for aortic VR (AVR) and CABG with AVR.

Of note, NHSN has two defined procedure categories in regard to OHS. More specifically, NHSN – CABG data is collected for CABG procedures, and NHSN – CARD data pertains to VR procedures. Within these categories, the contributions of patient and facility factors are considered when the standardized infection ratio (SIR) is calculated. The SIR is used to track hospital acquired infections over time. In 2016, 2 CABG SWI's were reported to NHSN, resulting in SIR of 1.625 [Figure 1]. In the first 6 months of 2017, 2 SWI's were identified for the CABG population with SIR of 0.796.

During the implementation phase of the project, we deployed a variety of key quality and safety measures. This included the use of CHG showers the night before surgery and strict blood glucose control in diabetic patients.[4] Although this manuscript was not designed to assess the impact of individual perioperative modifications on patient outcomes, similar results have been reported in the literature describing the use of a multifactorial approach.[6] The CTS team strives to be in the top decile performance range of the SIR classification. Application of the above-mentioned perioperative strategies enabled SLUHN to reach the top decile NHSN benchmark with a CABG SIR of 0.00 by the conclusion of the reporting period.

This report has certain limitations. Most importantly, we presented a single-center, retrospective analysis, which may not be readily translatable to other medical centers. Additionally, we noted some heterogeneity within our diabetes education and control outcomes. Consequently, emphasis was placed on empowering CTS team members to help improve patient compliance with insulin therapy as a conduit to reducing SWIs. This continues to be an active area of our focus, including detailed tracking of key diabetic endpoints on an ongoing basis. Lastly, due to the multiple variables contributing to individual outcomes, it is difficult to ascertain which interventions were pivotal to decreasing SWIs. Identifying the specific factors that play the largest role in predisposing patients to SWIs could lead to further and more targeted improvements in our processes and practices. The outcomes demonstrated here, however, suggest that defining a strict perioperative OHS protocol, can have a profound impact on the incidence of SWIs.

Conclusions: Sternal wound infections are serious, costly, and potentially avoidable complications of OHS. By implementing structured perioperative protocols and interventions, we were able to significantly reduce SWI rates in the OHS population at our University Hospital. The changes that were integrated into our perioperative care protocols will continue to be monitored and the rates of SWIs will continue to be reported on a bi-monthly basis. Prevention of SSIs remains a top priority for the CTS team. Our current effort led to significant improvement in key process outcomes, resulting in a “3 star” rating for CABG procedures.

  1. Sharif M, Wong CH, Harky A. Sternal wound infections, risk factors and management – How far are we? A literature review. Heart Lung Circ 2019;28:835-43.
  2. El Oakley RM, Wright JE. Postoperative mediastinitis: Classification and management. Ann Thorac Surg 1996;61:1030-6.
  3. Kotnis-Gąska A, Mazur P, Olechowska-Jarząb A, Stanisz A, Bulanda M, Undas A. Sternal wound infections following cardiac surgery and their management: A single-centre study from the years 2016-2017. Kardiochir Torakochirurgia Pol 2018;15:79-85.
  4. Abu-Omar Y, Kocher GJ, Bosco P, Barbero C, Waller D, Gudbjartsson T, et al. European Association for Cardio-Thoracic Surgery expert consensus statement on the prevention and management of mediastinitis. Eur J Cardiothorac Surg 2017;51:10-29.
  5. Kles CL, Murrah CP, Smith K, Baugus-Wellmeier E, Hurry T, Morris CD. Achieving and sustaining zero: Preventing surgical site infections after isolated coronary artery bypass with saphenous vein harvest site through implementation of a staff-driven quality improvement process. Dimens Crit Care Nurs 2015;34:265-72.
  6. Lindblom RP, Lytsy B, Sandström C, Ligata N, Larsson B, Ransjö U, et al. Outcomes following the implementation of a quality control campaign to decrease sternal wound infections after coronary artery by-pass grafting. BMC Cardiovasc Disord 2015;15:154.
  7. Stoodley L, Lillington L, Ansryan L, Ota R, Caluya J, Camello E, et al. Sternal wound care to prevent infections in adult cardiac surgery patients. Crit Care Nurs Q 2012;35:76-84.
  8. Lu JC, Grayson AD, Jha P, Srinivasan AK, Fabri BM. Risk factors for sternal wound infection and mid-term survival following coronary artery bypass surgery. Eur J Cardiothorac Surg 2003;23:943-9.
  9. Goh SS. Post-sternotomy mediastinitis in the modern era. J Card Surg 2017;32:556-66.
  10. Hsu HE, Kawai AT, Wang R, Jentzsch MS, Rhee C, Horan K, et al. The impact of the medicaid healthcare-associated condition program on mediastinitis following coronary artery bypass graft. Infect Control Hosp Epidemiol 2018;39:694-700.

   Abstract Number 3 Top

   High Observation Trauma II Algorithm for Patients with Traumatic Brain Injury Top

P. Thomas, R. Wilde-Onia, A. M. Szoke, D. O'Rourke, G. Biundo, N. Lohrman, T. Jones, A. Alam, A. Green, S. DeTurk, N. Stewart

Scientific contributors (alphabetically): N. Alderfer, D. Bennett, A. Benton, J. Concilio, K. Donatelli, J. Florkowski, K. Hayes, H. Kuhns, J. Meckes, H. Moulding, S.P. Stawicki, H. Weber, L. Wilson

St. Luke's Regional Level I Trauma Center, Nursing Services, Department of Neurosurgery, and Department of Research and Innovation, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Year of Submission: 2018

Introduction: In 2004, the World Health Organization (WHO) Collaborating Center for Neurotrauma Prevention, Management and Rehabilitation Task Force on Mild Traumatic Brain Injury (WHO Task Force) published what was proposed as a standardized definition of mild traumatic brain injury (mTBI).[1] The WHO Task Force defined mTBI as an acute traumatic head injury resulting in a Glasgow Coma Scale (GCS) of 13 – 15 sustained beyond 30 minutes following insult or arrival to a healthcare facility, plus at least one of the following: confusion or disorientation, loss of consciousness <30 minutes, post-traumatic amnesia <24 hours, and/or other transient neurologic abnormalities such as focal signs, seizures, and intracranial lesions not requiring surgery.[1] The need for a standardized, common language as to what mTBI is, stems from the fact that 70-90% of all TBI's are classified as mild, contributing to as many as 100-300/100,000 new hospital treated cases each year worldwide.[2] Due to the definitional variability of mTBI in the literature, it comes as no surprise that treatment strategies have also varied.[3]

Our Level I trauma center has historically admitted mTBI patients defined as having a GCS >14 and a positive computed tomography (CT) scan (i.e., intracranial hemorrhage) to an intensive care unit (ICU) for hourly neurologic assessments. However, recent literature suggests that a significant proportion of these patients may not require monitoring in an ICU setting. A meta-analysis of 49 primary studies, which included patients with mTBI and a GCS 13-15, showed rates of clinical deterioration, neurosurgical intervention and death of 11.7%, 3.5% and 1.4%, respectively.[4] Data from a retrospective review of our institutional trauma registry suggested that few of these patients deteriorate clinically and a majority of patients have an ICU length of stay (ILOS) <1 day. Consequently, we devised and implemented the High Observation Trauma (HOT) policy in 2009 for a select patient population at St. Luke's University Hospital Bethlehem (SLUHB). The initial patient group included those with isolated mTBI as defined by our criteria, no history of abnormal clotting or coagulation disorders, and absence of any antiplatelet or anticoagulation therapy. After six years of successful implementation and management, in 2015 a multidisciplinary team was convened to take our findings to the next level in what we referred to as HOT II. The new inclusion criteria would now include the addition of trauma patients who presented with mTBI while on aspirin, clopidogrel, or warfarin. The need to expand upon our earlier institutional protocol came from the understanding that although nearly 99% of mTBI patients are managed nonoperatively, a uniformly accepted standardized protocol for managing these patients does not yet exist.[5],[6]

A retrospective review of our annual admissions determined that 38% of trauma patients admitted with an mTBI in the setting of prescribed antiplatelet or anticoagulation therapy met HOT II inclusion criteria. Our team identified the opportunity to improve care for mTBI patients while reducing hospital resource utilization and lengths of stay (HLOS). This quality improvement study sought to reduce HLOS while optimizing the utilization of critical care resources and ILOS in the mTBI patient population by changing the current admission practice patterns.

