Hospital Standardized Mortality Ratio Research Articles

Hospital Responses to Mortality Measures: A Survey of Hospital Administrative Leaders.

Standardized hospital mortality ratios (SHMRs) are widely used for quality improvement, hospital ratings, and health care payment. (1) To characterize the programs implemented at hospitals in response to SHMRs.(2) To describe hospital leaders' perceptions of SHMRs as indicators of care quality. Electronic survey of administrative leaders at US academic medical centers who subscribed to Vizient leadership networks. Forty-seven administrative leaders from 37 US academic medical centers completed the survey. Respondents reported that SHMRs had the largest role in the decision to implement inpatient hospice programs, electronic early warning systems, and clinical documentation specialist programs at their institution. Respondents perceived clinical documentation specialist programs and condition-specific care pathways as the most effective programs to improve performance on SHMRs. Only 29% of respondents agreed that SHMRs accurately reflect the number of preventable deaths in hospitals, but 78% agreed that SHMRs have helped their hospital reduce preventable deaths. Hospitals have employed various strategies in response to SHMRs-including clinical programs that focus on reducing preventable deaths and other programs that target improvement in SHMR performance without reducing preventable deaths. Hospital administrative leaders identify significant benefits and flaws of SHMRs as quality indicators.

A hundred heart failure deaths: lessons learnt from the Dr Foster heart failure hospital mortality alert

BackgroundDespite advances in evidence-based pharmacotherapy, the latest National Heart Failure Audit (NHFA) has shown that in-hospital mortality of heart failure (HF) remains high with large interhospital variations. University Hospitals Coventry...

A Novel Model Demonstrates Variation in Risk-Adjusted Mortality Across Pediatric Cardiac ICUs After Surgery.

To develop a postoperative mortality case-mix adjustment model to facilitate assessment of cardiac ICU quality of care, and to describe variation in adjusted cardiac ICU mortality across hospitals within the Pediatric Cardiac Critical Care Consortium. Observational analysis. Multicenter Pediatric Cardiac Critical Care Consortium clinical registry. All surgical cardiac ICU admissions between August 2014 and May 2016. The analysis included 8,543 admissions from 23 dedicated cardiac ICUs. None. We developed a novel case-mix adjustment model to measure postoperative cardiac ICU mortality after congenital heart surgery. Multivariable logistic regression was performed to assess preoperative, intraoperative, and immediate postoperative severity of illness variables as candidate predictors. We used generalized estimating equations to account for clustering of patients within hospital and obtain robust SEs. Bootstrap resampling (1,000 samples) was used to derive bias-corrected 95% CIs around each predictor and validate the model. The final model was used to calculate expected mortality at each hospital. We calculated a standardized mortality ratio (observed-to-expected mortality) for each hospital and derived 95% CIs around the standardized mortality ratio estimate. Hospital standardized mortality ratio was considered a statistically significant outlier if the 95% CI did not include 1. Significant preoperative predictors of mortality in the final model included age, chromosomal abnormality/syndrome, previous cardiac surgeries, preoperative mechanical ventilation, and surgical complexity. Significant early postoperative risk factors included open sternum, mechanical ventilation, maximum vasoactive inotropic score, and extracorporeal membrane oxygenation. The model demonstrated excellent discrimination (C statistic, 0.92) and adequate calibration. Comparison across Pediatric Cardiac Critical Care Consortium hospitals revealed five-fold difference in standardized mortality ratio (0.4-1.9). Two hospitals had significantly better-than-expected and two had significantly worse-than-expected mortality. For the first time, we have demonstrated that variation in mortality as a quality metric exists across dedicated cardiac ICUs. These findings can guide efforts to reduce mortality after cardiac surgery.

