Hospital Standardized Mortality Ratio Research Articles

Measuring the impact of pharmaceutical care bundle delivery on patient outcomes: an observational study.

Clinical pharmacists perform activities to optimise medicines use and prevent patient harm. Historically, clinical pharmacy quality indicators have measured individual activities not linked to patient outcomes. To determine the proportion of patients who receive a pharmaceutical care bundle (PCB) (consisting of a medication history, medication review, discharge medication list and medicines information on the discharge summary) as well as investigate the relationship between delivery of this PCB and patient outcomes. Pharmaceutical care bundle activities were defined within state-wide (Queensland, Australia)clinical information systems and datasets were linked. An observational study using routinely recorded data was performed at ten participating sites for adult patients who had a non-same day hospital stay. The association between extent of PCB delivery and three patient outcomes were investigated: length of stay (LOS), unplanned readmission, and mortality. In total 283,813 patient hospital stays were evaluated. The delivery of the PCB occurred in 26.9% of patients at the ten participating hospital sites, ranging from 0.6 to 61.2% across sites. Patients with a longer LOS were more likely to receive delivery of the complete PCB (P < 0.001). There was no correlation between PCB and hospital standardised mortality ratio (r = 0.03, p = 0.93). Higher rates of delivery of the PCB were associated with lower rates of unplanned readmission within 30 days (r = - 0.993, p < 0.001). A complete PCB was delivered to 26.9% of patients and was associated with a significantly lower rate of unplanned readmission within 30 days.

Critically ill patients with high predicted mortality: Incidence and outcome

ObjectiveAs calculated by the severity scores, an unknown number of patients are admitted to the Intensive Care Unit (ICU) with a very high risk of death. Clinical studies have poorly addressed this population, and their prognosis is largely unknown. DesignPost hoc analysis of a multicenter, cohort, longitudinal, observational, retrospective study (CIMbA). SettingSixteen Portuguese multipurpose ICUs. PatientsPatients with a Simplified Acute Physiology Score II (SAPS II) predicted hospital mortality above 80% on admission to the ICU (high-risk group); A comparison with the remaining patients was obtained. InterventionsNone. Main Variables of InterestHospital, 30 days, 1 year mortality. ResultsWe identified 4546 patients (59.9% male), 12.2% of the whole population. Their SAPS II predicted hospital mortality was 89.0±5.8%, whilst the observed mortality was lower, 61.0%. This group had higher mortality, both during the first 30 days (aHR 3.52 [95% CI 3.34–3.71]) and from day 31 to day 365 after ICU admission (aHR 1.14 [95%CI 1.04–1.26]), respectively. However, their hospital standardized mortality ratio was similar to the other patients (0.69 vs. 0.69, P=.92). At one year of follow-up, 30% of patients in the high-risk group were alive. ConclusionsRoughly 12% of patients admitted to the ICU for more than 24h had a SAPS II score predicted mortality above 80%. Their hospital standardized mortality was similar to the less severe population and 30% were alive after one year of follow-up.

Hospital standardized mortality ratio: limits and potential of the indicator for assessing hospital performance in the Brazilian Unified National Health System

Comparative analyses based on clinical performance indicators to monitor the quality of hospital care have been carried out for decades in several countries, most notably the hospital standardized mortality ratio (HSMR). In Brazil, studies and the adoption of methodological tools that allow regular analysis of the performance of institutions are still scarce. This study aimed to assess the use of HSMR to compare the performance of hospitals funded by the Brazilian Unified National Health System (SUS). The Hospital Information System was the source of data on adult hospitalizations in Brazil from 2017 to 2019. The methodological approach to estimate HSMR was adapted to the available data and included the causes of hospitalization (main diagnosis) responsible for 80% of deaths. The number of expected deaths was estimated using a logistic regression model that included predictor variables widely described in the literature. The analysis was conducted in two stages: (i) hospitalization level and (ii) hospital level. The final risk adjustment model showed a C-statistic of 0.774, which is considered adequate. The variation in HSMR was wide, especially among the worst-performing hospitals (1.54 to 6.77). Private hospitals performed better than public hospitals. Although the limits of the available data and the challenges still face its more refined use, HSMR is applicable and has the potential to become an important tool for assessing hospital performance in the SUS.

