Subject learning objectives (SLOs)
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. Identify and compare 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 and discuss 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.
Course intended learning outcomes (CILOs)
This subject also contributes specifically to the following graduate attributes:
Propose relevant problem solving and human factors theories to the analysis of common issues inherent in the management and evaluation of healthcare services.
Justify and demonstrate appropriate leadership styles and skills necessary to manage, evaluate and innovate healthcare services utilising contemporary local, national and international perspectives.
Develop and contribute to research and quality improvement activities in order to maintain knowledge currency and influence healthcare practice and policy.
Validate the importance of integrating stakeholder partnerships in all healthcare decision making activities. (3.2) Communicates effectively and appropriately in challenging, complex and diverse situations.
Determine and recommend modes of communication necessary to optimise outcomes across differing audiences, purposes and contexts within healthcare practice.
This subject is taught using a variety of teaching and learning strategies. The strategies used emphasise active and applied approaches to developing students' ability to understand health data and to use health data for decision making. An overarching theme of the approaches to teaching and learning is to support students to actively analyse an assembled patient dataset using Microsoft Excel in computer labs, where they can interact and receive feedback from the facilitator and other students.
Understanding and critical thinking skills of health information will be encouraged via a range of activities, including:
Pre-class activities:
Online computer and Microsoft Excel assessments to prepare students to take part in related workshops available at UTS library.
Self-directed learning using on-line resources to prepare students to actively participate in lectures and computer lab activities.
Face-to-face activities:
Lectures and briefings, including:
Examples and illustrations of how health data is analysed and used in decision making. In class discussions on students' experience with real health data issues.
Group activities to help student to develop critical thinking ability to identify possible problems that could result in poor quality data and hand-on skills to solve these problems. Activities will include whole class brainstorming, discussions, and demonstrations.
Feedback:
Advice and guidance on assessment tasks will be given during the face-to-face session and via email. Real-time verbal feedback from lecturer/tutor and peers will be given in class during discussion sessions. Written feedback will be given in assessments.
Content (topics)
The content of this subject includes:
Basic data analysis and presentation in Microsoft Excel Minimum data sets, classifications and terminologies Information literacy
Data linkage Standardisation of data Health Level 7 (HL7) Health informatics
Unintended errors in technology Privacy, security and confidentiality
Project management software & GANTT charts
Program
Preparation & Orientation Weeks
Preparation week will prepare students to successfully undertake the subject. Students will familiarise themselves with the subject via UTSOnline where they will find the subject outline and resources for the subject.
The preparation week will also provide students with an opportunity to engage with subject readings and resources so they are fully prepared for the intensive on-campus learning days.
Essential preparation for computer lab activities: self-assessment and skill development.
Students to complete 2 skills self-assessment activities on UTSOnline: Fundamental computer skills
Microsoft Excel skills
Students should utilise their results from these self-assessments to undertake necessary skill development via links to the UTS Library on UTSOnline.
Introduction & overview Minimum datasets Terminologies & classifications:
Australian Refined Diagnosis Related Groups (AR-DRG) International Classification of Diseases (ICD-10) Systematized Nomenclature Of Medicine Clinical Terms (SNOMED-CT)
Feedback of computer skills and Excel skills assessment
Information literacy Data linkage Standardisation of data Health level 7 (HL7)
Data types and data analysis Feedback of assessment 1
Feedback of Excel skills from the 1st computer lab 13:00 - 17:00
Data presentation
Using UTS Hospital data and/or externally available data
Privacy, security and confidentiality Project management and other software
Feedback of Excel skills from the 2nd computer lab 13:00 - 17:00
Application & presentation of data (in computer labs)
Notes:
Pre-class activity:
Search for password security, pattern and combination, for class discussion.
Read the AIHW report via UTSOnline, focus on Content, Format and Presentation of tables and figures.
In-class activity: In Lecture
Whole discussion on password security, pattern and combination.
In Computer lab
Class discussion on how to create a new variable to separate patients died vs alive.
Volunteer students to review and demonstrate how to create a new variable in Excel.
Health informatics Electronic health record
Application and reporting of routinely collected data Feedback of assessment 2
Assessment task 1: Workbook Exercise
Intent: This assessment item focuses on the elements of administrative data and related key concepts and how to analyse health data.
Objective(s): This assessment task addresses subject learning objective(s):
Task:
1. Students are required to answer a series of questions via UTSOnline. A selection of multiple choice, multiple answer, true/false, matching statements, short text response, and ordering questions may be included.
Questions may:
require manipulation of the supplied UTS Hospital data using Microsoft Excel;
require access to sources on the International Classification of Diseases and the list of Australian Refined Diagnosis Related Groups, available through the UTS library and/or UTSOnline;
relate to casemix, including, but not limited to, AR-DRGs; relate to basic statistical concepts;
relate to important concepts and definitions.
Length: Online assessment using the supplied spreadsheet and other resources
Assessment task 2: Case Study
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.
Objective(s): This assessment task addresses subject learning objective(s): A, B, C, D and E
Task:
1. Read the following case scenario.
2. Provide a response 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 the response, apply your findings to the hospital so as to assist the executive group in decision making and planning.
