effective general practice: audit & feedback for the
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EFFECTIVE GENERAL PRACTICE: AUDIT & FEEDBACK FOR THE IMMUNISATION
PRIMARY SERIES.Gary Reynolds, Mareta Timo, Anjileena Dev, Susannah Holt, Tracey Poole, &
Nikki Turner
Immunisation Advisory CentreUniversity of Auckland, New Zealand
September 2015
QUOTE “Someone told me that each equation I included in the
book would halve the sales”
Stephen Hawking –Theoretical physicist
BACKGROUND – NZ Context High Immunisation rates are a Govt. Target
The Target - 95% by age 2 years
With Imm Rates just over 93%, the last 2% towards the Govt & WHO target of 95% - Just how challenging?
Targets – 95% immunised by 8 months since late 2013
Recent years - Emphasis on timeliness
There is enormous variation in immunisation delivery between General Practices
Establishing the denominator – ie. the number eligible for immunisation hase been a priority
BACKGROUND –Audit & Feedback
A Plethora of Stats generated for General Practice
Meaningful? Accurate? Relevant?
Need to develop audits that are useful and identify deficiencies in vaccine delivery
Use of A&F Tools allow quality improvements
Audit = collection of data (clinical performance)
Feedback = data to practice with or without recommendation
Need to develop best practice systems
AIM To examine the Primary Series (6w, 3m & 5m) delivery
in General Practice in NZ
To determine the accuracy of current auditing systems for the primary series
To establish a rigorous auditing system for the primary series
To identify the rules of vaccine delivery that improve outcomes
OBJECTIVES To confirm the accuracy of NIR data and different practice
based audits for individual general practices for the primary series
To develop an audit system which establishes an accurate numerator (no. immunised) and denominator (no. eligible) for immunisation
To specifically examine the last 5 -10% of delivery towards the target 95% immunisation rates
To examine European, Maori and Pasifika providers
To re audit the same practices an interval later
METHODS
Setting 1-Pasifika Provider Pasifika Horizon Healthcare – Inner West Auckland Pasifika practice - 1640 patients PMS - Medtech 32
Setting 2- Practice 1 A typical Practice – Inner SW suburbs of Auckland Practice 1 - 3000 enrolled patients PMS – Medtech 32 So-called “average” practice –(90% immunisation rate)
Setting 3-Maori Provider A Maori Provider –Urban South Auckland 5500 enrolled patients PMS – Medtech 32
METHODS The Dataset
“All infants enrolled at these practices requiring a primary series of 6w, 3m and 5m immunisationsaccording to the 2011 NZ immunisation schedule within a set 1 year period.”
1-2-2011 to 1-2-2012.
1-6-2011 to 1-6-2012.
1-6-2012 to 1-6-2013
METHODS Obtained the NIR report for the 1 year period for each
practice
Run a PMS Audit for each practice with the same NIR parameters (PMS Audit = NIR report)
The audit would identify the number immunised (Numerator) eligible (Denominator)
Run the Multi-Audit (A&F tool) on all practices
Correlate all immunisation events with the NIR Database
METHODS-The A&F Tool The Multi Audit = Multiple sub audits: 1) The 5m immunisation = the PMS based audit = NIR
report parameters. 2) The 6w immunisation not in the 5m audit. 3) The 3m immunisation not in the 6w and 5m audit. 4) No immunisations at 5m. 5) Total children born 6 months before the period to the
end of the period not in all the above audits.
Confirmation of Immunisation History Using NIR
ResultsRESULTS-Pasifika Horizon Healthcare
RESULTS – Pasifika audits All 3 initial audits were different NIR report 31/36 (86.1%) PMS 5m audit 39/43 (90.7%) {Manual audit 41/48 (85.4%)}
Requested a printout of NHI numbers from the NIR of the no. eligible (denominator)
RESULTS – Pasifika A&F Tool The Multi Audit identified (x=57) The 5m immunisation = the PMS based audit. (Group A =
43). The 6w immunisation not in the 5m audit (Group B = 7). The 3m immunisation not in the 6w and 5m audit (Group
C = 3). No immunisations at 5m (Group D = 2). Total children born 6 months before the period to the end
of the period not in all the above audits (Group E = 2).
RESULTS - exclusions A total of 10 of the total 58 candidates (17.2%) were
excluded for following reasons.
Input errors = 2 (3.4%)
Transferred out of the practice before the primary series was complete = 5 (8.6%)
Casual Patients seen but enrolled at other practices = 2 (3.4%)
Enrolled after the period = 1 (1.7%)
Multi-Audit
RESULTS-Multi Audit All 48 (100%) started their primary series
95.8% (46/48) fully immunised for their primary series. NIR report 31/36 (86.1%) -9.7% difference PMS 5m audit 39/43 (90.7%) {Manual audit 41/48 (85.4%)}
4.2% (2/48) were partially immunised missing only one of their injections.
None (0%) were unimmunised despite the initial audit revealing 2
RESULTS - timeliness Using strict timeliness criteria, 52.1% (25/48) were
immunised on time by 5 months
43.7% (21/48) were late for some or all of their immunisations
A total of 83.3% (40/48) would satisfy the new target of completion of the primary series immunisations by the 8-month milestone.
