The IMIA History Working Group: Inception through the IMIA History Taskforce, and Major Events Leading Up to the 50th Anniversary of IMIA.

Background: The 50th Anniversary of IMIA will be celebrated in 2017 at the World Congress of Medical Informatics in China. This takes place 50 years after the International Federation of Information Processing (IFIP) Societies approved the formation of a new Technical Committee (TC) 4 on Medical Information Processing, which was the predecessor of IMIA, under the leadership of Dr. Francois Grémy. The IMIA History Working Group (WG) was approved in 2014 to document and write about the history of the field and its organizations. Objectives: The goals of this paper are to describe how the IMIA History WG arose and developed, including its meetings and projects, leading to the forthcoming 50th Anniversary of IMIA. Methods: We give a chronology of major developments leading up to the current work of the IMIA History WG and how it has stimulated writing on the international history of biomedical and health informatics, sponsoring the systematic compilation and writing of articles and stories from pioneers and leaders in the field, and the organization of workshops and panels over the past six years, leading towards the publication of the contributed volume on the 50th IMIA Anniversary History as an eBook by IOS Press. Conclusions: This article leads up to the IMIA History eBook which will contain original autobiographical retrospectives by pioneers and leaders in the field, together with professional organizational histories of the national and regional societies and working groups of IMIA, with commentary on the main themes and topics which have evolved as scientific and clinical practices have changed under the influence of new insights, technologies, and the changing socio-economic, cultural and professional circumstances around the globe over the past 50 years.

Biomedical informatics in the desert--a new and unique program at Arizona State University.

OBJECTIVES: A new academic Biomedical Informatics (BMI) Program in Phoenix, Arizona, embodies a unique organizational structure to draw on the strengths of a computer science and informatics school and the biomedical and clinical strengths of a college of medicine, in an effort to infuse informatics approaches broadly. METHODS: The program reflects a partnership of two state universities that situates the Arizona State University (ASU) Department of BMI on a new downtown Phoenix Biomedical Campus with the University of Arizona (UA) College of Medicine in partnership with ASU (COM-PHX). Plans call for development of faculty and expertise in the four major subdomains of BMI, as well as in various cross-cutting capabilities. RESULTS: Coming into existence in a state that is investing significantly in biomedical science and technology, BMI has already developed Masters and PhD degree programs, is working with COM-PHX to integrate informatics intensively into the education of the medical students, and has been authorized to plan for an undergraduate program in BMI. Reflecting the statewide emphasis on the biomedical and health sector, the growing faculty are engaged in a number of research partnerships and collaborative centers. CONCLUSIONS: As one of the newest academic BMI programs is taking shape in Arizona, it is embarking on a wide-ranging educational program and a broad research agenda that are now in their earliest stages.

Decision support at the point of care: challenges in knowledge representation, management, and patient-specific access.

Many applications in a clinical information system can benefit from the incorporation of medical knowledge to provide patient-specific, point-of-care decision support. These include computer-based provider order entry, referral, clinical result interpretation, consultation, adverse event monitoring, scheduling, shared patient-doctor decision-making, and generation of alerts and reminders, among others. To be executable, knowledge must be represented in the form of rules, constraints, calculations, guidelines, and other logical/algorithmic formats. The main difficulty is that the integration of such knowledge into clinical applications, when it occurs, tends to be very system- and application-specific, often encoded in a programming language, or even in the formating specifications of a user interaction display. Also, the data references and services invoked are highly dependent on the system/platform and electronic medical record implementation. This makes it difficult and time-consuming to encode authoritative evidence-based knowledge, severely limits the ability to disseminate and share successes, and hampers efforts to review and update the logic as medical knowledge changes. Solutions to this problem involve the development of standards-based representations for medical knowledge, and tools for authoring/editing, dissemination, adaptation to local environments, and execution. Numerous approaches are being pursued, all of which will be described in this presentation.

Characteristics of consumer terminology for health information retrieval.

OBJECTIVES: As millions of consumers perform health information retrieval online, the mismatch between their terminology and the terminologies of the information sources could become a major barrier to successful retrievals. To address this problem, we studied the characteristics of consumer terminology for health information retrieval. METHODS: Our study focused on consumer queries that were used on a consumer health service Web site and a consumer health information Web site. We analyzed data from the site-usage logs and conducted interviews with patients. RESULTS: Our findings show that consumers' information retrieval performance is very poor. There are significant mismatches at all levels (lexical, semantic and mental models) between the consumer terminology and both the information source terminology and standard medical vocabularies. CONCLUSIONS: Comprehensive terminology support on all levels is needed for consumer health information retrieval.

