Modular structures and the delivery of inpatient care in hospitals: a Network Science perspective on healthcare function and dysfunction.

BACKGROUND: Reinforced by the COVID-19 pandemic, the capacity of health systems to cope with increasing healthcare demands has been an abiding concern of both governments and the public. Health systems are made up from non-identical human and physical components interacting in diverse ways in varying locations. It is challenging to represent the function and dysfunction of such systems in a scientific manner. We describe a Network Science approach to that dilemma. General hospitals with large emergency caseloads are the resource intensive components of health systems. We propose that the care-delivery services in such entities are modular, and that their structure and function can be usefully analysed by contemporary Network Science. We explore that possibility in a study of Australian hospitals during 2019 and 2020. METHODS: We accessed monthly snapshots of whole of hospital administrative patient level data in two general hospitals during 2019 and 2020. We represented the organisations inpatient services as network graphs and explored their graph structural characteristics using the Louvain algorithm and other methods. We related graph topological features to aspects of observable function and dysfunction in the delivery of care. RESULTS: We constructed a series of whole of institution bipartite hospital graphs with clinical unit and labelled wards as nodes, and patients treated by units in particular wards as edges. Examples of the graphs are provided. Algorithmic identification of community structures confirmed the modular structure of the graphs. Their functional implications were readily identified by domain experts. Topological graph features could be related to functional and dysfunctional issues such as COVID-19 related service changes and levels of hospital congestion. DISCUSSION AND CONCLUSIONS: Contemporary Network Science is one of the fastest growing areas of current scientific and technical advance. Network Science confirms the modular nature of healthcare service structures. It holds considerable promise for understanding function and dysfunction in healthcare systems, and for reconceptualising issues such as hospital capacity in new and interesting ways.

Identifying risk factors and patterns for unplanned readmission to a general medical service.

OBJECTIVE: To identify factors and patterns associated with 7- and 28-day readmission for general medicine patients at a tertiary public hospital. METHODS: A retrospective observational study was conducted using an administrative database at a general medicine service in a tertiary public hospital between 1 January 2007 and 31 December 2011. Demographic and clinical factors, as well as readmission patterns, were evaluated for the association with 7- and 28-day readmission. RESULTS: The study cohort included 13 802 patients and the 28-day readmission rate was 10.9%. In multivariate analysis, longer hospital stay of the index admission (adjusted relative risk (ARR) 1.34), Charlson index ≥ 3 (ARR 1.28), discharge against medical advice (ARR 1.87), active malignancy (ARR 1.83), cardiac failure (ARR 1.48) and incomplete discharge summaries (ARR 1.61) were independently associated with increased risk of 28-day readmission. Patients with diseases of the respiratory system, neurological or genitourinary disease, injury and unclassifiable conditions were likely to be readmitted within 7 days. Patients with circulatory and respiratory disease were likely to be readmitted with the same system diagnosis. CONCLUSION: Readmission of general medicine patients within 28 days is relatively common and is associated with clinical factors and patterns. Identification of these risk factors and patterns will enable the interventions to reduce potentially preventable readmissions.

Predicting admission of patients by their presentation to the emergency department.

OBJECTIVE: The present study aims to determine the importance of certain factors in predicting the need of hospital admission for a patient in the ED. METHODS: This is a retrospective observational cohort study between January 2010 and March 2012. The characteristics, including blood test results, of 100,123 patients who presented to the ED of a tertiary referral urban hospital, were incorporated into models using logistic regression in an attempt to predict the likelihood of patients' disposition on leaving the ED. These models were compared with triage nurses' prediction of patient disposition. RESULTS: Patient age, their initial presenting symptoms or diagnosis, Australasian Triage Scale category, mode of arrival, existence of any outside referral, triage time of day and day of the week were significant predictors of the patient's disposition (P < 0.001). The ordering of blood tests for any patient and the extent of abnormality of those tests increased the likelihood of admission. The accuracy of triage nurses' admission prediction was similar to that offered by a model that used the patients' presentation characteristics. The addition of blood tests to that model resulted in only 3% greater accuracy in prediction of patient disposition. CONCLUSIONS: Certain characteristics of patients as they present to hospital predict their admission. The accuracy of the triage nurses' prediction for disposition of patients is the same as that afforded by a model constructed from these characteristics. Blood test results improve disposition accuracy only slightly so admission decisions should not always wait for these results.

The efficient price: an opportunity for funding reform.

