Patient Flow Process Research Articles

POCT in Emergency Rooms: One Key Factor for Process Streamlining with Lean Management

Overcrowding is a common problem in Emergency Departments (ED) worldwide and has a negative impact on patient satisfaction and, more importantly, patient safety. So, the Emergency Department of the secondary-care hospital Paul Gerhardt Stift in Wittenberg, Germany, was faced with increasing numbers of patients. Lean management was introduced to analyse, optimise, and standardise ED processes. Consequently, a project group concentrated on “cycling muda” which is to identify waste and cost drivers along a representative patient path using one suitable Lean tool: mapping the current state in a value stream. As a result, it became clear that both patients and staff suffered from immense waiting times that lead to risky patient care and employee frustration. By subsequently eliminating the waste drivers and designing a high-quality patient flow process creating standards supported by state-of-the-art technology, the hospital’s ED turned into a streamlined department with reduced waiting times offering employees a satisfactory and modern workplace where patients benefit from first-class care.

Utilizing integrated facility design to improve the quality of a pediatric ambulatory surgery center

The aim was to use Integrated Facility Design (IFD) to design a surgery center that enhances the delivery of health care by developing processes that provide highly efficient patient, family, and provider flows while adding value and utilizing costly resources effectively. Integrated Facility Design is an adaptation of the Toyota 3P (Production, Preparation, Process) Program. The goal of IFD is to accelerate development time and lower start-up costs. The use of IFD produced a savings of $30 million in project costs and enabled a completion date 3.5 months ahead of schedule. The designed patient flow processes resulted in dramatic improvements in patient, family, and provider throughput. The use of IFD in the design of a pediatric ambulatory clinic and surgery resulted in significant cost savings and improved clinical efficiency.

Modeling the impact of changing patient flow processes in an emergency department: Insights from a computer simulation study

We report on the use of discrete-event simulation modeling to support process improvements in a hospital emergency department (ED), namely the implementation of a split-flow process. Our partner hospital was effective in treating patients, but wait time and congestion in the ED created patient dissatisfaction, unsafe conditions and staff morale issues. The split-flow concept is an emerging approach to manage ED processes by splitting patient flow according to patient acuity and enabling parallel processing. We contrast the split-flow operational model to other types of ED triage. While early implementations of the split-flow concept have demonstrated significant improvements in patient wait times, a systematic evaluation of operational configurations is lacking.We created a discrete-event simulation model and established its face validity for Saint Vincent Hospital in Worcester, USA, a community-teaching, Level II Trauma Center. Seventeen scenarios were tested to estimate the likely impact of a split-flow process redesign, including staffing level changes and patient volume changes. The scenarios were compared in terms of Door-to-Doctor time and length-of-stay for different patient acuity levels.Findings from the study supported implementation of the split-flow improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient length-of-stay was reduced. To gauge the success of the current split-flow process at Saint Vincent we compare performance metrics from three different sources: benchmark metrics, hospital data prior to split-flow implementation, and performance metrics post implementation.

Abstract WP241: Value Stream Analysis Reduces Door-To-Needle Times in Acute Stroke

Introduction: An important goal in treating acute stroke is minimizing door-to-needle times, consistent with evidence based practice and quality goals. Utilizing methods to streamline the process of patient and information flow in the emergency department (ED) is necessary to reduce treatment times. Hypothesis: Use of value stream analysis to improve efficiency in processing acute stroke patients in the ED should reduce door-to-needle times. Methods: Value stream is the movement of patient and information to deliver the desired outcome. The process from door to treatment was divided into individual steps. We defined process time (the time each step takes) and wait time (the time between each step). The mission was to identify which steps were absolutely necessary before a patient could receive IV-tPA, and to streamline those steps to reduce the process time. All patients who received IV-tPA in 2011 and thus far in 2012 are shown in the graph. Each bar represents an individual patient. Results: Prior to stream analysis, our average door-to-needle time was 122.2 minutes. After the process was rolled-out, the average door-to-needle time was down to 73.8 minutes. Before stream analysis we treated 7.6% of our IV-tPA patients in less than 60 minutes. After analysis we treated 69.2% of patients in less than 60 minutes. The number of patients treated rose from 11 in 2011 (5%) to 17 thus far in 2012 (mid-year, projected 15%). We also found that the number of thrombectomy treated patients was reduced from 39% prior to analysis to 23% following analysis, perhaps partly due to reduced processing time and more patients evaluated within the window for IV-tPA. Conclusion: Streamlining the process of patient flow in the ED by value stream analysis resulted in several outcome improvements in acute stroke patients: 1) Reduced door-to-needle times. 2) Increased number of patients treated within the golden hour. 3) Increased number of patients treated with IV-tPA.

