Reference no: EM13838529

Using Health Care Data for Decision Making

Upon successful completion of this subject students should be able to:

A. Access and manipulate supplied data in order to generate reports and make recommendations;

B. Consider the relationship between data, information, knowledge and wisdom and how these elements inform practice, management, and policy in the context of international trends;

C. Examine the relationship between datasets and information literacy;

D. Explain the data elements in contemporary health data terminologies;

E. Create a variety of ways in which complex issues can be effectively communicated for a variety of target audiences.

Propose relevant problem solving and human factors theories to the analysis of common issues inherent in the management and evaluation of healthcare services.

Determine and recommend modes of communication necessary to optimise outcomes across differing audiences, purposes and contexts within healthcare practice.

Assessment task 1: Short Statements

Intent: This assessment item focuses on the ability to concisely respond to specific questions and to demonstrate an understanding of the management and application of health data.

Word Limit: Maximum 1500 words.

Task: 1. Read the following case scenario.

2. Provide a response to each of the five 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.

3. In each response, apply your findings to the hospital so as to assist the executive group in decision 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 new buildings and refurbishing old buildings.

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. Each insurance fund negotiates the rate it pays for each AR-DRG individually each year with the hospital. 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 casemix 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 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 per diem basis (based on the national average bed day cost).

Provide a short statement (~ 300 words) for the executive that identifies the pros and cons of funding on a flat per diem rate compared to funding on a DRG basis. Provide the executive with a recommendation.

2. The CIO disagreed 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 (~300 words) with two or three examples of where the DRG had been split to allow for patients of different age or complexity in the most recent version of AR-DRGs in use in Australia. Given the nature of UTS Hospital's patients, outline to the executive the implications of changing AR-DRG versions.

3. The CIO was also sceptical of the practices of the coding staff: that the AR-DRGs assigned to episodes of care may not be the appropriate one. He suggested that clinical coders might not always select the correct diagnostic and procedure information from the notes, leading to incorrect classification.

Provide a short statement (~300 words) that identifies potential issues with coding practices, including the potential rate of incorrect codes, suggested causes, and ramifications. Identify potential solutions for the executive to implement to address the issues identified.

4. The CFO expressed the view that it was not incorrectly classified AR-DRG, age or complexity of the patients that was the problem. She noted that the average length of stay was exactly that - an average - and because UTS hospital patients were old and complicated they were likely to stay longer than average and be on the expensive side of the average cost for a given AR-DRG.

Provide a short statement (~300 words) that identifies one or more potential issues with using the average for a measure of central tendency. Describe for the executive how this issue (or issues) may be addressed in casemix (AR-DRG) funding approaches.

5. The CEO said that there was no reason to believe that UTS hospital 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 (~300 words) on the way peer hospitals are compared within Australia. Identify, for the executive, the areas of the hospital or type of patients where there could be a particular issue with falling outside the benchmarks, and the implications for the hospital.

Assessment task 2: Analysis of supplied patient data

Intent: This assessment item focuses on the development of data analysis and presentation skills in order to make recommendations that are congruent with contemporary literature.

Word Limit: Maximum 2500 words.

Task: You have been engaged as a consultant to the Local Health District (LHD). The LHD governing council requires you to develop a report based on the ‘UTS Hospital' data to address issues related to Outlier Admissions (patients with a length of stay over 30 days) . The governing council is primarily interested in the analysis, and expects clear recommendations that apply to, and are implementable by, ‘UTS Hospital'.

1. Locate the UTS Hospital data file from the Data Files folder in UTSOnline.

2. Produce a written report no longer than 2500 words for the LHD council based on the supplied data (UTS Hospital data file) and the following topic items that includes tables or graphs as appropriate (for example, to show comparisons). NB The word limit excludes tables and appendices.

3. The report must contain a data analysis strategy, the analysis, and appropriate reference to the literature.

Topic Items

1. With reference to Australian and overseas literature, briefly describe: the current percentage of patients who stay longer than expected for their AR-DRG, including those identified as outlier admissions factors (internal and external) likely to influence length of stay approaches used to prevent outliers and reduce length of stay for these admissions

2. Analyse the UTS Hospital dataset as follows:

i. Create a profile of outlier patients (those who stay more than 30 days)

a. include both individual and episode characteristics (e.g. age and others you identify in the literature)

b. include the proportion of the dataset that these patients comprise

c. compare this to a profile of all patients in this dataset

ii. For outlier patients: describe the most common individual and episode variables that occur in these long stay cases

identify the most common principal and secondary diagnoses identify the most common principal and secondary procedures

compare the length of stay of outlier patients to non-outlier patients in the same (or related) AR-DRG and/or the same MDCs

3. Based primarily on your analyses, but with reference to the literature:

identify important points that might suggest interventions to reduce the length of stay for outlier patients

make specific recommendations to reduce the number of outliers and reduce their length of stay

Requirements / Notes

1. Investigate the issues by:

researching the issue in the literature designing a data analysis strategy conducting the analysis making recommendations

2. Appropriately reference all material, however you do not need to reference the supplied dataset.

3. Ensure you read the marking criteria.

4. Develop an understanding of each of the data elements in the dataset. Do not limit your analysis to the most obvious variables.

5. The supplied dataset is relatively small so it is not expected that the analyses here would be definitive. You are expected to treat the dataset as if there were more cases than there actually are, so that the solutions you may suggest should be considered to be more valid than they will actually be with this selection of data. You do not need to note this in the report.

6. Secondary diagnoses are defined as any diagnosis after the principal.

7. Appendices may be included but will not contribute to the grade for this assessment

8. Report formats vary, but usually contain: