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Risk Assessment Platform for Age Care domain- New grounds for Data Mining By Raghavi Yarasi Contents Table of Contents ..................................................................................... Error! Bookmark not defined. Abstract ................................................................................................................................................... 3 Introduction ............................................................................................................................................ 4 Literature Review .................................................................................................................................... 5 Research Methodologies ...................................................................................................................... 15 Nature of Data in Age Care Domain...................................................................................................... 18 Application of Data Mining Methods .................................................................................................... 19 Case Study ............................................................................................................................................. 20 Conclusion ............................................................................................................................................. 21 References ............................................................................................................................................ 22 Abstract IT innovations and reforms in the age care sector have leaded a path way to the implementation of Electronic Health Record Systems and e-Business systems. This leads to capturing of data electronically. Applying data mining (DM) techniques on this data can benefit all the parties involved in this sector. This trend of using the available data in new dimensions will lead to the design of better strategies for developing services by the age care providers. These techniques will not only benefit the age care providers - but also help government in setting up/revising the funding types for age care sector. Introduction Australian population is aging ( Australian Bureau of Statistics 2011). In order to provide better services to the growing population in this sector, sophisticated systems and methods are required by the industry. The industry has started capturing the data electronically in various areas a decade ago(YU, 2012). This voluminous data has not been processed or analysed to make best use of it. Data mining is increasingly becoming popular in the IT world and is used intensively in various sectors. Age care sector is one of the areas that has not been explored seriously for the application of Data Mining Techniques This article is an attempt of exploring data mining techniques on the data that is electronically available in age care domain. The directions and applications of DM concepts are still to be explored. The last section of this literature review discusses five new applications of DM concepts. Literature Review LITERATURE REVIEW Tr ends in Population Growth Required services and IT applications Data Mining Methodology Study of DM techniques in Health care Industry Trends in Population Growth The Australian Bureau of statics for the year 2013 mentions that one in 4 of Australians will be aged over 65 and over by 2056. Below is the a figure showing the age structure of Australia in the past and future. Figure 1: Ageing population statistics Source : Australian Bureau of Statistics This increase in the ageing population influences the health related services currently available. This also puts a lot of demand on the care that will be required by the growing aging population. By 2020 more number of older people will be living alone due to increased divorce rates, smaller families and fewer old people living with their children. The current immigrants will start aging by 2020. These immigrants are from diverse cultural backgrounds and linguistics ( Mayer Foundation. 2004). With these wide requirements the expectations from the age care service provider’s increase. Future Service Requirements and Financial Implications The above mentioned trend in the growing aged population derives the fact that the existing aged care services will not be able to deliver the quantity and quality of services we will need over the next two decades. Starting from 2020 the demand for aged care will accelerate with the increase in number of older people. This increase in the older population is not directly proportional to the work force required by the industry. As a result there will be a scarce in the services. Modelling work done by The Allen Consulting Group estimates that by 2020 the costs of age care services will increase by 60%. These costs will increase as we create solutions to the significant problems we already face. The Age Pension provides the bare essentials. The fact that the proportion of people reaching retirement age, coupled with increased life expectation calls for steps that will pump money into future for providing subsides by the government . Government will also need to work on new strategies that will help boost up the revenue for the coming future (AMP 2009). As a result a greater importance is imposed on the necessity to save for retirement. This planning for the future retirement both by the individuals and government will help to afford for the services. Current Services Australian Health and Welfare website lists the following type of cares currently available to the clients of age care industry Table 1: Care Types Care Type Categories Low Level Care Community Age Care High Level Care Permanent residential age Care Residential Age Care Respite Care This research is being conducted on the residential age care. So the next set of literature review concentrates on the residential age care providers Information Technology and Residential Age Care The available data in aged care sector and the increasing demands shows that there needs a change that would take the advantage of the current data to improve the quality of life of aged Australians in future( KPMG . 