Selection of medical oncology electronic medical record (EMR): A case study.

327 Background: Selection of an ideal EMR is an important but a complicated process, especially because there are few established guidelines available. We describe a case study on the process of selection of medical oncology EMR from our experience at St. Joseph Regional Cancer Center, Lewiston ID. Methods: A multidisciplinary team was developed, including a medical oncologist to develop workflow diagrams capturing tasks completed for patient care. This allowed the team to identify requirements and functionality important to have within an EMR. Requirements rating as high, medium or low. The scoring methodology was based on a rating scale (0-5, where 0 does not meet the fundamental requirement and 5 fully meets the fundamental requirement; 0-2, where 0 does not meet the differentiator requirement and 2 fully meets the differentiator requirement) to capture if system met fundamental and differentiator requirements. The team developed a list of the ten most important needs within a system to focus on while viewing vendor demos. The top 10 requirements focused on lab result integration, other facility integration, alerts, billing flow of information, electronic prescriptions, local assessments, electronic consent forms, flexibility in chemo calculations, sign-offs of treatments throughout the patient flow process, and the ability to order cycles of treatment in a multi-visit treatment plan. Finally, use case scenarios were developed and vendors were given the opportunity to demo their product using workflow scenarios. Results: Out of 10 medical oncology systems in consideration, the evaluation included four 3rd party vendor systems and one EMR platform system. Four systems were chosen for evaluation based on rankings from the 2011 Oncology IS KLAS report. Complete evaluation of the systems and the scorings were based on vendor demo webinars and multidisciplinary input. This methodical approach allowed the team to gain a broad, balanced approach in narrowing the search down to the top three finalists. Conclusions: A systematic approach that includes an objective scoring system and research by interviewing other sites is useful in selection of a medical oncology EMR.

Development of Emergency Department Load Relief Area—Gauging Benefits in Empirical Terms

The primary goal of this investigation was to develop a simulation model to evaluate the various internal and external factors affecting patient flow and crowding in the emergency department (ED). In addition, a few recommendations are proposed to reconfigure the patient flow to improve ED capacity while maintaining service quality. In this research, we present a simulation study conducted in the ED at the "S Hospital" located in Seoul. Based on patient flow data and process analysis, a simulation model of patient throughput in the ED has been developed. We evaluated simulations of diverting the specific patient load in the light of our proposed recommendations to a separately managed area named as the ED load relief area (ED-LRA) and analyzing potential effects on overall length of stay (LOS) and waiting time (WT). What-if analyses have been proposed to identify key issues and investigate the improvements as per our proposed recommendations. The simulation results suggest that specific patient load diversion is needed to ensure desired outcomes. With the diversion of specific patient load to ED-LRA, there is a reduction of 40.60% in mean LOS and 42.5% in WT with improved resource utilization. As a result, opening of an ED-LRA is justified. Real-world systems are often too intricate for analytical models and often too expensive to trial with directly. Simulation models allow the modeling of this intricacy and enable experimentation to make inferences about how the actual system might perform. Our simulation study modeled that diverting the specific patient load to ED-LRA produced an improvement in overall ED's LOS and WT.