Toward a representation format for sharable clinical guidelines.

Clinical guidelines are being developed for the purpose of reducing medical errors and unjustified variations in medical practice, and for basing medical practice on evidence. Encoding guidelines in a computer-interpretable format and integrating them with the electronic medical record can enable delivery of patient-specific recommendations when and where needed. Since great effort must be expended in developing high-quality guidelines, and in making them computer-interpretable, it is highly desirable to be able to share computer-interpretable guidelines (CIGs) among institutions. Adoption of a common format for representing CIGs is one approach to sharing. Factors that need to be considered in creating a format for sharable CIGs include (i) the scope of guidelines and their intended applications, (ii) the method of delivery of the recommendations, and (iii) the environment, consisting of the practice setting and the information system in which the guidelines will be applied. Several investigators have proposed solutions that improve the sharability of CIGs and, more generally, of medical knowledge. These approaches can be useful in the development of a format for sharable CIGs. Challenges in sharing CIGs also include the need to extend the traditional framework for disseminating guidelines to enable them to be integrated into practice. These extensions include processes for (i) local adaptation of recommendations encoded in shared generic guidelines and (ii) integration of guidelines into the institutional information systems.

Sharable representation of clinical guidelines in GLIF: relationship to the Arden Syntax.

Clinical guidelines are intended to improve the quality and cost effectiveness of patient care. Integration of guidelines into electronic medical records and order-entry systems, in a way that enables delivery of patient-specific advice at the point of care, is likely to encourage guidelines acceptance and effectiveness. Among the methodologies for modeling guidelines and medical decision rules, the Arden Syntax for Medical Logic Modules and the GuideLine Interchange Format version 3 (GLIF3) emphasize the importance of sharing encoded logic across different medical institutions and implementation platforms. These two methodologies have similarities and differences; in this paper we clarify their roles. Both methods can be used to support sharing of medical knowledge, but they do so in complementary situations. The Arden Syntax is suitable for representing individual decision rules in self-contained units called Medical Logic Modules (MLMs), which are usually implemented as event-driven alerts or reminders. In contrast, GLIF3 is designed for encoding complex multistep guidelines that unfold over time. As a consequence, GLIF3 has several mechanisms for complexity management and additional constructs that may require overhead unnecessary for expressing simple alerts and reminders. Unlike the Arden Syntax, GLIF3 encourages a top-down process of guideline modeling consisting of three levels that are created in order: Level 1 comprises a human-readable flowchart of clinical decisions and actions. Level 2 comprises a computable specification that can be verified for logical consistency and completeness; and Level 3 comprises an implementable specification that includes information required for local adaptation of guideline logic as well as for mapping guideline variables onto institutional medical records. A major emphasis of the current GLIF3 development process has been to create the computable specification that formally represents medical decision and eligibility criteria. We based GLIF3's formal expression language on the Arden Syntax's logic grammar, making the necessary extensions to the Arden Syntax's data structures and operators to support GLIF3's object-oriented data model. We discuss why the process of generating a set of MLMs from a GLIF-encoded guideline cannot be automated, why it can result in information loss, and why simple medical rules are best represented as individual MLMs. We thus show that the Arden Syntax and GLIF3 play complementary roles in representing medical knowledge for clinical decision support.

Clinical decision support systems for the practice of evidence-based medicine.

BACKGROUND: The use of clinical decision support systems to facilitate the practice of evidence-based medicine promises to substantially improve health care quality. OBJECTIVE: To describe, on the basis of the proceedings of the Evidence and Decision Support track at the 2000 AMIA Spring Symposium, the research and policy challenges for capturing research and practice-based evidence in machine-interpretable repositories, and to present recommendations for accelerating the development and adoption of clinical decision support systems for evidence-based medicine. RESULTS: The recommendations fall into five broad areas--capture literature-based and practice-based evidence in machine--interpretable knowledge bases; develop maintainable technical and methodological foundations for computer-based decision support; evaluate the clinical effects and costs of clinical decision support systems and the ways clinical decision support systems affect and are affected by professional and organizational practices; identify and disseminate best practices for work flow-sensitive implementations of clinical decision support systems; and establish public policies that provide incentives for implementing clinical decision support systems to improve health care quality. CONCLUSIONS: Although the promise of clinical decision support system-facilitated evidence-based medicine is strong, substantial work remains to be done to realize the potential benefits.

Sharable computer-based clinical practice guidelines: rationale, obstacles, approaches, and prospects.

