Transcript Continuity of care – national examples Sweden

National Register for Quality
Improvement in Primary Care
Andy Maun
University of Gothenburg, Sweden
Declaration of conflicts of interest or
relationship
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Speaker Name: Andy Maun
GP Trainee, PhD student
Member of the Swedish Quality Council
Member of the Development Team of a National
Register for Quality Improvement in Primary Care
• I have no conflicts of interest to disclose with regard to
the subject matter of this presentation
Healthcare systems in Sweden
In health care and certainly primary healthcare:
21 counties and regions
differing in:
payment systems
IT – systems
follow–up of quality
Reform on Choice of Care 2008
Aim: Increase the number of healthcare centres
• Patients can choose a centre but not personal GP centres compete!
• Resulted in a lot of new centres mostly run by
great companies owned by risk capitalists.
Trends in most Counties
• Payment by individual capitation based on
– age
– socio-economy
– morbidity burden (ACG - adjusted clinical groups)
• The centre pays all costs for laboratory
services, x-ray and drugs
Quality surveillance
Existing quality registers
• mandatory to report to the National Diabetes
Register
• often also mandatory to report to other
registers (heart failure, asthma, COPD, etc.)
• Problem: most existing registers are
constructed by and for hospital clinicians
Public debate
National Register for Quality
Improvement in Primary Care?
• The Swedish Association of Local Authorities and
Regions (SALAR) stimulates the development of a
national register (550.000 Euro 2012)
• 3 initiatives merged to 1 national group:
– SFAM – Swedish Association of General Practice (Vision of a
database for research)
– Quality Consil / pvkvalitet.se feedback and benchmarking
– Register for Quality Improvement of the Western Region VGR
National development team for the register
National database
• Malin André (chairperson), GP, docent, chairperson SFAM research council
• Jörgen Månsson, GP, docent, CMO Carema
Register of the Western Region VGR
• Claes Hegen GP, chairperson of the VGR register
• Fredrik Bååthe, senior projectleader RC VGR
pvkvalitet.se
• Sven Engström, GP, PhD, chairperson SFAM quality council
• Andy Maun, GP-Trainee, PhD student
Assignment
• Define relevant variables from daily practice that
can be collected automated from regional
databases
• in a legally applicable system
• Target groups:
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Healthcare centres - internal improvements
Academy - scientific research
Other Registers - delivery and sharing of data
Political management - results, follow-up
Patient – empowerment
National coordination!
Relevant variables from daily practice?
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Ryggvärk
M54*
Myalgier
M790, M791
M797
Arthros M16, M17
LedvärkM254, M255, M256
M250
Tendinit/bur. M70*, M75*, M766, M771
M770
Osteoporos M80*-M828
Diabetes
E10* - E14*
AngP/Isch/AF I20*-I25*
Astma/KOL J45*, J44*
J46*
Stroke
I64*, I67P*. I69*
I65-68*
Luftvägsinf. B27, B34*, J01*-J06*, J18*, J22, R05* J00*, J12*-18*, J20*-21* (finns gen. viros)
UVI
N30*
N39.0
Hudinf.
L01*, L02*, L03*, L08*, A46, A692
Depression F32*, F33*, F39*
F34*, F38*
Ångest
F410, F41*
Sömnstörn. F51*
Demens
G30*, F01*, F03
F00*, F02*
Stressreaktion
F43*
-F43.2
Experiences from earlier projects
Experiences from other countries
medical outcomes, structural / process measures?
How to avoid silos?
Feasibility?
Legally applicable?
IT?
Pvkvalitet.se - Philosophy
• Quality indicators developed by clinical
active GPs
• We GPs think that we follow guideline to
much greater extent than we actually do!
• We have to study how we do in practice to
understand that we need to work
differently!
Asthma
Registration form
Health centre
GP
Emergency/
unplanned
visits last
year
Yes
No
Period
Smoking
registred
Yes
No
Have
inhaled
corticosteroids
Yes
No
Spirometry
last 2 years
Yes
Notes
No
• Note indicators in the form
for each sample patient
• Summarize the results
Sum
Proportion who had emergency/
unplanned visit for asthma last year
Proportion who had a check up including
spirometry last 2 years
common conditions and chronic diseases
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Tonsillitis
Cystitis in women
Asthma
COPD
Heart failure
Leg ulcer
Pneumonia
Atrial fibrillation
Urinary incontinence
Otitis media
Quality improvement
• Review ones own work, my own “exceptions”
• Discuss together: What could we do better?