319 Variability in Patient Care Process Measures Across 7 Massachusetts Academic Emergency Departments

Study Objectives: To identify areas of variability and consistency in clinical operations and practice parameters across academic emergency departments (EDs). Methods: A retrospective cross-sectional descriptive review of Massachusetts EDs where emergency medicine residents spend 1 month of required training time. Of the 13 eligible, 7 became participants by identifying a site PI, obtaining local institutional review board approval, and collecting the requested data. Seven domains of data were collected for 2011 including: patients, care space and disposition; clinical staffing; triage type; lab and radiology testing; process improvement trials; documentation and billing; acute coronary syndrome and congestive heart failure clinical practice. We have reported averages and 1 standard of deviation to describe central tendency and variability. Results: Average ED daily volume (177 +/− 79) and space (3.4 +/− 1.4 patients per sq ft per yr) are quite variable across facilities. Attending physician staffing has low variability (2.5 +/− 0.5 hrs per day) while nursing staffing is more variable (1.9 +/−0.8 nursing hrs per patients seen). When PAs and NPs are included with residents as physician extenders, affiliate site attending physicians with their extenders see fewer patients per hour than those at primary residency sites (0.8 patients per hr +/− 0.2 versus 1.5 +/− 0.4). However, affiliate site EDs bill a higher percentage of level 5 APC charts (36%) compared to the primary sites (24%), which are also the higher volume receiving centers for high acuity patients. Most EDs use some degree of RN triage (87%), but 43% have a combination model using some degree of direct to room or physician triage. EDs with observation units did not have shorter ED length of stay (LOS) for admitted patients as compared to those without (6.5 +/−0.8 hrs versus 5.0, +/−0.5hrs). ED LOS for discharged patients demonstrates relatively low variability (3.9 +/− 0.5 hrs). In the past 10 years, 85.7% of EDs have worked on patient flow, triage and inpt admission processes. 71.4% have addressed care-space use, fast track areas, lab testing services, and consultations. Only 57.1% have worked on radiology testing services. The greatest variability in payer mix occurs amongst private insurance (28.9%, +/−25.6) and self-pay patients (25.5%, +/−22.3), with much less variability amongst Medicare (24.2% +/−11.7) and Medicaid (21.5% +/−12.0) patients. All EDs use either a Troponin T or an ultrasensitive Troponin I. The average interval for 2 troponin assays as part of an acute coronary syndrome evaluation was 6hrs. All discharged a maximum of 10% of patients with a primary diagnosis of congestive heart failure from the ED. Conclusion: As academic EDs cope with increasing volume and patient complexity, a variety of innovative approaches have been taken to improve throughput and care quality. However, throughput to discharge seems relatively homogeneous. Larger EDs are managing with fewer resources and a poorer income generation scenario. The area of least process improvement innovation was radiology testing. Regarding cardiovascular care, there is potential practice and throughput improvement for patients being evaluated for acute coronary syndrome with a more evidence-based use of cardiac enzymes by using intervals shorter than 6 hours, and with congestive heart failure patients where less than 10% are discharged.

Predicting Emergency Department Inpatient Admissions to Improve Same‐day Patient Flow

The objectives were to evaluate three models that use information gathered during triage to predict, in real time, the number of emergency department (ED) patients who subsequently will be admitted to a hospital inpatient unit (IU) and to introduce a new methodology for implementing these predictions in the hospital setting. Three simple methods were compared for predicting hospital admission at ED triage: expert opinion, naïve Bayes conditional probability, and a generalized linear regression model with a logit link function (logit-linear). Two months of data were gathered from the Boston VA Healthcare System's 13-bed ED, which receives approximately 1,100 patients per month. Triage nurses were asked to estimate the likelihood that each of 767 triaged patients from that 2-month period would be admitted after their ED treatment, by placing them into one of six categories ranging from low to high likelihood. Logit-linear regression and naïve Bayes models also were developed using retrospective data and used to estimate admission probabilities for each patient who entered the ED within a 2-month time frame, during triage hours (1,160 patients). Predictors considered included patient age, primary complaint, provider, designation (ED or fast track), arrival mode, and urgency level (emergency severity index assigned at triage). Of the three methods considered, logit-linear regression performed the best in predicting total bed need, with a receiver operating characteristic (ROC) area under the curve (AUC) of 0.887, an R(2) of 0.58, an average estimation error of 0.19 beds per day, and on average roughly 3.5 hours before peak demand occurred. Significant predictors were patient age, primary complaint, bed type designation, and arrival mode (p < 0.0001 for all factors). The naïve Bayesian model had similar positive predictive value, with an AUC of 0.841 and an R(2) of 0.58, but with average difference in total bed need of approximately 2.08 per day. Triage nurse expert opinion also had some predictive capability, with an R(2) of 0.52 and an average difference in total bed need of 1.87 per day. Simple probability models can reasonably predict ED-to-IU patient volumes based on basic data gathered at triage. This predictive information could be used for improved real-time bed management, patient flow, and discharge processes. Both statistical models were reasonably accurate, using only a minimal number of readily available independent variables.

