joe scuteri (ppt 595 kb large file)
TRANSCRIPT
Can ‘short lists’ facilitate the collection of data on ‘diagnosis’, ‘intervention’ and ‘presenting issue’ in
community health and outpatient care services?
Casemix 2008, Adelaide, Australia
Joe Scuteri & Lisa Fodero
• Classification systems and code sets provide a common framework for the collection and analysis of data.
• Clinical classification systems and code sets enable consistent description of diagnoses, procedures, therapies, and presenting problems, etc.
• Standardisation is necessary to permit health information to be recorded in a way that accurately conveys the same meaning to all who access the information.
• Classification systems also provide the basis on which data can be aggregated and analysed for a range of purposes including:
epidemiological surveillance;
enabling clinical review of care;
assisting with both internal and external utilisation review; and
supporting casemix-adjusted payment.
Introduction – Classification systems
Capturing classified clinical data
• Most common model for using classification systems is to engage trained clinical coders to assign the code(s) to a case by abstracting information from the clinical notes.
used for collecting diagnosis and procedures data for inpatients;
relies on training coders in the use of the relevant classification systems; and
having sufficient resources to fund the required staff.
• Not suitable for community health and outpatient care settings as the:
the volume of services is too high;
not sufficient funds available to pay for trained clinical coders; and
not sufficient clinical coders available even if funds could be found.
• Community health and hospital outpatient services account for a large and growing proportion of the work of the NSW health system.
• Little is known about the nature of the services provided, the patients receiving those services, and their ongoing needs and future demands.
• Outpatient services increasingly substitute for same day patient services in NSW largely due to funding incentives, resulting in information loss.
• New AHCAs will deliver reform “through recognition of the ways in which primary care and acute care interact; introduction of activity-based funding across the country … and strict performance measures”.
Need to capture community health and outpatient care data
Community Health and Outpatient Care Information Project
• CHOCIP was developed by the NSW Health Department in response to this
rapidly developing service delivery environment.
• CHOCIP encompasses the collection of patient level data for hospital
outpatient care clinics and publicly funded community health services.
• Largest project of its type ever attempted in Australia and when complete
will result in the annual collection of some 25 million unit records.
• Project began in 2006 with three phases planned
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 July 2009)
CHOCIP : Phase Two
What: Finalise Minimum Data Set (produce Data Dictionary) and associated business rulesWhy: To standardise and detail data requirements and build system specifications. To standardise interpretation and application of rules in implementing Data CollectionWhen: Start Dec 06, End Mar 09Who: Project Team, Consultancies
1.1 Data dictionary1.2 Business Rules
What:Identify data requirements, build a system to collect data, register all clinics/service teamsWhy: To register and uniquely identify all reporting entities, standardise what is meant by ‘reporting entity’When: Start Jan 07, End Dec 08Who: Project Team, Consultancy
2 Reporting Entity Registration
What: Mandate electronic Area-level registration of ambulatory clientsWhy: Uniquely identify each client at the Area level, improve data integrity, minimise data entry burdenWhen: Start Jan 07, End Dec 07Who: 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 collectionWhen: Start Jun 07, End Jun 09Who: 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 tablesWhy: Securely store data and make it available for analysisWhen: Start Jun 08, End Jun 09Who: 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 availableWhy: To enable efficient collection of ancillary dataWhen: Start Aug 07, Strategy completed by end Dec 07, Implement strategy post Jan 09Who: 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 collectionWhen: Start TBD, will extend post Jun 09Who: 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 availableWhy: Data available for clinicians, managers and for assessment of data qualityWhen: Start Jul 09, End Dec 09Who: Project Team, DPE
10 Performance Reports
What: Testing of alternative data collection tools and performance reports in selected sitesWhy: To fine tune the tools and identify any further issues for implementation of the CollectionWhen: Start Jan 09, End Jun 09Who: Relevant Health Services, Project Team, Consultancy
8 Proof-of-Concept
What: Select and develop web-based and paper-based solutionsWhy: To enable collection of data across all servicesWhen: Start Jan 09, End Jun 09Who: Project Team, SIM, HT, Consultancy/contractors
7 Alternative Data Collection Tools
What: Finalise Minimum Data Set (produce Data Dictionary) and associated business rulesWhy: To standardise and detail data requirements and build system specifications. To standardise interpretation and application of rules in implementing Data CollectionWhen: Start Dec 06, End Mar 09Who: Project Team, Consultancies
1.1 Data dictionary1.2 Business Rules
What: Finalise Minimum Data Set (produce Data Dictionary) and associated business rulesWhy: To standardise and detail data requirements and build system specifications. To standardise interpretation and application of rules in implementing Data CollectionWhen: Start Dec 06, End Mar 09Who: Project Team, Consultancies
