Keywords (MeSH): Health Information Systems; Data Collection; Datasets as Topic; Aged; Australia

Transcription

1 Information management for aged care provision in Australia: development of an aged care minimum dataset and strategies to improve quality and continuity of care Jenny Davis, Amee Morgans and Stephen Burgess Abstract Background: Efficient information systems support the provision of multi-disciplinary aged care and a variety of organisational purposes, including quality, funding, communication and continuity of care. Agreed minimum data sets enable accurate communication across multiple care settings. However, in aged care multiple and poorly integrated data collection frameworks are commonly used for client assessment, government reporting and funding purposes. Objective: To determine key information needs in aged care settings to improve information quality, information transfer, safety, quality and continuity of care to meet the complex needs of aged care clients. Method: Modified Delphi methods involving five stages were employed by one aged care provider in Victoria, Australia, to establish stakeholder consensus for a derived minimum data set and address barriers to data quality. Results: Eleven different aged care programs were identified; with five related data dictionaries, three minimum data sets, five program standards or quality frameworks. The remaining data collection frameworks related to diseases classification, funding, service activity reporting, and statistical standards and classifications. A total of 170 different data items collected across seven internal information systems were consolidated to a derived set of 60 core data items and aligned with nationally consistent data collection frameworks. Barriers to data quality related to inconsistencies in data items, staff knowledge, workflow, system access and configuration. Conclusion: The development an internal aged care minimum data set highlighted the critical role of primary data quality in the upstream and downstream use of client information; and presents a platform to build national consistency across the sector. Keywords (MeSH): Health Information Systems; Data Collection; Datasets as Topic; Aged; Australia Introduction In Australia, the Australian Institute of Health and Welfare (AIHW) is responsible for the management of national information standards and data set specifications; including three core national data dictionaries related to the health, community and housing assistance sectors (Australian Institute of Health and Welfare 2012, 2014b). The AIHW maintains an accessible electronic data standards registry, METeOR (METadata Online Registry), which integrates and presents information specifically related to the National Health Data Dictionary, the National Community Services Data Dictionary, the National Housing Assistance Data Dictionary, National Minimum Data Sets and Data Set Specification (Australian Institute of Health and Welfare 2007). The nationally agreed minimum data sets and component data elements determine what data are to be collected and reported in relation to aged care and include those related to: the Aged Care Assessment Program (ACAP), the Home and Community Care (HACC), the National Respite Care Program (NRCP) and the Veterans Home Care Community Nursing Program (Australian Institute of Health and Welfare 2014b). The purpose of established national data standards is to enable nationally comparable and consistent information to be produced, regardless of how the data are collected; specify nationally agreed meaning and output representation of data; enable consistent comparison across different jurisdictions, settings and sectors; and promote more efficient data collection by reducing duplication of effort (Australian Institute of Health and Welfare 2007). Key data needed to support secondary information purposes (e.g. reporting, policy, governance, decision support) should be derived from primary data (e.g. point of care service delivery). Additionally, this secondary data should be used to provide feedback to improve and enhance primary service delivery HEALTH INFORMATION MANAGEMENT JOURNAL 1

