Co-Morbidities and Using Data to Manage Population Health

Managing the quality and cost of co-morbid populations is one of the most challenging aspects of health leadership. In this Discussion, you are challenged with selecting those data which will be most helpful in the management of Medicare populations. As health information exchanges (HIEs) progress at the state, federal, and nation level, health leaders are tasked to participate in the development of analytics tools that can be used to pull data and inform policy practice.

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Scenario: Review the high volume Medicare Data Scenario located in the Learning Resources. In this scenario you are asked to work with a complex dataset of co-morbidity data of patients that have three concurrent co-morbid conditions (Chronic Condition Triads: Prevalence and Medicare Spending). How can data from HIT systems be used to formulate useful information to facilitate in the management of this population?

To prepare:

  • Using the health care information systems standards for clinical and financial data discussed in Week 6 (Chapter 10 of Health Care Information Systems: A Practical Approach for Health Care Management), identify specific types of data (data sets, standards, examples of those data) that can be redeveloped into Big Data tools and used to address the management of population health initiatives.
  • Define a “Big Data” analysis dataset to include in a data warehouse by identifying two specific types of clinical and financial data from the Chronic Condition Triads: Prevalence and Medicare Spending dataset in your Learning Resources that you feel could be used to drive behavior change in the patient and provider populations. This Big Data dataset will become the focus of your Discussion.

By Day 3


Explain why the two specific types of clinical and financial data you selected as your Big Data dataset would best affect behavior change in the type of co-morbid Medicare populations served in the scenario. Explain and assess how this Big Data dataset can change the behaviors of health care providers in the scenario. Assuming that your Big Data dataset is going to be shared in a regional health information exchange, explain how the Centers for Medicare and Medicaid Services and private payers might use these regional data sets to increase value in delivering services to co-morbid Medicare patient populations in the region.




Wager, K. A., Lee, F. W., & Glaser, J. P. (2013). Health care information systems: A practical approach for health care management (3rd ed.). San Francisco, CA: Jossey-Bass.

  • Chapter 2, “Health care Data Quality” (pp. 49–65)

Reddy, C. K., & Aggerwal, C. C. (2015). Healthcare data analytics. Boca Raton, FL: CRC Press.

  • Chapter 4, “Mining of Sensor Data in Healthcare: A Survey” (pp. 91–126)
  • Chapter 7, “Natural Language Processing and Data Mining for Clinical Text” (pp. 219–250)
  • Chapter 10, “A Review of Clinical Prediction Models” (pp. 343–378)

Amarasingham, R., Patzer, R. E., Huesch, M., Nguyen, N. Q., & Xie, B. (2014). Implementing electronic health care predictive analytics: Considerations and challenges. Health Affairs, 33(7), 1148–1154.

Note: Retrieved from the Walden Library databases.

Barclay, G., Sabina, A., & Graham, G. (2014). Population health and technology: Placing people first. American Journal of Public Health, 104(12), 2246–2247.

Note: Retrieved from the Walden Library databases.

Block, D. J. (2014). Is your system ready for population health management? Physician Executive, 40(2), 20–22, 24.

Note: Retrieved from the Walden Library databases.

Coffin, J., Duffie, C., & Furno, M. (2014). The patient-centered medical home and meaningful use: A challenge for better care. The Journal of Medical Practice Management: MPM, 29(5), 331–334.

Note: Retrieved from the Walden Library databases.

Foldy, S., Grannis, S., Ross, D., & Smith, T. (2014). A ride in the time machine: Information management capabilities health departments will need. American Journal of Public Health, 104(9), 1592–1600.

Note: Retrieved from the Walden Library databases.

Fry, D. E., Pine, M., Locke, D., & Pine, G. (2015). Prediction models of Medicare 90-day post discharge deaths, readmissions, and costs in bowel operations. The American Journal of Surgery, 209(3), 509–514.

Note: Retrieved from the Walden Library databases.

Grover, A., & Joshi, A. (2015). An overview of chronic disease models: A systematic literature review. Global Journal of Health Science, 7(2), 210–227.

Note: Retrieved from the Walden Library databases.

Kringos, D. S., Boerma, W., van der Zee, J., & Groenewegen, P. (2013). Europe’s strong primary care systems are linked to better population health but also to higher health spending. Health Affairs, 32(4), 686–694.

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