One of the more difficult data threads for healthcare providers to follow is a patient’s medication history. Between multiple past providers and the likelihood of different prescription nomenclature systems, current providers can spend a lot of time and energy on ensuring they’ve effectively reconciled a patient’s medication history, and they still might not get it right.
Recently at HealthcareITNews, Bill Siwicki took a look at one provider – King’s Daughters Medical Center, a 99-bed community hospital in Brookhaven, Mississippi – who was concerned that patient records could potentially contain incomplete or inaccurate medication history data, and decided that the best way to improve the safety of medication reconciliation was to automate the transcription of “sig data” – the shorthand prescribing instructions – into their patients’ EHRs.
“Doing that meant finding a way to convert free-text into programmable data yielding discrete sig components within a patient’s medication history,” says Siwicki. “The hospital hoped that doing so would reduce clicks and keystrokes, ensure a more accurate patient medication history, and reduce adverse drug events.”
According to Joe Farr, RN, clinical applications coordinator at King’s Daughters, the hospital turned to its e-prescribing partner, health IT vendor DrFirst, “for implementation of an AI-powered solution that uses Natural Language Processing and machine learning to process and validate results, and codifies sigs into each facility’s standard terminology – for example, ‘by mouth’ versus ‘oral’ or ‘PO. The automation operates entirely in the background, without clinician intervention, and uses statistical validation and clinical analysis to translate sigs in real-time.”
The new system also helps staff resolve gaps by supplying alternative drug IDs for best-case drug matching, and details for incomplete or uncommon sigs, Siwicki adds. “Multiple safety checks disqualify transactions that are deemed clinically invalid. The system is designed to prefer no data to wrong data. If it determines that a sig data point poses a safety risk, it errs on the side of not transcribing it.”
The upshot?
According to Siwicki, “the automated sig translation system helped King’s Daughters reduce the number of incomplete or error-filled patient medication records, which in turn minimized pharmacy call-backs, workflow disruptions and patient treatment delays. It also significantly reduced the average number of keyboard clicks required for medication reconciliation, resulting in additional staff time and cost savings.”
More precisely, explained Farr, “(b)ased on 19,390 annual patient visits and an average of five medications per patient, the resulting time savings of 34 hours per month for clinicians, or 404 hours per year, translates into about $11,000 in recaptured nursing productivity over 12 months,” Farr noted. “This easily justifies the minimal investment of time and resources necessary to deploy the solution.”
And best of all, at least from a healthcare standpoint, “the new system appears to have contributed to increased patient safety and improved health outcomes.”