• Read patients records
– Sample small but enough to see trends
– Possible to find things like unplanned/emergency
visits for asthma
– quality of patient records, diagnosis
Pvkvalitet.se
• 261 Health Centres participating
• 37 000 patients reviewed
= 950 local improvement projects
supported!
Results
• Antibiotics
Areas with systematic use
– Quinolones for cystitis in women 6%  1% (2006 - 2007)
• Asthma
– Patients with spirometry last 2 years 38%  62% (2006 - 09)
• Heart failure
– Proportion investigated with UCG 65%  81%
– Patients treated with ACE / AII 71%  83%
(2006-2009)
(p < 0.05)
(2006 - 2007)
(p = 0.002)
Development of a register for Quality
Improvement of the Western Region
• Aim: regional primary healthcare register with
the potential for a national register
• Target group:
– Healthcare centres - internal improvements
– Academy - scientific research
– Political management - results, payment
– Patient – choice of healthcare centre
Get a new…
…perspective
Indicators
• Five chronic diseases: (< age 75)
– Diabetes (National Diabetes Register)
– Ischemic heart disease
– Hypertension
– Asthma
– COPD
Medical variabels
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Diagnosis
Smoking
Weight
Length
Waistlines
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Spirometry
HbA1c
Blood lipids
Blood pressure
• Age / Gender
Results can be linked to
- other registers e.g. stroke register
- prescription register
- socioeconomic data
Effects?
70 000
Number of individuals
60 000
50 000
Before/after ACG
(Payment for
morbidity burden)
40 000
30 000
Diabetes diagnosis
20 000
10 000
0
Primary Healthcare,
Western Region
Staffan Björck, Analysis Unit Western Region
Co-morbidity
Identifying high-risk groups
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RÖKNING
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8
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LDL>2,5
34
35
TRYCK>140/90
Percentage of individuals with high
blod pressure, high LDL cholesterol
and smoking.
Effects?
Difficulities for
centres in
poor districts?
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5
CNI
4
Relation between
socioeconomic index and
percentage of patients
with HbA1c < 52
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2
1
0
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10
20
30
40
50
60
70
80
90
HbA1c<52
Preliminary data Staffan Björck, Analysis Unit Western Region
Percentage of patients with atrial fibrillation that receive Warfarin
in different healthcare centres
Preliminary data Staffan Björck, Analysis Unit Western Region
60%
Percentage of patients atrial fibrillation and Warfarin
Different parts of the Western Region
50%
40%
30%
20%
10%
0%
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12 VGR
Preliminary data Staffan Björck, Analysis Unit Western Region
Percentage of patients atrial fibrillation and Warfarin
by Sex
60%
50%
40%
30%
20%
10%
0%
Män
Male
Kvinnor
Female
Preliminary data Staffan Björck, Analysis Unit Western Region
Percentage of patients atrial fibrillation and Warfarin
by Age
60%
50%
40%
30%
20%
10%
0%
15-29
30-44
45-59
60-74
75-89
90Preliminary data Staffan Björck, Analysis Unit Western Region
Percentage of patients atrial fibrillation and Warfarin
by Age and Sex
70%
60%
50%
40%
30%
20%
10%
0%
15-29
30-44
45-59
60-74
75-89
90Preliminary data Staffan Björck, Analysis Unit Western Region
Pilot study - continuity
• Aim: to examine the feasibility of a larger study,
where the correlation between provider
continuity and health outcomes is to be explored
• Method:
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retrospective study (Oct 2009-Febr 2012)
four primary care centres (33485 individuals)
health outcomes (blood pressure, HbA1c)
usual provider continuity (UPC) and continuity of care
index (COC) for physician/nurse
Results – No distinct correlations
• No distinct correlations could be found
between interpersonal continuity with
physician/nurse and blood pressure and
HbA1c values
• A timeline-study on the whole population of
the region (1,5 million inhabitants) is feasible
and necessary to gain more knowledge
Benefit?
• See the whole population /
”your own” population
– new thoughts and discussions
• Knowledge on effects of treatments in
“real populations” vs study populations
• Primary Care influence strengthened
– on guidelines
– on development of healthcare system
The challenge remains
• systems that measure quality and stimulate
improvement
• validity / complexity / interpretation of data
• no evidence of benefit of P4P but some
evidence of harm
• the hard part is to ensure the change and
stimulate improvement
Thank you for your attention!