Methods: Prior to implementation, patients with mTBI were treated according to department guidelines for High Observation Trauma (HOT). Under this policy, patients with an mTBI could be admitted to general inpatient floor if they had a GCS of 14-15, no significant injuries, were not taking an anticoagulant medication, and had no abnormal clotting parameters. A positive head CT did not constitute an automatic exclusion from these guidelines. Under the new HOT II policy implemented in 2015, patients taking aspirin, clopidogrel, or warfarin, were also considered eligible for inclusion. Care for these patients was evaluated by a multidisciplinary team of trauma leadership, physicians, advanced practitioners, nursing administration, nurses, and educators in the development of the HOT II admission policy.

A retrospective review of 150 mTBI patients was first performed to determine baseline data, including patient volume and staffing needs estimations. Data was taken from Trauma Collector Registry, a commercial software database program mandated by the Pennsylvania Trauma Systems Foundation (PTSF, Harrisburg, Pennsylvania, USA). The following data elements were analyzed: Date of admission, age, GCS, Injury Severity Score (ISS), admission destination, ILOS, HLOS, mortality, and pre-admission type of antiplatelet and anticoagulant therapy. This data exploration showed that 50% of the patients were prescribed a reversible anticoagulant, 38% of whom would meet updated admission criteria. The volume of trauma patients meeting proposed HOT II parameters was estimated as 1.2 patients per week. Early concerns raised by nursing staff regarding staffing, training, and general support were resolved with administrative support and extensive education. Patients were to be admitted to general care, with a maximum 1:4 nurse-to-patient ratio. Nursing education was provided via online learning, as well as a tip sheet. For patients admitted under HOT II protocol, hourly neurologic checks were conducted over 24 hours by nursing staff. This included pupillary assessment, GCS determination, muscle strength assessment, and orientation. Instructions on how to proceed in case of any acute change(s) in neurologic status were included with the tip sheet. Data were collected using the aforementioned Trauma Collector Registry database, with an additional user-defined element added to track the patients. Real-time chart reviews were done to provide direct support and education to the nursing teams and ensure appropriate patient assessments.

Results: Two cycles of protocol-specific changes were implemented over 15 months. After 6 months of utilizing the policy, results were reviewed and found to support the continuation of the policy. Neurologic checks were completed 93% of the time. However, there were opportunities to improve neurologic check compliance in the emergency department (ED). This was addressed by distributing the HOT II nursing education to the ED nursing staff. Cycle two showed that neurologic check completion improved to 97%. Another area to address was an increase in brain CT scans per patient admission from 1.61 to 1.86 (note: this included a proportion of unnecessary “recheck” follow-up scans). The data from the initial quality assessment cycle were shared with physicians at both our group practice and trauma multidisciplinary performance improvement meetings. Following these meetings CT scan utilization appropriately decreased to 1.38 per patient. After cycle two was completed, the HOT II policy remained in place. Descriptive statistics of health care utilization and financial savings are summarized in [Table 1], [Table 2], [Table 3].
Table 1: Initial proposed high observation trauma II analysis

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Table 2: Financial savings in first 6 months of high observation trauma II

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Table 3: Analysis of high observation trauma II project

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We noted that our primary goals of ILOS, HLOS, and cost reduction were met within the first six months of implementation [Table 1] and [Table 2], and were subsequently sustained. During the first 6 months HLOS and ILOS decreased by 0.16 days and 1.25 days respectively [Table 1] resulting in significant estimated cost savings [Table 2]. Average ISS increased during the 6 months from 9.41 to 10.34. The HOT II policy also led to longer-term, sustainable results. More specifically, after 3 years, HLOS and ILOS decreased an average of 0.59 and 1.11, respectively [Figure 1] and [Table 3]. This resulted in estimated health care cost related savings of nearly $6 million [Table 4]. Since implementing the policy there have been no mortalities reported in the HOT II patient group.
Table 4: Long.term financial impact of high observation trauma II protocol implementation

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Figure 1: Patient and healthcare utilization over 3 years

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Discussion: Previous publications have highlighted the variations in management guidelines for patients with mTBI.[6],[7] In this report, we demonstrated that the HOT II guidelines were successful in reducing HLOS, ILOS, and unnecessary head CT scan use, without affecting patient morbidity or mortality. Other studies have shown that mTBI can be managed in general inpatient settings instead of the ICU, without over-reliance on repeat cranial CT scans.[8],[9],[10] Moreover, several retrospective studies suggest that patients with a mTBI while on anticoagulation may not be at increased risk of intracranial bleeding or related complications.[11],[12],[13],[14] Consistent with this previously published evidence, the implementation of the HOT II protocol at our institution helped standardize and optimize management guidelines for patients with mTBI and active anticoagulation status.

In the current analysis, two key findings became apparent after implementing the HOT II protocol. Firstly, the utilization of healthcare resources became more optimal. Secondly, there was no associated increase in patient morbidity or mortality. Moreover, there was a decrease in HLOS, ILOS, as well as the average number of head CT scans per patient. This resulted in a significant savings over a 3-year period, or an estimated $6 million in a single-institution setting. At the same time, the observed increase in ISS indicates that the observed reduction in cost-of-care was not attributable to lower overall severity of injuries.

Identification and remediation of potential barriers to HOT II protocol implementation was critical to the project's success. Through discussions with nursing staff, concerns were raised regarding the staffing resources and the provision of appropriate level of training. By ensuring a nurse-to-patient ratio of 4 to 1, staffing resources were readily accommodated. Nursing knowledge and skills were successfully addressed through educational handouts and sessions provided to staff. After the completion of the initial phase of the project, areas for potential improvement were identified. An opportunity was identified in the ED, including evidence of non-compliance with regularly performed neurological checks. To address this, ED nurses received the same training materials provided to the inpatient nursing staff. This resulted in increased compliance, from 93% to 97%. Another area of opportunity involved the number of CT scans performed. In the first 6 months of this quality improvement project, the number of head CT scans per patient increased. Physicians were more likely to order multiple CT scans due to concerns for patient complications that might go unnoticed outside of the ICU. However, by sharing the project outcomes with a team of physicians, the number of unnecessary CT scans normalized to pre-implementation baseline.

The current report has several important limitations. First, this is a single-institution, retrospective review of outcomes in a narrowly defined group of mTBI patients. Thus, it may not be readily applicable to other TBI scenarios and/or settings. Second, we only examined patients who were either naive to antiplatelet and anticoagulant therapy or were taking aspirin, warfarin, or clopidogrel. Due to the growing number of patients now taking novel oral anticoagulants, future investigations including this patient population will be needed to better reflect the changing anticoagulant trends in our trauma population. Such future endeavors may help guide the development of a standardized protocol for the optimal management of mTBI patients taking novel anticoagulants. Third, we only observed patients up until the time of discharge, with limited outpatient follow-up. It would be beneficial to evaluate longer-term results of our patient cohort. More extensive post-discharge follow-up may help elucidate any late complications, factors associated with readmissions, as well as the appropriateness of anticoagulation in conjunction with optimal timing of re-starting anticoagulation.[15]

Conclusion: Following the implementation of HOT II protocol developed at our Level I Trauma Center, we were able to effectively address important questions regarding level-of-care and clinical optimization of clinical management for patients with mTBI who were receiving either aspirin, warfarin, or clopidogrel at the time of their injury. Under the guidelines set forth, we were able to reduce the need for ICU admissions, HLOS, and overall cost of hospital care. Further work is warranted in this important area of clinical investigation, including the incorporation of patients receiving direct oral anticoagulants (DOAC) or non-vitamin K oral anticoagulants (NOAC) into our existing paradigm.