Improving Patient Flow Through a Better Discharge Process

Hospitals are now facing a dramatic challenge: improve the quality of care while simultaneously lowering costs. The Affordable Care Act of 2010 will add an estimated 32 million additional patients to the system (CBO 2011), and hospitals will need to improve efficiency so that capacity be expanded while still providing quality care.One way hospitals improve efficiency is to increase patient throughput, and many hospitals have a strategic goal to do just that. Yet, short of spending capital to expand facilities, hospitals struggle to sustain gains in this area. Patient throughput is complex, and many factors must be considered when balancing quality of care and speed through the system. The key to improving throughput is to focus on patient flow. Hospitals that succeed in improving patient flow realize that this complex process must include a disciplined and proven process improvement methodology. Patient flow must be viewed as a system, and the issues causing obstacles to smooth flow must be identified and eliminated or mitigated. Exhibit 1 shows some of the interconnected processes within a hospital. By systematically addressing key aspects of this system through rigorous analysis and redesign of the system and its interactions, effective patient flow be achieved.The patient flow improvement process has many stakeholders, so garnering senior leadership support is a critical first step toward developing a better process. Next, make sure the improvement activities are clearly defined and manageable. Most organizations don't have massive resources to dedicate for such a large, complex project; therefore, you need to break patient flow into essential parts. Covithen's Customer Excellence group emphasizes that an organization must embrace a Kaizen (continuous improvement) philosophy. Kaizen involves always improving and promotes a spirit of can do, breaking even huge problems into smaller components to make improvement efforts more manageable. Exhibit 1 is an example of breaking patient flow into six major subprocesses. Each of these subprocesses holds opportunities for improvement; one of the best opportunities involves improving the inpatient discharge process, the last subprocess within the hospital setting.While this column will focus on improving the inpatient discharge process, the first step involves understanding the patient flow process - also referred to as the patient flow value stream - in great detail. A value stream is an end-to-end series of activities that create or achieve a result for a customer (in this case, the patient). Analyzing the patient flow value stream involves gathering stakeholders from each process step from admitting to discharge and from a wide range of hospital functions: registration, lab, imaging, transport, nursing, physicians, housekeeping, and more. This extended team focuses on mapping the flow of a patient through the entire system (this process is shown in Exhibit 2). The value stream mapping allows team members to see the real process and always reveals variation and wasteful activities that create inefficiencies and reduce the quality of care. Once the key stakeholders have analyzed the interconnected system, clear goals for the desired future be established. Typical roadblocks to improved throughput include lack of collaboration between key functions, lack of effective communication systems and methods, and lack of standard work methodology. Lean Six Sigma methodologies and value stream analysis tools promote significant employee engagement and result in sustainable models for improvement.As the team maps the current state value stream and begins to analyze opportunities for improvement, they often discover a need to focus on the discharge process. As the last step in the value stream, discharge is a significant constraint on patient flow, because newly admitted patients cannot move into a bed until a bed is available for them. Thus, the discharge process becomes the flow regulator, and efficiency of the discharge process have significant impact on overall patient flow. …

The Development of an Obstetric Triage Acuity Tool

The purpose of this article is to describe the journey a multicampus hospital system took to improve the obstetric triage process. A review of literature revealed no current comprehensive obstetric acuity tool, and thus our team developed a tool with a patient flow process, revised and updated triage nurse competencies, and then educated the nurses about the new tool and process. Data were collected to assess the functionality of the new process in assigning acuity upon patient arrival, conveying appropriate acuities based on patient complaints, and initiating the medical screening examination, all within prescribed time intervals. Initially data indicated that processes were still not optimal, and re-education was provided for all triage nurses. This improved all data points. The result of this QI project is that our patients are now seen based on their acuity within designated time frames.

