Myths of ideal hospital occupancy

Large-scale real-life implementation of technology-enabled care to maximize hospitals' medical surge preparedness during future infectious disease outbreaks and winter seasons: a viewpoint

Hospitals can be overburdened with large numbers of patients with severe infectious conditions during infectious disease outbreaks. Such outbreaks or epidemics put tremendous pressure on the admission capacity of care facilities in the concerned region, negatively affecting the elective program within these facilities. Such situations have been observed during the recent waves of the coronavirus disease pandemic. Owing to the imminent threat of a "tripledemic" by new variants of the coronavirus disease (such as the new Omicron XBB.1.16 strain), influenza, and respiratory syncytial virus during future winter seasons, healthcare agencies should take decisive steps to safeguard hospitals' surge capacity while continuing to provide optimal and safe care to a potentially large number of patients in their trusted home environment. Preparedness of health systems for infectious diseases will require dynamic interaction between a continuous assessment of region-wide available hospital capacity and programs for intensive home treatment of patients who can spread the disease. In this viewpoint, we describe an innovative, dynamic coupling system between hospital surge capacity and cascading activation of a nationwide system for remote patient monitoring. This approach was developed using the multi-criteria decision analysis methodology, considering previously published real-life experiences on remote patient monitoring.

Solutions for improved hospital-wide patient flows - a qualitative interview study of leading healthcare providers

BACKGROUND

Hospital productivity is of great importance for patients and public health to achieve better availability and health outcomes. Previous research demonstrates that improvements can be reached by directing more attention to the flow of patients. There is a significant body of literature on how to improve patient flows, but these research projects rarely encompass complete hospitals. Therefore, through interviews with senior managers at the world's leading hospitals, this study aims to identify effective solutions to enable swift patient flows across hospitals and develop a framework to guide improvements in hospital-wide patient flows.

METHODS

This study drew on qualitative data from interviews with 33 senior managers at 18 of the world's 25 leading hospitals, spread across nine countries. The interviews were conducted between June 2021 and November 2021 and transcribed verbatim. A thematic analysis followed, based on inductive reasoning to identify meaningful subjects and themes.

RESULTS

We have identified 50 solutions to efficient hospital-wide patient flows. They describe the importance for hospitals to align the organization; build a coordination and transfer structure; ensure physical capacity capabilities; develop standards, checklists, and routines; invest in digital and analytical tools; improve the management of operations; optimize capacity utilization and occupancy rates; and seek external solutions and policy changes. This study also presents a patient flow improvement framework to be used by healthcare managers, commissioners, and decision-makers when designing strategies to improve the delivery of healthcare services to meet the needs of patients.

CONCLUSIONS

Hospitals must invest in new capabilities and technologies, implement new working methods, and build a patient flow-focused culture. It is also important to strategically look at the patient's whole trajectory of care as one unified flow that must be aligned and integrated between and across all actors, internally and externally. Hospitals need to both proactively and reactively optimize their capacity use around the patient flow to provide care for as many patients as possible and to spread the burden evenly across the organization.

A Model to Compare International Hospital Bed Numbers, including a Case Study on the Role of Indigenous People on Acute 'Occupied' Bed Demand in Australian States