The ‘SAFE’ initiative – An innovative approach to safer patient care in a tertiary hospital setting

Introduction: Out of hours inpatient care within Australia, defined as the hours outside of 0800 to 1600 hours Monday to Friday, is traditionally provided by on-call medical teams, working in silos, supported by onsite junior medical staff. This model can be associated with poor communication both between and within teams, lack of accountability, failure of escalation, and a reactive model of care. International literature reveals that the outcomes of patients admitted to hospital out of hours are poorer, resulting in a discrepancy in mortality between in and out of hours admissions.Methods: We aimed to reduce the discrepancy in mortality between in and out of hours admissions, as well as reducing overall inpatient mortality. Using a resilience engineering approach, we introduced a novel model of out of hours care - the ‘Safety After Hours for Everyone’ (SAFE) Team. This incorporated a departmental model, with clear and robust internal leadership, external accountability, and formal processes for identification, review and follow up of at risk patients, as well as protocolised escalation processes.Results: The introduction of the SAFE model has been associated with a continuous reduction in the overall Hospital Standardised Mortality Ratio (HSMR) from 0.71 to 0.54 (periods January to March 2015 vs January to March 2018. In addition, the SAFE model has been associated with a reduction in out of hours mortality (defined as admissions from 1600 to 0800) from 0.98 to 0.38 (periods January to March 2015 vs January to March 2018). This has been accompanied by a qualitative improvement in the quality of care delivered out of hours, and improved satisfaction with working conditions and training delivered out of hours. Due to a drastic reduction in unplanned Resident Medical Officer (RMO) overtime associated with the introduction of the model, implementation was near cost neutral.Conclusion: The introduction of the SAFE model has been associated with improved hospital outcomes, in conjunction with improved medical and nursing staff experiences, at a low marginal cost. This model has scope to be applied to similar tertiary level hospitals, or modified to fit within most hospital structures. A key component to the success of this model’s innovation, is acknowledgement of the importance of after hours care provision to patients, highlighted by the formation of a department of after hours medicine as part of the SAFE model.

Mortalidade hospitalar em pacientes idosos no Sistema Único de Saúde, região Sudeste

ABSTRACTOBJECTIVE To evaluate factors associated with hospital death in older inpatients for specific diseases of the circulatory system in the Brazilian Unified Health System considering the risk-adjusted hospital mortality as an indicator of effectiveness.METHODS The data were extracted from the Brazilian Hospital Information System. A total of 385,784 hospitalizations of older were selected for hypertensive diseases, ischemic heart disease, congestive heart failure, and stroke in the Brazilian Southeast region between 2011 and 2012. Age, sex, emergency admission, principal diagnosis, and two comorbidity indexes were included in the logistic regression for the risk adjustment of hospital death. The analyses were developed at two levels: hospitalization and hospital.RESULTS A greater chance of death was observed with increasing age, emergency hospitalizations, stroke, presence of comorbidities, especially pneumonia and weight loss, hospitalizations for clinical care, and use of intensive care units. The risk-adjusted hospital mortality rate was 11.1% in for-profit private hospitals, 12.3% in non-profit private hospitals, and 14.4% in public hospitals, but there was great variability among the hospitals. The hospital standardized mortality ratio (ratio between observed and predicted deaths) ranged from 103.3% in non-profit private hospitals to 118.2% in for-profit private hospitals.CONCLUSIONS Although the information source has its shortcomings, the ability for discrimination of the risk adjustment model was reasonable. The variability in the risk-adjusted hospital mortality was great and comparatively higher in for-profit private hospitals. Despite the limits, the results favor the use of the risk-adjusted hospital mortality in the monitoring of the quality of hospital care provided to the older adult.

Test-Retest Reliability of an Experienced Global Trigger Tool Review Team.

During a comprehensive patient safety program at a 550-bed regional hospital in the Capital Region of Denmark, we observed an unexpected and unexplained doubling of the median patient harm rate from 56 to 109 harms per 1000 patient days measured by the Institute for Healthcare Improvement Global Trigger Tool (GTT). Meanwhile, other measures of patient safety, including hospital standardized mortality ratio, were stable or improving. Moreover, the review team was very experienced and stable during this period. Thus, we hypothesized that the increase in harm rate was not a true reflection of increased risk of patient harm but the result of the team getting better at identifying harms during GTT reviews. We examined the ability of the GTT review team to reproduce the rate of harm of two separate periods in the same hospital: period 1 (January-June 2010) and period 2 (October 2011-March 2012). For each period, we examined two samples: the original sample that was drawn and used for the ongoing monitoring of harm at the hospital during the safety campaign and a second that we drew and analyzed for this study. We found increased harm rates both between review 1 and review 2 and between period 1 and period 2. The increase was solely in category E, minor temporary harm. The very experienced GTT team could not reproduce harm rates found in earlier reviews. We conclude that GTT in its present form is not a reliable measure of harm rate over time.

