Eneida A. Mendonça

An Electronic Health Record Based on Structured Narrative

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 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 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 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 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.

Approach to mobile information and communication for health care.

Evidence suggests that inadequate access to information and ineffective communication are proximal causes of errors and other adverse events in-patient care. Within the context of reducing these proximal causes of errors, we explore the use of novel information-based approaches to improve information access and communication in health care settings. This paper describes the approaches for and the design of extensions to a clinical information system used to improve information access and communication at the point of care using information-based handheld wireless applications. These extensions include clinical and information resources, event monitoring, and a virtual whiteboard (VWB).

PalmCIS: A wireless handheld application for satisfying clinician information needs

Wireless handheld technology provides new ways to deliver and present information. As with any technology, its unique features must be taken into consideration and its applications designed accordingly. In the clinical setting, availability of needed information can be crucial during the decision-making process. Preliminary studies performed at New York Presbyterian Hospital (NYPH) determined that there are inadequate access to information and ineffective communication among clinicians (potential proximal causes of medical errors). In response to these findings, the authors have been developing extensions to their Web-based clinical information system including PalmCIS, an application that provides access to needed patient information via a wireless personal digital assistant (PDA). The focus was on achieving end-to-end security and developing a highly usable system. This report discusses the motivation behind PalmCIS, design and development of the system, and future directions.

Conceptual knowledge acquisition in biomedicine: A methodological review

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.

Gene symbol disambiguation using knowledge-based profiles

MOTIVATION The ambiguity of biomedical entities, particularly of gene symbols, is a big challenge for text-mining systems in the biomedical domain. Existing knowledge sources, such as Entrez Gene and the MEDLINE database, contain information concerning the characteristics of a particular gene that could be used to disambiguate gene symbols. RESULTS For each gene, we create a profile with different types of information automatically extracted from related MEDLINE abstracts and readily available annotated knowledge sources. We apply the gene profiles to the disambiguation task via an information retrieval method, which ranks the similarity scores between the context where the ambiguous gene is mentioned, and candidate gene profiles. The gene profile with the highest similarity score is then chosen as the correct sense. We evaluated the method on three automatically generated testing sets of mouse, fly and yeast organisms, respectively. The method achieved the highest precision of 93.9% for the mouse, 77.8% for the fly and 89.5% for the yeast. AVAILABILITY The testing data sets and disambiguation programs are available at http://www.dbmi.columbia.edu/~hux7002/gsd2006

Representing nursing activities within a concept-oriented terminological system: evaluation of a type definition.

OBJECTIVE A type definition, as a component of the categorical structures of a concept-oriented terminology, must support nonambiguous concept representations and, consequently, comparisons of data that are represented using different terminologies. The purpose of the study was to evaluate the adequacy and utility of a proposed type definition for nursing activity concepts. DESIGN Nursing activity terms (n = 1039) from patient charts and intervention terms from two nursing terminologies (Home Health Care Classification and Omaha System) were decomposed into the attributes of the proposed type definition-Delivery Mode, Activity Focus, and Recipient. MEASUREMENTS Attributes of the type definition were coded as present or absent for each term by multiple raters. In addition, Delivery Mode was rated as Explicit or Implicit and Recipient was rated as Explicit, Implicit, or Ambiguous. The data were summarized using descriptive statistics. Inter-rater reliabilities were calculated for each attribute of the type definition. RESULTS All attributes of the type definition were present in 73.9 percent of the chart terms, 91.3 percent of Home Health Care Classification intervention terms, and 63.5 percent of Omaha System intervention terms. While Delivery Mode and Activity Focus were almost universally present, Recipient was problematic. It was rated as ambiguous in 4.8 percent of the chart terms, 8.7 percent of Home Health Care Classification intervention terms, and 36.5 percent of Omaha System intervention terms. CONCLUSIONS The study findings supported the adequacy and utility of the type definition. Further research is needed to refine the type definition and its use for representing nursing activity concepts within a concept-oriented terminological system.

Genetic data and electronic health records: a discussion of ethical, logistical and technological considerations

Objective The completion of sequencing the human genome in 2003 has spurred the production and collection of genetic data at ever increasing rates. Genetic data obtained for clinical purposes, as is true for all results of clinical tests, are expected to be included in patients’ medical records. With this explosion of information, questions of what, when, where and how to incorporate genetic data into electronic health records (EHRs) have reached a critical point. In order to answer these questions fully, this paper addresses the ethical, logistical and technological issues involved in incorporating these data into EHRs. Materials and methods This paper reviews journal articles, government documents and websites relevant to the ethics, genetics and informatics domains as they pertain to EHRs. Results and discussion The authors explore concerns and tasks facing health information technology (HIT) developers at the intersection of ethics, genetics, and technology as applied to EHR development. Conclusions By ensuring the efficient and effective incorporation of genetic data into EHRs, HIT developers will play a key role in facilitating the delivery of personalized medicine.

PhenoGO: an integrated resource for the multiscale mining of clinical and biological data

The evolving complexity of genome-scale experiments has increasingly centralized the role of a highly computable, accurate, and comprehensive resource spanning multiple biological scales and viewpoints. To provide a resource to meet this need, we have significantly extended the PhenoGO database with gene-disease specific annotations and included an additional ten species. This a computationally-derived resource is primarily intended to provide phenotypic context (cell type, tissue, organ, and disease) for mining existing associations between gene products and GO terms specified in the Gene Ontology Databases Automated natural language processing (BioMedLEE) and computational ontology (PhenOS) methods were used to derive these relationships from the literature, expanding the database with information from ten additional species to include over 600,000 phenotypic contexts spanning eleven species from five GO annotation databases. A comprehensive evaluation evaluating the mappings (n = 300) found precision (positive predictive value) at 85%, and recall (sensitivity) at 76%. Phenotypes are encoded in general purpose ontologies such as Cell Ontology, the Unified Medical Language System, and in specialized ontologies such as the Mouse Anatomy and the Mammalian Phenotype Ontology. A web portal has also been developed, allowing for advanced filtering and querying of the database as well as download of the entire dataset http://www.phenogo.org.

A Phase III Skin Cancer Chemoprevention Study of DFMO: Long-term Follow-up of Skin Cancer Events and Toxicity

Decreasing the incidence of nonmelanoma skin cancer (NMSC) is of great importance in regards to future healthcare services. Given the previously reported preventive effects of α-difluoromethylornithine (DFMO) in skin and colon cancer trials, we determined appropriate cause to update the clinical data on the subjects from the recently reported randomized, double-blind, placebo-controlled phase III skin cancer prevention study of DFMO. Our intention was to retrospectively assess the further incidence of skin cancer, other malignancies, and adverse events of patients accrued to our phase III skin cancer prevention study of DFMO. Clinical records of 209 University of Wisconsin (UW) Health subjects were reviewed, and 2,092.7 person years of on study (884.3 person years) and poststudy (1,208.4 person years) follow-up for these patients were assessed for new NMSC events and recurrence rates from the on study period, the poststudy period, and the two study periods combined. No evidence of increased significant diagnoses or serious adverse events was observed in the DFMO participants. The initially observed, marginally significant reduction (P = 0.069) in NMSC rates for DFMO subjects relative to placebo continued without evidence of rebound. Event rates after discontinuation from study for total NMSCs (DFMO 0.236 NMSC/person/year, placebo 0.297, P = 0.48) or the subtypes of basal cell carcinomas (BCC; DFMO 0.179 BCC/person/year, placebo 0.190, P = 0.77) and squamous cell carcinomas (SCC; DFMO 0.057 SCC/person/year, placebo 0.107, P = 0.43) are listed. Follow-up data revealed a persistent but insignificant reduction in new NMSCs occurring in DFMO subjects without evidence of latent or cumulative toxicity relative to placebo subjects. Cancer Prev Res; 5(12); 1368–74. ©2012 AACR.

A multi-level model of information seeking in the clinical domain

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 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 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.