Summary Hospital-level Mortality Indicator Research Articles

Hospital workforce engagement, satisfaction, burnout and effects on patient mortality: Findings from the English national health service staff surveys.

Previous studies of healthcare organizations' workforces and their performance have focused on burnout and its impact on care. The aim of this research is to expand on this and examine the association of positive organizational states, engagement and recommendation of employer as a place to work, in comparison to burnout on Hospital performance. Methods: This was a panel study of the respondents to the 2012-2019 yearly Staff Surveys of the English National Health Service (NHS) hospital Trusts with hospital performance measured by the adjusted inpatient Summary Hospital-level Mortality Indicator (SHMI). Results: In univariable regression, all three organizational states significantly and negatively correlated with SHMI, with recommendation and engagement showing a nonlinear effect. In multivariable analysis, all three states remained significant predictors of SHMI. Engagement and recommendation showed mutual correlation, with engagement being a more prevalent state than recommendation. Conclusion: Our study indicates that organizations could benefit from monitoring multiple workforce variables to preserve or enhance workforce well-being, while optimizing organizational performance. The surprising finding that higher burnout was associated with improved short-term performance requires further investigation, as does the finding of less frequent staff recommendation of work compared to staff engagement with their work.

Staffing levels and hospital mortality in England: a national panel study using routinely collected data

ObjectivesExamine the association between multiple clinical staff levels and case-mix adjusted patient mortality in English hospitals. Most studies investigating the association between hospital staffing levels and mortality have focused on...

Hospital Mortality and Trainee Experiences: How General Medical Council Survey Findings Correlate With Summary Hospital-Level Mortality Indicator.

This study aimed to investigate whether components of junior doctor satisfaction are associated with patient mortality within the United Kingdom. We conducted a cross-sectional study of publicly available data (the General Medical Council [GMC] National Survey and the Summary Hospital-level Mortality Indicator [SHMI]) pertaining to subjective physician trainee satisfaction and patient mortality within 80 United Kingdom-based healthcare institutions. The direction and strength of correlation between components of the GMC National Survey and relative patient mortality as described by the SHMI were calculated. Additional outcomes included mean GMC survey scores for reported domains and mean SHMI by healthcare institution. SHMI for included healthcare institutions ranged from 0.69 to 1.21 (mean [SD], 1.01 [0.1]). Mean GMC domain scores ranged between 44.61 and 88.62 (mean [SD], 71.16 [10.84]). Statistically significant correlations were observed for clinical supervision, clinical supervision out of hours, rota design, overall satisfaction, and teamwork. After application of Bonferroni correction, statistically significant correlations remained for both clinical supervision and clinical supervision out of hours. There is a significant association between components of subjective trainee satisfaction and patient mortality within the United Kingdom. Further investigation to examine these relationships, perhaps to target intervention, may prevent avoidable patient harm.

The impact of management practices on relative patient mortality: Evidence from public hospitals.

A small, but growing, body of empirical evidence shows that the material and persistent variation in many aspects of the performance of healthcare organisations can be related to variation in their management practices. This study uses public data on hospital patient mortality outcomes, the Summary Hospital-level Mortality Indicator (SHMI) to extend this programme of research. We assemble a five-year dataset combining SHMI with potential confounding variables for all English NHS non-specialist acute hospital trusts. The large number of providers working within a common system provides a powerful environment for such investigations. We find considerable variation in SHMI between trusts and a high degree of persistence of high- or low performance. This variation is associated with a composite metric for management practices based on the NHS National Staff Survey. We then use a machine learning technique to suggest potential clusters of individual management practices related to patient mortality performance and test some of these using traditional multivariate regression. The results support the hypothesis that such clusters do matter for patient mortality, and so we conclude that any systematic effort at improving patient mortality should consider adopting an optimal cluster of management practices.

Organisational values of National Health Service trusts in England: semantic analysis and relation to performance indicators

BackgroundOrganisational values are widely assumed to have positive effects on performance and staff. National Health Service (NHS) trusts in England have accordingly chosen their own organisational values. However, there has...

The association between physician staff numbers and mortality in English hospitals.