2 (Australian Institute of Health and Welfare 2007). With this in mind, both upstream and downstream data needs related to aged care provision more broadly must be considered, whether for primary and secondary use or internal and external purposes, including consideration of the providers and end users of those data. Established aged care related data sets are informed by the aforementioned core national data dictionaries and national standards, yet they operate specifically at program level with an associated data dictionary developed for such purposes, as distinct from other system based national data collections such the National Hospital Morbidity Database (NHMD) and associated data source the National Inpatient Minimum Data Set (NIMDS) (Australian Institute of Health and Welfare, 2014a). Other related data classification systems aligned to national data standards and in support of nationally consistent data collection and reporting include: the Australian Bureau of Statistics standards and classifications; the International Statistical Classification of Disease and Related Health Problems Tenth Revision Australian Modification (ICD-10-AM); the International Classification of Functioning, Disability and Health (ICF) (Australian Institute of Health and Welfare 2014a). The Aged Care Funding Instrument (ACFI), while primarily for funding and regulation purposes in the residential aged care setting, is partially informed by the Aged Care Assessment Program (ACAP) minimum data set upon creation of the Aged Care Client Record (ACCR) if the person is deemed eligible for care in that setting. While there are several data collection frameworks relevant to community and residential aged care settings, these do not align well across systems and therefore do not adequately address additional information needs to optimise client care. In practice there are multiple requirements, information sources and collections of aged care client data across often poorly integrated information systems. The pathway for client information is often misaligned with the client s physical movement between care settings. Communication between aged care and healthcare providers is even more challenging when there are different data requirements across service sectors and transfer of information can often be suboptimal (Robinson et al. 2009). Timely and efficient access to accurate client information is necessary for the safety and quality of care provision at every service level and setting. There are also different expectations in relation to information management within aged and healthcare settings, particularly between government, peak body, service providers and consumers. These features of information management in the aged care sector impact upon: (a) identification of clients with complex health care needs; (b) staff ability to coordinate care effectively and efficiently; and (c) continuity of care between health and aged care providers. The setting for this study was a large not-for profit aged care provider in Victoria, Australia. They offer a range of aged care services including residential, in-home care, respite and housing services together with specialised care and expertise in dementia, palliative care, and culturally appropriate care for people from non-english speaking backgrounds. This study examined existing data collection frameworks to determine key information needs in aged care settings with the aim of improving information quality, information transfer, safety, quality and continuity of care to meet the complex needs of aged care clients. We present a proposed consolidated minimum data set for the aged care sector. Methods The study used modified Delphi methods involving five stages to establish consensus for a derived minimum data set for aged care that is suitable for internal and external organisational purposes. The process was informed by a previously validated data development framework described below. Stage1: Existing aged care related data collection frameworks Aged care related data collection frameworks were reviewed to identify the national data dictionaries, minimum data sets and other collections specific to aged care programs and service provision. The purpose of this review was to establish the relationship of the required data items (identified through consultation as required for internal purposes) to existing aged care program data dictionaries and minimum data sets, including similarities and differences between them. The following list represents the multiple aged care related programs and associated data dictionaries, each with variously specified data components and collection requirements in addition to changes in program titles within current aged care reforms. Several Australian Bureau of Statistics (ABS) Classification Standards were also identified as relevant to these data collection frameworks and these are summarised in Box 1. Aged Care Assessment Program Data Dictionary (ACAP DD) Aged Care Assessment Program Minimum Dataset (MDS) 2 HEALTH INFORMATION MANAGEMENT JOURNAL

