23 PCSI Conference, Lido, Venice · 2014-03-14 · 23rd PCSI Conference, Lido, Venice Lisa Fodero & Joe Scuteri • Integrating health services will not only improve patient outcomes

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


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.


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


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


When: Start TBD, will extend post Jan 2008


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









1.1 Data dictionary

1.2 Business Rules









1.1 Data dictionary

1.2 Business Rules





entity’








entity’





ambulatory clients







ambulatory clients












Vendors/Contractors









Vendors/Contractors





reference tables







9 Data Repository



reference tables







9 Data Repository















13 Project and Data Collection Governance

11 Change Management,

Training, Communication







Vendors/Contractors









Vendors/Contractors



12 Health Service

Implementation Plans




are available



data quality







are available



data quality













8 Proof-of-Concept










8 Proof-of-Concept




across all services





Tools




across all services





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


















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


















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


















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

Documents

23 PCSI Conference, Lido, Venice · 2014-03-14 · 23rd PCSI Conference, Lido, Venice Lisa Fodero & Joe Scuteri • Integrating health services will not only improve patient outcomes