Comparing international or regional hospital bed numbers is not an easy matter, and a pragmatic method has been proposed that plots the number of beds per 1000 deaths versus the log of deaths per 1000 population. This method relies on the fact that 55% of a person's lifetime hospital bed utilization occurs in the last year of life-irrespective of the age at death. This is called the nearness to death effect. The slope and intercept of the logarithmic relationship between the two are highly correlated. This study demonstrates how lines of equivalent bed provision can be constructed based on the value of the intercept. Sweden looks to be the most bed-efficient country due to long-term investment in integrated care. The potential limitations of the method are illustrated using data from English Clinical Commissioning Groups. The main limitation is that maternity, paediatric, and mental health care do not conform to the nearness to death effect, and hence, the method mainly applies to adult acute care, especially medical and critical care bed numbers. It is also suggested that sensible comparison can only be made by comparing levels of occupied beds rather than available beds. Occupied beds measure the expressed bed demand (although often constrained by access to care issues), while available beds measure supply. The issue of bed supply is made complex by the role of hospital size on the average occupancy margin. Smaller hospitals are forced to operate at a lower average occupancy; hence, countries with many smaller hospitals such as Germany and the USA appear to have very high numbers of available beds. The so-called 85% occupancy rule is an "urban myth" and has no fundamental basis whatsoever. The very high number of "hospital" beds in Japan is simply an artefact arising from "nursing home" beds being counted as a "hospital" bed in this country. Finally, the new method is applied to the expressed demand for occupied acute beds in Australian states. Using data specific to acute care, i.e., excluding mental health and maternity, a long-standing deficit of beds was identified in Tasmania, while an unusually high level of occupied beds in the Northern Territory (NT) was revealed. The high level of demand for beds in the NT appears due to an exceptionally large population of indigenous people in this state, who are recognized to have elevated health care needs relative to non-indigenous Australians. In this respect, indigenous Australians use 3.5 times more occupied bed days per 1000 deaths (1509 versus 429 beds per 1000 deaths) and 6 times more occupied bed days per 1000 population (90 versus 15 beds per 1000 population) than their non-indigenous counterparts. The figure of 1509 beds per 1000 deaths (or 4.13 occupied beds per 1000 deaths) for indigenous Australians is indicative of a high level of "acute" nursing care in the last months of life, probably because nursing home care is not readily available due to remoteness. A lack of acute beds in the NT then results in an extremely high average bed occupancy rate with contingent efficiency and delayed access implications.

Ambulances Required to Relieve Overcapacity Hospitals: A Novel Measure of Hospital Strain During the COVID-19 Pandemic in the United States

Study objective

Methods

Results

Conclusion

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

Use of a Novel Patient-Flow Model to Optimize Hospital Bed Capacity for Medical Patients

BACKGROUND

There is no known method for determining the minimum number of beds in hospital inpatient units (IPs) to achieve patient waiting-time targets. This study aims to determine the relationship between patient waiting time-related performance measures and bed utilization, so as to optimize IP capacity decisions.

METHODS

The researchers simulated a novel queueing model specifically developed for the IPs. The model takes into account salient features of patient-flow dynamics and was validated against hospital census data. The team used the model to evaluate inpatient capacity decisions against multiple waiting time outcomes: (1) daily average, peak-hour average, and daily maximum waiting times; and (2) proportion of patients waiting strictly more than 0, 1, and 2 hours. The results were published in a simple Microsoft Excel toolbox to allow administrators to conduct sensitivity analysis.

RESULTS

To achieve the hospital's goal of rooming patients within 30 to 60 minutes of IP bed requests, the model predicted that the optimal daily average occupancy levels should be 89%-92% (182-188 beds) in the Medicine cohort, 74%-79% (41-43 beds) in the Cardiology cohort, and 72%-78% (23-25 beds) in the Observation cohort. Larger IP cohorts can achieve the same queueing-related performance measure as smaller ones, while tolerating a higher occupancy level. Moreover, patient waiting time increases rapidly as the occupancy level approaches 100%.

CONCLUSION

No universal optimal IP occupancy level exists. Capacity decisions should therefore be made on a cohort-by-cohort basis, incorporating the comprehensive patient-flow characteristics of each cohort. To this end, patient-flow queueing models tailored to the IPs are needed.

Does volume or occupancy influence emergency access block? A multivariate time series analysis from a single emergency department in Sydney, Australia during the COVID-19 pandemic

OBJECTIVE

The study aims to determine whether ED presentation volume or hospital occupancy had a greater impact on ED performance before and during the COVID-19 health response at a tertiary referral hospital in Sydney, Australia.

METHODS

Single centre time series analysis using routinely collected hospital and ED data from January 2019 to September 2020. The primary outcome was ED access block measured by emergency treatment performance (ETP; i.e. percentage of patients who were discharged or transferred to a ward from ED within 4 h of ED arrival time). Secondary outcomes were hospital occupancy, elective theatre cases and ambulance ramping. Multivariate time series analysis was performed using vector autoregression, to model effects of changes in various endogenous and correlated variables on ETP.

