Patient Flow Analysis Research Articles

Patient Flow Analysis with a Custom Simulation Engine

Patient flow simulation and analysis is one of the oldest IT -based methods used to optimize patient care processes and hospital management. During the pandemic, interest in this domain suddenly increased due to the various constraints and recommendations to reduce the likelihood of further infections in the hospital. Suddenly, metrics such as the number of patients waiting in the same area, the maximum time a patient could stay in a single room, and the minimum distance between patients became important issues to monitor and optimize. Using data and modelling concepts from various hospitals, our team developed a simulation tool that used bpmn models to define an emergency department. We then modified a single day's usual patient flow with various real-world inspired edge cases to evaluate how the simulated flow would change and which stations would become bottlenecks, where the quality of patient care would deteriorate and rooms would become overcrowded. To execute the models, we developed our own tool based on the open-source Camunda modeling tool and the Business Process Model Notation (BPMN) file format. To execute the generated models, we use our own Python-based execution environment based on the SpiffWorkflow library, which permits extensive logging and extensive customization of the attributes analysed. In addition, the modelling toolkit of Camunda was narrowed down and compiled so that it could be easily used by researchers who are not programmers. In the paper, we present both the modeling process and the scenario design process, as well as the results obtained through the runs, including the maximum waiting times during the model runs and the maximum number of patients waiting at once, which allowed us to validate the effectiveness of the framework.

Computer-aided hospital layout optimization based on patient flow analysis: A case study from China

Chinese hospitals face challenges with overwhelming patient flow and efficiency. This study focuses on optimizing the outpatient building layout of a general hospital to reduce patients’ travel distances. Electronic medical records (EMRs) (N = 64,315) were analyzed using process mining and systematic layout planning to understand patient flows. Optimal path planning was then used to simulate travel routes and calculate travel distances. The existing layout was optimized using a genetic algorithm to minimize total travel distance for general patients, high-volume department patients, and those with mobility limitations. The final layout was selected manually considering relocation expense, local building codes and regulations, functional continuity, and design flexibility. The layout effectively met the optimization objectives, reducing total travel distance for general outpatients by 5.84%, high-volume department patients by 4.58%, and limited mobility patients by 1.90%. Additionally, the final layout demonstrates outstanding performance in spatial adjacency, site-building relationship, and flow disturbance reduction. This study proposes a computer-aided design framework to improve outpatient building renovations. Utilizing EMR data with a large sample size, it broadens the research scope, refines travel distance calculation methods, and includes practical architectural indicators for a comprehensive analysis. The novelty of the study lies in integrating the optimal path planning method considering travel costs of a multi-story building into the framework of Hospital Layout Problems (HLPs) based on Patient Flow Analysis (PFA), and incorporating the opinions of stakeholders at various stages of computerized design. The method offers the potential to enhance hospital building performance and efficiency with minimal adjustments to existing structures.

Implementing Point-of-Care Ultrasound for Acute Scrotal Pain in the Pediatric Emergency Department: Screening Testicular Torsion and Patient Flow Analysis.

Testicular torsion (TT) is a pediatric surgical emergency that requires prompt treatment. This study investigated the feasibility of point-of-care ultrasound (POCUS) for diagnosing TT in the pediatric emergency department (ED). We retrospectively reviewed the medical records of patients, aged 18 years or younger, who visited a university-affiliated hospital pediatric ED with acute scrotal pain without trauma history and underwent diagnostic ultrasounds between January 2010 and October 2022. This study included 731 patients (median age: 9 years), Of these, 315 (43%) were in the POCUS-performed group: 188 in the POCUS-only group, and 127 in the POCUS-and-RADUS group. The other 416 patients (56.9%) were in the RADUS-only group. In total, 45 patients (6.2%) were diagnosed with TT (19 in the POCUS-performed group and 26 in the RADUS-only group). The sensitivity, specificity, and positive and negative predictive values of POCUS for diagnosing TT were 94.7%, 92.9%, 46.2%, and 99.6%, respectively. The median time to perform POCUS was shorter than RADUS (23 versus 61 minutes, P < .001). The POCUS-performed group had a shorter ED length of stay than the RADUS-only group (93 versus 170 minutes, P < .001). Among the patients diagnosed with TT, performing POCUS first did not significantly delay the ED process, including time to operation (250 versus 205 minutes, P = .142). For patients with acute scrotal pain, evaluation performed by pediatric emergency physicians using POCUS performs well in screening TT, and can decrease length of stay in the ED.