Hospital quality measures: are process indicators associated with hospital standardized mortality ratios in French acute care hospitals?

BackgroundResults of associations between process and mortality indicators, both used for the external assessment of hospital care quality or public reporting, differ strongly across studies. However, most of those studies were conducted in North America or United Kingdom. Providing new evidence based on French data could fuel the international debate on quality of care indicators and help inform French policy-makers. The objective of our study was to explore whether optimal care delivery in French hospitals as assessed by their Hospital Process Indicators (HPIs) is associated with low Hospital Standardized Mortality Ratios (HSMRs).MethodsThe French National Authority for Health (HAS) routinely collects for each hospital located in France, a set of mandatory HPIs. Five HPIs were selected among the process indicators collected by the HAS in 2009. They were measured using random samples of 60 to 80 medical records from inpatients admitted between January 1st, 2009 and December 31, 2009 in respect with some selection criteria. HSMRs were estimated at 30, 60 and 90 days post-admission (dpa) using administrative health data extracted from the national health insurance information system (SNIIR-AM) which covers 77% of the French population. Associations between HPIs and HSMRs were assessed by Poisson regression models corrected for measurement errors with a simulation-extrapolation (SIMEX) method.ResultsMost associations studied were not statistically significant. Only two process indicators were found associated with HSMRs. Completeness and quality of anesthetic records was negatively associated with 30 dpa HSMR (0.72 [0.52–0.99]). Early detection of nutritional disorders was negatively associated with all HSMRs: 30 dpa HSMR (0.71 [0.54–0.95]), 60 dpa HSMR (0.51 [0.39–0.67]) and 90 dpa HSMR (0.52 [0.40–0.68]).ConclusionIn absence of gold standard of quality of care measurement, the limited number of associations suggested to drive in-depth improvements in order to better determine associations between process and mortality indicators. A smart utilization of both process and outcomes indicators is mandatory to capture aspects of the hospital quality of care complexity.

The degree of severity and trends in hospital standardized mortality ratios in Japan between 2008 and 2012: a retrospective observational study.

Hospital standardized mortality ratio (HSMR), an indicator that adjusts hospital mortality for case-mix differences, is used as a hospital performance measure. The aim of this study is to build a new HSMR model in Japan and examine HSMR trends according to the degree of severity. Observational retrospective study. Data from the Japanese Administrative Database. A total of 3 813 492 admissions from 278 Japanese acute-care hospitals were extracted from the database (patients between 2008 and 2012, from July to December in each year). We estimated the probability of in-hospital death by fitting a logistic regression model, and assessed the performance of the models with the c-index. In each year, HSMRs were obtained by calculating the ratio of the number of observed deaths to the number of expected deaths. The HSMR trends, including trends in comorbidity subgroups defined by the Charlson comorbidity index, were analysed. The c-index value was 0.871 for the HSMR model. The HSMR followed a constant decreasing trend over time; it fell by 18.8% from 110.3 in 2008 to 91.5 in 2012. The reduction in HSMR was not present in the severe comorbidity group, while the reduction trend was observed in the mild comorbidity group. Our model demonstrated excellent discrimination without detailed clinical data. The Japanese HSMR followed a constant decreased trend, while the reduction trend was not present in the severe patients. Our study implies the need to consider severe patients for assessing hospital quality by HSMR.

P003: Emergency department quality assurance sepsis project: why are more people dying in southwestern Ontario?

