This publication of the SHMI relates to discharges in the reporting period March 2023 - February 2024. The SHMI is the ratio between the actual number of patients who die following hospitalisation at the trust and the number that would be expected to die on the basis of average England figures, given the characteristics of the patients treated there. The SHMI covers patients admitted to hospitals in England who died either while in hospital or within 30 days of being discharged. To help users of the data understand the SHMI, trusts have been categorised into bandings indicating whether a trust's SHMI is 'higher than expected', 'as expected' or 'lower than expected'. For any given number of expected deaths, a range of observed deaths is considered to be 'as expected'. If the observed number of deaths falls outside of this range, the trust in question is considered to have a higher or lower SHMI than expected. The expected number of deaths is a statistical construct and is not a count of patients. The difference between the number of observed deaths and the number of expected deaths cannot be interpreted as the number of avoidable deaths or excess deaths for the trust. The SHMI is not a measure of quality of care. A higher than expected number of deaths should not immediately be interpreted as indicating poor performance and instead should be viewed as a 'smoke alarm' which requires further investigation. Similarly, an 'as expected' or 'lower than expected' SHMI should not immediately be interpreted as indicating satisfactory or good performance. Trusts may be located at multiple sites and may be responsible for 1 or more hospitals. A breakdown of the data by site of treatment is also provided, as well as a breakdown of the data by diagnosis group. Further background information and supporting documents, including information on how to interpret the SHMI, are available on the SHMI homepage (see Related Links).

This statistic shows the improvement in mortality rates 2007-2009 amongst all hospitals in the United States, sorted by mortality rates for inhospital care as well as ** and *** days following hospitalization. In addition to presenting information on improvement in the United States overall, this graph includes further data on hospitals of differing quality ratings. In the United States overall, mortality rates improved by *** percent, but in five-star hospitals, mortality rates improved by **** percent.

Number and percentage of deaths, by place of death (in hospital or non-hospital), 1991 to most recent year.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 3 procedures performed (Carotid Endarterectomy, Pancreatic Resection, and Percutaneous Coronary Intervention) in California hospitals. The 2023 IMIs were generated using AHRQ Version 2024, while previous years' IMIs were generated with older versions of AHRQ software (2022 IMIs by Version 2023, 2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

This statistic depicts the 30-day mortality rate for patients with select conditions in U.S. hospitals who were discharged, between 2010 and 2016. Among heart attack, stroke, heart failure and pneumonia patients, the 30-day mortality rate for discharged patients averaged **** percent between 2013 and 2016.

In the United States from 2022 to 2024, the 30-day mortality rate in hospital at home programs for patients with respiratory infections and inflammations with MCC was around ** deaths per 1,000. In comparison, the mortality rate in comparable hospitals for the same diagnosis related groups was almost *** deaths per 1,000.

The dataset contains risk-adjusted mortality rates, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 6 procedures performed (Abdominal Aortic Aneurysm Repair, Carotid Endarterectomy, Craniotomy, Esophageal Resection, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2014 and 2015 IMIs were generated using AHRQ Version 5.0, while the 2012 and 2013 IMIs were generated using AHRQ Version 4.5. The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to hospital table for hospital rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings

Notes:

In 2022, the highest in-hospital mortality rate in Spain was recorded in the Canary Islands, with a total of **** deaths for every 100 discharges. Galicia followed, with **** deaths per 100 discharges. In comparison, the Spanish communities with the lowest in-hospital mortality rates were Madrid and Catalonia.

This dataset contains the number of cases, number of in hospital/30 day deaths, observed, expected and risk- adjusted mortality rates for cardiac surgery and percutaneous coronary interventions (PCI) by hospital. Regions represent where the hospitals are located. The initial Health Data NY dataset includes patients discharged between January 1, 2008, and December 31, 2010. Analyses of risk-adjusted mortality rates and associated risk factors are provided for 2010 and for the three-year period from 2008 through 2010. For PCI, analyses of all cases, non-emergency cases (which represent the majority of procedures) and emergency cases are included. Subsequent year reports data will be appended to this dataset. For more information check out: http://www.health.ny.gov/health_care/consumer_information/cardiac_surgery/ or go to the “About” tab.

This dataset contains Mortality Statistics for years 2015 and 2016 from Quality Based Reimbursement (QBR) Program for hospitals in Maryland. It includes Hospital ID, Hospital Name, Mortality Rate, Ratio of Observed to Predicted Mortality Rate, Risk Adjusted Mortality and Survival Rates, Number of Dead and time period covered for the data collected.

This scatter chart displays hospital beds (per 1,000 people) against death rate (per 1,000 people). The data is about regions.

In 2018, over *** million people died due to poor quality of care in hospitals in the south Asian country of India. Furthermore, over *** people died due to insufficient access to healthcare in the country during that time.

These mortality indicators provide information to help the National Health Service (NHS) monitor success in preventing potentially avoidable deaths following hospital treatment.

The National Confidential Enquiry into Patient Outcome and Death (NCEPOD) have, over many years, consistently shown that some deaths are associated with shortcomings in health care. The NHS may be helped to prevent such potentially avoidable deaths by seeing comparative figures and learning lessons from the confidential enquiries, and from the experience of hospitals with low death rates.

The indicators presented measure mortality rates for patients, admitted for certain conditions or procedures, where death occurred either in hospital or within 30 days of the emergency admission or operative procedure.

There are five 'deaths within 30 days' indicators:

Operative procedures:

• Deaths within 30 days of a hospital procedure: surgery (non-elective admissions)

• Deaths within 30 days of a hospital procedure: coronary artery bypass graft

Emergency admissions:

• Deaths within 30 days of emergency admission to hospital: fractured proximal femur

• Deaths within 30 days of emergency admission to hospital: myocardial infarction

• Deaths within 30 days of emergency admission to hospital: stroke

This scatter chart displays death rate (per 1,000 people) against hospital beds (per 1,000 people) in the Americas. The data is filtered where the date is 2021. The data is about countries per year.

In 2023, the hospital mortality rate totaled *** percent in Hungary, marking a decrease from the previous year. The highest figure was recorded in 2021 at *** percent.

This scatter chart displays death rate (per 1,000 people) against hospital beds (per 1,000 people) in Asia. The data is filtered where the date is 2021. The data is about countries per year.

In 2019, the crude mortality rate per thousand inhabitants in Dubai amounted to ****. In the same year, the total number of deaths inside the hospitals in Dubai amounted to about *** thousand deaths taking place in private and governmental hospitals.

This dataset is about regions in the Americas. It has 4 rows. It features 3 columns: hospital beds, and death rate.