Critically ill patients with high predicted mortality: Incidence and outcome

ObjectiveAs calculated by the severity scores, an unknown number of patients are admitted to the Intensive Care Unit (ICU) with a very high risk of death. Clinical studies have poorly addressed this population, and their prognosis is largely unknown. DesignPost hoc analysis of a multicenter, cohort, longitudinal, observational, retrospective study (CIMbA). SettingSixteen Portuguese multipurpose ICUs. PatientsPatients with a Simplified Acute Physiology Score II (SAPS II) predicted hospital mortality above 80% on admission to the ICU (high-risk group); A comparison with the remaining patients was obtained. InterventionsNone. Main Variables of InterestHospital, 30 days, 1 year mortality. ResultsWe identified 4546 patients (59.9% male), 12.2% of the whole population. Their SAPS II predicted hospital mortality was 89.0±5.8%, whilst the observed mortality was lower, 61.0%. This group had higher mortality, both during the first 30 days (aHR 3.52 [95% CI 3.34–3.71]) and from day 31 to day 365 after ICU admission (aHR 1.14 [95%CI 1.04–1.26]), respectively. However, their hospital standardized mortality ratio was similar to the other patients (0.69 vs. 0.69, P=.92). At one year of follow-up, 30% of patients in the high-risk group were alive. ConclusionsRoughly 12% of patients admitted to the ICU for more than 24h had a SAPS II score predicted mortality above 80%. Their hospital standardized mortality was similar to the less severe population and 30% were alive after one year of follow-up.

Convergent Validity of 2 Widely Used Methodologies for Calculating the Hospital Standardized Mortality Ratio in Flanders, Belgium.

To assess their construct validity, we compared results from 2 models used for estimating hospital standardized mortality ratios (HSMRs) in Belgium. The method of the Flemish Hospital Network (FHN) is based on a logistic regression for each of the 64 All Patient Refined Diagnosis-Related Groups that explain 80% of mortality and uses the Elixhauser score to correct for comorbidities. (H)SMRs published on the 3M-Benchmark-Portal are calculated by a simpler indirect standardization for All Patient Refined Diagnosis-Related Groups and risk of mortality (ROM) at discharge. We used administrative data from all eligible hospital admissions in 22 Flemish hospitals between 2016 and 2019 (FHN, n = 682,935; 3M, n = 2,122,305). We evaluated model discrimination and accuracy and assessed agreement in estimated HSMRs between methods. The Spearman correlation between HSMRs generated by the FHN model and the standard 3M model was 0.79. Although 2 of 22 hospitals showed opposite classification results, that is, an HSMR significantly <1 according to the FHN method but significantly >1 according to the 3M model, classification agreement between methods was significant (agreement for 59.1% of hospitals, κ = 0.45). The 3M model (c statistic = 0.96, adjusted Brier score = 26%) outperformed the FHN model (0.87, 17%). However, using ROM at admission instead of at discharge in the 3M model significantly reduced model performance (c statistic = 0.94, adjusted Brier score = 21%), but yielded similar HSMR estimates and eliminated part of the discrepancy with FHN results. Results of both models agreed relatively well, supporting convergent validity. Whereas the FHN method only adjusts for disease severity at admission, the ROM indicator of the 3M model includes diagnoses not present on admission. Although diagnosis codes generated by complications during hospitalization have the tendency to increase the predictive performance of a model, these should not be included in risk adjustment procedures.

Changes in the Hospital Standardized Mortality Ratio Before and During the COVID-19 Pandemic: A Disaggregated Analysis by Region and Hospital Type in Korea.

