DNP 805 Topic 5 Discussion 1
Select a specific clinical problem and post a clinical question that could
potentially be answered using data mining. Identify data mining techniques you
would apply to this challenge, and provide your rationale. Are there any
specific data mining techniques you would not use? Support your decision.
A specific clinical problem is unplanned hospital readmissions. Hospital
readmission costs an estimated $25 billion dollars annually, and compromises
patient safety (Olson, et al., 2016). A clinical question that could potentially
be answered using data mining is “Which patients are at highest risks for
hospital readmissions”. The data mining technique that appears most
appropriate to analyze this clinical question would be the data relationship of
classes which is using data to organize patients into groups to predict outcomes
(Grand Canyon University, 2014). Data I would analyze for relationship trends
might include:
1. Patients re-hospitalized within 30 days – to identify the patients in question
2. Age – to look for age groups at risk for re-hospitalization
3. Gender – to see of males or females is a possible predictor of re-
hospitalization
4. Diagnoses – to identify diagnosis groups that increase risk of re-
hospitalization
5. Complications – to identify any complications during hospitalization that
may increase risk of re-hospitalization
6. Length of stay – to identify if length of stay trend may pose a risk of re-
Select a specific clinical problem and post a clinical question that could
potentially be answered using data mining. Identify data mining techniques you
would apply to this challenge, and provide your rationale. Are there any
specific data mining techniques you would not use? Support your decision.
A specific clinical problem is unplanned hospital readmissions. Hospital
readmission costs an estimated $25 billion dollars annually, and compromises
patient safety (Olson, et al., 2016). A clinical question that could potentially
be answered using data mining is “Which patients are at highest risks for
hospital readmissions”. The data mining technique that appears most
appropriate to analyze this clinical question would be the data relationship of
classes which is using data to organize patients into groups to predict outcomes
(Grand Canyon University, 2014). Data I would analyze for relationship trends
might include:
1. Patients re-hospitalized within 30 days – to identify the patients in question
2. Age – to look for age groups at risk for re-hospitalization
3. Gender – to see of males or females is a possible predictor of re-
hospitalization
4. Diagnoses – to identify diagnosis groups that increase risk of re-
hospitalization
5. Complications – to identify any complications during hospitalization that
may increase risk of re-hospitalization
6. Length of stay – to identify if length of stay trend may pose a risk of re-
