Challenges Facing Casemix Funding System - Assignment Example

HEALTH FINANCIAL MANAGEMENT [Student’s Name] [Professor’s Name] [Institution] [Course Title] 11th August, 2011 HEALTH FINANCIAL MANAGEMENT Change to ‘casemix’ funding hospital services is one of the key features of the health services reform process that is underway in Australia. Casemix is a term which is used to refer to the types of patients that are treated either by a hospital or a health unit. Casemix funding hospital services is a funding model which is in use in the health care services in Australia to necessitate cost reimbursement of patient care. The casemix funding system is a good measurement model for the performance of the hospitals. According to the Department of Health (2010), casemix funding system aims at rewarding the initiatives of the hospitals which lead to an increase in efficiency. The information from casemix funding is important to policy makers as it allows them to have a clear understanding of the nature of service delivery in health care and its related complexities. Casemix funding model uses the ‘Diagnosis Related Groups’, DRGs classification system. Under this classification system, acute inpatient incidents are classified into different groupings based on both resource consumption and clinical condition. One DRG is allocated for every single acute incident of inpatient care using clinical information (coded) derived from the medical record of the patient. The information manager in charge of health codes this information and allocates a DRG. A weight is then allocated to each of the DRG. The weight depends on the cost of inputs (average cost) that is needed to get the best patient outcome. Input costs may include; diagnostic services, nursing, procedures among others. A reimbursement of a predetermined amount is made for each patient incident. AR-DRG version 6 ‘Australian Refined Diagnosis Related Group’, AR-DRG version 6 is the relevant form of casemix to the patients in acute hospitals. It incorporates ICD-10-AM/ACHI (6th edition) codes within the fundamental structure of AR-DRG (version 5.2). Features of AR-DRG version 6 The features of AR-DRG version 6 include; It is contiguous It reflects the surgical and some other hierarchies in MDCs There is high number of separations Recognition of A10Z “insertion of ventricular assist devices”, which allow particular identification of procedures of heart pump. Importance of AR-DRG version 6 i. It provides an allowance for the specific identification of the procedures of mini heart pump ii. It ensures that the classification is consistent iii. It enables separation of the various resource consumption incidents into the required cost-differentiated ‘Diagnosis Related Groups’ within its adjacent. This ensures that the classification is clinically relevant and statistically sound. iv. The DRGs in Australia can now reflect the clinical environment of the hospital. This is because of the information that is provided which is useful in gaining understanding and the management of patients in acute care. Allocation of AR-DRG code There are several processes that enable information on a stay by a patient in an Australian hospital to be allocated an AR-DRG code. Only one AR-DRG is assigned for a case treatment by an acute care hospital. Complex algorithm forms the bases of this assignment (Casio, 1998). The items of information used include; the sex, the age, the principal and secondary diagnosis, the procedures performed and the discharge status of the patient. All these details are obtained from the information which has already been collected through HPE (Hospital In-Patient Enquiry) The following are the major steps used in the process to allocating a DRG to an inpatient stay; i. Discharges are separated according to principal diagnosis in main diagnostic groupings which is based on the systems of the body for a specialty that cares for the patients. Note that there are several MDCs that are currently in use. ii. Depending on whether there was a procedure performed there will be a further splitting of cases within the MDC. This is done with an appropriate surgical or medical divide. iii. The medical cases are further subdivided according to the primary diagnosis. Surgical cases are also categorized by the procedure used based on resource usage. iv. Diagnostic and further surgical subcategories are established. This is done in relation to such factors like age, procedures that do not need operations, co-morbidities and complications, secondary diagnosis and discharge status. However, these factors are usually used in cases where they are known to show considerable effect on the length of in-patient’s stay. Costs Allocation Several processes are involved in the allocation of costs to a patient’s stay in hospital. This enables the stay of the patient in the hospital to be included in the National Casemix Cost Data Collection. The major casemix funding that is provided to public hospitals for acute in-patient care is obtained from the funds produced through the allocation of AR-DRG and the corresponding cost weights (Cleveland, Murphy & Williams, 1998). However, there are additional casemix funding elements which makes provisional payments to care hospitals. They include; ‘training and development’ grant, special grants, and, data from emergency department. Two boundary points are assigned to each AR-DRG (high boundary point, HBP and low boundary point, LBP). The boundary points are a representation of the