23 pcsi conference, lido, venice · 2014-03-14 · 23rd pcsi conference, lido, venice lisa fodero...
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Integrated health services, integrated data sets, what comes first?
23rd PCSI Conference, Lido, Venice
Lisa Fodero & Joe Scuteri
• Integrating health services will not only improve patient outcomes but will also result in more cost efficient care by eliminating service duplication and redundancy.
• Service integration implies a patient centred approach to the collection of data that can be shared by multiple providers in multiple health care settings.
• Most healthcare providers capture significant data on patients and treatments provided, but few have the ability to share that information with other providers for the benefit of the patient.
• The data that are collected by providers are largely not standardised and therefore difficult to share.
Introduction
Introduction
• More than the 60 discretely funded programs form the Australian health
care system including:
federally funded programs including the Medicare Benefits Scheme (MBS)
and Pharmaceutical Benefits Schemes (PBS).
programs such as Public Hospitals and the Home and Community Care
Program (HACC) that are jointly funded by the Federal and state
governments.
disease specific programs such as palliative care, mental health, breast
cancer screening etc that are funded typically by the Federal Government.
• Each of these program have different accountability requirements and, as a
result, different data sets are collected as part of the process of providing
care and to account for the use of funds.
• There have been national attempts to standardise data collection through the
development of the National Health Data Dictionary (NHDD).
• Often the data elements defined in the national dictionary do not meet the
specific needs of a program, so new program specific data element definitions
and associated data domains are developed.
• Results in an increase in the data collection effort for service providers and
inconsistencies in the available data making interpretation and analysis across
data sets difficult, if not impossible.
Introduction
• Project undertaken by NSW Health Department.
• Aims to develop a patient level data collection across all community health
and outpatient care settings in the State.
• Largest project of its kind ever attempted in Australia.
• Annual collection of approximately 25 million patient level records
describing the characteristics of the patients treated and the nature of
services provided in the community health and outpatient care settings.
Community Health and Outpatient Care Information Project
Community Health and Outpatient Care Information Project
Need for the development of a patient level data collection has built up in recent
years for a number of reasons:
1) Community health and hospital outpatient services account for a large and
growing proportion of the workload of the NSW health care system, yet little
is known about the nature of the services provided and about the patients
receiving those services, their ongoing needs and future demands.
2) There has been a considerable shift in the mix of outpatient and sameday
patient services largely due to funding incentives which has resulted in
information loss on services provided (detailed unit record data are collected
on sameday patients but only aggregate data are collected on outpatients).
3) Although the current Australian Health Care Agreements (between Federal
and State Governments) only require the reporting of outpatient data at
aggregate level, it is anticipated that the next round of AHCAs will require
the collection of patient level data for outpatient care.
4) The availability of enterprise systems such as Cerner and iPM in hospitals
and CHIME in community health that have patient scheduling modules has
made collection of patient level data for outpatient and community health
services more feasible.
Community Health and Outpatient Care Information Project
Scope of CHOCIP
Types of NSW public sector services required to report to the CHOCIP include:
• Public hospitals (including public dental and public psychiatric hospitals), covering:
Outpatient medical and nursing services;
Outpatient allied health services;
Outpatient day procedures; and
Outreach services.
• Public community health services:
Centre/campus based services;
Home based services;
Mobile/Outreach services;
Multi-purpose services;
Mothercraft services; and
Community acute and post acute care services (other than Hospital in the
Home service that are reported to the Admitted Patient Data Collection).
• Justice Health services; and
• Health One services.
Overview of CHOCIP
• CHOCIP began in 2006
• Three phases of CHOCIP:
Phase One: Where are we now? Where do we want to be? How do we get there?
Phase Two: Infrastructure development (current phase)
Phase Three: The roll out (expected start date 1st September 2008)
What: Finalise Minimum Data Set (produce Data
Dictionary) and associated business rules
Why: To standardise and detail data requirements
and build system specifications. To standardise
interpretation and application of rules in
implementing Data Collection
When: Start Dec 06, End Mar 08
Who: Project Team, Consultancies
1.1 Data dictionary
1.2 Business Rules
What:Identify data requirements, build a system to
collect data, register all clinics/service teams
Why: To register and uniquely identify all reporting
entities, standardise what is meant by ‘reporting
entity’
