I heard this great quote this week in a presentation and I decided to do some research on options in healthcare analytics. When we provide analytics to our clients we are typically just showing current state outcomes and not necessarily predictive analytics (see below). When considering service line agreements (SLAs), for example, the analytics are to provide evidence of achieving outlined goals. That’s helpful for showing outcomes to targets, but doesn’t really provide evidence of opportunity.
The question really is, how can we provide data that drives performance improvement and is outlining actionable data?
I remember in my Six Sigma training some years back the concept of “Define Measure Analyze Improve Control” process (DMAIC). It’s a concept that any consult can directly apply to your client’s project.
The basics are:
• Define the problem or hypothesis, stakeholders and scope of analysis.
• Measure relevant data and conduct basic analysis to spot anomalies.
• Analyze via correlations and patterns, provide key visualizations.
• Improvement based on insights and showing several options to explore.
• Control the change by monitoring agreed on Key Performance Indicators (KPIs).
The other thing to consider are the various tools you may have in providing your client with analytics.
• Embedded Analytics – Amp up applications for clients within their EHR and use the data content within the EHR applications. It provides relevant information and analytical tools designed so end users can work smarter and more efficiently in their various modules. Epic does this very well through all kinds of reporting options, I’m sure Cerner and MEDITECH have comparable capabilities.
• Predictive and Prescriptive Analytics – Sharpen insights and improve accuracy of decisions on actions to be taken based on reporting. Data analytics leads naturally to predictive analytics using collected data to predict what might happen. Predictions are based on historical data and rely on human interaction to query data, validate patterns, create and then test assumptions. I saw a great presentation by my company that offers this in our DMA products….very cool.
• AI-Assisted Analytics – Offer a smarter user experience with search, voice, and narration options. AI is a combination of technologies, and machine learning is becoming ever more popular in healthcare. AI machine learning makes assumptions, reassesses the model and reevaluates the data.
As a consultant we may be faced with variations of client requests when it comes to tracking performance, providing data on deliverables, and/or showing performance improvement trends. I think we need to make sure we are tracking those areas of true value and reporting on them in a more progressive future state goal driven manner vs current state monitoring that just shows minimal achievements. Did I just write that? LOL. That is a mouthful, but I think it makes sense and aligns with the quote I heard around ‘news vs actionable’ data.
What are your thoughts on driving actionable data? Have any successes in this area? Share your comments below.