Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper)



healthcare analytics 2Over the last few years, there has been a flurry of activity around the topic of healthcare analytics (the discovery and communication of meaningful patterns in data) and even more recently, the use of “big data” (the collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications). This somewhat sudden interest in the topic can be traced back to a 2001 phone call between Dale Sanders, then serving as Director of Enterprise Data Warehousing for Intermountain Healthcare in Utah, and Pat Taylor of Blue Cross/Blue Shield of Alabama, during which time they founded the Healthcare Data Warehousing Association (HDWA) to accelerate the adoption and exploitation of analytics in healthcare. That phone call between two colleagues grew into a professional group that now includes over 300 organizational members in the U.S. and Canada.1

Culminating years of work in this arena and anticipating the healthcare industry’s needs, Sanders published a commentary in 2012 in which he released the inaugural version of the Healthcare Analytics Adoption Model (HAAM), a proposed framework to measure the adoption and meaningful use of data warehouses and analytics in healthcare in ways similar to the well-known HIMSS Analytics EMRAM model.2 After consultations and feedback from the industry, the second version of the HAAM is now being released.


What seems to be emerging in healthcare is a repeat of the trend of computerization and data management in other industries. Phase 1 of an industry’s computerization is portrayed by systems that are designed specifically for supporting transaction-based workflow and data collection. In healthcare, this phase is characterized by widespread electronic medical record (EMR) adoption. In Phase 2, the need for sharing data among members of the workflow team becomes apparent. In the case of healthcare, this phase is characterized by health information exchanges (HIEs).

In Phase 3 of computerization, organizations realize that the data they are collecting and sharing can be used to analyze aspects of the workflow that are reflected in the patterns of aggregated data. Healthcare is now entering Phase 3, the data analysis phase, which will be characterized by the adoption of enterprise data warehouses (EDW), now becoming synonymous with the term “Big Data.”

This same three-phase evolution seen at the industry-level also applies at the micro- level within an organization. Early adopters of EMRs are thus more likely to have transitioned through these three phases, even though the healthcare industry as a whole has yet to do so. Organizations such as Intermountain Healthcare, using the HELP EMR, and the U.S. Veterans Affairs (VA) Health Care system using Vista, were also early pioneers in reaching Phase 3 of data management. Examples of integrated care models in the United States and beyond demonstrate that, when incentives are aligned and the necessary enablers are in place, the impact of leveraging big data can be very significant. The VA health system generally outperforms the private sector in following recommended processes for patient care, adhering to clinical guidelines, and achieving greater rates of evidence-based drug therapy. These achievements are largely possible because of the VA’s performance-based accountability framework and disease-management practices enabled by EMRs and analytics allows them to frequently close the loop on clinical practices.


The Analytics Adoption Model was designed purposely to mimic the benefits of a structured educational curriculum based on over 20 years of industry observation and lessons learned in healthcare. The curriculum is designed to ensure that organizations establish

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