Introduction: London Health Sciences Centre (LHSC) includes two academic, urban hospitals in London, Canada. The hospital-standardized mortality ratio (HSMR) is consistently higher than provincial and national averages. Unpublished data reveals that sepsis contributes the largest number of statistically unexpected deaths to LHSC’s HSMR calculation. Factors contributing to in-hospital sepsis mortality are hypothesized to include demography, emergency department (ED) flow or sepsis treatment. Methods: Retrospective chart review of patients aged >=18 years, presenting to an LHSC ED between 01 Nov 2014 and 31 Oct 2015, with >=2 SIRS criteria and/or ED suspicion of infection and/or ED or hospital discharge sepsis diagnosis (ICD-10 diagnostic codes A4xx and R65). Data were abstracted from electronic health records. Regional, provincial and national data was retrieved from CIHI and Statistics Canada. Results: Median age and sex in London and across Canada are similar (48.2 years vs 48.9 years; 48% male vs 49% male). Baseline prevalences of diabetes, hypertension, COPD and mood disorders were similar in the Local Health Integration Network and Ontario (6% vs 7%, 19% vs 19%, 3% vs 4%, and 10% vs 8%). Median “Physician Initial Assessment,” (PIA) times for sepsis patients at LHSC were faster than median Canadian PIA times for CTAS I and II patients (CTAS I: 7 min vs 11 min, CTAS II: 34 min vs 54 min), and slower for CTAS III-V patients (CTAS III: 98 min vs 79 min, CTAS IV: 99 min vs 66 min, CTAS V: 132 min vs 53 min). Median ED length of stay for admitted, high acuity (CTAS I-III) patients was 6 h at LHSC versus 10 h across Canada.Median [IQR] time to intravenous fluid resuscitation was 60.5 min [29.8-101.2] for septic shock patients and 77.0 min [36.0-127.0] for expired patients. Median [IQR] time to antibiotics was 130 min [73.0-229.0] for sepsis patients, 106 min [60.0-189.0] for severe sepsis patients, and 82 min [42.2-142] for septic shock patients. Conclusion: Excess sepsis-related mortality at LHSC is not convincingly related to patient demographics or ED flow. Gains may be made by improving time to antibiotics and IV fluids.

The effect of cross-jurisdictional linked hospital and death data on estimating risk-adjusted grouped hospital standardised mortality ratios in Australia

ABSTRACT
 ObjectivesThe Population Health Research Network (PHRN) was established to increase data linkage capacity in Australia. A proof of concept study investigating cross border hospital use and hospital mortality was undertaken to demonstrate the effectiveness of increased data linkage capacity in supporting nationally significant health research. The objective of this study was to evaluate whether cross-jurisdictional linkage of hospital and death records across Australian states could refine estimation of Hospital Standardised Mortality Ratios (HSMRs).
 ApproachIn Australia, administrative hospital and death data are collected by individual state governments. The newly established Centre for Data Linkage created a cross-jurisdictional linkage key that brought together hospital and death records belonging to individuals across four Australian states over a five year period (1st July 2004 – 30th June 2009). Hospital inpatient records from public, psychiatric and private hospitals and private day surgery centres were provided by New South Wales, Western Australia and Queensland. South Australia provided public hospital inpatient records only. The linked data underwent extensive cleaning and standardisation to improve the validity of interstate comparisons.
 The final cohort comprised 7.7 million hospital patients. In-hospital deaths and deaths within 30 days of hospital discharge from the four state jurisdictions were used to estimate the SMR of hospital groups defined by geography and type of hospital (grouped HSMR) under three record linkage scenarios; 1) cross-jurisdictional person-level linkage, 2) within-jurisdictional (state-based) person-level linkage and 3) unlinked records. All public and private hospitals in New South Wales, Queensland, Western Australia and public hospitals in South Australia were included in this study. Death registrations from all four states were obtained from state-based registries of births, deaths and marriages.
 Results Cross-jurisdictional linkage identified 11,116 cross-border hospital transfers of which 170 resulted in a cross-border in-hospital death. An additional 496 cross-border deaths occurred within 30 day of hospital discharge. The inclusion of cross-jurisdictional person-level links to unlinked hospital records reduced the coefficient of variation amongst the grouped HSMRs from 0.19 to 0.15; the inclusion of 30 day deaths reduced the coefficient of variation further to 0.11. There were minor changes in grouped HSMRs between cross-jurisdictional and within-jurisdictional linkages, although the impact of cross-jurisdictional linkage increased when restricted to geographic regions with high cross-border hospital use such as the New South Wales and Queensland border area.
 ConclusionCross-jurisdictional data linkage modified estimates of grouped HSMRs, particularly for hospitals groups that were likely to receive a high proportion of cross-border users.

Improving the Estimation of Risk-Adjusted Grouped Hospital Standardized Mortality Ratios Using Cross-Jurisdictional Linked Administrative Data: A Retrospective Cohort Study.