The coronavirus disease 2019 (COVID-19) pandemic has led to a global shortage of medical resources; therefore, we investigated whether COVID-19 impacted the quality of non-COVID-19 hospital care in Korea by comparing hospital standardized mortality rates (HSMRs) before and during the pandemic. This retrospective cohort study analyzed Korean National Health Insurance discharge claim data obtained from January to June in 2017, 2018, 2019, and 2020. Patients' in-hospital deaths were classified according to the most responsible diagnosis categories. The HSMR is calculated as the ratio of expected deaths to actual deaths. The time trend in the overall HSMR was analyzed by region and hospital type. The final analysis included 2 252 824 patients. In 2020, the HSMR increased nationwide (HSMR, 99.3; 95% confidence interval [CI], 97.7 to 101.0) in comparison to 2019 (HSMR, 97.3; 95% CI, 95.8 to 98.8). In the COVID-19 pandemic zone, the HSMR increased significantly in 2020 (HSMR, 112.7; 95% CI, 107.0 to 118.7) compared to 2019 (HSMR, 101.7; 95% CI, 96.9 to 106.6). The HSMR in all general hospitals increased significantly in 2020 (HSMR, 106.4; 95% CI, 104.3 to 108.5) compared to 2019 (HSMR, 100.3; 95% CI, 98.4 to 102.2). Hospitals participating in the COVID-19 response had a lower HSMR (HSMR, 95.6; 95% CI, 93.9 to 97.4) than hospitals not participating in the COVID-19 response (HSMR, 124.3; 95% CI, 119.3 to 129.4). This study suggests that the COVID-19 pandemic may have negatively impacted the quality of care in hospitals, especially general hospitals with relatively few beds. In light of the COVID-19 pandemic, it is necessary to prevent excessive workloads in hospitals and to properly employ and coordinate the workforce.

Empirical use of temocillin in hospitalized patients: results from a retrospective audit.

Following a global shortage of piperacillin/tazobactam in 2017, a formulary decision was taken at a large District General Hospital in the East of England to partly replace piperacillin/tazobactam with either temocillin as monotherapy or as part of a combination regimen. A retrospective audit was then conducted to assess the clinical effectiveness of temocillin therapy. Data from patients admitted to Watford General Hospital between May and August 2017 and treated with temocillin were reviewed retrospectively. Clinical characteristics of patients, data related to the episode of infection, clinical success, tolerance and mortality were analysed. Temocillin was used in 126 patients with median age of 73 years. Infection episodes mostly originated from the abdomen (n = 46), the lung (n = 40) and the urinary tract (n = 21). Seventy-seven patients received temocillin as first-line therapy and 106 received it empirically, with temocillin prescribed in combination with another antibiotic in 82% of the empirically treated cases. Clinical success was observed in 88.9% of cases with no difference between patients treated empirically and others (89.6% versus 85%) or in efficacy among abdominal (91%), pulmonary (87.5%) and urinary (81%) infections. One case of Clostridioides difficile infection was reported in a patient treated with four different antibiotics. During the shortage period, the hospital's standardized mortality ratio was significantly lower when compared with the same period of the preceding year (85 versus 96). Using temocillin as part of an empirical strategy is feasible and safe as long as appropriate antibiotic combination is recommended based upon the indication and the likely bacterial pathogen.

Expected and observed in-hospital mortality in heart failure patients before and during the COVID-19 pandemic: Introduction of the machine learning-based standardized mortality ratio at Helios hospitals.

