Linking ambulance, emergency department and hospital admissions data: understanding the emergency journey

Mastering Linked Datasets: The Future of Emergency Health Care Research

Objectives: The aim of this work is to describe routine integration of prehospital emergency health records into a health master linkage file, delivering ongoing access to integrated patient treatment and outcome information for ambulance-attended patients in Queensland.Methods: The Queensland Ambulance Service (QAS) data are integrated monthly into the Queensland Health Master Linkage File (MLF) using a linkage algorithm that relies on probabilistic matches in combination with deterministic rules based on patient demographic details, date, time and facility identifiers. Each ambulance record is assigned an enduring linkage key (unique patient identifier) and further processing determines whether each record matches with a corresponding hospital emergency department, admission or death registry record. In this study, all QAS electronic ambulance report form (eARF) records from October 2016 to December 2018 where at least 1 key linkage variable was present (n = 1,771,734) were integrated into the MLF.Results: The majority of records (n = 1,456,502; 82.2%) were for transported patients, and 90.1% (n = 1,312,176) of these transports were to public hospital facilities. Of these transport records, 93.9% (n = 1,231,951) matched to emergency department (ED) records and 59.3% (n = 864,394) also linked to admitted patient records. Of ambulance non-transport records integrated into the MLF, 23.6% (n = 74,311) matched with ED records.Conclusion: This study demonstrates robust linkage methods, quality assurance processes and high linkage rates of data across the continuum of care (prehospital/emergency department/admitted patient/death) in Queensland. The resulting infrastructure provides a high-quality linked dataset that facilitates complex research and analysis to inform critical functions such as quality improvement, system evaluation and design.

Linking Ambulance Records with Hospital and Death Index Data to Evaluate Patient Outcomes

Objective

Linkage of electronic administrative datasets is becoming increasingly common, offering a powerful resource for research and analysis. However, routine linkage of prehospital data with emergency department (ED) presentation and hospital admission datasets is rare. We describe a methodology used to link ambulance data with hospital ED presentations, admissions, and death records, and examine potential biases between matched and unmatched patients.

Methods

Iterative deterministic linkage methodologies were employed to link clinical, operational, and secondary triage ambulance data to ED presentations, hospital admissions, and death records in Victoria, Australia. Descriptive analyses and standardised differences were used to examine potential biases between matched and unmatched patients.

Results

A total of 2,813,913 ambulance records were available for linkage. Of the patients that were transported to a public ED (n=1,753,268), 83.3% matched with an ED record. Only small differences were observed between matched and unmatched patients for sex, year, time of day and attending crew type. The data elements with the largest standardised differences were patient age (0.25) and paramedic diagnosis (0.25). Matched patients were older (mean ± standard deviation: 55.6±25.7 vs 49.0±26.0 years) and more likely to have a paramedic-suspected cardiac, respiratory, neurological, or gastrointestinal/genitourinary condition, suspected infection/sepsis, or pain.

Conclusion

This linked dataset will facilitate a large body of research into prehospital care and patient outcomes. Although future analysis of matched patients should acknowledge the linkage error rate, our findings suggest that results are likely to be generalisable to the broader ambulance population.

[Linking Health Claims Data and Records of Emergency Medical Services: Building a Bridge via Patient's Health Insurance Number?]

INTRODUCTION

In Germany, Emergency Medical Services (EMS) were involved in a total of 7.3 million emergency cases in 2016/2017. Information on prehospital care is stored in several secondary data sources, yet combined analysis of these data at the level of individual patients or EMS cases happens rarely. Research is needed on which methods and variables are suitable for the linkage of these data sources.

METHODS

We linked EMS records from five Bavarian emergency service districts to health claims data belonging to ten statutory health insurers (data from 2016). Two linkage approaches at the level of individual patient's EMS case/reimbursement case were demonstrated. First, a deterministic linkage was conducted based on the patient's unique identifying health insurance number. The second linkage was probabilistic. As linkage variables, it comprised the only partially available health insurance number plus several non-unique key variables, the latter being a patient's health insurance provider, sex, year of birth and distance travelled. In order to verify the deterministic and the probabilistic linkages' quality, rates of accordance of several variables present in both data sources were calculated.

