Faculty Bibliography

OBJECTIVES/OBJECTIVE:This study sought to design and validate a reliable instrument to assess the quality of physician documentation. DESIGN/METHODS:Adjectives describing clinician attitudes about high-quality clinical documentation were gathered through literature review, assessed by clinical experts, and transformed into a semantic differential scale. Using the scale, physicians and nurse practitioners scored the importance of the adjectives for describing quality in three note types: admission, progress, and discharge notes. Psychometric methods including exploratory factor analysis were applied to provide preliminary evidence for the construct validity and internal consistency reliability. RESULTS:A 22-item Physician Documentation Quality Instrument (PDQI) was developed. Exploratory factor analysis (n = 67 clinician respondents) on three note types resulted in solutions ranging from four (discharge) to six (admission and progress) factors, and explained 65.8% (discharge) to 73% (admission and progress) of the variance. Each factor solution was unique. However, four sets of items consistently factored together across all note types: (1) up-to-date and current; (2) brief, concise, succinct; (3) organized and structured; and (4) correct, comprehensible, consistent. Internal consistency reliabilities were: admission note (factor scales = 0.52-88, overall = 0.86), progress note (factor scales = 0.59-0.84, overall = 0.87), and discharge summary (factor scales = 0.76-0.85, overall = 0.88). CONCLUSION/CONCLUSIONS:The exploratory factor analyses and reliability analyses provide preliminary evidence for the construct validity and internal consistency reliability of the PDQI. Two novel dimensions of the construct for document quality were developed related to form (Well-formed, Compact). Additional work is needed to assess intrarater and interrater reliability of applying of the proposed instrument and to examine the reproducibility of the factors in other samples.

OBJECTIVE:Clinicians often have difficulty translating information needs into effective search strategies to find appropriate answers. Information retrieval systems employing an intelligent search agent that generates adaptive search strategies based on human search expertise could be helpful in meeting clinician information needs. A prerequisite for creating such systems is an information seeking model that facilitates the representation of human search expertise. The purpose of developing such a model is to provide guidance to information seeking system development and to shape an empirical research program. DESIGN/METHODS:The information seeking process was modeled as a complex problem-solving activity. After considering how similarly complex activities had been modeled in other domains, we determined that modeling context-initiated information seeking across multiple problem spaces allows the abstraction of search knowledge into functionally consistent layers. The knowledge layers were identified in the information science literature and validated through our observations of searches performed by health science librarians. RESULTS:A hierarchical multi-level model of context-initiated information seeking is proposed. Each level represents (1) a problem space that is traversed during the online search process, and (2) a distinct layer of knowledge that is required to execute a successful search. Grand strategy determines what information resources will be searched, for what purpose, and in what order. The strategy level represents an overall approach for searching a single resource. Tactics are individual moves made to further a strategy. Operations are mappings of abstract intentions to information resource-specific concrete input. Assessment is the basis of interaction within the strategic hierarchy, influencing the direction of the search. CONCLUSION/CONCLUSIONS:The described multi-level model provides a framework for future research and the foundation for development of an automated information retrieval system that uses an intelligent search agent to bridge clinician information needs and human search expertise.

Colon cancer screening rates in women are low. Whether screening for breast and cervical cancer is associated with colon cancer screening behavior is unknown but could provide linkage opportunities. To identify the extent to which both breast and cervical cancer screening increases uptake of colon cancer screening among women in New York City. Women at least 50 years old completed questionnaires for the New York Cancer Project. Analyses compared rates of endoscopic colon cancer screening with adherence to screening recommendations for breast and cervical cancer. Of the 3,386 women, 87.8% adhered to breast and cervical cancer screening guidelines, yet only 42.1% had received endoscopic colon cancer screening. Most women with colon cancer screening (95%) also reported past mammogram and Pap-smear. In multivariable analysis, women who adhered to the other two procedures were more likely to have had colon cancer screening than women with no prior history (OR = 4.4; CI = 2.36, 8.20), after accounting for age, race/ethnicity, insurance status, family history of cancer and income. Significant predictors of endoscopic colon cancer screening included: age over 65 years (OR = 1.63; CI = 1.23, 2.15) with 50-65 years old as the reference, any health insurance (OR = 2.18; CI = 1.52, 3.13) and a family history of cancer (OR = 1.38; CI = 1.17, 1.61). Colorectal cancer screening remains low, even among women who undergo other cancer screening tests. Opportunities to link cancer screening tests to encourage colon cancer screening merit closer attention.

OBJECTIVE:To develop an electronic health record that facilitates rapid capture of detailed narrative observations from clinicians, with partial structuring of narrative information for integration and reuse. DESIGN/METHODS:We propose a design in which unstructured text and coded data are fused into a single model called structured narrative. Each major clinical event (e.g., encounter or procedure) is represented as a document that is marked up to identify gross structure (sections, fields, paragraphs, lists) as well as fine structure within sentences (concepts, modifiers, relationships). Marked up items are associated with standardized codes that enable linkage to other events, as well as efficient reuse of information, which can speed up data entry by clinicians. Natural language processing is used to identify fine structure, which can reduce the need for form-based entry. VALIDATION/RESULTS:The model is validated through an example of use by a clinician, with discussion of relevant aspects of the user interface, data structures and processing rules. DISCUSSION/CONCLUSIONS:The proposed model represents all patient information as documents with standardized gross structure (templates). Clinicians enter their data as free text, which is coded by natural language processing in real time making it immediately usable for other computation, such as alerts or critiques. In addition, the narrative data annotates and augments structured data with temporal relations, severity and degree modifiers, causal connections, clinical explanations and rationale. CONCLUSION/CONCLUSIONS:Structured narrative has potential to facilitate capture of data directly from clinicians by allowing freedom of expression, giving immediate feedback, supporting reuse of clinical information and structuring data for subsequent processing, such as quality assurance and clinical research.