BackgroundReduced hospital admission rates for heart failure (HF) and evidence of increased in‐hospital mortality were reported during the COVID‐19 pandemic. The aim of this study was to apply a machine learning (ML)‐based mortality prediction model to examine whether the latter is attributable to differing case mixes and exceeds expected mortality rates.Methods and ResultsInpatient cases with a primary discharge diagnosis of HF non‐electively admitted to 86 German Helios hospitals between 01/01/2016 and 08/31/2020 were identified. Patients with proven or suspected SARS‐CoV‐2 infection were excluded. ML‐based models were developed, tuned, and tested using cases of 2016–2018 (n = 64,440; randomly split 75%/25%). Extreme gradient boosting showed the best model performance indicated by a receiver operating characteristic area under the curve of 0.882 (95% confidence interval [CI]: 0.872–0.893). The model was applied on data sets of 2019 and 2020 (n = 28,556 cases) and the hospital standardized mortality ratio (HSMR) was computed as the observed to expected death ratio. Observed mortality rates were 5.84% (2019) and 6.21% (2020), HSMRs based on an individual case‐based mortality probability were 100.0 (95% CI: 93.3–107.2; p = 1.000) for 2019 and 99.3 (95% CI: 92.5–106.4; p = .850) for 2020. Within subgroups of age or hospital volume, there were no significant differences between observed and expected deaths. When stratified for pandemic phases, no excess death during the COVID‐19 pandemic was observed.ConclusionApplying an ML algorithm to calculate expected inpatient mortality based on administrative data, there was no excess death above expected event rates in HF patients during the COVID‐19 pandemic.

Five-Year Outcomes After Implementing a Pain, Agitation, and Delirium Protocol in a Mixed Intensive Care Unit.

Objective: Assess for continued improvements in patient outcomes after updating our institutional sedation and analgesia protocol to include recommendations from the 2013 Society of Critical Care Medicine (SCCM) Pain, Agitation, and Delirium (PAD) guidelines. Methods: Retrospective before-and-after study in a mixed medical/surgical intensive care unit (ICU) at an academic medical center. Mechanically ventilated adults admitted from September 1, 2011 through August 31, 2012 (pre-implementation) and October 1, 2012 through September 30, 2017 (post-implementation) were included. Measurements included number of mechanically ventilated patients, APACHE IV scores, age, type of patient (medical or surgical), admission diagnosis, ICU length of stay (LOS), hospital LOS, ventilator days, number of self-extubations, ICU mortality, ICU standardized mortality ratio, hospital mortality, hospital standardized mortality ratio, medication data including as needed (PRN) analgesic and sedative use, and analgesic and sedative infusions, and institutional savings. Results: Ventilator days (Pre-PAD = 4.0 vs. Year 5 post = 3.2, P < .0001), ICU LOS (Pre-PAD = 4.8 days vs. Year 5 post = 4.1 days, P = .0004) and hospital LOS (Pre-PAD = 14 days vs. Year 5 post = 12 days, P < .0001) decreased after protocol implementation. Hospital standardized mortality ratio (Pre-PAD = 0.69 vs. Year 5 post = 0.66) remained constant; while, APACHE IV scores (Pre-PAD = 77 vs. Year 5 post = 89, P < .0001) and number of intubated patients (Pre-PAD = 1146 vs. Year 5 post = 1468) increased over the study period. Using the decreased ICU and hospital LOS estimates, it is projected the institution saved $4.3 million over the 5 years since implementation. Conclusions: Implementation of an updated PAD protocol in a mixed medical/surgical ICU was associated with a significant decrease in ventilator time, ICU LOS, and hospital LOS without a change in the standardized mortality ratio over a five-year period. These favorable outcomes are associated with a significant cost savings for the institution.

Role of a rapid response system and code status discussion as determinants of prognosis for critical inpatients: An observational study in a Japanese urban hospital.