Hospitals and death registries in Australia are operated under individual state government jurisdictions. Some state borders are located in heavily populated areas or are located near to major capital cities. Mortality indicators for hospital located near state borders may not be estimated accurately if patients are lost as they cross state borders. The aim of this study was to evaluate how cross-jurisdictional linkage of state hospital and death records across state borders may improve estimation of the hospital standardized mortality ratio (HSMR), a tool used in Australia as a hospital performance indicator. Retrospective cohort study of 7.7 million hospital patients from July 2004 to June 2009. Inhospital deaths and deaths within 30 days of hospital discharge from four state jurisdictions were used to estimate the standardized mortality ratio of hospital groups defined by geography and type of hospital (grouped HSMR) under three record linkage scenarios, as follows: (1) cross-jurisdictional person-level linkage, (2) within-jurisdictional (state-based) person-level linkage, and (3) unlinked records. All public and private hospitals in New South Wales, Queensland, Western Australia, and public hospitals in South Australia were included in this study. Death registrations from all four states were obtained from state-based registries of births, deaths, and marriages. Cross-jurisdictional linkage identified 11,116 cross-border hospital transfers of which 170 resulted in a cross-border inhospital death. An additional 496 cross-border deaths occurred within 30 days of hospital discharge. The inclusion of cross-jurisdictional person-level links to unlinked hospital records reduced the coefficient of variation among the grouped HSMRs from 0.19 to 0.15; the inclusion of 30-day deaths reduced the coefficient of variation further to 0.11. There were minor changes in grouped HSMRs between cross-jurisdictional and within-jurisdictional linkages, although the impact of cross-jurisdictional linkage increased when restricted to regions with high cross-border hospital use. Cross-jurisdictional linkage modified estimates of grouped HSMRs in hospital groups likely to receive a high proportion of cross-border users. Hospital identifiers will be required to confirm whether individual hospital performance indicators change.

Learning from hospital mortality

Understanding and learning from hospital deaths is an important component of good clinical practice but current approaches and measures are complex, controversial and difficult to understand. Patients who die are not a homogeneous group but fall into three distinct categories; most learning will be achieved by recognising this and investigating categories of deaths in different ways, relying heavily on qualitative approaches. Numerical measures of overall hospital mortality, such as hospital standardised mortality ratio (HSMR) or measures of 'preventable' deaths, are most unlikely to be helpful at a hospital level and may even give false reassurance, as accuracy of measurement is strongly influenced by factors apart from quality of care.

Improved hospital mortality with a low MET dose: the importance of a modified early warning score and communication tool.

Rapid response systems have been mandated for the recognition and management of the deteriorating patient. Increasing medical emergency team (MET) dose may be associated with improved outcomes. Large numbers of MET calls may divert resources from the program providing the service unless additional personnel are provided. To describe the implementation and outcomes of a multifaceted rapid response system (RRS) in a teaching hospital, we conducted an observational study. The RRS consisted of the introduction of a MET together with 1) redesign of the ward observation chart with the vital sign variables colour-coded to identify variation from normal; 2) mandated minimum frequency of vital sign measurement; 3) three formal levels of escalation based on the degree of physiological instability as measured by a modified early warning score (MEWS); 4) COMPASS© education and e-learning package with a two-hour face-to-face small group tutorial; 5) practise in escalation and communication using the ISBAR (Identify, Situation, Background, Assessment, Response/Recommendation) communication tool. The primary outcome measures were all-cause hospital mortality rate and hospital standardised mortality ratio (HSMR) compared to peer hospitals calculated by the Health Round Table. There were 161,153 separations and 1,994 hospital deaths from July 2008 to December 2012. The MET call rate was 11.3 per 1000 separations in 2012. There was a decline in all-cause hospital mortality from 13.8 to 11 deaths/1000 separations. The HSMR decreased from 95.7 in 2008 to 66 in the second half of 2012 (below the three standard deviation control limit). A low MET dose may be associated with improved hospital mortality when combined with a MEWS and an intervention to improve communication.