Using patient flow analysis with real-time patient tracking to optimize radiation oncology consultation visits

PurposeClinical efficiency is a key component of the value-based care model and a driver of patient satisfaction. The purpose of this study was to identify and address inefficiencies at a high-volume radiation oncology clinic.Methods and materialsPatient flow analysis (PFA) was used to create process maps and optimize the workflow of consultation visits in a gastrointestinal radiation oncology clinic at a large academic cancer center. Metrics such as cycle times, waiting times, and rooming times were assessed by using a real-time patient status function in the electronic medical record for 556 consults and compared between before vs after implementation of the PFA recommendations.ResultsThe initial PFA revealed four inefficiencies: (1) protracted rooming time, (2) inefficient communications, (3) duplicated tasks, and (4) ambiguous clinical roles. We analyzed 485 consult-visits before the PFA and 71 after the PFA. The PFA recommendations led to reductions in overall median cycle time by 21% (91 min vs 72 min, p < 0.001), in cumulative waiting times by 64% (45 min vs 16 min; p < 0.001), which included waiting room time (14 min vs 5 min; p < 0.001) and wait for physician (20 min vs. 6 min; p < 0.001). Slightly less than one-quarter (22%) of consult visits before the PFA lasted > 2 h vs. 0% after implementation of the recommendations (p < 0.001). Similarly, the proportion of visits requiring < 1 h was 16% before PFA vs 34% afterward (p < 0.001).ConclusionsPFA can be used to identify clinical inefficiencies and optimize workflows in radiation oncology consultation clinics, and implementing their findings can significantly improve cycle times and waiting times. Potential downstream effects of these interventions include improved patient experience, decreased staff burnout, financial savings, and opportunities for expanding clinical capacity.

S1481 Impact of Time-Motion Patient Flow Analysis on the Productivity of an Endoscopy Unit in a Large Safety-Net Health Center

Introduction: The COVID-19 pandemic disrupted health care delivery, particularly for high-volume procedural areas. To improve productivity in the Los Angeles County + University of Southern California Medical Center (LAC + USC) Endoscopy Unit, we initiated an iterative rapid cycle quality improvement process to identify inefficiencies and implement changes to our workflow. Methods: A time-motion analysis of patient flow through the LAC + USC Endoscopy Unit was used to construct a time-tracked flow sheet to track individual patients as they moved through the Unit. Data were collected weekly over 3 9-10 week phases, and intervening plan-do-study-act (PDSA) cycles were conducted to direct interventions for subsequent phases. Following phase 1 (9/1/21 to 11/9/21) we implemented targeted interventions at the start of phase 2 (12/1/21 to 2/1/22) and phase 3 (3/15/22 to 5/31/22). Phase 2 was focused on our anesthesia supported endoscopy room which requires greater resource coordination. Metrics were compared to published benchmarks. Linear regression was used to compare outcome parameters for the lean process flow improvement project. Results: Our phase 1 analysis showed operational delays in room turnover time for all procedures and pre-operative assessment and first-case on time start percentage for procedures supported by anesthesia, when compared to published benchmarks (Table 1). In phase 2 we implemented an intervention of combining pre-anesthesia visits with endoscopy teaching visits for patients designated to have anesthesia support. This significantly improved both turnover time and throughput for the anesthesia room (Table 1). In phase 3 we initiated a policy of preparing the first patient of the day in the procedure room which dramatically increased first-case on time start percentage. We further streamlined inter-procedure processes by simultaneously consenting, placing monitoring equipment and documenting in the time between procedures, leading to a greater than 20% increase in total procedure volume (Table 1). Procedure throughput for the anesthesia supported procedure room increased from 4.5 to 7 to 9 procedures per room per day for phases 1, 2, and 3 respectively (Table 1). Endoscopy Unit staffing remained unchanged throughout the study period. Conclusion: Time-motion analysis of patient flow may be used to perform targeted interventions with significant improvements in Endoscopy Unit efficiency. This may be achieved without costly interventions such as hiring additional support staff or faculty. Table 1. - Core Productivity and Efficiency Parameters of LAC+USC Endoscopy Unit Phase 1 FIRST INTERVENTION 12/1/2021: pre-anesthesia visit Phase 2 SECOND INTERVENTION 3/15/2022:First patient prepared in-room and streamlined inter-procedure processes Phase 3 Sep-2021 Oct-2021 Nov-2021 Dec-2021 Jan-2022 Feb-2022 Mar-2022 Apr-2022 May-2022 Total Procedures 539 556 450 511 467 501 504 647 612 Moderate Sedation 515 539 426 488 432 468 465 608 573 Anesthesia Supported 24 17 24 23 35 33 39 39 39 Mean Room Turnover Time (minutes - for anesthesia room) 45 (±40) 31 (±40) 30 (±24) First-Case On Time Start % (moderate sedation) 75.7% 78.3% 87.5% First-Case On Time Start % (anesthesia supported) 0% 0% 92.5% Anesthesia Room Throughput (mean # of procedures per day) 4.5 7.0 9.0