Rapid response systems (RRS) have been introduced worldwide to reduce unpredicted in-hospital cardiac arrest (IHCA) and in-hospital mortality. The role of advance care planning (ACP) in the management of critical patients has not yet been fully determined in Japan.We retrospectively assessed the characteristics of all inpatients with unpredicted IHCA in our hospital between 2016 and 2018. Yearly changes in the number of RRS activations and the incidence of unpredicted IHCA with or without code status discussion were evaluated from 2014 to 2018. Hospital standardized mortality ratios were assessed from the data reported in the annual reports by the National Hospital Organization.A total of 81 patients (age: 70.9 ± 13.3 years) suffered an unpredicted IHCA and had multiple background diseases, including heart disease (75.3%), chronic kidney disease (25.9%), and postoperative status (cardiovascular surgery, 18.5%). Most of the patients manifested non-shockable rhythms (69.1%); survival to hospital discharge rate was markedly lower than that with shockable rhythms (26.8% vs 72.0%, P < .001). The hospital standardized mortality ratios was maintained nearly constant at approximately 50.0% for 3 consecutive years. The number of cases of RRS activation markedly increased from 75 in 2014 to 274 patients in 2018; conversely, the number of unpredicted IHCA cases was reduced from 40 in 2014 to 18 in 2018 (P < .001). Considering the data obtained in 2014 and 2015 as references, the RRS led to a reduction in the relative risk of unpredicted IHCA from 2016 to 2018 (ie, 0.618, 95% confidence interval 0.453–0.843). The reduction in unpredicted IHCA was attributed partly to the increased number of patients who had discussed the code status, and a significant correlation was observed between these parameters (R2 = 0.992, P < .001). The reduction in the number of patients with end-stage disease, including congestive heart failure and chronic renal failure, paralleled the incidence of unpredicted IHCA.Both RRS and ACP reduced the incidence of unpredicted IHCA; RRS prevents progression to unpredicted IHCA, whereas ACP decreases the number of patients with no code status discussion and thus potentially reducing the patient subgroup progressing to an unpredicted IHCA.

The Effect of Advanced Diagnostic Imaging on Mortality and Length of Stay in Tehran General University Hospitals

Background: As fast and accurate techniques, advanced medical imaging technologies (AMIT) allow healthcare professionals to better diagnose and treat various health conditions, which translates into higher use of non-invasive operational procedures. Objectives: The current study intended to investigate the effect of inpatient use of MRI and CT scan on the inpatient mortality and length of stay (LOS) in Tehran general university hospitals.&#x0D; Methods: Data were collected from all general university hospitals in Tehran in 2017. A multiple linear regression model was constructed for each combination of technology and outcomes (i.e., mortality and LOS), and all models were controlled for patients’ demographic and clinical characteristics and structural profile of hospitals. In calculating hospital standardized mortality ratio (HSMR) for each of 72 diagnosis groups related to death, a binary logistic regression model was fitted with predictors including LOS, admission type, comorbidity level, sex, and age.&#x0D; Results: The use of CT varied from 0.39 to 149.35, and MRI from 0.24 to 80.23 exams per 100 discharges. The HSMR ranged from 76.8% to 146%, and the average length of stay (ALOS) was 3 - 8.46 days. MRI and CT had no significant effect on the HSMR and ALOS.&#x0D; Conclusions: Further use of AMIT was not linked with improved efficiency and quality but was associated with better resource management in healthcare organizations. Effective management of the AMIT use requires clear rules and regulations with assertive commitment, in addition to establishing clinical guidelines with the support of insurance companies.

A new standardized mortality ratio method for hospital quality evaluation

Evaluation of hospital quality is of great significance for the promotion of the development of medical care. Hospital standardized mortality ratio (HSMR) is the ratio of hospital observed mortality to expected mortality (O/E ratio) and is an important indicator for the evaluation of hospital performance. Given the importance of HSMR, accurate estimation of HSMR confidence intervals is essential. All existing methods assume that the distributions of the O/E ratios are close to a normal distribution. However, this assumption is not reasonable. In this article, we propose a new method for calculating the HSMR confidence intervals. We derive the confidence intervals for the O/E ratios by calculating the confidence intervals of log(O/E). Then, we use the coverage probability of the confidence intervals to compare the performance of our method with the performance of existing methods. In the scenarios with different true relative risks, if the mortality rate is less than or equal to 1%, the bias of our method is substantially lower than that of the existing methods. The simulation results show that our method provides a more accurate estimate of the confidence intervals of the O/E ratios in the case of low mortality rates than that provided by the existing methods.

