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Healthcare Data and Decision Making: Case of All Saints Hospital Report

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Introduction

The role that data plays in informing important decision-making processes can never be underestimated. Many program managers in different fields, including healthcare, often refer to their databases to inform critical decision-making in addition to facilitating effective program management (Bandy, Burkhauser, & Metz, 2009, para. 1). Within the healthcare context, the decisions made through such data are essential if services are to be delivered cost-effectively. Indeed, many health institutions around the world are increasingly investing in information and communication technology to consolidate their databases to improve service delivery (Pelletier & Diers, 2004, p 1). This paper aims at using All Saints Hospital, Capricornia, to show how healthcare data can be invariably used in important decision-making processes.

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Pros and Cons of Funding on a Flat Rate compared to funding on DRG Basis

According to Shaman et al (1994), health facilities must aim at achieving high-quality healthcare at the least possible expense if they are to maximize cost-effectiveness (p. 52). To achieve this objective, hospital funding agencies are leaning more towards the outcome-based model that funds hospitals for what they do (Cheah & Chee, 1999, para. 2). In Australia, payment for hospitals in the private sector is mainly done through a combination of per diem and case-mix-based approaches such as the

Australian Refined Diagnosis Related Groups (APR-DRGs). Both systems have their advantages and disadvantages (Reynolds, 2008, p. 27; Courtney & Briggs, 2004, p. 10).

The per diem rate is advantageous as it gives health facilities the leeway to consider and calculate all the costs based on individual treatment procedures. Unlike DRGs, the per day funding rate sufficiently accounts for disparities in therapeutic philosophies, necessitating health managers to take a more detailed approach while calculating the level of funding needed for each patient based on the national bed day cost (Reynolds, 2008, p. 56). In healthcare facilities with specialized needs, DRGs are often accused of taking a simplistic view of disparities in patients’ needs, including the complexity of illness and co-morbidities (Cheah & Chee, 1999, para. 11).

While the DRG model is good at assisting clinicians and managers track the actual cost of providing healthcare to clusters of patients with analogous diagnostic characteristics, it may prove disadvantageous to some health facilities if the level of funding is tied to the national average bed day cost (Ferguson, 2004, p. 324). Factors such as a high elderly population often occasion the costs to “blow out beyond the national or state determined costs weights” (Reynolds, 2008, p.27). Such a scenario may occasion financial difficulties as witnessed in All Saints hospital since the health facilities use much more resources than they get from the funding agencies. The flat per diem rate can act to remedy the situation in such circumstances.

However, the per diem rate fails to provide a framework for motivating health personnel to treat more patients within the average cost structure (Reynolds, 2008, p.27). It is more profit-oriented and therefore may work against the objectives of some charitable health facilities such as All Saints hospital. Also, the per diem rate does not in any way present adequate motivations for efficiency improvement and innovation. This goes against All Saints hospital’s renowned reputation for innovativeness.

It is imperative to note that efficient use of limited resources is of paramount importance for health facilities in developed countries such as Australia since they end up consuming 30 – 40 percent of total health care expenditure (Ghaffari, Doran, & Wilson, 2008, para. 4). In this respect, efficiency and innovation can only be guaranteed by the DRG model due to its ability to characterize similar health conditions into categories for funding purposes.

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DRG Model, Age Factors, Coding Issues, and Latest Version

Elderly patients are assumed to require a longer time in health facilities in addition to using more resources – financial and material (Reynolds, 2008, p. 27). It is against this backdrop that modern DRGs must incorporate probable confounder influences such as age, principal diagnosis, medical procedures, and genetic predisposition if they are to be successful in meeting their objectives (Eager, Garrett, & Lin, 2002, p. 128).

The Australian DRGs have been split into various categories that allow health managers to classify patients according to the medical requirements of their respective age groups. The first Australian DRG model, released in 1992, was shallow in age representation as it only had the pediatric–adult age split, but at the age of 10 years (KAROL, 2008, p. 20). However, later versions of the model have been split and reformulated to cater to patients of different age groups.