Patient-Flow Analysis for Planning a Focused Hospital Layout: Tampere Heart Hospital Case.

The objective of this study is present how a patient movement-based patient-flow analysis is performed for planning the new Heart Hospital of Tampere University Hospital and how patient transfer distances can be shortened by this method. The Heart Hospital had served patients as a service line organization for years. However, the Heart Hospital layout rather looked like functional layout instead of service line layout because the units of the Heart Hospital have been spread out around the large university hospital campus. The flow routes of patients treated over the course of 1 year were analyzed by information technology systems in the hospital planning phase. Then, the proximity ranking of the main functions of the Heart Hospital was made. Layout planning was performed based on the proximity ranking. Nine months after the opening of the new Heart Hospital, the distances between the various hospital functions were calculated for the old Heart Hospital and the new one. In the old Heart Hospital, patients' transfer distance was 5,654 km (3,513 miles), while the corresponding figure for the new Heart Hospital was 3,797 km (2,359 miles), which means the distance was reduced by 33%. The patient-flow analysis works as it generated substantially shorter patient transfer distances in the new Heart Hospital. Shorter distances have supported more fluent patient flows that, in turn, has contributed higher productivity and quality of care.

Optimizing Radiation Oncology Consult Visits Using Patient Flow Analysis (PFA)

An efficient workflow in the oncology ambulatory care setting can improve patient experience and decrease provider burnout. The purpose of this study was to identify inefficiencies, develop an optimized workflow, and measure the resulting post-implementation impact in a high-volume radiation oncology department.We conducted an IRB-approved study in Gastrointestinal Radiation Oncology (GIRO) at a large academic cancer center. Patient Flow Analysis (PFA) was used to track 556 consults from check-in to check-out. Process maps were created and an improved clinical workflow was designed based on the findings. The specific roles and responsibilities of each clinical team member were defined and clearly communicated. Timepoints were collected using the electronic medical record (EPIC) status board, which was updated by clinical staff. Pre- vs. post-implementation metrics, including total clinic cycle times, waiting times, rooming times, and time spent with each clinical team member were compared.Initial PFA led to recommendations targeting four principal inefficiencies: (1) protracted patient rooming, (2) delays due to inefficient communication, (3) duplicated tasks, and (4) ambiguous clinical roles. There were 485 pre- and 71 post-implementation consults available for analysis. The optimized workflow resulted in reduction in overall median cycle times by 21% (91 vs. 72 min; P < 0.001). Consults > 2 hours in duration occurred in 22% of pre-implementation vs. 0% of post-implementation visits (P < 0.001). Similarly, the proportion of visits requiring < 1 hour was 16% pre- vs. 34% post-implementation (P < 0.001). Patients spent significantly less time in the waiting room (14 vs. 5 min; P < 0.001) despite no significant differences in the proportion of patients arriving early, on-time, or late. Overall, wait times at each step in the visit process were reduced by 55-70% (Table 1).PFA can be used to identify clinical inefficiencies and optimize workflows in radiation oncology. Utilizing this patient-centric model reduced waiting times and total consult duration, which may improve patient satisfaction, decrease staff burnout, and provide a framework for financial savings through innovative staffing models. Efforts are currently underway to expand this process across all sections within our department.