When: Start Jan 07, End Dec 07
Who: Project Team, Consultancy
2 Reporting Entity Registration
What: Mandate electronic Area-level registration of
ambulatory clients
Why: Uniquely identify each client at the Area level,
improve data integrity, minimise data entry burden
When: Start Jan 07, End Oct 07
Who: Project Team, Consultancy
3 Electronic Client Registration
What: Create State Base Build of ambulatory
booking/data collection modules. Implement
automated data extracts from these applications
Why: Standardise data collection
When: Start Jul 07, End Jun 08
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
4.1 Modifications to Enterprise App.
4.2 Data Extracts from Enterprise App.
What: Create a repository for
incoming data and associated
reference tables
Why: Securely store data and make
it available for analysis
When: Start Mar 07, End Aug 08
Who: BIS Program Office, Project
Team, SIM, Health Services, DPE,
Consultants, Independent Testers
9 Data Repository
What: Develop cost effective strategy for ensuring
data on ancillary services is available
Why: To enable efficient collection of ancillary data
When: Start Jan 08, Strategy completed by end
Mar 08, Implement strategy post Sep 08
Who: Project Team, SIM, Health Technology
6 Data Extracts for Ancillary Services
13 Project and Data Collection Governance
11 Change Management,
Training, Communication
What: Implement system modifications to comply
with Minimum Data Set. Implement automated data
extracts from these applications
Why: Standardise data collection
When: Start TBD, will extend post Jan 2008
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
5.1 Modifications to Other Source App.
5.2 Data Extracts from Other Source App.
12 Health Service
Implementation Plans
What: Develop reports relevant to
key stakeholders that can be
automatically generated once data
are available
Why: Data available for clinicians,
managers and for assessment of
data quality
When: Start Aug 07, End Aug 08
Who: Project Team, DPE
10 Performance Reports
What: Testing of alternative data
collection tools and performance
reports in selected sites
Why: To fine tune the tools and
identify any further issues for
implementation of the Collection
When: Start Mar 08, End Jun 08
Who: Relevant Health Services,
Project Team, Consultancy
8 Proof-of-Concept
What: Select and develop web-
based and paper based solutions
Why: To enable collection of data
across all services
When: Start Jan 08, End Jun 08
Who: Project Team, SIM, HT,
Consultancy/contractors
7 Alternative Data Collection
Tools
What: Finalise Minimum Data Set (produce Data
Dictionary) and associated business rules
Why: To standardise and detail data requirements
and build system specifications. To standardise
interpretation and application of rules in
implementing Data Collection
When: Start Dec 06, End Mar 08
Who: Project Team, Consultancies
1.1 Data dictionary
1.2 Business Rules
What: Finalise Minimum Data Set (produce Data
Dictionary) and associated business rules
Why: To standardise and detail data requirements
and build system specifications. To standardise
interpretation and application of rules in
implementing Data Collection
When: Start Dec 06, End Mar 08
Who: Project Team, Consultancies
1.1 Data dictionary
1.2 Business Rules
What:Identify data requirements, build a system to
collect data, register all clinics/service teams
Why: To register and uniquely identify all reporting
entities, standardise what is meant by ‘reporting
entity’
When: Start Jan 07, End Dec 07
Who: Project Team, Consultancy
2 Reporting Entity Registration
What:Identify data requirements, build a system to
collect data, register all clinics/service teams
Why: To register and uniquely identify all reporting
entities, standardise what is meant by ‘reporting
entity’