RESULTS

The starting point for our data linkage were 106,371 EMS records (independent of certain health insurance companies) and 432,693 EMS services reimbursed by health insurers (independent of specific EMS providers). 4,327 EMS records could be linked to health claims data - out of 5,921 EMS records that coded a health insurance company contributing claims data to Inno_RD. With a probabilistic linkage, it was possible to increase this number to a total of 5,379 linked EMS records. All checks carried out indicated a high linkage quality for both the deterministic and the probabilistic approach.

CONCLUSION

A linkage of EMS records with health claims data is possible. In Inno_RD, a probabilistic approach has proven a valuable alternative to deterministic linkage via health insurance number since EMS records can be linked meaningfully even if the health insurance number is unavailable or where a minority of non-unique key variables show non-accordance or missing values.

Patient-specific record linkage between emergency department and hospital admission data for a cohort of people who inject drugs: methodological considerations for frequent presenters

Background

People who inject drugs (PWID) have been identified as frequent users of emergency department (ED) and hospital inpatient services. The specific challenges of record linkage in cohorts with numerous administrative health records occurring in close proximity are not well understood. Here, we present a method for patient-specific record linkage of ED and hospital admission data for a cohort of PWID.

Methods

Data from 688 PWID were linked to two state-wide administrative health databases identifying all ED visits and hospital admissions for the cohort between January 2008 and June 2013. We linked patient-specific ED and hospital admissions data, using administrative date-time timestamps and pre-specified linkage criteria, to identify hospital admissions stemming from ED presentations for a given individual. The ability of standalone databases to identify linked ED visits or hospital admissions was examined.

Results

There were 3459 ED visits and 1877 hospital admissions identified during the study period. Thirty-four percent of ED visits were linked to hospital admissions. Most links had hospital admission timestamps in-between or identical to their ED visit timestamps (n = 1035, 87%). Allowing 24-h between ED visits and hospital admissions captured more linked records, but increased manual inspection requirements. In linked records (n = 1190), the ED ‘departure status’ variable correctly reflected subsequent hospital admission in only 68% of cases. The hospital ‘admission type’ variable was non-specific in identifying if a preceding ED visit had occurred.

Conclusions

Linking ED visits with subsequent hospital admissions in PWID requires access to date and time variables for accurate temporal sorting, especially for same-day presentations. Selecting time-windows to capture linked records requires discretion. Researchers risk under-ascertainment of hospital admissions if using ED data alone.

A validation of machine learning-based risk scores in the prehospital setting

BACKGROUND

The triage of patients in prehospital care is a difficult task, and improved risk assessment tools are needed both at the dispatch center and on the ambulance to differentiate between low- and high-risk patients. This study validates a machine learning-based approach to generating risk scores based on hospital outcomes using routinely collected prehospital data.

METHODS

Dispatch, ambulance, and hospital data were collected in one Swedish region from 2016-2017. Dispatch center and ambulance records were used to develop gradient boosting models predicting hospital admission, critical care (defined as admission to an intensive care unit or in-hospital mortality), and two-day mortality. Composite risk scores were generated based on the models and compared to National Early Warning Scores (NEWS) and actual dispatched priorities in a prospectively gathered dataset from 2018.

RESULTS

A total of 38203 patients were included from 2016-2018. Concordance indexes (or areas under the receiver operating characteristics curve) for dispatched priorities ranged from 0.51-0.66, while those for NEWS ranged from 0.66-0.85. Concordance ranged from 0.70-0.79 for risk scores based only on dispatch data, and 0.79-0.89 for risk scores including ambulance data. Dispatch data-based risk scores consistently outperformed dispatched priorities in predicting hospital outcomes, while models including ambulance data also consistently outperformed NEWS. Model performance in the prospective test dataset was similar to that found using cross-validation, and calibration was comparable to that of NEWS.

