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Improvements in clinical quality in
English primary care
A longitudinal study of the Quality
and Outcomes Framework
Evan Kontopantelis
NPCRDC, University of Manchester
Timeline
• 80’s: Determinism
– Quality can’t be measured
– There’s no such thing as a bad doctor
• Early 90’s: Wind of change
– Government: improving health care became a priority. Care is too
variable but can be expensive to improve
– Academics: developed methods for measuring Q
– Doctors: cultural shift towards accepting that Q needs to be
measured and improved.
• By 1997: Reversal of perception, guidelines & standards
– Quality can be measured
– Care is too variable and can improved
– Providing high Q care is expensive
– Doctors want to be rewarded for providing high Q care
QuIP
• Design
– Longitudinal time series, 4 time points
– Sample: 42 representative English practices
• Aim
– Evaluate the impact of the 2004 GMS contract and QOF on
the quality of care provided in general practice, with attention
to both the losses and gains
1998 2003 2005 2007
NGMS/
QOF
QUASAR QuIP
1998 2003 2005 2007
NGMS/
QOF
QUASAR QuIP
1998-2003: pre QOF
• Quality was already
improving
• How will the new
contract affect quality of
care…
– No change
– Change in level not slope
– Change in level & slope
– Change: quality fall
59%
62% 60%
76%
70% 70%
0%
10%
20%
30%
40%
50%
60%
70%
80%
CHD Diabetes Asthma
1998
2003P<0.01 P<0.01 P<0.01
newcontract
2004: Quality and Outcomes
Framework introduced
• What is it?
– A complex set of 146 quality indicators that relates
to 25% of GPs’ income
• Costs
– Additional funding of £1.8 billion
• What might its effects be?
– Improved care
– Un-incentivised areas get worse care
– Change in professional values
1998-2005: QOF in the middle
• Quality is still improving
• But: Is the rate of improvement significantly more than
what was expected from previous trends?
• In other words: is there a QOF effect?
59% 62% 60%
76%
70% 70%
85% 84%81%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
CHD Diabetes Asthma
1998 2003 2005
Method
• Data Structure
– Longitudinal time series
– Data extracted from medical records of random cross-sectional
samples of patients with asthma, CHD and diabetes registered with
42 English family practices
– 2300 patients in 1998, 1495 in 2003 and 1882 in 2005
• Problems
– Ceiling effect
– Time-series with only 3 time points
• Method
– Performance in 2005 was compared to that predicted by a logit
model, based on observed trends between 1998 and 2003
– Comparison for both overall scores and individual indicators
– Sensitivity analysis with the more conservative linear model
produced the same results
Did QOF improve care?
• Mean practice achievement scores for 2005, in our
sample of 42 English practices:
CHD Diabetes Asthma
Actual score 85.0 81.4 84.3
Predicted* 80.7 73.2 72.3
Significance of difference 0.066 0.002 <0.001
*on logit model using the 98-03 trend
• Performances for incentivised vs non-incentivised
indicators were compared
• No significant difference found for any condition
3.0%
1.0% 1.8% 2.6% 3.7%
6.1%
10.5%
21.7%
49.5%
0.6% 0.4% 0.3% 0.8% 1.2% 2.2%
5.0%
12.1%
77.3%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 to <650 650 to < 700 700 to < 750 750 to < 800 800 to < 850 850 to < 900 900 to < 950 950 to < 1000 1000 to 1050
No. of points
Percentageofpractices
2004/05
2005/06
The big picture: post QOF achievement
across all English practices
2004-5
Median
2005-6
Median
Overall 83.4% 87.1%
CHD 85.7% 88.7%
Diabetes 80.1% 83.7%
Asthma 80.5% 83.6%
• Data extracted automatically from clinical computing
systems for 7935 English practices in 04/05 & 05/06:
Overall weighted achievement
Overview
• QOF improved the rate of increase in quality of
care for certain conditions
• There is no evidence that quality improved more
for incentivised indicators compared to non-
incentivised ones
• QOF could be corrosive for professional values but
“internal motivation has not been damaged by
external incentives” (McDonald, Harrison, Checkland,
Campbell, Roland. BMJ, In Press)
Thank you for listening