2009). A study of current trends and predictions based on the available data will provide better insight into the problems and requirements that could arise in the future. This calls for data mining principles to be applied for the data available in this domain. There are already systems in place that are collecting and storing the data. There is a need to further identify the important parameters so that the data generation points can be clearly identified. Data marts could then be created for data analysis purposes. To further support this Hovenga mentions Residential aged care is largely considered a ‘green field’ with regard to Information Technology(Hovenga, 2007). Systems that already exist usually have their own system architectures, which results in a lack of interoperability within the aged care sector as a whole. The provision, administration and funding of aged care consist of a complex and varied set of arrangements which requires an IT infrastructure that meets the needs of many stakeholders including nurses and personal carers in the aged care residential sector. These health workers must be able to comply with contemporary best practice, and meet all quality and reporting requirements. In this scenario, the implementation of Electronic Health Records (EHRs) is a key strategy for improving the quality, safety and efficiency of residential aged care delivery. The openEHR approach (http://www.openEHR.org) is one of the most recognised approaches for EHR systems. The definition and use of aged care openEHR Archetypes (clinical models representing semantic constructs) can contribute to interoperability of EHRs as well as various health information systems. Current IT Applications The adoption and use of ICT in the aged care sector is largely a ‘green field’as mentioned earlier. (Young, 2006) gives an example of an EHR project related to aged care is Health elink, the first pilot of an Electronic Health Record for New South Wales. This has initially been made available for people aged 65 years or more and located in certain regions of NSW (NSW Health Website, 2006).With regard to further current implementations that are indirectly related to the implementation of Electronic Health Records in the aged care sector, the Aged Care Association Australia lists a number of software products at their website (http://agedcareassociation.com.au). In this context of various software products being available in the domain - capturing the data electronically is possible in most of the areas. There is no industry specific research for data mining in this area. The section below in the literature review covers the data mining methodology adapted for this research followed by the data mining applications. Most of the information has been extracted for the Data Mining applications in health care so that they can be implied to the age care domain. Introduction to Data Mining Methodologies Data Mining is a relatively new methodology and technology that came into prominence in late 20th century(Trybula, 1997). It is aimed to identify useful and understandable corelations in the data (Chung, 1999). Data mining techniques can be broadly classified based on what they can do, namely description and visualization; association and clustering; and classification and estimation, which is predictive modelling. Description and visualization can contribute greatly towards understanding a data set, especially a large one, and detecting hidden patterns in data, especially complicated data containing complex and non-linear interactions. CRISP-DM (CRoss-Industry Standard Process for Data Mining) has been adapted for the Data Mining process in the current context. (Shearer 2000) CRISP-DM is an industry-, tool-, and application-neutral model. This model encourages best practices and offers organizations the structure needed to realize better, faster results from data mining. CRISP-DM organizes the data mining process into six phases: Business Understanding Data Understanding Data Preparation Modelling Evaluation Deployment These phases help organizations understand the data mining process and provide a road map to follow while planning and carrying out a data mining project. Figure 2:Phases of CRISP-DM [Source : (Shearer 2000)] Phase One: Business Understanding The business understanding Phase involves following key steps Determining business objectives Assessing the situation Determining the data mining goals Producing the project plan Phase Two: Data Understanding The data understanding Phase includes the following steps: Collect the Initial Data Describing the Data Explore the Data Verify the Data Quality Phase Three: Data Preparation The five steps in data preparation are The selection of data The cleansing of data The construction of data The integration of data The formatting of data Phase Four: Modelling Modelling steps include The selection of the modelling technique The generation of test design The creation of models The assessment of models Phase Five: Evaluation The key steps here are The evaluation of