BackgroundPhysician medical specialties place specific demands on medical staff. Often patients have multiple co-morbidities, frailty is common, and mortality rates are higher than other specialties such as surgery. The key intervention for patients admitted under physician subspecialties is the care provided on the ward. The current evidence base to inform staffing in physician medical specialty wards is limited. The aim of this analysis is to investigate the association between medical staffing levels within physician medical specialties and mortality.MethodsThis study is a cross-sectional analysis of national data, which is aggregated at provider level. Medical beds per senior, middle grade and junior physicians employed in physician medical specialties were calculated from national employment records for acute hospitals in England, in 2017. Outcome measures included unadjusted mortality rate and Summary Hospital-level Mortality Indicator (SHMI) in physician medical specialties. Both Raw mortality and SHMI include deaths during admission or within 30 days following discharge. Linear regression models were constructed for each medical staffing grade for unadjusted mortality, SHMI and SHMI adjusted for local provider factors.FindingsThe mean number of medical beds per senior, middle grade and junior physicians were 7.3(SD 2.5), 19.7(11.5), 10.1(3.1) respectively. Lower bed numbers per medical staff grade were associated with lower than expected mortality by SHMI; senior(Coefficient 0.012(95%CI:0.005–0.018),p = 0.001), middle grade(0.002(0.0002–0.005),p = 0.032) and junior(0.008(0.002–0.015),p = 0.014). Hospital providers were more likely to achieve a better than expected mortality (SHMI<1) if beds per physician were lower than; 5.3, 14.6 and 9.0 for senior, middle grade and junior doctors respectively.InterpretationAcute hospital providers with fewer beds per medical staff of all grades are associated with lower than expected mortality.FundingNo external funding is associated with this analysis.

Hospital Workforce Engagement and Inpatient Mortality Rate: Findings from the English National Health Service Staff Surveys.

Healthcare workforce engagement may represent a proactive approach against provider burnout, a widely prevalent condition that is associated with poor patient outcomes. We examine whether workforce engagement is associated with better hospital performance, measured as lower inpatient mortality, in English National Health Services (NHS) acute Trusts. Panel study using cross-lagged regression, applying an optimally time-lagged value of the dependent variable as covariate to account for unmeasured Trust characteristics. NHS acute Trusts and respondents to the NHS Staff Surveys, 2012-2018. We measured engagement using three survey questions corresponding to validated engagement factors, and hospital performance using the Summary Hospital-level Mortality Indicator (SHMI). In the first analyses, associations of SHMI (dependent variable) with workforce engagement in the current, prior, and subsequent years were studied to find the optimum lag period for lagged regression analysis. In the subsequent cross-lagged regression analysis, bi-directional associations between SHMI and engagement were studied. Heterogeneity in engagement components across Trusts was studied in detail for the year 2017. In the first analyses, current SHMI was negatively associated with engagement in the current year (ß = - 0.044; p = 0.035) more than with the prior year (ß = - 0.037; p = 0.049). In the second analysis, (a) engagement predicted same-year SHMI after controlling for prior-year SHMI (ß = - 0.044; p = 0.035). A 1-unit higher engagement score was associated with 4.4% lower SHMI. (b) SHMI predicted engagement in the same year (ß = - 0.066; p = 0.001) after controlling for prior-year engagement. More in-depth analysis showed high inter-trust heterogeneity on all three engagement factors (I2 > 85%). Higher workforce engagement predicts lower mortality which in turn predicts engagement. Heterogeneity in workforce well-being suggests an opportunity to foster mutual learning across Trusts.

A detailed analysis of patients included in the Summary Hospital-level Mortality Indicator (SHMI) for myocardial infarction (MI)—all is not what it seems?

BackgroundThe Summary Hospital-level Mortality Indicator (SHMI) for Myocardial Infarction (MI) is the ratio of the observed to the expected number of deaths due to MI. We aimed to assess (1)...

Safe medical staffing: the association between medical staff numbers and mortality in English hospitals

The Royal College of Physicians’ Guidance on safe medical staffing (GoSMS) report proposes staff numbers per 30-bed medical ward for expert clinical decision makers (ECDM), senior clinical decision makers (SCDM) and competent clinical decision makers (CCDM). The report recognised that outcome data in relation to medical staffing levels are limited. The Summary Hospital level Mortality Indicator (SHMI) is the adjusted measure of mortality calculated for hospital providers comparing actual with expected …

A Culture Of Openness Is Associated With Lower Mortality Rates Among 137 English National Health Service Acute Trusts.

The English National Health Service (NHS) started to implement reforms in 2016 to create a culture of openness, transparency, and accountability across the entire hospital system. However, there is a debate among policy makers and researchers about whether and to what extent openness is related to significant improvements in health system performance or lower mortality rates. Drawing on data from 137 English acute trusts (or hospital systems) for the period 2012-14, we used multivariate regression models to test whether mortality rates, taken from the Summary Hospital-level Mortality Indicator, were lower in hospitals that had higher levels of openness among staff members, a measure derived from the NHS National Staff Survey. When we adjusted for hospital operating capacity, our results showed that a one-point increase in the standardized openness score was associated with a 6.48percent reduction in hospital mortality rates. These findings have important policy implications: They offer empirical evidence to support further efforts to increase openness in the English hospital system, since doing so has improved health care quality.

