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92917 Using Health Care Data For Decision Making

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92917 Using Health Care Data For Decision Making Question: 1 Provide a response to each of the three sections that demonstrates an understanding of the application and management of health data and refers to literature related to the identified issues and associated tasks. 2 In each response, apply your findings to the hospital so as to assist the executive group indecision making and planning. Case Scenario – UTS Hospital UTS hospital is a well-established charitable hospital operated on a not for profit basis. It has 250 beds in an inner-city location. The population of the local community, from which it draws the majority of its patients, is ageing: 40% are over the age of 65 years. UTS hospital has an excellent reputation for innovative care, rapid uptake of new technologies, teaching and research. It gets very little support from the government for running costs, although previous governments have been generous in meeting the cost of constructing new buildings and refurbishing old ones. The hospital is in financial difficulty. Over 90% of the funding to the hospital for acute inpatients comes from private health insurers. The remainder is from the Department of Veterans Affairs, patients who pay for their own admissions, compensable patients from motor vehicle and workplace insurance, and patients whose stay is paid from a research grant. The rate of reimbursement from private insurers is based on a negotiated rate for each AR-DRG. Every year, insurance companies negotiate with the hospital the rate it pays for each AR-DRG (i.e. a type of case mix- or activity-based funding). The fees are based on the average length of stay for each AR-DRG using the Australian cost weights. The Chief Executive Office (CEO) has called a special meeting of the executive to discuss the issues facing the hospital and to plan the action they need to take. Present at the meeting are the Director of Nursing (DON), the Chief Financial Officer (CFO) and the Chief Information Officer (CIO). 1: The DON suggested that the problem is that case mix-based funding using AR- DRGs are not the best method to record performance because they do not suit the type of patients treated by UTS Hospital. She said that the majority of patients are older and more complex, and need to stay longer than the average length of stay for each AR-DRG. She suggested that AR-DRGs are useless for measuring the hospital’s performance when the length of stay of the patients was different to that of the average hospital. She was of the view that the hospital should go back to the insurance funds and negotiate a return to the funding of patients on a fixed per diem basis. Provide a short statement for the executive that identifies the pros and cons of case mix-based funding approach compared to a fixed per diem rate. Provide the executive with a recommendation. 1.The CIO disagree that the age and complexity of the patient made the DRG system useless. He noted that there were many examples where older patients or those with more complex care, that needed a longer length of stay, had been classified into a different AR-DRG. UTS hospital is using AR-DRG version 5, and he was not sure if this was the most recent version. Provide a short statement with two or three examples of where the AR-DRG had been split to allow for patients of different age or complexity in the current version of AR-DRGs in use in Australia. Given the nature of the UTS Hospital’s patients. outline to the executives the implications of changing AR-DRG versions. The CFO said that there was no reason to believe that UTS hospital’s patients in a given AR-DRG classification were older or more complex than the patients in the same AR-DRG at a different hospital. He noted that there were established methods in use to compare the performance of similar hospitals. Provide a short statement on the way peer hospitals are compared with in Australia. Identify, for the executive, the peer groups used in Australia. Provide an example of a benchmark used for comparing hospitals and identify the type of patients where there could be a particular issue with falling outside that benchmark. Describe the implications for the hospital. Defends the advantages and disadvantages of case mix and per-diem funding and their potential impact on a hospital Explain the different approaches to resource splits in AR-DRG. Considers data quality and accuracy challenges inherent in health communication such as medical coding, medical notes and discharge summaries Answer: Statement 01: AR-DRG system or Australian refined diagnosis related groups can be defined as the contemporary patient classification system that uses scientific and clinically meaningful patient tagging system that relays information about the number and type of the patients admitted in the health care facility. This method of patent tagging is characterised a mixed case type patient classification, where the number of patients are compartmentalized depending on the type of care they are seeking (Hamada, Sekimoto and Imanaka 2012). This method of statistically relating the patients helps the health care facility to divide and subdivide patients into different groups according to the care type they are going to receive in a broader sense and decide the payment bundle according the grouping. This method allows the health care team to bill the patients according to the care items they are purchasing according their care needs which brings ease and clarity in the charging system and makes the billing procedure in health care much more transparent (Thomson et al. 2012). However this classification system is a