1.1 Data dictionary1.2 Business Rules
What:Identify data requirements, build a system to collect data, register all clinics/service teamsWhy: To register and uniquely identify all reporting entities, standardise what is meant by ‘reporting entity’When: Start Jan 07, End Dec 08Who: Project Team, Consultancy
2 Reporting Entity Registration
What:Identify data requirements, build a system to collect data, register all clinics/service teamsWhy: To register and uniquely identify all reporting entities, standardise what is meant by ‘reporting entity’When: Start Jan 07, End Dec 08Who: Project Team, Consultancy
2 Reporting Entity Registration
What: Mandate electronic Area-level registration of ambulatory clientsWhy: Uniquely identify each client at the Area level, improve data integrity, minimise data entry burdenWhen: Start Jan 07, End Dec 07Who: Project Team, Consultancy
3 Electronic Client Registration
What: Mandate electronic Area-level registration of ambulatory clientsWhy: Uniquely identify each client at the Area level, improve data integrity, minimise data entry burdenWhen: Start Jan 07, End Dec 07Who: 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 collectionWhen: Start Jun 07, End Jun 09Who: 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 collectionWhen: Start Jun 07, End Jun 09Who: 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 tablesWhy: Securely store data and make it available for analysisWhen: Start Jun 08, End Jun 09Who: 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 tablesWhy: Securely store data and make it available for analysisWhen: Start Jun 08, End Jun 09Who: 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 availableWhy: To enable efficient collection of ancillary dataWhen: Start Aug 07, Strategy completed by end Dec 07, Implement strategy post Jan 09Who: Project Team, SIM, Health Technology
6 Data Extracts for Ancillary Services
What: Develop cost effective strategy for ensuring data on ancillary services is availableWhy: To enable efficient collection of ancillary dataWhen: Start Aug 07, Strategy completed by end Dec 07, Implement strategy post Jan 09Who: 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 collectionWhen: Start TBD, will extend post Jun 09Who: 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 collectionWhen: Start TBD, will extend post Jun 09Who: 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 availableWhy: Data available for clinicians, managers and for assessment of data qualityWhen: Start Jul 09, End Dec 09Who: Project Team, DPE
10 Performance Reports
What: Develop reports relevant to key stakeholders that can be automatically generated once data are availableWhy: Data available for clinicians, managers and for assessment of data qualityWhen: Start Jul 09, End Dec 09Who: Project Team, DPE
10 Performance Reports
What: Testing of alternative data collection tools and performance reports in selected sitesWhy: To fine tune the tools and identify any further issues for implementation of the CollectionWhen: Start Jan 09, End Jun 09Who: Relevant Health Services, Project Team, Consultancy
8 Proof-of-Concept
What: Testing of alternative data collection tools and performance reports in selected sitesWhy: To fine tune the tools and identify any further issues for implementation of the CollectionWhen: Start Jan 09, End Jun 09Who: Relevant Health Services, Project Team, Consultancy
8 Proof-of-Concept
What: Select and develop web-based and paper-based solutionsWhy: To enable collection of data across all servicesWhen: Start Jan 09, End Jun 09Who: Project Team, SIM, HT, Consultancy/contractors
7 Alternative Data Collection Tools
What: Select and develop web-based and paper-based solutionsWhy: To enable collection of data across all servicesWhen: Start Jan 09, End Jun 09Who: Project Team, SIM, HT, Consultancy/contractors
7 Alternative Data Collection Tools
• The investment associated with creating a patient level data collection for community health and outpatient care services would not be justified unless data were collected on:
the reason a patient attended a service (presenting issue);
what was wrong with them at the time of service (diagnosis); and
what was done to them during the service (intervention).
• Problem was to identify a practical method for collecting and classifying the data, hence the exploration of the short lists concept.
• NSW Health defined 132 service types covering all publicly funded community health and outpatient services.
• Short lists specific to each service type needed to be developed representing 396 short lists.
Short list development project
.
What is a ‘short l ist’?
• Short lists allow self coding of the 10 to 20 most commonly occurring codes in the classification system for each service with the balance being coded to
‘other’.
• Results in specific information about variables such as diagnoses, interventions and presenting issue being available for the great majority of the patients seen.
• The short list idea has been used in a number of data collection projects in Australia and elsewhere in the world.
• A search of the literature showed that the development of short lists on such a large scale has never been attempted anywhere in the world.
Project Methodology
The project methodology consisted of five major processes:
• A range of classification systems were evaluated to assess their suitability as the underlying base for the short lists for the data elements diagnosis, intervention and presenting issue.
• Draft short lists were developed for the three data elements for each of the 132 service types (i.e. 396 short lists) by reviewing the literature, conducting initial consultation with clinicians, and collecting and analysing any available (coded) data.
