Inpatient Flow Research Articles

Innovations in inpatient flow and bed management

Purpose– The purpose of this paper is to address a gap in operations management empirical research through the use of diffusion of innovation (DOI) theory to develop further insight into patient flow and bed management, a problem that has been taxing healthcare organizations across the world.Design/methodology/approach– The study used an action research (AR) approach and was conducted over an 18-month period within an acute hospital in the north east of England. Data were generated through enacting AR cycles, interviews, participant observation, document analysis, diaries, meetings, questionnaires and statistical analysis.Findings– The research conducted within this study has not only led to practical outcomes for the hospital in terms of the successful adoption of a new patient flow system but has also led to new knowledge about the determinants of diffusion for technological and process innovations in healthcare organizations which are complex and highly political.Research limitations/implications– AR is not suited to all organizations and is most appropriate within those that are culturally attuned to participative and democratic ways of working. The results from this study are not generalizable but some similar organizations may see merits in this approach.Social implications– The AR approach has supported the hospital in adopting the new system, PFMS. This system is helping to improve the quality of patient care, providing facilities to support the work of clinicians, aiding timely discharge of well patients back into the community and saving the hospital money in terms of not needing to open emergency “winter” wards.Originality/value– From an operations management perspective this work has demonstrated the potential to bring theory, in this case DOI theory, and practice closer together as well as show how academic research can impact organizations. Local-H intends to continue developing its AR approach and take it into other systems projects.

Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time

One key factor contributing to emergency department (ED) overcrowding is prolonged waiting time for admission to inpatient wards, also known as ED boarding time. To gain insights into reducing this waiting time, we study operations in the inpatient wards and their interface with the ED. We focus on understanding the effect of inpatient discharge policies and other operational policies on the time-of-day waiting time performance, such as the fraction of patients waiting longer than six hours in the ED before being admitted. Based on an empirical study at a Singaporean hospital, we propose a novel stochastic processing network with the following characteristics to model inpatient operations: (1) A patient’s service time in the inpatient wards depends on that patient’s admission and discharge times and length of stay. The service times capture a two-time-scale phenomenon and are not independent and identically distributed. (2) Pre- and post-allocation delays model the extra amount of waiting caused by secondary bottlenecks other than bed unavailability, such as nurse shortage. (3) Patients waiting for a bed can overflow to a nonprimary ward when the waiting time reaches a threshold, where the threshold is time dependent. We show, via simulation studies, that our model is able to capture the inpatient flow dynamics at hourly resolution and can evaluate the impact of operational policies on both the daily and time-of-day waiting time performance. In particular, our model predicts that implementing a hypothetical policy can eliminate excessive waiting for those patients who request beds in mornings. This policy incorporates the following components: a discharge distribution with the first discharge peak between 8 a.m. and 9 a.m. and 26% of patients discharging before noon, and constant-mean allocation delays throughout the day. The insights gained from our model can help hospital managers to choose among different policies to implement depending on the choice of objective, such as to reduce the peak waiting in the morning or to reduce daily waiting time statistics. This paper was accepted by Assaf Zeevi, stochastic models and simulation.

Identifying reasons for delays in acute hospitals using the Day-of-Care Survey method

This paper describes a new tool called 'Day-of-Care Survey', developed to assess inpatient delays in acute hospitals. Using literature review, iterative testing and feedback from professional groups, a national multidisciplinary team developed the survey criteria and methodology. Review teams working in pairs visited wards and used case records and bedside charts to assess the patient's status against severity of illness and service intensity criteria. Patients who did not meet the survey criteria for acute care were identified and delays were categorised. From March 2012 to December 2013, nine acute hospitals across Scotland, Australia and England were surveyed. A total of 3,846 adult general inpatient beds (excluding intensive care and maternity) were reviewed. There were 145 empty beds at the time of surveys across the nine sites, with 270 definite discharges planned on the day of the survey. The total number of patients not meeting criteria for acute care was 798/3,431 (23%, range 18-28%). Six factors accounted for 61% (490/798) of the reasons why patients not meeting acute care criteria remained in hospital. This survey gives important insights into the challenges of managing inpatient flow using system level information as a method to target interventions designed to address delay.

