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In the business of health care, patient appointments mean revenue. If there are no appointments, there is no revenue. If appointment-scheduling software goes amok and appointments are lost or are error-ridden, revenue is lost.
When one health care system purchased new software for the management of EMRs, insurance billing and appointment scheduling improved, and it was a big efficiency win. Multiple software systems would now become a single solution of streamlined software, and the major revenue-generating functions of the hospital would work together on a more efficient platform.
But this win quickly turned into an implementation nightmare.
As a phase of the migration project, the IT department was tasked with moving patient appointments to the new platform, with a go-live deadline to take place within months. As with many health care systems, merger and consolidation of regional hospitals into the system had been common over the past decade. This meant a continually increasing volume of appointment records, creating many data-accuracy challenges. Moreover, records were typically created by manual data entry, which often meant typographical errors, missing data fields, and other inaccuracies caused by human error.
To further compound the challenge, the hospital had been acquiring private-physician practice groups that used a variety of formats and types of database records for appointment scheduling. Different practice groups kept and required different patient data for appointment scheduling, which resulted in different database record formats and varying database fields for the same type of appointment, depending on which hospital department originated the record. In other words, records were not consistent across the enterprise. This lack of consistency regarding current appointment-scheduling conventions throughout the various hospital records caused major challenges in determining whether appointment-scheduling records would correctly transfer during the migration project.
Typically, data accuracy issues are “fixed” through the stopgap of adding bodies to manually deal with it. The hospital IT department, however, had limited FTE bandwidth and limited ability to hire contract labor to work on data-entry corrections and to check whether records transferred over correctly. In any case, the time required for this “solution” to the accuracy-checking problem would have caused an unacceptable delay in the go-live. Given the huge volume of appointments scheduled across the enterprise, and the deadline for go-live, data accuracy was going to be impossible to determine. There was huge potential for crash and burn on the go-live. The only feasible solution was to manually spot-check records to determine whether they were accurately moved to the new software platform, leaving the majority of the records unchecked, unverified, and with unknown accuracy.
With the Naveego complete data-accuracy platform, revenue loss due to lost or botched appointment-scheduling records is avoided. By using Naveego’s complete data-accuracy platform, any health care system can automate data accuracy for a migration project. The Naveego platform is technology agnostic and can connect to any data source to verify data accuracy. Naveego can connect to the old health care software platforms as well as the new, verify accuracy in real time during data migration, and monitor the data accuracy of appointment scheduling moving forward, keeping the data clean and thereby reducing loss of revenue from missed appointments due to data errors.
With the Naveego complete data-accuracy solution, data quality checks are easily set to verify that records transfer without error. Naveego means migration-project success, with the go-live deadline met and $0 lost revenue from data errors.
Katie is a former IP attorney at Microsoft, and leverages her Silicon Valley background and industrial engineering skills, for start-up scaling and growth. She has been recognized at U.S. Congress as a business leader for innovative business models.