  1. Carroll LJ, Cassidy JD, Holm L, Kraus J, Coronado VG; WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. Methodological issues and research recommendations for mild traumatic brain injury: The WHO Collaborating Centre Task Force on Mild Traumatic Brain Injury. J Rehabil Med 2004;43:113-25.
  2. Holm L, Cassidy JD, Carroll LJ, Borg J; Neurotrauma Task Force on Mild Traumatic Brain Injury of the WHO Collaborating Centre. Summary of the WHO collaborating centre for neurotrauma task force on mild traumatic brain injury. J Rehabil Med 2005;37:137-41.
  3. Foks KA, Cnossen MC, Dippel DW, Maas A, Menon D, van der Naalt J, et al. Management of mild traumatic brain injury at the emergency department and hospital admission in Europe: A survey of 71 neurotrauma centers participating in the CENTER-TBI study. J Neurotrauma 2017;34:2529-35.
  4. Marincowitz C, Lecky FE, Townend W, Borakati A, Fabbri A, Sheldon TA. The risk of deterioration in GCS13-15 patients with traumatic brain injury identified by computed tomography imaging: A systematic review and meta-analysis. J Neurotrauma 2018;35:703-18.
  5. Joseph B, Friese RS, Sadoun M, Aziz H, Kulvatunyou N, Pandit V, et al. The BIG (brain injury guidelines) project: Defining the management of traumatic brain injury by acute care surgeons. J Trauma Acute Care Surg 2014;76:965-9.
  6. Tavender EJ, Bosch M, Green S, O'Connor D, Pitt V, Phillips K, et al. Quality and consistency of guidelines for the management of mild traumatic brain injury in the emergency department. Acad Emerg Med 2011;18:880-9.
  7. Patel A, Vieira MM, Abraham J, Reid N, Tran T, Tomecsek K, et al. Quality of the development of traumatic brain injury clinical practice guidelines: A systematic review. PLoS One 2016;11:e0161554.
  8. Joseph B, Pandit V, Haider AA, Kulvatunyou N, Zangbar B, Tang A, et al. Improving hospital quality and costs in nonoperative traumatic brain injury: The role of acute care surgeons. JAMA Surg 2015;150:866-72.
  9. Washington CW, Grubb RL Jr. Are routine repeat imaging and intensive care unit admission necessary in mild traumatic brain injury? J Neurosurg 2012;116:549-57.
  10. Bardes JM, Turner J, Bonasso P, Hobbs G, Wilson A. Delineation of criteria for admission to step down in the mild traumatic brain injury patient. Am Surg 2016;82:36-40.
  11. Kaen A, Jimenez-Roldan L, Arrese I, Delgado MA, Lopez PG, Alday R, et al. The value of sequential computed tomography scanning in anticoagulated patients suffering from minor head injury. J Trauma 2010;68:895-8.
  12. Peck KA, Sise CB, Shackford SR, Sise MJ, Calvo RY, Sack DI, et al. Delayed intracranial hemorrhage after blunt trauma: Are patients on preinjury anticoagulants and prescription antiplatelet agents at risk? J Trauma 2011;71:1600-4.
  13. Schoonman GG, Bakker DP, Jellema K. Low risk of late intracranial complications in mild traumatic brain injury patients using oral anticoagulation after an initial normal brain computed tomography scan: Education instead of hospitalization. Eur J Neurol 2014;21:1021-5.
  14. Uccella L, Zoia C, Perlasca F, Bongetta D, Codecà R, Gaetani P. Mild traumatic brain injury in patients on long-term anticoagulation therapy: Do they really need repeated head CT scan? World Neurosurg 2016;93:100-3.
  15. Hon HH, Elmously A, Stehly CD, Stoltzfus JC, Granson MA, Stawicki SP, et al. Inappropriate preinjury warfarin use in trauma patients: A call for a safety initiative. J Postgrad Med 2016;62:73-9.

   Abstract Number 4 Top

   Decreasing Length of Stay in Patients Admitted to Cardiac Observation for Chest Pain Top

V. Yellapu, A. Elshaikh, B. Kim, I. Taha, C. Ruggeri, J. P. Psaila, S. P. Stawicki, D. Agresti, J. Wilson

Departments of Research and Innovation and1 Internal Medicine, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Year of Submission: 2017

Introduction: Chest pain (CP) is the second most common chief complaint in the Emergency Department (ED) accounting for 8-10 million ED visits annually.[1],[2] The importance of CP as a high-volume chief compliant becomes apparent when one considers that 20-25% of patients with CP may fall into high-risk acute coronary syndrome (ACS) scenario with 39% categorized as low risk patients and the remaining group falling into a moderate risk stratum.[1],[3],[4],[5]

Over the past 20 years, there have been substantial work flow improvements that allow for increasingly more accurate and rapid identification of low-risk patients, which can help with shifting focus to the high risk patients. These methods rely largely on rapid risk stratification using various scores, such as the HEART (history-electrocardiogram-age-risk factors-troponin); GRACE (Global Registry of Acute Coronary Events); and TIMI (thrombolysis in myocardial infarction) scores.[6]

Obtaining a thorough history is of critical importance in the initial ED evaluation of CP patients, which includes an accurate description of the chest pain, associated anginal symptoms, history of coronary artery disease, and presence of any additional risk factors. While certain classical characteristics may be concerning for ischemic CP, approximately one-third of patients with myocardial ischemia will present with atypical symptoms.[7] Combining the historical information with cardiac biomarker testing and electrocardiography (ECG) allows clinicians to accurately stratify patients into high-, intermediate-, and low-risk groups. Appropriate risk stratification allows for additional targeted diagnostic testing, such as serial cardiac markers and more definitive imaging.

While risk factors for coronary artery disease can help identify at-risk patients, they do little to predict the actual occurrence of an active cardiac event. The evaluation for active ischemia starts with an ECG because it is inexpensive, noninvasive, and can be performed rapidly. An ECG can promptly diagnose ST elevation MI and reveal ischemic changes not rising to the level of STEMI. Unfortunately, an ECG within normal limits alone is insufficient to rule out active ischemia. In addition to the ECG, serial cardiac biomarker measurement (specifically troponin) is recommended to aid in the detection of active ischemia. Even in the presence of a “normal” ECG, the detection of elevated troponin levels is highly sensitive for cardiac ischemia.

After a cardiac injury, serum troponin levels become detectable in the patient's circulation within 2 to 3 hours of chest pain onset. Levels will continue to rise and peak between 12 to 48 hours. In most individuals, serum troponin levels return to baseline (0 ng/ml) anywhere between 4 to 10 days. Due to the 2 to 3 hour delay in initial serum troponin elevation repeat testing is essential if the patient had a chest pain episode within 3 hours of presenting to the ED. The expected delayed elevation, peak and decline of serum troponin is a key factor in the diagnosis of myocardial infarction when distinguishing this from other causes of elevated troponin levels.[8],[9]

Since initial ECG findings are present in only 5% of cases, additional diagnostic modalities may be required.[1],[2],[3],[4],[5],[6],[7] There are two pathways that can be used to assess further work-up of CP. An early assessment pathway used for high-risk patients includes use of echocardiography (to identify regional wall motion abnormalities).[7] Other diagnostic tests performed in high-risk patients include cardiac magnetic resonance (MR), coronary computed tomographic angiography (CTA) with resting single photon emission CT (SPECT) and cardiovascular magnetic resonance (CMR) imaging. The second pathway is referred to as the “observational pathway,” and it involves serial analysis of cardiac biomarkers to rule in or out ACS. Patients within this pathway are usually admitted for observation, including the performance of a diagnostic echocardiography, which has a negative predictive value (NPV) of 82% to 98% for acute MI (AMI).[7],[10]

Low-risk CP patients that are admitted for observation for >24 hours or admitted for diagnostic work-up may be considered to represent suboptimal utilization of hospital resources. With risk stratification scores such as HEART having a negative predictive value of 97-99% and serial troponin associated with NPV of 99.5%, there exists a substantial opportunity to better manage low risk patients.[1],[2],[3],[11]

In 2009, the Centers for Medicare and Medicaid Services (CMS) stated that patients can be categorized under “observation” status if they were in the hospital for 48 hours or less. Patient stays >48 hours are considered “inpatient stays”. This time constraint is especially difficult when it comes to managing CP patients. With low-risk CP patients there is high chance of exceeding the allotted observation time as one awaits diagnostic test results (echo, troponin, etc.). Thus, we implemented targeted strategies to expedite echocardiograms and increase ordering of outpatient echocardiograms.[12],[13]

Methods: This was a quality improvement (QI) project focused on reducing the duration of clinical observation in ACS patients. We obtained data from software Insights® (CLINclient, Austin, TX) that provided detailed information on all patients with CP. We used the APR-DRG (All Patients Refined Diagnosis Related Groups) code 203 to identify all CP patients that were admitted to the ED, observation, or inpatient. We then narrowed our data pool to only include patients admitted to observation. The data were obtained for the following time periods: (a) prior to intervention (June 2016-November 2017) and (b) post-intervention (January 2018-June 2018). An order set, workflow, and educational directives were created and used to educate our staff regarding management of low risk cardiac patients. The order set and educational interventions were introduced during the months of November and December 2017 (e.g., the transition period).