A Multidisciplinary Care Pathway Significantly Increases the Number of Early Morning Discharges in a Large Academic Medical Center

In an environment where there is increased demand for hospital beds, it is important that inpatient flow from admission to treatment to discharge is optimized. Among the many drivers that impact efficient patient throughput is an effective and timely discharge process. Early morning discharge helps align inpatient capacity with clinical demand, thereby avoiding gridlock that adversely affects scheduled surgical procedures, diagnostic procedures, and therapies. At our large, academic medical center, we hypothesized that an interdisciplinary approach to scheduled discharge order entry would increase the percentage of discharges occurring before 11:00 AM and improve overall discharge time. The pilot study involved moving rate-limiting steps to earlier in the discharge process, specifically medication reconciliation to the night before discharge and "discharge to home" order entry before 9:00 AM the morning of discharge. The baseline rate of discharges before 11:00 AM was 8% and significantly increased to 11% after the intervention (P = .02). Moreover, in the subset of patients (21%) for whom early medication reconciliation and discharge to home order entry were both executed, the percentage of patient discharges occurring before 11:00 AM increased to 29.7%, with an associated average discharge time of more than 3 hours earlier. No patient harm events were associated with this pilot project. There was no significant change in length of stay, and 30-day readmission rate improved significantly from 13.8% to 10.3% (P = .002). Our study demonstrates that a multidisciplinary approach using prescribed order entry and medication reconciliation is a low cost, safe, and effective way to increase early morning discharges and improve patient flow for large hospitals with high volumes of scheduled patient admissions.

Patient Flow from Emergency Department to Inpatient Wards: Empirical Observations from a Singaporean Hospital

This document presents a comprehensive empirical study on the inpatient flow management in a Singaporean hospital. The empirical study uses high resolution patient flow data from 2008 to 2010. This document details the statistics of waiting times for patients admitted from the emergence department (ED) to inpatient wards, bed occupancy rate of the inpatient wards, and overflow proportions (proportions of patients that are admitted to a non-primary ward). The document also reports various statistics related to patient arrival, inpatient discharge, length of stay (LOS), and pre- and post-allocation delays incurred during the bed-assignment process.

Enhancing Community-Based Rehabilitation for Stroke Survivors: Creating a Discharge Link

Background and Objective: Intensive poststroke rehabilitation is critical to maximizing outcomes, improving inpatient flow, and decreasing long-term costs. This regional initiative investigated the impact of improving access to timely intensive home-based stroke rehabilitation follow-up care on hospital length of stay (LOS) and readmission rates. The enhanced service was made available across the region’s rural geography to new stroke survivors transitioning home who were unable to access outpatient care. Methods: All new stroke survivors with ongoing rehabilitation needs being discharged from the hospital and eligible for home care received timely enhanced intensity of home-based rehabilitation services from existing community rehabilitation providers for 2 months after discharge. Five hundred twenty-four stroke survivors received this service over a 3-year period. The service priority rating was increased to reduce wait times to less than 5 days. Collaborative planning across sectors occurred through discharge link meetings. Comparative analysis was used to investigate health system measures before and after service implementation. Results: A 15.7-day decrease in hospital LOS and decreased hospital readmission rates were observed after enhanced service implementation. Functional Independence Measure (FIM) efficiency improved for those discharged from inpatient rehabilitation. Average wait time for community rehabilitation services decreased from 44 days to 4.4 days. The mean total number of community therapy visits more than doubled to an average of 12 per client. Conclusion: The provision of timely more intensive home-based rehabilitation services for new stroke survivors transitioning home and unable to access outpatient care was associated with decreased hospital LOS and decreased readmission rates.