Patient flow analysis in a children’s clinic

Patient flow analysis was used to assess the waiting time of patients referred to a large paediatric outpatient department, and also the lengths of the consultations of the paediatricians and interns. The average waiting times to see the paediatricians and interns were 77 minutes and 7.8 minutes, respectively. The average lengths of consultation for the paediatricians and interns were 3.4 minutes, and 7.7 minutes, respectively

Patient Flow, Health Delivery Processes, and Areas for Improvement in the UP Health Service (UPHS) during May to June 2020 of the COVID-19 Pandemic

Background. Workplace or employees’ clinics play a vital role in disease outbreaks as there could be an influx of sick personnel. Processes and patient flows during pandemics should be documented to identify good practices and sources of operational inefficiencies.&#x0D; Objective. To describe the patient flow, health delivery processes, and areas for improvement at the UPHS during the early phase of the COVID-19 pandemic from May to June 2020.&#x0D; Methods. This was a cross-sectional study involving patient flow analysis of processes at the employees’ clinic of the University of the Philippines-Philippine General Hospital. The study was divided into two major components: clinic process time measurement and process flow mapping. Data collection involved time elements and narrative descriptions of good practices and problems in the process flow.&#x0D; Results. The UPHS staff attended to 1,514 employees’ visits during the 15 working days from May to June 2020. The total UPHS service time from arrival to end of consultation of an employee with a COVID-19-related concern was an average of 1 hour 3 minutes (SD±39 minutes) with a mean total waiting time of 46 minutes (SD±37 minutes). Good practices identified were personnel flexibility in doing other tasks, good communication, and infection control measures. Areas for improvement included symptom screening, implementation of physical distancing, and disinfection practices.&#x0D; Conclusion. The process flows in the UPHS clinic consisted of COVID-19 related consultations, non-COVID-19 related concerns, and swabbing services. Good communication, staff flexibility, infection control measures, and leadership were identified as good practices. Occasional lapses in symptom screening at triage, physical distancing among employees in queuing lines, and inconsistent disinfection practices were the areas for improvement.

Using Patient Flow Analysis to Streamline Intakes at a Community Mental Health Clinic.

This case study reports on a quality improvement strategy used by a community mental health clinic (CMHC) to improve the intake process at the clinic. Patient Flow Analysis (PFA) was used to assess outpatient intakes, identify possible areas of improvement, and test an intervention to improve the intake process. At baseline, the mean time to complete intakes for 22 clients was 106.9min. Using these data, an intervention was designed to reduce the mean intake time, with a target time of 90min. Post-intervention data revealed that the mean time to complete intakes decreased to 94.5min for 28 clients. Patient Flow Analysis is a cost-effective way to assess current processes and identify areas of improvement in the intake flow at CMHCs. The present study used PFA in a CMHC to improve the intake process and saw favorable results from this quality improvement initiative.

Exploratory Data Analysis for Social Big Data Using Regression and Recurrent Neural Networks

In general the health care system and hospital takes a major role in the service sector. The clinical treatment successfully increases in the year for the treatment of both the medical and the technical innovations. A sample theoretical challenge exists in the patient flow analysis for real data. An existing method target on the audience and less concentration on the secondary statistical analysis, where the data obtain from the hospital not suitable for the analysis. So this limitation can overcome using the Exploratory Data Analysis (EDA), which helps in analysis of the patients flow in large hospital. The proposed frame work uses a machine learning method for the data classification processes. The feature extraction processes for the patient data applied for the larger hospital dataset and the individual hospital data. Some similar features are allowed to train over the Recurrent Neural Network (RNN) classifier for data modeling using the large hospital dataset. The output of the classifier has the specific details about the patients taken for the EDA method. The linear regression algorithm be the one kind of statistical tool for predicting the relationship between the variables. The proposed frame work is implemented using Mat lab R2014a software and the results were simulated. The relationship between the patient details and hospital information shows the status of the hospital as healthy and un healthy status.