ANALYSIS OF SURGICAL PATIENT FLOW AT WINNIPEG HEALTH SCIENCES CENTRE

The Health Sciences Centre (HSC) in Winnipeg is the major trauma centre serving the entire province of Manitoba, Northwestern Ontario, and Nunavut. Therefore, it has to handle a high volume of both elective and emergent surgical patients. Because the facility always strives to provide quality care in a fast and effective manner, it initiated a research project to analyze its surgical patient flow and generate ideas on how it could be redesigned to improve the systems performance. After a year of careful analysis, all of the problems identified were grouped into six major categories, showcasing how various departments are affected by each problem. Based on this analysis, the true impact of each problem in the surgical patient flow process can be understood. Steps can now be taken to identify which problems need to be addressed, what changes should be made, and how this will benefit the entire system.

Discovering the Impact of Preceding Units' Characteristics on the Wait Time of Cardiac Surgery Unit from Statistic Data

IntroductionPrior research shows that clinical demand and supplier capacity significantly affect the throughput and the wait time within an isolated unit. However, it is doubtful whether characteristics (i.e., demand, capacity, throughput, and wait time) of one unit would affect the wait time of subsequent units on the patient flow process. Focusing on cardiac care, this paper aims to examine the impact of characteristics of the catheterization unit (CU) on the wait time of cardiac surgery unit (SU).MethodsThis study integrates published data from several sources on characteristics of the CU and SU units in 11 hospitals in Ontario, Canada between 2005 and 2008. It proposes a two-layer wait time model (with each layer representing one unit) to examine the impact of CU's characteristics on the wait time of SU and test the hypotheses using the Partial Least Squares-based Structural Equation Modeling analysis tool.ResultsResults show that: (i) wait time of CU has a direct positive impact on wait time of SU (); (ii) capacity of CU has a direct positive impact on demand of SU (); (iii) within each unit, there exist significant relationships among different characteristics (except for the effect of throughput on wait time in SU).ConclusionCharacteristics of CU have direct and indirect impacts on wait time of SU. Specifically, demand and wait time of preceding unit are good predictors for wait time of subsequent units. This suggests that considering such cross-unit effects is necessary when alleviating wait time in a health care system. Further, different patient risk profiles may affect wait time in different ways (e.g., positive or negative effects) within SU. This implies that the wait time management should carefully consider the relationship between priority triage and risk stratification, especially for cardiac surgery.

Did Renovation Project Need Renovation?

Did Renovation Project Need Renovation?

Swedish emergency department triage and interventions for improved patient flows: a national update

BackgroundIn Scandinavia, emergency department triage and patient flow processes, are under development. In Sweden, the triage development has resulted in two new triage scales, the Adaptive Process Triage and the Medical Emergency Triage and Treatment System. Both these scales have logistic components, aiming to improve patient flows. The aim of this study was to report the development and current status of emergency department triage and patient flow processes in Sweden.MethodsIn 2009 and 2010 the Swedish Council on Health Technology Assessment sent out a questionnaire to the ED managers in all (74) Swedish hospital emergency departments. The questionnaire comprised questions about triage and interventions to improve patient flows.ResultsNearly all (97%) EDs in Sweden employed a triage scale in 2010, which was an increase from 2009 (73%). Further, the Medical Emergency Triage and Treatment System was the triage scale most commonly implemented across the country. The implementation of flow-related interventions was not as common, but more than half (59%) of the EDs have implemented or plan to implement nurse requested X-ray.ConclusionsThere has been an increase in the use of triage scales in Swedish EDs during the last few years, with acceleration for the past two years. Most EDs have come to use the Medical Emergency Triage and Treatment System, which also indicates regional co-operation. The implementation of different interventions for improved patient flows in EDs most likely is explained by the problem of crowding. Generally, more studies are needed to investigate the economical aspects of these interventions.

Abstract No. 106: The Implementation of an RN Bedside PICC Team Under the Interventional Radiology Umbrella - A Patient Flow Process Improvement

Abstract No. 106: The Implementation of an RN Bedside PICC Team Under the Interventional Radiology Umbrella - A Patient Flow Process Improvement

Study Designs for Program Evaluation: How Do We Know What Works?