RESULTS

There was an increase in ETP, drop in ED presentations and decrease in hospital occupancy between April and June 2020. Elective surgery and hospital occupancy had significant effects up to 2 days prior on ETP, while there were no significant effects of either ED or ambulance presentations on ETP. Hospital occupancy itself increased with ED presentations after 2-4 days and decreased with elective surgery after 1 day. Shocks (a one standard deviation increase) in hospital occupancy had a peak impact nearly two times greater compared to ED presentations (-1.43, 95% confidence interval -1.92, -0.93 vs -0.73, 95% confidence interval -1.21, -0.25).

CONCLUSION

The main determinants of the reduction of ED overcrowding and access block during the pandemic were associated with reductions in hospital occupancy and elective surgery levels, and more research is required to assess more complex associations beyond the scope of this manuscript.

Impact of shortened length of stay for delivery on the required bed capacity in maternity services: results from forecast analysis on administrative data

BACKGROUND

We examine the implications of reducing the average length of stay (ALOS) for a delivery on the required capacity in terms of service volume and maternity beds in Belgium, using administrative data covering all inpatient stays in Belgian general hospitals over the period 2003-2014.

METHODS

A projection model generates forecasts of all inpatient and day-care services with a time horizon of 2025. It adjusts the observed hospital use in 2014 to the combined effect of three evolutions: the change in population size and composition, the time trend evolution of ALOS, and the time trend evolution of the admission rates. In addition, we develop an alternative scenario to evaluate the impact of an accelerated reduction of ALOS.

RESULTS

Between 2014 and 2025, we expect the number of deliveries to increase by 4.41%, and the number of stays in maternity services by 3.38%. At the same time, a reduction in ALOS is projected for all types of deliveries. The required capacity for maternity beds will decrease by 17%. In case of an accelerated reduction of the ALOS to reach international standards, this required capacity for maternity beds will decrease by more than 30%.

CONCLUSIONS

Despite an expected increase in the number of deliveries, future hospital capacity in terms of maternity beds can be considerably reduced in Belgium, due to the continuing reduction of ALOS.

Assessing the opportunity costs of patients with multidrug-resistant organisms in hospitals

OBJECTIVES

The concept of opportunity cost can be applied to the utilization of hospital beds with special focus on patients colonized or infected with multidrug-resistant organisms. Blocked beds due to isolation measures or increased length of stay may result in opportunity costs if newly arriving patients have to be rejected and the hospital is confronted with revenue foregone. However, the amount of these costs is unclear, since different approaches are used in the literature to determine the respective costs. Our paper develops a concept to assess opportunity costs from the perspective of a hospital.

METHODS

The analysis is two-stage. In a first step, the probability of rejecting a patient due to over-occupancy in a hospital is calculated with a queuing model and a Monte Carlo simulation taking various assumptions into account. In a second step, the amount of the opportunity costs is calculated as an expected value applying a stochastic approach based on a potential patient pool.

RESULTS

Opportunity costs will occur only with a probability that is influenced, among others, by current bed occupancy rates. They have to be measured by average net revenue foregone, i.e., by the difference between average revenue foregone and average costs avoided.

CONCLUSIONS

Previous studies have a tendency of overestimating the occurrence or the size of opportunity costs with regard to the use of hospital beds. Nonetheless, its influence on the hospital budget is crucial and should be determined exactly.

National Emergency Access Targets metrics of the emergency department-inpatient interface: measures of patient flow and mortality for emergency admissions to hospital

BACKGROUND

Movement of emergency patients across the emergency department (ED)-inpatient ward interface influences compliance with National Emergency Access Targets (NEAT). Uncertainty exists as to how best measure patient flow, NEAT compliance and patient mortality across this interface.

OBJECTIVE

To compare the association of NEAT with new and traditional markers of patient flow across the ED-inpatient interface and to investigate new markers of mortality and NEAT compliance across this interface.

METHODS

Retrospective study of consecutive emergency admissions to a tertiary hospital (January 2012 to June 2014) using routinely collected hospital data. The practical access number for emergency (PANE) and inpatient cubicles in emergency (ICE) are new measures reflecting boarding of inpatients in ED; traditional markers were hospital bed occupancy and ED attendance numbers. The Hospital Standardised Mortality Ratio (HSMR) for patients admitted via ED (eHSMR) was correlated with inpatientNEAT compliance rates. Linear regression analyses assessed for statistically significant associations (expressed as Pearson R coefficient) between all measures and inpatient NEAT compliance rates.