CONCLUSIONS

Machine learning-based risk scores outperformed a widely-used rule-based triage algorithm and human prioritization decisions in predicting hospital outcomes. Performance was robust in a prospectively gathered dataset, and scores demonstrated adequate calibration. Future research should explore the robustness of these methods when applied to other settings, establish appropriate outcome measures for use in determining the need for prehospital care, and investigate the clinical impact of interventions based on these methods.

Using deterministic record linkage to link ambulance and emergency department data: is it possible without patient identifiers? A case study from the UK

INTRODUCTION

Routine linkage of emergency ambulance records with those from the emergency department is uncommon in the UK. Our study, known as the Pre-Hospital Emergency Department Data Linking Project (PHED Data), aimed to link records of all patients conveyed by a single emergency ambulance service to thirteen emergency departments in the UK from 2012-2016.

OBJECTIVES

We aimed to examine the feasibility and resource requirements of collecting de-identified emergency department patient record data and, using a deterministic matching algorithm, linking it to ambulance service data.

METHODS

We used a learning log to record contacts and activities undertaken by the research team to achieve data linkage. We also conducted semi-structured interviews with information management/governance staff involved in the process.

RESULTS

We found that five steps were required for successful data linkage for each hospital trust. The total time taken to achieve linkage was a mean of 65 weeks. A total of 958,057 emergency department records were obtained and, of these, 81% were linked to a corresponding ambulance record. The match rate varied between hospital trusts (50%-94%). Staff expressed strong enthusiasm for data linkage. Barriers to successful linkage were mainly due to inconsistencies between and within acute trusts in the recording of two ambulance event identifiers (CAD and call sign). Further data cleaning was required on emergency department fields before full analysis could be conducted. Ensuring the data was not re-identifiable limited validation of the matching method.

CONCLUSION

We conclude that deterministic record linkage based on the combination of two event identifiers (CAD and call sign) is possible. There is an appetite for data linkage in healthcare organisations but it is a slow process. Developments in standardising the recording of emergency department data are likely to improve the quality of the resultant linked dataset. This would further increase its value for providing evidence to support improvements in health care delivery.

HIGHLIGHTS

Ambulance records are rarely linked to other datasets; this study looks at the feasibility and resource requirement to use deterministic matching to link ambulance and emergency department data for patients conveyed by ambulance to the emergency department.It is possible to link these data, with an average match rate of 81% across 13 emergency departments and one large ambulance trust.All trusts approached provided match-able data and there was an appetite for data linkage; however, it was a long process taking an average of 65 weeks.We conclude that deterministic matching using no patient identifiers can be used in this setting.

Developing new ways of measuring the quality and impact of ambulance service care: the PhOEBE mixed-methods research programme

Background

Ambulance service quality measures have focused on response times and a small number of emergency conditions, such as cardiac arrest. These quality measures do not reflect the care for the wide range of problems that ambulance services respond to and the Prehospital Outcomes for Evidence Based Evaluation (PhOEBE) programme sought to address this.

Objectives

The aim was to develop new ways of measuring the impact of ambulance service care by reviewing and synthesising literature on prehospital ambulance outcome measures and using consensus methods to identify measures for further development; creating a data set linking routinely collected ambulance service, hospital and mortality data; and using the linked data to explore the development of case-mix adjustment models to assess differences or changes in processes and outcomes resulting from ambulance service care.

Design

A mixed-methods study using a systematic review and synthesis of performance and outcome measures reported in policy and research literature; qualitative interviews with ambulance service users; a three-stage consensus process to identify candidate indicators; the creation of a data set linking ambulance, hospital and mortality data; and statistical modelling of the linked data set to produce novel case-mix adjustment measures of ambulance service quality.

Setting

East Midlands and Yorkshire, England.

Participants

Ambulance services, patients, public, emergency care clinical academics, commissioners and policy-makers between 2011 and 2015.

Interventions

None.

Main outcome measures

Ambulance performance and quality measures.

Data sources

Ambulance call-and-dispatch and electronic patient report forms, Hospital Episode Statistics, accident and emergency and inpatient data, and Office for National Statistics mortality data.