Scientific Inquiry provides a forum to facilitate the ongoing pro- cess of questioning and evaluating practice, presents informed prac- tice based on available data, and innovates new practices through research and experimental learning . W hether or not we set out to be researchers, many of us encounter evaluation imperatives in our work (Grembowski, 2001). Administrators, funders, and accrediting agencies all want answers to the same basic question: How do we know our programs are effective? When we set out to answer this question, we may wish we had taken an extra research methods course or two. Evaluation language can quickly become technical, and we may feel in over our heads. We must answer evaluation questions, but how to begin? The hallmark of pharmaceutical research, which is in some ways the prototype for all healthcare research, is the randomized controlled trial (RCT). RCTs are the backbone of meta-analyses, Cochrane reviews, and other systematic reviews of the literature, which in turn are the basis for evidence- based practice in health care. But do we really need a rand- omized control group to evaluate a new patient-flow process on our clinical unit? If so, how can we randomly assign some patients not to go through the new system? The RCT is so dominant in healthcare research that it can be hard to think outside this paradigm. RCTs can be used to study virtually any type of program or intervention in healthcare settings. But this doesn't mean they always should be used: Witness a review in the British Medical Journal denouncing parachute use as an unvalidated intervention for the prevention of acceleration trauma (i.e., harm incurred by falling out of airplanes), because the evidence consists only of anecdotal reports. Where are the data from a control group randomly assigned not to use a parachute (Smith & Pell, 2003)? Clearly, the assumption that we always need an RCT is not quite right.

Statistical process control part 2: using statistical process control to measure process improvement in patient flow

THE FIRST ARTICLE in this series, ?Statistical process control part 1: a primer for using statistical process control in health care process improvement? 1 (in this issue of the Journal), introduced the basic concepts of statistical process control (SPC) and its main tool, the control chart. While this set of techniques was originally developed in the manufacturing sector, there is growing realisation in recent years that SPC (and also other quality improvement techniques, such as Six Sigma and lean thinking) can be successfully applied to health care quality improvement.2 The reason for this is that SPC is a potent and powerful, yet simple tool for tracking, and detecting any variation in, process performance over time; which creates the opportunity for health professionals to promptly respond to any improvement or deterioration in the process. Perhaps the most valuable feature of SPC techniques however, is the ability to place a change in the outcome of a process in close temporal proximity to the redesign and improvement of the process. This means SPC can reliably evaluate the effectiveness of quality improvement initiatives implemented at the front line of health service delivery, despite the complexities of the hospital system and the challenges this often poses for health services research (for example, the inability to use robust research designs). The purpose of this companion article is to therefore demonstrate the practical application of SPC in a health care organisation. Specifically, the technique of control charting was used to track the impact of patient flow process improvement interventions in a public hospital, in the hope that this will exemplify to health care professionals the value and simplicity in applying SPC as part of their daily work.

Cutting-edge Discussions of Management, Policy, and Program Issues in Emergency Care

Cutting-edge Discussions of Management, Policy, and Program Issues in Emergency Care

403-O. Patient Flow Redesign

In 2002, the emergency department of Montefiore Medical Center in Bronx, NY, was identified as the 15th-busiest emergency department in the United States by the American Hospital Association Annual Survey of Hospitals. To meet the demand of the increased volume, a more efficient method for rapid and accurate sorting of incoming patients was needed. A plan was developed to initiate a 5-level triage acuity rating scale. However, the patient-flow process in the emergency department needed to be addressed prior to implementing the new scale.

From a radial operating theatre to a self‐contained operating table

Equipment congestion and a disarray of wires, tubes and lines (the spaghetti syndrome) is a common scenario in operating theatres. The radial arrangement of input and output signals and their interconnecting lines has been identified as the main source of clutter and congestion in this environment. Our aim was to present a comprehensive design concept for reducing electrical and physical clutter in the operating theatre. Data were collected from different operating theatres, including identification and sorting of equipment, cables, tubes and lines according to the direction and the features of the transmitted information and materials. We suggest a concept of a self-contained, 'built-in' operating table as a design solution for avoiding the clutter and congestion caused by the radial configuration. The operating table will function as a central integrated unit for management of the entire process of patient flow and control of supply systems and environmental conditions.