Using guidelines set by the American College of Cardiology (ACC) and the HEART score, we asked staff to stratify patients with CP into low-risk and high-risk categories. The full set of criteria used to define low-risk patients are listed in [Table 1]. Accompanying flow diagram is shown in [Figure 1]. After the decision was made to place a patient in observation we initiated the order set associated with either Low-Risk ACS or High-Risk ACS. This included serial troponin assessments, bedside evaluation for changes in clinical status or symptoms, and continuous ST-segment monitoring. Data were re-assessed in June 2018 to identify the effect of the intervention. We used Microsoft Excel® (Redmond, Washington, USA) to calculate the descriptive statistics. Student's t-test was utilized to analyze differences between normally distributed groups, with significance set at α=0.05.
Figure 1: This figure shows the standardized workflow introduced to both Emergency Department and admitting physicians

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Table 1: Criteria utilized to identify patients classified as low-risk for acute coronary syndrome

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Results: Data collected prior to the intervention (July 2016-November 2017) showed that the average duration of observation for CP was 25.47±2.56 hours. Within 2 months of the intervention, we were able to decrease the average duration of observation to <24 hours. After intervention, we collected data from 1,276 patients with 289,369 hours of total observation between January 2018 and June 2018. After the interventions had been implemented, the average duration of observation between January-June 2018 decreased to 20.69±2.14 hours. When comparing mean observation hours in the first 6 months of 2017 to the first 6 months of 2018, there was a statistically significant decrease in the duration of CP observation [p<0.004, [Table 2]. [Figure 2] shows the mean monthly CP observation hours over time. [Figure 3] shows the aggregate CP observation times pre-and post-intervention.
Table 2: This table shows the significant decrease of chest pain observation hours post-intervention

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Figure 2: This graph shows length of stay averages for each month and the decrease in length of stay after the implementation of our intervention (arrow)

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Figure 3: This graph shows length of stay averages based on financial year. Based on the graph we can see that there is a decrease to <24 h

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Discussion: There has been an increasing trend to reduce the length of stay among low severity patients to improve institutional operations and associated resource utilization/performance metrics.[14],[15] Previously published ED data indicate that CP is consistently within the top 10 complaints for admission.[16],[17],[18],[19] It has also been noted that the work-up of CP can be costly, with up to $12 billion spent annually across the nation.[18],[20] The current QI project demonstrated that simple interventions, such as creating appropriate order sets and educating patient-facing staff, can make a significant impact.

Although our interventions may superficially appear simplistic and trivial, their implementation resulted in substantial reduction in hospital resource utilization and financial savings, without any appreciable effect on the quality of patient care. The current QI project followed pertinent ACC guidelines very closely, which was important in achieving our quality, safety, and resource reduction goals rapidly. More specifically, we were able to see sustained decreases in the duration of CP observation period within 2 months of programmatic implementation. Whereas prior to the implementation the average duration of observation was >24 hours, the average post-implementation duration of CP observation decreased to anywhere between 18.1-23.6 hours. This allows us to meet the CMS cut-off for observation status in much greater proportion of cases.

When a patient has to be transitioned from “observation” to “inpatient” status, the cost of this change can range between $1,000-$2,200.[21],[22] While there is no significant change in direct patient care, the expense that is incurred by both the patient and institution varies greatly. Previously published studies of similar scenarios demonstrate that a substantial opportunity for healthcare savings exists when the process is optimized to avoid unnecessary “observation” to “inpatient” transitions.[23],[24]

The current report has some important limitations. First, our analyses are based on a retrospective review of a short-term, single-institution intervention. Thus, potential exists for biases inherent to this type of study design. Second, interventions described herein may not be readily applicable to other institutions and/or settings. Third, because we focused on the operational aspects of the CP observation process, our analyses did not include several important factors, such as individual patient acuity, demographics, and other clinical determinants that may be relevant to our primary outcomes.

Conclusion: By simplifying workflow, creating and streamlining a specialized order set, and educating staff we were able to reduce the mean duration of CP clinical observation hours by nearly 10%, sustainably ensuring patient discharge in <24 hours. Associated non-trivial estimated cost savings were noted. The above were accomplished without compromising quality of care.

  1. Amsterdam EA, Kirk JD, Bluemke DA, Diercks D, Farkouh ME, Garvey JL, et al. Testing of low-risk patients presenting to the emergency department with chest pain: A scientific statement from the American Heart Association. Circulation 2010;122:1756-76.
  2. Howell JM, Eddy OL, Lukens TW, Thiessen ME, Weingart SD, Decker WW, et al. Clinical policy: Critical issues in the evaluation and management of emergency department patients with suspected appendicitis. Ann Emerg Med 2010;55:71-116.
  3. Laureano-Phillips J, Robinson RD, Aryal S, Blair S, Wilson D, Boyd K, et al. HEART score risk stratification of low-risk chest pain patients in the emergency department: A systematic review and meta-analysis. Ann Emerg Med 2019;74:187-203.
  4. Lee TH, Goldman L. Evaluation of the patient with acute chest pain. N Engl J Med 2000;342:1187-95.
  5. Selker HP, Beshansky JR, Griffith JL, Aufderheide TP, Ballin DS, Bernard SA, et al. Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial. Ann Intern Med 1998;129:845-55.
  6. Poldervaart JM, Langedijk M, Backus BE, Dekker IMC, Six AJ, Doevendans PA, et al. Comparison of the GRACE, HEART and TIMI score to predict major adverse cardiac events in chest pain patients at the emergency department. Int J Cardiol 2017;227:656-61.
  7. Emergency Department Patients With Chest Pain Writing Panel, Rybicki FJ, Udelson JE, Peacock WF, Goldhaber SZ, Isselbacher EM, et al. 2015 ACR/ACC/AHA/AATS/ACEP/ASNC/NASCI/SAEM/SCCT/SCMR/SCPC/SNMMI/STR/STS appropriate utilization of cardiovascular imaging in emergency department patients with chest pain: A joint document of the American College of Radiology Appropriateness Criteria Committee and the American College of Cardiology appropriate use criteria task force. J Am Coll Radiol 2016;13:e1-e29.
  8. Mirkin J, Radecki R, Spiegel R. Deriving peace of mind: In search of a fifth-generation troponin testing threshold to safely rule out acute myocardial infarction: March 2019 annals of emergency medicine journal club. Ann Emerg Med 2019;73:317-9.
  9. Tahir K, Pauley E, Dai X, Smith SC Jr, Sweeney C, Stouffer GA. Mechanisms of ST elevation myocardial infarction in patients hospitalized for noncardiac conditions. Am J Cardiol 2019;123:1393-8.
  10. Berman DS, Hachamovitch R, Shaw LJ, Friedman JD, Hayes SW, Thomson LE, et al. Roles of nuclear cardiology, cardiac computed tomography, and cardiac magnetic resonance: Assessment of patients with suspected coronary artery disease. J Nucl Med 2006;47:74-82.
  11. Carlton EW, Khattab A, Greaves K. Identifying patients suitable for discharge after a single-presentation high-sensitivity troponin result: A comparison of five established risk scores and two high-sensitivity assays. Ann Emerg Med 2015;66:635-450.
  12. Baugh CW, Schuur JD. Observation care-high-value care or a cost-shifting loophole? N Engl J Med 2013;369:302-5.
  13. Feng Z, Wright B, Mor V. Sharp rise in Medicare enrollees being held in hospitals for observation raises concerns about causes and consequences. Health Aff (Millwood) 2012;31:1251-9.
  14. Stambough JB, Nunley RM, Curry MC, Steger-May K, Clohisy JC. Rapid recovery protocols for primary total hip arthroplasty can safely reduce length of stay without increasing readmissions. J Arthroplasty 2015;30:521-6.
  15. Mahler SA, Riley RF, Hiestand BC, Russell GB, Hoekstra JW, Lefebvre CW, et al. The HEART Pathway randomized trial: Identifying emergency department patients with acute chest pain for early discharge. Circ Cardiovasc Qual Outcomes 2015;8:195-203.
  16. Owens PL, Barrett ML, Gibson TB, Andrews RM, Weinick RM, Mutter RL. Emergency department care in the United States: A profile of national data sources. Ann Emerg Med 2010;56:150-65.
  17. Than M, Aldous S, Lord SJ, Goodacre S, Frampton CM, Troughton R, et al. A 2-hour diagnostic protocol for possible cardiac chest pain in the emergency department: A randomized clinical trial. JAMA Intern Med 2014;174:51-8.
  18. Foy AJ, Liu G, Davidson WR Jr, Sciamanna C, Leslie DL. Comparative effectiveness of diagnostic testing strategies in emergency department patients with chest pain: An analysis of downstream testing, interventions, and outcomes. JAMA Intern Med 2015;175:428-36.
  19. Pitts SR, Niska RW, Xu J, Burt CW. National Hospital Ambulatory Medical Care Survey: 2006 emergency department summary. Natl Health Stat Report 2008;7:1-38.
  20. Limkakeng AT, Carr C, Mehrotra A, Smith L, Pajewski N, Burke GL, et al. chest pain care patterns across the Carolinas: Determining the readiness for widespread HEART pathway dissemination Ann Emerg Med 2017;70:S1.
  21. Weingarten SR, Riedinger MS, Conner L, Lee TH, Hoffman I, Johnson B, et al. Practice guidelines and reminders to reduce duration of hospital stay for patients with chest pain. An interventional trial. Ann Intern Med 1994;120:257-63.
  22. Forberg JL, Henriksen LS, Edenbrandt L, Ekelund U. Direct hospital costs of chest pain patients attending the emergency department: A retrospective study. BMC Emerg Med 2006;6:6.
  23. Kontos MC, Schmidt KL, McCue M, Rossiter LF, Jurgensen M, Nicholson CS, et al. A comprehensive strategy for the evaluation and triage of the chest pain patient: A cost comparison study. J Nucl Cardiol 2003;10:284-90.
  24. Mikhail MG, Smith FA, Gray M, Britton C, Frederiksen SM. Cost-effectiveness of mandatory stress testing in chest pain center patients. Ann Emerg Med 1997;29:88-98.