3 Aged Care Funding Instrument (ACFI) Aged Care Innovation Pool program Aged Care Standards and Accreditation Agency Ltd (Accreditation Agency) (until 1 Jan 2014) Australian Aged Care Quality Agency (Quality Agency) (from 1 Jan 2014) Community Aged Care Packages Program (CACP) (until 1 Aug 2013) Community Care Common Standards (CCCS) (until 1 Aug 2013) Commonwealth Home Support Programme (including HACC & NRCP from 1 July 2015) ICD-10-AM 10 th Revision (diseases & health related problems) Home and Community Care Program (HACC) (incl. Victoria from ; Western Australia from ) Home and Community Care Data Dictionary (HACC DD) Home and Community Care Minimum Dataset (MDS) Home Care Program [Levels 1-4] (previously CACP, EACH, EACH-D) (new packages from 1 Aug 2013) Home Care Standards (previously CCC Standards) (from 1 Aug 2013) Multi-Purpose Services Program National Aboriginal and Torres Strait Islander Flexible Aged Care Program National Aboriginal and Torres Strait Islander Flexible Aged Care Program Quality Framework National Respite for Carers Program (NRCP) (until 1 July 2015) National Community Services Data Dictionary (NCS DD) National Health Data Dictionary (NHDD) State government based programs incl. Post-Acute Care (PAC), Hospital in the Home (HITH) Transition Care Program (TCP) Veterans Home Care Program (VHC) Veterans Home Care Data Dictionary (VHC DD) Veterans Community Nursing Program Veterans Community Nursing Minimum Dataset (MDS). Stage 2: Validation of data items - need, tradition, professional judgement, empiricism A data item template was created based on existing aged care sector data collection frameworks as identified above, and incorporating existing data items collected across the organisation s seven internal information systems. Within the organisational setting of this study, information system content and process experts were identified and a workshop convened to review the data items within the template. These information system experts represented key roles with the organisation, including nursing, customer service advisor, information technology, quality manager, risk and governance manager, finance and administration, and community services manager (information system champions). The system experts were asked to nominate which of the data items within the template were required and those considered optional within each. It should be noted that while the data items could be collected in different formats, the goal that distinguished each was clearly evident. For example, last name could also be represented as surname, or first name could be represented as given name. The required data items were subsequently collated and organised into a summary table of results, which Box 1: Australian Bureau of Statistics Standards and Classifications Australian Bureau of Statistics: A directory of concepts and standards for social, labour and demographic statistics (1995) Australian Bureau of Statistics: Australian Classification of Languages (2011) Australian Bureau of Statistics: Australian Standard Classification of Occupations, 2nd Ed (1997) Australian Bureau of Statistics: Interim Standards for Statistics on Aboriginal/Torres Strait Islander status (ABS Directory of concepts and standards for social, labour and demographic statistics, 1995) Australian Bureau of Statistics: National Localities Index (2002) Australian Bureau of Statistics: Standard Australian Classification of Countries (1998) Australian Bureau of Statistics: Standard for Statistics on Cultural and Language Diversity (1999) Australian Bureau of Statistics: Standard Geographical Classification (ASGC) (2009) Australian Bureau of Statistics: Survey of Disability, Ageing and Carers (1998) Sources: Australia Bureau of Statistics (2009, 2011, 2012, 2013); Australian Consortium for Classification Development (2014); Australian Government (2013); Australian Institute of Health and Welfare (2014b); Department of Health (2012). HEALTH INFORMATION MANAGEMENT JOURNAL 3

4 then represented a draft organisation wide minimum or core data set relevant to aged care. Consultation and feedback was also sought from community based health care professionals (e.g. physiotherapist, geriatrician) regarding the data items contained within the draft version of the minimum data set. This process was undertaken to confirm and validate the required data items from external aged care provider perspectives and should be used to inform any future information sharing processes or systems that may consequently be developed. Stage 3: Assessment of data quality within and across systems A system of real time, automated exception reporting was established by the IT department for the seven information systems across the organisation to determine the completeness of the capture of the data items nominated as required. This provided a baseline for assessment and monitoring of information system quality based on the derived data set across the organisation and the results were used to inform the development of an organisation wide information quality improvement plan. Stage 4: Information quality improvement plan including education and training To improve quality of data entry, education and training initially targeted key users at residential sites, followed by community based aged care users with identified responsibilities for data capture and completion across information systems (e.g. Incident Management System [RiskMan], Client Management Systems [Carelink Plus, Epicor, icarehealth, Magic, and two in-house systems). Face-to-face training sessions were conducted onsite with key users and the required data items were examined and discussed. The importance of their role to data quality was emphasised, and training included a practical demonstration of system access, functionality and navigation tips and hints. Organisation wide information system user training across both residential and community care settings was then planned in association with scheduled information systems upgrades across all user levels. Stage 5: Sustainability Ongoing monitoring of data quality within an established system of exception reporting included regular constructive feedback to users on performance and data quality trends over time. The inclusion of these data quality measures and reporting mechanisms for user feedback on performance now feature within the continuous improvement plans across residential, community and organisational levels. Timely feedback is an essential component of any sustainable education and training program (Delany & Molloy 2009). Data development framework The framework used to develop this process was based on previous research undertaken in the development of a non-government mental health standard data set (Wood & Pennebaker 2001). This framework provides a standard approach for data content standards and development that is consistent with the information development literature (Leginksi et al. [1989] cited in Wood & Pennebaker 2001). The chosen framework recommends that data items considered for inclusion in a proposed data set address the concepts of need (items critical to processing and categorising); tradition (representative of setting idiosyncrasies); professional judgement (informed by key stakeholder experience and knowledge); and empiricism (Leginskiet. al. 1989). Identifying internally required data items will, by necessity, more efficiently support reporting of secondary data for external purposes. This framework was adapted for the aged care context of this study, including consideration of existing National Health Information Models (NHIM), National Health Data Dictionaries (NHDD) and related aged care services data dictionaries and collection frameworks. Modified Delphi methods were used in order to refine the chosen framework inclusion components for data items to address need, tradition, professional judgement and empiricism as specified in the framework applied to this project, thus ensuring rigor in the method. Results This study utilised previously identified data collection frameworks relevant to aged care provision in Australia (see Methods), including state government based aged care and healthcare related programs such as the Post-Acute Care Program in Victoria, Australia, applicable to the geographical setting for this study. There were eleven different aged care related programs identified and one relevant state based (post-acute care) program. Across these programs, there are five related data dictionaries, three minimum data sets, and five Program Standards or Quality Frameworks. The remaining data collection frameworks relate to disease classification (ICD-10-AM), funding (ACFI), and service activity reporting (e.g. NRCP). Nine related ABS classification standards (see Box 1) and systems were identified, which guide data collection, including the National Classification of Community Services and the Australian government 4 HEALTH INFORMATION MANAGEMENT JOURNAL

5 English Proficiency Scale utilised within the HACC MDS. A total of 170 different data items were collected across seven internal information systems prior to the workshop. As part of the Delphi process used in this study, the information system champions reached consensus on a derived set of 85 required data items. Following this workshop, these data items were consolidated and validated by the first author as a qualified Health Information Manager to the final list of 60 core data items based upon alignment with the nationally consistent data collection frameworks identified earlier (see Appendix Table 1A). In the course of this study, multiple client assessment and eligibility data requirements were identified, which also support the provision of aged care services. However, a full examination of this area is beyond the scope of this work. For the purposes of reporting within this study, client health related data is broadly captured in recording of medical conditions, medical history, allergies and medications within the data set under development here. Table 1A (Appendix) presents aged care data items by category within the information domains: client, service, and funding/ regulation. Several data limitations were identified within the Delphi process in the context of existing aged care related data collection frameworks that the