Transcript Slide 1

Centre Fédéral d’Expertise
des Soins de Santé
Federaal Kenniscentrum
voor de Gezondheidszorg
Inventory of
healthcare databases
KCE reports vol. 30A
KCE reports vol. 30 Supplement
2006
and related problems
Stefaan Van de Sande (KCE), Dirk De Wachter (KCE), Nathalie Swartenbroeckx (KCE), Jan Peers (Deloitte), Hans Debruyne
(Deloitte), Ingrid Moldenaers (Deloitte), Baudouin Lejeune (Deloitte), Virginie Van Damme (Deloitte), Mark Leys (KCE)
http://kce.fgov.be
2007-09-20
Database inventory
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Aims
 Inventory of databases with healthcare data
 Inventory of databases relevant for equity research in
healthcare
 Foreign experiences with development of HIS
(Scotland, Denmark, Germany)
 Assessment and ‘best practice’ recommendations
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Results (1)
 Belgium has a lot of healthcare data
 Dispersion of data sources
 Accessibility problems: no central inventory
neither a central contact for retrieval of
information
 Few available data quality plans (relevance,
accuracy, completeness, consistency, coherence,
timeliness)
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Roadmap (public) HC data
Protocol
29-03-2000
Gemeenschappen
Gewesten &
Lokale besturen
Overlijdensoorzaken
Openbare
statistiek
NIS
Geboortenstatistiek
Wetensch.
onderzoek
NSIH
MORBIDAT
Rijksregister
RRN / INSZ
MKG, MVG, MPG, MUG,
Finhosta, ...
ZIV-wet
Gezondheidszorg
AO-wet
FOD Sociale
zaken
Dossiers
gehandicapten
NIC – IMA
7 VI’s
FAO
Bestanden SS & Pop
EVA
Zorgprogramma’s
FBZ
?
HZV
Interv. cardio
Oncologie, e. a.
Research registers:
Intego – eerste lijn
Legenda :
Uitkeringen
sociale luik
Statistische tabellen
AZV, Pharmanet
Kankeregister
Zorgverstrekkers
Zorginstellingen
Universiteiten
DWH
arbeidsmarkt
Sociale zekerheid
RIZIV
Stichting
Kankerregister
IKAROS,
O.N.E.
BDMS,
SPE, ...
Populatie
FOD
Volksgezondheid
SPMA
NSIH
RESOPRIM
HA peilpraktijken
Zorgregisters
Demografie
DWTC
Federaal
wetensch.beleid
WIV/ISP/IPH
Morbiditeits
-registers
Federale
overheid
Nationaal instituut
voor statistiek
Protocol
21-03-2001
Infectieziekten,
TBC,
Diabetes,
MS,
HIV, ...
Federaal niveau
Samenwerking
& uitwisseling
Regulering
Bestuurlijke
domeinen
Hulp- en
Voorzorgskas voor
Zeevarenden
® KCE
?
Actoren m.b.t. data
gezondheids(zorg)
Registers
Gezondheids(zorg)data
Actoren
Openbare statistiek
Inventaris
metadata
Input data
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Results (2)
 The problem of the Unique Personal
Identifier (UPI) or pseudonyme (UPP)
– Legal issues
– Problems with linkage of the data
 Longitudinal analyses frequently impossible
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Results (3)
 Gaps in (public domain) data
–
–
–
–
–
–
–
Primary care
Trajectories of healthcare
Non compulsory health insurance
Few data for nursing and rest homes RVT-ROB
Few useful data on psychiatry & mental healthcare
HC Technology
Non reimbursed consumption
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Results (4)
 International experiences
Scotland Germany Denmark
Centralisation
++
±
++
Data quality plan ++ decentralized ++
UPI (micro data) (CHI)
Privacy protection ++
aggregation
Accessability &
+
+
dissemination
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Database inventory
UPI
++
++
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Recommendations (1)
 An integrated healthcare data model
– Conceptual framework, incl.
• database standards & relationships
• data protection & dissemination policy
– Formal participation of providers, users and TTP’s
– Efficient use of (financial) means + assessment of
means needed
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Recommendations (2)
 Centralisation of information about
databases, data & metadata (cHIS)
 Healthcare data quality- & validation plan
 Importance of suitable UPI-UPP strategy
 Integrated legal framework
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Centre Fédéral d’Expertise
des Soins de Santé
Federaal Kenniscentrum
voor de Gezondheidszorg
Personal (health) data
& their protection
Stefaan Van De Sande, M.D.
Medical supervisor KCE
Outline
General concepts and ….
some definitions
Visions for the future
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Unique Personal Identifier - UPI
 Certified uniqueness
 Not necessarily content free: may contain
 date of birth
 sex indicator
 Typically used in public domain
 e.g. in Belgium : Social security number (INSZ /
NISS) ≈ National number (RRN / NRN)
 Allows ‘easy’ identification :
 no need for ‘ad hoc’ conversion tables
 nor decryption
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Individually identifiable health
data/information
“Data/information about past, present or future
physical or mental state of health of a (directly
or indirectly) identifiable individual”
(Belgian Law on establishment and organisation of Crossroads Bank for
Social Security - art. 2, 7°, 15 january 1990)
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Categories of personal data
Sample / population size
Risks of identification
100%
Privacy
Anonymized
(not identifiable)
Coded
Non coded
(indirectly identifiable)
(directly identifiable)
Threshold
n=?
‘singularity’
Small cell area
0%
N=1
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Detail & quantity of information
(indirect or direct identification data)
Database inventory
© KCE
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‘Singularity paradox’
I have’nt any ID
card!
But.. see that scar? It’s definitely
‘Jack the Bear’
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Pseudonymisation for clinical /
scientific research / HSR

