Hospital Trust Level Research Articles

How do hospitals respond to payment unbundling for diagnostic imaging of suspected cancer patients?

Payments for some diagnostic scans undertaken in outpatient settings were unbundled from Diagnosis Related Group based payments in England in April 2013 to address under-provision. Unbundled scans attracted additional payments of between £45 and £748 directly following the reform. We examined the effect on utilization of these scans for patients with suspected cancer. We also explored whether any detected effects represented real increases in use of scans or better coding of activity. We applied difference-in-differences regression to patient-level data from Hospital Episodes Statistics for 180 NHS hospital Trusts in England, between April 2010 and March 2018. We also explored heterogeneity in recorded use of scans before and after the unbundling at hospital Trust-level. Use of scans increased by 0.137 scans per patient following unbundling, a 134% relative increase. This increased annual national provider payments by £79.2 million. Over 15% of scans recorded after the unbundling were at providers that previously recorded no scans, suggesting some of the observed increase in activity reflected previous under-coding. Hospitals recorded substantial increases in diagnostic imaging for suspected cancer in response to payment unbundling. Results suggest that the reform also encouraged improvements in recording, so the real increase in testing is likely lower than detected.

The association between mechanical ventilator compatible bed occupancy and mortality risk in intensive care patients with COVID-19: a national retrospective cohort study

BackgroundThe literature paints a complex picture of the association between mortality risk and ICU strain.In this study, we sought to determine if there is an association between mortality risk in intensive care units (ICU) and occupancy of beds compatible with mechanical ventilation, as a proxy for strain.MethodsA national retrospective observational cohort study of 89 English hospital trusts (i.e. groups of hospitals functioning as single operational units). Seven thousand one hundred thirty-three adults admitted to an ICU in England between 2 April and 1 December, 2020 (inclusive), with presumed or confirmed COVID-19, for whom data was submitted to the national surveillance programme and met study inclusion criteria. A Bayesian hierarchical approach was used to model the association between hospital trust level (mechanical ventilation compatible), bed occupancy, and in-hospital all-cause mortality. Results were adjusted for unit characteristics (pre-pandemic size), individual patient-level demographic characteristics (age, sex, ethnicity, deprivation index, time-to-ICU admission), and recorded chronic comorbidities (obesity, diabetes, respiratory disease, liver disease, heart disease, hypertension, immunosuppression, neurological disease, renal disease).ResultsOne hundred thirty-five thousand six hundred patient days were observed, with a mortality rate of 19.4 per 1000 patient days. Adjusting for patient-level factors, mortality was higher for admissions during periods of high occupancy (> 85% occupancy versus the baseline of 45 to 85%) [OR 1.23 (95% posterior credible interval (PCI): 1.08 to 1.39)]. In contrast, mortality was decreased for admissions during periods of low occupancy (< 45% relative to the baseline) [OR 0.83 (95% PCI 0.75 to 0.94)].ConclusionIncreasing occupancy of beds compatible with mechanical ventilation, a proxy for operational strain, is associated with a higher mortality risk for individuals admitted to ICU. Further research is required to establish if this is a causal relationship or whether it reflects strain on other operational factors such as staff. If causal, the result highlights the importance of strategies to keep ICU occupancy low to mitigate the impact of this type of resource saturation.

Correlation between compensated patient claims and 30-day mortality.