LO085: Canadian in-hospital mortality for patients with emergency-sensitive conditions

Introduction: The emergency department (ED) hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve outcomes (emergency-sensitive conditions). This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR. Methods: Data were extracted from hospital discharge databases from April 2009 to March 2012. The ED-HSMR was calculated as the ratio of observed deaths among patients with emergency-sensitive conditions in a hospital during a year (2010-11 or 2011-12) to the expected deaths for the same patients during the reference year (2009-10), multiplied by 100. The expected deaths were estimated using predictive models fitted from the reference year for different hospital peer-groups (teaching, large, medium and small hospitals) adjusted for comorbidities, age, diagnosis, and hospital length of stay. Thirty-seven validated emergency-sensitive conditions were included (e.g., stroke, sepsis, shock). Aggregated provincial ED-HSMR values were derived from patient-level probabilities of death. A HSMR above or below 100 respectively means that more or fewer deaths than expected occurred in hospital within a province. Results: During the study period, 1,335,379 patients were admitted to 629 hospitals across 11 provinces and territories with an emergency-sensitive condition as the most responsible diagnosis, of which 8.9% died. More in-hospital deaths (95% confidence interval) than expected were respectively observed for the years 2010-11 and 2011-12 in Newfoundland [124.3 (116.3-132.6) & 117.6 (110.1-125.5)] and Nova Scotia [116.4 (110.7-122.5) & 108.7 (103.0-114.5)], while mortality was as expected in Prince Edward Island and Manitoba, and less than expected in other provinces and territories [Territories 67.3 (48.3-91.3) & 73.2 (55.0-95.5); New Brunswick 87.7 (82.5-93.1) & 90.4 (85.2-95.8); British Columbia 92.0 (89.6-94.4) & 87.1 (84.9-89.3); Saskatchewan 92.3 (87.1-97.4) & 90.8 (86.2-95.6); Ontario 94.0 (92.6-95.4) & 88.0 (86.6-89.3); Alberta 94.1 (91.1-97.2) & 91.0 (88.2-93.9); Québec 95.7 (93.8-97.6) & N/A]. Conclusion: Our study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The results should trigger more in-depth evaluations to identify the causes for these regional variations.

The National Emergency Access Target (NEAT) and the 4-hour rule: time to review the target.

We explored the relationship between the National Emergency Access Target (NEAT) compliance rate, defined as the proportion of patients admitted or discharged from emergency departments (EDs) within 4 hours of presentation, and the risk-adjusted in-hospital mortality of patients admitted to hospital acutely from EDs. Retrospective observational study of all de-identified episodes of care involving patients who presented acutely to the EDs of 59 Australian hospitals between 1 July 2010 and 30 June 2014. The relationship between the risk-adjusted mortality of inpatients admitted acutely from EDs (the emergency hospital standardised mortality ratio [eHSMR]: the ratio of the numbers of observed to expected deaths) and NEAT compliance rates for all presenting patients (total NEAT) and admitted patients (admitted NEAT). ED and inpatient data were aggregated for 12.5 million ED episodes of care and 11.6 million inpatient episodes of care. A highly significant (P <0.001) linear, inverse relationship between eHSMR and each of total and admitted NEAT compliance rates was found; eHSMR declined to a nadir of 73 as total and admitted NEAT compliance rates rose to about 83% and 65% respectively. Sensitivity analyses found no confounding by the inclusion of palliative care and/or short-stay patients. As NEAT compliance rates increased, in-hospital mortality of emergency admissions declined, although this direct inverse relationship is lost once total and admitted NEAT compliance rates exceed certain levels. This inverse association between NEAT compliance rates and in-hospital mortality should be considered when formulating targets for access to emergency care.

Variation in hospital performance according to payment sources in the states of São Paulo and do Rio Grande do Sul, Brazil

In Brazil, the combined presence of public and private interests in financing and provision of healthcare services stands out clearly in hospital care. Financing arrangements adopted by hospitals (the public Brazilian Unified National Health System - SUS and/or health plans and/or out-of-pocket payment) can affect quality of care. Studies have analyzed the hospital standardized mortality ratio (HSMR) in relation to quality improvements. The objective was to analyze HSMR according to source of payment for the hospitalization and the hospital's financing arrangement. The study analyzed secondary data and causes that accounted for 80% of hospital deaths. HSMR was calculated for each hospital and payment source. Hospitals with worse-than-expected performance (HSMR > 1) were mostly large public hospitals. HSMR was higher in the SUS, including between admissions in the hospital. Despite the study's limitations, the findings point to inequalities in results of care. Efforts are needed to improve the quality of hospital services, regardless of the payment sources.