results The process review Determination of next steps. Phase Six: Deployment The key steps in this Phase are Plan deployment Plan monitoring Maintenance The production of the final report Review of the project. Data Mining techniques and Age Care Industry Data can be a great asset to age care organizations, but they have to be first transformed into information. As there are not enough studies about data mining in age care we will first examine data mining applications in health care and extend them to age care domain. An important factor motivating the use of data mining applications in healthcare is the realization that data mining can generate information that is very useful to all parties involved in the health care industry. Data mining applications also can benefit health care providers, by identifying effective service delivery pattern and best practices. A research in health care industry mentions that with the Balanced Budget Act of 1997, the Centres for Medicare and Medicaid Services must implement a prospective payment system based on classifying patients into case-mix groups, using empirical evidence that resource use within each case-mix group is relatively constant. CMS has used data mining to develop a prospective payment system for inpatient rehabilitation(Relles,2002) . If we extend this concept to the age care domain, we could have a system that could look into the health records of the age care clients and group them based on their conditions so that they can be located in a same location and the care workers can be appropriately rostered to provide services more effectively. (Silver M)Blue Cross has been implementing data mining initiatives to improve outcomes and reduce expenditures through better disease management. For instance, it uses emergency department and hospitalization claims data, pharmaceutical records, and physician interviews to identify unknown asthmatics and develop appropriateinterventions.1 Data mining also can be used to identify and understand highcost patients. To aid healthcare management, data mining applications can be developed to better identify and track chronic disease states and high-risk patients, design appropriate interventions, and reduce the risky situations that could be life threating to the age care clients. For example, to develop better diagnosis and treatment protocols, the Arkansas Data Network looks at readmission and resource utilization and compares its data with current scientific literature to determine the best treatment options, thus using evidence to support medical care.( Kincade,1998) Also, the Group Health Cooperative stratifies its patient populations by demographic characteristics and medical conditions to determine which groups use the most resources, enabling it to develop programs to help educate these populations and prevent or manage their conditions.1 Group Health Cooperative has been involved in several data mining efforts to give better healthcare at lower costs. In the Seton Medical Center, data mining is used to decrease patient length-of-stay, avoid clinical complications, develop best practices, improve patient outcomes, and provide information to physicians—all to maintain and improve the quality of healthcare(Dakins,2001). Data mining can be used to analyse massive volume of data and statistics to search for patterns that might indicate an attack by bio-terrorists(Piazza,2002). The Lightweight Epidemiological Advanced Detection Emergency Response System (LEADERS) is one such effort. In the past, LEADERS has uncovered several disease outbreaks. Data mining also can be used for hospital infection control12 or as an automated early-warning system in the event of epidemics. A syndromic system, based on patterns of symptoms, is likely to be more efficient and effective than a traditional system that is based on diagnosis. An early warning of the global spread of the SARS virus is an example of the usefulness of a syndromic system based on data mining(Brewin,2003). While customer relationship management is a core approach in managing interactions between commercial organizations—typically banks and retailers—and their customers, it is no less important in a healthcare context. Customer interactions may occur through call centers, physicians’ offices, billing departments, inpatient settings, and ambulatory care settings. As in the case of commercial organizations, data mining applications can be developed in the healthcare industry to determine the preferences, usage patterns, and current and future needs of individuals to improve their level of satisfaction. (Biafore, 1999) These applications also can be used to predict other products that a healthcare customer is likely to purchase, whether a patient is likely to comply with prescribed treatment or whether preventive care is likely to produce a significant reduction in future utilization. (Koh ,?? )Through the use of data mining, Customer Potential Management Corp. has developed a Consumer Healthcare Utilization Index that provides an indication of an individual’s propensity to use specific healthcare services, defined by 25 major diagnostic categories, selected diagnostic related groups or specific medical service areas(Paddison,2000). This index, based on millions of healthcare transactions of several million patients, can identify patients who can benefit most from specific healthcare services, encourage patients who most need specific care to access it, and continually refine the channels and messages used to reach appropriate audiences for improved health and long-term patient relationships and loyalty. The index has been used by OSF Saint Joseph Medical