Coronavirus Disease 2019 ICU Patients Have Higher-Than-Expected Acute Physiology and Chronic Health Evaluation-Adjusted Mortality and Length of Stay Than Viral Pneumonia ICU Patients.

To compare Acute Physiology and Chronic Health Evaluation-IV-adjusted mortality and length of stay outcomes of adult ICU patients who tested positive for coronavirus disease 2019 with patients admitted to ICU with other viral pneumonias including a subgroup with viral pneumonia and concurrent acute respiratory distress syndrome (viral pneumonia-acute respiratory distress syndrome). Retrospective review of Acute Physiology and Chronic Health Evaluation data collected from routine clinical care. Forty-three hospitals contributing coronavirus disease 2019 patient data between March 14, and June 17, 2020, and 132 hospitals in the United States contributing data on viral pneumonia patients to the Acute Physiology and Chronic Health Evaluation database between January 1, 2014, and December 31, 2019. One thousand four hundred ninety-one patients with diagnosis of coronavirus disease 2019 infection and 4,200 patients with a primary (n = 2,544) or secondary (n = 1,656) admitting diagnosis of noncoronavirus disease viral pneumonia receiving ICU care. A subset of 202 viral pneumonia patients with concurrent acute respiratory distress syndrome was examined separately. None. Mean age was 63.4 for coronavirus disease (p = 0.064) versus 64.1 for viral pneumonia. Acute Physiology and Chronic Health Evaluation-IV scores were similar at 56.7 and 55.0, respectively (p = 0.060), but gender and ethnic distributions differed, as did Pao2 to Fio2 ratio and WBC count at admission. The hospital standardized mortality ratio (95% CI) was 1.52 (1.35-1.68) for coronavirus disease patients and 0.82 (0.75-0.90) for viral pneumonia patients. In the coronavirus disease group, ICU and hospital length of stay were 3.1 and 3.0 days longer than in viral pneumonia patients. Standardized ICU and hospital length of stay ratios were 1.13 and 1.46 in the coronavirus disease group versus 0.95 and 0.94 in viral pneumonia patients. Forty-seven percent of coronavirus disease patients received invasive or noninvasive ventilatory support on their first ICU day versus 65% with viral pneumonia. Ventilator days in survivors were longer in coronavirus disease (10.4 d) than in viral pneumonia (4.3 d) patients, except in the viral pneumonia-acute respiratory distress syndrome subgroup (10.2 d). Severity-adjusted mortality and length of stay are higher for coronavirus disease 2019 patients than for viral pneumonia patients admitted to ICU. Coronavirus disease patients also have longer time on ventilator and ICU length of stay, comparable with the subset of viral pneumonia patients with concurrent acute respiratory distress syndrome. Mortality and length of stay increase with age and higher scores in both populations, but observed to predicted mortality and length of stay are higher than expected with coronavirus disease patients across all severity of illness levels. These findings have implications for benchmarking ICU outcomes during the coronavirus disease 2019 pandemic.

Associations between hospital deaths (HSMR), readmission and length of stay (LOS): a longitudinal assessment of performance results and facility characteristics of teaching and large-sized hospitals in Canada between 2013–2014 and 2017–2018

ObjectivesTo examine the association between hospital deaths (hospital standardised mortality ratio, HSMR), readmission, length of stay (LOS) and eight hospital characteristics.DesignLongitudinal observational study.SettingA total of 119 teaching and large-sized hospitals...

Appropriateness, effectiveness and safety of care delivered in Canadian hospitals: a longitudinal assessment on the utility of publicly reported performance trend data between 2012–2013 and 2016–2017

ObjectivesTo assess the utility of publicly reported performance trend results of Canadian hospitals (by hospital size/type and jurisdiction).DesignLongitudinal observational study.Setting489 hospitals in Canada between fiscal years 2012–2013 and 2016–2017.ParticipantsAnalysis focused...

