your life in their hands mortality as a measure of clinical performance david prytherch, jeff sirl,...
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Your life in their hands
Mortality as a measure of clinical
performance
David Prytherch, Jeff Sirl, Paul Weaver, Paul Meredith, Michael Booth
………Hospitals and the NHS could tell you about throughput (number of patients treated), bed occupancy (the proportion of beds occupied in the hospital), and, latterly, the costs involved. But, generally speaking, quality of outcome was a closed book.
Chapter 27 para 2
“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001
Why look at Clinical Outcomes?
At national level, the indicators of performance should be comprehensible to the public as well as to healthcare professionals. They should be fewer and of high quality, rather than numerous but of questionable or variable quality.
Recommendations para 153
“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001
Why Mortality?
…… Variables such as case mix and where possible, in the case of surgery, operative risk must be allowed for, so that, wherever feasible, it is possible to compare like with like.
Chapter 27 para 49
“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001
Why Case-mix adjust?
For the future the multiple methods and systems for collecting data must be reduced. Data must be collected as the by-product of clinical care.
Summary para 96
“Learning from Bristol”:The Report of the Public Inquiry into children’s heart surgeryat the Bristol Royal Infirmary 1984 to 1995I Kennedy, HMSO 2001
How to collect the data?
What modelling provides:Stratified (=case mix adjusted model) enables: Comparison of expected and observed
outcomes
Comparison of outcomes / performance between hospitals and clinicians
Meaningful league tables based on clinical performance
Better understanding of the process of clinical care?
Provides a ruler
Previous / existing models (General Surgery) require data beyond that routinely stored in “core” systems.
Can useful models be constructed from the more limited data set stored in “core” systems?
An interesting question
General Surgery
results adapted from:
Towards a national clinical minimum datasetfor general surgery.
D. R. Prytherch, J. S. Sirl, P. C. Weaver,P. Schmidt, B. Higgins, G.L SuttonBritish Journal of Surgery, in print
5 year period, 1st August 1997 to 31st July 2002 28925 General Surgical in-patient episodeswith necessary dataModels constructed from years 1 and 2 andtested prospectively against years 3, 4 and 5
Data items used in BHOM modelsfor General Surgery:
•Urea•Na•K•Haemoglobin•White Cell Count•Age on admission•Sex•BUPA operative severity score•Mode of admission – elective or emergency•Mortality at discharge
General Surgery - total mortality
Ten 6- month periods 1st August 1997 31st J uly 2002
0
20
40
60
80
100
120
1 2 3 4 5 6 7 8 9 10
Predicted Deaths
Reported Deaths
p-value (prospective)6 df = 0.96
Risk Bands (%)
Dis-charges
Mean Risk (%)
Predicted Deaths
Reported Deaths
2
0 to 5 2227 0.98 22 24 0.23
> 5 to 10 261 7.09 19 21 0.36
> 10 to 15
119 12.13 14 14 0.01
> 15 to 25
91 19.13 17 20 0.48
> 25 to 50
59 34.33 20 19 0.12
> 50 to 100 24 64.17 15 14 0.36
0 to 100 2751 3.91 108 112 1.56
General Surgery Study
Final 6 month period 1st February – 31st July 2002
2 = 1.56, 6 d.f, P = 0.96, no evidence of lack of fit
General Medicine
General Medicine Study
Data from HIS and Biochemistryand Haematology modules of pathology system
4 year period, 1st January 1998 - 31st December 2001
37283 discharges from GM with necessary data
Models constructed from 3 months (Jan – Mar 01) and tested prospectively against the other 45 months of the study.
Data items used in BHOM modelsfor General Medicine:•Urea
•Albumin•Creatinine•Na•K•Haemoglobin•White Cell Count•Age on admission•Sex•Mortality at discharge
General Medicine - Mortality at Discharge
Monthly, J anuary 1998 to December 2001
0
20
40
60
80
100
120
Predicteddeaths
Reporteddeaths
p-value
(prospective)
45 df = 0.11
Risk (%) Dis-charges
Mean Risk (%)
Predicted Deaths
Reported Deaths
2
0 to 5 1266 2.38 30 34 0.50
>5 to 7.5 281 6.52 18 22 0.79
>7.5 to 10 326 8.88 29 24 0.93
>10 to 12.5
160 11.47 18 21 0.43
>12.5 to 15
171 13.93 24 29 1.31
>15 to 20 158 17.72 28 30 0.18
>20 to 25 74 23.16 17 13 1.30
>25 to 33 67 28.58 19 19 0.00
>33 to 50 28 39.50 11 9 0.63
>50 to 100 13 64.46 8 7 0.64
0 to 100 2544 7.99 202 208 6.71
General Medicine Study
Final 3 month period 1st October – 31st December 2001
2 = 6.71
10 d.f
P = 0.75
no evidence of
lack of fit
How applicable are the models?
Discharges by speciality (2001: PHT + Elderly)
13.2% - General Medicine
8.7% - General Surgery
2.2% - Geriatric Medicine
0.3% - Ederly Acute
13.0% - Nephrology
6.6% - Gynaecology
56.0% - Others
Mortality at discharge by speciality (2001: PHT + Elderly)
45.4% - GeneralMedicine
9.7% - GeneralSurgery
15.3% - EderlyAcute
10.0% - GeriatricMedicine
19.6% -Others
Conclusions
Clinical data obtained from a single venesection
Clinical data are used operationally in care of individuals
No “extra” effort is required to collect data
Clinical data used are subject to extensive quality assurance
Available in all hospitals
Candidate National Clinical Minimum dataset
Sources of funding
Portsmouth NHS R&D Consortium
Portsmouth Hospitals NHS Trust
University of Portsmouth