   Abstract Number 5 Top

   Development of a Post-Acute Skilled Nursing Facility Network: A Triple AIM Approach Top

A. A. Mira, P. Kaur, L. Giovanni, L. Kohler, S. P. Stawicki, V. Wagner, M. Wilson

Scientific contributors (alphabetically): J. Amon, D. Ankrom, M. Barnhart, F. Botek, J. Broniec, J. DeArmas, H. Depaolis, C. Fleckenstein, C. Lewis, M. L. Pagliante-Webb, D. Sabol

Departments of Ambulatory Care Management, St Luke's Home Health and Hospice, BPCI Program, Warren Physical Therapy, Finance, Heart Center, Care Management, St. Luke's Hospital, Warren Campus, St Luke's Senior Care, St Luke's Care Network, Phillipsburg, New Jersey; Quality, Department of Research and Innovation; St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Year of Submission: 2017

Introduction: The Affordable Care Act of 2010 called for historic revisions to the health care delivery model. The Federal Government charged the Centers for Medicare and Medicaid (CMS) with developing and testing new payment models under the Centers for Medicare and Medicaid Innovation (CMMI) using the Triple AIM goals: (a) Improving the health of populations; (b) Improving the patient experience through quality and satisfaction; and (c) decreasing the per capita cost.[1],[2]

Innovative models of care include Accountable Care Organizations (ACO), Bundle Payment for Care Improvement (BPCI), Medicare Shared Savings Programs (MSSP), and Patient Centered Medical Homes (PCMH). Historically, Medicare Fee-for-Service (FFS) care was not patient-centric, and has been associated with inefficient spending within the Medicare system. In response, BPCI was introduced in an attempt to encourage healthcare organizations to increase accountability for both acute and post-acute care (up to 90 days after hospital discharge) as a single episode.[1] More specifically, the episode of care is defined as all Medicare services provided by an entity entirely owned or run by the admitting hospital in the 72 hours before admission, the hospital facility services delivered during the hospital stay, and services offered during the 90-day post discharge period (PDP) at any of the admitting hospital's locations.[2] Incorporated in the 90-day PDP are inpatient hospital readmissions, skilled nursing facility (SNF) services, home health agency (HHA) services, inpatient rehabilitation facility (IRF) services, long-term care hospital services, hospital outpatient services, independent outpatient therapy services, clinical laboratory services, durable medical equipment, and Medicare Part B drugs and pharmacy services.[2] One in five Medicare beneficiaries is readmitted to the hospital within 30 days of discharge while one in three is admitted within 90 days.[3] Moreover, approximately two-thirds of medical patients and about half of surgical patients were readmitted or died within the first year following discharge. Also, the re-hospitalized patients stayed on average 0.6 days longer than patients in the same diagnosis related group (DRG).[3] One recent study found that only 49% of patients had timely follow-up within 30 days, and those without follow-up were 10 times more likely to be readmitted.[4]

According to the Medicare Payment Advisory Commission, 12% of readmissions were potentially avoidable, and an effective method of preventing even 10% of these readmissions could result in annual Medicare savings of $1 billion.[5] Actively assessing various models across different provider types, length of time of the bundle (30-, 60-, 90-days post discharge), as well as prospective versus retrospective payment strategy can help guide both the optimization of care and overall cost reduction.[6] Within the US healthcare matrix, bundled payment programs offer unique prospect for developing interprofessional partnerships to transform the system of care and improve patient outcomes. Collaborative team approaches can foster a positive culture, which can improve care through cross-continuum support and incorporation of evidence-based best practices.[1],[7] The aim of this quality improvement project was to decrease average SNF length of stay (LOS) to 20 days and decrease readmissions by 5% in the BPCI population by 2016 through building a robust, patient-centric, SNF network.

Methods: In 2013, members of the BPCI Executive Leadership and Post-Acute BPCI Committees analyzed pertinent referral patterns, CMS claims data, and CMS 5-star ratings. The team selected 16 area SNFs, sent requests for information (RFI), and met collectively to explain the program goals and to propose a collaborative framework to improve the quality and optimize care within the healthcare system. The customer-centric aspects of this initiative included the creation of a positive experience for the patients and families.

Out of the gate, six facilities elected to work collaboratively to improve the regional care of our Medicare FFS patient population. An additional 10 institutions participated in regular quarterly meetings, without fully joining the initiative. The overarching goal of the quarterly briefings was to discuss common areas of concern, such as handoffs/communication, transfer of information, and data sharing. Additionally, the meetings allowed for an open forum where facilities could offer what their “best practices” are at the time. It was also an opportunity for discussing basic operational concerns such as staffing, education and recruitment-related issues. The group's initial meetings included discussions of Practitioner Orders for Life Sustaining Treatment (POLST) and Nurses Improving Care for Health system Elders (NICHE). The team was truly interdisciplinary, including nursing, rehabilitation, marketing, and medical leadership. Targeted education and awareness campaign was provided for participating facilities.

In all, we developed eight clinical pathways for the most common co-morbid conditions seen in the SNFs which included. This included the pre-admission BOOST tool assessment [Exhibit 1]; care planning incorporating BOOST metrics starting on arrival; 72-hour care plan [including the length of stay (LOS) estimation]; 72-hour meeting with the patient and family to share the care plan and discuss estimated discharge date(s); clinical assessment; discharge planning; and physician-to-physician hand-off involving high-risk patients.

The physician team was led by the Chief of Geriatrics. The group focused on improving the quality of care and promoting patient centric outcomes, through setting the overall clinical strategy, post-acute care management goals, as well as providing the foundation of clinical performance and practice standards. Specific measures included the implementation of clinical pathways and initiatives that measure, assess and improve the quality and consistency of care delivered to all patients. Physicians were also involved in the interdisciplinary team meetings to address patients' physical, medical and social needs. Physicians used the BOOST tool on admission [Exhibit 1] to identify patients at risk for recurrent emergency department (ED) visits, hospitalization and increased LOS. Patients were monitored closely and managed aggressively for their clinical conditions. In addition, the group emphasized physician-to-physician handoffs on admission to SNF and other transitions-of-care. Finally, support was provided for nurse communication with on-call providers using the Situation, Background, Assessment, Recommendation (SBAR) tool, especially in the management of frail, complex elderly patients.

Staff development and educational initiatives led by providers were implemented to help reinforce nursing clinical skills in post-acute care. Our Health Network's congestive heart failure (CHF) and COPD coordinators also assisted in educating the SNFs on disease signs, symptoms, and management pathways. This included outreach to the SNFs regarding individual patients and weekly SNF visits by an advanced practitioner specializing in CHF. We also utilized a field registered nurse (FRN) that attended weekly utilization management meetings at the SNFs. This allowed the Network to have oversight of the patient care experience. The FRN was able to identify areas of compliance with pathways, clinical issues, and teaching opportunities that could be escalated to the Post-Acute BPCI Committee or Chief of Geriatrics as appropriate.

Institution's Home Health involved RNs in the discharge planning meetings at several SNFs. These meetings occurred prior to the patient's discharge, included each discipline reporting on the progress of the patient, and set individual patient goals ahead of return home. Some SNFs had meetings with the patient and family. Early in the BPCI process the meetings took an educational focus, where home health professionals assisted patients, families and SNF staff to understand what types of services home health could provide post-discharge. We also provided SNF patients with preferred provider lists, focusing on CMS 5-star rating and physician availability. Case managers received scripts to accurately describe the quality and outcomes of our preferred provider network.