To test if compensation claims from patients (reported to the Norwegian System of Patient Injury Compensation) are correlated with the existing quality indicator of 30-day mortality (based on data from Norwegian Patient Registry). This correlation has not been previously evaluated. The association between patient claims and 30-day mortality at hospital trust level was assessed by the Pearson correlation coefficient. The Norwegian System of Patient Injury Compensation is a governmental agency under the Ministry of Health and Care Services and deals with patient-reported complaints about incorrect treatment in the public and private healthcare services. Patient-reported claims may be an indicator of healthcare quality, as 30-day mortality. All 19 Norwegian hospital trusts. : None. Patient claims rates, 30-day mortality and Pearson correlation coefficient. Both number of deaths within 30 days and number of claims have declined over time. High correlation (0.77, P < 0.001) was found between number of deaths within 30 days and the total number of claims. In addition, an even stronger association was found with approved claims, with a correlation coefficient of 0.83 (P < 0.001). Moreover, adjusted 30-day mortality was significantly correlated with the patient-claim rate using number of bed-days as denominator, but not when using number of discharges. The results from the present study indicate an association between compensation claims from patients and 30-day mortality, suggesting that both parameters reflect the latent quality of care for the hospital trusts, but they may capture different aspects of care.

Patient experiences with interdisciplinary treatment for substance dependence: an assessment of quality indicators based on two national surveys in Norway.

Purpose: The quality of health care is often measured using quality indicators, which can be utilized to compare the performance of health-care providers. Conducting comparisons in a meaningful and fair way requires the quality indicators to be adjusted for patient characteristics and other individual-level factors. The aims of the study were to develop and test a case-mix adjustment model for quality indicators based on patient-experience surveys among inpatients receiving interdisciplinary treatment for substance dependence, and to establish whether the quality indicators discriminate between health care providers.Patients and methods: Data were collected through two national surveys involving inpatients receiving residential treatment in Norway in 2013 and 2014. The same questionnaire was used in both surveys, and comprised three patient-experience scales. The scales are reported as national quality indicators, and associations between the scales and patient characteristics were tested through multilevel modeling to establish a case-mix model. The intraclass correlation coefficient was computed to assess the amount of variation at the hospital-trust level.Results: The intraclass correlation coefficient for the patient-reported experience scales varied from 2.3% for “treatment and personnel” to 8.1% for “milieu”. Multivariate multilevel regression analyses showed that alcohol reported as the most frequently used substance, gender and age were significantly associated with two of the three scales. The length of stay at the institution, pressure to be admitted for treatment, and self-perceived health were significantly related to all three scales. Explained variance at the individual level was approximately 7% for all three scales.Conclusion: This study identified several important case-mix variables for the patient-based quality indicators and systematic variations at the hospital-trust level. Future research should assess the association between patient-based quality indicators and other quality indicators, and the predictive validity of patient-experience indicators based on on-site measurements.

30-day mortality after systemic anticancer treatment for breast and lung cancer in England: a population-based, observational study