• The draft short lists were refined by working with the Clinical Advisory Groups (CAGs) formed specifically to advise on the suitability of the short lists for one or more of the 132 service types (in progress at the time of preparing this paper).
• The draft short lists will be pilot tested at 15 to 20 service delivery sites that are representative of community health and outpatient care services in NSW.
• The pilot test results will be analysed (including frequency counts of the use of the short list categories) and the short lists will be refined and finalised.
Classification system evaluation
• Wide range of classification system already in use in NSW across community health and outpatient services;
• care needed to be taken in choosing appropriate classification systems to ensure that existing data sets are protected while the consistency of what is collected in CHOCIP is preserved;
• to choose between the leading classification system options, a set of evaluation criteria was formulated against which each option for each data element
(diagnosis, intervention and presenting issue) was assessed.
• the cost of using the classification system as the basis for short list development.Cost
• the extent to which the system can support the development of short lists;Suitability
• the research and development process supporting the maintenance and continuing refinement of the classification system;Research and development
• the ease of use of the classification system, including consistency with commonly used clinical terminology;Ease of use
• the extent to which the classification system describes the issues encountered and services provided by the service types (out of the list of 132);Applicability
• the extent to which the classification system is already used in community health and outpatient care services in NSW, in Australia, and internationally;Existing use
InterpretationCriterion
Classification system evaluation criteria
• No one system would be suitable as the base for the short lists across the 132 service types (range of service types is just too broad).
• The evaluation showed that, for hospital outpatient clinics, it was most logical to use ICD-10-AM and ACHI.
maximum continuity and consistency of the outpatient data with hospital inpatient data
maximum opportunity to leverage off the existing clinical coding skill base in hospitals
Classification system evaluation results – outpatient care
• ICD-10-AM (diagnosis and presenting issue) and ACHI (intervention) was one option.
• A classification system specifically for community health services had been formulated as part of a major project to develop an enterprise system for community health, the Australian Classification and Terminology of Community Health (CATCH).
• Other major candidate was ICPC-2 PLUS (for all data elements), which is primarily used in general practice settings in Australia.
• Some in-scope services used CATCH and/or ICD-10-AM and ACHI but no services used ICPC-2 PLUS.
• Many intervention categories in CATCH were based on ACHI codes. • There was a strong desire to link admitted patient data to outpatient and
community health data for the same journey (e.g. breast cancer) so ICD-10-AM and ACHI were chosen.
• Exceptions will be made for some services (eg domestic violence) where it is known that ICD-10-AM and ACHI do not provide a suitable framework.
Classification system evaluation results – community health
Development of the short lists
• Little data available on diagnosis, intervention and presenting issue within existing systems to support the development of short lists.
• ` For outpatients, first draft short lists were based on some data:
examined unit record level admitted patient data to develop draft diagnosis and intervention short lists for outpatients;
no data for presenting issue so these short lists were derived from first principles for outpatients.
• For community health, a data extract from the CHIME system was obtained for diagnosis, intervention and presenting issue.
data were found to be of variable quality, particularly as many services had set the system up differently and developed their own local codes in addition to the standard code set;
proved to be a better basis for drafting the short lists than the admitted patient data used for outpatient services.
• CAGs proved to be very valuable in the development process.
Case study - cardiology
• The service types classification developed by the NSW Health Department includes seven categories that relate to cardiology services are:
• The first draft short lists for the cardiology unit, nfd were prepared by using admitted patient data for non-emergency inpatient admissions for patients with cardiac disease (AR-DRGs F60A to F75C).
• These lists were submitted to the cardiology services CAG for review and refinement at a two-hour face-to-face meeting.
• The meeting results were analysed to develop second draft short lists, which were circulated to CAG members by email for final review.
