Faculty Bibliography

XML has been widely adopted as an important data interchange language. The structure of XML enables sharing of data elements with variable degrees of nesting as long as the elements are grouped in a strict tree-like fashion. This requirement potentially restricts the usefulness of XML for marking up written text, which often includes features that do not properly nest within other features. We encountered this problem while marking up medical text with structured semantic information from a Natural Language Processor. Traditional approaches to this problem separate the structured information from the actual text mark up. This paper introduces an alternative solution, which tightly integrates the semantic structure with the text. The resulting XML markup preserves the linearity of the medical texts and can therefore be easily expanded with additional types of information.

At Columbia-Presbyterian Medical Center, free-text "Signout" notes are typed into the electronic record by clinicians for the purpose of cross-coverage. We plan to "unlock" information about adverse events contained in these notes in a subsequent project using Natural Language Processing (NLP). To better understand the requirements for parsing, Signout notes were compared to other common medical notes (ambulatory clinic notes and discharge summaries) on a series of quantitative metrics. They are shorter (mean length 59.25 words vs. 144.11 and 340.85 for ambulatory and discharge notes respectively) and use more abbreviations (26.88% vs. 20.07% and 3.57%). Despite being terser, Signout notes use less ambiguous abbreviations (8.34% vs. 9.09% and 18.02%). Differences were found using Relative Entropy and Squared Chi-square Distance in a novel fashion to compare these medical corpora. Signout notes appear to constitute a unique sublanguage of medicine. The implications for parsing free-text cross-coverage notes into coded medical data are discussed.

Too often, online searches for health information are time consuming and produce results that are not sufficiently precise to answer clinicians' or patients' questions. The PERSIVAL project is designed to circumvent this problem by personalizing and tailoring searches and presentation to the demands of the user and the particular clinical context. This paper focuses on a cognitive evaluation of one component of this project, a Query User Interface (QUI). The study examines the system's ability to allow users to easily and intuitively express their information needs. We performed several analyses including a cognitive walkthrough of the interface and quantitative estimations of cognitive load. The paper also presents a preliminary analysis of usability testing. The analyses suggest that there are features in the QUI that contribute to a greater cognitive load and result in greater effort on the part of the subject. The results of usability testing are consistent with these findings. However, subjects found it to be relatively easy and intuitive to generate well-formed queries using the interface. This study contributed to the iterative design of the interface and to the next generation of the PERSIVAL system.

The large and rapidly growing number of information sources relevant to health care, and the increasing amounts of new evidence produced by researchers, are improving the access of professionals and students to valuable information. However, seeking and filtering useful, valid information can be still very difficult. An online information system that conducts searches based on individual patient data can have a beneficial influence on the particular patient's outcome and educate the healthcare worker. In this paper, we describe the underlying model for a system that aims to facilitate the search for evidence based on clinicians' needs. This paper reviews studies of information needs of clinicians, describes principles of information retrieval, and examines the role that standardized terminologies can play in the integration between a clinical system and literature resources, as well as in the information retrieval process. The paper also describes a model for a digital library system that supports the integration of clinical systems with online information sources, making use of information available in the electronic medical record to enhance searches and information retrieval. The model builds on several different, previously developed techniques to identify information themes that are relevant to specific clinical data. Using a framework of evidence-based practice, the system generates well-structured questions with the intent of enhancing information retrieval. We believe that by helping clinicians to pose well-structured clinical queries and including in them relevant information from individual patients' medical records, we can enhance information retrieval and thus can improve patient-care.

With the growing use of Natural Language Processing (NLP) techniques as solutions in Medical Informatics, the need to quickly and efficiently create the knowledge structures used by these systems has grown concurrently. Automatic discovery of a lexicon for use by an NLP system through machine learning will require information about the syntax of medical language. Understanding the syntactic differences between medical and non-medical corpora may allow more efficient acquisition of a lexicon. Three experiments designed to quantify the syntactic differences in medical and non-medical corpora were conducted. The results show that the syntax of medical language shows less variation than non-medical language and is likely simpler. The differences were great enough to question the applicability of general language tools on medical language. These differences may reduce the difficulty of some free text machine learning problems by capitalizing on the simpler nature of narrative medical syntax.

The vast amount of information collected and stored in clinical systems can be a significant challenge in the integration of digital libraries and electronic medical records, especially the selection of clinical data to be used in the search, retrieval, and summarization processes. In this study, we describe the use of information retrieval measures with natural language processor output to identify critical information in narrative reports. Our hypothesis is that clinical data that occur often in narrative reports are less important to clinicians than findings that occur rarely. We used the information retrieval methods to analyze one year of discharge summaries. We then conducted a performance study, using physicians as subject. Results show that the methods can be used for filtering critical information from reports. Further studies need to be done on evaluation of the method based on an evaluation of the system performance in the context of a digital library.

A variety of methods have been proposed for presenting medical data visually on computers. Discussion of and comparison among these methods have been hindered by a lack of consistent terminology. A taxonomy of medical data presentations based on object-oriented user interface principles is presented. Presentations are divided into five major classes-list, table, graph, icon, and generated text. These are subdivided into eight subclasses with simple inheritance and four subclasses with multiple inheritance. The various subclasses are reviewed and examples are provided. Issues critical to the development and evaluation of presentations are also discussed.

The expanding health information infrastructure offers the promise of new medical knowledge drawn from patient records. Such promise will never be fulfilled, however, unless researchers first address policy issues regarding the rights and interests of both the patients and the institutions who hold their records. In this article, the authors analyze the interests of patients and institutions in light of public policy and institutional needs. They conclude that the multicenter study, with Institutional Review Board approval of each study at each site, protects the interests of both. "Anonymity" is no panacea, since patient records are so rich in information that they can never be truly anonymous. Researchers must earn and respect the trust of the public, as responsible stewards of facts about patients' lives. The authors find that computer security tools are needed to administer multicenter patient records studies and describe simple approaches that can be implemented using commercial database products.