OBJECTIVE: Proposed Australian healthcare reforms describe a move towards partial Commonwealth funding of public hospitals, whereby hospitals will be paid an 'efficient price' for each separation, incorporating both the costs and benefits of services. This paper describes a potential approach to setting the efficient price using risk adjusted cost-effectiveness (RAC-E) analysis. METHODS: RAC-E analysis uses a decision analytic framework to estimate lifetime costs and survival for individual patients, which are standardised by comparing observed and expected values. Analysis of standardised costs and effects at different hospitals identifies efficient hospitals, from which efficient prices can be defined. RESULTS: A RAC-E analysis of services for stroke patients at the four main public hospitals in South Australia demonstrates the need to account for costs and benefits in identifying efficient hospitals. The hospital with the best patient outcomes incurred additional costs relative to less effective hospitals. If an investment of AU$14760 to gain an additional life year in stroke patients is deemed to be a cost-effective use of resources, then the most effective hospital is also the most efficient hospital. CONCLUSIONS: The applied RAC-E analysis demonstrates a framework for comparing the economic efficiency of care provided at different hospitals, which provides a basis for defining the efficient price and appropriate funding incentives to achieve better patient outcomes.

The selection of acute medical admissions for a short-stay unit.

Objective of this study is to evaluate the selection of patients to be admitted to a hospital medical short-stay unit (SSU) where acute medical admissions with a predicted length of stay of between 24 and 72 h are managed. This is a retrospective observational study evaluating outcomes of all admissions to the medical SSU between January 2005 and December 2008. Factors that influence inappropriate allocation of patients to the SSU or alternative longer stay medical units were evaluated. Length of stay (LOS), mortality, Charlson score, admission to intensive care unit (ICU) (from the SSU), discharge diagnosis, and 7-day readmission rate were analysed. Over 4 years, 45% of the general medical inpatient take, 9,125 admission episodes, were managed by the medical SSU. On an average, 72% of these admissions to the SSU stayed fewer than 72 h. After excluding in-hospital deaths, there were 8,381 admissions to the general medical unit discharged within 72 h, and 77% of these were managed by the SSU during the study period. Inappropriate admissions to the SSU (LOS more than 72 h) tended to be older patients with more complex medical comorbidities. Other factors contributing to prolonged stay in the SSU included weekend admissions, and transfers to the ICU. The 7-day readmission rate was low at 3%; the all-cause hospital mortality for patients admitted to the medical SSU was 2% despite a 32% increase in workload in the medical SSU over these 4 years. In the context of fixed resources and a steeply increasing patient workload, a large proportion of general medical patients can be managed in a medical SSU with the majority being discharged home within 72 h while keeping all-cause in-hospital mortality and readmission rates low. More accurate identification of appropriate patients on admission by using a physiological clinical score and addressing operational issues particularly on weekends could lead to a more efficient SSU.

Routine use of administrative data for safety and quality purposes--hospital mortality.

Worldwide, current practice is to report hospital mortality using the hospital standardised mortality ratio (HSMR). An HSMR is generated by comparing an indirectly standardised expected mortality rate against a hospital's observed mortality rate. A hospital's HSMR can be compared with the overall outcomes for all hospitals in a population, or with peer hospitals. HSMRs should be used as screening tools that alert institutions to the need for further investigation, rather than as definitive measures of the quality of care provided by individual hospitals. HSMRs are computed from existing hospital administrative data sources, which are fit for such a purpose. The addition of clinical or physiological data does not, at present, add to the discriminative powers of the risk adjustment models used to adjust HSMR values for differences in hospitals' casemixes. There has been concern that HSMRs may be too variable over time for individual values to be interpretable. A study of HSMR outcomes in Australian hospitals confirmed earlier reports of the stability of the measure. Considerable progress has been made with developing Australian HSMRs for use as routine measures to improve the safety and quality of Australian hospital care.

Outcomes of establishing an acute assessment unit in the general medical service of a tertiary teaching hospital.

OBJECTIVE: To evaluate the impact of an acute assessment unit (AAU) on length of hospital stay (LOS), emergency department (ED) waiting times, direct discharge rate, unplanned readmission rate and all-cause hospital mortality of general medical patients. DESIGN AND SETTING: Retrospective comparison of data for general medical patients admitted to a tertiary teaching hospital in Adelaide, South Australia, before and after the establishment of an AAU (reference years, 2003 [before] and 2006 [after]). MAIN OUTCOME MEASURES: Mean LOS, ED waiting times and all-cause hospital mortality during calendar years 2003 (pre-establishment) and 2006 (post-establishment). RESULTS: Following the establishment of an AAU, the mean LOS shortened (from 6.8 days in 2003 to 5.7 days in 2006; P < 0.001) despite a 50.5% increase in the number of admissions (from 2652 to 3992). The number of admitted patients waiting in the ED more than 8 hours for a hospital bed decreased (from 28.7% to 17.9%; P < 0.001), as did the number waiting more than 12 hours (from 20.2% to 10.4%; P < 0.001). The rates of unplanned readmission within 7 and 28 days did not change. The all-cause hospital mortality for general medical admissions was 4.6% in 2003 v 3.7% in 2006 (P = 0.056). CONCLUSION: The establishment of an AAU within the general medical service coincided with decreases in both LOS and ED waiting times, despite a 50% increase in admissions. This structural reform in the process of acute medical care may have contributed to the improvement in these key health care performance indices without compromising the quality of patient care.