For instance, AN-DRG version 3 more than doubled the number of groups defined by age. Age categories were introduced as class boundaries in addition to developing age splits of 5 years interval, from a patient of 5 years of age to 80 years. Later versions have also dwelt on the age issue and refined the age categories to fewer groups that offer more representation across the board. In version 4, the proportion of DRGs defined by age split plummeted from 20 to 8 (KAROL, p. 23). However, the formulated classes adequately catered for patients of all age groups.

The DRG model will be thrown into disarray if incorrect codes are used to enter the patients’ fundamental information into the various classes. Wrong coding will occasion an incorrect selection of diagnostic and medical intervention information from the notes, resulting in inappropriate allocations of DRGs (KAROL, 2008, p. 34-37). Incorrect coding can be occasioned by several factors, including late and incomplete documentation of critical patients’ data, incorrect use of terminology and abbreviations, illegible documentation of crucial information, inadequate understanding of the diagnosis and procedure coding booklets and manuals, and improper recording and coding of primary diagnosis and another related diagnosis (KAROL). To effectively tackle the difficulties, the hospital should switch to AR-DRG Version 6.0, released in November 2008 (DHA, 2009).

Advantages of using the Average

The average has often been used in the computation of important healthcare data, including the average length of stay in the hospital, the average age of the patient, and the average nursing hours per patient day. According to Doherty (n.d.), the average is a measure of central tendency (p. 2).

One of the advantages of using the average is that it allows healthcare professionals to refine their analysis of healthcare data to come up with certain key indicators such as the performance and efficiency of health institutions. Secondly, an average such as the median can be effectively used to compute healthcare data since it is neither affected nor influenced by extreme scores. The median is the central point of distribution. In this context, using the median score as an average will reveal to the hospital’s administration why many patients tend to reside on the expensive side of the average.

The mode is yet another type of average that can be used to reveal the troubles affecting the hospital. Its advantage lies in the fact that it can effectively help the hospital administration to know the most common categories of the age of patients and related ailments. Such information is prudential as it will assist the hospital administration to present its case to the funding agencies in regards to the issue of old age.

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The weaknesses of the DRG Reimbursement System

According to Reynolds (2008), population factors and medical complexities are critical in determining the length of stay in the hospital (p.27). Elderly patients and others with complex ailments such as mental complications may end up staying longer in hospitals, thereby consuming more resources. All Saints hospital has a combination of these factors since most of its patients are waiting to relocate to long-term aged care while a huge number are in the mental health unit. This scenario has obvious cost implications. As such, it may be counterproductive to reimburse funding to this facility if such factors are not put into consideration.

According to Scotton (1991), acute hospital care as witnessed above is “by far the most costly component of healthcare – both in terms of unit and total expenditure” (p. 9). The above factors are known to add an aspect of complexity especially when reimbursement is based on negotiated fixed rates. The negotiated payments advanced by the funding agencies are based on the average length of stay calculated using the Australian cost weights.

However, many hospitals across Australia differ in many respects, including the composition of patients under their care as well as the availability of specialist facilities (Scotton, p.14). In this regard, it may be unfair to reimburse money to the health facilities if the computations are sorely based on average Australian cost weights. The national average cost weights are on the lower end of the scale when compared to high-complexity DRGs. In addition, the negotiated reimbursement rates do not cater for risk adjustment since many are reimbursed at a flat rate (Antioch & Walsh, 2004, p. 23).

A good alternative form of case-mix is the Risk Adjustment Specified Grants (Antioch & Walsh, 2004, p. 27). This approach will ensure that classifications requiring more attention and funding are adequately reimbursed on top of what they are supposed to get under the average cost weights. Another effective case approach is the Resident Classification Scale, which categorizes “patients according to several different criteria and then groups’ patients with different care needs for funding purposes” (Ferguson, 2004, p. 319). Still, the commonwealth government can be asked to provide direct funding to some distinct health classifications such as mental health and elderly care (Scotton, 1991, p. 17).

