Gaining Efficiency and Reducing Cost: The Re-design of a Preoperative Screening Clinic

Purpose: The purpose of this project was to focus on the redesign of the preoperative screening clinic (PSC) at a 410-bed acute care facility. The process change took place at a healthcare facility where surgical volume has been growing annually since 2011, with an average growth rate of 3% per year. The facility has projected business plans to add capacity for 6 operating rooms in the next three years due to the increased growth. The organization needed the ability to support continued surgical growth prior to the development of adding operating rooms. Understanding the current PSC operations with the need to support future growth, was the motivation for the development of this project to redesign the PSC operations. Methods: A literature review was preformed prior to the development of a plan for change in the PSC. This project is based on using an all registered nurse (RN) group to staff the prescreening clinic for patients needing anesthesia services. The intent was to demonstrate reduced day of surgery cancellations. To complete this process, specific nurse assignments with sequential assembly of the medical chart, and patient information was used. Following approved permissions for use, the Prosci's change management methodology, and the ADKAR model were used to guide the change process. Quantitative data was collected over two separate six month time periods, to compare metrics before and after the change. Results: The z-test was used to determine the significance of the changes made in the pre screening clinic. The results suggest that the changes made to the operational design in the pre screening clinic were significant in reducing day of surgery cancellations. Day of surgery cancellation rate for avoidable causes decreased from 15 cases per month to just two from December 2014 to April 2015. Conclusions: The implementation of the project achieved the goal of decreasing day of surgery cancellations. Additional benefits from the changes implemented included reduced patient wait times in the PSC to an average of less than 15 minutes, and an increased number of patient visits per day by 55%. These changes resulted in an increase in patient satisfaction. Data sources: Data was obtained using Epic's electronic software, which included the Cadence scheduling software. Additional software programs that were used to obtain data were the Kronos time keeping software, and Cisco phone reports. Daily schedules were developed by the manager to coordinate nursing assignments. Researched data sources used included; PubMed, Cochrane Collaboration, CINAHL, and Google Scholar. Key words: pre-screening clinic, pre-admission testing, pre-surgical labs, pre-operative anesthesia consultations, set-up and functioning of pre-anesthesia clinic, cost effective preoperative clinic, design of pre-operative clinic, surgery cancellation rate and the pre-operative clinic.Doctor of Anesthesia Practice (DAP)School of Health Professions and Studies: Doctor of Anesthesia PracticeUniversity of Michiganhttps://deepblue.lib.umich.edu/bitstream/2027.42/137960/1/Flint2016.pd

Adoption of Evidence-based Practices in Stroke Transitions of Care

Background: Stroke is a leading cause of mortality in the U.S. Individuals who suffer from stroke or transient ischemic attack are at risk for further cerebrovascular events. Prevention requires thorough diagnostic evaluation and care coordination, particularly from inpatient to outpatient settings. Transition of Care programs are effective methods for developing long-term treatment, establishing follow-up, and preventing further complications. Aims/Objectives: The project purpose was to integrate evidence-based recommendations into a Transitions of Care program for patients with ischemic stroke or transient ischemic attack. The aims were to evaluate whether implementation of Stroke Transitions of Care improved outpatient follow-up and stroke readmission rates. Methods: This quality improvement project occurred in a Primary Stroke center and Neurology clinic. Participants were hospitalized individuals diagnosed with ischemic stroke or transient ischemic attack, assigned to the Neurology service, and discharged home. Process improvements included the nurse practitioner meeting with patients, establishment of a designated patient contact, scheduling follow-up, and performing a post-discharge call with medication reconciliation. Process measures were tracked with run charts over twelve weeks. Pre- and post-implementation data were collected. Results: There were n=45 patients prior to intervention and n= 24 during intervention. There was a statistically significant 34% increase in proportion of patients attending clinic after intervention (83% vs 62%, p=0.012). Readmission rates were maintained at less than 10% post-implementation. Implications/Conclusions: Overall, the process improvement measures led to an increase in outpatient follow-up. Adopting evidence-based practices in stroke transitions of care can lead to increased patient retention in the outpatient setting, which may improve overall patient care

Discrete Event Simulations

Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book reflects many different points of view about DES, thus, all authors describe how it is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself reflects the plurality that these points of view represent. The book embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into five groups. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor firmly believes that this book will be interesting for both beginners and practitioners in the area of DES

Process mining in healthcare : opportunities beyond the ordinary

Nowadays, in a Hospital Information System (HIS) huge amounts of data are stored about the care processes as they unfold. This data can be used for process mining. This way we can analyse the operational processes within a hospital based on facts rather than fiction. In order to enhance the uptake of process mining within the healthcare domain we present a healthcare reference model which exhaustively lists the typical types of data that exists within a HIS and that can be used for process mining. Based on this reference model, we elaborate on the most interesting kinds of process mining analyses that can be performed in order to illustrate the potential of process mining. As such, a basis is provided for governing and improving the processes within a hospital. Keywords: healthcare, process mining, reference mode