The correlation between National Health Service trusts' clinical trial activity and both mortality rates and care quality commission ratings: a retrospective cross-sectional study

ObjectivesEvidence supporting the notion that clinical research activity in itself is of benefit to organisations as a whole is inconclusive. In the recent past, a positive association between research activity and reduced mortality has been shown. This study aimed to ascertain if clinical research activity is associated with established organisational outcome measures. Study designRetrospective cross-sectional study. MethodsFor 129 English National Health Service hospital Trusts, National Institute for Health Research study activity data, Summary Hospital-level Mortality Indicator (SHMI) scores and Care Quality Commission (CQC) ratings were collected. Research activity was controlled for Trust size by dividing it by clinical staffing levels. Multiple linear regression and Spearman correlation analyses were performed. ResultsAlthough there is a significant association between the number of studies and participants with both SHMI score and CQC rating, one particular variable is correlated more significantly than others: the number of participants recruited into interventional studies. It shows a significant correlation with better CQC ratings (standardised coefficient beta 0.26, P-value 0.003) and lower SHMI scores (standardised coefficient beta −0.50, P-value 0.001). ConclusionsThe mortality-related results corroborate with other published data showing a correlation between increased research and reduced deaths. Furthermore, there is also a statistically significant association between clinical trials activity and improved CQC ratings. However, these tie-ins are predominantly driven by the number of participants in interventional research rather than observational research activity.

Do expanded seven-day NHS services improve clinical outcomes? Analysis of comparative institutional performance from the \u201cNHS Services, Seven Days a Week\u201d project 2013\u20132016

BackgroundThe cause of adverse weekend clinical outcomes remains unknown. In 2013, the “NHS Services, Seven Days a Week” project was initiated to improve access to services across the seven-day week. Three years on, we sought to analyse the impact of such changes across the English NHS.MethodsAggregated trust-level data on crude mortality rates, Summary Hospital-Level Mortality Indicator (SHMI), mean length of stay (LOS), A&E admission and four-hour breach rates were obtained from national Hospital Episode Statistics and A&E datasets across the English NHS, excluding mental and community health trusts. Trust annual reports were analysed to determine the presence of any seven-day service reorganisation in 2013–2014. Funnel plots were generated to compare institutional performance and a difference in differences analysis was performed to determine the impact of seven-day changes on clinical outcomes between 2013 and 2014, 2014–2015 and 2015–2016. Data was summarised as mean (SD).ResultsOf 159 NHS trusts, 79 (49.7%) instituted seven-day changes in 2013–2014. Crude mortality rates, A&E admission rates and mean LOS remained relatively stable between 2013 and 2016, whilst A&E four-hour breach rates nearly doubled from 5.3 to 9.7%. From 2013 to 2014 to 2014–2015 and 2015–2016, there were no significant differences in the change in crude mortality (2014–2015 p = 0.8, 2015–2016 p = 0.9), SHMI (2014–2015 p = 0.5, 2015–2016 p = 0.5), mean LOS (2014–2015 p = 0.5, 2015–2016 p = 0.4), A&E admission (2014–2015 p = 0.6, 2015–2016 p = 1.0) or four-hour breach rates (2014–2015 p = 0.06, 2015–2016 p = 0.6) between trusts that had implemented seven-day changes compared to those which had not.ConclusionsAdverse weekend clinical outcomes may not be ameliorated by large scale reorganisations aimed at improving access to health services across the week. Such changes may negatively impact care quality without additional financial investment, as demonstrated by worsening of some outcomes. Detailed prospective research is required to determine whether such reallocation of finite resources is clinically effective.