“Pseudonymisation is data de-identification such that data belonging
to the same individual in the clinical environment bear the same
relation to each other in the de-identified research version”
(Ecole de Technologie Superieure - Canada)


How :

Removal of overt identifying items and replacement by a ‘content
free’ identifier that can only be reversed by the provider or its
nominated trusted third party.

One way hashing of UPI results in UPP (Unique Personal
Pseudonym)
Aim: preventing direct identification
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Unique Personal Pseudonyme
Unique : ‘collision free’
Meaningless : ‘content free’
Hashing / encryption by provider or/and TTP
Concise : ‘as short as possible’
Allowing for error detection (correction)
Irreversible (at least conditional reversibility)
Evolutive : ‘best practice’
Retroactive : preserves longitudinality
Domain specific / project specific
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Domain & project specificity in HC
registries
1ary exploitation
(by data 'owner')
Local domains :
Local UPPs
HC institution,
medical praxis, ..
(Operational) database
Regional domains :
Domain specific UPPs
regional hospital association,
regional GP guard duty cooperation
L
e
Recoding local UPPs  project
specific UPPs by data provider
g
Merging of (operational) databases
Recoding local UPPs  domain
specific UPPs by TTP of choice
i
Recoding domain specific UPPs
 project specific UPPs by TTP
Extension 'ownership'
Supraregional domains :
supraregional hospital association,
Health Insurance Funds, IMA/AIM, ...
t
Merging of (operational) databases
i
Recoding domain specific UPPs
 project specific UPPs by TTP
regulated by gouvernmental authorities
t
Project specific UPPs
h
o
i
s
a
a
t
c
National cancerregistry
Recoding domain specific UPPs
 project specific UPPs by TTP
Project specific UPPs
Data transfered for well defined
research projects
Project specific UPPs
research projects
EHR network
‘Genomic’ databases (DNA
sequences)
n
Database inventory
Data transfered for well defined
research projects
I Data transfered for well defined
o
y
2007-09-20
Data transfered for well defined
research projects
Domain specific UPPs
(Administrative) HC databases
Federal gouvernment / RIZIV
Project specific UPPs
u
m
Extension / transfer 'ownership'
Public domains :
A
r
Domain specific UPPs
Recoding source UPPs  new
domain specific UPPs by TTP of choice
2ary exploitation
(by other party)
Pseudonymisation
instance
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Outline
General concepts and ….
some definitions
Visions for the future
2007-09-20
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Key challenge
How to reconcile personal data
privacy (data protection)
and public health interest
(data accessibility & dissiminaton)
in the era of emerging EHR?
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Belgian Telematics Committee
of the Ministry of Health
HEPI-GO
Health Electronic Personal Identification
Final Report
Date 1/8/2006
Version 0.3b
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Een visie op elektronische samenwerking in de gezondheidszorg, gebaseerd op
de ervaring in de sociale sector, en de mogelijke rol van een
Be-Health platform
2007-02-15
Frank Robben
Administrateur-generaal Kruispuntbank Sociale Zekerheid
Gedelegeerd bestuurder Smals - MvM
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Personal
visions
What we need is ...
a national
Coordination & Certification Agency
≈ CIHI - Canada
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Personal
visions
CCA – Core missions

National authority under custody Privacy Commission (‘watchdog’)

Provider of UPP’s for HC data registries


Trusted third party (TTP) for recoding & linkage health(care) data

UPI-UPP datawarehousing
HC data & metadata ‘clearinghouse’


Central DWH & mDWH?
Center of excellence: ‘best practice’ directives

Coordination of data models, data linkage, data processing, …

Certification of databases: (meta)data quality, data protection

Accreditation of data research groups: scientific credentials & ISP

Standards of telematics, EHR, software applications, ISP …

National & international ‘networking’
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Personal
visions
Person X
Name, first name, date
of birth, sex, full
address, ...
UPP DWH
CBSS
RRNx / INSZx
Privacy
Commission
CCA
Legally protected TTP in public HC domain
INSZ x

Domain UPP X,1-n

Project UPP x,A-Z
domain 1
domain 2
domain 3
domain n
UPP X,1
UPP X,2
UPP X,3
UPP X,n
= ‘restriction principle’
RRNx / INSZx = personal social security number
Research
project A
= domain A
Research
project B
= domain B
UPP X,A
UPP X,B
CCA = Coordinating& Certification Agency
CBSS = Crossroads Bank Social Security
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Requirements
Multi-sectoral agreement
Political commitment
Public funding
It’s a (very) long way to Tipperary …
but not impossible !
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