복합만성질환 입원환자의 중증도 보정 사망비에 대한 융복합 연구

This study was to develop the predictive model for severity-adjusted mortality of inpatients with multiple chronic conditions and analyse the factors on the variation of hospital standardized mortality ratio(HSMR) to propose the plan to reduce the variation. We collect the data Korean National Hospital Discharge In-depth Injury Survey from 2008 to 2010 and select the final 110,700 objects of study who have chronic diseases for principal diagnosis and who are over the age of 30 with more than 2 chronic diseases including principal diagnosis. We designed a severity-adjusted mortality predictive model with using data-mining methods (logistic regression analysis, decision tree and neural network method). In this study, we used the predictive model for severity-adjusted mortality ratio by the decision tree using Elixhauser comorbidity index. As the result of the hospital standardized mortality ratio(HSMR) of inpatients with multiple chronic conditions, there were statistically significant differences in HSMR by the insurance type, bed number of hospital, and the location of hospital. We should find the method based on the result of this study to manage mortality ratio of inpatients with multiple chronic conditions efficiently as the national level. So we should make an effort to increase the quality of medical treatment for inpatients with multiple chronic diseases and to reduce growing medical expenses.

Palliative care coding practices in Canada since the introduction of guidelines and the HSMR indicator

ObjectivesThis study examines palliative care (PC) coding practices since the introduction of a national coding standard and assesses a potential association with hospital standardised mortality ratio (HSMR) results.SettingAcute-care hospitals in...

Development of a Hospital Standardized Mortality Ratio for Emergency Department Care

Experts have recommended including measures of mortality in emergency department (ED) performance evaluation frameworks. This study aims to develop a hospital standardized mortality ratio (HSMR) for patients admitted to the hospital with conditions for which ED care may reduce mortality (emergency-sensitive conditions). Data were extracted from Canadian hospital discharge databases from April 1, 2009, to March 31, 2012. The ED HSMR is the ratio of the observed deaths among patients with emergency-sensitive conditions in a hospital during a year to the expected deaths for the same patients during the reference year (2009-2010). The expected deaths were estimated with predictive models fitted for different hospital peer groups (teaching hospitals and large, medium, and small community hospitals). The data set included 1,770,809 admissions (9.2% deaths). The ED HSMR was calculated for 47% (294/629) of all Canadian hospitals. The majority of exclusions (98%) were for small community hospitals with fewer than 20 expected deaths. Predictive models had good calibration and discrimination, with areas under the curve ranging from 0.80 to 0.81. In comparisons of 2010-2011 and 2011-2012, the classification of hospitals by ED HSMR quartile was stable, with the majority remaining within the same quartile (43.5%) or moving up or down a single quartile (40.2%). Peer-group-level comparisons between ED HSMR measured at different points after admission (2, 7, and 30 days, and hospital discharge) did not demonstrate any significant differences. The ED HSMR appears to be a reliable measure with high discrimination, calibration, and forecasting properties that can be used to guide assessment of ED performance.

Predicting mortality rates: Comparison of an administrative predictive model (hospital standardized mortality ratio) with a physiological predictive model (Acute Physiology and Chronic Health Evaluation IV)—A cross-sectional study

IntroductionDirect comparison of mortality rates has limited value because most deaths are due to the disease process. Predicting the risk of death accurately remains a challenge. MethodsA cross-sectional study compared the expected mortality rate as calculated with an administrative model to a physiological model, Acute Physiology and Chronic Health Evaluation IV. The combined cohort and stratified samples (<0.1, 0.1-0.5, or >0.5 predicted mortality) were considered. A total of 47,982 patients were scored from 1 July 2013 to 30 June 2014, and 46,061 records were included in the analysis. ResultsA moderate correlation was shown for the combined cohort (Pearson correlation index, 0.618; 95% confidence interval [CI], 0.380-0.779; R2 = 0.38). A very good correlation for the less than 10% stratum (Pearson correlation index, 0.884; R2 = 0.78; 95% CI, 0.79-0.937) and a moderate correlation for 0.1 to 0.5 predicted mortality rates (Pearson correlation index, 0.782; R2 = 0.61; 95% CI, 0.623-0.879). There was no significant positive correlation for the greater than 50% predicted mortality stratum (Pearson correlation index, 0.087; R2 = 0.007; 95% CI, −0.23 to 0.387). ConclusionAt less than 0.1, the models are interchangeable, but in spite of a moderate correlation, greater than 0.1 hospital standardized mortality ratio cannot be used to predict mortality.