Capturing the trends in hospital standardized mortality ratios for pneumonia: a retrospective observational study in Japan (2010 to 2018)

BackgroundPneumonia has a high human toll and a substantial economic burden in developed countries like Japan, where the crude mortality rate was 77.7 per 100,000 people in 2017. As this trend is going to continue with increasing number of the elderly multi-morbid population in Japan; monitoring performance over time is a social need to alleviate the disease burden. The study objective was to determine the characteristics of hospital standardized mortality ratios (HSMRs) for pneumonia in Japan from 2010 to 2018 to describe this trend.MethodsData of the DPC (Diagnostic Procedures Combination) database were used, which is an administrative claims and discharge summary database for acute care in-patients in Japan. HSMRs were calculated using the actual and expected numbers of in-hospital deaths, the latter of which was calculated using logistic regression model, with a number of explanatory variables, e.g., age, sex, urgency of admission, mode of transportation, patient volume per month in each hospital, A-DROP score, and Charlson comorbidity index (CCI). We constructed two HSMR models: a single-year model, which included hospitals with > 10 in-patients per month and, a 9-year model, which included those hospitals with complete 9-year data. Predictive accuracy of the logistic models was assessed using c-index (area under receiver operating curve).ResultsTotal 230,372 patients were included for the analysis over the 9-year study period. Calculated HSMRs showed wide variation among hospitals. The proportion of hospitals with HSMR less than 100 increased from 36.4% in 2010 to 60.6% in 2018. Both models showed good predictive ability with a c-statistic of 0.762 for the 9-year model, and no less than 0.717 for the single-year model.ConclusionThis study denoted that HSMRs of pneumonia can be calculated using DPC data in Japan and revealed significant variations among hospitals with comparable case-mixes. Therefore, HSMR can be used as yet another measure to help improve quality of care over time if other indicators are examined in parallel and to get a clear picture of where hospitals excel and lack.

Trends in hospital standardized mortality ratios for stroke in Japan between 2012 and 2016: a retrospective observational study.

Stroke is one of the leading causes of death and disability, and imposes a major healthcare burden. The aim of this study was to determine the characteristics of hospital standardized mortality ratios (HSMRs) for stroke in Japan for the year 2012-16 to describe the trend. Retrospective observational study. Data from the Japanese administrative database. All hospital admissions for stroke were identified from diagnostic procedures combination (DPC) database from 2012 to 2016. HSMR was calculated using the actual number of in-hospital deaths and expected deaths. To obtain the expected death number, a logistic regression model was developed to get the coefficient with a number of explanatory variables. Predictive accuracy of the logistic models was assessed using c-index and calibration was evaluated using the Hosmer-Lemeshow test. A total of 63084 patients admitted for stroke from January 2012 to December 2016 were analyzed. HSMRs showed declining tendency over these 5years, suggesting stroke-related mortality has been improving. While the HSMRs varied from year to year, a wide variation was also seen among the different hospitals in Japan. The proportion of hospitals with HSMR less than 100 increased from 41.0% in 2012 to 59.0% in 2016. This study demonstrated that HSMR can be calculated using DPC data and found wide variation in HSMR of stroke among hospitals in Japan and enabled us to image the trend. By examining these trends, facilities, authorities and provinces can initiate designs that will ultimately lead to an upgraded healthcare delivery system.

Digital transformation of the emergency department-inpatient interface (EDii): integration for future innovation.

As the focus of clinicians and government shifts from speciality-based care to system-based key performance indicators such as the National Emergency Access Target (NEAT) or the 4-h rule, integration between emergency department (ED) and inpatient clinical workflows and information systems is becoming increasingly necessary. Such system measures drive the implementation of integrated electronic medical records (ieMR) to digitally integrate these workflows. The objective of this case study was to describe the impact of digital transformation of the ED-in-patient interface (EDii) of a large tertiary hospital on process measures and clinical outcomes for patients requiring emergency admission to hospital. Data were collected from routine clinical and administrative information systems to measure process and clinical outcome measures, including ED length of stay, compliance with the 4-h rule and in-patient mortality between 28 November 2014 and 28 February 2017. The 4-h rule compliance for all patients, as well as for the EDii group (admitted to hospital excluding short stay ward), declined after digitisation. There were 55 fewer deaths in the postintervention group (15% relative reduction; P = 0.02) and a 10% relative reduction in adjusted mortality as measured by the Hospital Standardised Mortality Ratio for emergency patients (eHSMR), which did not reach statistical significance. Digital deceleration in ED performance did occur with an ieMR rollout, but worsening of key patient outcomes was not observed.

Canadian in-hospital mortality for patients with emergency-sensitive conditions: a retrospective cohort study

BackgroundThe emergency department (ED) sensitive hospital standardized mortality ratio (ED-HSMR) measures risk-adjusted mortality for patients admitted to hospital with conditions for which ED care may improve health outcomes. This study aimed to describe in-hospital mortality across Canadian provinces using the ED-HSMR.MethodsHospital discharge data were analyzed 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. Aggregated provincial ED-HSMR values were calculated. A HSMR value above or below 100 respectively means that more or fewer deaths than expected occurred within a province.ResultsDuring the study period, 1,335,379 patients were admitted to hospital in Canada with an emergency-sensitive condition as the most responsible diagnosis. 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 [99.9 (86.5–114.8) & 100.7 (87.5–115.3)] and Manitoba [99.2 (94.5–104.1) & 98.3 (93.5–103.3)], and less than expected in all other provinces and territories.ConclusionsOur study revealed important variation in risk-adjusted mortality for patients admitted to hospital with emergency-sensitive conditions among Canadian provinces. The ED-HSMR may be a useful outcome indicator to complement existing process indicators in measuring ED performance.Trial registrationN/A – Retrospective cohort study.

The impact of disease severity adjustment on hospital standardised mortality ratios: Results from a service-wide analysis of ischaemic stroke admissions using linked pre-hospital, admissions and mortality data.

BackgroundAdministrative data are used to examine variation in thirty-day mortality across health services in several jurisdictions. Hospital performance measurement may be error-prone as information about disease severity is not typically available in routinely collected data to incorporate into case-mix adjusted analyses. Using ischaemic stroke as a case study, we tested the extent to which accounting for disease severity impacts on hospital performance assessment.MethodsWe linked all recorded ischaemic stroke admissions between July, 2011 and June, 2014 to death registrations and a measure of stroke severity obtained at first point of patient contact with health services, across New South Wales, Australia’s largest health service jurisdiction. Thirty-day hospital standardised mortality ratios were adjusted for either comorbidities, as is typically done, or for both comorbidities and stroke severity. The impact of stroke severity adjustment on mortality ratios was determined using 95% and 99% control limits applied to funnel plots and by calculating the change in rank order of hospital risk adjusted mortality rates.ResultsThe performance of the stroke severity adjusted model was superior to incorporating comorbidity burden alone (c-statistic = 0.82 versus 0.75; N = 17,700 patients, 176 hospitals). Concordance in outlier classification was 89% and 97% when applying 95% or 99% control limits to funnel plots, respectively. The sensitivity rates of outlier detection using comorbidity adjustment compared with gold-standard severity and comorbidity adjustment was 74% and 83% with 95% and 99% control limits, respectively. Corresponding positive predictive values were 74% and 91%. Hospital rank order of risk adjusted mortality rates shifted between 0 to 22 places with severity adjustment (Median = 4.0, Inter-quartile Range = 2–7).ConclusionsRankings of mortality rates varied widely depending on whether stroke severity was taken into account. Funnel plots yielded largely concordant results irrespective of severity adjustment and may be sufficiently accurate as a screening tool for assessing hospital performance.