It was also decided that all data would be reviewed by the Post-Acute BPCI Committee on a monthly basis via monitoring claims and manually collected SNF LOS information. Adherence to clinical pathways would be audited on a quarterly basis [Exhibit 2]. Additionally, SNFs were required to submit pre-defined metrics [Exhibit 3] on a quarterly basis, again focusing on identifying and tracking factors that may be associated with longer LOS and higher readmissions.

Results: In terms of general results, our team was able to achieve the stated goal of building a robust preferred SNF provider network. Within this network, providers achieved an average LOS of 18 days and a 90-day readmission rate of 27.4%. The SNF LOS decline has been sustained for an extended period [Figure 1]. Similarly, the readmission rate has been maintained around 20-30% [Figure 2]. There is noted variability in the readmission rate; however, it is well below the historic benchmark. When combined, the above results contributed to significant cost savings, well in excess of $10 million (more detailed estimates are not available due to certain regulatory data sharing restrictions).
Figure 1: Line graph showing quarterly skilled nursing facility length of stay performance. Since program inception, the average monthly length of stay has been consistently decreasing and remained well below adjusted historic (baseline) levels

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Figure 2: Line graph showing quarterly skilled nursing facility readmission rates. Although variable, the results were consistently better than the adjusted historic baseline (horizontal green line)

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Discussion: As the healthcare regulatory and legislative landscape continues to evolve, there is need to update and optimize existing clinical pathways, with ongoing focus on generating value, quality, and better clinical results. The relative lack of integration among different teams, combined with inadequate communication among team members during transitions of care, can result in delays and suboptimal patient outcomes. Approaches to redesigning delivery of care involve the development of dedicated, patient-centric clinical pathways as well as the focus on optimization of patient experience. Innovations in care coordination highlight the development of effective longitudinal care models.

Besides developing eight clinical pathways designed to address the most common co-morbid conditions seen in the SNFs (pre-admission BOOST tool, care planning incorporating BOOST metrics starting on arrival, 72-hour care plan meeting incorporating LOS discussion, 72-hour meeting with the patient and family sharing the care plan and estimated discharge date, clinical assessment, discharge planning, and physician to physician hand-off on high risk patients), participating physicians also engaged in the interdisciplinary team meetings to address patients' physical, medical and social needs. This resulted in significant improvement in patient care planning and coordination, and is thought to be a major contributor to decreased LOS and re-hospitalizations.

Physicians are perfectly positioned to identify patients at risk for recurrent emergency department (ED) visits, hospitalizations and increased LOS by utilizing the BOOST tool on SNF admission. The “Project BOOST” paradigm was established for use among clinicians, with an emphasis on ensuring patient-centered care, appropriate risk estimation, fostering teamwork, and ultimately achieving successful discharge process. As of 2013, more than 180 sites have implemented Project BOOST.[8] Early data from six pilot sites that implemented BOOST revealed a reduction in 30-day readmissions from 14.2% to 11.2%, a 3% decrease and a 21% reduction in 30-day all-cause readmission rates.[9] Piedmont Hospital in Atlanta described a 9.1% absolute reduction in re-hospitalization rate for patients <70 years of age to just 3.97% from 13.05%. Readmissions for patients >70 years of age were also reduced by 4.73%, to 11.17% from 15.9%.[10],[11] Based on another health center experience in Missouri, significant drop in 30-day readmissions was accompanied by a substantial increase in patient satisfaction.[9]

Initiatives such as physician-to-physician handoffs on admission to nursing homes or subsequent discharge to community; and nurse communication with on-call providers using the SBAR tool were used for the management of frail and medically complex elderly patients. This tool is intended to provide nurses with sufficient information to prevent uninformed and potentially dangerous actions, such as unnecessary tests, patient transfers, or other interventions. At the same time, SBAR empowers nurses to become more confident in their clinical evaluation, resulting in improved communication and collaboration among care team members.[12],[13],[14]

Within our new paradigm, patients are monitored closely and managed proactively to optimize their clinical outcomes. Screening and identifying high-risk patients allowed our team to decrease exacerbations of chronic conditions, and thus associated complications. We also placed increased emphasis on palliative care discussions. Research indicates that supportive and palliative care can lower re-hospitalization rates and can lead to increased patient satisfaction.[15] Home health care constitutes another important component that is critical to improving outcomes while reducing hospitalizations.[16]

Staff development and education initiatives led by our providers have improved nursing clinical skills in post-acute care, enhancing both patient safety and quality of care. During earlier pilot studies, feedback was provided that teams saw their mentors as critical catalysts of change. It was noted that the customized mentoring was particularly beneficial and effective, and provided accountability while encouraging innovation.[8] The enhanced mentoring model facilitating the sharing of knowledge and education with staff and case management offered yet another opportunity for early intervention to reduce re-hospitalization risk among high-risk patients.[17],[18] This education also helps reinforce the concept that clinical goal completion can continue to occur at home, and can be facilitated through tasks carried out by the patient-caregiver-home visiting staff collaborative team.[19] To corroborate this point, recently reported data show that the proportion of 30-day readmissions was higher among patients discharged to a SNF compared to patients discharged to home (27% vs. 21%, respectively).[20] In another study, patients who received multidisciplinary education about their disease, physical therapy interventions, and dietary modifications, experienced nearly 50% lower 30-day and 90-day readmissions when compared to the non-treatment/control group.[21]

As part of our overall intervention, the Preferred SNF network has been adopted by Care Network (CN) which includes over 1,300 physicians and advanced practice clinicians, as well as home health and durable medical suppliers. This high performing structure was developed to improve clinical outcomes, patient experience, increase operational efficiency, all while positioning the organization to succeed in the value-based reimbursement regime. In a retrospective case review looking at transitions-of-care in heart failure patients, the presence of 'transitionalist' teams (board certified in Family Medicine with special training in CHF management) was highly effective in patient education, information sharing, and handoff communication.[22] In fact, the same study demonstrated that 30-day, all-cause readmissions decreased significantly (4.1% from 26%) while 90-day, all-cause readmissions decreased from 69.8% to 27.3%.[22] To accomplish its stated mission of improving health through prevention and chronic disease management, the CN invited participation from providers who share the same vision of high quality, cost efficient care, delivered in a patient-centered manner. As our CN looked to include post-acute care facilities, the decision was made to incorporate the BPCI preferred providers. The performance outcomes during the post-acute phase, monitored by the Post-Acute BPCI Committee and acted on by the preferred providers, naturally align with CN commitment to quality and care optimization.

As outlined earlier in this report, each patient was provided with an informational SNF packet, highlighting providers with CMS 5-star rating and availability. To operationalize this objective, case managers received specialized training that helped facilitate communication about the high quality and clinical outcomes within our preferred provider network. CMS star rating is an important tool that can help make informed care decisions, where a rating of 1 or 2 indicates “below average” performance and 4 or 5 indicates “above average” quality.[23],[24] In one retrospective analysis, an inverse relationship was found between CMS star rating and incidence of readmissions in patients with total knee arthroplasty (TKA) and total hip arthroplasty (THA).[25] Consequently, as the overall SNF star rating approached 5 stars, the incidence of all-cause 30-day readmissions decreased from 6.4% to 5.0% for TKA and from 9.1% to 6.2% for THA.[25] Comparable trends were seen among patients who were followed up to 90 days, with relative reductions of 25.9% for TKA and 28.0% for THA. The usage – and significance – of star ratings is likely to increase as our healthcare system evolves towards the value-driven paradigm.[26]

We also utilized FRNs that attended weekly utilization management meetings at the SNFs. This allowed the Network to maintain oversight, and input into, the patient care experience. The “overall staffing rate” measure was used to evaluate SNF nursing quality. It is a combination of “RN hours per SNF resident per day” and “total staff hours per SNF resident per day”. It was previously reported that as nursing staff ratings improved, both 30-day and 90-day all-cause readmissions decreased in retrospective analyses of TKA and THA patients.[25] Other reports corroborate the relationship between nurse staffing and favorable patient outcomes.[27] A substantial proportion of readmissions have been attributed to lack of proper provider-SNF communication about discharge orders, especially in resource- and staffing-limited environments.[28] In one randomized controlled study of readmissions, 23% of transfers were thought to be preventable and 36% could have been managed within the SNF, with estimated readmission “preventability” of between 31% and 39%.[29]

Our current data demonstrate that we need to learn more about the various potential root causes for readmissions, up to and including the so-called social determinants of health. To address these important questions, we plan to re-assess our transitions out of the SNF and the utilization of the BOOST tool to develop a better understanding of some of the key nuances related to transitions-of-care within our SNF network. To that end, our ambulatory care management team continually develops new goals and targeted interventions to improve patient experience and quality of care within each 90-day episode. Additionally, we are looking at integration of the preferred home health network using a methodology similar to that used for our SNFs. This, it is hoped, might lead to enhanced harmonization of care and improved quality of life. Inherent to this approach is the expanded role of the patient and/or family members in providing such extended care. This will require significant educational efforts to enhance the understanding of the disease process and management.[30],[31] Assisting the patient and family as one unit is imperative to delivering exceptional home care, especially in the context of transitions-of-care.[31] Accordingly, we began constructing clinical pathways within Network Home Health and are actively planning the incorporation of ambulatory care managers into the established framework of weekly team meetings to better facilitate patient-centered care transitions.