A Two-Time-Scale Approach to Time-Varying Queues for Hospital Inpatient Flow Management

We analyze a time-varying Mperi/Geo2timeScale/N queueing system. The arrival process is periodic Poisson. The service time of a customer has components in different time scales: length of stay (LOS) in days and departure time (hdis) in hours. This queueing system has been used to study patient flows from the emergency department (ED) to hospital inpatient wards. In that setting, the LOS of a patient is simply the number of days she spends in a ward and her departure time hdis is the discharge hour on the day of her discharge. We develop a new analytical framework that can perform exact analysis on this novel queueing system. This framework has two steps: first analyze the midnight customer count process and obtain its stationary distribution, then analyze the time-dependent customer count process to compute various performance measures. We also develop approximation tools that can significantly reduce the computational time. In particular, via Stein's method, we derive explicit expressions to approximate the stationary distribution of the midnight count. We provide error bounds for these approximations and numerically demonstrate that they are remarkably accurate for systems with various sizes and load conditions. Our theoretical and numerical analysis have produced a number of insights that can be used to improve hospital inpatient flow management. We find that the LOS term affects the overnight wait caused by the mismatch between daily arrivals and discharges, whereas the hdis term affects the intra-day wait caused by the non-synchronization between the arrival and discharge time patterns. Thus, reducing LOS or increasing capacity can impact the daily average performance significantly; shifting the discharge timing to earlier times of a day can alleviate the peak congestion in the morning and mainly affects the time-dependent performance.

Multi-Criteria Knapsack Problem for Disease Selection in an Observation Ward

The aging population and the introduction of Thailand universal healthcare have increased inpatients and outpatients to public hospitals, particularly to a hospital that provides special and comprehensive health services. Many inpatient wards have experienced large influx of inpatients as the hospitals have to admit all patients regardless their conditions. These overcrowding wards cause stress to medical staffs, block access between medical departments, hospital-acquired infections, and ineffective uses of resources. One way to manage such inundated inpatient is to select some patients whose conditions require less clinical attention or whose lengths of stay are predictable and short and, then, place them at an observation ward. This intermediate ward increases turnover of beds and reduces unnecessary paperwork as patients are considered to be outpatients. In this article, we studied inpatient data of a tertiary care hospital in which an observation ward was considered to alleviate the overcrowding problem at Internal Medicine Department. The analysis of data showed that the hospital can balance inpatient flow by managing a group of patients who is admitted because of treatments ordered by its special clinics. Having explored several alternatives, we suggested patient selection criteria and proposed a layout at an observation ward. The hospital should increase medical beds in a new building ward because the current observation ward can handle 27.3% of total short stay patients, while the observation ward is projected to handle 80% of total short stay patients.

America's “best”

In this edition of the American Journal of Emergency Medicine, authors Mullins et al [1] expose that some of our “best” hospitals are, in fact, dysfunctional. Their measurement is wait times in the emergency department (ED), but this is not an ED issue. A crowded ED is a sign of a dysfunctional hospital. The evidence from the scientific literature, government analysis, and the lay press is clear [2-7]—longwaits (and crowding) in the US EDs are primarily due to large number of inpatients kept in the ED awaiting an inpatient bed. When 10%, 20%, 50%, or even 80% of treatment spaces in an ED are filled with inpatients, time to treatment for all patients increases. There are many causes for inpatients remaining in the ED (often termed boarding)—some hospitals have closed inpatient beds because of shortage of nurses and some fill their inpatient beds preferentially with higher profit elective surgical cases and transfers from outside institutions, leaving the less profitable emergency patients to accumulate in the ED. Regardless of the cause, the result is a crowded ED. There are consequences to excessive waits and boarding of inpatients in the ED, all of them bad. We know that the boarding of inpatients in the ED leads to delay in care of all patients in the ED; lower patient satisfaction; higher rates of patients leaving without care; and, most importantly, higher mortality and morbidity for those patients who are waiting and those who seek care [2-7]. A recent abstract by Drew Richardson of Australia suggests that mortality is increased by 0.3% per hour of waiting in the ED. That suggests that patients held in an ED for 600 minutes may have a 3% increase in mortality [8]. Although Dr Richardson has not determined the cause of this increase, the article byMullins et al [1] suggests that many quality measures follow the same trend as wait times; increased infections and increased readmissions are more common in institutions that perform poorly on the ED to inpatient floor flow measures. So if waiting is bad for patients, why do we permit it? It may be that our hospitals do not recognize how bad the situation has become. Crowding is relative. When I served as ACEP President a few years ago, I had the opportunity to visit many EDs and talk to physicians across the nation. Crowding and long waits for inpatient beds are concerns for most emergency physicians. Some complained about 1 or 2 inpatient boarders, whereas others had become accustom to 40 to 60 inpatients. While some were concerned when patients stayed for 2 to 4 hours after being admitted, others were happy if they only stayed 1