information system champions identified as of particular significance for client care. Most notably were inconsistent data collection of allergies, cultural background, delegated decision making (e.g. power of attorney), advance care planning, medications, and medical conditions. These data are essential to guide a client s ongoing healthcare. The derived data set and data item relationships were also compared to data collection frameworks in primary healthcare to investigate support for crosssector information sharing; however none were found to represent a national minimum data set. An earlier evaluation of primary healthcare data identified potential data gaps across all reviewed data collections, highlighting the lack of a national minimum data set, and recommending implementation at a national level (Australian Institute of Health and Welfare 2008). There is a strong case to argue for consideration of a national baseline of client information as it is in other settings, and therefore worthy of inclusion within an aged care minimum data set. Barriers and enablers Multi-level factors were identified as contributing to data quality within existing information systems across the organisation including: Data items: lack of clarity: inconsistent data fields, definitions, terminology; inconsistent requirements for required or optional data items across different systems. Staff and workflow: duplication of effort across multiple information systems, lack of clarity regarding roles and responsibilities for data collection, lack of staff knowledge and understanding of systems and their importance. Security: inconsistent user level permissions across staff roles and sites. System level: inconsistent availability of data fields across user level permissions; duplicate records; inactive and incomplete records. Strategies to improve quality Security and system level factors related to user level permissions and availability of data fields were addressed directly with the IT department in consultation with site level or role managers to ensure a standardised approach for user access and authorisation processes according to organisational role and position. Key users, particularly Care Coordinators, Case Managers and Team Leaders were identified as having inconsistent user level permissions across the organisation, with the process for approval according to role now streamlined and standardised. A review of available data fields and required data items within existing information systems was undertaken in collaboration with the IT department, which allowed further systems configuration alignment with the key data collection frameworks. This included minimising the use of free text data entry where dropdown options were available, which aligned with key data collections or classifications, such as ICD-10-AM. Systems configuration that was not achievable at organisational level was taken up with the commercial vendors of these systems for inclusion in future systems upgrades. Staff and workflow matters were addressed in the following ways; review of roles and responsibilities of staff involved directly in data collection, including workflow patterns to more closely align the client journey to processes of data collection. For example, at first contact with a client, potential client or family member, a client record was established upon which further information could be built. Each time the client record is accessed, the staff member is expected to make further contributions to the data already contained within the record. Examples of education included, searching for existing clients within the system database to avoid duplicate records and duplication of effort in data collection and more efficient use of staff time. Education for both data collectors HEALTH INFORMATION MANAGEMENT JOURNAL 5

6 and data users highlighted the purpose and importance of capturing data accurately in the first instance, based on the principle of creating once and using often (Grain & Procter 2009). Discussion This study successfully developed a derived set of data items to meet internal data collection and business function requirements, and additionally to support external reporting purposes by minimising duplication of effort and improving data quality. These findings could be used to build a platform for future development of a national minimum data set suitable for the aged care sector more broadly. The methods used in this study enabled identification of key data requirements, gaps and limitations of data capture, storage, retrieval and communication of client related information within the organisation. More broadly, this study facilitated a review of organisation wide data management processes, structures, systems capacity, and development of an information improvement strategy plan. Improved staff knowledge and understanding of information systems, their