Summary Hospital-level Mortality Indicator (SHMI) reports on mortality at trust level across the NHS in England. It covers all deaths reported of patients admitted to non-specialist acute NHS trusts who either die while in hospital, or within 30 days of discharge.

1. Summary Hospital-Level Mortality Indicator
(SMHI)
26 November 2012

2. Overview and background
Summary Hospital-level Mortality Indicator (SHMI) reports on mortality at
trust level across the NHS in England
Covers all deaths reported of patients admitted to non-specialist acute
NHS trusts who either die while in hospital or within 30 days of discharge
Indicates whether a trust’s mortality ratio is as expected, higher than
expected or lower than expected
Produced and published as an experimental official statistic on a quarterly
basis by the HSCIC, with the first publication in October 2011

3. Overview and background
Data sets used
• Hospital Episode
Statistics (HES) can be
used to identify
whether a patient dies
in hospital
• HES does not capture
deaths occurring
outside hospital
• Linking HES to ONS
deaths data creates a
richer dataset
• Allows the identification
of deaths which occur
outside of hospital
within 30 days of
discharge
Exclusion criteria
• Specialist hospitals
• Mental health trusts
• Community trusts
• Day cases
• Regular attenders – day
and night
• Stillbirths
Contextual indicators
• Also publish a range of
contextual indicators
alongside the SHMI to
aid in its interpretation.
• The following
contextual indicators
are currently available:
• Palliative care
• Admission method
• In and out of hospital
deaths
• Social deprivation

4. How SHMI can / cannot be used
What it is intended
to be
Indication
Used with other
more detailed
indicators
Smoke alarm/
trigger for further
investigation
What it is NOT
intended to be
League table
Direct measure/
comparison
A definitive
judgement

5. Why are we producing SHMI?
National review
of hospital
summary
mortality ratios
(HSMR)
Review commissioned because of
concerns about
• different indicators in use,
• the lack of consistency and
• lack of clarity about the way some
were being calculated
Review looked at both
technical &
audience/use issues Following the recommendations
from this review, the Department
of Health committed to
implementing the SHMI as the
single mortality indicator which
could be adopted across the NHS

6. How the SHMI was developed
A steering group to define the high-level requirements
A detailed independent statistical modeling and analysis
exercise carried out by ScHARR, University of Sheffield
An expert technical group to agree on the specifications
HSCIC commissioned to lead the continued development
and improvement of the SHMI, working with a range of
stakeholders as well as publishing on a quarterly basis
Quarterly meetings of technical group who act as expert
peer reviewers, and review through the Indicator
Assurance Process

7. Calculation of SHMI
Observed
Deaths
• the number of patients
who die following
treatment at the trust
Expected
Deaths
Risk adjusted
based on
patient
characteristics
• the number of patients who would
be expected to die on the basis of
average England figures, given the
characteristics of the patients
treated there
• calculated using logistic regression
• Diagnosis group
• Age
• Gender
• Comorbidities
• Admission method
SHMI =
ObservedDeaths
ExpectedDeaths

8. Calculation of risk
Risk(Age)
Risk(Adm
Method)
Risk(Sex)
Risk(Co-morbidity)
logodds
Patient1
Risk
F(log
odds)
푒log 표푑푑푠
1 + 푒log 표푑푑푠

9. Calculation of expected deaths
Patient1
Risk
Patient2
Risk
Patient..
Risk
Expected
Deaths

10. Calculation of observed deaths
HES
In
hospital
deaths
HES ONS
Out of
hospital
deaths
Observed
Deaths

11. SHMI funnel plot
• Baseline SHMI value is 1
(observed = expected)
• Providers who do not
conform to the national
baseline (with associated
control limits) are
indicating special cause
variation
• This variation has not
been explained by the
baseline model, many
possibilities for reasons
why, but this warrants a
follow-up
Higher than expected
As expected
Lower than expected
Upper
Control
Limits
Lower
Control
Limits

12. SHMI VLAD chart
Variable Life-Adjusted Display
(VLAD) charts are for:
• a single trust
• a single diagnosis group
For each patient observed
outcome (0 for survived and 1
for died) is subtracted from
the risk of dying and this is
plotted cumulatively
Time
A downward trend indicates a run of more deaths than expected
An upward trend indicates a run of fewer deaths than expected

13. Comparison of control charts
Funnel Plot
• Snapshot of calculated outcome
over reporting period
• Identification of outliers
• Comparison of overall SHMI value
for trust with national baseline
VLAD Chart
• Cumulative display of longitudinal
data over time
• Detect changes in a series of
outcomes
• At diagnosis group level

14. Investigating alerts from control charts
Individual
Process of care
Structure / resource
Patient case-mix
Data
Pyramid of investigation
Lilford R., Mohammed M. A., Spiegelhalter D., Thomson R. Use and misuse of process and outcome data in managing
performance of acute medical care: avoiding institutional stigma. Lancet 2004; 363: 1147-54.

15. New developments
Report identifying ‘repeat outliers’ released in January 2013
• Quarterly reporting of ‘repeat outliers’
• Taken forward by Sir Bruce Keogh as trusts to be investigated for part of the review into the
quality of care and treatment provided by the NHS, following the Francis Inquiry 2013
Establishing the SHMI as a National Statistic
• Recommended by the Francis Inquiry 2013 (recommendation no. 271)
SHMI Data extract service
• Sharing of underlying record-level SHMI data with trusts
• Also providing trusts with VLAD charts for some SHMI diagnosis groups
Contextual indicators
• Development of additional contextual indicators on depth of coding, data quality and transfers
between trusts

16. Issues handling
Issues Log
Methodology
Changes
Publication

17. Current methodological challenges
• Non-specialist trusts with hospices within their
organisation
• Guidance on coding for palliative care is subject to
interpretation and varies considerably between trusts
Palliative care
• Issues with model convergence for some SHMI
diagnosis groups
• Investigating ways of improving this including using
more data to build the models, automated re-categorisation
to ensure that all case-mix categories
have more than a threshold number of events
Model convergence
• SHMI uses Charlson comorbidity index
• Carrying out research into using the Elixhauser index,
which includes more conditions
• Investigating calibrating the weights of the
comorbidity index on SHMI data
Measures of comorbidity

18. Questions
Any Questions?
Thank you
Clinical Indicators Team
Health and Social Care Information Centre
clinical.indicators@hscic.gov.uk

19. Connect with us
www.hscic.gov.uk
@hscic
www.slideshare.net/hscic
0300 303 5678