SummaryBackground30-day mortality might be a useful indicator of avoidable harm to patients from systemic anticancer treatments, but data for this indicator are limited. The Systemic Anti-Cancer Therapy (SACT) dataset collated by Public Health England allows the assessment of factors affecting 30-day mortality in a national patient population. The aim of this first study based on the SACT dataset was to establish national 30-day mortality benchmarks for breast and lung cancer patients receiving SACT in England, and to start to identify where patient care could be improved.MethodsIn this population-based study, we included all women with breast cancer and all men and women with lung cancer residing in England, who were 24 years or older and who started a cycle of SACT in 2014 irrespective of the number of previous treatment cycles or programmes, and irrespective of their position within the disease trajectory. We calculated 30-day mortality after the most recent cycle of SACT for those patients. We did logistic regression analyses, adjusting for relevant factors, to examine whether patient, tumour, or treatment-related factors were associated with the risk of 30-day mortality. For each cancer type and intent, we calculated 30-day mortality rates and patient volume at the hospital trust level, and contrasted these in a funnel plot.FindingsBetween Jan 1, and Dec, 31, 2014, we included 23 228 patients with breast cancer and 9634 patients with non-small cell lung cancer (NSCLC) in our regression and trust-level analyses. 30-day mortality increased with age for both patients with breast cancer and patients with NSCLC treated with curative intent, and decreased with age for patients receiving palliative SACT (breast curative: odds ratio [OR] 1·085, 99% CI 1·040–1·132; p<0·0001; NSCLC curative: 1·045, 1·013–1·079; p=0·00033; breast palliative: 0·987, 0·977–0·996; p=0·00034; NSCLC palliative: 0·987, 0·976–0·998; p=0·0015). 30-day mortality was also significantly higher for patients receiving their first reported curative or palliative SACT versus those who received SACT previously (breast palliative: OR 2·326 99% CI 1·634–3·312; p<0·0001; NSCLC curative: 3·371, 1·554–7·316; p<0·0001; NSCLC palliative: 2·667, 2·109–3·373; p<0·0001), and for patients with worse general wellbeing (performance status 2–4) versus those who were generally well (breast curative: 6·057, 1·333–27·513; p=0·0021; breast palliative: 6·241, 4·180–9·319; p<0·0001; NSCLC palliative: 3·384, 2·276–5·032; p<0·0001). We identified trusts with mortality rates in excess of the 95% control limits; this included seven for curative breast cancer, four for palliative breast cancer, five for curative NSCLC, and seven for palliative NSCLC.InterpretationOur findings show that several factors affect the risk of early mortality of breast and lung cancer patients in England and that some groups are at a substantially increased risk of 30-day mortality. The identification of hospitals with significantly higher 30-day mortality rates should promote review of clinical decision making in these hospitals. Furthermore, our results highlight the importance of collecting routine data beyond clinical trials to better understand the factors placing patients at higher risk of 30-day mortality, and ultimately improve clinical decision making. Our insights into the factors affecting risk of 30-day mortality will help treating clinicians and their patients predict the balance of harms and benefits associated with SACT.FundingPublic Health England.

Risk adjustment in measurements of predicted mortality after myocardial infarction.

In 2014, the government introduced elements of quality-based funding (pay-for-performance) for the hospital sector. Survival is included as a quality indicator. If such quality indicators are to be used for funding purposes, it must be established that the observed variations are caused by conditions that the hospital trusts are able to influence, and not by any underlying variables. The objective of this study was to investigate how the predicted mortality after myocardial infarction was influenced by various forms of risk adjustment. Data from the Norwegian Patient Register on 10,717 patients who had been discharged with the diagnosis of myocardial infarction in 2009 were linked to data on socioeconomic status, comorbidity, travel distances and mortality. The predicted 30-day mortality after myocardial infarction was analysed at the hospital-trust level, using three different models for risk adjustment. Unadjusted 30-day mortality was highest in the catchment area of Førde Hospital Trust (12.5%) and lowest in Asker og Bærum (5.2%). Risk adjustment changed the estimates of mortality for many of the hospital trusts. In the model involving the most comprehensive risk adjustment, mortality was highest in the catchment area of Akershus University Hospital (10.9%) and lowest in the catchment areas of Sunnmøre Hospital Trust (5.2%) and Nordmøre og Romsdal Hospital Trust (5.2%). The variation in treatment quality between the hospital trusts, as measured by predicted mortality after myocardial infarction, is influenced by the methods used for risk adjustment. If the quality-based funding scheme is to continue, well-documented models for risk adjustment of the quality indicators need to be established.

Association of hospital structures with mortality from ruptured abdominal aortic aneurysm.