length of the inpatient’s stay. The figure for LBP is derived from a calculation that divides the ‘state average LOS’ by three while the figure for HBP is derived from a calculation that multiplies the ‘state average LOS’ by three. This results to an inliers and outlier inpatient stay. An inlier inpatient stay is if the period of in-patient stay falls within the 2 boundary points. A low inpatient outlier is if the period of in-patient stay falls below the LBP while a high inpatient outlier is if the period of in-patient stay falls above the HBP. Determination of weight is done from the data collected on costs from the hospital in a given time period (Francis & Roger, 2001). Note that patients with similar AR-DRG can have different cost weights due to the differences in stay for each one of them. These weights therefore help to reflect the discrepancy in length of days. Each AR-DRG has a provision for cost weights for the circumstances that follow: stays for same day, stays for one day, and stays for multi day among others. The WIES is multiplied by a standard figure to obtain the cost weight. This cost weight provides the various scenarios of period of stay that is related to it. Private patients that get treatment within the main hospital care providers have a provision for different amounts (Viswesvaran, 2006). The health department uses AR-DRG version 6 and applies the cost weights that is derived from acute hospital care data from public hospitals. There exist variations in prices which reflect differences in funding policy between metropolitan and rural or country hospitals. The size of funds that is held in the casemix funding kitty also varies. For instance, acute admitted inpatients are usually funded at; FFS hospital get $4404 Metropolitan FFS hospitals get $3605 Country FFS hospitals get $3154 It can therefore be difficult to make a reasonable comparison of the performance of the hospitals based on casemix funding. The resultant resource allocation will also not be representative of the financial cost of the different hospitals. CHALLENGES FACING CASEMIX FUNDING The health service requirements and modalities keep changing hence casemix funding and the models used should be refined to keep abreast (Spoehr, 1999). Using casemix funding tool shows variations across Australia. The system is very much developed and deeply rooted in funding process in some states whereas other states are still not advanced in its use. This hampers the consistent in the national casemix cost data collection (Hazel, 2001). The processes of allocating an AR-DRG code and cost allocation to inpatient stay encounter problems like; There is lack of harmonization when it comes to the classification of hospitals that offer services which are far much above the acute inpatients These processes do not strictly apply the allocation processes and the costing standards. However, an “activity based funding, ABF” will address most of these problems as it will; Bring about a harmonized classification system whereby those hospitals that their services exceed the acute inpatients will not be short-changed. It will lay more emphasis on following the set allocation processes and costing standards. The different types of patients will be more explicitly defined. According to Johnston and Clark (2008), it will provide a benchmark data that is more reliable across jurisdiction. Casemix’s models design and application and processes across Australia will be more consistent. This will allow a benchmark data that is meaningful and that can be used in the comparison of the levels of activity and the cost incurred by a patient between jurisdictions (William, 2000). CONCLUSION Casemix funding system is a useful tool that boosts the financial management in health care service delivery as it improves the operational efficiency of the hospitals. In particular, casemix funding analyzes the relationship between patient care and its associated service costs. This allows comparison of activity levels among health units hence providing equitable funding of these units (Brown, et.al. 2001). Resource allocations based on business decisions that are sound provide effective and efficient use of scarce resources. This ensures that the hospitals provide the maximum level of care. REFERENCES Brown, S, Blackmon, K and Cousins, P., 2001. Operations management: policy, practice and performance improvement. Butterworth: Heinemann. Casio, W., 1998. Applied psychology in human resource management. Upper Saddle River, NJ: Prentice Hall. Cleveland, J., Murphy, K. & Williams, E., 1998. "Multiple uses of performance appraisals: Prevalence and correlates," Journal of Applied Psychology, 74(1), 130-135. Department of Health, 2010. Casemix Funding for Hospitals: Methodology. Rundle Mall: South Australian Department of Health Francis, H. & Roger, F., 2001. Case mix: global views, local actions: evolution in twenty countries. Sydney: IOS Press. Hazel, B., 2001. Euthanasia, death with dignity with and the law. Hart publishing: Port land. Johnston, R. and Clark, G., 2008. Service operations management: improving service industry. New York: Prentice Hall. Spoehr, J., 1999. Beyond the Contract State: Ideas for Social and Economic Renewal in South Australia. Melbourne: Wakefield Press. Viswesvaran, C., 2006. "Comparative analysis of the reliability of job performance ratings," Journal of Applied Psychology 81(5), 557-574. William, M., 2000. Euthanasia: Medical treatment in aid of an easy death. Abe Books Read More