Polishing the Stone – Where to Start with Healthcare Analytics

by | Dec 22, 2014 | Healthcare

Reading Time: 4 minutes

polishing the stoneThe world of healthcare is changing at an accelerating pace, and navigating it as a healthcare provider requires agility. There are three main factors that are driving this change:

  • Healthcare transformation:

    Regulations are putting pressure on providers to provide higher quality at a lower cost. There are also new ways providers are reimbursed (pay for performance vs. fee for service). These factors are leading to greater consolidation in the healthcare industry.

  • Healthcare information evolution:

    To support this transformation, there are massive investments in information systems, particularly in EHRs.

  • Technology innovation:

    New advancements provide additional advantages and opportunities to healthcare providers; however, healthcare technology is changing at a pace that exceeds the ability of even large organizations to keep up.

Many of these challenges lie between these three areas, and analytics has the potential to serve as a bridge in addressing them. However, for many healthcare organizations, wrapping their heads around analytics is a difficult proposition. Organizations know they should do something with analytics, but knowing where and how to get started may be less clear.

Where to start with analytics

During his final appearances as the National Coordinator for Healthcare Information Technology, Dr. Farzad Mostashari proposed an approach to healthcare analytics that he described as “polishing the stone”.

Dr. Mostashari’s metaphor suggests using data you already understand as the starting point of your “analytics journey”. For most healthcare organizations, that consists of information derived from administrative claims.

Now this may be somewhat of a surprise coming from a passionate advocate of EHR technology and someone responsible for encouraging its adoption. However, Dr. Mostashari’s point is that effective analytics requires a solid foundation. And that foundation is only as strong as the integrity and reliability of the data used to construct it.

No doubt, the clinical data captured in certified EHRs holds a great deal of analytical promise. But it also represents a layer of information that requires a reliable foundation to be truly useful.

There are a few reasons why Dr. Mostashari’s suggestion makes good sense:

  1. Claims data uses well defined standards, such as ICD-9/10, CPT-4 and UB-04, to define data elements. It is also quite reliable, with high levels of data integrity, since it is used as the basis for financial transactions between payers and providers. These qualities make it ideal for objective analysis.
  2. Both government payers, such as CMS, and private payers use claims data for many of the quality and outcome measures used to evaluate provider performance. Understanding claims-based data from an analytical perspective is essential to proactively managing the performance of these measures.
  3. Building, analyzing and optimizing a claims-based data set is an ideal way to develop a base set of analytical competencies within your organization.

Combined with information from various operational systems, this data set can be used to illuminate performance patterns in areas such as utilization, efficiency and productivity.

Adding value to analytics

But the data elements that comprise standard claims data are just the starting point for laying the foundation.  A great deal of value can be added by integrating information from readily available sources.  The Healthcare Cost and Utilization Project, which is sponsored by the Agency for Healthcare Research and Quality (AHRQ), provides a family of supplemental data sets that can be added to claims data, including:

  • Clinical Classifications

    : Provides clinical meaningful categories using ICD-9/10 and CPT coded data.

  • Comorbidities

    : Identifies clinically significant groups of conditions.

  • Procedure Classes

    : Classifies procedures into major (OR-based), minor, diagnostic and therapeutic groupings.

  • Utilization Flags

    : Reveals usage patterns for procedures and services such as ICU, diagnostic tests and therapies.

  • Surgery Flags

    : Identifies surgical procedures and encounters.

AHRQ also provides free Quality Indicator Software that can be used to compute quality and safety measures designed to assist with quality performance efforts.

Incorporating data from these sources into a basic claims data set, and refreshing it daily, will provide a powerful information asset that can be used to better understand utilization patterns, segment populations, and improve outcomes. This is certainly a valuable, and practical, first step to consider for your analytics journey.

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