Amar K. Das

EON: A Component-Based Approach to Automation of Protocol-Directed Therapy

Provision of automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time-stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a time-oriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed, evaluated, and reported independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who are following clinical trial protocols for AIDS or HIV infection. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.

A Logical Foundation for Representation of Clinical Data

OBJECTIVE A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. DESIGN Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. RESULTS The proposed framework was used to model a simple radiology report, which included temporal references. CONCLUSION Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach.

Position paper: Temporal representation and reasoning in medicine: Research directions and challenges

OBJECTIVE The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. BACKGROUND Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. METHODOLOGY The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. RESULTS We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader--including those who are unfamiliar with the topic--to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. CONCLUSIONS We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research.

Gender differences in posttraumatic stress disorder among primary care patients after the World Trade Center attack of September 11, 2001.

BACKGROUND Debate surrounds the nature of gender differences in rates of posttraumatic stress disorder (PTSD). OBJECTIVE The goal of this study was to quantify and explore the reasons for gender differences in rates of PTSD in low income, primary care patients after the World Trade Center (WTC) attack of September 11, 2001. METHODS A survey was conducted at a large primary care practice in New York City 7 to 16 months after the WTC attack. The study involved a systematic sample of primary care patients aged 18 to 70 years. The main outcome measures were the Life Events Checklist, the Posttraumatic Stress Disorder Checklist-Civilian Version, and the Primary Care Evaluation of Mental Disorders Patient Health Questionnaire, all administered by a bilingual research staff. RESULTS A total of 3807 patients were approached at the primary care clinic. Of the 1347 who met eligibility criteria, 1157 (85.9%) consented to participate. After the addition of the WTC/PTSD supplement to the study, the total number of patients was 992, of whom 982 (99.0%) completed the survey. Both sexes had high rates of direct exposure to the WTC attack and high rates of lifetime exposure to stressful life events. Overall, females had lower rates of exposure to the attack compared with males (P < 0.05). Hispanic females had the highest rate of PTSD in the full sample. Gender differences in rates of PTSD were largely accounted for by differences in marital status and education. The rate of current major depressive disorder (MDD) was higher in females than in males (P < 0.001), and the reverse was true for substance abuse (P < 0.001). Gender differences for MDD and substance abuse persisted even after adjustments for demographic differences between the sexes. CONCLUSIONS The increased rate of PTSD in women attending a primary care clinic was mediated by their social and economic circumstances, such as living alone without a permanent relationship and with little education or income. The increased rate of MDD in women appeared to be less dependent on these circumstances. These findings have implications for the treatment of women with PTSD in primary care and for research on gender differences in rates of psychiatric disorders.

Integration of Temporal Reasoning and Temporal-Data Maintenance into a Reusable Database Mediator to Answer Abstract, Time-Oriented Queries: The Tzolkin System

The ability to reason with time-oriented data is central to the practice of medicine. Monitoring clinical variables over time often provides information that drives medical decision making (e.g., clinical diagnosis and therapy planning). Because the time-oriented patient data are often stored in electronic databases, it is important to ensure that clinicians and medical decision-support applications can conveniently find answers to their clinical queries using these databases. To help clinicians and decision-support applications make medical decisions using time-oriented data, a database-management system should (1) permit the expression of abstract, time-oriented queries, (2) permit the retrieval of data that satisfy a given set of time-oriented data-selection criteria, and (3) present the retrieved data at the appropriate level of abstraction. We impose these criteria to facilitate the expression of clinical queries and to reduce the manual data processing that users must undertake to decipher the answers to their queries. We describe a system, Tzolkin, that integrates a general method for temporal-data maintenance with a general method for temporal reasoning to meet these criteria. Tzolkin allows clinicians to use SQL-like temporal queries to retrieve both raw, time-oriented data and dynamically generated summaries of those data. Tzolkin can be used as a standalone system or as a module that serves other software systems. We implement Tzolkin with a temporal-database mediator approach. This approach is general, facilitates software reuse, and thus decreases the cost of building new software systems that require this functionality.

