Faculty Bibliography

Providing knowledge at the point of care offers the possibility for reducing error and improving patient outcomes. However, the vast majority of the physician's information needs are not met in a timely fashion. The research presented in this paper characterizes an expert librarian's search strategies as it pertains to the selection and use of various electronic information resources. The 10 searches conducted by the librarian to address the physician's information needs varied in terms of complexity and question type. The librarian employed a total of 10 resources and used as many as 7 in a single search. The longer term objective is to model the sequential process in sufficient detail as to be able to contribute to the development of intelligent automated search agents.

Transdisciplinary research accelerates scientific progress. Despite the value of social network analysis to characterize interdepartmental collaboration, institutions have been slow to adopt the approach. We use the approach to characterize collaboration among obesity researchers at our institution, identifying cores of researchers engaged in frequent collaborations. Providing an objective view of research across an institution, social network analysis is a baseline for efforts to facilitate transdisciplinary collaboration.

This work has the objective to implement a search strategy model for high precision retrieval of documents in reply to a clinical query. To achieve this goal we designed a series of search filters on a staged configuration that allow documents to be retrieved from the most precise to the less specific. We found that for most questions the most relevant articles are retrieved at early stages of the search.

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.

In this paper we examine frequently performed clinical research activities with the objective of identifying aspects of workflow that could be amenable to informatics-based re-engineering. This paper is part of a series of studies under the NIH Roadmap initiative, which examines workflow of clinical research in community practices. We describe three common work activities, detailing the main actors involved, the tools used and the challenges faced. These activities illustrate inefficiencies in the clinical research workflow which include: a) lack of supporting tools to perform routine work activities, b) redundancy, low reuse of data and poor interoperability between systems and c) the fragmented and distributed nature of the workflow. We identify opportunities for re-engineering at both a micro (activity) and macro level (organization).

BACKGROUND:The National Institutes of Health (NIH) Roadmap for Medical Research aims to increase the efficiency and speed of clinical research. We report results and lessons learned from a key component of the Roadmap, the Clinical Research Networks initiative. METHODS:Twelve diverse, experienced, large, clinical research networks were funded for 3 years to develop strategies for integrating, expanding, and increasing the interoperability of clinical research networks in support of the Roadmap goals. Network leaders met periodically in person and by teleconference to describe common challenges encountered and solutions used for expansion and increased interoperability. RESULTS:These networks developed innovative solutions to technical challenges, including strategies for interoperability of information systems and management of complex information system technologies (eg, "brokering" to address data system incompatibility, data transfer, and security requirements), and solutions to human factor challenges at the individual, group, intraorganizational, and interorganizational levels (eg, applying collaborative organizing and decision-making processes based on key principles). CONCLUSIONS:These solutions can provide guidance to existing and future clinical research networks, particularly those forming as part of the NIH Clinical Translation Science Award program. Remaining technical and human factor challenges, however, as well as the largely unmet need for consistent funding for network infrastructure and maintenance, stand in the way of fulfilling the vision of a robust future role for clinical research networks.

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.