Co-Morbidities And Using Data To Manage Population Health

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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.
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.
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Post:
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.
References
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.© 2016 Laureate Education, Inc. Page 1 of 1

Medicare Data Scenario Examine the CMS Chronic Conditions Triads: Prevalence and Medicare Spending spreadsheet located in your Learning Resources. Familiarize yourself with CMS data regarding chronic conditions and Medicare spending (CMS.gov), beginning with the first tab in the spreadsheet, titled Overview, that summarizes the data sources, study population chronic conditions, and socio-demographic variables involved in the data. Note that the remainder of this data set presents five years of data on various triads of chronic conditions that represent material co-morbidities studied by CMS. With the CMS development of ACO’s (accountable care organizations) there is an emphasis on managing certain chronic conditions to minimize hospital readmissions. The pro-active medical management of heart failure, specifically CHF (congestive heart failure), is a focus in trying to prevent unnecessary hospital admissions. In the medical management of this condition and associated comorbidities such as diabetes chronic kidney disease and hyperlipidemia, patients must manage both their fluid intake and maintain a rigorous regime of medication such as beta-blockers. A lack of medication compliance and or fluid management in these patients often results in repeated emergency room visits and or hospital readmissions to stabilize physiologic parameters. In this scenario assume you are an administrator of an integrated delivery network who is working with CMS on developing an ACO. Using these historical, five year data on CMS patients with comorbidities related to Heart Failure and per capita spending, you are asked to work with an IT analyst to lead the design of the functional requirements for the data warehouse. This business intelligence application will upload information from your organization to CMS as a part of the ACO. Senior leaders want to understand which HIT systems and which data within those HIT systems will be required to contribute relevant information to CMS regarding comorbidities on heart failure patients. They also want to understand the availability of those data and the level of quality of those data in the organization, as they will be key to the financial parameters set within the ACO agreement.

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