A Method for Representing and Querying Temporal Information in OWL

Ontologies are becoming a core technology for supporting the sharing, integration, and management of information sources in Semantic Web applications. As critical as ontologies have become, ontology languages such as OWL typically provide minimal support for modeling the complex temporal information often contained in these sources. As a result, ontologies often cannot fully express the temporal knowledge needed by many applications, forcing users and developers to develop ad hoc solutions. In this paper, we present a methodology and a set of tools for representing and querying temporal information in OWL ontologies. The approach uses a lightweight temporal model to encode the temporal dimension of data. It also uses the OWL-based Semantic Web Rule Language (SWRL) and the SWRL-based OWL query language SQWRL to reason with and query the temporal information represented using our model.

Customizing clinical narratives for the electronic medical record interface using cognitive methods

OBJECTIVE As healthcare practice transitions from paper-based to computer-based records, there is increasing need to determine an effective electronic format for clinical narratives. Our research focuses on utilizing a cognitive science methodology to guide the conversion of medical texts to a more structured, user-customized presentation in the electronic medical record (EMR). DESIGN We studied the use of discharge summaries by psychiatrists with varying expertise-experts, intermediates, and novices. Experts were given two hypothetical emergency care scenarios with narrative discharge summaries and asked to verbalize their clinical assessment. Based on the results, the narratives were presented in a more structured form. Intermediate and novice subjects received a narrative and a structured discharge summary, and were asked to verbalize their assessments of each. MEASUREMENTS A qualitative comparison of the interview transcripts of all subjects was done by analysis of recall and inference made with respect to level of expertise. RESULTS For intermediate and novice subjects, recall was greater with the structured form than with the narrative. Novices were also able to make more inferences (not always accurate) from the structured form than with the narrative. Errors occurred in assessments using the narrative form but not the structured form. CONCLUSIONS Our cognitive methods to study discharge summary use enabled us to extract a conceptual representation of clinical narratives from end-users. This method allowed us to identify clinically relevant information that can be used to structure medical text for the EMR and potentially improve recall and reduce errors.

Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

OBJECTIVE To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. DESIGN 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 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 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.

Computers in psychiatry: a review of past programs and an analysis of historical trends.

In a variety of clinical settings, computers are playing an increasing role in managing or retrieving clinical information. A recent survey of physician computer use suggests that psychiatrists, in comparison to other types of medical specialists, may be using computers less in routine care. In this paper, we present a literature review of 57 articles on computer programs in psychiatry that were published since 1966 in five major peer-reviewed journals. We divide the types of programs that have been developed into four categories: (1) diagnostic and decision support, (2) patient screening and therapy, (3) data collection and management, and (4) data modeling. Among the first three categories, we found trends in publications during the past three decades of research. We provide a discussion of representative computer programs. Our analysis of past programs reveals a number of design problems that may be a barrier to the more widespread use of computers in psychiatry.

Using Semantic Web Technologies for Knowledge-Driven Querying of Biomedical Data

Software applications that work with biomedical data have significant knowledge-management requirements. Formal knowledge models and knowledge-based methods can be very useful in meeting these requirements. However, most biomedical data are stored in relational databases, a practice that will continue for the foreseeable future. Using these data in knowledge-driven applications requires approaches that can form a bridge between relational models and knowledge models. Accomplishing this task efficiently is a research challenge. To address this problem, we have developed an end-to-end knowledge-based system based on Semantic Web technologies. It permits formal design-time specification of the data requirements of a system and uses those requirements to drive knowledge-driven queries on operational relational data in a deployed system. We have implemented a dynamic OWL-to-relational mapping method and used SWRL, the Semantic Web Rule Language, as a high-level query language that uses these mappings. We have used these methods to support the development of a participant tracking application for clinical trials and in the development of a test bed for evaluating biosurveillance methods.