Treatment-process analysis of orthopaedic trauma surgery patients at an outpatient setting in a university hospital

The cost pressure in acompetitive environment forces hospitals and physicians to optimize clinical processes. In order to secure competitive advantages, a continuous evaluation of relevant processes is necessary. Administrative and medical processes in a university outpatient department for orthopedics and traumatology were evaluated using the lean method in order to reduce patient waiting times. Over aperiod of 2 weeks all patients who were treated in the department for orthopedic and trauma surgery on an outpatient basis were included in the assessment of the process. Personnel in the policlinic were prepared and trained to record times for appointments made by telephone, arrival time at the hospital, first contact, administrative procedure, first contact with the doctor, length of stay in the radiology and anesthesiology departments and completion of treatment. In addition, potential inefficiencies were identified through patient flow analysis and personal interviews with personnel in the administration and outpatient departments as well as residents and senior physicians. A total of 126 patients were enrolled in the study. The average length of stay of patients in the outpatient clinic was 144 min (range 30-371 min). A necessary imaging examination increased the length of stay by an average of 53 min and a necessary premedication by an average of 78 min compared to patients with no further consultations. By analyzing the pathways and times of patients, various reasons for waiting times in the university outpatient clinic could be shown. This study shows that astructured application of lean management and adedicated analysis create added value for patients by reducing waiting times.

Patient Flow Analysis in General Hospitals: How Clinical Disciplines Affect Outpatient Wait Times

Background: Overcrowding is an important problem for outpatient services in healthcare facilities. Patient flow analysis (PFA) is a useful method for identifying inefficiencies in and facilitating patient flow. Objective: PFA was used to estimate patient wait time and determine how different clinical disciplines impact wait times in the studied hospital. Methods: This cross-sectional study investigated a study population comprised of outpatients who referred to clinics at a general hospital in Tehran, Iran. A total of 3836 samples were selected from different stations. Nonrandomized quota sampling was used, and data was gathered using workflow checklists, the content validity of which was proven by experts and hospital authorities. SPSS statistical software was used for data analysis. Results: Total patient stay in the outpatient setting was an estimated 77 minutes (without considering para-clinic units and pharmacy). More than 90% of this time was spent waiting. The wait time of patients at the clinic was greater than that at other stations, and it was less at the cash station than at other stations. Wait times varied at different clinics (P value &lt; 0.001) and were correlated with physician delay (P value &lt; 0.001). Conclusion: The most important result of using PFA in the outpatient setting was managing wait times. This study indicated that a considerable amount of a patient’s total stay in the system was related to waiting for physical examinations. This suggests that the first priority in improving the system should be managing patient wait times for physical examinations.

Process evaluation of implementation fidelity of the integrated chronic disease management model in two districts, South Africa

BackgroundThe Integrated Chronic Disease Management (ICDM) model has been implemented in South Africa to enhance quality of clinical services in Primary Healthcare (PHC) clinics in a context of a high prevalence of chronic conditions and multi-morbidity. This study aimed to assess the implementation fidelity (adherence to guidelines) of the ICDM model.MethodsA cross-sectional study in 16 PHC clinics in two health districts in South Africa: Dr. Kenneth Kaunda (DKK) and West Rand (WR). A fidelity assessment tool with 89 activities and maximum score of 158 was developed from the four interrelated ICDM model components: facility re-organization, clinical supportive management, assisted self-management and strengthening of support systems. Value stream mapping of patient flow was conducted to analyse waiting time and identify operational inefficiencies. ICDM items were scored based on structured observations, facility document reviews and structured questionnaires completed by healthcare workers. Fidelity scores were summarized using medians and proportions and compared by facilities and districts using Chi-Square and Kruskal Wallis test.ResultsThe monthly patient headcount over a six-month period in these 16 PHC clinics was a median of 2430 (IQR: 1685–2942) individuals over 20 years. The DKK district had more newly diagnosed TB patients per month [median 5.5 (IQR: 4.00–9.33) vs 2.0 (IQR: 1.67–2.92)], and fewer medical officers per clinic [median 1 (IQR: 1–1) vs 3.5 (IQR:2–4.5)] compared to WR district. The median fidelity scores in both districts for facility re-organization, clinical supportive management, assisted self-management and strengthening of support systems were 78% [29/37, IQR: 27–31)]; 77% [30/39 (IQR: 27–34)]; 77% [30/39 (IQR: 28–34)]; and 80% [35/44 (IQR: 30–37)], respectively. The overall median implementation fidelity of the ICDM model was 79% (125/158, IQR, 117–132); WR was 80% (126/158, IQR, 123–132) while DKK was 74% (117/158, IQR, 106–130), p = 0.1409. The lowest clinic fidelity score was 66% (104/158), while the highest was 86% (136/158). A patient flow analysis showed long (2–5 h) waiting times and one stream of care for acute and chronic services.ConclusionThere was some variability of scores on components of the ICDM model by PHC clinics. More research is needed on contextual adaptations of the model.