Benchmarking Data: Comparing Peer Hospitals

In healthcare, benchmarking can be defined as an incessant, systematic procedure for assessing the products, services, and work processes of health facilities that are documented as representing best practices for the functions of organizational improvement (Ferguson, 2004, p. 329). Health facilities learn from their peers – industry partners and competitors – about how to function much more efficiently. First, peer hospitals can be compared using internal benchmarking activities, which identify differences in performances, work processes, and hospital-wide practices that might be occasioned by differences in historical backgrounds, geographical locations, levels of training, and preferences of both health managers and clinicians. This type of benchmarking can improve the efficiency and effectiveness of service delivery by establishing best practices and organizational-wide internal performance standards. Examples include antibiotic therapy usage and clinical indicator rates.

Peer hospitals can also be compared through competitive benchmarking activities, which require that one health facility gains precise information and knowledge about the products, services, and other work processes of a peer health institution to make performance-related comparisons and identify gaps in service delivery between the two health facilities (Ferguson, 2004, p. 329). These comparisons are ultimately aimed at enhancing the effectiveness and efficiency of care delivery. Competitive benchmarking is exhibited when health clinicians meet to discuss apparent differences and similarities of indicators such as the average length of stay per DRG and the average cost of treatment per DRG within the health facilities.

Australian health institutions can still be compared using functional process benchmarking. This procedure mainly depends on administrative information to benchmark business processes within different organizations as a principle means of establishing the best way to execute organizational processes and set performance standards for those processes (Fergusson, 2004, p. 330). Examples include comparing the human resource or accounting processes of various health institutions to come up with procedures that guarantee efficiency in running organizational affairs.

Reference List

Antioch, R.M., & Walsh, M.K. (2004). “The risk-adjustment vision beyond casemix (DRG) funding in Australia.” The European Journal of Health Economies, Vol. 5, No. 2

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Bandy, T., Burkhauser, M., & Metz, A.J.R. (2009). Data-driven decision making in out- Of-school time programs. Web.

Cheah, J., & Chee, Y.C. (1999). “Case mix – for better or for worse?” Singapore Medical Journal Vol. 40, No. 1. Web.

Courtney, M., & Briggs, S. (2004). Health care financial management. Elsevier Australia. Web.

Department of Health and Ageing. (2009). Classifications: AR-DRG Version 6.0. Web.

Dolhenty, J. (n.d.). A basic guide to statistics. Web.

Eager, K., Garrett, P., & Lin, V. (2002). Health planning: Australian Perspectives. Allen & Unwin. Web.

Evelyn, H., & Sheree, L. (2006). “Working with information and knowledge.” In M.G.Harris (Eds), Managing health services: concepts and practice. Marrickville, N.S.W: Elsevier Australia. Web.

Ferguson, L. (2004). Casemix: key issues for health service managers. Pearson Education.

Ghaffari, S., Doran, C.M., & Wilson, A. (2008). “Casemix in the Islamic Republic of Iran: Current knowledge and attitudes of healthcare staff.” Eastern Mediterranean Health Journal Vol. 14, No 4. Web.

KAROL Consulting. (2008). Macedonian DRG Manual Draft for Discussion. Web.

Pelletier, D., & Diers, D. (2004). “Developing data for practice and management.” Computers, Informatics, Nursing, Vol. 22, No. 4.

Reynolds, L. (2008). Understanding the Australian healthcare system. Elsevier Health Sciences. Web.

Scotton, R. (1991). National health insurance in Australia: New concepts and new applications. Web.

Shamian, J., Hagen, B., Hu, T., & Forgarty, T.E. (1994). “The Relationship between length of stay and required nursing care hours.” JONA, Vol. 24, No. 7-8, pp. 52-58. Web.

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