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The Hospital Standardized Mortality Ratio (HSMR) indicator is available in Your ������ϱ� System: Insight, CIHI’s interactive, secure web tool.

Your ������ϱ� System: Insight currently houses a number of ������ϱ�indicators. The tool is updated monthly with open-year data so users can monitor their performance on key indicators throughout the year. In addition to seeing province-, region- and hospital-level results, approved users can drill down to their own chart-level information to identify factors that may be driving their organization’s results.

If you do not have access to Your ������ϱ� System: Insight, please to request access. Should you have any questions, please contact us at help@cihi.ca.

As the pandemic has evolved, there have been significant changes in crude COVID-19 mortality rates across Canada. In some instances, the crude mortality rate was 3 times lower in 2022–2023 compared with 2020–2021. To accurately reflect the changing circumstances and ensure that HSMR results align with the current state of affairs, it was necessary to update the risk-adjustment model for the COVID-19 patient group.

For the risk adjustment of the COVID-19 patient group in 2022–2023, the coefficients were derived using a single year of data (2022–2023). The results for 2022–2023 (released privately in August 2023 and publicly in December 2023) were calculated using the updated risk-adjustment model for the COVID-19 patient group. The results for previous years remain unchanged. Similarly, there were no changes to the risk-adjustment models for other patient groups as their mortality rates remained stable. All HSMR models will be updated in the future per our regular methodology update cycle.

This process of reviewing and updating the risk-adjustment model is crucial to account for the evolving nature of the pandemic and to ensure an accurate assessment of COVID-19 mortality rates. By using updated data and coefficients, the HSMR results can better reflect the impact of the pandemic on patient outcomes and provide valuable insights for health care professionals and policy-makers.

• HSMR results are calculated with an updated baseline using 2018–2019 to 2020–2021 data. The previous baseline was calculated using 2015–2016 to 2017–2018  data.
• Based on the 2018–2019 to 2020–2021 data, the list of conditions responsible for 80% of in-hospital mortality includes 74 diagnosis groups. Compared with the previous list, starting in August 2022,
  • 1 diagnosis group is removed from the list:
    • C80: Malignant neoplasms of ill-defined, secondary and unspecified sites
  • 3 diagnosis groups are now included in the list:
    • K83: Other diseases of biliary tract
    • R41: Other symptoms and signs involving cognitive functions and awareness
    • U07: COVID and COVID-related condition

• HSMR results were recalculated using 2015–2016 to 2017–2018 data. The previous baseline was calculated using 2012–2013 data.
• The list of conditions responsible for 80% of in-hospital mortality was updated. Compared with the previous list, starting in September 2019,
  • 3 diagnosis groups were removed from the list:
    • C85: Other and unspecified types of non-Hodgkin’s lymphoma
    • E86: Volume depletion
    • I24: Other acute ischemic heart diseases
  • 3 diagnosis groups were included in the list:
    • I33: Acute and subacute endocarditis
    • J10: Influenza due to identified seasonal influenza virus
    • A49: Bacterial infection of unspecified site
• Starting in 2019, HSMRs began to be compared with the national HSMR for provincial- and regional-level reporting, as well as with the peer group HSMR for hospital- and site-level reporting. This enhances the credibility of the measure, as it enables more meaningful comparisons to be drawn between hospitals.
• An HSMR above its peer group average indicates that the hospital’s mortality rate is higher than the average rate of its peer group. An HSMR below its peer group average indicates that the hospital’s mortality rate is lower than the average rate of its peer group.
• The ICD-10-CA codes for stroke were revised so they are more clinically accurate; therefore, the previous diagnosis group “stroke” was relabelled “cerebrovascular disease (CVD).” Please refer to the Hospital Standardized Mortality Ratio Methodology Notes (PDF) for details.
• Since 2019, HSMR subgroups have been based on the risk-adjusted model for the All Cases HSMR, rather than using separate models for each subgroup population.

The HSMR methodology calculates the indicator using conditions that account for 80% of in-hospital mortality. In the current methodology, the list of 74 conditions was established based on data from 2018–2019 to 2020–2021. COVID-19 (U07 as the most responsible diagnosis) has been added to the list of conditions.

COVID-19 was also added as a risk factor to the risk-adjustment models for all other 73 diagnosis groups. COVID-19 as a risk factor is determined as diagnosis code U07.1 or U07.2 with diagnosis type M, 1, C, 2, W, X or Y recorded on the discharge abstract.

Medical assistance in dying (MAID) was legalized in Canada with the enactment of Bill C-14 in June 2016. Information on MAID performed in acute care hospitals is submitted to the Discharge Abstract Database (DAD). MAID cases are excluded from HSMR calculations for all DAD-submitting provinces and territories. Please note that it is not possible to identify MAID cases in data from Quebec; therefore, MAID cases are not excluded from Quebec results. Findings based on 2016–2017 data indicate that the impact of including or excluding MAID cases is minimal for HSMR results.