Results

Seventy-two candidate measures were generated from systematic reviews in four categories: (1) ambulance service operations (n = 14), (2) clinical management of patients (n = 20), (3) impact of care on patients (n = 9) and (4) time measures (n = 29). The most common operations measures were call triage accuracy; clinical management was adherence to care protocols, and for patient outcome it was survival measures. Excluding time measures, nine measures were highly prioritised by participants taking part in the consensus event, including measures relating to pain, patient experience, accuracy of dispatch decisions and patient safety. Twenty experts participated in two Delphi rounds to refine and prioritise measures and 20 measures scored ≥ 8/9 points, which indicated good consensus. Eighteen patient and public representatives attending a consensus workshop identified six measures as important: time to definitive care, response time, reduction in pain score, calls correctly prioritised to appropriate levels of response, proportion of patients with a specific condition who are treated in accordance with established guidelines, and survival to hospital discharge for treatable emergency conditions. From this we developed six new potential indicators using the linked data set, of which five were constructed using case-mix-adjusted predictive models: (1) mean change in pain score; (2) proportion of serious emergency conditions correctly identified at the time of the 999 call; (3) response time (unadjusted); (4) proportion of decisions to leave a patient at scene that were potentially inappropriate; (5) proportion of patients transported to the emergency department by 999 emergency ambulance who did not require treatment or investigation(s); and (6) proportion of ambulance patients with a serious emergency condition who survive to admission, and to 7 days post admission. Two indicators (pain score and response times) did not need case-mix adjustment. Among the four adjusted indicators, we found that accuracy of call triage was 61%, rate of potentially inappropriate decisions to leave at home was 5–10%, unnecessary transport to hospital was 1.7–19.2% and survival to hospital admission was 89.5–96.4% depending on Clinical Commissioning Group area. We were unable to complete a fourth objective to test the indicators in use because of delays in obtaining data. An economic analysis using indicators (4) and (5) showed that incorrect decisions resulted in higher costs.

Limitations

Creation of a linked data set was complex and time-consuming and data quality was variable. Construction of the indicators was also complex and revealed the effects of other services on outcome, which limits comparisons between services.

Conclusions

We identified and prioritised, through consensus processes, a set of potential ambulance service quality measures that reflected preferences of services and users. Together, these encompass a broad range of domains relevant to the population using the emergency ambulance service. The quality measures can be used to compare ambulance services or regions or measure performance over time if there are improvements in mechanisms for linking data across services.

Future work

The new measures can be used to assess different dimensions of ambulance service delivery but current data challenges prohibit routine use. There are opportunities to improve data linkage processes and to further develop, validate and simplify these measures.

Funding

The National Institute for Health Research Programme Grants for Applied Research programme.

Using Patient Flow Information to Determine Risk of Hospital Presentation: Protocol for a Proof-of-Concept Study

Background

Every day, patients are admitted to the hospital with conditions that could have been effectively managed in the primary care sector. These admissions are expensive and in many cases are possible to avoid if early intervention occurs. General practitioners are in the best position to identify those at risk of imminent hospital presentation and admission; however, it is not always possible for all the factors to be considered. A lack of shared information contributes significantly to the challenge of understanding a patient’s full medical history. Some health care systems around the world use algorithms to analyze patient data in order to predict events such as emergency presentation; however, those responsible for the design and use of such systems readily admit that the algorithms can only be used to assess the populations used to design the algorithm in the first place. The United Kingdom health care system has contributed data toward algorithm development, which is possible through the unified health care system in place there. The lack of unified patient records in Australia has made building an algorithm for local use a significant challenge.

Objective

Our objective is to use linked patient records to track patient flow through primary and secondary health care in order to develop a tool that can be applied in real time at the general practice level. This algorithm will allow the generation of reports for general practitioners that indicate the relative risk of patients presenting to an emergency department.

Methods

A previously designed tool was used to deidentify the general practice and hospital records of approximately 100,000 patients. Records were pooled for patients who had attended emergency departments within the Eastern Health Network of hospitals and general practices within the Eastern Health Network catchment. The next phase will involve development of a model using a predictive analytic machine learning algorithm. The model will be developed iteratively, testing the combination of variables that will provide the best predictive model.

