Reference no: EM131673037

Discussion Background

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 Objectives

Students 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

Instructions for the Discussion:

Discussion Topic: 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?

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.

To prepare:

- 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.

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.