RESULTS - rules for PHHC Give injections sequentially 6w, 3m then 5m
Patient transfers out of practice Advise NIR -?other way round
Patient transferred in Run Status query with NIR database to update your PMS immunisaton history
Partially immunised need to stay in the inbox until the catch up injections have happened
Casual patients immunise and enter into NIR database. Invoice but don’t count. Complete a NIR Status
query
Avoiding Input Errors Don’t input all vaccines on the same day. Run NIR status query
Shared care patients Important for Maori -Run NIR status query
ResultsRESULTS-Practice 1-The Average
NIR report for Practice 1
RESULTS- Practice 1
Audit Practice 1
NIR 38/42 (90.5%)
PMS 37/40 (92.5%)
Multi 44/49 (89.8%)
Requested a printout of NHI numbers from the NIR of the no. eligible (denominator)
RESULTS- Comparison
Multi Audit Practice 1
Total 44/49 (89.8%)
5 month 37
6 week 2
3 month 0
NO 5 month imms 1
Total Babies 4
RESULTS- Audit Exclusions
Audit Practice 1
Multi 20/69 (30%)
Input errors 2 (2.8%)
Transfer Out 5 (7.2%)
Went Overseas 3 (4.3%)
Enrolled Outside Period 10 (14.5%)
RESULTS- Comparison
Multi Audit Practice 1
Multi 44/49 (89.8%)
Unimmunised 1/49 (2%)
Partial imms 0
Decline 4/49 (8.2%)
Timeliness 43/49 (87.8%)
Government Targets 95% by 8 months of Age
Results –Practice 1 to 95%
Excellent systems and NIR contact 44/49 (89.8%) Declines 4/49 (8.2%) Unimmunised referred to outreach 1/49 (2.0%) Met the new govt. target of 8m 43/49 (87.8%)
Practice 1NIR Audit 38/42 90.5%
PMS Audit 37/40 92.5%
Multi-Audit 44/49 89.8%
Feedback for Practice 1 Excellent nurse –NIR interaction Avoiding input errors Partially immunised stay in their inbox (outreach) No partially immunised –once vaccinated, they finished to
primary series Plan of attack for “declining” parents
ResultsRESULTS-The Maori Provider
Maori Provider NIR report
RESULTS- Maori
Audit Maori
NIR 115/144 (80%)
PMS 139/162 (85.8%)
Multi 159/194 (82%)
Requested a printout of NHI numbers from the NIR of the no. eligible (denominator)
RESULTS- A&F Tool
Multi Audit Maori
Total 159/194(82%)
5 month 139
6 week 15
3 month 1
NO 5 month imms 2
Total Babies 2
RESULTS- Audit Exclusions
Audit Maori
Multi 120 /314 (38.2%)
Input errors 21 (6.6%)
Transferred 54 (17.2%)
Went Overseas-Died 2 (0.3%)
Enrolled Outside Period 43 (13.7%)
RESULTS- Comparison
Multi Audit Maori
Multi 159/194(82%)
Unimmunised 7/194 (3.6%)
Partial imms 35/194 (18%)
Decline 3/194 (1.5%)
Timeliness ?/194 (%)
Government Targets 95% by 8 months of Age
Results –Maori to 95%
Systems and NIR contact 159/194 (82%) Declines very low 3/194 (1.5%) Partially immunised 35/194 (18%) Unimmunised ?outreach 7/ 194 (3.6%) Met the new govt. target of 8m ?/194 (?%) Transient population 54/314 (17.2%)
Maori
NIR Audit 115/144 (80%)
PMS Audit 139/162 (85.8%)
Multi-Audit 159/194 (82%)
RESULTS - rules for Maori Provider
Give injections sequentially 6w, 3m then 5m
Patient transfers out of practice –extreme care Advise NIR -?other way round
Patient transferred in –extreme care Run Status query with NIR database to update your PMS immunisaton history
Partially immunised need to stay in the inbox until the catch up injections have happened
Casual patients immunise and enter into NIR database. Invoice but don’t count. Complete a NIR Status query
Avoiding Input Errors Don’t input all vaccines on the same day. Run NIR status query Nurse education re NIR interaction esp a NIR Status query
Shared care patients Important for Maori -Run NIR status query eg Born to and 6w at practice 3m elsewhere 5m elsewhere again. Plunket Is particularly helpful to this practice Care with NIR Assignation
CONCLUSIONS Possible to improve immunisation rates just with accurate counting
The Pasifika practice under reported rates by 9.7% on NIR (86.1%) when actually running at 95.8%. Above the current NZ government and WHO target
The “average” practice in this study will struggle to hit 95% due to the numbers declining vaccination
The Maori population in this study is highly mobile often receiving the primary series at multiple providers
Each audit had different Numerators & Denominators ?Why. The A&F Tool identifies a more accurate denominator
The A&F Tool identifies deficiencies in vaccine delivery
The NIR ‘status query’ is an excellent tool to determine immunisation history
Direct reconciliation with the NIR database is a key step for improving accuracy.
FUTURE Confirm and refine the A&F Tool
More Discussion with NIR
Reconcile the NIR and Practice Data
Best Practice Vaccine Delivery Systems
Acknowledgements IMAC / CONECTUS Staff
Mareta Timo
Anjleena Dev
Leane Els
Tracey Poole
RNZCGP funding
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