Assessment task: 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.

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 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 Insipid us or Gestational Diabetes). The governing council is primarily interested in the analysis, and expects clear recommendations that apply to, and are implementable by, 'UTS Hospital'.

• Locate the UTS Hospital data file from the Data Files folder in UTSOnline.
• Produce a written report no longer than 2000 words for the LHD council based on the supplied data (UTS Hospital data file) and the following topic.
• 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.
• Includes tables or figures as appropriate (for example, to profile the target group or to show comparisons).

Topic: Diabetes Mellitus (ICD10-AM Codes E10-E14.9; NOT Diabetes Insipidus or Gestational 20/07/2016 (Spring 2016) © University of Technology Sydney Page 7 of 10 Topic: Diabetes Mellitus (ICD10-AM Codes E10-E14.9; NOT Diabetes Insipidus or Gestational Diabetes)

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):

ii. Create a profile of surgical patients with diabetes mellitus as a secondary diagnosis (i.e. any diagnosis after the principal)

iii. Include the proportion of the dataset that these patients comprise Compare this to a profile of all patients in this dataset

iv. 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 i.

ii. Make specific recommendations to reduce length of stay for these patients

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.

Attachment:- Assignment.rar