Abstract

1. Patient EMR Clinician Input Content Comparison Clinician Decision Support Dashboard: Extracting value from Electronic Medical Records using Text MiningIcchaSethi, Zhaohui Sun, Harold R. Garner Related Literature Similar EMRs Case Reports Clinical Trials Scientific Hypothesis Generation Best matches based on patient information Predictions Diagnosis Treatments Outcomes Warnings Side effects Rx Interactions Hidden Dx Abstract The conversion of medical records to electronic form is proceeding quickly; for example, Carilion Clinics is one of the leading patient care organizations with over 70% (and heading towards 100% in the next few years) of its patients have Electronic Medical Records (EMRs). Although EMRs have been a benefit for efficiency and administrative needs such as billing and logistics, doctors want to see enhanced benefits for the patient and their delivery of care, and patient care facilities want to see more cost saving enhancements without sacrificing quality or delivery of care. Therefore, what is needed is a system that will analyze a patient’s evolving EMR in context with all available biomedical knowledge and the accumulated experience recorded in the EMRs of other patients. The aim of the Clinician Decision Support (CDS) Dashboard is to provide interactive, automated, actionable, EMR text-mining tools that helps improve both the patient and clinical care staff experience. The CDS Dashboard, in a secure network, will help physicians find de-identified EMRs similar to their patient's medical record thereby aiding them in diagnosis, treatment, prognosis and outcomes. Because some cases involve complex disorders, it will also allow physicians to search medical literature, recent research findings, clinical trials and medical cases, enabling clinicians to also become researchers, while simultaneously making certain their patients get the latest in care. Another feature of the Dashboard is that it will also provide suggestions for drug alternatives, counter indications and drug-drug interactions. Text similarity searching technology (etblast.org) developed by our group will be used as the similarity search engine for the Dashboard. Another critical part is the development of local databases to which the patient’s EMR is compared, and these include MedLine, the Orange Book, the Physician Desk Reference and others which then link the latest in medical knowledge to a given case. The design, features and functionality of the CDS Dashboard are described. They require a multidisciplinary approach to the construction of the system, including computer science, medicine, biomedical research, marketing and human-machine interfacing. Proposed Interface Introduction An Electronic Medical Record is an electronic record of health-related information on an individual that is created, gathered, managed, and consulted by licensed clinicians and staff from a single organization who are involved in the individual's health and care. The Clinical Decision Support Dashboard aims to extend the purpose of EMRs beyond information storage and use the data to help doctors find similar de-identified electronic medical records and other related medical information from medical literature and journals, thereby aiding them to provide better health care. Design Conclusion The clinical decision support dashboard described is a system which integrates text mining methodology with various sciences like medicine, marketing, human computer interaction and medical informatics thereby providing a comprehensive approach to medical healthcare. References Errami, M., J. M. Hicks, et al. (2008). "Deja vu--a study of duplicate citations in Medline." Bioinformatics 24(2): 243-249. Errami, M., J. D. Wren, et al. (2007). "eTBLAST: a web server to identify expert reviewers, appropriate journals and similar publications." Nucleic Acids Res 35(Web Server issue): W12-15. Lewis, J., S. Ossowski, et al. (2006). "Text similarity: an alternative way to search MEDLINE." Bioinformatics 22(18): 2298-2304. Report, I. (1999). "To Err is Human: Building a Safer Health System." National Academies Press. Zhou, L., C. S. Soran, et al. (2009). "The Relationship between Electronic Health Record Use and Quality of Care over Time." Journal of the American Medical Informatics Association 16(4): 457-464. • Challenges • There are numerous challenges in processing EMRs, a few of which are listed below: • There are a variety of ways each physician makes entries into the medical record; the use their own set of acronyms and shortcuts. • The presence of billing and CPT codes. • Combinations of unstructured and structured laboratory data. • Presence of lexical variants and synonyms.