Impact Insights

Reporting, a Building Block for Data Analytics

It goes without saying that the effective capture of data is a foundational step for reporting and data analytics, but where does the aspect of reporting stop and data analytics begin?  After working in the reporting area the last several years, I am a bit embarrassed to say that the line has been blurry and people use the terms interchangeably.  However, the current organization I am working with has helped bring new clarity to these terms and has further delineated the need to satisfy both areas.

My current role is Reporting Advisor where I assist with anything data reporting related, an expanding area I continue to find fascinating.  My current client is an academic, integrated delivery system with advanced reporting and business intelligence capabilities already in place.   We are currently in the system build phase of an EHR implementation and have supplemented our approach by interviewing a majority of system and entity executives to ensure current reporting needs are understood and addressed prior to go-live so there are no surprises.

Based upon their feedback, many rely on a small number of reports that are emailed to them.  I was somewhat surprised that more did not have interactive dashboards that they access via their mobile devices, but many preferred going to email and accessing what they need via attachment.  Additional questions we asked included who were the key people in their organization they relied on to answer questions about their reports or to pull new information.  Inevitably, we almost always received the names of three to six people who were the “go-to data people” and we proceeded to interview those individuals.  These go-to people, as we’ll call them going forward, are the data super users.

These data super users, or “data geeks” as many of them affectionately referred to themselves as, were the individuals receiving the reports and files from several systems and combining them into databases or other reports for their executives.  While the executives were more focused on a final number, measure, or set of figures, the data super users were actually performing the data analytics and detailed analysis.  Their jobs in many cases were answering the tough questions their executives would ask, such as “what are our admissions by facility compared to the last two years?”, or, “why have our infection rates increased in areas where we implemented recent changes?”  As our implementation progresses, we will likely use standard reports and customized versions to satisfy our executives, but our data super users  will need not just reports, but other advanced tools including universes, data cubes, and data marts to satisfy their needs; a much higher bar than just providing a report.

A report in simple terms can be a list of data – a list of patients, a list of debits and credits, or a list of medications or supplies.  It can be detailed or not, summarized or not, but likely is static and shows information from a snapshot in time.  Reporting, in the context of an EHR implementation, is the activity of creating and generating reports from a computer system.  Many vendors provide standard reports as well as technical tools to create custom reports.  The implementation team will learn the tools and typically implement a combination of off-the-shelf and custom reports to satisfy reporting needs.

While this type of reporting satisfies much of the organization, those that are charged with finding answers using the data, or data super users, is where data analytics comes in.  Data super users will receive reports, but that is only the start.  Their needs cover reports and data from many sources, so if one is asked to determine the effectiveness of a service line, they will likely have many report and data needs including patient visits, charge, cost, therapy, medication, staffing, reimbursement and patient experience data to help make a determination on how successful the launch performed.  The success of a program can be measured in terms of quality, patient experience, financial outcomes and many other factors.  Generally speaking, the faster you can get the data, the more time you have for analysis, but as experience has shown, more time is spent today in creating reports than performing analysis.

As the implementation continues, additional reporting needs will be identified.  While much of the organization will use reports, our data super users will use all the reporting tools in the EHR tool box to perform their data analytics functions.  Reporting will continue to be foundational, but the data analytics needs of an organization are limitless.


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