OBJECTIVE:To characterize global structural features of large-scale biomedical terminologies using currently emerging statistical approaches. DESIGN/METHODS:Given rapid growth of terminologies, this research was designed to address scalability. We selected 16 terminologies covering a variety of domains from the UMLS Metathesaurus, a collection of terminological systems. Each was modeled as a network in which nodes were atomic concepts and links were relationships asserted by the source vocabulary. For comparison against each terminology we created three random networks of equivalent size and density. MEASUREMENTS/METHODS:Average node degree, node degree distribution, clustering coefficient, average path length. RESULTS:Eight of 16 terminologies exhibited the small-world characteristics of a short average path length and strong local clustering. An overlapping subset of nine exhibited a power law distribution in node degrees, indicative of a scale-free architecture. We attribute these features to specific design constraints. Constraints on node connectivity, common in more synthetic classification systems, localize the effects of changes and deletions. In contrast, small-world and scale-free features, common in comprehensive medical terminologies, promote flexible navigation and less restrictive organic-like growth. CONCLUSION/CONCLUSIONS:While thought of as synthetic, grid-like structures, some controlled terminologies are structurally indistinguishable from natural language networks. This paradoxical result suggests that terminology structure is shaped not only by formal logic-based semantics, but by rules analogous to those that govern social networks and biological systems. Graph theoretic modeling shows early promise as a framework for describing terminology structure. Deeper understanding of these techniques may inform the development of scalable terminologies and ontologies.

Signout is an unofficial clinical document used traditionally to facilitate patient handoff. Qualitative studies have suggested its importance in clinical care. We used a novel technique to quantify the use of signout by analyzing clinical information system logfiles. Viewing and editing events were collected for 1,677 unique patients admitted to our internal medicine service. We found the average patient's signout on a given day is viewed frequently (>6x) and edited frequently (>2x) with multiple unique viewers (>3) and editors (>1). We also found that signouts are used throughout a 24-hour period, not just at the time of handoff. Finally, we showed that they are viewed months and even years after their creation. Signout is therefore a highly utilized, collaborative, clinical document used for more than patient handoff. Our findings also suggest that clinical information systems may benefit from the introduction of collaborative tools such as subscription, versioning, and author-attribution utilities.

Clinicians perform many tasks in their daily work requiring summarization of clinical data. However, as technology makes more data available, the challenges of data overload become ever more significant. As interoperable data exchange between hospitals becomes more common, there is an increased need for tools to summarize information. Our goal is to develop automated tools to aid clinical data summarization. Structured interviews were conducted on physicians to identify information from an electronic health record they considered relevant to explaining the patients medical history. Desirable data types were systematically evaluated using qualitative and quantitative analysis to assess data categories and patterns of data use. We report here on the implications of these results for the design of automated tools for summarization of patient history.

Natural language processing, an important tool in biomedicine, fails without successful segmentation of words and sentences. Tokenization is a form of segmentation that identifies boundaries separating semantic units, for example words, dates, numbers and symbols, within a text. We sought to construct a highly generalizeable tokenization algorithm with no prior knowledge of characters or their function, based solely on the inherent statistical properties of token and sentence boundaries. Tokenizing clinician-entered free text, we achieved precision and recall of 92% and 93%, respectively compared to a whitespace token boundary detection algorithm. We classified over 80% of punctuation characters correctly, based on manual disambiguation with high inter-rater agreement (kappa=0.916). Our algorithm effectively discovered properties of whitespace and punctuation in the corpus without prior knowledge of either. Given the dynamic nature of biomedical language, and the variety of distinct sublanguages used, the effectiveness and generalizability of our novel tokenization algorithm make it a valuable tool.

Automated speech recognition (ASR) is used in many areas of medicine today. However, not many studies have evaluated the usefulness of ASR applications for capturing clinician information needs in noisy environments. We evaluated 72 ASR transcribed clinician-generated questions and assessed them for semantic and syntactic errors. The results showed that basic user training is not sufficient in order to capture the semantics of recordings.

The use of conceptual knowledge collections or structures within the biomedical domain is pervasive, spanning a variety of applications including controlled terminologies, semantic networks, ontologies, and database schemas. A number of theoretical constructs and practical methods or techniques support the development and evaluation of conceptual knowledge collections. This review will provide an overview of the current state of knowledge concerning conceptual knowledge acquisition, drawing from multiple contributing academic disciplines such as biomedicine, computer science, cognitive science, education, linguistics, semiotics, and psychology. In addition, multiple taxonomic approaches to the description and selection of conceptual knowledge acquisition and evaluation techniques will be proposed in order to partially address the apparent fragmentation of the current literature concerning this domain.