Minding the Data Freshness Gap

While every healthcare organization achieves the same basic task—open door, see patient, diagnose patient, bill patient, pay provider—the business processes involved can be very different, based on the stated mission, population served, and financial structure of the organization. Best practices that help the organization find the nexus of these competing goals might be generally defined, but business process flow can vary widely.

Mind The Gap

Every healthcare information software system can be molded to serve the existing process flow, but some organizations find it is easier to change the processes to fit the HCIS. Push comes from the opposite side of the system as well. Business processes are constantly evolving to meet the data-heavy reporting, quality, and delivery goals of healthy organizations. Even the best HCIS systems may need additional applications to help deliver information where business processes cannot reasonably be further modified to close the data delivery gap.

Addressing the Data Delivery Gap

Let’s look at MEDITECH’s proprietary Data Repository as a case in point. Because the MEDITECH HCIS is separate from MEDITECH DR, multiple issues surface that require IT monitoring and support:

  • transaction queue build up and delay
  • transaction job failures
  • SQL server out of space
    (no data gets in, even though MEDITECH attempts to send)
  • data truncation
    (data types in MEDITECH out of sync with MEDITECH DR)

For these reasons, a wide spectrum of MEDITECH DR data users, from executives and directors to managers and supervisors, find data pulled from the DR incomplete or out of sync from time to time.

With Data, Timing is Everything

When the CFO of a regional hospital network wants to know exactly how much money is owing to them, she needs confidence that every last billable item or event is appearing on the B/AR report. When daily census reports are used to create daily housekeeping schedules, the “freshness” of data is realized here more than when that same census data is used to report HEDIS quality measures a month later.

The default way to check data completeness is to run reports from both MEDITECH and the Repository, and compare manually. Both data inquiries and completeness assurance tasks fall to IT staff in the form of reports written using a specialized application. Just as learning a programming language is best done through practice, employing data reporting tools with accuracy and speed also requires an expertise typically best gained through practice. It is common for a large organization to have just a small staff with report writing experience.

While it might seem smarter to have more people trained in report writing, that can also lead to unintended consequences. If you have ever watched the promise of a SharePoint database devolve into uselessness because of poor file creation and editing practices, you know exactly what I mean! Information governance applies to output as much as it does input.

The complexity of data management means that business and executive teams who rely on information contained within the DR are often likely to underestimate effort to support a healthy DR. The trouble is that it takes a lot of time to review data quality, identify intermediate loads, respond to error reports, check queue length, and more. The time and effort required are often underestimated by IT, namely the executives at the highest level responsible for success.

Though the usual (or default) method of auditing DR is to manually compare reports, some enterprise teams do automate these audit tasks. Additionally, what works for a large hospital network may not be financially possible for smaller organizations. However, most homegrown data validation efforts cost more and deliver less than some readily available products.

Standing in the Gap

There are applications designed to stand in the gap. Blue Elm offers a group of products specifically designed to help minimize data latency issues in the MEDITECH DR. For the IT staff, their DR Dashboard product provides a real time view into transaction queues and pending activity. The DR Auditor application automates the data validation process, removing the need for manual auditing.

If you are looking to make MEDITECH data more accessible, Blue Elm’s OpenGate could be a smart choice. It allows staff to use the more ubiquitous SQL against the MEDITECH proprietary data structure. It checks a SQL statement against the MEDITECH mapping. This opens data for use by non-MEDITECH experts, and frees MEDITECH BI analysts from a barrage of report requests. How’s that for minding the data delivery gap?

This post was originally published on LinkedIn.


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