Care coordination must be a continuous process that begins prior to acute illness, continues during the hospitalization, and seamlessly facilitates patient transition back into the community. The presence of seamless communication and collaboration between hospitals, home care agencies, geriatric specialists, primary care physicians, and SNFs will be critical to improving both processes and outcomes.

Conclusions: Measured benefits from CMS BPCI at our tertiary referral center include decreased LOS and a 90-day readmission rate, with corresponding substantial cost savings. Ensuring the implementation of clinical pathways and initiatives designed to assess and improve the quality and consistency of care is fundamental in improving patient outcomes, care quality metrics and overall cost-effectiveness.

  1. Hardin L, Kilian A, Murphy E. Bundled payments for care improvement: Preparing for the medical diagnosis-related groups. J Nurs Adm 2017;47:313-9.
  2. Centers for Medicare and Medicaid Services. Bundled Payments for Care Improve – Ment (BPCI) Initiative: General Information. Available from: http://innovation.cms.gov/initiatives/bundled-payments/index.html. [Last accessed on 2019 Sep 05].
  3. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med 2009;360:1418-28.
  4. Misky GJ, Wald HL, Coleman EA. Post-hospitalization transitions: Examining the effects of timing of primary care provider follow-up. J Hosp Med 2010;5:392-7.
  5. Medicare Payment Advisory Commission. Report to the Congress: Promoting Greater Efficiency in Medicare. Washington: Medicare Payment Advisory Commission; 2014. p. 189.
  6. Press MJ, Rajkumar R, Conway PH. Medicare's New Bundled Payments: Design, Strategy, and Evolution. JAMA 2016;315:131-2.
  7. Brandt B, Lutfiyya MN, King JA, Chioreso C. A scoping review of interprofessional collaborative practice and education using the lens of the Triple Aim. J Interprof Care 2014;28:393-9.
  8. Williams MV, Li J, Hansen LO, Forth V, Budnitz T, Greenwald JL, et al. Project BOOST implementation: Lessons learned. South Med J 2014;107:455-65.
  9. Robeznieks A. Project BOOST Aims to Cut Readmission Rates; 2010.
  10. Subramanian S, Whitcomb W, Vidyarthi A. Project BOOST: A Return on Investment Analysis. BOOSTing Care Transitions; 2010. Available from: http://scha.org. [Last accessed on 2019 Dec 03].
  11. Andrews M. Hospitals Aim to Reduce the Number of Patients Readmitted after Discharge. Washington Post; 2011.
  12. Ouslander JG, Bonner A, Herndon L, Shutes J. The interventions to reduce acute care transfers (INTERACT) quality improvement program: An overview for medical directors and primary care clinicians in long term care. J Am Med Dir Assoc 2014;15:162-70.
  13. Achrekar MS, Murthy V, Kanan S, Shetty R, Nair M, Khattry N. Introduction of situation, background, assessment, recommendation into nursing practice: A prospective study. Asia Pac J Oncol Nurs 2016;3:45-50.
  14. Müller M, Jürgens J, Redaèlli M, Klingberg K, Hautz WE, Stock S. Impact of the communication and patient hand-off tool SBAR on patient safety: A systematic review. BMJ Open 2018;8:e022202.
  15. Brumley R, Enguidanos S, Jamison P, Seitz R, Morgenstern N, Saito S, et al. Increased satisfaction with care and lower costs: Results of a randomized trial of in-home palliative care. J Am Geriatr Soc 2007;55:993-1000.
  16. Clarke JL, Bourn S, Skoufalos A, Beck EH, Castillo DJ. An innovative approach to health care delivery for patients with chronic conditions. Popul Health Manag 2017;20:23-30.
  17. Boxer RS, Dolansky MA, Frantz MA, Prosser R, Hitch JA, Piña IL. The bridge project: Improving heart failure care in skilled nursing facilities. J Am Med Dir Assoc 2012;13:83.e1-7.
  18. Ouslander JG, Berenson RA. Reducing unnecessary hospitalizations of nursing home residents. N Engl J Med 2011;365:1165-7.
  19. Adib-Hajbaghery M, Maghaminejad F, Abbasi A. The role of continuous care in reducing readmission for patients with heart failure. J Caring Sci 2013;2:255-67.
  20. Manemann Sheila M, Chamberlain Alanna M, St. Sauver J, Weston Susan A, Jiang R, Roger Veronique L. Abstract 15301: Skilled Nursing facilities and 30-day hospital readmissions in heart failure. Circulation 2014;130 Suppl 2:A15301.
  21. Howie-Esquivel J, Carroll M, Brinker E, Kao H, Pantilat S, Rago K, et al. A strategy to reduce heart failure readmissions and inpatient costs. Cardiol Res 2015;6:201-8.
  22. Ota KS, Beutler DS, Gerkin RD, Weiss JL, Loli AI. Physician-directed heart failure transitional care program: A retrospective case review. J Clin Med Res 2013;5:335-42.
  23. Centers for Medicare and Medicaid Services Nursing home Compare; 2018. Available from: https://www.medicare.gov/nursinghomecompare/search.html. [Last accessed on 2018 Oct 20].
  24. Design for Nursing Home Compare Five-Star Quality Rating System: Technical Users' Guide. Centers for Medicare and Medicaid Services; 2018. Available from: https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/CertificationandComplianc/downloads/usersguide.pdf. [Last accessed on 2019 Sep 05].
  25. Kimball CC, Nichols CI, Nunley RM, Vose JG, Stambough JB. Skilled Nursing Facility Star Rating, Patient Outcomes, and Readmission Risk After Total Joint Arthroplasty. J Arthroplasty 2018;33:3130-7.
  26. Heath S. How to effectively use the CMS hospital quality star-rating. In. How to Effectively Use the CMS Hospital Quality Star Ratings. 2018. Available from: https://patientengagementhit.com/news/how-to-effectively-use-the-cms-hospital-quality-star-ratings. [Last accessed on 2019 Dec 03].
  27. Blegen MA, Goode CJ, Spetz J, Vaughn T, Park SH. Nurse staffing effects on patient outcomes: Safety-net and non-safety-net hospitals. Med Care 2011;49:406-14.
  28. Jacobs B. Reducing heart failure hospital readmissions from skilled nursing facilities. Prof Case Manag 2011;16:18-24.
  29. Ouslander JG, Naharci I, Engstrom G, Shutes J, Wolf DG, Alpert G, et al. Root cause analyses of transfers of skilled nursing facility patients to acute hospitals: Lessons learned for reducing unnecessary hospitalizations. J Am Med Dir Assoc 2016;17:256-62.
  30. Adams RJ. Improving health outcomes with better patient understanding and education. Risk Manag Healthc Policy 2010;3:61-72.
  31. Seow H, Bainbridge D. A review of the essential components of quality palliative care in the home. J Palliat Med 2018;21:S37-44.