Centre to get the right messages and services to the most appropriate patients at strategic times. The end result is more effective and efficient communications as well as increased revenue. (Paddison,2000) Miller has suggested that the data mining of patient survey data can help set reasonable expectations about waiting times, reveal possible ways to improve service, and provide knowledge about what patients want from their healthcare providers. Also, Hallick(Hallick,2001) has suggested that CRM in healthcare can help promote disease education, prevention, and wellness services. (Kolar,2001) (Veletsos,2003) also have reported that Florida Hospital has used data mining to segment Medicare patients as well as develop commercial applications that enable credit scoring, debt collection, and analysis of financial data. Rafalski(Rafalski,2002) has studied Sinai Health System’s use of data mining for healthcare marketing and CRM. Lastly, pharmaceutical companies can benefit from healthcare CRM and data mining, too. By tracking which physicians prescribe which drugs and for what purposes, pharmaceutical companies can decide whom to target, show what is the least expensive or most effective treatment plan for an ailment, help identify physicians whose practices are suited to specific clinical trials (for example, physicians who treat a large number of a specific group of patients), and map the course of an epidemic to support pharmaceutical salespersons, physicians, and patients.(Brannigan,1999 ) Pharmaceutical companies can also apply data mining to huge masses of genomic data to predict how a patient’s genetic makeup determines his or her response to a drug therapy. (Thompson,2000) (Koh)Data mining applications that attempt to detect fraud and abuse often establish norms and then identify unusual or abnormal patterns of claims by physicians, laboratories, clinics, or others. Among other things, these applications can highlight inappropriate prescriptions or referrals and fraudulent insurance and medical claims. For example, the Utah Bureau of Medicaid Fraud has mined the mass of data generated by millions of prescriptions, operations and treatment courses to identify unusual patterns and uncover fraud.( Milley, 2000) Limitations of Data Mining Data mining applications can greatly benefit the healthcare industry. However, they are not without limitations. Healthcare data mining can be limited by the accessibility of data, because the raw inputs for data mining often exist in different settings and systems, such as administration, clinics, laboratories and more. Hence, the data have to be collected and integrated before data mining can be done. While several authors and researchers have suggested that a data warehouse be built before data mining is attempted, that can be a costly and time-consuming project. On a positive note, a data warehouse has been successfully built by Intermountain Health Care from five different sources— a clinical data repository, acute care case-mix system, laboratory information system, ambulatory case-mix system, and health plans database—and used to find and implement better evidence-based clinical solutions. Oakley29 has suggested a distributed network topology instead of a data warehouse for more efficient data mining, and Friedman and Pliskin30 have documented a case study of Maccabi Healthcare Services using existing databases to guide subsequent data mining. Secondly, other data problems may arise. These include missing, corrupted, inconsistent, or non-standardized data, such as pieces of information recorded in different formats in different data sources. In particular, the lack of a standard clinical vocabulary is a serious hindrance to data mining. (Cios,2002) have argued that data problems in healthcare are the result of the volume, complexity and heterogeneity of medical data and their poor mathematical characterization and non-canonical form. Further, there may be ethical, legal and social issues, such as data ownership and privacy issues, related to healthcare data. The quality of data mining results and applications depends on the quality of data. (Chopoorian ,2001)Thirdly, a sufficiently exhaustive mining of data will certainly yield patterns of some kind that are a product of random fluctuations.( Hand, 1998) This is especially true for large data sets with many variables. Hence, many interesting or significant patterns and relationships found in data mining may not be useful. Murray34 and Hand33 have warned against using data mining for data dredging or fishing, which is randomly trawling through data in the hope of identifying patterns. Fourthly, the successful application of data mining requires knowledge of the domain area as well as in data mining methodology and tools. Without a sufficient knowledge of data mining, the user may not be aware of or be able to avoid the pitfalls of data mining(Brannigan,1999) . Collectively, the data mining team should possess domain knowledge, statistical and research expertise, and IT and data mining knowledge and skills. Finally, healthcare organizations developing data mining applications also must make a substantial investment of resources, particularly time, effort, and money. Data mining projects can fail for a variety of reasons, such as lack of management support, unrealistic user expectations, poor project management, inadequate data mining expertise, and more. Data mining requires intensive planning and technological preparation work. In addition, physicians and executives have to be convinced of the usefulness of data mining and be willing to change work processes. Further, all parties involved in the data mining