There is significant variation in the mortality rates of patients with a ruptured abdominal aortic aneurysm (rAAA) admitted to hospital in England. This study sought to investigate whether modifiable differences in hospital structures and processes were associated with differences in patient outcome. Patients diagnosed with rAAA between 2005 and 2010 were extracted from the Hospital Episode Statistics database. After risk adjustment, hospitals were grouped into low-mortality outlier, expected mortality and high-mortality outlier categories. Hospital Trust-level structure and process variables were compared between categories, and tested for an association with risk-adjusted 90-day mortality and non-corrective treatment (palliation) rate using binary logistic regression models. There were 9877 patients admitted to 153 English NHS Trusts with an rAAA during the study. The overall combined (operative and non-operative) mortality rate was 67·5 per cent (palliation rate 41·6 per cent). Seven hospital Trusts (4·6 per cent) were high-mortality and 15 (9·8 per cent) were low-mortality outliers. Low-mortality outliers used significantly greater mean resources per bed (doctors: 0·922 versus 0·513, P < 0·001; consultant doctors: 0·316 versus 0·168, P < 0·001; nurses: 2·341 versus 1·770, P < 0·001; critical care beds: 0·045 versus 0·019, P < 0·001; operating theatres: 0·027 versus 0·019, P = 0·002) and performed more fluoroscopies (mean 12·6 versus 9·2 per bed; P = 0·046) than high-mortality outlier hospital Trusts. On multivariable analysis, greater numbers of consultants, nurses and fluoroscopies, teaching status, weekday admission and rAAA volume were independent predictors of lower mortality and, excluding rAAA volume, a lower rate of palliation. The variability in rAAA outcome in English National Health Service hospital Trusts is associated with modifiable hospital resources. Such information should be used to inform any proposed quality improvement programme surrounding rAAA.

Antibacterial usage in English NHS hospitals as part of a national Antimicrobial Stewardship Programme

Antimicrobial resistance (AMR) has become a global problem for health care services, with fewer antimicrobials entering the market and some pathogenic organisms becoming resistant to commonly used antimicrobials. Antimicrobial stewardship (AS), including evidence-based standard setting, education and communication, and audits of practice, has become a key method of preventing the rise in the rise in AMR. Data on antibiotic consumption are often obtained through prospective and retrospective point prevalence audits of antibiotic usage, but such studies are very resource intensive and only provide a snapshot of consumption. The objective of the study reported here was to examine longitudinal total antibacterial usage at a national level and cross-sectional usage at an individual hospital trust level using a commercial database that captures antimicrobial prescribing from at least 99% of English hospital Trusts.

Machine learning and sentiment analysis of unstructured free-text information about patient experience online

BackgroundTraditional measures of patient experience have included surveys and, more recently, structured patient-reported outcome measures. There are also large amounts of unstructured, free-text information about the quality of health care available on the internet from blogs, social networks, and health-care rating websites that we are not scrutinising. In other industries, real-time natural language processing, such as sentiment analysis, of large datasets has provided a useful analytical approach to find patterns and understand data. If these techniques can be applied to health care, it opens up a novel approach to analyse large volumes of textual information about patient experience. The large number of free-text comments on the UK NHS Choices website allows an opportunity to examine these data through sentiment analysis. These comments are matched with the users' own quantitative ratings of the service, presenting an opportunity to measure the accuracy of natural language processing methods against the patient's own assessment. Simultaneously, the NHS has a developed programme of patient experience measurement via a national survey of hospital inpatients. Using these data sources, we have a natural opportunity to compare our sentiment analysis of comments to traditional patient surveys at an organisational level. MethodsWe tried to predict whether a patient would recommend a hospital, whether the hospital was clean, and whether they were treated with dignity from their free-text descriptions. We applied machine learning and natural language processing techniques to all (6400) online comments about hospitals on the NHS Choices website in 2010. We used open-source Weka data mining software. We used comments from NHS Choices data from 2008, 2009, and 2011 to train the software. Data from 2010 were used to test the predicting accuracy of the approach. We included our own a priori classification of the 1000 most common words and phrases in the analysis. Having calculated the accuracy of our prediction algorithm, we compared the results obtained with the national inpatient survey results for the same year (2010) at the hospital trust level with Spearman's test for rank correlation. FindingsWe were able to predict patients' rating of their care from their free-text comments with an accuracy of 81% for hospital cleanliness, 83% for treatment with dignity, and 89% for overall recommendation. We observed mild to moderate associations between our machine learning predictions and patient survey quantitative responses for the three categories examined: cleanliness (Spearman ρ=0·37, p<0·0001), dignity (ρ=0·50, p<0·0001), and overall recommendation (ρ=0·46, p<0·0001). InterpretationThe prediction accuracy that we have achieved using this machine learning process suggests that we are able to predict, from free text, a reasonably accurate assessment of patients' opinion about different performance aspects of a hospital. We also find that these machine learning predictions are associated to an extent with results of more conventional surveys. This work is ongoing and an iterative process, but suggests that it might be possible to monitor the so-called online cloud of patient experience in real-time and by doing so harness the value of patient opinion. FundingImperial College London is grateful for support from the National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care scheme, the National Institute for Health Research Biomedical Research Centre Funding scheme, and the Imperial Centre for Patient Safety and Service Quality.