A DISCRETE EVENT SIMULATION (DES) BASED APPROACH TO MAXIMIZE THE PATIENT THROUGHPUT IN OUTPATIENT CLINIC

The healthcare system is a complex system which exhibits conditions of uncertainty, ambiguity emergence that incurs incoming patient congestion. Discrete event simulation (FlexSim) is considered as a viable decision support tool in analyzing a system for improvement. Using a data-driven discrete event simulation approach, this paper portrays a comprehensive analysis to maximize the number of patients in an on-campus clinic, located at Mississippi State University. The outcome of the analysis of current system exhibits that deploying a few nurse practitioners results in bottlenecks which decreases the systems’ throughput substantially due to the overall longer patients’ waiting time.  Access to the laboratory is characterized through multi-server queuing network, arrival process is followed discrete distributions, and batch sizes and arrival times are stochastic in nature. In an effort to plummet inpatient congestion at the outpatient clinic, by using empirically calibrated simulation model, we will figure out the best balance between the number of the lab technician and incoming patient during working hour. An analysis of optimal solutions is demonstrated, which is followed by recommendation and avenues for future research

Patient-Specific Factors Associated with Surgical Delay in a Large Academic Hospital

The high cost of healthcare is driving the search for more efficient practice, especially in high-stakes locations like the operating room. In addition to financial losses, patients suffer physical and emotional distress, including an increased risk of morbidity or mortality when surgical cases are delayed due to inefficiency. While patient-related causes of delay have been implicated, it is unclear which specific factors are most significant. This study aimed to identify specific patient factors correlated with surgical delay and develop a predictive risk algorithm that describes the relationship between patient-specific factors and surgical delay. A retrospective review of 36,543 patients’ charts who underwent surgery at a large academic hospital over a 5-year period was conducted. Patient-specific factors, including demographics, insurance type, proximity to the hospital, anesthesia type, American Society of Anesthesiologists (ASA) classification, system-specific comorbidities, and medication usage, were identified. Bivariate analysis using chi-square analysis was conducted to determine if any of these factors were significantly correlated with surgical delay. The significant patient-specific factors were entered into a logistic regression model. Black race, ASA =\u3e3, renal failure, insulin, steroid, and several surgical specialties (colorectal, gynecologic oncology, hepatobiliary, neurosurgery, ophthalmology, and plastic surgery) were associated with an increased odds of surgical delay in this sample. Obesity, general anesthesia, and cardiovascular anesthesia were associated with a decreased odds of surgical delay. The model explains approximately 3.8-5.3% of surgical delays in this sample. The overall predictive rate of the model was 57.1%. Despite previous studies attributing a significant amount of surgical delay to patient factors, reasons other than patient factors were responsible for 94-95% of surgical delay in this sample. Further research in other populations or studies using different methods such as a prospective approach are necessary to fully understand the role of patient-specific factors in surgical delay. On the other hand, the power of this study permitted the discovery of seemingly small disparities that are nonetheless clinically significant. This study demonstrates that there are certain types of patients more at risk for surgical delay and therefore a diminished access to care

Machine Learning Predictions of No-Show Appointments in a Primary Care Setting

No-show appointments are a significant financial and operational burden for the entire healthcare system. In primary care, the rate of no-show appointments ranges from 19% [1] to 42% [2]. The data set contains just over 988,000 unique encounters spanning 7 years’ worth of appointment, clinical, demographic, and financial data from a large rural Federally Qualified Health Center. Prediction of the probability of a patient missing a scheduled appointment in a primary health care center can be a significant advantage to operational and financial success for a primary care practice. Using the predictive data generated, in combination with targeted interventions, can benefit FQHC practices which typically operate on very small margins. Nine machine learning algorithms were tested against each other to determine the most predictive model generator. Compared to the results achieved using the most commonly used algorithm previously, Logistic Regression, Adaptive Boosting showed a statistically significant improvement in accuracy and recall

INTEGRATING BEHAVIORAL HEALTH INTO PEDIATRIC DEPARTMENTS AT A PRIMARY CARE ORGANIZATION, AND RESPONDING TO NEW NEEDS DURING THE COVID-19 PANDEMIC: A PROGRAM EVALUATION AND ITS CHALLENGES

It is estimated that 14 million, or 21% of children residing in the United States meet diagnostic criteria for a mental health and/or substance use disorder (American Academy of Pediatrics, 2016) } id= 2086950881 \u3e(American Academy of Pediatrics, 2016). Additionally, 16% of children and adolescents have impaired mental health functioning that does not meet criteria for a mental health disorder. Unfortunately, approximately 75% to 85% of children with behavioral health concerns do not receive mental health specialty services, and many of them do not receive any treatment at all. Limited resources and long wait times for services are among the many barriers to Behavioral Health care for this population. In addition to quality of life issues, untreated child mental health disorders have profound societal economic consequences. This study set out to evaluate a pilot project that integrates Behavioral Health (BH) care into pediatric departments at a large multi-site, multispecialty primary care organization as a strategy for reducing wait times for child BH services and improving patient outcomes. Data started to be collected and some preliminary findings were obtained but the program did not reach its planned endpoint. In March of 2020 operations at the primary care organization were severely disrupted by an organizational and financial crisis caused by the necessary safety precautions designed to reduce the spread of infection during the onset of the Covid-19 pandemic. Behavioral Health care at the organization subsequently shifted to a telehealth platform. The in-person pilot program was terminated and reorganized into two new telehealth programs with the same goal of increasing access to BH care by reducing wait times for services. The first of these programs called the Pediatric Behavioral Health Covid Response (PBHCR) team was designed to address the emerging urgent child mental health needs caused by the pandemic. The second program called The Virtual Integration Program was designed to provide general BH care to children. Data for the new pilot program is incomplete, but preliminary results indicate that wait times and no show rates were reduced, and pediatric providers and their patients generally found the program helpful. The Virtual Integration Program was not evaluated. An exploration of operational data for the PBHCR program found that the use of a single session and psychoeducation in the form of Tip Sheets may be an effective strategy for increasing access to child behavioral health care