Reference no: EM133153327

We consider the case of a system developed in 2015, hereafter referred to as the Australia case study. This case was aimed at improving diabetes management for patients across 36 different medical clinics in Australia by continuously analyzing daily generated electronic medical records (EMRs). Within these 36 clinics, an analytics infrastructure was implemented that linked the daily generated EMRs of the diabetes patients. A centralized statistical engine automatically conducted a weekly predictive analysis. An application on the physicians' office computer used the centralized weekly analysis to predict which patients were at risk of developing diabetes. Despite its potential for improving the quality of diabetes disease management, the Australia case study failed to deliver its objectives. The source of this failure was traced to the poor quality of the information being entered into the EMRs in clinics.

You are about to design a research method that can find out the reasons why physicians in these 36 clinics made no effort to put quality data into electronic medical records.

1. What are your hypotheses? Clearly define your causes and effects. Also indicate if an effect is the cause of another effect, too. Discuss why you think these hypotheses can be valid.
2. Discuss your variables in the scope of interest and variables outside of the scope of interest. For each hypothesis, identify the following:
   1. Independent variables
   2. Dependent variables
   3. Are there any constants? If yes, name them.
   4. Are there any confounders, controls, or background variables? If yes, name them.
3. How do you ensure the internal validity of the study? Address all biases to internal validity and explain how these biases can be avoided.