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.

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.

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.
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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