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QOF improvements in Primary Care (2006)

  • 1. Improvements in clinical quality in English primary care A longitudinal study of the Quality and Outcomes Framework Evan Kontopantelis NPCRDC, University of Manchester
  • 2. Timeline • 80’s: Determinism – Quality can’t be measured – There’s no such thing as a bad doctor • Early 90’s: Wind of change – Government: improving health care became a priority. Care is too variable but can be expensive to improve – Academics: developed methods for measuring Q – Doctors: cultural shift towards accepting that Q needs to be measured and improved. • By 1997: Reversal of perception, guidelines & standards – Quality can be measured – Care is too variable and can improved – Providing high Q care is expensive – Doctors want to be rewarded for providing high Q care
  • 3. QuIP • Design – Longitudinal time series, 4 time points – Sample: 42 representative English practices • Aim – Evaluate the impact of the 2004 GMS contract and QOF on the quality of care provided in general practice, with attention to both the losses and gains 1998 2003 2005 2007 NGMS/ QOF QUASAR QuIP 1998 2003 2005 2007 NGMS/ QOF QUASAR QuIP
  • 4. 1998-2003: pre QOF • Quality was already improving • How will the new contract affect quality of care… – No change – Change in level not slope – Change in level & slope – Change: quality fall 59% 62% 60% 76% 70% 70% 0% 10% 20% 30% 40% 50% 60% 70% 80% CHD Diabetes Asthma 1998 2003P<0.01 P<0.01 P<0.01 newcontract
  • 5. 2004: Quality and Outcomes Framework introduced • What is it? – A complex set of 146 quality indicators that relates to 25% of GPs’ income • Costs – Additional funding of £1.8 billion • What might its effects be? – Improved care – Un-incentivised areas get worse care – Change in professional values
  • 6. 1998-2005: QOF in the middle • Quality is still improving • But: Is the rate of improvement significantly more than what was expected from previous trends? • In other words: is there a QOF effect? 59% 62% 60% 76% 70% 70% 85% 84%81% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% CHD Diabetes Asthma 1998 2003 2005
  • 7. Method • Data Structure – Longitudinal time series – Data extracted from medical records of random cross-sectional samples of patients with asthma, CHD and diabetes registered with 42 English family practices – 2300 patients in 1998, 1495 in 2003 and 1882 in 2005 • Problems – Ceiling effect – Time-series with only 3 time points • Method – Performance in 2005 was compared to that predicted by a logit model, based on observed trends between 1998 and 2003 – Comparison for both overall scores and individual indicators – Sensitivity analysis with the more conservative linear model produced the same results
  • 8. Did QOF improve care? • Mean practice achievement scores for 2005, in our sample of 42 English practices: CHD Diabetes Asthma Actual score 85.0 81.4 84.3 Predicted* 80.7 73.2 72.3 Significance of difference 0.066 0.002 <0.001 *on logit model using the 98-03 trend • Performances for incentivised vs non-incentivised indicators were compared • No significant difference found for any condition
  • 9. 3.0% 1.0% 1.8% 2.6% 3.7% 6.1% 10.5% 21.7% 49.5% 0.6% 0.4% 0.3% 0.8% 1.2% 2.2% 5.0% 12.1% 77.3% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 0 to <650 650 to < 700 700 to < 750 750 to < 800 800 to < 850 850 to < 900 900 to < 950 950 to < 1000 1000 to 1050 No. of points Percentageofpractices 2004/05 2005/06 The big picture: post QOF achievement across all English practices 2004-5 Median 2005-6 Median Overall 83.4% 87.1% CHD 85.7% 88.7% Diabetes 80.1% 83.7% Asthma 80.5% 83.6% • Data extracted automatically from clinical computing systems for 7935 English practices in 04/05 & 05/06:
  • 11. Overview • QOF improved the rate of increase in quality of care for certain conditions • There is no evidence that quality improved more for incentivised indicators compared to non- incentivised ones • QOF could be corrosive for professional values but “internal motivation has not been damaged by external incentives” (McDonald, Harrison, Checkland, Campbell, Roland. BMJ, In Press)
  • 12. Thank you for listening