Stability of plasma creatinine concentrations in acute complex long-stay admissions to a general medical service.

Assessment of glomerular filtration rate (GFR) is essential for calculating safe dosages of renally cleared drugs. Formulae for estimating reliable GFRs assume that plasma creatinine concentrations are stable. This study evaluates the variability of plasma creatinine (PCr) concentrations in patients admitted acutely to hospital. From 2,293 newly admitted patients, those in whom a subsequent clinically significant change (> 20%) in PCr had occurred were identified. Median age was 81.1 years. Median baseline PCr was 90 umol/l (eGFR 60 ml/min). In total, 46.3% of the patients had a PCr that varied > 20% from baseline three to seven days following admission. A 10-year increase in age increased the odds of a rise in PCr over the next week by 11.1% (odds ratio = 1.11, 95% confidence interval = 1.03, 1.20; p = 0.007). Overall, baseline creatinine was a poor predictor of subsequent variation in PCr. GFR formulae for calculating renally-cleared drug dosages should be used with caution in elderly patients admitted acutely to hospital.

Measurement for improvement: a survey of current practice in Australian public hospitals.

OBJECTIVE: To identify patient safety measurement tools in use in Australian public hospitals and to determine barriers to their use. DESIGN: Structured survey, conducted between 4 March and 19 May 2005, designed to identify tools, and to assess current use of, levels of satisfaction with, and barriers to use of tools for measuring the domains and subdomains of: organisational capacity to provide safe health care; patient safety incidents; and clinical performance. PARTICIPANTS AND SETTING: Hospital executives, managers and clinicians from a nationwide random sample of Australian public hospitals stratified by state and hospital peer grouping. MAIN OUTCOME MEASURES: Tools used by hospitals within the three domains and their subdomains; patient safety tools and processes identified by individuals at these hospitals; satisfaction with the tools; and barriers to their use. RESULTS: Eighty-two of 167 invited hospitals (49%) responded. The survey ascertained a comprehensive list of patient safety measurement tools that are in current use for measuring all patient safety domains. Overall, there was a focus on use of processes rather than quantitative measurement tools. Approximately half the 182 individual respondents from participating hospitals reported satisfaction with existing tools. The main reported barriers were lack of integrated supportive systems, resource constraints and inadequate access to robust measurement tools validated in the Australian context. Measurement of organisational capacity was reported by 50 (61%), of patient safety incidents by 81 (99%) and of clinical performance by 81 (99%). CONCLUSION: Australian public hospitals are measuring the safety of their health care, with some variation in measurement of patient safety domains and their subdomains. Improved access to robust tools may support future standardisation of measurement for improvement.

Patient journeys: the process of clinical redesign.

*Clinical process redesign is a successful improvement method that has been used to increase access to health services in 60 public hospitals across New South Wales, and at Flinders Medical Centre (FMC) in South Australia. *The method focuses on the patient journey as the primary improvement locus, and uses process mapping to identify the value-adding steps in that journey; it involves redesign teams identifying and eliminating non-value-adding steps to improve flow and reduce delays in access to emergency and elective care. *The method engages clinicians, managers, patients and carers, and delivers real gains in health care delivery. *This article outlines the clinical process redesign programs being used by NSW Health and at FMC.

Clinical process redesign for unplanned arrivals in hospitals.

*Emergency department performance had been deteriorating in NSW Health facilities and at Flinders Medical Centre before a fundamentally new approach involving a redesign method, additional bed capacity and more rigorous hospital performance management was applied. *Redesign was undertaken in over 60 hospitals in New South Wales. *Numerous disconnections and misalignments in the process of care delivery have been uncovered during the diagnostic phase of this redesign. *Solutions addressed the entire patient journey through the hospital, to produce smoother patient flow along the continuum of care. *To achieve a sustained improvement in performance, numerous solutions must be simultaneously implemented in each hospital. *With this multipronged approach, a turnaround in NSW emergency access performance has been achieved in the face of rising demand for services; the improvement has continued over 3 years. *This article reports on our findings from system-wide redesign for unplanned hospital attendances.

Redesigning care at the Flinders Medical Centre: clinical process redesign using "lean thinking".

*The Flinders Medical Centre (FMC) Redesigning Care program began in November 2003; it is a hospital-wide process improvement program applying an approach called "lean thinking" (developed in the manufacturing sector) to health care. *To date, the FMC has involved hundreds of staff from all areas of the hospital in a wide variety of process redesign activities. *The initial focus of the program was on improving the flow of patients through the emergency department, but the program quickly spread to involve the redesign of managing medical and surgical patients throughout the hospital, and to improving major support services. *The program has fallen into three main phases, each of which is described in this article: "getting the knowledge"; "stabilising high-volume flows"; and "standardising and sustaining". *Results to date show that the Redesigning Care program has enabled the hospital to provide safer and more accessible care during a period of growth in demand.