Week 7: Using HIT to Strategically Manage the Health of Populations
Everyday private enterprise organizations are mining data and using that data to effectively manage their business. Best practices in transforming day-to-day operational data into strategic organizational assets are gleaned from financial services, retail, airlines and the insurance industries. This week allows you to strategize how health care organizations can likewise benefit from using their clinical and financial transactional data in a more strategic manner.
Behavioral change is also addressed because as leaders we recognize that transformation comes from facilitating behavioral change at many levels. This includes affecting the behavior of health care consumers as well as internal operations and medical staff. Using tools such as data warehouses and database queries leaders can be more data driven in the clinical and financial decisions that they make.
Medicare collects data from providers on a regular basis. Much of the Medicare population is treated for many co-morbid issues that drive a great deal of expenditure in the health care system. Health providers are seeking technology solutions in the form of building data warehouses that involve these Medicare data sets as well as their own data sets from internal electronic health record information systems. This week you will examine a Medicare data set of patients with complex co-morbidities and analyze what kind of health care information technology data elements would be used by a provider organization to partner with CMS and use that data set strategically to address cost and quality goals.
Learning ObjectivesStudents will:
- Assess how information technology can affect behavioral change in individuals and organizations
- Analyze how Big Data Analytics can increase value for providers, consumers and payers in delivering services
Discussion: 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?
- Using the health care information systems standards for clinical and financial data discussed in Week 6 (Chapter 11 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.