roles and responsibilities and importance of data quality through targeted initial and ongoing education has demonstrated improvement in completeness and accuracy of client data. Identification of the barriers and enablers impacting on data quality within existing organisational systems and the strategies developed to address these have enabled more efficient data collection with reduced duplication of effort and more efficient use of staff time. More broadly, strategies to improve and sustain data quality and information management processes across the organisation have highlighted the importance of education and training and aligned this with: staff induction, ongoing staff education, ICT infrastructure planning, governance, risk and quality systems, and proactive monitoring of ongoing aged care system reforms for changes in information requirements. In the context of this study we have demonstrated the need and feasibility for establishing an aged care minimum data set at national level. This derived minimum data set was achieved through internal stakeholder consensus within existing organisational systems. Data quality improvement has been identified by ongoing monitoring of data quality within a system of exception reporting, including regular, constructive feedback to users on performance and data quality trends over time. This has directly impacted upon the quality of client information that is collected, communicated and available to support safe and comprehensive client care both internally and externally and importantly is sustainable for the future. Plans for ongoing monitoring of data quality and regular feedback to users, embedded as part of business systems and continuous improvement, further enhance system sustainability and maintain the heightened organisational awareness and focus on key data necessary for information quality. It was beyond the scope and capacity of this study to create a formal aged care data dictionary. This project should be considered as essential preparatory work preceding formal development of both a national aged care data dictionary and minimum data set, subject to further funding for such a significant project being made available in the future. Limitations This study has been limited to the context of one large aged care provider in Victoria, Australia and therefore may not be nationally representative or generalisable to other aged care service providers operating in other state or territory jurisdictions, although much of the regulation that applies to aged care is set at a national level, so in practice this limitation should be relatively minimal. It is anticipated this preliminary work will provide a platform for future development of a national minimum data set suitable for the aged care sector more broadly to better meet the complex care needs of clients and associated information needs of service providers and policy makers. While the first author is a qualified Health Information Manager with knowledge and experience of Australian health information management principles and practices the study was limited to publicly available information and was unable to access authoritative expertise of bodies such as NeHTA, Standards Australia, the Australian Institute of Health and Welfare or Australian Bureau of Statistics. The involvement of such bodies in the development of a subsequent national aged care minimum dataset would obviously be ideal. However, this study has clearly demonstrated the strong capability for local working groups with operational aged care experience and information system knowledge to make an equally valuable contribution to improving information quality and continuity of care. Conclusion This paper presents the strategies and techniques used to improve aged care related data capture, information quality and continuity of care by one aged care provider, also relevant to other aged care settings. Adopting measures to improve data efficiency and data quality in the aged care sector are essential 6 HEALTH INFORMATION MANAGEMENT JOURNAL

7 to support the provision of aged care. Aged care providers will be able to adapt or adopt these practical strategies and techniques into their setting to support their information management quality improvement strategy plan. Importantly, providers can educate and provide feedback to staff on the critical role of data in the cycle of client information impacting delivery, quality, funding, communication and continuity of care. The development of an internal minimum data set is a critical feature of this study, and included identifying information needs across the aged care sector. However, the national alignment and integration of data collection frameworks across health and aged care sectors would more significantly improve information quality, information transfer, safety, quality and continuity of care to meet the complex