effort have to collaborate and cooperate(Gillespie,2000). Research Methodologies The age care industry can benefit greatly from data mining applications. The objective of this section is to explore relevant Data Mining applications by providing a detailed description of the Research Methodology that is considered for this study This research has been conducted by studying and working on the software applications for 2 years in one of the leading service provider in Age Care Industry. Experimental Research Method is used for the current research. (Clarker, 2005) Experimental Research is defined as a process in which researchers try to isolate and control every relevant condition which determines the events investigate, so as to observe the effects when the conditions are manipulated. The experimental research methodology is further classified into the following four types Pre-experimental: unreliable assumptions are made despite the lack of control over variables True experimental: rigorous check of the identical nature of groups before testing the influence of a variable on a sample of them under controlled circumstances Quasi-experimental: not all conditions of true experimental design can be fulfilled but the shortcomings are identified Correlation and ex post facto: correlation looks for cause and effect relationships between two sets of data whereas ex post facto reverse experimentation interprets the cause of phenomenon observing its effects Based on the above definitions the current research can be further classified as a quasiexperimental type. First we will look at the structure of the applications in age care domain. In the context of this research, the applications in aged care domain( Age Care e-connect website) are broadly classified into five different categories [This classification does not include the applications used for internal operations – like mailing services, intranets, messaging systems, IT Service Management systems and so on] Business Financial Applications(BFA) Applications used for the financial purposes – Accounting Systems, Budgeting Systems, Funding Maintenance Systems Payroll and HR Systems (PHRS):The payroll systems , HR Systems Rostering and Attendance Applications(RAA) These applications are used for rostering and managing the attendance of the care workers Client Record Systems (CRS)These systems are used to maintain the information about the client. These systems when implemented efficiently can get transformed into electronic health recording ( eHR ) systems. Web Applications ( WA)The web sites about the organization, web sites for marketing. Figure 3 : IT Applications used in Aged Care In this section the research method is combined with the concept of CRISP-DM phases to provide the details about the research method used for the entire process. There are many IT applications currently used in by an age care service provider. In most of the situations the applications are used without interfacing with the other systems – as a result there are discrepancies between the data. For Ex : The Rostering systems are not interfaced with the payroll systems as a result most of the employee data is entered manually which could lead to manual error. This causes inconsistency and manual correction when processing the pay roll. Also the rostering applications don’t interact with the budget applications – due to which there is no way to identify if the rostering is over or under the budget. Figure 3 shows that there are electronic data collection points of most of the information used in a age care service organization. But this data is not sythesized to explore new grounds for better opportunities. Business is constantly looking for the following information: Objective 1 : Best possible usage of IT applications by reducing manual intervention when dealing with more than one application. This reduces manual errors . The above figure shows that most of the information is driven by the payroll system. If we could integrate all the applications with pay d financial applications we could achieve this objective Objective 2 : Interface all the applications with the financial applications to identify the areas in the organizations that are running over the budget. This can be achieved by deriving the data from the budgeting application and combine with the data from other applications. Objective 3: Use the clinical records and records of a client to find patterns that would help identify risks to the client, so that preventive measures can be undertaken. Objective 4: The churning rate and the reasons for the client switching to a different service provider can be identified to retain the existing customers. Objective 5: Increase the new customer acquisition which is achieved by analysing the data from CRS and WA applications. Nature of Data in Age Care Domain <TODO> Application of Data Mining Methods <TODO> Case Study <TODO> Conclusion Data mining applications in age care can have tremendous potential and usefulness. However, the success of age care data mining hinges on the availability of clean age care data. In this respect, it is critical that the age care industry consider how data can be better captured, stored, prepared, and mined. Possible directions include the standardization of the vocabulary in the domain and the sharing of data across organizations to enhance the benefits of age care data mining applications. Further, as age care data are not limited to just quantitative data, such as notes from the registered nursed or electronic records related to the clients, it is necessary to also explore the use of text mining to expand the scope and nature of what age care data mining can currently do. In particular, it is useful to be able to integrate data from various sources. References 1. AMP.NATSEM Income and Wealth Report Issue 2009, Don’t stop thinking about tomorrow ,Issue 24,University of Canberra,Canberra 2. Australian Bureau of Statistics (2013), Health of Older People,Australia,viewed 03 April 2013 <http://www.abs.gov.au/ausstats/[email protected]/mf/4833.0.55.001> 3. Australian Institute of Health and Welfare(2013), Aged Care,Australia,viewed 03 April 2013 < http://www.aihw.gov.au/aged-care/ > 4. Biafore, S., 1999, “Predictive solutions bring more power to decision makers”, Health Management Technology,vol. 20,no. 10,pp. 12-14. 