Improving Patient Satisfaction with the Virtual Handoff Process through the Utilization of Educational Pamphlets in the Emergency Department

Boarding patients in the emergency room while waiting to transfer the patient to the proper unit can be harmful to clinical care and have significant financial opportunity costs. At one local hospital it was found that on average patients were being boarded in the emergency room (ED) for approximately 85 minutes waiting to be transferred. Several barriers that caused this delay were found including, delay in room cleaning, nurse staff shortage, and inability to give report to the nurse receiving the patient. In an attempt to combat this delay which may be caused by a difficulty in giving patient report, this organization is rolling out a virtual bedside handoff process. While virtual technology is not a new concept, there are many patients that may not be comfortable with the technology. The purpose of the evidence-based project was to provide a written educational pamphlet that details the how’s and why’s of the virtual handoff process to the patient to be given upon admission. The goal of the educational pamphlet was to increase the patients’ satisfaction with the process. A pre-survey was given to a group of patients after they experienced the virtual handoff process to assess their comfort level. These results were compared to the post-survey results of patients that received the educational pamphlet prior to experiencing the virtual handoff process. Ten pre-surveys and seven post-surveys were analyzed utilizing SPSS and descriptive statistics. The analysis concluded that the participants who received the educational pamphlet felt more prepared for the virtual handoff process

Using data to answer questions of public health importance for ACT Health, with an emphasis on routinely-collected linked data

My field placement was with the Epidemiology Section in the Population Health Protection and Prevention Division at ACT Health. Within this placement, I have completed four projects for this thesis: an analysis of Emergency Department (ED) data; a gastroenteritis outbreak investigation; an evaluation of a population health survey and, for my main project, a study of unplanned hospital readmissions. One of the motivations for undertaking these projects was to promote better use of the routinely-collected linked data to answer questions of public health importance for ACT Health. My data analysis project was an analysis of frequent ED use in the Australian Capital Territory (ACT). This is the first study to quantify and characterise ED frequent users in the ACT. The results support existing evidence that frequent users tend to be older, female, and/or single, and commonly present with pain-related conditions. The data also showed that compared to non-frequent ED users, frequent users were more likely to be referred by police, corrective or community services; arrive by ambulance, not wait to be assessed, or leave at their own risk. In addition, we investigated visit intervals, rarely reported on in other studies. This study found around one third of frequent users returned within 7 days, with 41% of their visits having the same diagnosis as the last visit. Early identification and follow-up in the community for frequent users will assist in the development of targeted strategies to improve health service delivery to this vulnerable group. Unexpected return to hospital has negative impacts on families and healthcare systems. We examined which conditions have the highest rates of readmission and contribute most to 30-day unplanned readmissions in the ACT, and which patient characteristics are associated with readmissions. The study identified a 30-day unplanned readmission rate of 6.2%, with admission rates highest for alcohol-related liver disease (19.2%), and heart valve disorders (17.4%). Older age and comorbidities are strong predictors for 30-day unplanned readmissions. For some conditions the rates were relatively high, suggesting areas to target for reducing readmissions. Therefore, when developing preventative strategies and post-discharge plans, particular consideration should be given to patients at older age or with underlying comorbidities. As part of the ACT Health Survey Program (HSP), the ACT General Health Survey (GHS) is a computer-assisted telephone interviewing survey conducted every year among ACT residents. My evaluation of the GHS found that it is a useful tool to monitor trends of overweight, obesity, nutrition and physical activity for adults and children in the ACT. The data collected are used to provide evidence to understand and analyse overweight and obesity patterns in the ACT and create awareness of unhealthy lifestyles. However, improvements could be made in a few areas, including: developing a proper evaluation plan and a data quality statement, increasing the sample size and the proportion of young people in the sample population. I also carried out an outbreak investigation of foodborne gastroenteritis that occurred among staff and public members at a large national institution in Canberra. I conducted two studies for this outbreak – a retrospective cohort study and a case control study. The epidemiological, environmental and laboratory evidence suggested the outbreak was caused by C. perfringens toxin Type A, with the likely vehicles of transmission being butter chicken and rice. The findings of this investigation suggest that a breakdown in temperature control and good food handling practices may have resulted in C. perfringens bacterium growing rapidly and producing a toxin which caused the illness. This project also indicated that the value of a second epidemiological study was questionable given the limited time and resources available

Why Not the Best? Results From a National Scorecard on U.S. Health System Performance

Compares the national average healthcare system performance to benchmarks of higher performance. Provides a mechanism for monitoring change over time across goals of health outcomes, quality, access, efficiency, and equity