Results

Records of approximately 97,000 patients who have attended both a general practice and an emergency department have been identified within the database. These records are currently being used to develop the predictive model.

Conclusions

Records from general practice and emergency department visits have been identified and pooled for development of the algorithm. The next phase in the project will see validation and live testing of the algorithm in a practice setting. The algorithm will underpin a clinical decision support tool for general practitioners which will be tested for face validity in this initial study into its efficacy.

Measuring care trajectories using health administrative databases: a population-based investigation of transitions from emergency to acute care

Background

A patient’s trajectory through the healthcare system affects resource use and outcomes. Data fields in population-based administrative health databases are potentially valuable resources for constructing care trajectories for entire populations, provided they can capture patient transitions between healthcare services. This study describes patient transitions from the emergency department (ED) to other healthcare settings, and ascertains whether the discharge disposition field recorded in the ED data was a reliable source of patient transition information from the emergency to the acute care settings.

Methods

Administrative health databases from the province of Saskatchewan, Canada (population 1.1 million) were used to identify patients with at least one ED visit to provincial teaching hospitals (n = 5) between April 1, 2006 and March 31, 2012. Discharge disposition from ED was described using frequencies and percentages; and it includes categories such as home, transfer to other facilities, and died. The kappa statistic with 95 % confidence intervals (95 % CIs) was used to measure agreement between the discharge disposition field in the ED data and hospital admission records.

Results

We identified N = 1,062,861 visits for 371,480 patients to EDs over the six-year study period. Three-quarters of the discharges were to home, 16.1 % were to acute care in the same facility in which the ED was located, and 1.6 % resulted in a patient transfer to a different acute care facility. Agreement between the discharge disposition field in the ED data and hospital admission records was good when the emergency and acute care departments were in the same facility (κ = 0.77, 95 % CI 0.77, 0.77). For transfers to a different acute care facility, agreement was only fair (κ = 0.36, 95 % CI 0.35, 0.36).

Conclusions

The majority of patients who attended EDs did not transition to another healthcare setting. For those who transitioned to acute care, accuracy of the discharge disposition field depended on whether the two services were provided in the same facility. Using the hospital data as reference, we conclude that the discharge disposition field in the ED data is not reliable for measuring transitions from ED to acute care.

Improved outcomes for emergency department patients whose ambulance off-stretcher time is not delayed

OBJECTIVE

To describe and compare characteristics and outcomes of patients who arrive by ambulance to the ED. We aimed to (i) compare patients with a delayed ambulance offload time (AOT) >30 min with those who were not delayed; and (ii) identify predictors of an ED length of stay (LOS) of >4 h for ambulance-arriving patients.

METHODS

A retrospective, multi-site cohort study was undertaken in Australia using 12 months of linked health data (September 2007-2008). Outcomes of AOT delayed and non-delayed presentations were compared. Logistic regression analysis was undertaken to identify predictors of an ED LOS of >4 h.

RESULTS

Of the 40 783 linked, analysable ambulance presentations, AOT delay of >30 min was experienced by 15%, and 63% had an ED LOS of >4 h. Patients with an AOT <30 min had better outcomes for: time to triage; ambulance time at hospital; time to see healthcare professional; proportion seen within recommended triage time frame; and ED LOS for both admitted and non-admitted patients. In-hospital mortality did not differ. Strong predictors of an ED LOS >4 h included: hospital admission, older age, triage category, and offload delay >30 min.

CONCLUSION

Patients arriving to the ED via ambulance and offloaded within 30 min experience better outcomes than those delayed. Given that offload delay is a modifiable predictor of an ED LOS of >4 h, targeted improvements in the ED arrival process for ambulance patients might be useful.

Expanding emergency department capacity: a multisite study

OBJECTIVES

The aims of the present study were to identify predictors of admission and describe outcomes for patients who arrived via ambulance to three Australian public emergency departments (EDs), before and after the opening of 41 additional ED beds within the area.

