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Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Wang Y, Steinhubl SR, Defilippi C, Ng K, Ebadollahi S, Stewart WF, Byrd RJ - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR.PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems.To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

View Article: PubMed Central - PubMed

Affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.

ABSTRACT
Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems. To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

No MeSH data available.


Example of matched and unmatched prescriptions extracted from two sources.
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f3-2090064: Example of matched and unmatched prescriptions extracted from two sources.

Mentions: Comparison of medications documented on the same date from two sources (i.e., MED_REC entries and clinical notes) illustrates the need for medication reconciliation. As an example. Figure 3 shows the medication discrepancies found for a single patient: 21 out of 25 were mentioned in both sources, while of the remaining four, two appeared only in MED_REC and two were found only in the encounter note. In an application setting, these discrepancies would be presented to a healthcare professional for reconciliation and appropriate follow-up. Note that this example is based on a single time point – the encounter date. In an actual reconciliation application, we’ll need to create and exploit a more general timeline of prescription information for the patient. Of course, prescription timelines will require full semantic analysis of prescription mentions, including the semantics of prescription verbs, such as “start”, “stop”, “reduce”, “discontinue”, etc.


Prescription Extraction from Clinical Notes: Towards Automating EMR Medication Reconciliation.

Wang Y, Steinhubl SR, Defilippi C, Ng K, Ebadollahi S, Stewart WF, Byrd RJ - AMIA Jt Summits Transl Sci Proc (2015)

Example of matched and unmatched prescriptions extracted from two sources.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525262&req=5

f3-2090064: Example of matched and unmatched prescriptions extracted from two sources.
Mentions: Comparison of medications documented on the same date from two sources (i.e., MED_REC entries and clinical notes) illustrates the need for medication reconciliation. As an example. Figure 3 shows the medication discrepancies found for a single patient: 21 out of 25 were mentioned in both sources, while of the remaining four, two appeared only in MED_REC and two were found only in the encounter note. In an application setting, these discrepancies would be presented to a healthcare professional for reconciliation and appropriate follow-up. Note that this example is based on a single time point – the encounter date. In an actual reconciliation application, we’ll need to create and exploit a more general timeline of prescription information for the patient. Of course, prescription timelines will require full semantic analysis of prescription mentions, including the semantics of prescription verbs, such as “start”, “stop”, “reduce”, “discontinue”, etc.

Bottom Line: Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR.PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems.To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

View Article: PubMed Central - PubMed

Affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.

ABSTRACT
Medication in for ma lion is one of [he most important clinical data types in electronic medical records (EMR) This study developed an NLP application (PredMED) to extract full prescriptions and their relevant components from a large corpus of unstructured ambulatory office visit clinical notes and the corresponding structured medication reconciliation (MED REC) data in the EMR. PredMED achieved an 84.4% F-score on office visit encounter notes and 95.0% on MED"REC data, outperforming two available medication extraction systems. To assess the potential for using automatically extracted prescriptions in the medication reconciliation task, we manually analyzed discrepancies between prescriptions found in clinical encounter notes and in matching MED_REC data for sample patient encounters.

No MeSH data available.