Electronic Information Sharing to Improve Post-Acute Care Transitions

Hospitals frequently transfer patients to skilled nursing facilities (SNFs) for post-acute care; information sharing between these settings is critical to ensure safe and effective transitions. Recent policy and payer initiatives have encouraged hospitals and SNFs to work together towards improving these care transitions, and associated patient outcomes such as avoidable re-hospitalizations. Exchanging information electronically, through health information exchange (HIE), can help facilitate information transfer, and has shown benefits to patient care in other contexts. But, it is unclear whether this evidence translates to the post-acute care context given the vulnerability of this patient population and complexities specific to coordination between acute and post-acute care settings. Chapter One estimates the national prevalence of hospital’s engagement in HIE with post-acute providers, and explores potential factors prompting this investment. 56% of hospitals report some level of HIE with post-acute care providers. This investment appears strategically to be more incidental than intentional; hospitals’ overall level of sophistication and investment in electronic health records and HIE strongly predicts whether HIE is occurring in the post-acute transition context. However, we see some evidence of association between participation in delivery and payment reforms and hospital use of HIE with post-acute providers. This suggests that HIE may increasingly be considered part of a comprehensive strategy to improve coordination between hospitals and post-acute care providers, though may lack the necessary customization to achieve meaningful value in this context. Chapter Two utilizes a difference-in-differences approach to assess HIE impact on patient outcomes in the post-acute context, exploiting one focal hospital’s selective implementation of HIE with just three partnering local SNFs. I find no measurable effect of HIE implementation on patient likelihood of re-hospitalization, relative to patients discharged to SNFs without HIE. However, log files that capture when and how these SNF providers use available HIE technology reveal significant variation in usage patterns. HIE was more often utilized following discharge situations where transitional care workflows may not be particularly robust, such as discharge from the ED or observation rather than an inpatient unit. However, the system was less likely to be used for more complex patients, and for patients discharged on the weekend – when SNFs operate at reduced staffing and may not have the bandwidth to leverage available technology. When we connect variation in usage patterns to likelihood of readmission, realizing patient care benefits depended on the timing (relative to patient transfer) and intensity (depth of information retrieved) of use. Chapter Three employs qualitative methods – semi-structured interviews with the focal hospital and five proximate SNFs – to better understand hospital-to-SNF transitions, and perceived opportunities and challenges in using HIE functionality to address information gaps. We capture five specific dimensions of information discontinuity; utilizing IT to address these issues is hindered by lack of process optimization from a sociotechnical perspective. Some SNFs lacked workflows to connect those with HIE access to the staff seeking information. Further, all facilities struggled with physician-centric transition processes that restricted availability of critical nursing and social work documentation, and promoted organizational changes that strengthened physician-to-physician handoff while unintentionally weakening inter-organizational transitional care processes. HIE has the potential to address information discontinuity that compromises post-acute transitions of care. These findings facilitate targeted efforts to help hospitals and SNFs pursue HIE in ways that are most likely to result in improved care quality and patient outcomes.PHDHealth Services Organization & PolicyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146031/1/dacross_1.pd

Implementation of Teach-Back for Discharge Teaching in a Critical Access Hospital: A Quality Improvement Project

A lack of comprehension of discharge instructions may cause high readmission and emergency room revisit rates for organizations. At the project site, there was no current evidence-based practice to ensure patient comprehension of discharge instructions. The purpose of this quantitative, quasi-experimental quality improvement project was to determine if the implementation of the Institute for Healthcare Improvement (IHI) Always Use Teach-Back Toolkit would impact emergency room revisit rates among adult medical-surgical patients in a critical access hospital in rural Minnesota over four weeks. Afaf Meleis’ transitions theory and the Iowa model for evidence-based practice were the scientific underpinnings of the project. The total sample size was 87, n = 47 in the comparison and n = 40 in the intervention groups. Data was extracted from the facility’s electronic health record. A chi-squared test was used, and results indicated no statistically significant reduction in the ED revisit rates X 2 (1, n=87) =2.00, p=0.157. Clinical significance is found in reducing the ED revisit rates by 1.38% over the four weeks. Therefore, the implementation of the IHI’s Always Use Teach-Back Toolkit may reduce emergency room revisit rates in this population and setting. Recommendations include sustaining the practice, adding teach-back to the discharge planning protocol, and disseminating the project findings

