title: Content and structure of clinical problem lists: a corpus analysis.
abstract: In the interest of designing an automated high-level, longitudinal clinical summary of a patient record, we analyze traditional ways in which medical problems pertaining to the patient are summarized in the electronic health record. The patient problem list has become a commonly used proxy for a summary of patient history and automated methods have been proposed to generate it. However, little research has been conducted on how to structure the problem list in a manner most effective for supporting clinical care. This study analyzes the structure and content of the Past Medical History (PMH) sections of a large corpus of clinical notes, as a proxy for problem lists. Findings show that when listing patients history, physicians convey several semantic types of information, not only problems. Furthermore, they often group related concepts in a single line of the PMH. In contrast, traditional problem lists allow only a simple enumeration of coded terms. Content analysis goes on to reiterate the value of more complex representations as well as provide valuable data and guidelines for automated generation of a clinical summary.
authors.affiliation: Department of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Tielman T
authors.initials: TT
authors.lastname: Van Vleck
authors.forename: Adam
authors.initials: A
authors.lastname: Wilcox
authors.forename: Peter D
authors.initials: PD
authors.lastname: Stetson
authors.forename: Stephen B
authors.initials: SB
authors.lastname: Johnson
authors.forename: Noémie
authors.initials: N
authors.lastname: Elhadad
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002985:Clinical Protocols; D016247:Information Storage and Retrieval / Q000379:methods*; D008487:Medical History Taking / Q000379:methods*; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data*; D008501:Medical Records, Problem-Oriented / Q000706:statistics & numerical data*; D009323:Natural Language Processing; D009518:New York; D010363:Pattern Recognition, Automated / Q000379:methods*; D013358:Subject Headings