Developing a narrated PowerPoint presentation describing an aspect of a â€œbig dataâ€ application to address a community/population health problem. The goal for the assignment is for you to understand the specific methodologies used for â€œbig dataâ€ analysis and examine the potential of big data in improving population health by exploring the feasibility, challenges and/or issues surrounding its application to another setting or scaling up for widespread use.
Select an example of a methodology that is used for a â€œbig dataâ€ study in health care. Describe the methodology, the data requirements and the software that is necessary to conduct the analysis. What makes the study a â€œbig dataâ€ study: volume, velocity, variety, (variability, veracity, value)? Provide an example of how the method was applied to a clinical, financial and/or administrative problem in health care. What were some of the challenges in conducting the study in your example? What are the recommendations for future use of the methodology? Is this a methodology that could readily be used in a small health care organization, nursing home, physician practice, or other clinical setting that has access to a similar population to improve the health of the population? Did the study use open source or proprietary software? (Note, you will not be expected to understand the statistical analyses, but rather, examine the significance and usefulness of the results). The approach taken in your presentation is whether this method could be used in a â€œyour organization,â€ which happens to be like the one described in your example. Did the example cited provide actionable data?
Cite sources used for this assignment in APA format on the last slide.
Resources for Completing this Assignment
Here are some examples of â€œbig dataâ€ research (available on ERES):
Chase, H. S., Mitrani, L. R., Lu, G. G., & Fulgieri, D. J., (2017). Early recognition of multiple sclerosis using natural language processing of the electronic health record. BMC Medical Informatics and Decision Making, 17(1), 24. https://doi.org/10.1186/s12911-017-0418-4
Gibbons, C., Richards, S., Valderas, J. M., & Campbell, J. (2017). Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy. Journal of Medical Internet Research, 19(3), e65. https://doi.org/10.2196/jmir.6533
Oliveira, A., Faria, B. M., Gaio, A. R., & Reis, L. P. (2017). Data mining in HIV-AIDS surveillance system: Application to Portuguese data. Journal of Medical Systems, 41(4), 51. https://doi.org/10.1007/s10916-017-0697-4
Pruinelli, L., Yadav, Hangsleben, A., Johnson, J., Dey, S., McCarty, M., Kumar, V., Delaney, C. W., Steinbach, M., Westra, B. & Simon, G. J. (2016, July 20). A data mining approach to determine sepsis guideline impact on inpatient mortality and complications. AMIA Joint Summits on Translational Science Proceedings, 194-202. eCollection 2016. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5001751/
Westra, B. L., Christie, B., Johnson, S. G., Pruinelli, L., LaFlamme, A., Sherman, S. G.,â€¦ Speedie, S. (2017). Modeling flowsheet data to support secondary use. Computers, Informatics, Nursing: CIN, 35(9), 452-458. https://doi.org/10.1097/CIN.0000000000000350