METHODS

The present study was a retrospective comparative cohort study using deterministically linked health data collected between 3 September 2006 and 2 September 2008. Data included ambulance offload delay, time to see doctor, ED length of stay (LOS), admission requirement, access block, hospital LOS and in-hospital mortality. Logistic regression analysis was undertaken to identify predictors of hospital admission.

RESULTS

Almost one-third of all 286037 ED presentations were via ambulance (n=79196) and 40.3% required admission. After increasing emergency capacity, the only outcome measure to improve was in-hospital mortality. Ambulance offload delay, time to see doctor, ED LOS, admission requirement, access block and hospital LOS did not improve. Strong predictors of admission before and after increased capacity included age >65 years, Australian Triage Scale (ATS) Category 1-3, diagnoses of circulatory or respiratory conditions and ED LOS >4h. With additional capacity, the odds ratios for these predictors increased for age >65 years and ED LOS >4h, and decreased for ATS category and ED diagnoses.

CONCLUSIONS

Expanding ED capacity from 81 to 122 beds within a health service area impacted favourably on mortality outcomes, but not on time-related service outcomes such as ambulance offload time, time to see doctor and ED LOS. To improve all service outcomes, when altering (increasing or decreasing) ED bed numbers, the whole healthcare system needs to be considered.

Impact of opening a new emergency department on healthcare service and patient outcomes: analyses based on linking ambulance, emergency and hospital databases

BACKGROUND

Emergency department (ED) crowding caused by access block is an increasing public health issue and has been associated with impaired healthcare delivery, negative patient outcomes and increased staff workload.

AIM

To investigate the impact of opening a new ED on patient and healthcare service outcomes.

METHODS

A 24-month time series analysis was employed using deterministically linked data from the ambulance service and three ED and hospital admission databases in Queensland, Australia.

RESULTS

Total volume of ED presentations increased 18%, while local population growth increased by 3%. Healthcare service and patient outcomes at the two pre-existing hospitals did not improve. These outcomes included ambulance offload time: (Hospital A PRE: 10 min, POST: 10 min, P < 0.001; Hospital B PRE: 10 min, POST: 15 min, P < 0.001); ED length of stay: (Hospital A PRE: 242 min, POST: 246 min, P < 0.001; Hospital B PRE: 182 min, POST: 210 min, P < 0.001); and access block: (Hospital A PRE: 41%, POST: 46%, P < 0.001; Hospital B PRE: 23%, POST: 40%, P < 0.001). Time series modelling indicated that the effect was worst at the hospital furthest away from the new ED.

CONCLUSIONS

An additional ED within the region saw an increase in the total volume of presentations at a rate far greater than local population growth, suggesting it either provided an unmet need or a shifting of activity from one sector to another. Future studies should examine patient decision making regarding reasons for presenting to a new or pre-existing ED. There is an inherent need to take a 'whole of health service area' approach to solve crowding issues.

Tracking the patient journey by combining multiple hospital database systems

With new national targets for patient flow in public hospitals designed to increase efficiencies in patient care and resource use, better knowledge of events affecting length of stay will support improved bed management and scheduling of procedures. This paper presents a case study involving the integration of material from each of three databases in operation at one tertiary hospital and demonstrates it is possible to follow patient journeys from admission to discharge. What is known about this topic? At present, patient data at one Queensland tertiary hospital are assembled in three information systems: (1) the Hospital Based Corporate Information System (HBCIS), which tracks patients from in-patient admission to discharge; (2) the Emergency Department Information System (EDIS) containing patient data from presentation to departure from the emergency department; and (3) Operation Room Management Information System (ORMIS), which records surgical operations. What does this paper add? This paper describes how a new enquiry tool may be used to link the three hospital information systems for studying the hospital journey through different wards and/or operating theatres for both individual and groups of patients. What are the implications for practitioners? An understanding of the patients’ journeys provides better insight into patient flow and provides the tool for research relating to access block, as well as optimising the use of physical and human resources.

The development of a data-matching algorithm to define the ‘case patient’

Objectives. To describe a model that matches electronic patient care records within a given case to one or more patients within that case. Method. This retrospective study included data from all metropolitan Ambulance Victoria electronic patient care records (n = 445 576) for the time period 1 January 2009–31 May 2010. Data were captured via VACIS (Ambulance Victoria, Melbourne, Vic., Australia), an in-field electronic data capture system linked to an integrated data warehouse database. The case patient algorithm included ‘Jaro–Winkler’, ‘Soundex’ and ‘weight matching’ conditions. Results. The case patient matching algorithm has a sensitivity of 99.98%, a specificity of 99.91% and an overall accuracy of 99.98%. Conclusions. The case patient algorithm provides Ambulance Victoria with a sophisticated, efficient and highly accurate method of matching patient records within a given case. This method has applicability to other emergency services where unique identifiers are case based rather than patient based. What is known about the topic? Accurate pre-hospital data that can be linked to patient outcomes is widely accepted as critical to support pre-hospital patient care and system performance. What does this paper add? There is a paucity of literature describing electronic matching of patient care records at the patient level rather than the case level. Ambulance Victoria has developed a complex yet efficient and highly accurate method for electronically matching patient records, in the absence of a patient-specific unique identifier. Linkage of patient information from multiple patient care records to determine if the records are for the same individual defines the ‘case patient’. What are the implications for practitioners? This paper describes a model of record linkage where patients are matched within a given case at the patient level as opposed to the case level. This methodology is applicable to other emergency services where unique identifiers are case based.

Is the increase in emergency short-stay admissions sustainable? Trends across Melbourne, 2000 to 2009

OBJECTIVES

To describe the trends in emergency admissions over 10 years in terms of volume, age-specific rates, hospital length of stay (LOS) and clinical reasons.

METHODS

A retrospective analysis of population-based linked Department of Health ED and hospital admission data for metropolitan Melbourne 1999/2000 to 2008/2009 was conducted. Outcome measures included: hospital admission numbers (total, single day/overnight, ≥2 days LOS); admission rates per 1000 person-years (total, single day/overnight, ≥2 days LOS); hospital LOS.

RESULTS

The volume of patients admitted to hospital through EDs rose by 56% over the 10 years to June 2009. The number of same day/overnight admissions rose by 60%, equating to a 6.1% average annual increase beyond that accounted for by demographic change (95% CI 5.7-6.5%). The volume of patients admitted for ≥2 days also increased; however, the admission rate per 1000 persons for these longer-stay patients declined over the decade by 9% (95% CI 5-12%). The most frequent discharge diagnoses were injury or poisoning, and disorders of the circulatory, respiratory or digestive systems. The numbers and mortality rate for ED admissions declined over the decade.

CONCLUSION

Emergency hospital admissions have risen over the last decade even after adjustment for population changes. There was a disproportionate rise in same day/overnight admissions, with overrepresentation of the elderly. This is possibly related to changes in ED models of care, including introduction of short-stay units, improved diagnostic and therapeutic capability, and risk-averse management to optimise safe discharge, within the context of time-based performance targets.

The methodology of the Australian Prehospital Outcomes Study of Longitudinal Epidemiology (APOStLE) Project

This paper describes the methodology of a large emergency medical services (EMS) data linkage research project currently under way in the statewide EMS system of New South Wales, Australia. The paper is intended to provide the reader with an understanding of how linkage techniques can be used to facilitate EMS research. This project, the Australian Prehospital Outcomes Study of Longitudinal Epidemiology (APOStLE) Project, links data from six statewide sources (computer-assisted dispatch, EMS patient health care reports, emergency department data, inpatient data, and two death registries) to enable researchers to examine the patient's entire journey through the health care system, from the emergency 0-0-0 call to the emergency department and inpatient setting, through to discharge or death, for approximately 2.6 million patients transported by the Ambulance Service of New South Wales to emergency departments between June 2006 and July 2009. Manual, deterministic, and probabilistic data linkages are described, and potential applications of linked data in EMS research are outlined.