Bed occupancy and hospital mortality

British Journal of Healthcare ManagementVol. 22, No. 7 Money MattersBed occupancy and hospital mortalityRod JonesRod JonesSearch for more papers by this authorRod JonesPublished Online:18 Jul 2016https://doi.org/10.12968/bjhc.2016.22.7.380AboutSectionsView articleView Full TextPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References Boyle J, Zeitz K, Hoffman R, Khanna S, Beltrame J et al. (2013) Probability of severe adverse events as a function of hospital occupancy. IEEE J Biomed Health Inform 18(1): 15–20. doi: https://doi.org/10.1109/JBHI.2013.2262053. Crossref, Google ScholarGriffiths P, Ball J, Murrells T et al. (2015) Registered nurse, healthcare support workers, medical staffing levels and mortality in English hospital trusts: a cross-sectional study. BMJ Open 5: e008751. doi: https://doi.org/10.1136/bmjopen-2015-008751 Crossref, Google ScholarHealth and Social Care Information Centre (2016) Summary Hospital-level Mortality Indicator. Available at: www.hscic.gov.uk/SHMI (accessed 15 June 2016) Google ScholarJones R (2011) Hospital bed occupancy demystified and why hospitals of different size and complexity must operate at different average occupancy. BJHCM 17(6): 242–8 Google ScholarJones R (2013) Optimum bed occupancy in psychiatric hospitals. www.priory.com/psychiatry/psychiatric_beds.htm (accessed 15 June 2016) Google ScholarJones R (2015) Recurring outbreaks of an infection apparently targeting immune function, and consequent unprecedented growth in medical admission and costs in the United Kingdom: A review. British Journal of Medicine and Medical Research 6(8): 735–70. doi: https://doi.org/10.9734/BJMMR/2015/14845 Crossref, Google ScholarJones R (2016a) Hospital bed occupancy and deaths (all-cause mortality) in 2015. BJHCM 22(5): 283–5 Google ScholarJones R (2016b) Is cytomegalovirus involved in recurring periods of higher than expected death and medical admissions, occurring as clustered outbreaks in the northern and southern hemispheres? British Journal of Medicine and Medical Research 11(2): 1–31. doi: https://doi.org/10.9734/BJMMR/2016/20062 Crossref, Google ScholarKaier K, Meyer E, Dettenkofer M, Frank U (2010) Epidemiology meets econometrics: using time-series analysis to observe the impact of bed occupancy rates on the spread of multidrug-resistant bacteria. J Hosp Infect 76(2): 108-13. doi: https://doi.org/10.1016/j.jhin.2010.04.010 Crossref, Google ScholarKaier K, Mutters N, Frank U (2012) Bed occupancy rates and hospital-acquired infections – should beds be kept empty? Clin Microbiol Infect 18(10): 941–5. doi: https://doi.org/10.1111/j.1469-0691.2012.03956.x Crossref, Google ScholarKuntz L, Mennicken R, Scholtes S (2014) Stress on the ward: Evidence for safety tipping points in hospitals. Manage Sci 61: 754-771. doi: https://doi.org/10.1287/mnsc.2014.1917 Crossref, Google ScholarNHS England (2016) Bed availability and occupancy data. https://www.england.nhs.uk/statistics/statistical-work-areas/bed-availability-and-occupancy/bed-data-overnight/ (accessed 15 June 2016) Google ScholarOzdemir B, Sinha S, Karthikesalingam A, et al. (2016) Mortality of emergency general surgical patients and associations with hospital structures and processes. Brit J Anaesthesia 116(1): 1–9. doi: https://doi.org/10.1093/bja/aev372 Crossref, Google ScholarPublic Health England (2016) National End of Life Care Intelligence Network. www.endoflifecare-intelligence.org.uk/data_sources/place_of_death (accessed Google ScholarTeitelbaum A, Lahad A, Calfon N, Gun-Usishkin M, Lubin G, Tsur A (2016) Overcrowding in psychiatric wards is associated with increased risk of adverse events. Medical Care 54(3): 296–302. doi: https://doi.org/10.1097/MLR.0000000000000501 Crossref, Google Scholar FiguresReferencesRelatedDetails 2 July 2016Volume 22Issue 7ISSN (print): 1358-0574ISSN (online): 1759-7382 Metrics History Published online 18 July 2016 Published in print 2 July 2016 Information© MA Healthcare LimitedPDF download

Clear the decks of the Summary Hospital-level Mortality Indicator

Clear the decks of the Summary Hospital-level Mortality Indicator

Lowering levels of bed occupancy is associated with decreased inhospital mortality and improved performance on the 4-hour target in a UK District General Hospital

ObjectiveTo evaluate whether there is an association between an intervention to reduce medical bed occupancy and performance on the 4-hour target and hospital mortality.MethodsThis before-and-after study was undertaken in a...

Avoidability of hospital deaths and association with hospital-wide mortality ratios: retrospective case record review and regression analysis

Objectives To determine the proportion of avoidable deaths (due to acts of omission and commission) in acute hospital trusts in England and to determine the association with the trust’s hospital-wide...

