4 Steps for IT Success
In my experience, there are 4 areas of focus that need to be addressed for an individual health IT project to succeed. I can help Epic organizations in all 4 areas. To see my take on these elements and how they connect with larger health IT issues, take a look at my Substack piece on health IT failures (or success).
These four steps ideally create a positive feedback loop that facilitates users' work, generates data and new insights that in turn help to develop even more new insights.
Usability
First, the system needs to be designed in a usable manner. The findings of KLAS' Arch Collaborative demonstrate that even very advanced systems can be deployed poorly, while comparatively basic systems may outperform expectations. In Epic organizations, there are often opportunities in connecting what the system can do and what will help an end user. In particular, the IT analyst may know of a particular tool or technique that is accessible but the clinical end user may not know that such a tool exists. Conversely, a clinical end user may have a particular need but the analyst team may not know that they have the need because they frame their requests in terms that are familiar to them such as alerts and order sets. Finally, if you can make the system work well for the end user, there are mechanisms in Epic to create good data just because the users are using the system well.
Data Governance and Curation
The data exhaust of user workflows should produce clean, accurate, and discrete data. However, raw data generally lacks meaning. In Epic, much of this extraction and governance can be facilitated, standardized and curated using tools like registries, flowsheets and SmartData. In my experience, most organizations miss out on the opportunity to capture enormous quantities of data because they don't fully leverage these tools.
Data Processing
Once we have data (ideally well curated and clean), you need to process it. This processing consists of taking multiple data points, combining them into a logical framework to come up with a new insight. The current hype around AI fits in this bucket. However, "processing" also includes more ordinary descriptive analytics and even simple pivot tables. It is still astounding to me how many organizations want to jump straight to neural networks and LLMs but still struggle with basic descriptive analytics. Let me leverage my knowledge of Epic's data structures to help your BI and data science teams come up with better reports and advanced models.
New Insights
From the processing step, you will get new insights and opportunities for new tools. This can range from understanding the top 3 reasons patients' discharges are delayed to coming up with a new way to score stroke risk. If you do it right, you will head back to step 1 and create elegant ways that make it painless for end users to incorporate these new insights. Let me help you close the circle, transforming these insights into Epic workflows that don't require a lot of training for your end users.