Electronic Medical Records for Clinical Research: Application to the Identification of Heart Failure | Page 3

Published Online: June 01, 2007
Serguei Pakhomov, PhD; Susan A. Weston, MS; Steven J. Jacobsen, MD, PhD; Christopher G. Chute, MD, DrPH; Ryan Meverden, BS; and V??ronique L. Roger, MD, MPH
This study also has unique strengths. It used a large dataset (more than 3000 patients) that was developed over a period of 3 years and involved complete manual records  abstraction for validation. Another strength is that this study addressed identification of HF patients, whose diagnosis is complex, and relies in part on the language found in the unrestricted text of the EMR. The 2 methods described here were tested on the same population, which made it possible to determine their respective yields in the same dataset and to define their potential application separately or in combination with predictable results. Another advantage of the NLP approach is that it is not restricted to a specific data element or a specific location in the EMR. The Mayo Clinic EMR maintains a diagnostic problem list that is used to summarize the main findings entered into the  clinical note. The notes contain problem-list entries as numbered items inside the Impression/Report/Plan and the Final Diagnosis sections. The NLP algorithm described in this article does not take advantage of the problem-list items. Instead, the search is performed across the entire text of the note in the attempt to capture symptom information. Therefore, our NLP strategy may be used in EMR systems that do not routinely use problem-list entries. The feasibility of using the NLP strategy in other EMR systems and for other conditions will be assessed in subsequent work.

Implications for Clinical Research
Although the highly sensitive NLP method may be more appropriate as a screening mechanism for observational studies, the predictive modeling method may be more suitable for clinical trials, when stricter inclusion criteria may be required. Indeed, the NLP method often involves subsequent manual abstraction of medical records, and a highly sensitive screening tool will direct manual data collection. The predictive modeling method is based on selected populations and is more concerned with the efficient enrollment of patients who fit study inclusion and exclusion criteria. For clinical trials, the predictive-modeling approach with a higher PPV is a better screening mechanism.

Acknowledgments
We acknowledge Kay Traverse, RN, and Susan Stotz, RN, for manual review of patient records.



Author Afiliations: From the Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minn (SP, SAW, CGC, RM); the Department of Research and Evaluation, Kaiser Permanente, Pasadena, Calif (SJJ); and the Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic, Rochester, Minn (VLR).

Funding Sources: This work was supported by NIH grants RO1-72435, GM14321, and AR30582; NLM Training Grant in Medical Informatics (T15 LM07041-19); and the NIH Roadmap Multidisciplinary Clinical Research Career Development Award Grant (K12/NICHD-HD49078).

Correspondence Author: Serguei V. Pakhomov, PhD, Department of Health Science Research, Mayo Clinic, 200 First St SW, Rochester, MN 55905. E-mail: pakhomov.serguei@mayo.edu.

Author Disclosure: The authors (SP, SAW, SJJ, CGC, RM, VLR) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter discussed in this manuscript.

Authorship Information: Concept and design (SP, SJJ, CGC, VLR)); acquisition of data (SP, SJJ, RM, VLR); analysis and interpretation of data (SP, SAW, SJJ, CGC, RM); drafting of the manuscript (SP, SJJ); critical revision of the manuscript for important intellectual content (SP, SAW, SJJ, VLR); statistical analysis (SP, SAW, RM, VLR); provision of study materials or patients (SP); and obtaining funding (SP, VLR); administrative, technical, or logistic support (SP, CGC, VLR); supervision (SP, CGC, VLR).

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