DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
DNP 805 Topic 5 DQ 1 Select a specific clinical problem and post a clinical question that could potentially be answered using data mining
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Topic 5 DQ 1
May 12-14, 2022
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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.
REPLY TO DISCUSSION
Data mining, according to Alexander et al. (2019), is the process of analyzing vast amounts of data to uncover important and intelligible patterns. Such patterns have been shown to be helpful in medicine and the healthcare business for projecting trends, enhancing product safety and usability, and improving patient experience. Electronic health records have greatly enhanced data collecting and have aided data mining in the prevention and reduction of medical errors. Data mining can provide an answer to the following question: Is telemedicine useful in reducing hospital readmissions for patients with congestive heart failure (CHF)? According to Reddy and Borlaug (2019), CHF is a common cause of hospitalization that accounts for almost $30 billion in US spending. Over five million people are affected by CHF, and studies suggest that readmission rates for those who were hospitalized due to the disease have increased (Garcia, 2017). Tracking patterns, association, and prediction are some data mining approaches that can be applied. Clustering analysis is a technique that I would not use.
References:
Alexander, S., Frith, K., & Hoy, H. (2019). Applied clinical informatics for nurses (2nd ed.). Jones & Bartlett Learning.
Reddy, Y. N. V., & Borlaug, B. A. (2019). Readmissions in heart failure: It’s more than just the medicine. Mayo
Clinic Proceedings, 94(10),
- https://doi-org.lopes.idm.oclc.org/10.1016/j.mayocp.2019.08.015
REPLY
Audimar,
That is an excellent question and a great potential use of data mining. I do agree with you that clustering analysis would not be the best technique to use. What is you hypothesis associated with this? Do you believe that it could reduce this due to the ability of visits? I can say from my experience it is challenging to get the generation that is most commonly suffering with heart failure comfortable with using telemedicine.
Great post Audimar—The work to reduce hospital readmissions is going to be ongoing for a long time because of the complexity of CHF, this is an issue with all hospitals. To reduce the number of preventable readmissions, the Centers for Medicare & Medicaid
Services (CMS) initiated the Hospital Readmissions Reduction Program (HRRP) in 2012. Also, they realized that only 30% of all the patients with CHF had a scheduled follow-up appointment with the PCP or cardiologist on discharge and that of all those who were discharged, only 37% kept their follow-up appointments and, about 41% were lost to follow-up visits. The hospitals started an intervention program to reduce the readmission rate by making sure that all the CHF patients had follow up appointments and they started a weekly or biweekly phone call to the patients, telemonitoring, and home visits. At the end, about 60% of the CHF patients when discharged had a scheduled follow-up appointment with a PCP or cardiologist within two weeks. At the end of the intervention about 56% of all discharged patients kept their follow-up appointments and they were able to reduce the 30-day readmission rates for CHF patients to 14%, which was a 50% reduction from the previous rates. These interventions have proven to help reduce the readmission rates of CHF patients as well as by having an adequate number of nursing staff to help with the education, optimizing of medical therapy and carrying out the interventions (Nair, Lak, Hasan, Gunasekaran, Babar, & Gopalakrishna, 2020).
References:
Nair, R., Lak, H., Hasan, S., Gunasekaran, D., Babar, A., & Gopalakrishna, K. V. (2020). Reducing all-cause 30-day hospital readmissions for patients presenting with acute heart failure exacerbations: A quality improvement initiative. Cureus. https://doi.org/10.7759/cureus.7420
REPLY
If the patient or family is unable to provide these details, obtaining a medical and psychiatric history can be extremely difficult. Having a database of patient information that includes any Emergency Department visits, Psychiatric services, any outpatient clinic or primary care visit, and current medications, discharge plans, or appointments that the patient was scheduled to attend. This is especially difficult when dealing with patients who have serious mental illnesses and require emergency services due to dangerous behavior or thoughts. The individual may be too ill to provide a medication list or historical data.
Having a database of medical and psychiatric history at the fingertips of every provider allows for the most efficient decisions for the patient. This removes barriers to treatment and can hasten the patient’s recovery. “HIE, or health information exchange, connects providers’ and clinicians’ electronic health record (EHR) systems, allowing them to securely share patient information and better coordinate care. Health Current is Arizona’s health information exchange, connecting over 900 Arizona organizations ranging from first responders to hospitals, labs, community behavioral health and physical health providers, as well as post-acute care and hospice providers.” (2022, Healthcurrent).
Healthcurrent. What is HIE?. https://healthcurrent.org/hie/what-is-hie/.2022
REPLY
The Covid 19 pandemic has left nursing across all disciplines forever changed. For some this has resulted in burn out and nursing leaving the profession. This in parallel to the nursing shortage has a potential for a negative impact in being able to provide care for those in need. The demand will continue to rise as the baby boomers continue to age in greater numbers than historically seen. What interventions are impactful in improving decreasing nursing turnover among nurses?
Data mining is looking at relationships and correlations to aims to predict outcomes. This is not a typical problem that you would think as being something that can be work on with data mining, but there are some opportunities using a principle component analysis. Data mining can reveal if there is a relationship between preventable nursing turnover and nurse salaries. Examples of unpreventable nursing turnover is retirement, relocation, death, and involuntary termination. Other variables to look at related to preventable nursing turn over would be leapfrog rating, CMS Stars, Magnet status, mandated patient ratios, workplace violence incidents, employee injuries, and union hospitals. The ability to data mine these items in comparison preventable nursing turnover will help guide what is most important to nursing to then have targeted interventions to decrease this turnover and keep nurses in the profession. One study did find a correlation with workplace violence and turnover in two large teaching hospitals (Yeh et al., 2020).
