Variations and inter-relationship in outcome from emergency admissions in England: a retrospective analysis of Hospital Episode Statistics from 2005–2010

Wide variations in clinical outcomes between hospitals have been described for a number of conditions. In addition, a number of high-profile failures of healthcare have been reported [1, 2]. One common theme has been a failure to deliver safe and high quality care, with subsequent poor clinical outcomes. In response to such variations and failures, there have been suggestions that rating systems for hospitals could be developed in tandem with stringent hospital inspections. The aim of such a system would be to inform key stakeholders on the quality of care delivered by providers and to provide a method to prevent problems from developing, detect failure before harm is done and to facilitate a timely response.

The feasibility of a single rating being able to achieve all these aims has been questioned by commentators, including a report by the Nuffield Trust [3, 4]. In particular, a rating system can only be useful if significant variations in outcome exist between hospitals, and can only be of value if the outcomes of different diagnoses are inter-related within individual hospitals [5]. Whether ratings should be limited to specific conditions, or whether they can be hospital-wide requires evaluation [6]. Therefore, it is important to define whether hospitals perform at a similar level of outcome across a range of conditions, or whether individual providers encompass a range of performance levels, dependent on specialty [7‐9].

The objectives of this study were to determine whether the risk-standardised clinical outcomes for emergency admissions varied significantly between providers, and whether outcomes were inter-related between different diagnoses at a hospital level.

The data source for this retrospective study was the national administrative dataset, the Hospital Episode Statistics (HES) data, for the period 1^st April 2005 to 31^st March 2010. The HES detail every admission into the NHS and allow patients to be tracked between NHS hospitals and across years through the use of a unique pseudo-anonymised identifier. The dataset can be considered an inclusive record of National Health Service (NHS) hospital activity in England as there is a requirement for every hospital to submit a “minimum dataset” to the Department of Health. Each hospital submits data from its own Patient Administration System (PAS) to the centralised HES data warehouse which is then validated and cleaned by the NHS Information Centre according to a pre-specified list of rules. HES contains publicly available admitted patient care data from 1989 onwards, with more than 12 million new records added each year. Each HES record contains a significant amount of information about the associated admission including patient demographics, the treating hospital, diagnostic and procedural coding, length of stay, discharge status and destination. Emergency and elective admissions can be differentiated by specific combinations of codes relating to type of admission and diagnostic/procedural code sub-groups. As a result, it is possible to identify patient cohorts with specific characteristics with a high degree of accuracy [10]. A recent report based on HES data describes the approximate number of emergency admissions in England as more than 4.5 million per annum [11].

A five-year study period was chosen to achieve a pragmatic balance between sample size and the cohort reflecting contemporary practice. The HES were linked with the Office of National Statistics (ONS) Registry data to provide longer-term and out-of-hospital mortality data. Clinical coding in the HES is through the OPCS-4 procedural coding system and ICD-10 diagnostic codes [12].

Twenty patient groups were used that covered the breadth of hospital emergency admissions in adults (>17years of age) to acute NHS (public) hospitals. The groups were chosen a-priori by quantifying the commonest emergency admission diagnoses with the highest numbers of deaths to all acute NHS hospitals using a sample of HES data (2010/2011) (Additional file 1: Tables S1 and S2). ICD-10, OPCS-4 and HRG codes were used to create clinically meaningful groups. Medical conditions were defined by ICD-10 codes whilst surgical groups were defined by OPCS-4 codes.

The primary outcome measure was in-hospital all-cause risk-standardised mortality rate (in-RSMR) and secondary outcomes were one-year all-cause risk-standardised mortality rate (1yr-RSMR) and 28-day all-cause emergency readmission rate (RSRR) [13]. In-hospital mortality was defined as death occurring after admission to an index hospital and before discharge either from the index hospital or any subsequent receiving hospital in cases where the patient was transferred from the index hospital (a definition equivalent to a “super-spell” whereby a “spell” is HES-specific terminology denoting one continuous admission at a hospital) [12, 14]. RSRRs were quantified using only patients discharged alive from hospital.

Established methods were used to extract and clean the data, and entries with missing key fields, such as operation dates, were excluded from analyses (representing 1.15% of the dataset) [15‐17]. Each patient was included only once, using the diagnostic or procedural code in the primary field in the first occurrence for allocation to a clinical group. This prevented a single patient being included multiple times due to complications of care, or multiple surgical procedures.

