nursing (health informatics)w10r

ASSIGNMENT

Instructions:

Describe a population health issue that could be informed or addressed through the use of data mining.

· What research question(s) would guide your data mining activities?

· What types of datasets would you examine?

· How would you use the results of your data mining analyses to support a population health intervention to address the issue?

Respond substantively to two of your colleague's posts by suggesting other data sources or questions to ask to address the issue.  Note: Please try to pick a colleague who has not yet received a response when responding.

Presenter 1

Describe a population health issue that could be informed or addressed through the use of data mining

Cancer is a major public health concern in the United States (U.S.); it is currently the second leading cause of death among U.S. men and women after heart disease. (Stewart, 2018).  Since this disease is not going to be cured anytime soon, the use of data mining is very beneficial to help one day cure this disease.  I would recommend using QCENTRIX.  QCENTRIX is able "to capture and analyze 250-plus data points within the cloud-based, modern architecture of our software—the first new technology to enter the cancer market in more than a decade."(qcentrix.com,, 2021).  

QCENTRIX:

· Identify referral patterns by facility or enterprisee a novel multi layered method combining clustering and decision tree techniques to build a cancer risk prediction system is proposed here which predicts lung, breast, oral, cervix, stomach and blood cancers and is also user friendly, time and cost saving.

· Discover trends in utilization of services by volume, disease sites, payer mix, stage and migration.

· Expose trends in disease categories by volume across enterprise or facility, migration, stage, payer mix and race.

· Benchmark your hospital’s cancer patient volumes by county against state volumes, facility, and location of diagnosis.

What research question(s) would guide your data mining activities?

How can certain type of cancers be predicted sooner?  For example:  lung or pancreatic cancer?

What type of treatments are going to work for the type of cancer the patient has?  For example:  Breast cancer:  Adjuvant or Neoadjuvant?

Will immunotherapy or chemotherapy work for the patient's type of cancer?

Which imaging test is would warrant the most accurate diagnosis? Are there prior imaging studies that can be reviewed if a patient had treatment at another location?

Is Radiation recommended for this particular cancer?  Is combination of radiation and chemotherapy beneficial for their disease?

Since cancer can affect every part of your body, I would recommend having a data set for each specific type of cancer.  "The dataset would comprise of demographic information, habits, and historic medical records." (Fernandes, 2017)

How would you use the results of your data mining analyses to support a population health intervention to address the issue?

Data mining is very beneficial for preventing, treating and hopefully one day curing cancer.  "Medical researchers can use large amounts of data on treatment plans and recovery rates of cancer patients in order to find trends and treatments that have the highest rates of success in the real world." (Durcevic, 2020).  Early prediction of cancer has been shown better prognosis. "Therefore, a novel multi layered method combining clustering and decision tree techniques to build a cancer risk prediction system which predicts lung, breast, oral, cervix, stomach and blood cancers and is also user friendly, time and cost saving is necessary." (Ramachandran, 2014).  It can be very overwhelming for patient's and families when the diagnosis of cancer is heard.  Data mining can hopefully lessen the anxiety of this very overwhelming process. 

References:

Durcevic, S.  (2020).  18 Examples Of Big Data Analytics In Healthcare That Can Save People.  https://www.datapine.com/blog/big-data-examples-in-healthcare/

Fernandes, K, Cardoso, J, and Fernandes, J.  (2017).  Cervical Cancer Data Set.  https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29

QCENTRIX.  (2021).  Realize the untapped potential.  https://www.q-centrix.com/our-technology/cancer-technology/.

Ramachandran, P, Girjia, N, and Bhuvaneswari, T.  (2014).  Early Detection and Prevention of Cancer using Data Mining Techniques.  https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.681.6233&rep=rep1&type=pdf

 Stewart, S, Hayesn N, Moore, A,  Bailey II, R, Brown, P,  and Wanliss, E.  (2017).  Combating Cancer Through Public Health Practice in the United States: An In-Depth Look at the National Comprehensive Cancer Control Program.  https://www.intechopen.com/chapters/61994

Presenter 2

A health issue that could be informed or addressed through the use of data mining is childhood obesity. The CDC states that, “obesity now affects 1 in 5 children and adolescents in the United States” (CDC, 2021).

 

Some of the research questions I would ask to guide my data mining activities would include:

1. What is considered childhood obesity

2. What are the statics of children under a certain age who are considered to be obese within a certain population

3. What are factors that increase risk of childhood obesity (socioeconomics, genetics, access to education)

4. Does childhood obesity have a higher association with specific populations

5. What educational tools are provided to sed populations regarding childhood obesity prevention

 

One dataset that I would evaluate includes the electronic health records within pediatric populations. I would compare these datasets from hospitals among different socioeconomic locations. According to our text, Nursing informatics and the foundation of knowledge, “EHR data mining can help with population health, informing administrative processes, providing metrics for quality improvement supporting value-based reimbursement, and providing data for registry software that helps with population health management” (McGonigle & Mastrian, 2021).

 

I would also examine clinical registries as they, “acquire and repurpose real-world data to empower an evolving set of mission-driven data initiatives” (Prometheus Research, 2020). The article, Rare Disease Registries Classification and Characterization: A Data Mining Approach, says, “interoperability between RDRs or rare disease registries is needed for research activities, validation of therapeutic treatments, and public health actions” (Santoro et al., 2015).

 

I would also utilize data from centers for disease control and prevention as they can provide specific information related to defining childhood obesity, providing statistics and evaluating causes and consequences of this population health issue.

 

I would use the results of my data mining analysis to support interventions related to this population health issue by identifying inefficacies and analyzing relationships between causative factors associated with childhood obesity. Upon identifying causative factors, I would implement strategies to provide education and tools that can be utilized to decrease the prevalence of childhood obesity.

 

References:

Centers for Disease Control and Prevention. (2021, April 9). Childhood Overweight & Obesity. Centers for Disease Control and Prevention. https://www.cdc.gov/obesity/childhood/.

McGonigle, D., & Mastrian, K. G. (2021). Nursing informatics and the foundation of knowledge. Jones & Bartlett Learning.

Prometheus Research – What We Do. Prometheus Research Data Management Solutions. (2020, December 4). https://www.prometheusresearch.com/what-we-do/.

Santoro, M., Coi, A., Lipucci Di Paola, M., Bianucci, A. M., Gainotti, S., Mollo, E., Taruscio, D., Vittozzi, L., & Bianchi, F. (2015). Rare Disease Registries Classification and Characterization: A Data Mining Approach. Public Health Genomics, 18(2), 113–122. https://doi.org/10.1159/000369993