Hospital level analysis to improve patient flow.

The complexity of hospital operations ensures that one-size-fits-all solutions seldom work. As hospitals turn to evidence based strategies to redesign flow, it is critical that they tailor the strategies to suit their individual service. This paper analyses the effect of hospital occupancy on inpatient and emergency department patient flow parameters at the Caboolture hospital in Queensland, Australia, and identifies critical levels, or choke points, that result in performance decline. The effect of weekdays and weekends on patient flow is also investigated. We compare these findings to a previous study that has analysed patient flow across Queensland hospitals grouped by size, and discover several differences in the interaction between rising occupancy and patient flow parameters including rates of patient flow, length of stay, and access block. We also identify significantly higher choke points for Caboolture hospital as compared to other similarly sized Queensland hospitals, which suggest that patient flow here can be redesigned to operate at higher levels of occupancy without degrading flow performance. The findings support arguments for hospitals to analyse patient flow at a service level to deliver optimum service improvement.

Unravelling relationships: Hospital occupancy levels, discharge timing and emergency department access block

To investigate the effect of hospital occupancy levels on inpatient and ED patient flow parameters, and to simulate the impact of shifting discharge timing on occupancy levels. Retrospective analysis of hospital inpatient data and ED data from 23 reporting public hospitals in Queensland, Australia, across 30 months. Relationships between outcome measures were explored through the aggregation of the historic data into 21 912 hourly intervals. Main outcome measures included admission and discharge rates, occupancy levels, length of stay for admitted and emergency patients, and the occurrence of access block. The impact of shifting discharge timing on occupancy levels was quantified using observed and simulated data. The study identified three stages of system performance decline, or choke points, as hospital occupancy increased. These choke points were found to be dependent on hospital size, and reflect a system change from 'business-as-usual' to 'crisis'. Effecting early discharge of patients was also found to significantly (P < 0.001) impact overcrowding levels and improve patient flow. Modern hospital systems have the ability to operate efficiently above an often-prescribed 85% occupancy level, with optimal levels varying across hospitals of different size. Operating over these optimal levels leads to performance deterioration defined around occupancy choke points. Understanding these choke points and designing strategies around alleviating these flow bottlenecks would improve capacity management, reduce access block and improve patient outcomes. Effecting early discharge also helps alleviate overcrowding and related stress on the system.

Geographic distribution of healthcare resources, healthcare service provision, and patient flow in Japan: A cross sectional study

Healthcare systems in developed countries are facing the challenge of dealing with changing social structures as a result of rapidly aging populations. This study examines the relationship among the geographical distribution of healthcare resources, healthcare service provision, and interregional patient flow in Japan. A cross-sectional study was performed using data from healthcare-related public surveys conducted in 2008, together with social, economic, and environmental variables. The geographical units of analysis were 348 Secondary Healthcare Service Areas, which provide and manage most healthcare services in Japan. The equity of the distribution of physicians among hospitals and clinics was evaluated using the Lorenz curve and the Gini coefficient. Multiple regression analysis was used to examine the relationships between the inpatient flow ratio and selected variables. Next, the 348 Secondary Healthcare Service Areas were divided into tertiles according to the inpatient flow ratio, and differences among these variables were examined using Bonferroni's correction for multiple comparisons. The Gini coefficient for physician distribution among hospitals was 0.209 and was 0.165 among clinics. Multiple regression analysis showed that hospital physician density, the elderly ratio, and hospital bed density were all correlated with the inpatient flow ratio (β = 0.396, −0.576, 0.425, respectively; R2 = 0.622, all ps < 0.001). Healthcare resources were significantly more scarce in the lowest tertile (outflow group) than in other groups in both hospitals and clinics. The provision of healthcare services was also imbalanced among tertiles. Our results imply that there is a need for reconstituting the geographical distribution of healthcare resources in Japan. Further research and healthcare-related databases are also needed to facilitate the creation of a more balanced geographical distribution and of a more effective healthcare system in Japan.

