Difference between revisions of "Final Progress"
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| + | <div style="background: #F5FFFA; padding: 12px; font-family: Arimo; font-size: 18px; font-weight: bold; line-height: 1em; text-indent: 15px; border-left: #2E8B57 solid 32px;"><font color="##4682B4">Abstract</font></div> | ||
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| + | The healthcare industry has always been concerned with gathering insights on patients and their no-show appointments. The number of no-show appointments has an impact on the cost and clinic utilization. It creates an opportunity cost for another patient who is unable to make use of the no-show appointment slot to get a consultation from a doctor or an allied health professional. In this study, we aim to identify significant variables that affect no-show appointments in Hospital X. The data used for this model building process is provided to us by our sponsor, a medical consultant at Hospital X. Taking references from past literature review, we will select and derive relevant variables to be used for modelling. We will develop logistic regression and decision tree models to predict the probability of no-shows for Hospital X using both patient information and individual clinical appointment attendance records. We will then compare the different models and assess the results. | ||
| + | Based on our findings, we will end the report with set of implications and results for Hospital X. | ||
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Revision as of 12:36, 15 April 2017
| Mid-Term Progress
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Final Progressnew! |
The healthcare industry has always been concerned with gathering insights on patients and their no-show appointments. The number of no-show appointments has an impact on the cost and clinic utilization. It creates an opportunity cost for another patient who is unable to make use of the no-show appointment slot to get a consultation from a doctor or an allied health professional. In this study, we aim to identify significant variables that affect no-show appointments in Hospital X. The data used for this model building process is provided to us by our sponsor, a medical consultant at Hospital X. Taking references from past literature review, we will select and derive relevant variables to be used for modelling. We will develop logistic regression and decision tree models to predict the probability of no-shows for Hospital X using both patient information and individual clinical appointment attendance records. We will then compare the different models and assess the results.
Based on our findings, we will end the report with set of implications and results for Hospital X.