An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions

ArticleInPressOne of the major challenges that confront medical experts during a pandemic is the time required to identify and validate the risk factors of the novel disease and to develop an effective treatment protocol. Traditionally, this process involves numerous clinical trials that may take up to several years, during which strict preventive measures must be in place to control the outbreak and reduce the deaths. Advanced data analytics techniques, however, can be leveraged to guide and speed up this process. In this study, we combine evolutionary search algorithms, deep learning, and advanced model interpretation methods to develop a holistic exploratory- predictive-explanatory machine learning framework that can assist clinical decision-makers in reacting to the challenges of a pandemic in a timely manner. The proposed framework is showcased in studying emergency department (ED) readmissions of COVID-19 patients using ED visits from a real-world electronic health records database. After an exploratory feature selection phase using genetic algorithm, we develop and train a deep artificial neural network to predict early (i.e., 7-day) readmissions (AUC = 0.883). Lastly, a SHAP model is formulated to estimate additive Shapley values (i.e., importance scores) of the features and to interpret the magnitude and direction of their effects. The findings are mostly in line with those reported by lengthy and expensive clinical trial studies

An initial case study of a readmission and emergency department revisit reduction program for high utilizer patients at a large community hospital system in Massachusetts

BACKGROUND: This dissertation presents an initial, mixed-methods case study of a hospital-based multidisciplinary care team (MCT) program designed to reduce hospital readmissions and emergency department (ED) revisits among patients with high hospital utilization without restrictions in a large, not-for-profit, non-teaching, community hospital system. METHODS: High utilizers were defined by either ≥10 ED visits or ≥4 inpatient stays within the past 12 months on a rolling basis. Electronic medical records and retrospective patient-level surveys completed by MCT staff provided insight into program reach and implementation, as well as initial impacts on hospital-based outcomes, non-hospital based outcomes, and staff-perceived impacts of MCT services on patients. Interviews with MCT patients, program staff, hospital administrators, community partners, and field experts were analyzed to understand the key challenges, best practices, and lessons learned to help inform transferability and sustainability of this type of program. RESULTS: Of the 1,680 patients who were identified as eligible high utilizers, about half received ≥2 telephone calls or face-to-face visits with the MCT. There were significant delays to patients receiving MCT services, especially for patients who met eligibility criteria within the first few months of the program initiation. Data reflected the high number of MCT encounters and breadth of services provided to MCT participants. On average, changes in post-period ED revisit and inpatient readmission rates were not significantly different from pre-period rates for MCT participants overall, or when broken down by initial classification as an inpatient or ED high utilizer. MCT staff reported improvements in housing stability, usual source of care, and substance use treatment or recovery for MCT participants. Staff perceived positive overall and specific impacts of MCT services for a large portion of patients, with greater perceived positive impacts on all outcomes with increasing program duration. Salient themes from the qualitative data analysis included the heterogeneity of the high utilizer population, internal communication and support, community integration, and financing. CONCLUSION: This initial study, conducted prior to the conclusion of the full MCT program, provided insight into the strengths, challenges, and early lessons learned from a hospital-based multidisciplinary care team program designed to reduce high readmission and revisit rates among high utilizers. This study also lays the groundwork for a full post-program evaluation in the future.2019-11-08T00:00:00

Optimizing medication therapy in older hospitalized patients. Identifying potentially inappropriate medications and testing an interdisciplinary intervention

The overall aim of this thesis is to provide knowledge on potentially inappropriate medications (PIMs) in hospitalized older patients and to investigate how clinical pharmacist services in an interdisciplinary setting can contribute to medication optimization and improve patient outcomes. First, we used national health registers to investigate how admissions to Norwegian geriatric hospital wards affected PIM use. More than half of the 715 patients included in the study used PIMs after discharge. Hospitalization did not reduce the use of PIMs but may have increased use depending on how we measured PIMs. Second, we designed a 5-step intervention, introducing a clinical pharmacist in the ward teams working by the integrated medicines management (IMM) model to optimize medication use and improve communication with primary care. The intervention was tested in older patients ≥70 years admitted to two internal medicines wards at the University Hospital of North Norway. We applied a non-blinded randomized controlled trial, where 516 acutely admitted patients were randomized into an intervention group and a standard care group (1:1). The primary outcome was the rate of emergency medical visits (readmissions and emergency department visits) 12 months after discharge. Many medication discrepancies and MRPs were identified and solved in intervention patients, suggesting that the intervention optimized medication use. However, no significant reduction in the rate of emergency medical visits was observed in intervention patients versus control patients, nor did we observe any significant effects on time to the first emergency medical visit, 30-days readmissions rate, length of index hospital stay or mortality. Overall, this thesis demonstrates a need to optimize medication therapy in older hospitalized patients. Including clinical pharmacists' services in hospital wards teams may contribute to optimizing medication use, but there is a need for further studies to identify interventions that simultaneously produce meaningful improvements in patient outcomes