A Markov decision process to dynamically match hospital inpatient staffing to demand

Appropriate inpatient staffing levels minimize hospital cost and increase patient safety. Hospital inpatient units dynamically adjust premium staffing (above base staffing) levels by attempting to match their daily demand. Historically, inpatient managers subjectively adjust daily staffing from observing the morning inpatient inventory. Inpatient units strive to match staff with demand in a complex patient throughput environment where service rates and non-stationary profiles are not explicitly known. Related queue control and throughput modeling literature do not directly match staffing with demand, require explicit service process knowledge, and are not formulated for an inpatient unit. This paper presents a Markov decision process (MDP) for dynamic inpatient staffing. The MDP explicitly attempts to match staffing with demand, has a statistical discrete time Markov chain foundation that estimates the service process, predicts transient inventory, and is formulated for an inpatient unit. Lastly, the MDP application to a telemetry unit reveals a computational myopic, an approximate stationary, and a finite horizon optimal policy that is validated through hospital expert experience. The application reveals difficult-to-staff inventory levels and shows that the removal of discharge seasonality can drastically decrease required size of the premium staffing pool and the probability of full occupancy thus improving the inpatient unit's patient flow.

An open source software project for obstetrical procedure scheduling and occupancy analysis

Increases in the rate of births via cesarean section and induced labor have led to challenging scheduling and capacity planning problems for hospital inpatient obstetrical units. We present occupancy and patient scheduling models to help address these challenges. These patient flow models can be used to explore the relationship between procedure scheduling practices and the resulting occupancy on inpatient obstetrical units such as labor and delivery and postpartum. The models capture numerous important characteristics of inpatient obstetrical patient flow such as time of day and day of week dependent arrivals and length of stay, multiple patient types and clinical interventions, and multiple patient care units with inter-unit patient transfers. We have used these models in several projects at different hospitals involving design of procedure scheduling templates and analysis of inpatient obstetrical capacity. In the development of these models, we made heavy use of open source software tools and have released the entire project as a free and open source model and software toolkit.

Short-Term Forecasting of Emergency Inpatient Flow

Hospital managers have to manage resources effectively, while maintaining a high quality of care. For hospitals where admissions from the emergency department to the wards represent a large proportion of admissions, the ability to forecast these admissions and the resultant ward occupancy is especially useful for resource planning purposes. Since emergency admissions often compete with planned elective admissions, modeling emergency demand may result in improved elective planning as well. We compare several models for forecasting daily emergency inpatient admissions and occupancy. The models are applied to three years of daily data. By measuring their mean square error in a cross-validation framework, we find that emergency admissions are largely random, and hence, unpredictable, whereas emergency occupancy can be forecasted using a model combining regression and autoregressive integrated moving average (ARIMA) model, or a seasonal ARIMA model, for up to one week ahead. Faced with variable admissions and occupancy, hospitals must prepare a reserve capacity of beds and staff. Our approach allows estimation of the required reserve capacity.

