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PRIMIS

PRIMIS. 23 rd April 2002 Metropole Birmingham. Data Feedback for Facilitators. Dr Dougal Darvill Emma Hallam PRIMIS. Today’s workshop. quick introduction to PDQ and SDQ a review of what the queries are all about viewing the data - in context

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PRIMIS

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  1. PRIMIS 23rd April 2002 Metropole Birmingham

  2. Data Feedback for Facilitators Dr Dougal Darvill Emma Hallam PRIMIS

  3. Today’s workshop • quick introduction to PDQ and SDQ • a review of what the queries are all about • viewing the data - in context • what we find - and its meaning for practices (PDQ, SDQ, CHD)

  4. Workshop themes • data analysis and interpretation depend on data quality • data quality can only be assessed accurately with practice-specific / local knowledge

  5. Focus on PDQ and SDQ • it’s what most schemes are using • PDQ is more meaningful to practices than DQ

  6. PDQ • more meaningful to practices than DQ • recording of risk factors in the last 5 years • drugs without diagnosis (last year) • cytology subset available • HbA1c • separate from diabetes subset • recording in last year

  7. PDQ: Risk Factor Recording Smoking status ever recorded Return 80 Control Next 70 60 50 Practice percentage 40 All 30 20 10 0 71860 71862 71882 71901 71913 71998 72010 72043 72100 72107 50 74 57 64 52 48 75 70 48 75 All

  8. PDQ: Risk Factor Recording Recording of smoking status recorded in the last 5 Years or a recording of never smoked Return 80 Control Next 70 60 50 Practice percentage 40 All 30 20 10 0 71860 71862 71882 71901 71913 71998 72010 72043 72100 72107 36 61 56 44 43 41 62 55 7 71 All

  9. SDQ • two main aims: • to find baseline prevalence recording of well defined diagnoses • to highlight data quality issues surrounding the recording of past medical history

  10. When diagnosis recorded

  11. Viewing the data

  12. PDQ: Practice Percentage

  13. PDQ: Age standardised

  14. PDQ: Apparent prevalence

  15. Diagnosis code Data quality gap Diagnosis and associated codes Before After Time series:Apparent prevalence

  16. PDQ: Males with female only conditions

  17. What do the data tell us? Using the local report-style queries to investigate further

  18. Possible explanations for data • practice-specific recording behaviour • system-related anomalies / variations • query limitations • Read coding issues • variability in clinical practice

  19. “It is a capital mistake to theorise before you have all the evidence. It biases the judgement.” Sherlock Holmes, 1888

  20. The meaning for practices • lots of work!

  21. Working with the practices • remedial action • correcting mistakes • case identification • training issues • time • going over the same ground • performance management • value of inter-practice comparison

  22. CHD • what does the CHD data mean to the practices? • detail about their CHD patients • detail about monitoring • NSF performance targets • it is very dependent on their data quality • clinical benefits

  23. The number of patients with an aspirin recording (prescribed or OTC) increased between first and second extracts

  24. CHD NSF standards have increased prescribing of statins

  25. PRIMIS 23rd April 2002 Metropole Birmingham

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