   Abstract Number 6 Top

A Rehabilitation-focused, Multidisciplinary, Patient Quality Process Aimed at Reducing Discharges to Rehab Facilities after Total Joint Arthroplasties

A. Verghese, J. Rowbotham, J. Patanella, B. Owens, V. Yellapu

Scientific contributors (alphabetically): W. Delong, J. Florkowski, J. Gregus, K. Hinds, A. Matika, A. Mazza, K. Mooney, C. Nevils, M. Noll, M. Paglianite-Webb, L. Paulshock, J. Ruland, T. Sakasits, S. P. Stawicki, A. Storm, K. Yoder

Departments of Inpatient Rehabilitation, Quality Resources, Physical Medicine, Research and Innovation, Emergency, Orthopedics, Anesthesia, Clinical Analytics, Patient Care, Accounting, Case Management, Surgical Services, St. Luke's University Health Network, Bethlehem, Pennsylvania, USA

Year of Submission: 2018

Introduction: Total joint Arthroplasties (TJA) are among the most widely performed elective surgeries in the United States, with over 1 million TJAs done each year.[1] It is estimated that by 2030, there will be a >170% increase in Total Hip Arthroplasty (THA) and a >670% increase in Total Knee Arthroplasty (TKA), corresponding to an estimated 4 million patients undergoing a THA and 7.4 million undergoing a TKA.[2],[3],[4] Medicare data from 1997-2009 show that >50% of patients discharged from hospitals were sent to a rehabilitation facility and physical therapy (PT) costs rose during this period.[5] By 2009, 40.5% of TKA patients and 40.8% of THA patients were discharged with home healthcare.[4]

The Center for Medicare and Medicaid Services (CMS) created the Bundled Payments for Care Improvement Initiative (BPCI) to optimize costs and improve quality of care.[2] The implementation of BPCI and shared risk programs resulted in declining reimbursements and an increased demand for high value care. This, in turn, necessitated better quality and cost-effective strategies for TJA. Discharge to a skilled nursing facility (SNF) as opposed to home discharge is associated with a significant risk of post-surgical complications and re-admissions, inclusive of patients with multiple medical co-morbidities.[6],[7],[8] It was in this context that a concerted effort was made to reduce discharge rate to rehabilitation facilities. A review of literature established that implementing an Integrated Care Pathway (ICP) for TJA could help achieve high value care.[9] Using data provided by Premier (Charlotte, North Carolina, USA) we created an ICP for TJA to coordinate the care continuum from the pre-operative surgical visit to final follow-up care.

Aims and Objectives: Baseline analysis of our hospital's TJA population data between September 2016 and May 2017 showed a discharge rate to rehabilitation facility (RF) of 25.9%. The goal was to reduce the hospital-to-RF discharge rate by 30% over a one-year period for patients who underwent elective TJA.

Methods: We examined TJA data for 8 months prior to our intervention – starting September 2016 and ending May 2017 – to identify our baseline discharges to RF. Additional baseline metrics of length of stay (LOS) and readmission rate were tracked before and after the intervention.

We identified key areas of opporunity including stakeholder buy-in, realignment of rehabilitation resources, the development of rehab-related multidisciplinary process maps [Figure 1], and the implementation of a rehabilitation clinical pathway. Rehab-related education and communication processes were standardized, both in the context of the patient and the care team. Therapy Milestones and Mobility (TMM) tracker was introduced to motivate patients with their functional progress, to follow-up on patient or staff-related missed opportunities for mobilization, and to reinforce the expectation for discharge home. A Patient Rounding Tool (PRT) was used to track the patient's experience of rehab care.
Figure 1: Pre and post-surgical process maps

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In order to set our goals, we compared our baseline data to the top 10% of hospitals nationally. The benchmark we strived to achieve after our intervention was: home health (HH) discharge rate of 54.5% and skilled nursing facility (SNF) discharge rate of 22.3%. Rehab rates include SNF and Acute Rehab Centers (ARC) discharge rates.

We also looked at readmission rate as a secondary outcome, with the expectation that readmission rates would decrease due to fewer complications, earlier mobilization, and decreased risk of morbidity. As one of our key interventions, we also adjusted therapy hours to provide afternoon and evening therapy options, from day of surgery to discharge. Additional staff was recruited to fill in when necessary. These extra resources facilitated increased patient care time, focusing on patients deemed to be high-risk for a RF discharge. A dedicated restorative staff member was assigned to work with this population, which was felt to improve both early and ongoing mobilization efforts, including both therapy and nursing staff interactions.

Therapists received specialized education on evidence-based practices in the acute care setting for patients with THR and TKR. Therapy students were mentored to research and disseminate current evidence-based practices for TJA. These measures provided a benchmark for standardization of care protocols and helped facilitate therapists' skills and confidence in working with this population. Moreover, therapists worked closely with the Orthopedic Joint Care Coordinators to pre-operatively mitigate modifiable home setup and care support barriers for home discharge. The Post-Surgical Rehab Process Map [Figure 1] helped therapists identify key elements of the clinical management continuum required to facilitate an optimal discharge to home instead of RF.

All discharges to RF were monitored proactively to identify missed opportunities. The team also introduced a tool to track therapy milestones and mobility for each patient. It consists of a simple list of various functional rehab interventions such as chair transfer, self-toileting, washing, ambulation distance, and stairs completion. Each task was checked off the day it was accomplished. This tool improved patient engagement by providing a visual way to track their progress, set functional goals, and reinforce the expectation of discharge home when appropriate goals are met. Nursing and restorative staff utilized this tool routinely when patients were mobilized out of bed for meals, to the bathroom, and for ambulation.

All members of the care team were instructed, whenever possible and reasonable, to reinforce patient expectations for a discharge to home. Therapists set rehab goals and proceeded accordingly. Any concerns regarding alternate discharge expectations from the therapist, patient, or family member were directed to the care coordinator. The care team worked collaboratively to mitigate all patient-related and medical barriers for a safe discharge home. Additionally, therapists were empowered to recommend an additional day of stay if that would facilitate a discharge to home.

Results: Our data for 2017 showed the RF discharge rate to be 25.9% while the discharge rate to home was 74.1%. During the same period, the readmission rate was 1.2%. Regarding our main objective, the target goal of this initiative was exceeded in 9 months. In comparison with the pre-intervention period, the post-intervention discharge rate to rehab fell by 57% and the discharge rate to home rose by 17% [Figure 2]. Data also showed a significant reduction in the proportion of Medicare patients discharged to RF. These changes were consistently sustained, in line with the pre-determined benchmark of the top decile when compared to the observation period. Also, our re-admission rates fell by 65% during the intervention period while the observed LOS rose by 3%. Cost savings are difficult to calculate given the multiplicity of payers and payment terms. Based on a generalized calculation conducted in consultation with the finance and business analytics departments, a conservative estimate of approximately $325,000 in shared savings throughout the continuum of care can be proposed.
Figure 2: Discharge rates to home and rehab

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Discussion: Discharging a patient home when it is appropriately safe to do so may be one of the most effective ways to decrease readmissions and prevent unnecessary complications. In one study, even in the setting of significant medical co-morbidities, the risk of post-operative complications was sufficiently low that the majority of TJA patients could safely be discharged home instead of being sent to a SNF.[10] Our study compared the pre-and post-implementation periods, demonstrating that the readmission rate fell by 65% and the readmission index fell by 71%.

In keeping with the “Six Aims for Quality” established by the Institute of Medicine, our overall goal for this project was to establish a patient-centered quality process that is equitable, timely, safe, efficient, and effective in reducing both costs and burden to patients and the healthcare system.[11] The project team performed a root cause analysis, used benchmark data, created tracking tools, and succeeded in standardizing the process and creating a corresponding pathway. This helped improve patient and team education and communication, resulting in the decrease in discharges to RF. Our data shows a significant reduction in patient discharge rate to rehab between the pre-and post-implementation periods [Figure 3]. Moreover, our nine-month results were sustained between the upper and lower control limits, demonstrating little variation in process during the tracking period. Overall, there was an increase in patients discharged home and a decrease in patients discharged to SNF, RF, or home health.
Figure 3: Distribution of discharge rates: baselines compared to post project

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Despite longer hospital LOS, we believe that our interventions contributed to overall healthcare savings in the long run. This is because the resources required to ensure patient readiness for home discharge may lead to significant reduction of downstream costs associated with SNF, RF, or home health discharge. Consequently, this finding may influence a strategic shift in focus within our increasingly value-driven healthcare system, favoring a hospital-to-home discharge over minimizing the LOS.

This project has some important limitations that need to be mentioned at this time. First, it is a retrospective evaluation of a quality improvement process. Thus, biases inherent to this methodological approach are likely to be present. Second, we describe a single-institution experience, which may not be readily applicable to other organizations or settings. Finally, additional bias may be present due to variables not formally considered in the current analysis (e.g., patient acuity, baseline functional status).

Conclusion: Our data show that the implementation of a quality improvement process focused on maximizing patient independence and functional status effectively increased the proportion of discharges to home. The successful deployment of this initiative at our hospital serves as a template for standardization of orthopedic rehab-related processes in other similar settings. Moreover, it is an excellent pathway to improve patient and team education, as well as communication related to medical and rehab processes. Further research is warranted examining the effect of our intervention on Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) outcomes, especially as they relate to discharges home and to outpatient physical therapy facilities.

  1. Sutton JC 3rd, Antoniou J, Epure LM, Huk OL, Zukor DJ, Bergeron SG. Hospital discharge within 2 days following total hip or knee arthroplasty does not increase major-complication and readmission rates. J Bone Joint Surg Am 2016;98:1419-28.
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  [Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5], [Figure 6], [Figure 7], [Figure 8], [Figure 9], [Figure 10], [Figure 11], [Figure 12], [Figure 13], [Figure 14], [Figure 15]

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]


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