Analytics professionals on the front-lines of healthcare
I firmly believe that analytics professional working in a healthcare environment should spend as much time working with and learning from front-line healthcare professionals as they spend in front of a computer developing analytical tools and studying data. The reasoning behind this is two-fold.
First, time spent working with front-line providers lets the analytics professional to better understand the context and nuances in practice and processes that ultimately end up as data being analyzed. Without this level of understanding of the data, analysis will be limited to simple descriptive statistics; any deeper analysis will be impossible.
Secondly, analytics professionals can (and do) bring a sense of what is “possible” (from an analytics perspective) to quality improvement teams. This experience and knowledge of data analysis and representation beyond simple static reports is where analytics professionals can really bring value to the teams.
Optimal healthcare quality team composition includes analytics expertise
Naturally, healthcare quality improvement teams consist of front-line healthcare providers who are subject matter experts (SMEs) on the particular area being addressed, as well as facilitators skilled in the various methodologies being used (such as Lean). It is relatively uncommon, however, for analytics professionals to be involved directly on the team.
Because of this, analytics expertise is typically sought after the fact – once the quality team has already decided which new reports, dashboards, analyses, and other analytics tools they feel need to be built or completed. Quality improvement teams, however, are often not fully versed on what data is available electronically from source systems or how that data can best be analyzed.
Avoiding more of the “same old”
Embedding analytics expertise on quality teams can provide healthcare quality teams insight into things like:
- what data is available,
- how the data can be accessed,
- what metrics are appropriate (and which have already been defined by other projects),
- in what ways the data can be analyzed, and
- what analytic and business intelligence tools (dashboards, reports, models, simulations, etc) can be built to support initiatives.
Without this analytics expertise and insight, it is possible that quality improvement teams may ask for analyses that are impossible or undesirable. This may be because the necessary data does not exist electronically or because the requested analyses do not fit the data. Without the necessary expertise, quality teams will continue to ask for the “same old” type of reporting that has always been done because that is all they know, and are unaware of the possibilities.
Analytics in practice
I have always encouraged quality improvement teams to include an analyst on their teams. This is good for the career development of the analytics professional, but also beneficial for the teams. I have been part of many process improvement projects where the analytics professional has greatly contributed to the success of the project by bringing analytics directly to the front line. Just a few examples of how analytics expertise have benefited quality improvement projects I have been involved with include:
- using simulation to visualize improvements to Emergency Department patient flow
- implementing real-time dashboards to highlight barriers to effective patient care
- developing useful (and relevant) process performance reports based on needs identified by front-line staff
(A few future blog posts will provide more detail about these tools used on quality improvement projects)
Keep it simple – and useful!
Keep in mind that analytics at the front-line don’t need to be earth-shattering. The analyses, reports, and applications created by analytics experts simply need to be useful. As healthcare quality improvement and decision-making becomes ever-more data-centric, an analytics professional is the best person on a quality improvement team to bridge the gap between a problem under investigation and how to analyze and represent its magnitude and progress made toward eliminating it.