When: Start Jan 07, End Dec 07
Who: Project Team, Consultancy
2 Reporting Entity Registration
What: Mandate electronic Area-level registration of
ambulatory clients
Why: Uniquely identify each client at the Area level,
improve data integrity, minimise data entry burden
When: Start Jan 07, End Oct 07
Who: Project Team, Consultancy
3 Electronic Client Registration
What: Mandate electronic Area-level registration of
ambulatory clients
Why: Uniquely identify each client at the Area level,
improve data integrity, minimise data entry burden
When: Start Jan 07, End Oct 07
Who: Project Team, Consultancy
3 Electronic Client Registration
What: Create State Base Build of ambulatory
booking/data collection modules. Implement
automated data extracts from these applications
Why: Standardise data collection
When: Start Jul 07, End Jun 08
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
4.1 Modifications to Enterprise App.
4.2 Data Extracts from Enterprise App.
What: Create State Base Build of ambulatory
booking/data collection modules. Implement
automated data extracts from these applications
Why: Standardise data collection
When: Start Jul 07, End Jun 08
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
4.1 Modifications to Enterprise App.
4.2 Data Extracts from Enterprise App.
What: Create a repository for
incoming data and associated
reference tables
Why: Securely store data and make
it available for analysis
When: Start Mar 07, End Aug 08
Who: BIS Program Office, Project
Team, SIM, Health Services, DPE,
Consultants, Independent Testers
9 Data Repository
What: Create a repository for
incoming data and associated
reference tables
Why: Securely store data and make
it available for analysis
When: Start Mar 07, End Aug 08
Who: BIS Program Office, Project
Team, SIM, Health Services, DPE,
Consultants, Independent Testers
9 Data Repository
What: Develop cost effective strategy for ensuring
data on ancillary services is available
Why: To enable efficient collection of ancillary data
When: Start Jan 08, Strategy completed by end
Mar 08, Implement strategy post Sep 08
Who: Project Team, SIM, Health Technology
6 Data Extracts for Ancillary Services
What: Develop cost effective strategy for ensuring
data on ancillary services is available
Why: To enable efficient collection of ancillary data
When: Start Jan 08, Strategy completed by end
Mar 08, Implement strategy post Sep 08
Who: Project Team, SIM, Health Technology
6 Data Extracts for Ancillary Services
13 Project and Data Collection Governance
11 Change Management,
Training, Communication
What: Implement system modifications to comply
with Minimum Data Set. Implement automated data
extracts from these applications
Why: Standardise data collection
When: Start TBD, will extend post Jan 2008
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
5.1 Modifications to Other Source App.
5.2 Data Extracts from Other Source App.
What: Implement system modifications to comply
with Minimum Data Set. Implement automated data
extracts from these applications
Why: Standardise data collection
When: Start TBD, will extend post Jan 2008
Who: SIM, HT, Health Services, Project Team,
Vendors/Contractors
5.1 Modifications to Other Source App.
5.2 Data Extracts from Other Source App.
12 Health Service
Implementation Plans
What: Develop reports relevant to
key stakeholders that can be
automatically generated once data
are available
Why: Data available for clinicians,
managers and for assessment of
data quality
When: Start Aug 07, End Aug 08
Who: Project Team, DPE
10 Performance Reports
What: Develop reports relevant to
key stakeholders that can be
automatically generated once data
are available
Why: Data available for clinicians,
managers and for assessment of
data quality
When: Start Aug 07, End Aug 08
Who: Project Team, DPE
10 Performance Reports
What: Testing of alternative data
collection tools and performance
reports in selected sites
Why: To fine tune the tools and
identify any further issues for
implementation of the Collection
When: Start Mar 08, End Jun 08
Who: Relevant Health Services,
Project Team, Consultancy
8 Proof-of-Concept
What: Testing of alternative data
collection tools and performance
reports in selected sites
Why: To fine tune the tools and
identify any further issues for
implementation of the Collection
When: Start Mar 08, End Jun 08
Who: Relevant Health Services,
Project Team, Consultancy
8 Proof-of-Concept
What: Select and develop web-
based and paper based solutions
Why: To enable collection of data
across all services
When: Start Jan 08, End Jun 08
Who: Project Team, SIM, HT,
Consultancy/contractors
7 Alternative Data Collection
Tools
What: Select and develop web-
based and paper based solutions
Why: To enable collection of data
across all services
When: Start Jan 08, End Jun 08
Who: Project Team, SIM, HT,
Consultancy/contractors
7 Alternative Data Collection
Tools
CHOCIP : Phase Two
Project Methodology
The project methodology consisted of five major processes:
1) Review of the proposed MDS to ensure that it would produce the information
required to meet the project objectives.
2) Review of a series of 11 data dictionaries for related data collections to extract
data element definitions and data domains for data elements that were
included in the CHOCIP MDS.
3) Preparation of draft data dictionary for distribution to stakeholders as the basis
of a series of workshops to gather input on the most suitable specification of
the data elements for the purposes of CHOCIP.
4) Analysis of the consultation findings to prepare the final data dictionary.
5) Preparation of a series of mappings for each data element in the CHOCIP
Data Dictionary with the entries in the 11 data dictionaries.
• National Health Data Dictionary Version 12;
• NSW Health Data Dictionary Version 1.2;
• NSW Health Drug and Alcohol Data Dictionary Version 5.0;
• NSW Health Emergency Department Data Dictionary Version 3.2;
• NSW Health Oral Health Data Dictionary Version 1.4;
• NSW Health Admitted Patient Data Collection Data Dictionary Version 1.0;
• NSW Health Sexual Assault Data Dictionary Version 1.0;
• NSW Health CHIME Data Dictionary of Classifications;
• NSW Mental Health Data Dictionary Version 3.0;
• Home and Community Care Data Dictionary Version 2.01; and
• Hunter Area Health Service Allied Health Data Dictionary (AHMIS).