Identifying systems delays in assessment, diagnosis, and operative management for testicular torsion in a single-payer health-care system

Testicular torsion (TT) is a common pediatric urologic emergency. Management of TT is time sensitiveand often confirmed on scrotal Doppler ultrasound (DUS). Acquiring DUS, however, can result in delays in the management of TT, affecting testicular salvage rates. The objective of this study is to identify delays in the assessment and diagnosis for patients presenting with TT to a Canadian academic hospital using patient flow analysis. A retrospective review was performed for patients presenting to the emergency department (ED) who received a scrotal DUS to rule out possible TT between 2012 and 2017. The primary outcome measured cycle-time measurements (median time) between points along the clinical flow pathway for a patient with suspected TT. The secondary outcome assessed diagnostic sensitivity, specificity, positive predictive value (PPV), and negative predictive value(NPV) of standard scrotal DUS components (Doppler flow, arterial waveform, heterogeneous echotexture). A total of 609 patients presented with an acute scrotum warranting a scrotal DUS to rule out TT; of which, 46 underwent scrotal exploration. Testicular salvage rate was 82.6% in the series (38 testes salvaged, 8 required orchiectomy). Median time from symptomonset to ED presentation for patients with possible TT was 4h. After triage, a median of 79.8min was required for ED physician assessment and an additional 48min for scrotal DUS to be performed. Absence of Doppler flow on scrotal DUS had a 97.4% PPV for diagnosing TT confirmed during scrotal exploration. Almost 4h of in-ED time is required from triage to surgical intervention for potential TT at the institution. One area of delay is the time needed to conduct a scrotal DUS (48-128min; Fig.1). This represents an area of opportunity for patient flow optimization through the use of standardized clinical pathways and diagnostic adjuncts, such as point-of-care ultrasound. This study is limited in its retrospective nature and does not include patients with overt signs of TT who underwent surgical detorsion without need for scrotal DUS. Patient flow delays to surgical intervention for patients with TT representa preventable cause of orchiectomy in young men. This study identifies intervention points in patient-care flow pathways where delays to surgical intervention can be potentially reduced by up to 2h. The findings support the need for further studies into the optimization of patient flow and management protocols to reduce delays in the diagnosis and management of TT.

Forecasting arrivals and occupancy levels in an emergency department

This is a sequel to Whitt and Zhang (2017), in which we developed an aggregate stochastic model of an emergency department (ED) based on the publicly available data from the large 1000-bed Rambam Hospital in Haifa, Israel, from 2004–7, associated with the patient flow analysis by Armony et al. (2015). Here we focus on forecasting future daily arrival totals and predicting hourly occupancy levels in real time, given recent history (previous arrival and departure times of all patients) and useful exogenous variables. For the arrival forecasting, we divide the dataset into an initial training set for fitting the models and a final test set to evaluate the performance. By using 200 weeks of data instead of the previous 25, we identify (i) long-term trends in both the arrival process and the length-of-stay distributions and (ii) dependence among successive daily arrival totals, which were undetectable before. From several forecasting methods, including artificial neural network models, we find that a seasonal autoregressive integrated moving average with exogenous (holiday and temperature) regressors (SARIMAX) time-series model is most effective. We then combine our previous ED model with the arrival prediction to create a real-time predictor for the future ED occupancy levels.

Application of a physical science model in the analysis of patient flow in a hospital

One of the most relevant aspects in hospital management relies on how to properly control and predict the patient flow, that is, the paths and the time sequence a whole set of patients run in their journey inside the hospital, as they look for treatment. This issue is of the utmost importance since it interferes in the quality of the healthcare delivered to a person and also has a huge impact on both the costs for the patient and the operational costs for the hospital. This work intends to collaborate with the comprehension of the patient flow analysis and to offer a mathematical model analogous to a physical model capable of, qualitatively at first sight, describing the main variables and properties of this flow. We also present the logical elements that allow the manager to develop quantitative flow evaluations adaptable to a specific institution, based on local measurements of the variables described here. This theoretical formulation can directly be applied to practical situations concerning the management of patient flow. The relevant variables and their mathematical relations can be used by the manager in order to quantify each relevant patient circuit in a hospital. This way, it is expected that recurring problems derived from the unwanted variations in the patient flow can be anticipated and corrected by the manager.