The seedbank of a young surface mine wetland: C. A. Cole, Wetlands Ecology & Management, 1(3), 1991, pp 173–184

30-day mortality might be a useful indicator of avoidable harm to patients from systemic anticancer treatments, but data for this indicator are limited. The Systemic Anti-Cancer Therapy (SACT) dataset collated by Public Health England allows the assessment of factors affecting 30-day mortality in a national patient population. The aim of this first study based on the SACT dataset was to establish national 30-day mortality benchmarks for breast and lung cancer patients receiving SACT in England, and to start to identify where patient care could be improved.In this population-based study, we included all women with breast cancer and all men and women with lung cancer residing in England, who were 24 years or older and who started a cycle of SACT in 2014 irrespective of the number of previous treatment cycles or programmes, and irrespective of their position within the disease trajectory. We calculated 30-day mortality after the most recent cycle of SACT for those patients. We did logistic regression analyses, adjusting for relevant factors, to examine whether patient, tumour, or treatment-related factors were associated with the risk of 30-day mortality. For each cancer type and intent, we calculated 30-day mortality rates and patient volume at the hospital trust level, and contrasted these in a funnel plot.Between Jan 1, and Dec, 31, 2014, we included 23 228 patients with breast cancer and 9634 patients with non-small cell lung cancer (NSCLC) in our regression and trust-level analyses. 30-day mortality increased with age for both patients with breast cancer and patients with NSCLC treated with curative intent, and decreased with age for patients receiving palliative SACT (breast curative: odds ratio [OR] 1·085, 99% CI 1·040–1·132; p<0·0001; NSCLC curative: 1·045, 1·013–1·079; p=0·00033; breast palliative: 0·987, 0·977–0·996; p=0·00034; NSCLC palliative: 0·987, 0·976–0·998; p=0·0015). 30-day mortality was also significantly higher for patients receiving their first reported curative or palliative SACT versus those who received SACT previously (breast palliative: OR 2·326 99% CI 1·634–3·312; p<0·0001; NSCLC curative: 3·371, 1·554–7·316; p<0·0001; NSCLC palliative: 2·667, 2·109–3·373; p<0·0001), and for patients with worse general wellbeing (performance status 2–4) versus those who were generally well (breast curative: 6·057, 1·333–27·513; p=0·0021; breast palliative: 6·241, 4·180–9·319; p<0·0001; NSCLC palliative: 3·384, 2·276–5·032; p<0·0001). We identified trusts with mortality rates in excess of the 95% control limits; this included seven for curative breast cancer, four for palliative breast cancer, five for curative NSCLC, and seven for palliative NSCLC.Our findings show that several factors affect the risk of early mortality of breast and lung cancer patients in England and that some groups are at a substantially increased risk of 30-day mortality. The identification of hospitals with significantly higher 30-day mortality rates should promote review of clinical decision making in these hospitals. Furthermore, our results highlight the importance of collecting routine data beyond clinical trials to better understand the factors placing patients at higher risk of 30-day mortality, and ultimately improve clinical decision making. Our insights into the factors affecting risk of 30-day mortality will help treating clinicians and their patients predict the balance of harms and benefits associated with SACT.Public Health England.