Once it is identified what seems to be the most important components that keep nurses in their roles will allow for focusing on those things to improve and then market that when recruiting nurses into the organization. With the shortage it is important to retain the nurses that you have and creatively market new ones in. This includes taking more new graduate nurses than historically taken.
Reference
Yeh, T.-F., Chang, Y.-C., Feng, W.-H., Sclerosis, M., & Yang, C.-C. (2020). Effect of Workplace Violence on Turnover Intention: The Mediating Roles of Job Control, Psychological Demands, and Social Support. Inquiry : A Journal of Medical Care Organization, Provision and Financing, 57, 46958020969313. https://doi-org.lopes.idm.oclc.org/10.1177/0046958020969313
REPLY
Thank you for your post. I agree with you that data mining is looking at relationships and correlations to aims to predict outcomes. Prediction is a very powerful aspect of data mining that represents one of four branches of analytics. Predictive analytics use patterns found in current or historical data to extend them into the future. Thus, it gives organizations insight into what trends will happen next in their data. There are several different approaches to using predictive analytics. Some of the more advanced involve aspects of machine learning and artificial intelligence. However, predictive analytics does not necessarily depend on these techniques, it can also be facilitated with more straightforward algorithms. (Zentut,2018).
The work to reduce hospital readmissions is going to be ongoing for a long time because of the complexity of CHF, this is an issue with all hospitals. To reduce the number of preventable readmissions, the Centers for Medicare & Medicaid Services (CMS) initiated the Hospital Readmissions Reduction Program (HRRP) in 2012. Also, they realized that only 30% of all the patients with CHF had a scheduled follow-up appointment with the PCP or cardiologist on discharge and that of all those who were discharged, only 37% kept their follow-up appointments and, about 41% were lost to follow-up visits. The hospitals started an intervention program to reduce the readmission rate by making sure that all the CHF patients had follow up appointments and they started a weekly or biweekly phone call to the patients, telemonitoring, and home visits. At the end, about 60% of the CHF patients when discharged had a scheduled follow-up appointment with a PCP or cardiologist within two weeks. At the end of the intervention about 56% of all discharged patients kept their follow-up appointments and they were able to reduce the 30-day readmission rates for CHF patients to 14%, which was a 50% reduction from the previous rates. These interventions have proven to help reduce the readmission rates of CHF patients as well as by having an adequate number of nursing staff to help with the education, optimizing of medical therapy and carrying out the interventions (Nair, Lak, Hasan, Gunasekaran, Babar, & Gopalakrishna, 2020).
References:
Nair, R., Lak, H., Hasan, S., Gunasekaran, D., Babar, A., & Gopalakrishna, K. V. (2020). Reducing all-cause 30-day hospital readmissions for patients presenting with acute heart failure exacerbations: A quality improvement initiative. Cureus. https://doi.org/10.7759/cureus.7420
It can be extremely difficult to obtain a patients medical and psychiatric history if the patient or family is unable to provide these details. Having a database of patient information including any Emergency Department visits, Psychiatric services, any outpatient clinic or primary care visit and current medications or discharge plans or appointments that the patient was to attend. This is especially difficult with patients who suffer from serious mental illness and require emergency services due to unsafe behavior or thoughts. The person may not be well enough to provide a medication list or provide historical data.
Having a database of medical and psychiatric history at every providers fingertips allows the most efficient decisions possible for the patient. This removes barriers for treatment and can accelerate wellness for the patient. “Health information exchange or HIE connects the electronic health record (EHR) systems of providers and clinicians allowing them to securely share patient information and better coordinate care. Health Current is Arizona’s health information exchange, connecting over 900 Arizona organizations, from first responders, hospitals, labs, community behavioral health and physical health providers to post-acute care and hospice providers.” (Healthcurrent, 2022).
Healthcurrent. What is HIE?. https://healthcurrent.org/hie/what-is-hie/.2022
The Covid 19 pandemic has left nursing across all disciplines forever changed. For some this has resulted in burn out and nursing leaving the profession. This in parallel to the nursing shortage has a potential for a negative impact in being able to provide care for those in need. The demand will continue to rise as the baby boomers continue to age in greater numbers than historically seen. What interventions are impactful in improving decreasing nursing turnover among nurses?
Data mining is looking at relationships and correlations to aims to predict outcomes. This is not a typical problem that you would think as being something that can be work on with data mining, but there are some opportunities using a principle component analysis. Data mining can reveal if there is a relationship between preventable nursing turnover and nurse salaries. Examples of unpreventable nursing turnover is retirement, relocation, death, and involuntary termination. Other variables to look at related to preventable nursing turn over would be leapfrog rating, CMS Stars, Magnet status, mandated patient ratios, workplace violence incidents, employee injuries, and union hospitals. The ability to data mine these items in comparison preventable nursing turnover will help guide what is most important to nursing to then have targeted interventions to decrease this turnover and keep nurses in the profession. One study did find a correlation with workplace violence and turnover in two large teaching hospitals (Yeh et al., 2020).
Once it is identified what seems to be the most important components that keep nurses in their roles will allow for focusing on those things to improve and then market that when recruiting nurses into the organization. With the shortage it is important to retain the nurses that you have and creatively market new ones in. This includes taking more new graduate nurses than historically taken.
Reference
Yeh, T.-F., Chang, Y.-C., Feng, W.-H., Sclerosis, M., & Yang, C.-C. (2020). Effect of Workplace Violence on Turnover Intention: The Mediating Roles of Job Control, Psychological Demands, and Social Support. Inquiry : A Journal of Medical Care Organization, Provision and Financing, 57, 46958020969313. https://doi-org.lopes.idm.oclc.org/10.1177/0046958020969313
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