A new survey suggests that problems with duplicate patient records and patient identification are still costing hospitals a tremendous amount of money.
The survey, which was conducted by Black Book Research, collected responses from 1,392 health technology managers using enterprise master patient index technology. Researchers asked them what gaps, challenges and successes they’d seen in patient identification processes from Q3 2017 to Q1 2018.
Survey respondents reported that 33% of denied claims were due to inaccurate patient identification. Ultimately, inaccurate patient identification cost an average hospital $1.5 million last year. It also concluded that the average cost of duplicate records was $1,950 per patient per inpatient stay and more than $800 per ED visit.
In addition, researchers found that hospitals with over 150 beds took an average of more than 5 months to clean up their data. This included process improvements focused on data validity checking, normalization and data cleansing.
Having the right tools in place seemed to help. Hospitals said that before they rolled out enterprise master patient index solutions, an average of 18% of their records were duplicates, and that match rates when sharing data with other organizations averaged 24%.
Meanwhile, hospitals with EMPI support in place since 2016 reported that patient records were identified correctly during 93% of registrations and 85% of externally shared records among non-networked provider.
Not surprisingly, though, this research doesn’t tell the whole story. While using EMPI tools makes sense, the healthcare industry should hardly stop there, according to Gartner Group analyst Wes Rishel.
“We simply need innovators that have the vision to apply proven identity matching to the healthcare industry – as well as the gumption and stubbornness necessary to thrive in a crowded and often slow-moving healthcare IT market,” he wrote.
Wishel argues that to improve patient matching, it’s time to start cross-correlating demographic data from patients with demographic data from third-party sources, such as public records, credit agencies or telephone companies, what makes this data particularly helpful is that it includes not just current and correct attributes for person, but also out-of-date and incorrect attributes like previous addresses, maiden names and typos.
Ultimately, these “referential matching” approaches will significantly outperform existing probabilistic models, Wishel argues.
It’s really shocking that so many healthcare organizations don’t have an EMPI solution in place. This is especially true as cloud EMPI has made EMPI solutions available to organizations of all sizes. EMPI is needed for the financial reasons mentioned above, but also from a patient care and patient safety perspective as well.
