Adoption Model for Analytics Maturity (AMAM)
The HIMSS Adoption Model for Analytics Maturity (AMAM) measures the analytics capabilities that healthcare organizations have gained from having a strong analytics strategy and competency, and advances an organization’s healthcare analytics regardless of the technologies installed.
Analytics serve to improve many facets of a healthcare business beyond clinical decision support, such as an organization’s operational and financial aspects. With the AMAM, turn data into actionable insight as you assess and optimize capabilities gained through the adoption and implementation of a healthcare analytics strategy, its surrounding processes, and your workforce’s analytics competency. Organizations can leverage the AMAM to improve the predictive analytics and governance and workforce dimensions of digital health.
Grow Data Content
Grow your organization’s data content to improve operational, clinical and financial performance.
Build Data Infrastructure
Develop a strategy for sourcing data, and use it across your health system.
Shape Data Governance
Manage your data assets and align your analytics efforts with overall organizational strategy.
Guide Analytics Competency
Develop your analytics resources, and refine skills in a coordinated and structured manner.
- Leverage advanced data sets, such as genomics and biometrics data, to support the uniquely tailored and specific prescriptive healthcare treatments of personalized medicine.
- Deliver mass customization of care combined with prescriptive analytics.
- Demonstrate maturity in use of predictive analytics
- Demonstrate expanded focus on advanced data content and clinical support
- Demonstrate expanded point-of-care oriented analytics and support of population health.
- Align data governance to support quality-based performance reporting and bring further understanding around the economics of care.
- Direct analytical data assets, skills and infrastructure squarely toward improving clinical, financial and operational program areas.
- Make a concerted effort to understand and optimize clinical care by honing analytics resources that support evidence-based care, track and report care and operational variability, and identify and minimize clinical and operational waste.
- Demonstrate mastery of descriptive reporting broadly across the enterprise.
- Varying parts of the organization are able to effectively corral data, work with it, and produce historical and current period reporting with minimal effort.
- Data quality is stable and predictable, tools are standardized and broadly available, and data warehouse access is managed and reliable.
- Data is presented in a formal data warehouse as an enterprise resource (as opposed to a silo-oriented and narrowly used resource), with master data management that supports ad hoc queries and descriptive reporting.
- The enterprise begins maturing data governance while leveraging this environment in support of basic clinical and operational tasks, such as patient registries.
- All activities are aligned with the organization’s overall strategic goals.
- Analytic skills, standards and education are managed through an analytics competency center.
- Organizations begin to accumulate and manage data in a centralized location, like an operational data store or data warehouse, supporting historical reference and consolidated access.
- Document and begin execution of an analytics strategy that brings basic data together from appropriate systems of record and learns to manage (data governance) and define data to be used and referenced by a broad cross section of analysts.
- All organizations start their analytics journey at Stage 0, with a desire to learn about developing analytics capabilities in response to business demands and market pressures, and a need to develop further insights into the important decisions they make every day.