1. Primary Care Prescribing Variation
November 2016

2. Objectives Principles of Medicines Optimisation
Availability of Prescribing Data
Total Prescribing Spend
Total Prescription Items
Medicines Optimisation Incentive Scheme Interventions (including antibiotic prescribing)
Factors affecting uptake of interventions
How does the Medicines Optimisation Team use the data
Changing Clinical behaviour – the Evidence Base

3. Principles of Medicines Optimisation

4. Availability of Prescribing Data
Robust (used as the basis for reimbursing community pharmacies and dispensing practices)
Timely – available six weeks after the end of the month in which the prescription was dispensed
Available electronically – month, prescriber and down to individual product
Doesn’t capture patient specific information
National indicators available via the BSA PPD information portal

5. Total Prescribing Spend
Spend relates to those prescriptions dispensed in the relevant month (not all prescriptions are cashed by patients)
National weighted population denominator available – the ASTRO PU
Prices fixed Nationally (branded and generic)
Off FP10 supply can skew CCG comparisons (e.g. tube feeds, dressings), rebates paid locally and different arrangements regarding shared care

6. ASTRO PU
Age, Sex, Temporary Resident Prescribing Unit – although the PPD no longer include TRs!)
Only weights for the age and sex of the practice population
Weighting based on historic prescribing patterns (last updated 2013) therefore doesn’t take account of recent changes in prescribing patterns such as the NOACs (AF predominantly affects older people)
Updated every quarter

7. ASTRO PU Does not include any measures of disease prevalence
special populations such as care home residents
individual patients on particularly expensive medicine regimens
Therefore the spend per ASTRO PU needs to be viewed alongside the known health needs of the practice population
STAR PUs (Specific Therapeutic Area Related Prescribing Units) are available to compare certain therapeutic areas (e.g. antibiotics)

8. ASTRO PU WEIGHTING Note CCG spend per ASTRO PU in 15/16 was…….
Cost-based – ASTRO PU 2013
Age Band
Male
Female
0-4
1.0
0.9
5-14
0.7
15-24
1.2
1.4
25-34
1.3
1.8
35-44
2.6
45-54
3.1
3.7
55-64
5.3
5.4
65-74
8.7
7.6
75+
11.3
9.9

9. Total Prescription Items
A prescription item refers to a single item prescribed by a prescriber on a prescription form (FP10). If a prescription form includes three medicines it is counted as three prescription items
Comparisons using items can therefore be skewed by practice policies on duration of repeat prescriptions
Useful to compare acute prescribing (e.g. antibiotics) and ratios of different products within individual practices

10. Medicines Optimisation Incentive Scheme Interventions (including antibiotic prescribing)
‘Simple’ switches – for example Vensir, branded inhalers and buprenorphine patches
clinically non-contentious
led by medicines optimisation team
small number of patients might be concerned, or the switch might be inappropriate
short term shortages (community pharmacy can’t supply) can increase workload and stress for practices
dispensing practices – might be conflict with discounts

11. Medicines Optimisation Incentive Scheme Interventions (including antibiotic prescribing)
More complex interventions – such as reviewing pregabalin and co-proxamol prescribing
often involves review of the patient’s diagnosis, Read Code and clinical history
often requires consultation with patient
might require discussion with a specialist
identifying suitable patients is more involved for medicines optimisation team

12. Medicines Optimisation Incentive Scheme Interventions (including antibiotic prescribing)
pressure from patients (however studies show GPs tend to over-estimate the actual pressure)
belief that prescribing an antibiotic reduces workload (studies show it increases the likelihood of the patient re-attending with similar symptoms)
conflicting messages regarding not prescribing antibiotics but treating patients urgently with antibiotics to prevent sepsis

13. Factors affecting uptake of interventions
Practice factors
Individual Prescriber Behaviour
Patients
Medicines Optimisation Team input
Secondary care influence
Pharmaceutical industry influence

14. Practice factors Stress workload staffing levels financial
practice disputes
complaints, litigation, previous significant incidents
demographics
etc.

15. Practice factors
There are GPs in this room who are extremely engaged but who sit in practices with high antibiotic prescribing rates, high pregabalin costs and high co-proxamol usage
It is not as simple as whether the practice is engaged or not

16. Individual Prescriber Behaviour
Practice may be engaged but individual prescribers may not
Knowledge base including latest guidance at both national and local level
Personal factors such as levels of stress/morale/other commitments
Ownership of role as guardian of NHS resources
Previous experience e.g. patients experiencing negative side effects/complications/complaint/litigation
Relationship with the patient and ability to communicate risk/benefit of treatments

17. Patients Patient’s beliefs are crucial to good medicines concordance
Remember the principles of medicines optimisation
Patients vary as to how responsible they feel for using NHS resources appropriately
Patient understanding of medicines-ordering processes varies

18. Medicines Optimisation Team
Time needed to work up interventions (evidence base, clinical system searches, ensure supply, etc.)
Workload (team and the GPs)
Practical issues such as room in practices, remote access, GP clinical system and templates

19. Secondary Care Specialists
Not aware of CCG formulary, preferred medicines, interventions
Choices of medicines often reflect the different patient populations being treated
Products stocked by hospitals based on procurement decisions might differ from primary care preferred products

20. Pharmaceutical Industry
Direct marketing of certain products to patients (e.g. blood glucose test strips, nutrition products)
Indirect advertising to patients both of medicines and medical conditions (magazines e.g. fungal nail paints, urinary incontinence)
Support of in-house medical and nursing education and training
Support of clinics and patient education evenings

21. How does the Medicines Optimisation Team use the data
Budgetary performance including forecast outturn
Savings monitor for QIPP plan
Trends for interventions at CCG level compared to National mean
Individual practice trends and monthly snapshot for total spend and MOIS interventions
Practice data discussed at practice incentive scheme meetings (initial meeting and mid-year review)