The Elimination of Transfer Distances Is an Important Part of Hospital Design.

The objective of the present study was to describe how a specific patient flow analysis with from-to charts can be used in hospital design and layout planning. As part of a large renewal project at a university hospital, a detailed patient flow analysis was applied to planning the musculoskeletal surgery unit (orthopedics and traumatology, hand surgery, and plastic surgery). First, the main activities of the unit were determined. Next, the routes of all patients treated over the course of 1 year were studied, and their physical movements in the current hospital were calculated. An ideal layout of the new hospital was then generated to minimize transfer distances by placing the main activities with close to each other, according to the patient flow analysis. The actual architectural design was based on the ideal layout plan. Finally, we compared the current transfer distances to the distances patients will move in the new hospital. The methods enabled us to estimate an approximate 50% reduction in transfer distances for inpatients (from 3,100 km/year to 1,600 km/year) and 30% reduction for outpatients (from 2,100 km/year to 1,400 km/year). Patient transfers are nonvalue-added activities. This study demonstrates that a detailed patient flow analysis with from-to charts can substantially shorten transfer distances, thereby minimizing extraneous patient and personnel movements. This reduction supports productivity improvement, cross-professional teamwork, and patient safety by placing all patient flow activities close to each other. Thus, this method is a valuable additional tool in hospital design.

Use of Patient Flow Analysis to Improve Patient Access and Clinical Efficiency in an Ambulatory Women's Clinic [18F

INTRODUCTION: Pregnancy and prenatal care follow-ups require frequent care visits. Studies show that excessive wait time can be a source of patient and employee dissatisfaction and are associated with lower patient satisfaction (P<.05). METHODS: From March 2013 to February 2015, the VMB was utilized as a Patient flow analysis (PFA) to track patient access in the women's clinic. RESULTS: By using VMB, the staff were able to identify inefficiencies in the patient visits, suggest areas in need of improvement, institute changes and test the effectiveness of clinic interventions. Mean wait time in the obstetrical clinic was decreased from 34 minutes to 33 minutes and in the gynecological clinic wait time decreased from 49 minutes to 28 minutes. The delay time before new obstetrical visits could be scheduled decreased from 11 to 7 days after changes were implemented. From January 2014 to August 2014, the volume of initial gynecologic visits increased by 38%, 739 to 1020 appointments), return gynecologic visits increased by 29%, 795 to 1026 appointments, initial obstetrical visits increased by 15%, 152 to 174, repeat obstetrical visits decreased by 2.4%, 1198 to 1169, and using a 10 point Likert-scale revealed that mean patient satisfaction scores increased from 67% to 84%. CONCLUSION: Patient flow analysis (PFA) via VMB is a simple, cost-effective method assesses function of ambulatory health care sites to measure patient flow, identify any inefficiency in the administrative and clinical processes and efficiently collect patient and provider flow data.

A Patient Flow Analysis: Identification of Process Inefficiencies and Workflow Metrics at an Ambulatory Endoscopy Unit.

Background. The increasing demand for endoscopic procedures coincides with the paradigm shift in health care delivery that emphasizes efficient use of existing resources. However, there is limited literature on the range of endoscopy unit efficiencies. Methods. A time and motion analysis of patient flow through the Hotel-Dieu Hospital (Kingston, Ontario) endoscopy unit was followed by qualitative interviews. Procedures were directly observed in three segments: individual endoscopy room use, preprocedure/recovery room, and overall endoscopy unit utilization. Results. Data were collected for 137 procedures in the endoscopy room, 139 procedures in the preprocedure room, and 143 procedures for overall room utilization. The mean duration spent in the endoscopy room was 31.47 min for an esophagogastroduodenoscopy, 52.93 min for a colonoscopy, 30.47 min for a flexible sigmoidoscopy, and 66.88 min for a double procedure. The procedure itself accounted for 8.11 min, 34.24 min, 9.02 min, and 39.13 min for the above procedures, respectively. The focused interviews identified the scheduling template as a major area of operational inefficiency. Conclusions. Despite reasonable procedure times for all except colonoscopies, the endoscopy room durations exceed the allocated times, reflecting the impact of non-procedure-related factors and the need for a revised scheduling template. Endoscopy units have unique operational characteristics and identification of process inefficiencies can lead to targeted quality improvement initiatives.