care needs of aged care clients. Acknowledgements This work has been supported as part of the Department of Social Services Better Health Care Connections (BHCC) Framework Project: Sub Acute Linkage in Later Years the SALLY Project and funded by the Australian Government Department of Social Services. The authors would like to thank Arpi Cseh, Jane Korneyko and the Benetas information system champions for their support and co-operation in the study. References Australian Bureau of Statistics. (2011) Australian Statistical Geography Standard (ASGS): Volume 1 - Main Structure and Greater Capital City Statistical Areas, July Canberra, Australian Bureau of Statistics. Australian Bureau of Statistics. (2013) Australian Demographic Statistics, Sept Available at: (accessed 6 May 2013). Australian Bureau of Statistics (2011). Guide to Migrant Statistical Sources, Edition 2. Department of Health and Ageing, System for the Payment of Aged Residential Care (SPARC). Canberra, ABS, Commonwealth of Australia. Australian Bureau of Statistics. (2012) Country of Birth Standard, 2012, Version 1.0.Canberra, Australian Bureau of Statistics. Australian Bureau of Statistics. (2013). Statistics on the Indigenous peoples of Australia. Available at: Previousproducts/1301.0Feature%20Article51994?opend ocument&tabname=summary&prodno=1301.0&issue= 1994&num=&view= (accessed 3 May 2013). Australian Bureau of Statistics (2009) Australian Standard Classification of Occupations (ASCO) Second Edition Available at: abs@.nsf/0/1761c8ec7130bd69ca25697e001840e?op endocument (accessed 17 March 2013). Australian Consortium for Classification Development (2014). International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification. ICD-10-AM. ACCD, Australian Consortium for Classification Development. Australian Government (2013). Aged Care Assessment Program Data Dictionary Version 3.0 Canberra, Department of Health and Ageing. Australian Institute of Health and Welfare (2007). A guide to data development Cat. no. HWI 94. Canberra, National Data Development and Standards Unit, AIHW. Australian Institute of Health and Welfare. (2008). Review and evaluation of Australian information about primary health care: a focus on general practice. Cat. no. HWI 103. Canberra, AIHW. Australian Institute of Health and Welfare (2012). National Health Data Dictonary 2012 version 16. Cat. no. HWI 119. Canberra, AIHW. Australian Institute of Health and Welfare (2014a). Australian hospital statistics Health Services Series no. 54. Cat. no. HSE 145. Canberra, AIHW. Australian Institute of Health and Welfare (2014b). National Community Services Data Dictionary, version 8, Cat. no. HWI 126. Canberra, AIHW. Delany, C. and Molloy, E. (2009). Clinical education in the health professions. Chatswood, NSW, Churchill Livingstone. Department of Health (2012). Quality framework for the National Aboriginal and Torres Strait Islander Flexible Aged Care Program. Canberra, Department of Health. Grain, H. and Procter, P. (2009). Using health data: applying technology to work smarter. Chatswood, NSW, Elsevier Australia. Leginski, W., Croze, C., Driggers, J.et. al. (1989). Data standards for mental health decision support systems. A report of the Task Force to Revise the Data Content and System Guidelines of the Mental Health Statistics Improvement Program. Washington, D.C., US Department of Health and Human Services. Robinson, A., Emden, C., Lea, E., Elder, J., Turner, P., andvickers, J. (2009). Information issues for providers of services to people with dementia living in the community in Australia: breaking the cycle of frustration. Health andsocial Care in the Community, 17(2): doi: /j x. Wood, C. and Pennebaker, D. (2001). Non-government sector Mental Health Data Dictionary and Standard Data Set. Health Information Management Journal30(4): HEALTH INFORMATION MANAGEMENT JOURNAL 7

8 Corresponding author: Jenny Davis, BHIM (Hons), RN, RM, MMid, BAppSc(Nsg), GDip Periop, GDipCritCare, GCertHEd, CertIVT&A Benetas Level 1/789 Toorak Road Hawthorn East VIC 3123 Tel: jenny.davis@benetas.com.au Doctoral Candidate Monash University Victoria Australia Amee Morgans, PhD, BA, BAppSci(Hons), GCertHEd, DipMgmt Adjunct Senior Research Fellow Monash University Victoria Australia Benetas Hawthorn East VIC 3123 Stephen Burgess, BHlthSc, GCertHlthProfEd, GDipEmergHlth, MPH, PhD (candidate) Adjunct Senior Lecturer and Doctoral Candidate Monash University Victoria Australia Benetas Hawthorn East VIC HEALTH INFORMATION MANAGEMENT JOURNAL

