Faculty Bibliography

The use of nursing documents from different electronic health record (EHR) systems is challenging due to inconsistency in document naming across systems and institutions. Mapping each local document name to standard document ontology may enable health care professionals to navigate and retrieve documents efficiently for multiple purposes such as quality assurance, outcomes research or public health reporting. The purpose of this study was to evaluate the sufficiency of the Health Level 7 (HL7)/Logical Observation Identifiers, Names, and Codes (LOINC) document ontology for representing nursing document names. We collected 94 nursing document types from the Eclipsys Clinical Information System (CIS) and the Columbia Medical Entities Dictionary (MED) and mapped them to the components of the HL7/LOINC document ontology. Seventy-five (79.8%) nursing document names were completely represented and 19 (20.2%) document names were partially represented. In order for the HL7/LOINC document ontology to be of more use in implementing EHRs that support nursing documentation, Subject Matter Domain and Type of Service axes require extension and clarification.

Temporal information plays a critical role in the understanding of clinical narrative (i.e., free text). We developed a representation for marking up temporal information in a narrative, consisting of five elements: 1) reference point, 2) direction, 3) number, 4) time unit, and 5) pattern. We identified 254 temporal expressions from 50 discharge summaries and represented them using our scheme. The overall inter-rater reliability among raters applying the representation model was 75 percent agreement. The model can contribute to temporal reasoning in computer systems for decision support, data mining, and process and outcomes analyses by providing structured temporal information.

Clinical research is vital to the translation of biomedical knowledge into standard clinical practice. Efforts are underway under the NIH Roadmap initiative to re-engineer the national research enterprise to sustain the rapid pace of innovation in the biomedical domain. As part of these efforts, we have embarked on an empirical evaluation of clinical research workflow in community practice settings. The reasons for this focus are three-fold. First, there is an increasing tendency by trial sponsors to conduct clinical trials in community, rather than academic, settings. Second, understanding workflow is critical to developing re-engineering strategies. Third, workflow associated with the conduct of clinical research in community practices have received virtually no attention in the scientific literature. In this paper, we describe a pilot study using time-motion observations, to determine the workflow of clinical research coordinators, the tools they use to conduct the constituent activities of those workflows, and their ultimate outcomes. The preliminary findings provide insights and understanding of clinical research workflow in community practice settings - knowledge that may significantly impact the way in which information technology based re-engineering can be deployed in such an environment.

eNote is an electronic health record (EHR) system based on semi-structured narrative documents. A heuristic evaluation was conducted with a sample of five usability experts. eNote performed highly in: 1)consistency with standards and 2)recognition rather than recall. eNote needs improvement in: 1)help and documentation, 2)aesthetic and minimalist design, 3)error prevention, 4)helping users recognize, diagnosis, and recover from errors, and 5)flexibility and efficiency of use. The heuristic evaluation was an efficient method of evaluating our interface.

Natural language processing (NLP) systems have demonstrated utility in parsing narrative texts for purposes such as surveillance and decision support. However, there has been little work related to NLP of nursing narratives. The purpose of this study was to compare the semantic categories of a NLP system (Medical Language Extraction and Encoding [MedLEE] system) with the semantic domains, categories, and attributes of the International Standards Organization (ISO) reference terminology models for nursing diagnoses and nursing actions. All but two MedLEE diagnosis and procedure-related semantic categories mapped to ISO models. In some instances, we found exact correspondence between the semantic structures of MedLEE and the ISO models. In other situations (e.g. aspects of Site or Location), the ISO model was not as granular as MedLEE. For clinical procedure and non-invasive examination, two ISO nursing action model components (Action and Target) mapped to a single MedLEE semantic category. The ISO models are applicable to NLP of nursing narratives. However, the ISO models require additional specification of selected semantic categories for the abstract semantic domains in order to achieve the objective of using NLP to parse and encode data from nursing narratives. Our analysis also suggests areas for extension of MedLEE particularly in regard to represent nursing actions.

The conduct of translational health research has become a vital national enterprise. However, multiple barriers prevent the effective translation of basic science discoveries into clinical and community practice. New information technology (IT) applications could help address these barriers. Unfortunately, owing to a combination of organizational, technical, and social factors, neither physician-investigators and research staff nor their clinical and community counterparts have harnessed such applications. Recently, at the request of the Institute of Medicine's Clinical Research Roundtable, a qualitative study of these factors was conducted at several leading academic medical centers. We explore the current status of IT in the translational research domain, describe the qualitative results, and conclude with a proposed set of initiatives to further increase the integration of IT into translational research.

OBJECTIVE:To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. DESIGN/METHODS:A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. MEASUREMENTS/METHODS:Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. RESULTS:An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). CONCLUSION/CONCLUSIONS:A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.

The purpose of this evaluation was to assess perceptions of usability of a new semi-structured electronic clinical note. Two focus groups were held, one with attending physicians and one with residents. Physicians described their experiences with eNote and their perceptions about the system. Transcripts of the focus groups underwent content analysis, and four major themes emerged. These were "time", "hardware-system issues", "eNote application issues", and "patients' perceptions."

BACKGROUND:Health messages are crucial to the field of public health in effecting behavior change, but little research is available to assist writers in composing the overall structure of a message. In order to develop software to assist non-expert message writers in constructing effective messages, the structure of existing health messages must be understood, and an appropriate method for analyzing health message structure developed. METHODS:72 messages from expert sources were used for development of the method, which was then tested for reproducibility using ten randomly selected health messages. Four raters analyzed the messages and inter-coder agreement was calculated. RESULTS:A method for analyzing the structure of the messages was developed using sublanguage analysis and discourse analysis. Overall kappa between four coders was 0.69, demonstrating "substantial agreement." CONCLUSION/CONCLUSIONS:A novel framework for characterizing health message structure and a method for analyzing messages appears to be reproducible.

Electronic health records (EHRs) are beginning to manage an increasing volume of narrative data, such as clinical notes pertaining to admission, patient progress, shift change, follow-up, consultation, procedures, etc. These documents fall into a wide variety of classes, based on who is writing them, for what purpose, and in which location, suggesting the need for a document ontology (DO) to model our knowledge of health care documents and their properties. This paper focuses on one aspect of the Health Level 7 (HL7)/ Logical Observation Identifiers, Names, and Codes (LOINC) DO, the Subject Matter Domain (SMD). We created a new polyhierarchical structure for the SMD that combines the current value lists from the LOINC database with another value list from the American Board of Medical Specialties (ABMS). We refined and evaluated the new structure through expert review of the ontology, a survey of medical specialty boards, and specification of SMDs for a corpus of clinical notes.