The pMD Blog
Leveraging pMD Data to Improve Patient Outcomes

As our customers grow and evolve their medical practices, they need to be able to do sophisticated analysis of their patient data. This is essential to ensuring that they’re able to run their businesses efficiently, and provide the best possible outcomes for their patient population. pMD has always had a set of reports that are available for our customers to run on demand, but, as our users have begun to utilize pMD to capture and track quality (MIPS, OPPE, etc), transition of care, and post-acute follow-up data, the breadth and depth of their datasets has grown significantly.

To support these growing reporting needs, we’re building a dynamic Online Analytical Processing (OLAP) reporting tool. The idea is to give our customers a mechanism to define the kinds of questions they want to ask of their data, then build a multidimensional data “cube” capable of providing realtime answers to those queries. Users will be able to export the raw data into a spreadsheet, and will also have the option of doing an analysis in our new visualization tool.

What types of questions are pMD customers asking? There is a large variety, but take for example a line of questioning a practice manager might have about his/her group’s readmission rate: “How many of the patients whom we discharged from the hospital in the last month had a readmission within one week?”; “What was the most common diagnosis”; “How many of those readmissions occurred on a weekend?”; “How many were attributed to each of my providers?”; etc. Or, consider a nephrology practice that might want to analyze their Monthly Capitation Payment (MCP) data: “Show me a breakdown of comprehensive vs. limited visits for each of my dialysis units for the last quarter”; “Of those, which provider had the most comprehensive visits?”; “What’s our most popular day of the week for dialysis, per unit?”.

This is just a small subset of the types of questions groups have told us they want to be able to ask. And the really interesting, and challenging aspect about supporting this type of reporting is that folks don’t always know in advance exactly what they might want to ask of the data. Finding the answer to one question often leads to several more questions. The ideal reporting tool tries to accommodate this by allowing the user to “drill-in” to their data, down to a very granular level, in order to understand their patient population. Armed with this business intelligence, we think the feature set we’re building will give our users a powerful new tool for running their businesses.