22. How does the Medicines Optimisation Team use the data
Summary of total spend shared with practice prescribing leads each month
Total spend and performance against key interventions discussed at Locality Medicines Optimisation Group meetings (attended by practice prescribing leads).
Used to prioritise team workload and input to practices

23. Total Spend and Items Practice Locality 2015/16 Spend (£)
Budget 2016/17 (£)
Apr16 -July16 Spend
Forecast 2016/17 Spend (£)
Forecast Increase over 2015/16 Spend (£)
Forecast Increase over 2015/16 Spend (%)
Forecast Outturn against Budget (£)
Forecast Outturn against Budget (%)
Total Cost based Astro PUs - June15
Total Cost based Astro PUs - June16
Change in ASTRO PUs (%)
Forecast 2016/17 Spend per ASTRO PU (£)
Forecast 2016/17 Spend per ASTRO PU (%)
Note 1 2 3 4 5 6 7 8 9 10 11 12 13
BARTON SURGERY
WNF
£2,488,336
£2,456,229
£812,235
£2,419,526
-£68,810
-2.8
-£36,703
-1.5
55,951
56,695
1.3
42.68
111
CHAWTON HOUSE SURGERY
£1,040,729
£1,027,301
£346,221
£1,031,341
-£9,388
-0.9
£4,040
0.4
32,016
32,216
0.6
32.01 83
CORNERWAYS MEDICAL CENTRE
£1,984,987
£1,959,374
£655,641
£1,953,055
-£31,932
-1.6
-£6,319
-0.3
53,346
53,755
0.8
36.33 94
FORDINGBRIDGE SURGERY
£2,108,743
£2,081,533
£700,090
£2,085,463
-£23,280
-1.1
£3,929
0.2
55,567
55,871
0.5
37.33 97
LYNDHURST SURGERY
£867,967
£856,767
£283,144
£843,443
-£24,524
-£13,325
23,737
24,272
2.3
34.75 90
NEW FOREST MEDICAL GROUP
£1,127,282
£1,112,737
£382,037
£1,138,030
£10,748
1.0
£25,293
35,597
35,949
31.66 82
NEW MILTON HEALTH CENTRE
£1,868,949
£1,844,833
£619,048
£1,844,053
-£24,896
-1.3
-£781
-0.0
47,754
47,692
-0.1
38.67
100
RINGWOOD MEDICAL CENTRE
£1,923,239
£1,898,424
£657,369
£1,958,205
£34,966
1.8
£59,782
3.1
50,314
50,948
38.44
THE ARNEWOOD PRACTICE MILTON MEDICAL CEN
£2,566,343
£2,533,230
£859,260
£2,559,605
-£6,738
£26,376
61,520
61,625
41.54
108
THE PRACTICE AT LYMINGTON
£17,195
£16,973
£13,671
£40,725
£23,530
136.8
£23,752
139.9
#DIV/0!
TWIN OAKS MEDICAL CENTRE
£659,667
£651,155
£219,542
£653,983
-£5,684
£2,828
18,314
18,344
35.65 92
WISTARIA & MILFORD SURGERIES
£3,135,561
£3,095,102
£1,026,659
£3,058,263
-£77,298
-2.5
-£36,840
-1.2
76,130
76,661
0.7
39.89
103
TOTAL
£84,785,330
£83,691,339
£28,480,775
£84,839,963
£54,632
0.1
£1,148,623
1.4
2,174,722
2,198,302
1.1
38.59

24. Key Interventions – note July data

26. Changing Clinical Behaviour – the Evidence Base
Educational materials – ineffective on their own, more effective if combined with other reinforcing strategies (e.g. detail aids and intervention briefs on website)
Educational meetings – large scale didactic meetings generally seen as ineffective (especially if trying to change complex behaviours). Smaller scale interactive meetings seen as more effective (e.g. Medicines Optimisation Group meetings)
Educational outreach visits – effective (notably in changing prescribing performance). May be particularly effective if combined with social marketing techniques (e.g. visits from MOT members and practice incentive scheme meetings)

27. Changing Clinical Behaviour – the Evidence Base
Opinion leaders – mixed effect on behaviour. Difficult to accurately identify who the local opinion leaders are (e.g. Dr John Duffy, Dr Tamara Everington - AF work, Dr Bruce Guthrie - polypharmacy work)
Audit and feedback – literature identifies mainly small to moderate positive effects. Key issues seem to include who provides the feedback, timeliness, data quality, relevance of content and level of clinician buy-in (e.g. GRASP-AF extremely successful in improving the treatment of AF)
Reminders (including computer decision-support) – can be moderately effective, particularly when computer decision-support is used to influence prescribing (e.g. DXS alerts)

28. Changing Clinical Behaviour – the Evidence Base
Patient-mediated interventions – passing information to patients through mass media may be effective in changing clinician behaviour, although it is unclear whether this effect is due to patients or clinicians themselves making use of the information (e.g. AF campaign)
Multifaceted interventions – can be more effective than single strategies, especially if barriers to change are identified and interventions tailored to address these. They are more costly. (e.g. AF work by medicines optimisation team uses a multifaceted approach)

29. Conclusions – Prescribing Variation
Primary care prescribing data is robust
Available denominators do not explain all prescribing variation
Comparative information therefore requires interpretation
Many factors affect the uptake of interventions (including practice, prescriber and patient factors)

30. Conclusions – Changing Clinical Behaviour
Complex issue, multifactorial in nature
Change requires the building of relationships with a wide range of stakeholders
The process requires an evidence-based, multifaceted approach to changing behaviour and sustaining transformation
Need to build on successes of what has worked well
There are quick fixes but fundamental change takes longer