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Measuring clinical performance using routinely collected clinical data.

Measuring clinical performance using routinely collected clinical data. Dr David Prytherch and Dr Jim Briggs Health Care Computing Group University of Portsmouth Mr Paul Weaver and Dr Paul Schmidt, University of Portsmouth

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Measuring clinical performance using routinely collected clinical data.

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  1. Measuring clinical performance using routinely collected clinical data. Dr David Prytherch and Dr Jim Briggs Health Care Computing Group University of Portsmouth Mr Paul Weaver and Dr Paul Schmidt, University of Portsmouth Professor Gary Smith, Portsmouth Hospitals NHS Trust, University of Bournemouth

  2. Why look at Clinical Outcomes? ………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. Why Mortality? 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. “Learning from Bristol”: The Report of the Public Inquiry into children’s heart surgery at the Bristol Royal Infirmary 1984 to 1995. I Kennedy, HMSO 2001

  3. Why case-mix adjust? …… 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. How to collect the data? 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. “Learning from Bristol”: The Report of the Public Inquiry into children’s heart surgery at the Bristol Royal Infirmary 1984 to 1995. I Kennedy, HMSO 2001

  4. Measuring Clinical Performance How do you measure clinical performance? You need to know what you expect – predicted outcomes. Compare predicted with reported. How do you predict outcomes? Case mix adjusted models Why case mix adjust? To gain clinician engagement To answer “my results are worse than … because my patients are sicker” Essentially it provides a ruler

  5. How do you case mix adjust? • Use clinical data that encapsulates the physiological state of the patient • Use this to predict a risk of “adverse outcome” • Trick is to collect necessary data in the clinical environment

  6. BHOM: Biochemistry and Haematology Outcome Modelling Aim of study was to see if data stored in core hospital systems could be used to predict (case mix adjust) clinical outcomes. Data from: PAS Biochemistry and Haematology modules of pathology system Data already collected / exists. No additional administrative or clinical burden.

  7. e.g., BHOM in General Medicine results adapted from: The use of routine laboratory data to predict in-hospital death in medical admissions D R Prytherch, J S Sirl, P Schmidt, P I Featherstone, P C Weaver, G B Smith. Resuscitation 2005; 66: 203-207 First demonstration of outcome prediction for General Medicine Data from PAS and Biochemistry and Haematology modules of pathology system 1st January 2001 - 31st December 2001 9497 discharges from GM with necessary data Model developed from Q1 and applied prospectively against Q2, Q3 and Q4

  8. Data items used in models for General Medicine: • Urea • Albumin • Creatinine • Na • K • Haemoglobin • White Cell Count • Age on admission • Sex • Mode of admission • Mortality at discharge

  9. General Medicine StudyFinal 3 month period1st October – 31st December 2001 2 = 8.00 10 d.f P = 0.63 no evidence of lack of fit c-index=0.757

  10. General Medicine StudyTotal Mortality through time – 1st January 1998 to 31st December 2001 (37283 discharges)

  11. Use to identify periods when performance deviates from norm

  12. General Medicine StudyExternal Validation: T2 v T1 2 = 7.09 10 d.f P = 0.72 no evidence of lack of fit c-index=0.92

  13. Key points: 1 • Clinical data obtained from a single venesection • Clinical data are used operationally in care of individuals • All data already stored on hospital core IT systems - no “extra” effort is required to collect data • Clinical data used are subject to extensive quality assurance

  14. Key points: 2 • Case mix adjusted and uses high quality data trusted by clinicians (no coded data) – more likely to win clinical acceptance • Data immediately available to inform decisions • Cannot be “gamed” • Performance and surveillance tool

  15. National Application of BHOM Vascular Society of Great Britain and Ireland National Vascular Database (Risk adjusted predictive models of death after index arterial operations using a minimal data set. D R Prytherch, BMF Ridler, S Ashley on behalf of the Audit and Research Committee VSGBI, Br J Surg 2005; 92: 714-718) NCEPOD (National Confidential Enquiry into Patient Outcome and Death) study into Abdominal Aortic Aneurysm www.ncepod.org.uk

  16. Sources of funding Portsmouth NHS R&D Consortium Portsmouth Hospitals NHS Trust University of Portsmouth

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