RESULTS

PANE and ICE were inversely related to inpatient NEAT compliance rates (r = 0.698 and 0.734 respectively, P < 0.003 for both); no significant relation was seen with traditional patient flow markers. Inpatient NEAT compliance rates were inversely related to both eHSMR (r = 0.914, P = 0.0006) and all-patient HSMR (r = 0.943, P = 0.0001).

CONCLUSIONS

Traditional markers of patient flow do not correlate with inpatient NEAT compliance in contrast to two new markers of inpatient boarding in ED (PANE and ICE). Standardised mortality rates for both emergency and all patients show a strong inverse relation with inpatient NEAT compliance.

The probability of readmission within 30 days of hospital discharge is positively associated with inpatient bed occupancy at discharge--a retrospective cohort study

BACKGROUND

Previous work has suggested that given a hospital's need to admit more patients from the emergency department (ED), high inpatient bed occupancy may encourage premature hospital discharges that favor the hospital's need for beds over patients' medical interests. We argue that the effects of such action would be measurable as a greater proportion of unplanned hospital readmissions among patients discharged when the hospital was full than when not. In response, the present study tested this hypothesis by investigating the association between inpatient bed occupancy at the time of hospital discharge and the 30-day readmission rate.

METHODS

The sample included all inpatient admissions from the ED at a 420-bed emergency hospital in southern Sweden during 2011-2012 that resulted in discharge before 1 December 2012. The share of unplanned readmissions within 30 days was computed for levels of inpatient bed occupancy of <95%, 95-100%, 100-105% and >105% at the hour of discharge. A binary logistic regression model was constructed to adjust for age, time of discharge, and other factors that could affect the outcome.

RESULTS

In all, 32,811 visits were included in the study, 9.9% of which resulted in an unplanned readmission within 30 days of discharge. The proportion of readmissions was 9.0% for occupancy levels of <95% at the patient's discharge, 10.2% for 95-100% occupancy, 10.8% for 100-105% occupancy, and 10.5% for >105% occupancy (p = 0.0001). Results from the multivariate models show that the OR (95% CI) of readmission was 1.11 (1.01-1.22) for patients discharged at 95-100% occupancy, 1.17 (1.06-1.29) at 100-105% occupancy, and 1.15 (0.99-1.34) at >105% occupancy.

CONCLUSIONS

Results indicate that patients discharged from inpatient wards at times of high inpatient bed occupancy experience an increased risk of unplanned readmission within 30 days of discharge.

StratBAM: A Discrete-Event Simulation Model to Support Strategic Hospital Bed Capacity Decisions

The ability to accurately measure and assess current and potential health care system capacities is an issue of local and national significance. Recent joint statements by the Institute of Medicine and the Agency for Healthcare Research and Quality have emphasized the need to apply industrial and systems engineering principles to improving health care quality and patient safety outcomes. To address this need, a decision support tool was developed for planning and budgeting of current and future bed capacity, and evaluating potential process improvement efforts. The Strategic Bed Analysis Model (StratBAM) is a discrete-event simulation model created after a thorough analysis of patient flow and data from Geisinger Health System's (GHS) electronic health records. Key inputs include: timing, quantity and category of patient arrivals and discharges; unit-level length of care; patient paths; and projected patient volume and length of stay. Key outputs include: admission wait time by arrival source and receiving unit, and occupancy rates. Electronic health records were used to estimate parameters for probability distributions and to build empirical distributions for unit-level length of care and for patient paths. Validation of the simulation model against GHS operational data confirmed its ability to model real-world data consistently and accurately. StratBAM was successfully used to evaluate the system impact of forecasted patient volumes and length of stay in terms of patient wait times, occupancy rates, and cost. The model is generalizable and can be appropriately scaled for larger and smaller health care settings.

