Identifying Inferential Statistics in Research Studies

Module 3a Written Assignment

Learning Objectives:

  1. Evaluate HIM research and its impact on the HIM body of knowledge.
  2. Identify types of inferential statistics utilized within HIM research.
  3. Extract data from the CDC National Vital Statistics Data Center and create an appropriate data display.

Total Points:   35 pts

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Part I:  Identifying Inferential Statistics in Research Studies (25 pts)

In this section, you will be reviewing five research articles published by AHIMA – Perspectives in Health Information Management and identifying the inferential statistics that were utilized to support the research performed.   Pages 178 – 184 of the Health Informatics Research Methods will help you understand the research tests that were used within each study.  As a HIM professional, it is important to delineate key ideas within research studies and apply these findings as you work to improve processes within your healthcare organizations.   You will answer the three questions listed here for each article:

  1. Identify the purpose of the research study in your own words. (1 pt)
  2. Identify the inferential statistics noted within each study in your own words. (2 pts)
  3. Delineate the outcomes of the research in your own words and describe how this adds to the HIM body of knowledge. (2 pts)

Peterson, J., & Turley, J. (2021).  Predictors of success on the RHIA exam.  Perspectives in Health Information Management. 

https://perspectives.ahima.org/predictors-of-success-on-the-rhia-exam/

Fox, D., Weibe, N., Southern, D., Quan, H., Kim, E., King, C., Grosu, O. & Eastword, C. (2021)

The prevalence of insomnia and sleep apnea in discharge abstract data: A call to improve data quality.   Perspectives in Health Information Management.

https://perspectives.ahima.org/the-prevalence-of-insomnia-and-sleep-apnea-in-discharge-abstract-data-a-call-to-improve-data-quality/

 

Ashley, C. & Berry, S. (2021). The association between race and stroke prevalence in a patient cohort in Mississippi.    Perspectives in Health Information Management.

https://perspectives.ahima.org/the-association-between-race-and-stroke-prevalence-in-a-patient-cohort-in-mississippi/

 

Beesely, K., McLeod, A., Hewitt, B. & Moczygema, J.  Health information management reimagined: Assessing current professional skills and industry demand.  Perspectives in Health Information Management.

https://perspectives.ahima.org/health-information-management-reimagined-assessing-current-professional-skills-and-industry-demand/

 

Liengsawangwong, R., Kumar, S., Ortiz, R. & Hill, J. (2021).  Health informatics tool toward sepsis screening.   Perspectives in Health Information Management.

https://perspectives.ahima.org/health-informatics-tool-toward-sepsis-screening/