•16.04: Pacemaker unit;
•38.02: Heart/lung transplantation unit.•16.03: Cardiology diagnostic unit;
•37.03: Cardiac surgery unit; and•16.02: Cardiac rehabilitation unit;
•16.05: Cardiac catheterisation unit;•16.01: Cardiology unit, nfd;
Case study – cardiology unit, nfd
Other
Valve diseases/disorders
Type 2 diabetes mellitus with circulatory complication
OtherSick sinus syndrome
TachycardiaPhlebitis and thrombophlebitis
Syncope and collapsePericarditis
Peripheral oedemaOtherMyocarditis
PalpitationsUpright tilt table testingIschaemic heart disease
Mechanical complication of heart valve prosthesisTrans-thoracic echocardiogram (TTE)Hypotension
Mechanical complication of cardiac electronic deviceTrans-oesphageal echocardiogram (TOE)Hypertension
Irregular heart rhythmPercutaneous central vein catheterisationHeart failure
Infection and inflammatory reaction due to other cardiac and vascular devices, implants and grafts
CardioversionHeart block
Identification of heart disease risk factorsCardiovascular stress testEndocarditis
DyspnoeaCardiac electrophysiological study Congenital malformations of the circulatory system
Dizziness EducationCardiomyopathy
Chest pain/discomfortCounsellingCardiac arrhythmia (excluding heart block)
BradycardiaConsultationAtrial fibrillation and/or flutter
Adjustment and management of cardiac deviceCase planning/care planningAtherosclerosis
Abnormal results of cardiovascular function studiesCase coordination and managementAngina pectoris
Abnormal findings on diagnostic imaging of heart and coronary circulationAssessmentAcute myocardial infarction
Presenting Issue descriptionIntervention descriptionDiagnosis description
Case study - audiology
• Audiology services represent a single service type in the NSW Health classification
12.02: Audiology/Audiometry Unit.
• The first draft short lists were developed using an extract of CHIME data.
• Unlike cardiology, where only admitted patient could be used, the CHIME data relate to non-admitted patient services.
• The first draft short lists were discussed with two practising audiologists resulting in a refined second draft which was submitted to the audiology services CAG for review at a two-hour face-to-face meeting.
• The resultant third draft short lists were circulated to CAG members for final review thereby producing the final draft.
Other
Training in use of communication device and/or technique
Risk of hearing loss – trauma
Risk of hearing loss – speech and language issue
Risk of hearing loss – ototoxicity
Risk of hearing loss – meningitis
Risk of hearing loss – learning/behavioural issueOtherOther
Risk of hearing loss – in-utero infectionVestibular function testsUnclear/inconclusive
Risk of hearing loss - family historyTympanometryAuditory neuropathy
Risk of hearing loss - developmental delaySpeech audiometryCentral hearing disorder
Risk of hearing loss – congenital abnormalityPsychoacoustic testsAuditory processing disorder
Risk of hearing loss – birth relatedOtoacoustic emission evaluationRetrocochlear pathology
Hearing impairment/reviewInterventions involving assistive or adaptive device, aids or equipment
Vestibular dysfunction
Hearing – TinnitusImmittance audiometrySensorineural hearing loss, asymmetric
Hearing – middle ear pathologyElectrocochleographySensorineural hearing loss, progressive
Hearing – general concernCounselling and/or education for hearing loss or aural disorder Sensorineural hearing loss, unilateral
Hearing – failed screenCentral auditory function testsSensorineural hearing loss, bilateral
Fitting of deviceAuditory evoked potentialsNon organic hearing loss
Employment/Medical requirementAudiometry function testsMixed hearing loss
Ear plugs (hearing/water protection)Assessment for assistive or adaptive device, aids or equipmentHearing loss, unspecified
Balance problemAssessment – intraoperativeHearing – normal
Assistive or adaptive device, aids or equipment issueAssessment – hearing deviceConductive hearing loss
Presenting issue descriptionIntervention descriptionDiagnosis description
Case study - audiology
Conclusion
• The case studies illustrate the nature of the short lists development process as well as the different issues faced for the service types.
• For all services, review of the available data assisted in the generation of some of the draft short lists, but the most important process was always refinement
of the draft short lists by working directly with the CAGs.
• It took considerable work to actively engaged clinicians in the process.
• The short lists have been shown to be relevant to each service type, largely because of the homogeneity of patients and services within each service type.
• The data resulting from the implementation of the short lists will be extremely valuable in planning, developing and funding community health and outpatient care services in NSW.
• The development process has highlighted the:
difference between the language used in classification systems and the terminology in common use by clinicians.
need to develop a glossary of terms that can be used as a reference source in assigning the short list categories.
inconsistencies in recording data on diagnosis and interventions in community health and outpatient care services.
Lessons learnt
• The final refinement process will consist of a pilot test of the short lists in 15 services across NSW, covering off at least one service type in each of the 132 categories across community health and outpatient services.
• The pilot test will be conducted over a period of six weeks using manual data collection.
• Results of the pilot test will be used to refine the short lists which will then be included in the CHOCIP Data Dictionary and enterprise systems.
• The results will also be used to decide the nature of the glossary of terms that needs to be developed as well as to shape the maintenance process that is required to keep the short lists consistent with clinical practice.
Next steps
Clinicians agreed that the short lists facilitate the collection of data that address the important questions of
why do patients present for services? (presenting issue);
what is wrong with them? (diagnosis); and
what services are provided to them? (intervention)
without the need for specialised clinical coders.
In summary
Acknowledgments
• NSW Department of Health
Deniza Mazevska
Brendan Ludvigsen