The effect of laboratory testing on emergency department length of stay: a multihospital longitudinal study applying a cross-classified random-effect modeling approach

Objectives

The objective was to examine the relationship between laboratory testing (including test volume and turnaround time [TAT]) and emergency department (ED) length of stay (LOS), using linked patient‐level data from four hospitals across 4 years.

Methods

This was a retrospective, multisite cohort study of patients presenting to any one of four EDs in New South Wales, Australia, during a 2‐month period (August and September) in 2008, 2009, 2010, and 2011. Data from ED information systems were linked to laboratory test data. A cross‐classified random‐effect modeling approach was applied to identify factors affecting ED LOS, taking into account the correlation between patients' presentations at the same hospital and/or in the same calendar year. Number of test order episodes (tests ordered at one point in time during the ED stay) and TAT (time from laboratory order receipt to result available) were examined.

Results

As the number of test order episodes increased, so did the duration of patient ED LOS (p < 0.0001). For every five additional tests ordered per test order episode, the median ED LOS increased by 10 minutes (2.9%, p < 0.0001); each 30‐minute increase in TAT was, on average, associated with a 5.1% (17 minutes; p < 0.0001) increase in ED LOS, after adjustment for other factors. Patients presenting to the ED at night (7 p.m. to 7 a.m.) had longer stays than those presenting during the daytime, although the median TATs at nights were shorter than those during the daytime.

Conclusions

Laboratory testing has a direct effect on patients' LOS in ED. Laboratory TAT, number of testing episodes, and test volume influence ED LOS. Targeted increases of ED resources and staffing after‐hours may also contribute to reductions in ED LOS.

High bed occupancy rates: Are they a risk for patients and staff?

AIM

In order to produce empirical evidence on the relationship between high bed occupancy and its potential hazards, this study correlates bed occupancy rates with hospital patient safety and staff overload indicators.

METHODS

Data from nine medium to large scale public hospitals, all pertaining to the Hospital Foundation of Minas Gerais, Brazil, were gathered for the period January 2007 to June 2011. Indicators were aggregated by month, resulting in 486 observations (54 months × 9 hospitals). Bivariate linear regressions were performed, aiming to estimate the effect of bed occupancy rates on each response variable (hospital infection rates, institutional mortality and sick leave incidence). In addition, to directly test the hypothesis that bed occupancy rates over 85% are unsafe, it was included in the models as a categorical instead of continuous variable, using 85% as the cut-off value.

RESULTS

Bed occupancy rates showed an inverse correlation to mortality rates (b = -0.056; P < 0.001) and presented no significant correlation to the nosocomial infection rates (P = 0.512). High bed occupancy (>85%) was associated with a slight increment of short sick leaves, especially short leaves (<7 days) (+0.14%; P = 0.008).

CONCLUSIONS

The increase in hospital loading was unexpectedly associated with reduced institutional mortality and was not related to nosocomial infection incidences. High bed occupancy was associated to a slight increment of short sick leaves.

Probability of severe adverse events as a function of hospital occupancy

A unique application of regression modeling is described to compare hospital bed occupancy with reported severe adverse events amongst inpatients. The probabilities of the occurrence of adverse events as a function of hospital occupancy are calculated using logistic and multinomial regression models. All models indicate that higher occupancy rates lead to an increase in adverse events. The analysis identified that at an occupancy level of 100%, there is a 22% chance of one severe event occurring and a 28% chance of at least one severe event occurring. This modeling contributes evidence toward the management of hospital occupancy to benefit patient outcomes.

Measuring patient flow in a children's hospital using a scorecard with composite measurement

BACKGROUND

Although patient flow is a focus for improvement in hospitals, commonly used single or unaggregated measures fail to capture its complexity. Composite measures can account for multiple dimensions of performance but have not been reported for the assessment of patient flow.

OBJECTIVES

To present and discuss the implementation of a composite measure system as a way to measure and monitor patient flow and improvement activities at an urban children's hospital.

METHODS

A 5-domain patient flow scorecard with composite measurement was designed by an interdisciplinary workgroup using measures involved in multiple aspects of patient flow.

RESULTS

The composite score measurement system provided improvement teams and administrators with a comprehensive overview of patient flow. It captured overall performance trends and identified operational domains and specific components of patient flow that required improvement.