4. The response should include appropriate and properly formatted tables and figures.
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 casemix- 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 Health Information Manager (HIM), the Chief Financial Officer (CFO) and the Chief Information Officer (CIO).
The HIM is of the opinion that casemix-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 states the majority of patients is older and more complex, and need to stay longer than the average length of stay for each AR-DRG. She suggests that AR-DRGs are useless for measuring the hospital's performance when the length of stay of the patients is different to that of the average hospital. She is of the view that the hospital should go back to insurance funds and negotiate a return to the funding of patients on a fixed per diem basis.
As the HIM's assistant, you are tasked with examining UTS Hospital data, and preparing a summary for the HIM to use in the upcoming meeting.
You are asked to include the following information in the summary:
1. Background
Definitions for per-diem and casemix funding.
Description of the differences of these two funding models.
Identification of the pros and cons of the casemix-based funding approach compared to a fixed per diem rate.
Description of how casemix funding is achieved in Australian hospitals. Conclude this section with a statement of the aim of the analysis.
2. Method
A brief description of software and techniques used to examine the data
3. Results
Visual presentation to describe the relationship between Length of Stay (LoS) and age for the entire dataset, accompanied by a description of the findings.
Tabulated presentation of the top most frequent AR-DRG for patients aged 70 years and older, accompanied by a description of the findings.
4. Discussions
A brief discussion of the findings in relation to the two funding models.
5. Conclusion and recommendation
Provide a short statement of conclusion and recommendation that is linked to the UTS Hospital.
Assessment task 3: 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.
Objective(s): This assessment task addresses subject learning objective(s):
Task: Background:
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 data from ‘UTS Hospital' (an Australian public hospital situated in New South Wales) to address issues related to Diabetes Mellitus (ICD10-AM Codes E10-E14.9; NOT Diabetes Insipidus or Gestational Diabetes) . The report must contain a data analysis strategy, the analysis, and appropriate reference to the literature. Note that the governing council is primarily interested in the analysis, and expects clear recommendations that apply to, and are implementable by, ‘UTS Hospital'.
Requirements:
1. Locate the UTS Hospital data file from the Data Files folder in UTSOnline.
2. Refer to the topic area below.
3. Investigate the issues you are asked to, by:
a. Researching the issue in the literature
b. Designing a data analysis strategy
c. Conducting the analysis
4. Produce a written report no longer than 2000 words for the LHD council based on the supplied data (UTS Hospital data file) on the topic given below.
5. The report must contain brief background literature, a data analysis, results (including tables and figures), and discussion and appropriate recommendations that integrate the findings and the background literature.
6. Includes tables or figures as appropriate (for example, to profile the target group or to show comparisons).
Notes
Report formats vary across disciplines. It is expected that students will use the following headings: Introduction (< 200 words)
Background (alternative heading: Literature) (< 300 words) Data Analysis (alternative heading: Method)
Results (alternative heading: Findings) Discussion & Recommendations (< 300 words) Conclusion
References Recommended process:
Research the background of the issue in the peer reviewed and ‘grey' literature. Design and document a data analysis plan.
Conduct and document the analysis described in the data analysis strategy. Present results with tables or figures as appropriate.
Discuss and make recommendations that integrate the background and the results.
Other notes:
Appropriately reference all material, however you do not need to reference the supplied dataset. Ensure you read the assessment criteria.
Develop an understanding of each of the data elements in the dataset. Do not limit your analysis to the most obvious variables.
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.
Secondary diagnoses are defined as any diagnosis after the principal.
Appendices may be included but will not contribute to the grade for this assessment.
Topic: Diabetes Mellitus (ICD10-AM Codes E10-E14.9; NOT Diabetes Insipidus or Gestational Diabetes)
a. With reference to Australian and overseas literature, briefly describe:
i. Current prevalence & incidence of diabetes mellitus, and any relevant trends
ii. Current admissions & length of stay for diabetes mellitus, and any relevant trends
iii. Approaches used to decrease length of stay for diabetes mellitus
b. Analyse the UTS Hospital dataset
i. Considering the individual/personal characteristics that are relevant to diabetes mellitus (e.g. age, gender and others identified in the literature):
Create a profile of surgical patients with diabetes mellitus as a secondary diagnosis
(i.e. any diagnosis after the principal)
a. Include the proportion of the dataset that these patients comprise
Compare this to a profile of all patients in this dataset
ii. For surgical patients with a secondary diagnosis of diabetes mellitus: Describe the length of stay of these patients, and compare it to:
a. Those patients without diabetes in the same AR-DRG
b. Those patients with a primary diagnosis of diabetes.
Describe any observed patterns and/or relationships between variables
c. Based primarily on your analyses, but with reference to the literature:
i. Identify important points that might suggest interventions to reduce length of stay for surgical patients with this condition
ii. Make specific recommendations to reduce length of stay for these patients
https://www.dropbox.com/s/ujzi6oohut0xus0/Data.rar?dl=0