The confounding effect of inter-hospital transfers on in-RSMR and RSRR was accounted for as patients can be tracked between hospitals within the HES. Previous studies have shown that linking concurrent admissions together and assigning the ultimate outcome to the index hospital (super-spells) provides the most accurate reflection of in-hospital death rates [12].

2,406,709 admissions were included across 20 emergency groups in the five-year period. Summary demographic and outcome data for each group are provided (Table 1).

This study analysed nearly 2.5 million emergency admissions to acute English NHS Trusts over a contemporary five-year period, with one year follow-up. The risk-standardised analyses covered the commonest acute medical and acute surgical diagnoses and procedures using granular, and clinically meaningful subgroups.

The key finding of this study was to demonstrate significant variations between hospitals in terms of the in-hospital mortality rates, one-year mortality rates and 28-day emergency readmission rates that followed non-elective medical or surgical admissions. Furthermore, highly relevant within-provider inter-relationships were seen such that the outcome of a particular condition in one hospital was strongly associated with the aggregated outcome of the other conditions in that hospital.

These data are suggestive of the presence of hospital-level factors that determine the outcomes of a variety of disparate emergency conditions [25, 26]. In some cases, this translates into the outcomes from the same diagnosis being significantly better in one hospital than in another hospital. Importantly, in addition to this inter-hospital variation, the outcomes of apparently disparate diagnoses within individual providers are inter-related, such that systemic structural and process factors are implicated that underlie clinical outcomes. These factors remain incompletely defined, but appear to contribute to a system-wide phenomenon that persisted in analyses of 28-day emergency readmission rates and one-year mortality. Whilst it is likely that some of these influences are definable structure or process factors, they might also be representative of more abstract concepts such as institutional attitudes that stimulate excellence [27, 28]. The scale of variation between hospital outcomes was concerning, and often exceeded both statistical and clinical boundaries of acceptability. At a time when the quality of care delivered is being closely examined, such variations require further investigation. Modifiable technical, organisational or hospital-related factors play an important role in patient care, and merit further study in order to optimise service delivery and to improve care. Hospital-level factors which have thus far been demonstrated to influence outcomes for certain defined clinical groups include availability of high-dependency beds with adequate medical staffing levels, co-localisation of ancillary specialties, nurse-to-patient ratios, teaching status and hospital size [29‐33]. Process factors are more heterogeneous and consequently can be more difficult to identify but have, nonetheless, also been shown to influence outcomes [34‐36]. This supposition is further supported by the findings of others that the association between hospital procedural volume and post-operative mortality lacks specificity [9]. Good governance practices such as complete and accurate data collection/submission, internal audit, transparent publication of results and benchmarking against defined quality standards perpetuate good performance. Gauging improvement against national Quality Improvement Frameworks requires objective appraisal of outcomes and must be accompanied by a willingness to change sub-standard practices and to embrace service remodelling if necessary. Robust local monitoring and mandatory submission of data is needed to ensure early reaction to divergent performance and will be of interest to commissioners who may use evidence of such practice to shape service delivery [37]. Transparent reporting of results with clear elaboration of statistical methodology used to risk-adjust data and identify outliers and peer-review of outcomes in an environment seeking to improve quality of care rather than to ostracize individuals or units is essential [14]. Interestingly, current prevailing opinion is that supposed patient empowerment through reporting of clinical outcomes in their current form is unlikely to drive service improvement through market forces as patients tend rely more on subjective information or patient experience measures (PEMs) such as hospital cleanliness [38].

On the basis of these results, the development of an outcomes rating system could be cautiously supported, as this work has shown that there is a need for improved outcomes and reduced variations in outcome in emergency care [39‐41]. However, in the first instance, any composite rating system must be based on clinically meaningful subgroups, with definable endpoints. A single pan-provider rating is unlikely to be the correct model [42‐44]. These results suggest that acute conditions require detailed appraisal at the speciality level at least. Furthermore, a single rating system that purports to cover both hospitals’ clinical and non-clinical performance will likely be misleading [3].

For emergency medical and surgical admissions in England, wide variations in outcome exist between providers. In addition, strong associations in outcome were found between disparate clinical groups within individual providers that suggested the presence of underlying global structure and process factors underpinning clinical outcomes. These results have implications for the way in which care is delivered and provides potential targets for global quality improvement.