A Recipe for Analytics, Key Ingredient #4 – Analytics Competency

James Gaston

This 4-part blog series focuses on building a successful analytics strategy with each of these 4 ingredients.

Key ingredients

  1. Very liberal amount of Data Content
  2. Proportional dose of Infrastructure
  3. Quart of Data Governance
  4. Several stalks of Analytics Competency

On the original Iron Chef TV cooking show they often spoke Japanese and it was unclear to other audiences what was being said.  But the tone of the voices, the excitement, and the actions of the cooks furiously sautéing, slicing, and simmering was magical to watch.  With only 1 hour of time they were challenged to prepare multi-course meals from scratch, each including a secret ingredient that was not revealed until right before the clock started ticking.

Such as the genesis of many analytics projects at healthcare organizations.  “We need answers, we need them right away, and this is the new data that you need to work with” to prepare a presentation. In these cases, having the competency - or well developed skills and understanding -  to know what to do, how to do it and how to deliver a satisfying result are critical.  Analytics competency is the yin to the yang of cooking in this analogy.  Let’s continue our cooking theme and talk about the last key ingredient for healthcare analytics, analytics competency.  

Key Ingredient #4: Analytics Competency

Analytics competency takes time and effort to develop.  Not to be confused with analytics culture, which is to say the use of analytics in everyday tasks and organizational processes, analytics competency is about the craft and skill of applying analytics.  Just as a good chef can make a recipe not only consistently taste amazing and adjust to changes in the availability and quality of ingredients, so must a good analyst do for any given analytical project. Procuring the data, understanding its features and flavor, as well as applying the proper preparation and presentation of findings are part and parcel of exceptional analytical skills.

Mastering analytical skills takes a concerted and focused effort.  If these skills are not used, encouraged, and integrated into the norm of corporate expectations they will begin to go bad, just like food that has been in the fridge too long.

The HIMSS Analytics Adoption Model for Analytics Maturity (AMAM) provides a roadmap to help healthcare organizations begin to develop and advance along their analytics journey, with one of the model’s focus areas being analytics competency.  The AMAM helps organizations develop analytical talent and skills with basic, logical steps along the AMAM roadmap.

The lower AMAM Stage analytics competency focus areas are designed to initiate and help organizations get a handle on what assets are in place and available, standardizing analytical approaches, resolving issues related to master data management, and providing consistent direction to enable data-driven decision making.

Higher level AMAM Stage analytical competency requirements ensure analytical proficiency and motives are aligned behind advanced business, clinical, and financial data-driven decisions.  Stage by Stage improvements of data governance are represented by these key ingredients:

Stage 0: Fragmented solutions and skills

Stage 1: Education, skills of analytics resources are profiled and inventoried

Stage 2: Analytics competency center, registry portfolio buildout

Stage 3: Consistent, efficient report production supporting operations and management

Stage 4: Focused on best practices, minimizing waste, reducing variability

Stage 5: Population health, precision registries

Stage 6: Analytic motive addresses high volume diagnosis-based cohorts

Stage 7: Prescriptive analytics, mass customization of care, wellness management

The AMAM analytical competency requirements for each Stage are designed as a roadmap to help healthcare providers assimilate a responsive, capable analytics environment that enables a wide variety of clinical, operational, and financial analytics insights (or recipes).

Other key ingredients in the analytic recipes call for fresh data, data governance, and a supportive infrastructure.  Click below to read all of the posts in this AMAM blog series to better understand the power of analytics.



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This post is part of a multipart series on healthcare analytics. Read Part 1 Part 2 and Part 3 here and continue to follow HIMSS Analytics for more on this topic. 

If you have questions about AMAM or healthcare analytics please reach out to our client relations team and read more here