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Machine Learning and NLP Improve Medication Reconciliation, Patient Safety

By Joe Farr, RN, and David Sellars

To improve patient safety, create efficiencies, and streamline the medication reconciliation process, King’s Daughters Medical Center automated the transcription of sig data into the EHR, improving clinical satisfaction and reducing 30-day readmissions by more than 11%.

Fully three-fourths of hospital executives worry that their patients’ medication history data are incomplete and/or inaccurate, according to a survey by the College of Healthcare Information Management Executives. The news was especially disappointing coming after years of investment in improving the medication reconciliation process. Many hospital leaders likely wondered what more could be done to ensure accurate medication data were available to help ensure patient safety and positive outcomes.

That was also a concern for leaders at King’s Daughters Medical Center (KDMC), a 99-bed community hospital in Brookhaven, Mississippi, 60 miles south of the state capital in Jackson. At the time, virtually all patient records at KDMC had incomplete medication histories, requiring manual—and therefore error-prone—data entry in the EHR by nurses at the point of care. In late 2018, KDMC sought to improve patient safety and streamline the medication reconciliation process by automating the transcription of critical medication data into the EHR.

The solution they came up with has contributed to increased patient safety and better health outcomes, as well as higher nurse productivity.

The Root of the Problem: Free-Text Sigs
Medication history discrepancies typically occur during the patient triage/intake process, when data from an outside EHR are transferred into the hospital’s resident EHR. One of the most difficult problems complicating the medication reconciliation process is that the distinct nomenclature of the different EHR system often results in missing or indiscernible “sigs”—the important shorthand prescribing instructions that mean the difference between a patient receiving 1.0 mg and 10 mg of a medication, or “qhs” being interpreted as “every hour” instead of “nightly at bedtime.”

An estimated 66% of data from the nation’s largest medication history database are missing essential sig information. To prevent adverse drug events, staff and clinicians can spend hours on the computer or phone conferring with outside providers and pharmacies to gather the missing sig data and fill in information missing from the sig.

Further complicating the issue, current prescription routing technologies provide free-text sig information for dosing instructions rather than the discreet text that is easily translatable to an EHR. This poses a challenge for pharmacists, who must manually sift through the sig data in order to create prescriptions, as well as for clinical staff responsible for medication reconciliation during triage/intake. At KDMC, nurses had to manually transcribe sigs from the medication history into the current visit list in the EHR.

The Solution: Automated Sigs
KDMC’s leadership decided the best way to improve the safety of medication reconciliation was to automate the transcription of sig data into the EHR. Doing that meant finding a way to convert free-text sig data into programmable data yielding discrete sig components within a patient’s medication history. The hospital hoped doing so would reduce medication reconciliation clicks and keystrokes, ensure a more accurate patient medication history, and fill important medication history gaps by deducing missing sigs.

For help, KDMC turned to its ePrescribing partner 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 (eg, “by mouth” vs “oral” or “PO”). The automated process operates entirely in the background, without clinician intervention, and employs statistical validation and clinical analysis to produce a real-time sig translation. In addition, the solution helps staff resolve these gaps by supplying alternative drug IDs for best-case drug matching and details for incomplete or uncommon sigs. Multiple safety checks enable disqualification of transactions that are deemed clinically invalid. The solution is programmed to prefer no data to wrong data, so if it determines that a sig datapoint poses a safety risk, it will withhold the data rather than transcribe them.

Challenges Along the Way
Implementing the automated sig-transcription solution required less than two hours and integrated seamlessly with the health system’s inpatient EHR. Implementation required KDMC’s EHR vendor to update its code to accommodate the solution, a task they accomplished quickly.

The hospital was also concerned about the possibility that nurses responsible for medication reconciliation would revert to the manual method of sig translation because the new process was unfamiliar. To ensure that end-users were prepared to make the transition to the new tool, KDMC created a two-minute educational video to ensure fast adoption with none of the typical barriers to rolling out a new technology.

Outcomes
Implementation of the sig translation automation solution at KDMC reduced the number of incomplete or error-filled patient medication records, which in turn minimized pharmacy callbacks and reduced workflow disruptions and patient treatment delays. It also significantly reduced the average number of computer “clicks” required for medication reconciliation, resulting in additional time and cost savings.

After implementing the tool, medication reconciliation required a total of 45,000 fewer “clicks” per month, based on an analysis of data from May and June 2019 compared with preimplementation data. The resulting time savings of 34 hours per month for clinicians (404 hours/year) translates into about $11,000 in recaptured nursing productivity over a 12-month period (based on 19,390 annual patient visits and an average of five medications per patient).

More significantly, the solution appears to have contributed to increased patient safety and improved health outcomes. In the first seven months following implementation of the sig translation solution, KDMC’s overall 30-day readmission rate fell by 11.3%, from 6.2% prior to implementation to 5.5% postimplementation. The hospital is currently reviewing the data to determine the exact impact of the solution on 30-day readmission rates. The hypothesis is that improved accuracy of medication dosage accounts for a significant portion of the decrease in readmissions, possibly due to a decline in postdischarge adverse drug reactions.

Anecdotally, KDMC’s nurses have expressed satisfaction with the tool, which has streamlined patient triage and saved time. In light of these results, health care organizations are advised to find ways to automate the transcription of sig data with tools that enhance patient safety and outcomes, while creating time savings.

— Joe Farr, RN, is clinical applications coordinator at King’s Daughters Medical Center (KDMC), a 99-bed community hospital in Brookhaven, Mississippi. Farr has supervisory oversight of the clinical IT team and reports to the CIO and to KDMC’s Information Systems Steering Committee.

— David Sellars is principal of product innovations at DrFirst, a provider of ePrescribing, price transparency, and medication management solutions. He has 18 years of health care experience with a deep emphasis in big data, artificial intelligence, health care interoperability, programming, and systems optimization.