5. Brannigan, M.,1999, “Quintiles seeks mother lode in health data mining.” ,Wall Street Journal, March vol. 2, no. 1. 6. Brewin, B. ,2003, New health data net may help in fight against SARS,Computerworld, vol. 37,no.17, p. 59. 7. Chopoorian, J.A., Witherell, R., Khalil, O.E.M., Ahmed, M., 2001,”Mind your own business by mining your data”, SAM Advanced Management Journal,vol. 66,no. 2, pp. 45-51. 8. Cios, K.J. , Moore, G.W. ,2002, Uniqueness of medical data mining. Artificial Intelligence in Medicine, pp. 1-24. 9. Courtney, K., Demiris,G., Alexander,G., 2005,” Information technology: Changing nursing processes at the point-of-care”, Nursing Administration Quarterly,vol. 29, no. 4,pp. 315-322. 10. Dakins, D.R. ,2001, “Center takes data tracking to heart”, Health Data Management, vol.9,no. 1, pp.32-36. 11. Devlin, B., Rogers. S.,Myers, J,2012,Big data comes ofaAge, Research Report, Enterprise Management Associates,Colorado. 12. Gillespie, G.,2000, “There’s gold in them thar’ databases”, Health Data Management, vol. 8,no.11, pp. 40-52. 13. Hallick, J.N. ,2001, Analytics and the data warehouse. Health Management Technology, pp. 24-25 14. Hand, D.J.,1998, Data mining: statistics and more? The American Statistician, pp. 112118. 15. Hovenga, J., Garde, S ., Carr, T ., 2007 , 'Innovative approaches and processes for capturing expert aged care knowledge for multiple purposes', Electronic journal of health informatics, vol. 2 , no . 1, pp. 131 - 133 16. Jamal,A. McKenzie,K.Clark,M. 2009.’ Systematic review: Impact of health information technology on quality of medical and health care’, Health information management journal , vol. 38,no. 3,pp. 29-32. 17. Kaye, R,. Kokia,E., Shalev, V., Idaer, D ., Chinitz, D., Parsons, D., 2010, ‘A practioners perspective: Barriers and successfactor in health information technology’,Journal of Management and Marketting in health care,vol.3,no.2,pp.163-175. 18. Kincade, K. , 1998, Data mining: digging for healthcare gold. Insurance & Technology 19. Kolar, H.R.,2001, Caring for healthcare. Health Management Technology, vol. 22,no. 4, pp.46-47. 20. KPMG, 2009, Golden OpportunITy - How information technology can rejuvenate Australia’s aged care sector, KPMG International, Australia 21. Liu, T., H, Ko., S, Oh., 2003, ‘Tele-primary care and patient satisfaction in Korea’, Journal of Korean Hospital Management, vol 9,no. 1,pp. 17-24. 22. Lord,R.,Sherrington,C.,2001, Falls in older people, CambridgeUniversity Press,,,pp. 23. Milley, A.,2000, Healthcare and data mining,Health Management Technology, vol. 21,no. 8,pp. 44-47. 24. NSW Health (2006), Trial of Electronic Health Records,viewd on 15 May 2012< http://www0.health.nsw.gov.au/news/2006/20060323_01.html > 25. Paddison, N. ,2000, Index predicts individual service use. Health Management Technology, pp. 14-17 26. Park, H.A., Kim, H.,Song, M., Song, T., Jung, Y., 2002,’Development of web-based health information service system for the elderly’, Journal of the Korean Society of Medical Informatics, vol. 8, no. 3,pp. 37-45. 27. Piazza, P. ,2002., Health alerts to fight bioterror. Security Management, p. 40. 28. Productivity Commission 2005, Economic Implications of an Ageing Australia, Research Report, Common Wealth of Australia,Canberra. 29. Rafalski, E.,2002, Using data mining and data repository methods to identify marketing opportunities in healthcare,Journal of Consumer Marketing, vol. 19,no.7,pp. 607-613. 30. Rehn, M., 2013, ‘Improving adjustments for older age in prehospital assessment and care’. Scandinavian Journal of Trauma,Resuscitation and Emergency Medicine, vol. 21,no.4,pp.2-3. 31. Relles, D., Ridgeway, G., Carter, G.,2002, Data mining and the implementation of a prospective payment system for inpatient rehabilitation.Health Services & Outcomes Research Methodology, pp.247-266. 32. Seo, Y., 2001, ‘Strategic orientation and behaviors of Korean hospitals’, Journal of Korean Hospital Management,vol. 6,no. 2,pp. 173-201. 33. Silver, M., Sakata, T., Su, H.C., Herman, C., Dolins, S.B., O’Shea, M.J., 2001, Case study: how to apply data mining techniques in a healthcare datawarehouse, Journal of Healthcare Information Management, 15, vol.2,pp. 155-164. 34. Sydney local health district (2012), Aged Care & Rehabilitation , Australia,viewed 03 April 2013 < http://www.slhd.nsw.gov.au/acrs/common.html > 35. The Myer Foundation, 2004 , Vision 2020 for aged care in australia, The Myer Foundation, Melbourne. 36. Thompson, W. Roberson, M.,2000, Making predictive medicine possible, 42,pp.6 37. Veletsos, A. ,2003, Getting to the bottom of hospital finances. Health Management Technology, vol. 24,no. 8,pp. 30-31 38. World Health Organization,2004, What are the main risk factors for falls amongst older people and what are the most effective interventions to prevent these falls?,Health Evidence Network, UK 39. Young R, 2006, CEO Aged Care Association Australia. Keynote address – Aged Care Landscape and Major Issues. 3rd Aged Care Informatics Conference Hobart, pp.8- 10