DISCUSSION

A patient flow scorecard with composite measurement holds advantages over a single or unaggregated measurement system, because it provides a holistic assessment of performance while also identifying specific areas in need of improvement.

Associations between in-hospital bed occupancy and unplanned 72-h revisits to the emergency department: a register study

Background

A possible downstream effect of high in-hospital bed occupancy is that patients in the emergency department (ED) who would benefit from in-hospital care are denied admission. The present study aimed at evaluating this hypothesis through investigating associations between in-hospital bed occupancy at the time of presentation in the ED and the probability for unplanned 72-hour (72-h) revisits to the ED among patients discharged at index. A second outcome was unplanned 72-h revisits resulting in admission.

Methods

All visits to the ED of a 420-bed emergency hospital in southern Sweden between 1 January 2011 and 31 December 2012, which did not result in admission, death, or transfer to another hospital were included. Revisiting fractions were computed for in-hospital occupancy intervals <85%, 85% to 90%, 90% to 95%, 95% to 100%, 100% to 105%, and ≥105%. Multivariate models were constructed in an attempt to take confounding factors from, e.g., presenting complaints, age, referral status, and triage priority into account.

Results

Included in the study are 81,878 visits. The fraction of unplanned 72-h revisits/unplanned 72-h revisits resulting in admission was 5.8%/1.4% overall, 6.2%/1.4% for occupancy <85%, 6.4%/1.5% for occupancy 85% to 90%, 5.8%/1.4% for occupancy 90% to 95%, 6.0%/1.6% for occupancy 95% to 100%, 5.4%/1.6% for occupancy 100% to 105%, and 4.9%/1.4% for occupancy ≥105%.

In the multivariate models, a trend to lower probability of unplanned 72-h revisits was observed at occupancy ≥105% compared to occupancy <95% (OR 0.88, CI 0.76 to 1.01). No significant associations between in-hospital occupancy at index and the probability of making unplanned 72-h revisits resulting in admission were observed.

Conclusions

The lack of associations between in-hospital occupancy and unplanned 72-h revisits does not support the hypothesis that ED patients are inappropriately discharged when in-hospital beds are scarce. The results are reassuring as they indicate that physicians are able to make good decisions, also while resources are constrained.

Does hospital occupancy impact discharge rates?

Objective. To understand what impact hospital inpatient occupancy levels have on patient throughput by analysing one hospital’s occupancy levels and the rate of patient discharge. Methods. A four-stage model was fit to hospital admission and separation data and used to analyse the per-capita separation rate according to the patient load and the impact of hospital over-census actions. Results. Per-capita separation rates are significantly higher on days when the hospital declares an over-census due to emergency department crowding. Per-capita separation rates are also higher or lower on days with 8−10% higher or lower patient loads, respectively, but the response is not nearly as strong as the response to an over-census declaration, and is limited to patients with an elapsed stay of 10 days or more. Within the medical division there is an increase in per-capita separation rates on over-census days, but no significant difference in per-capita release rates for different patient loads. Within the surgical division there is no significant difference in per-capita separation rates on over-census days compared with other days, but the patient load does make a significant difference. Conclusion. Staff do discharge a greater proportion of long-stay patients when the hospital is experiencing high demand and a lower proportion when occupancy is low, but the reasons driving those changes remains unclear. What is known about the topic? The evidence regarding safe and efficient levels of hospital occupancy is limited. There is minimal empirical evidence that confirms the relationship between occupancy and discharge rates. What does the paper add? Per-capita separation rates increase strongly on over-census days. The hospital increases per-capita separation rates on days of high occupancy and reduces it on days of low occupancy, mostly for long-stay patients with over 10 days of elapsed stay. The response to high occupancy is not as strong as the response to over-census. The medical division responds strongly to the over-census and the surgical division does not. The surgical division responds more to occupancy levels within its own division than does the medical division. What are the implications for practitioners? The implementation of over-census-type responses to periods of high occupancy may result in increased per-capita separation rate. Using mathematical analysis to understand patient load on per capita separation is important to create a better understanding of health service delivery, thereby aiding hospital managers, and has the potential to guide system improvement. The clinical drivers for these changes and the service design implications require further investigation.