From ‘reckless’ to ‘mindful’ in the use of outcome data to inform service-level performance management: perspectives from child mental health

In the recent report on patient safety in the National Health Service (NHS) in England, Don Berwick calls on the NHS to align the necessity for increased ‘accountability’ with the...

The Best Practice Tariff helps improve management of neck of femur fractures: a completed audit loop

The Best Practice Tariff incentivizes hospitals in the UK to improve the care they deliver, and includes a requirement to deliver multiprofessional care to patients with neck of femur fractures. The Best Practice Tariff for 2010-11 included six targets: (1) surgery within 36 hours, (2) admission under consultant-led joint orthopaedic-geriatric care, (3) admission using a multidisciplinary assessment protocol, (4) review by a geriatrician within 72 hours, (5) geriatrician-directed multi-professional rehabilitation, and (6) assessment for falls and bone protection. The authors chose to audit their Trust's compliance with these targets. A retrospective audit was conducted in 2011 at the authors' university-affiliated tertiary care hospital, which is a regional major trauma centre. Only patients 65 years or older, with fragility-type neck of femur fractures who were treated surgically at the authors' unit and were eligible for geriatric review and multiprofessional rehabilitation, were included. The results of this audit (2010-11 Best Practice Tariff targets) were analysed and a series of procedural and logistical measures were introduced. A re-audit was performed in April 2012 for 2011-12, and the results for the 2 years were compared using appropriate statistics (Chi square tests and analysis of variance). Thirty-day mortality was compared using the summary hospital-level mortality indicator. A total of 410 patients were eligible for Best Practice Tariff in 2010-11, which increased to 463 in 2011-12. The changes from the first year's audit helped increase the rates for 36-hour surgery from 48.3% to 73.4% and for 72-hour geriatric review from 68.8% to 81.8% (P<0.05). The annual Best Practice Tariff achievement increased from 31.7% to 61.3% (P<0.05). The summary hospital-level mortality indicator declined from 96.5 to 61.3. Focusing on poorly satisfied Best Practice Tariff indicators can produce a significant improvement in the per capita Best Practice Tariff achievement. Further studies are needed to assess the health and financial gain in detail.

Discussing avoidable deaths

408 British Journal of Healthcare Management 2013 Vol 19 No 8 © 2 01 3 M A H ea lth ca re L td the headlines with Mid-Staffs and the Healthcare Commission’s purely statistical calculation that the hospital may have had between 400 and 1200 excess deaths. The fact that 1200 was the extreme upper end of a fan of uncertainty, and quite simply not credible if you think about it, was entirely lost on most of Her Majesty’s Media. These apparently ‘excess’ deaths rapidly became ‘avoidable ones’—there is hardly a media outlet that has not published the ‘fact’ that ‘up to 1200 patients may have died unnecessarily’ at Mid-Staffs. A ‘fact’ that has gone all around the world and been established, long before the much muddier and more complex truth (or in this case, balance of probabilities) has got its boots on. The 14 Keogh trusts were chosen because they had high HSMRs or a high Summary Hospital Level Mortality Indicator, a slightly more modern and slightly more sophisticated measure than the HSMR—although as an illustration of the difficulty of using these numbers it is notable, as Sir Bruce pointed out, that the two measures provided different lists of potential trusts to be investigated. High mortality indicators are clearly a ‘smoke alarm’ as the Keogh review puts it. But they are subject to coding difficulties and potential explanation, and the mere fact that a hospital has a lower than average HSMR does not guarantee that all is well. As Sir Bruce put it in his report, ‘however tempting it may be, it is clinically meaningless and academically reckless to use such statistical measures to quantify actual numbers of avoidable deaths.’ The silver lining is that post-Sir Bruce’s review, Nick Black of the London School of Hygiene and Tropical Medicine and Lord Darzi, the former health minister, have been commissioned to conduct a review on the relationship between excess mortality rates and actual avoidable deaths. Their study will use ‘systematic and externally audited’ case note reviews in a mix trusts with lower, higher and expected to death rates in an attempt to come up with a saner measure of ‘avoidable deaths’. It will take time. Even if it succeeds, it will take much longer to wean the media from the belief that any excess death is by definition an avoidable one. But it does offer a chance that—while there can be no acceptance of appalling standards of care—we finally may get to a more rational discussion about how far uncaring care is actually producing avoidable deaths. BJHCM Nick Timmins, senior fellow at the Institute for Government and the King’s Fund, on how the NHS’s failings have been more prominent than its successes this summer Discussing avoidable deaths