much narrower classification system that groups patients into very basic groups like acute and newborn. It creates a lot discrepancies in the costing methods as the care of different patients differ drastically in spite of belonging to acute care group or new born care group (Hamada, Sekimoto and Imanaka 2012). As the patients for this hospital and predominantly aged the care On the other hand per diem fixed rate system is a per day allowance system for the patients that will allow the health care services to charge the patients for each and every care services they avail rather than a care bundle that they did not avail or availed much more than what the care bundle entails (Thomson et al. 2012). Statement 02:  The diagnosis related grouping has been in place in Australia since a very long time and the patterns have been changed and refined a lot of times. The very first version for ARDRG that was introduced in the health care of Australia, have been a very basic grouping system that compartmentalized patients depending on their care needs be it acute, general or neonatal. However as the health care industry progressed there have been a lot of numbers of variations along the years. And there have been a lot of factors that have been included in the system of ARDRG. The element of age as a grouping factor has been added in the ARDRG system in the third version ( 2017). The hospital has been using the version 5 of the ARDRG which is not the recent version of patient classification in place in Australia. The recent version of the ARDRG system is the version 8 which has had a lot more improvements included (Cheng, Chen and Tsai 2012). This version has been introduced in 2016 where phases of clinical complexity have been categorized as the grouping factor. The diagnosis related groups have been increased from 698 to 807 in the recent version. A lot of complex cases and relevant care needs have been incorporated in the different groups like anxiety disorders, sleep disorders, musculoskeletal disorders and endocrine and nutritional disorders which are very common occurrences within the elderly population. As the most of clientele for UTS hospital is elderly adapting to the version 8 can prove to be beneficial ( 2017). Statement 03:  Peer groups can be defined as the group of similar hospitals that have operational characteristics alike or identical. Peer groups within the health care sector have been compared a lot of times in the history to gauge the performance standards of different hospitals (Alkhenizan and Shaw 2011). The comparison in the operational characteristics of the similar hospitals and their care delivery and performance standards provides a wealth of information for the regulatory authorities to determine the progress in the health care industry and room for improvements. Australian institute of health and welfare compares peer hospital groups in regular intervals to judge the performance and progress standards across different hospitals with hopes to identify the health care facilities that do not meet the quality and performance benchmarks ( 2017). There are different benchmarks used to compare the hospitals in Australia, for instance the time the patients spend in the emergency departments. However, it has to be considered that there are a lot of inter related external and internal factors associated with the different operations within the health care sector. Taking the customer base of UTS hospital as an example the patients are mostly aged and ridden with a myriad of complex diseases ( 2017). The prolonged stay in the emergency departments for these patients is mostly due to the health related complications like diabetes, coronary heart complication they already have or the age related complications like respiratory infections they develop during the stay in the emergency departments. All this factors play an influential role in determining the recovery rate in the different departments of the hospital and prolong and complicate the care needs. The lack of proper staff training due to limited funds is another important factor that contributes to prolonged stay of patients in emergency wards (Bartram et al. 2012). Inadequately trained staff lack in the efficiency that is required for the emergency wards and proper funding for the hospital is required for the hospital to elevate performance standards. Reference: (2017). Australian Institute of Health and Welfare. [online] Available at: [Accessed 22 Apr. 2017]. Alkhenizan, A. and Shaw, C., 2011. Impact of accreditation on the quality of healthcare services: a systematic review of the literature. Annals of Saudi medicine, 31(4), p.407. Bartram, T., Casimir, G., Djurkovic, N., Leggat, S.G. and Stanton, P., 2012. Do perceived high performance work systems influence the relationship between emotional labour, burnout and intention to leave? A study of Australian nurses. Journal of Advanced Nursing, 68(7), pp.1567-1578. Cheng, S.H., Chen, C.C. and Tsai, S.L., 2012. The impacts of DRG-based payments on health care provider behaviors under a universal coverage system: a population-based study. Health Policy, 107(2), pp.202-208. Hamada, H., Sekimoto, M. and Imanaka, Y., 2012. Effects of the per diem prospective payment system with DRG-like grouping system (DPC/PDPS) on resource usage and healthcare quality in Japan. Health Policy, 107(2), pp.194-201. (2017). UTSOnline – Blackboard Learn. [online] Available at: [Accessed 22 Apr. 2017]. Thomson, S., Osborn, R., Squires, D. and Jun, M., 2012. International profiles of health care systems 2012: Australia, Canada, Denmark, England, France, Germany, Iceland, Italy, Japan, the Netherlands, New Zealand, Norway, Sweden, Switzerland, and the United States.

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