The activity of the Clinic of Ophthalmology as the subunit of the Faculty of Medicine of the University of Lithuania (Vytautas Magnus University) during 1922–1938

The aim of the study was to evaluate the activity and the validity of the expansion of the Clinic of Ophthalmology of the University of Lithuania (since 1930-Vytautas Magnus University) operating in Kaunas city between 1922 and 1938. The evaluation was based on the analysis of changes in inpatient and outpatient flow, the structure of cases of inpatient treatment, and the usage of beds. In the analysis, we used annual reports of the Clinic of Ophthalmology as well as data presented in statistical publications of the Department of Health for the studied period. The changes in the indices of the activity of the Clinic were evaluated using the logarithmic regression coefficient. A more rapid increase in the number of patients discharged from the Clinic of Ophthalmology was observed during 1922-1930 (on the average, by 9% per year). During 1931-1938, only the number of discharged men was increasing. During the studied period, the majority of the cases of inpatient treatment were lenticular diseases (19%), trachoma (16%), and corneal diseases (16%). During 1922-1930, the sharpest increase was observed in the number of inpatients with eyeball diseases and eye traumas (on the average, by 12.3% per year) and during 1931-1938, in the number of patients with trachoma (on the average, by 6.7% per year). The analysis of the indices of the activity of the inpatient unit confirmed the need for the expansion of the Clinic during 1922-1930, but revealed that the expansion of the material basis of the Clinic up to 50 beds during 1931-1938 was not efficient. In the outpatient unit of the Clinic of Ophthalmology, the number of visits per year and the number of admitted patients per year during the studied period increased by 2.5 and 3.5 times, respectively.

Modeling the emergency cardiac in-patient flow: an application of queuing theory

This study investigates the bottlenecks in the emergency care chain of cardiac in-patient flow. The primary goal is to determine the optimal bed allocation over the care chain given a maximum number of refused admissions. Another objective is to provide deeper insight in the relation between natural variation in arrivals and length of stay and occupancy rates. The strong focus on raising occupancy rates of hospital management is unrealistic and counterproductive. Economies of scale cannot be neglected. An important result is that refused admissions at the First Cardiac Aid (FCA) are primarily caused by unavailability of beds downstream the care chain. Both variability in LOS and fluctuations in arrivals result in large workload variations. Techniques from operations research were successfully used to describe the complexity and dynamics of emergency in-patient flow.

Access block causes emergency department overcrowding and ambulance diversion in Perth, Western Australia

Objective: Access block refers to the situation where patients in the emergency department (ED) requiring inpatient care are unable to gain access to appropriate hospital beds within a reasonable time...

Internet-accessible Emergency Department Workload Information Reduces Ambulance Diversion

Objective. To determine the effect of pre-emptive ambulance distribution based on the implementation of a real-time, Internet-accessible emergency department (ED) workload schematic andprehospital Australasian Triage Scale (ATS) allocations on ambulance diversion in Western Australia. Methods. Comparison of July–December 2002 andJuly–December 2003 metropolitan Perth ED cubicle occupancy, ambulance diversion, ambulance distribution, andambulance unloading delays at four inner andfour outer metropolitan EDs. Results. Ambulance diversion fell from 1,788 hours in 2002 to 1,138 hours in 2003 (p < 0.001) despite an increase in mean weekly ED cubicle occupancy from 31 patients (95% confidence internal [CI] 29–33) in 2002 to 39 patients in 2003 (95% CI 36–43, p < 0.001). Inner metropolitan ED ambulance attendances fell 2.7% from 27,475 in 2002 to 26,743 in 2003, andouter metropolitan correspondingly rose from 5,877 to 6,628 ambulance attendances (p < 0.001). Unloading delays were similar in 2002 (219, 0.66%) and2003 (223, 0.67%, p = 0.84); however, median duration of unloading delays increased from 38 minutes (interquartile range [IQR] 18–68) in 2002 to 50 minutes (IQR 25–108) in 2003 (p < 0.001). Conclusions. The implementation of pre-emptive ambulance distribution using Internet-accessible ED information andprehospital ATS allocations was associated with reduced ambulance diversion, probably consequent upon the redistribution of ambulances from inner to outer metropolitan EDs. The rise in ED cubicle occupancy between the study periods suggests that this approach to reducing ambulance diversion should be viewed only as complementary to direct efforts to reduce ambulance diversion by improving hospital inpatient flow andthe balance between acute andelective hospital inpatient accommodation.