9 APPENDIX Table 1A: Aged care service provision data items by category within the information domains: client, service, and funding/regulation DATA ITEM CATEGORY AGED CARE SECTOR: DATA ITEMS HEALTH PROFESSIONAL: DATA ITEMS PRE-WORKSHOP & EXISTING 1 ADDITIONAL 2 REQUIRED 3 Who receives services (Client) Title FINAL VERSION (DRAFT) Customer Code Family Name/ Preferred Name Family Name Surname First Given Name First Name Preferred Name Date of Birth Age; Date of Birth Date of Birth Approximation; Age Date of Birth Approximation Address Address Suburb/Town/Locali ty Region; Geographic LGA; Postcode Postcode Tel. No. Usual Place of Residence Title Suburb/Town/Locality Region Geographic LGA Mobile No. Tel. No. Usual Place of Residence Mobile No. Access to Property Access to Property Lives alone; Has a carer; Lives Alone Has a Carer Gender Sex Gender Sex Country of Birth Country of Birth Indigenous Status Indigenous Status Language spoken at home NOK Tel. No. GP or Medical Centre GP Name, Clinic Name, Street Address, Suburb, Postcode, GP Contact Tel. No.; Health condition/s Cultural Background Cultural Background Language(s) other than English spoken at home; Primary Language; Secondary Language; Next of Kin; Tel. No. NOK; NOK Relationship; Tel. No. Emergency Contact; Power of Attorney; Medical Enduring Power of Attorney (MEPOA); Advance Care Directive; Advance Care Plan; End of Life Wishes; Consent to treatment and information access/sharing Occupation Occupation Religion Language Spoken at Home Language(s) other than English spoken at home Next of Kin Tel. No. NOK NOK Relationship Tel. No. Emergency Contact Power of Attorney; Medical Enduring Power of Attorney (MEPOA); Advance Care Directive; Advance Care Plan; End of Life Wishes; Consent to treatment and information access/sharing Religion Funeral Notes; Funeral Arrangements; Funeral Notes Funeral Arrangements Doctor Phone no. (AH), Mobile no., Fax no., GP or Medical Centre GP Name, address; Clinic Name, Street Address, Suburb, Physician (managing) Consulting/Managing Postcode, GP Contact Tel. No.; Tel. physician details, Practicing at, Contact Tel., Other No. (AH), Mobile No., Fax No., relevant information; Address, Physician (managing) Consulting/Managing physician details, Name, Practicing at, Contact Tel., Medical History; Medical History Medical Condition/s; Medical Condition(s) Allergies Allergies Medications Medications - special Medications Allied Health details Allied Health Details HEALTH INFORMATION MANAGEMENT JOURNAL 9

10 DATA ITEM CATEGORY AGED CARE SECTOR: DATA ITEMS HEALTH PROFESSIONAL: DATA ITEMS PRE-WORKSHOP & EXISTING 1 ADDITIONAL 2 REQUIRED 3 What services are delivered (Services) Referral Date Referral Date Service Event Service Plan Details Community Care Clients: Service Plan No.; Area; Plan Start Date; Funding Start Date; Specific Service Plan Details; Enteral Feeding: start date Oxygen Treatment supplement: start date FINAL VERSION (DRAFT) Service Plan Details: Service Delivery At what cost services (Financial) Medicare number Medicare Exp. No.; Medicare Number Pension number DVA number Health Insurance Service Outcome (Evaluation) TBD Centrelink No.; Centrelink Asset Assessment; Govt. Pension Status; DVA Entitlement; Department ID; Veteran File No.; Item No.; Claim Form Period; CNC Visits: CNC Visits; Total CNC Hours; RN visits; Total RN hours; EN visits; Total EN hours; NSS visits; Total NSS hours; Carers Attending; Carer Training; Veteran s Home Care: VHC Domestic Assistance; VHC Personal Care; VHC Respite In Home; Health Fund; Membership No.; Fund Exp. Date; Government Program Support; Category; Community Aged Care Package: including PAG; Home Care Package: Level 1 and Level 2 approval, limitation date - approval; Level 3 and 4 approval, limitation date approval; Resident category: permanent residential care; residential respite care; Status; Leave details; Client Management Configuration: - Service: Service Types; Providers; Rate Types; - Funding: Funding Rates; Oxygen Rx; Enteral Feeding; Mobility Supplement; Dementia & Cognition Supplement; Veteran Supplement; Workforce Supplement; EACHD Top Up Supplement; - Funding Reconciliation: Level 1 4; Review Dates; Service Reconciliation; Service Post to AP; Service Accrual; Customer Account Details: Account details; Unpaid invoices; Invoices; Debt memos; Cash Receipts; Billing Address: different from primary address; primary carers billing address; Suburb, State, Postcode; Service Plan Details: Budget amount; Effective date; Total; Payment options; Service unrecorded transactions; Service recorded transactions; Leave/Supervision/Billing; Notes: 1 Data Items (Pre-workshop & existing Data Dictionaries/MDS) 2 Additional data Items (post - workshop) 3 Health Professional: Data Items (required) (validation/confirmation - focus group/interview) Medicare Expiry Date Government Pension Status Pension Number Centrelink Number DVA Entitlement Health Insurance Government Program Support Category: Community Aged Care Package Home Care Package Post-Acute Care Resident Category Status Leave Details Others within reforms Client Management Configuration: Service Type Funding Type Customer Account Details; Billing Address; Service Plan Details: Financial 10 HEALTH INFORMATION MANAGEMENT JOURNAL