Reviewed data dictionaries
Part One Data Dictionary analysis
• 28 data elements in Part One of CHOCIP Data Dictionary
17 data elements included in analysis which had specific data domains
or codes
excluded address, postcode and date data elements
Data Element
Number of Data
Dictionaries
containing data
element
Number of times
most common
entry occurs
Number of
different entries
Most common
entry used for
CHOCIP
Sex 11 8 2 Yes
Aboriginal and Torres Strait Islander origin 10 6 3 No
Country of birth 10 6 3 Yes
Disposition status 9 1 9 No
Preferred language 9 4 3 Yes
Source of referral 9 1 9 No
Billing category 7 1 7 No
DVA card type 7 4 4 Yes
Estimated date of birth flag 6 3 4 No
Interpreter required 6 6 1 No
Service delivery setting 6 1 6 Yes
Discipline of individual service provider(s) 4 1 4 No
Service contact mode 4 1 4 Yes
Outcome of offer 3 1 3 No
Group or individual service indicator 2 1 2 No
Anonymous/unidentified client indicator 1 1 1 No
Initial or subsequent service indicator 1 1 1 No
Analysis of data element entries in the 11 reference data dictionaries
Data Element
Number of Data
Dictionaries
containing data
element
Number of times
most common
entry occurs
Number of
different entries
Most common
entry used for
CHOCIP
Sex 11 8 2 Yes
Aboriginal and Torres Strait Islander origin 10 6 3 No
Country of birth 10 6 3 Yes
Disposition status 9 1 9 No
Preferred language 9 4 3 Yes
Source of referral 9 1 9 No
Billing category 7 1 7 No
DVA card type 7 4 4 Yes
Estimated date of birth flag 6 3 4 No
Interpreter required 6 6 1 No
Service delivery setting 6 1 6 Yes
Discipline of individual service provider(s) 4 1 4 No
Service contact mode 4 1 4 Yes
Outcome of offer 3 1 3 No
Group or individual service indicator 2 1 2 No
Anonymous/unidentified client indicator 1 1 1 No
Initial or subsequent service indicator 1 1 1 No
Analysis of data element entries in the 11 reference data dictionaries
Data Element
Number of Data
Dictionaries
containing data
element
Number of times
most common
entry occurs
Number of
different entries
Most common
entry used for
CHOCIP
Sex 11 8 2 Yes
Aboriginal and Torres Strait Islander origin 10 6 3 No
Country of birth 10 6 3 Yes
Disposition status 9 1 9 No
Preferred language 9 4 3 Yes
Source of referral 9 1 9 No
Billing category 7 1 7 No
DVA card type 7 4 4 Yes
Estimated date of birth flag 6 3 4 No
Interpreter required 6 6 1 No
Service delivery setting 6 1 6 Yes
Discipline of individual service provider(s) 4 1 4 No
Service contact mode 4 1 4 Yes
Outcome of offer 3 1 3 No
Group or individual service indicator 2 1 2 No
Anonymous/unidentified client indicator 1 1 1 No
Initial or subsequent service indicator 1 1 1 No
Analysis of data element entries in the 11 reference data dictionaries
Data Element
Number of Data
Dictionaries
containing data
element
Number of times
most common
entry occurs
Number of
different entries
Most common
entry used for
CHOCIP
Sex 11 8 2 Yes
Aboriginal and Torres Strait Islander origin 10 6 3 No
Country of birth 10 6 3 Yes
Disposition status 9 1 9 No
Preferred language 9 4 3 Yes
Source of referral 9 1 9 No
Billing category 7 1 7 No
DVA card type 7 4 4 Yes
Estimated date of birth flag 6 3 4 No
Interpreter required 6 6 1 No
Service delivery setting 6 1 6 Yes
Discipline of individual service provider(s) 4 1 4 No
Service contact mode 4 1 4 Yes
Outcome of offer 3 1 3 No
Group or individual service indicator 2 1 2 No
Anonymous/unidentified client indicator 1 1 1 No
Initial or subsequent service indicator 1 1 1 No
Analysis of data element entries in the 11 reference data dictionaries
Analysis of the use of reference data dictionaries for the purposes of CHOCIP
Data Element
Number of Data
Dictionaries containing
data element
Defined in National
Health Data
Dictionary
Defined in NSW
Health Data
Dictionary
Entry used
Aboriginal and Torres Strait Islander origin 10 Yes Yes NSW
Anonymous/unidentified client indicator 1 No No N/A
Billing category 7 Yes No Neither
Country of birth 10 Yes Yes Both
Discipline of individual service provider(s) 4 Yes No Neither
Disposition status 9 Yes No Neither
DVA card type 7 No Yes NSW
Estimated date of birth flag 6 Yes No Neither
Group or individual service indicator 2 No No N/A
Initial or subsequent service indicator 1 Yes No Neither
Interpreter required 6 Yes No Neither
Outcome of offer 3 No No N/A
Preferred language 9 Yes Yes NSW
Service contact mode 4 Yes Yes Neither
Service delivery setting 6 Yes No Neither
Sex 11 Yes Yes Both
Source of referral 9 Yes No Neither
Scenario: Impact of multiple data sets
Mr Smith, a 70 year old man is hit by a car as he was crossing the road. He
sustains a broken left leg and left arm. He is taken by ambulance to the
emergency department and treated there, but it is determined that the broken
leg needs to be pinned. He is admitted immediately as an inpatient and has
surgery the following day. He stays two days in hospital where discharge
planners determine that he requires physiotherapy as well as occupational
therapy to assess the suitability of his home environment given his restricted
mobility. He also requires home nursing to assist with activities of daily
living. He is required to return to outpatients two weeks after discharge to
consult the orthopaedic surgeon. He finds it difficult to cope with his reduced
mobility and inability to look after himself which results in depression
requiring the assistance of a psychologist.
Analysis of the services received by Mr Smith and the associated data collections
Service area Data Set
Emergency Department in hospital Emergency Department Data collection
Admitted patient unit in hospital Admitted Patient Data collection
Hospital based allied health departments Allied Health Data collection
Outpatient unit (orthopaedic surgeon) Aggregate outpatient statistics
Home nursing service Home and Community Care Data Collection
Community mental health team Mental Health Data Collection
How CHOCIP addresses these issues?
• Attempting to standardise the definitions and data domains for all elements in
the MDS for community health and outpatient settings.
• The intention is to rationalise existing reporting requirements across these
programs. This will mean that one standarised MDS will be reported and
supplementary information for the specific program area will be the only
additional data to report.
• The data collections flagged for integration include:
– Home and Community Care Data Collection;
– Drug and Alcohol Data Collection;
– Mental Health Outcomes and Assessment Tools Data Collection;
– Allied Health Data Collection; and
– Chronic Care Performance Indicators.
• In the case of Mr Smith, this development will mean that four of the six
collections that collect unit record data on the services he receives will collect
it according to standardised and consistent definitions for the common data
elements.
• It also means the common data elements will only be collected once to fulfil
the requirements of all these data collections.
• The Part 1 Data Dictionary project also worked on standardising data
definitions with the Emergency and Admitted Patient data collections although
there are no current plans to integrate these collections into CHOCIP. Thus
the CHOCIP work will contribute to the solution of, but not resolve all the
current problems.
How CHOCIP addresses these issues?
Conclusion
• The process of data set definition was found to follow the process of program
management in Australia.
• That is, most programs are funded in ‘silos’, they have their own program
eligibility criteria, often their own funding models and as a result their own
data sets and associated data collections.
• This phenomenon exists notwithstanding the stated aims of integrating the
services for patients of the health system to ensure continuity of care.
• The CHOCIP data dictionary development project has produced a data
dictionary that can be applied across the broad range of services covered by
the scope of community health and outpatient care.
• To date, this dictionary defines 28 data elements and their associated data
domains. It will be used to support the implementation of the CHOCIP
MDS from 1st September 2008.
• It will also be reviewed as part of the Part 2 Data Dictionary Project to
include data elements that describe more complicated concepts such as
diagnosis and intervention.
Conclusion
Lessons learnt
• The data dictionary exercise has demonstrated that developing integrated health
services requires the development of integrated health data sets.
• It is not a matter of what comes first; the reality is that one is not possible
without the other.
• Without consistent and comparable data across the range of services delivered
in community health and outpatient care, it is impossible to identify gaps in
service delivery and discontinuity in patient journeys.
• Data needs to be consistently collected and shared amongst service providers.
• CHOCIP represents an important step in the process of integrating data sets in
support of developing integrated health services.
• The next challenge will be the analysis of the resultant data to improve methods
of service delivery and funding thereby resulting in improved continuity of care
for patients of the health system.
Thank you
• NSW Health and NSW Health CHOCIP Project Team
– Deniza Mazevska
– Brendan Ludvigsen
– David Baty
– Durham Bennett