No, the HSMR does not include palliative care patients. For the purposes of HSMR calculation, palliative care cases are defined as those with a most responsible diagnosis of palliative care (patients whose hospitalization was for the purpose of palliative care or patients who received palliative care for the largest portion of their hospital stay). Note that in Quebec, due to different palliative care coding standards, palliative care patients who have cancer are identified as having cancer as the most responsible diagnosis and palliative care as another diagnosis.

The HSMR calculation does include acute care inpatients who received some palliative care (not representing the largest portion of their length of stay). An example of the type of case that would be included would be a patient who was admitted to an acute care hospital with a hip fracture and who had surgery but, at some point post-operatively, whose condition became progressively worse. The patient, family and care team then determined that the patient’s treatment program would consist of comfort care or palliative care. The patient subsequently died in hospital shortly after being switched over to palliative care.

The number of palliative care cases in a facility is available, along with other descriptive/summary analyses, in Your ������ϱ� System: Insight, CIHI’s secure web tool. Please note that if a facility does not have eligible HSMR cases, the number of palliative care cases is not shown in Insight.

The logistic regression models have been updated using 2018–2019 to 2020–2021 data from the Hospital Morbidity Database. A significant addition to the model is the inclusion of COVID-19 as a new risk factor.

No, the coefficients are derived using all records meeting the inclusion and exclusion criteria from all acute care hospitals in the 2018–2019 to 2020–2021 Hospital Morbidity Database.

In all logistic regression analyses, a reference category must be specified. For all of the HSMR logistic regression models, the following are the reference variables: type of admission = elective, sex = female, LOS group = 3, transfer = 0, Charlson group = 0 and COVID_19 = 0. These reference variable levels do not have coefficients in the coefficient files because reference groups have a coefficient of 0. Please refer to the Hospital Standardized Mortality Ratio Methodology Notes (PDF) on the HSMR web page for more details about the risk factors and coefficients.

Yes, the HSMR is adjusted for a patient’s age. All else being equal, older patients have a higher risk of dying in hospital than their younger counterparts and are adjusted for accordingly in the calculation.

A number of factors contribute to in-hospital mortality. The HSMR methodology adjusts for several of them. Complicated patients tend to be those who are older, are admitted under the urgent or emergent category, have a COVID-19 diagnosis and stay longer in the hospital. The methodology has taken these factors into account, which is consistent with the HSMR methods used in different countries.

In addition, the methodology adjusts for a patient’s Charlson Index score, which reflects preadmission diagnoses recorded on a patient’s discharge abstract. It provides a weighted score for each patient depending on the number and type of diagnoses on the discharge abstract. A higher score generally indicates a more complex case. The Charlson Index is an overall comorbidity score that has been shown to be highly associated with mortality and has been widely used in clinical research.

Transfers between hospitals are treated as separate admissions. For example, if a patient was transferred from hospital A (acute) to hospital B (acute) and then to hospital C (acute), they would be considered a transfer in for hospitals B and C and would be counted in the HSMR for all 3 hospitals (if inclusion and exclusion criteria are otherwise satisfied). For hospitals A and B, this patient would also be considered a transfer out. This is consistent with the approach taken in other countries’ HSMR calculations. The current methodology adjusts for transfers in, which are patients transferred from an acute care institution. Note that transfers from one hospital’s emergency department to another hospital are not adjusted for.

HSMRs excluding all acute transfers (in and out) are provided to help assess how transfers affect patient care in your organization.

HSMRs are not calculated for specialty facilities. A specialty facility is defined as one that provides care for a specific group of patients or for specific illnesses, and that tailors its care to fit the chosen condition, patient or procedure. Examples of specialty facilities are children’s hospitals, women’s hospitals and cancer centres.

HSMR cases from specialty facilities are included in provincial-, peer group–, regional- and multi-site hospital-level HSMRs.

The list of specialty facilities is as follows:

Patients with alternate level of care (ALC) days are not automatically excluded from the HSMR calculations. All patients who meet the inclusion criteria (see the Hospital Standardized Mortality Ratio Methodology Notes (PDF)) for the HSMR are included in our analysis; these may be patients with or without ALC days.

If a patient with ALC days meets the inclusion criteria for the HSMR, the total number of days spent in hospital, including ALC days, is used to calculate the total length of stay. Length of stay is one of the variables adjusted for in the regression model.

HSMR results for subgroups are based on the same risk-adjusted model as the All Cases HSMR.

Regional-level reports include HSMR cases and deaths from all acute hospitals (including specialty hospitals) in the region. This provides a comprehensive picture of the quality of care at the regional level. Note that if a site did not have cases in the HSMR top 80% list, it was not included in the roll-up.

The HSMR is calculated and provided at the facility, health region, provincial and national levels, provided that the inclusion criteria are met.

