In an earlier article for Health Data Management, I defined the three main disciplines of a sound information governance model for health care organizations.
Together, those disciplines – the business information, systems and network, and information asset– support the entire lifecycle at a health care organization.
I now want to take a closer look at a health care organization's most important information assets: its analytic assets.
Analytic assets have grown in number in recent years, and there’s a critical need to manage these assets with agility, because doing so can improve decision-making and enhance business performance.
Characteristics and examples of analytic assets
Like any information assets, analytic assets can take shape as documents, e-mails, Web content, business data, images, video and other physical and digital formats.
Regardless of the format it's in, an analytic asset has several defining characteristics. An analytic asset:
- Is organized to resolve a business issue;
- Has structure, definition, meaning and ownership;
- Can be used by one department or the entire organization;
- Is an application, metric or measure that is maintained and updated and updated based on business direction;
- Can be SOX-compliant in the form of either spreadsheet reports or dashboards;
- Increases in value when being used consistently across the enterprise;
- Increases in value based on the level of improved analysis; and
- Reflects high quality and accuracy and influences the analysis performed with it.
Examples of analytic assets include medical loss ratio (MLR); per-member, per-month count; pay for performance application; balance scorecard; balance sheet; and financial analytics.
A closer look at MLR illustrates the weightiness of one of many analytic assets. MLR is a crucial component of a health plan's compliance with the Patient Protection and Affordable Care Act (PPACA). If a plan is not within the acceptable ratio for MLR, there will be penalties, which may be significant. Scenario-based analysis of MLR, including assignment of cost containment expenses, general growth assumptions and health care reform impact rules, enables quick scrutiny of the payer's current and future direction at the segmentation level.
Managing analytic assets
Management of analytic assets should follow standard management principles:
- Responsibility and accountability for the asset should be clearly established and maintained. Accountability includes being able to explain decisions and processes that let the organization to certain decisions;
- Responsibility should be established for stewardship of definitions, usages and sharing requirements (within the governance disciplines outlined in the May 2011 article I referred to previously);
- Assets should be inventoried and managed as part of the overall data environment. Refinement of analytic models may require audit capability to track changes; and
- Assets should be reviewed for impact analysis when changes occur in the business.
Proper management of analytic assets supports much-needed consistency in the use of such assets. For example, to demonstrate regulatory compliance with CMS/STARS reporting for quality metrics, a repeatable, secure and auditable analytic model, including version control and documentation capabilities, is essential. This is particularly true in light of the changing environment for CMS and health care reform.
The information governance framework I outlined in the April article presents a structure for capturing and maintaining accountability for managing analytic assets within each business area. With health care reform likely to remain the law of the land for the foreseeable future, fully engaged management of analytic assets is essential not only to business success, but to health care reform compliance as well.
The views expressed herein are those of the author and do not necessarily reflect the views of
Ernst & Young LLP. For more information, please contact Mark Vreeland at firstname.lastname@example.org
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