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arrange the informational background on which effec- tive diagnostic reasoning is based.3 For these reasons, direct dictation of clinical narra- tives is still the preferred method to document a visit. For many years, speech recognition and computer-aided transcription systems have seamlessly supported physi- cian dictation work fl ow. The fi nal output of such systems is a textual clinical note.


Clinical language understanding bridges the gap Clinical language understanding (CLU) is a develop- ing technology that automatically captures problems and other key patient clinical data from dictated visit notes. This data is standardized and saved into the EMR and other clinical systems directly from dictation, saving physicians the time and tedium of entering data manu-


How does CLU work?


As physicians dictate their clincal notes into the EHR using voice-recognition technology, CLU analyzes the narrative and automatically extracts pertinent clinical facts, such as problems, symptoms, severity, allergies and medications. This data is automatically normalized and coded into standards, such as ICD-9 and SNOMED.


ally. As CLU becomes available for routine clinical use in hospitals and practices, the solution to the problem of creating and maintaining the problem lists will be more feasible.


The goal of clinical language understanding is to directly integrate and synchronize through language technologies constantly evolving clinical scenarios with their representation inside the EMR. For problem lists, as the CLU software extracts data from the note, these can be matched in real time with what’s in, or missing from, the list. A number of scenarios can be supported by this paradigm. For instance, problems documented in the note but absent from the problem list can be automatically added, completing the documentation. Furthermore, by utilizing ICD-9 codes to normalize different terminologies, the system can decide if a more detailed description of a problem becomes available and update the problem list accordingly.


Consider this sentence: “The otitis media for which the patient was seen last month appears to be fully re- solved.” CLU automatically and reliably assesses that the “otitis media” is “resolved” and thus should be removed from the list of current problems. Today, this action would require manual editing of the data. However, with CLU this happens automatically, with the physician confi rming the deletion.


The fi nal result is akin to the physician taking the time to enter the data into the EHR using menus and pick lists. However, with CLU, physicians can continue to dictate effi ciently, while preserving the patient clinical story and the clinical decision-making process that is so valuable in communicating the uniqueness of the patient conditions to other members of the clinical team. At the same time, the discrete and coded data is automatically available in the EHR to support and streamline other applications, such as clinical decision support tools, quality and regulatory reporting, trending and billing.


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Documenting patient visits is complex and does not lend itself to discrete and structured data entry. A problem name or its status, how well the condition is controlled, the onset or remission of complications, or the history of recurrences and other attributes are better described in dictated notes. The increasing prevalence of chronic conditions that require complex clinical manage- ment makes only more urgent the need for information- rich, fl exible documentation tools that can directly and intelligently interact with EMR applications. The purpose of CLU is to bridge the gap between dictated narratives and structured data in the EMR. As CLU applications fi nd their way into commercial EMR systems, the bridge will fi nally be established to align the ways in which both physicians and EMRs document, represent and interpret clinical data.


HMT


References 1. DesRoches, CM et al. “Electronic Health Records in Ambulatory Care – A National Survey of Physicians,” New England Journal of Medicine 2008; 359, no. 1: 50–60.


2. Blue Cross Blue Shield of Massachusetts press release (October 28, 2008). 3. Gordon D. Schiff, David W. Bates. “Can Electronic Clinical Documentation Help Prevent Diagnostic Errors? New England Journal of Medicine 2010; 362:1066- 1069.


HEALTH MANAGEMENT TECHNOLOGY November 2010 31


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