Research has suggested that a variety of factors both within and outside the health system may affect in-hospital mortality. Other factors present on admission may also matter (e.g., underlying health status of the population, severity of illness, organization and delivery of care). While the HSMR is adjusted for a number of factors known to affect the risk of in-hospital mortality, we were not able to control for everything.

Systematic under- or over-coding of comorbidities may affect HSMR estimates, although the most responsible diagnosis continues to be the most important predictor of in-hospital mortality in most cases. During the HSMR validation process, some organizations identified coding inconsistencies and have implemented processes to make improvements in this area. Further review will hopefully lead to more consistent coding and better data quality across the country.

������ϱ�advises all facilities to code according to the nationally mandated Canadian Coding Standards. Complying with the standards is essential to ensure the national consistency and quality of the data, leading to the most accurate results on all indicators (including but not limited to the HSMR). Should coders have questions about implementing these standards, they may contact the ������ϱ�Classifications team through the eQuery service.

A variety of approaches are continually used to improve the quality of the data, including establishment of coding and abstracting standards, automated edits on data submission and database closure, and ongoing education for hospital staff and others involved in the data submission process.

In addition to the All Cases HSMR, HSMR subgroups have been developed to enable clinical teams within organizations to compare results with those of their peers. HSMR results within subgroups are compared with corresponding averages. For example, the medical HSMR at the hospital level is compared with the medical peer group average; the surgical HSMR at the regional level is compared with the surgical national average.

95% confidence interval (CI) limits for the risk-adjusted rates are calculated to aid interpretation and comparisons. CIs are used to establish whether the indicator result is statistically different from the average. The width of the CI illustrates the degree of variability associated with the rate. Indicator values are estimated to be accurate within the upper and lower CI 19 times out of 20 (95% CI). Risk-adjusted rates with CIs that do not contain the Canada or peer group result can be considered statistically different.

HSMR results are available in both Your ������ϱ� System: In Depth/In Brief (publicly available) and in Your ������ϱ� System: Insight (secure). In Insight, HSMR results are based on available data as of the corresponding data submission cut-off dates. Data may change significantly throughout the course of the year due to continuous updates to the database. Thus HSMR results reported during the year in Insight may vary until the database is closed and closed-year HSMR results are calculated.

As of November 2016, HSMR results are shown using funnel plots in the Your ������ϱ� System web tool. The funnel plots provide a visual representation of performance relative to the national average, while considering the number of HSMR cases in various organizations. Indicator values outside of the funnel indicate out-of-the-ordinary results. For more details, consult the .

Peer ranges and peer quartiles (QRs) are also provided to allow for additional interpretation of the results in Your ������ϱ� System: Insight, CIHI’s secure web tool.

HSMR results are not publicly reported if

• A facility/region has fewer than 50 HSMR cases
• The number of expected deaths is less than 1 and the number of observed deaths is more than 0

However, the results are available in Your ������ϱ� System: Insight, CIHI’s secure web tool.

Since the beginning of this project, organizations have been monitoring HSMR results and interpreting their HSMR trends. Some organizations have added this measure to their balanced scorecards or quality monitoring/improvement programs, and they have indicated that the HSMR is reviewed regularly by their boards of directors. In addition, organizations have conducted further investigation and drill-down analysis using various resources and tools. They have identified areas for improvement and developed action plans to focus improvement efforts to reduce hospital mortality.

As a “big-dot” measure, the purpose of the HSMR is to provide a reflection of in-hospital mortality changes over time for a broad range of disease groups for an organization. ������ϱ�believes that the HSMR should be used along with other indicators to help assess quality of care in hospitals. While a single indicator offers useful information, it should be considered a starting point for further analysis. For example, medical, surgical and ICU HSMRs, where applicable, are provided to help further understand your hospital’s results. Potential starting points for identifying areas for improvement include reviewing mortality rates for meaningful groups (e.g., programs, diagnosis groups). also contains tools that may help you identify areas for improvement.

The HSMR is a broad system-level measure comparing observed to expected deaths. The expected is based on the national experience. Following your organization’s HSMR over time and further analysis of the source data may help to identify and target areas for improvement. At the individual patient level, there might be no obvious issue identified, but statistical measurement and the overall picture may provide a compelling clue to prompt further investigation into clinical processes and lead to improvement in care.

We do provide our clients with SAS code to calculate the HSMR. However, the code is provided as is, which means the way we use it for calculating the HSMR using ������ϱ�data. It has to be modified to suit the data structure and the data elements of the hospital’s data. The whole process requires intermediate (including knowledge of SAS macros) SAS skills.

The All Cases Coefficients are available in the coefficients Excel file (XLSX). For further questions on coefficients, send an email to hsp@cihi.ca.

Background information and additional reference material are available on CIHI’s website and in the Indicator Library.

We welcome your comments/questions about your facility’s results, how you are using the HSMR for quality improvement or the HSMR methodology. Please send an email to hsp@cihi.ca.