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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

PRIMIS

23rd April 2002

Metropole Birmingham


Data feedback for facilitators

Data Feedback for Facilitators

Dr Dougal Darvill

Emma Hallam

PRIMIS


Today s workshop
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)


Workshop themes
Workshop themes

  • data analysis and interpretation depend on data quality

  • data quality can only be assessed accurately with practice-specific / local knowledge


Focus on pdq and sdq
Focus on PDQ and SDQ

  • it’s what most schemes are using

  • PDQ is more meaningful to practices than DQ


Primis
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


Pdq risk factor recording
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


Pdq risk factor recording1
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


Primis
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







Time series apparent prevalence

Diagnosis code

Data quality gap

Diagnosis and associated codes

Before

After

Time series:Apparent prevalence


Pdq males with female only conditions
PDQ: Males with female only conditions


What do the data tell us

What do the data tell us?

Using the local report-style queries

to investigate further


Possible explanations for data
Possible explanations for data

  • practice-specific recording behaviour

  • system-related anomalies / variations

  • query limitations

  • Read coding issues

  • variability in clinical practice


It is a capital mistake to theorise before you have all the evidence it biases the judgement

“It is a capital mistake to theorise before you have all the evidence. It biases the judgement.”

Sherlock Holmes, 1888


The meaning for practices
The meaning for practices the evidence. It biases the judgement.”

  • lots of work!


Working with the practices
Working with the practices the evidence. It biases the judgement.”

  • remedial action

    • correcting mistakes

    • case identification

    • training issues

  • time

  • going over the same ground

  • performance management

  • value of inter-practice comparison


Primis
CHD the evidence. It biases the judgement.”

  • 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


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


Chd nsf standards have increased prescribing of statins
CHD NSF standards have increased prescribing of statins or OTC) increased between first and second extracts


Primis

PRIMIS or OTC) increased between first and second extracts

23rd April 2002

Metropole Birmingham