Application of Information Technology j MediClass : A System for Detecting and Classifying Encounter-based Clinical Events in Any Electronic Medical

j J Am Med Inform Assoc. 2005;12:517–529. DOI 10.1197/jamia.M1771. Electronic medical records (EMRs) promise to revolutionize the health care industry by making health care better and cheaper. A key aspect of this promise is comprehensive capture of patient-specific clinical data; the EMR provides a vehicle for communicating information about the patient across time and providers. In both electronic and paperbased medical records, narratives produced by providers about their patients play a critical role in this communication. Adopting the EMR also may improve health care and reduce costs by enabling process control and decision support via computations using data for individual patients as well as populations. These computations promote evidence-based quality and efficiency in the delivery of health care. In actual practice, these two key aspects of the EMR (supporting patient-specific clinical communications and workflow while enabling general process controls) often conflict with each other. For those care activities that heavily affect organizational goals, the care organization strives to increase coded data entry to enhance process control. However, clinicians often find coded data entry cumbersome because it interferes with individualized patient care and adds steps to an already busy workflow. Compared to traditional natural language narrative, coded entry captures only a fraction of the information produced in the clinical encounter (although what is coded can be less ambiguous). As with other mechanisms for coding complex real-world phenomena, there is a trade-off between sensitivity and specificity in clinician coding. Some phenomena will be coded with high accuracy, which will come at the expense of information captured about other aspects of the care. For instance, standardized coding of diagnoses has traditionally been a high-priority activity that efficiently communicates the common and well-understood health conditions that clinicians ascribe in their assessment of each patient. On the other hand, recording the important details of counseling about patient behavior is much more difficult to accomplish with a standardized coding scheme, in part because these details involve assessing and capturing the patient’s motivations and intentions. However, a clinician can relatively easily recall and record such details about the encounter in a progress note that quickly covers multiple aspects of the clinical encounter. We need new technologies that allow optimization of both clinical practice and process control in the care delivery system. MediClass (a ‘‘medical classifier’’) is a knowledge-based system that automatically classifies the content of a clinical encounter captured in the medical record. MediClass accomplishes this by applying a set of application-specific logical rules to the medical concepts that are automatically identified in both the free-text (e.g., counseling activities in progress notes) and precoded data elements (e.g., medication orders) of potentially any EMR. As explained in detail below, the MediClass system can process data from any EMR system for which data can be expressed in the Clinical Document Architecture [CDA] data standard that is managed by the Health Level Seven health care standards organization. Our initial target implementation of the system addressed Affiliations of the authors: Kaiser Permanente Center for Health Research, Portland, OR (BH, VJS), HRF Engineering, New London, NH (HRF); Department of Medical Informatics, Kaiser Permanente, Northwest, Portland, OR (DFS). This work was supported in part by a grant from the National Cancer Institute (U19 CA79689) for The HMO Cancer Research Network (CRN2). The authors acknowledge the work of Jack Hollis, Tom Vogt, Jonathan Winicoff, Ted Palen, Russ Glascow, Sabina Smith, Joan Hollup, Donna Rusinak, and Alanna Rahm, for their assistance in developing the knowledge used by MediClass for smoking cessation care assessment. They thank Rajesh Zade, Steve Balch, Mark Schmidt, Ron Norman, and Ping Shi for their help implementing the system. Jen Coury provided valuable assistance editing this manuscript. They thank Prakash Nadkarni for providing details about the NegFinder system and making publicly available the lexical and grammatical structures used by that system. Correspondence and reprints: Brian L. Hazlehurst, PhD, Center for Health Research, 3800 N. Interstate Ave., Portland, OR 97227; e-mail: <brian.hazlehurst@kpchr.org>. Received for review: 12/13/04; accepted for publication: 05/08/05. 517 Journal of the American Medical Informatics Association Volume 12 Number 5 Sep / Oct 2005 by gest on Jauary 8, 2016 ht://jam ia.oxfournals. org/ D ow nladed from detection of smoking cessation care delivery in accord with a widely adopted, evidenced-based care guideline. The guideline consists of a five-step, sequential program called the ‘‘5 A’s’’: (1) ASK patients about smoking status at every visit, (2) ADVISE all tobacco users to quit, (3) ASSESS a patient’s willingness to try to quit, (4) ASSIST the patient’s quitting efforts (provide smoking-cessation treatments or referrals), and (5) ARRANGE follow-up (provide or arrange for supportive follow-up contacts). In a companion paper, we present results from an evaluation study of the MediClass system’s performance at assessing smoking cessation care using EMR data from four health plans. We found the system to be similar in accuracy to trained human medical record abstractors. Using the trained abstractors as gold standard, the system performed with an average sensitivity of 82% and an average specificity of 93%. In this paper, we describe the design and function of MediClass, a general-purpose knowledge-based system created for detecting clinical events in any EMR. Such a system shows promise for addressing a wide variety of concerns in health services research and clinical operations including care quality, disease surveillance, and adverse event detection.