Clinical practice guideline automation at the point of care is of growing interest, yet most guidelines are authored in unstructured narrative form. Computer-based execution depends on a formal structured representation, and also faces a number of other challenges at all stages of the guideline lifecycle: modeling, authoring, dissemination, implementation, and update. This is because of the multiplicity of conceptual models, authoring tools, authoring approaches, intended applications, implementation platforms, and local interface requirements and operational constraints. Complexity and time required for development and structure are also huge obstacles. These factors argue for convergence on a common shared model for representation that can be the basis of dissemination. A common model would facilitate direct interpretation or mapping to multiple implementation environments. GLIF (GuideLine Interchange Format) is a formal representation model for guidelines, created by the InterMed Collaboratory as a proposed basis for a shared representation. GLIF currently addresses the process of authoring and dissemination; the InterMed team's major focus now is on tools to facilitate these tasks and the mapping to clinical information system environments. Because of limitations in what can be done by a single team with finite resources, however, and the variety of additional perspectives that need to be accommodated, the InterMed team has determined that further development of a shared representation would be best served as an open process in which the world community is engaged. Under the auspices of the HL7 Decision Support Technical Committee, a GLIF Special Interest Group has been established, which is intended to be a forum for collaborative refinement and extension of a standard representation that can support the needs of the guideline lifecycle. Significant areas for future work will need to include demonstrations of effective means for incorporating guide-lines at point of care, reconciliation of functional requirements of different models and identification of those most important for supporting practical implementation, im-proved means for authoring and management of complexity, and methods for automatically analyzing and validating syntax, semantics, and logical consistency of guidelines.

Handling expressiveness and comprehensibility requirements in GLIF3.

Clinical guidelines are aimed at standardizing patient care and improving its quality and cost effectiveness. Guidelines represented in a computer-interpretable (CI) format can be used to provide automatic decision support applied to individual patients during the clinical encounter. The process of creating computer-interpretable guidelines (CIG) re-moves ambiguities contained in paper-based guidelines, thus making the guideline more comprehensible. For these reasons, CIGs may have a larger impact on clinician behavior than paper-based guidelines. Since much effort goes into creating guidelines in a CI format, it is desirable that different institutions and software systems share them. In a guideline representation workshop hosted by the InterMed Collaboratory in March 2000, the need for a standard representation format for sharable CIGs was recognized. As a first step towards achieving this goal, we proposed a set of functional requirements for sharable CIGs. The requirements encompass the entire life cycle of a CIG: development, implementation, use and maintenance. In this paper we discuss requirements that are important during the development stage of a CIG. We have abstracted the requirements into two groups: expressiveness--the ability to ex-press the knowledge content of different types of guidelines--and comprehensibility--the ability to manage complexity, facilitate coherence, and visualize a guideline model to aid in human comprehension. The Guideline Interchange For-mat version 3 (GLIF3) is a language for structured representation of CIGs. It is under development to facilitate sharing CIGs among different institutions and systems. We illustrate how GLIF3 meets the specified development requirements.

Representation of clinical practice guidelines for computer-based implementations.

Representation of clinical practice guidelines is a critical issue for computer-based guideline development, implementation and evaluation. We studied eight types of computer-based guideline representation models. Typical primitives for these models include decisions, actions, patient states and execution states. Temporal constraints and nesting are important aspects of guideline structure representation. Integration of guidelines with electronic medical records can be facilitated by the introduction of formal models of patient data. Patient states and execution states are closely related to one another. Data collection, decision, patient state and intervention are four basic steps in a guideline's logic flow.

Patient and clinician vocabulary: how different are they?

Consumers and patients are confronted with a plethora of health care information, especially through the proliferation of web content resources. Democratization of the web is an important milestone for patients and consumers since it helps to empower them, make them better advocates on their own behalf and foster better, more-informed decisions about their health. Yet lack of familiarity with medical vocabulary is a major problem for patients in accessing the available information. As a first step to providing better vocabulary support for patients, this study collected and analyzed patient and clinician terms to confirm and quantitatively assess their differences. We also analyzed the information retrieval (IR) performance resulting from these terms. The results showed that patient terminology does differ from clinician terminology in many respects including misspelling rate, mapping rate and semantic type distribution, and patient terms lead to poorer results in information retrieval.

Training in medical informatics: combining onsite and online instruction.

The Internet is promoting active exchange of teaching materials and discussion among geographically distant collaborators. We envision that training in medical informatics can be better achieved if both onsite and online instruction are combined, provided that cultural and technological barriers are anticipated and the training program is prepared accordingly. We describe our Brazil/USA program in medical informatics, which includes components of on-site and online education, and discuss lessons learned during its ongoing implementation. Three onsite courses and one workshop have been planned, and two online courses are being developed.

Adaptation of the critical incident technique to requirements engineering in public health.

The introduction of modern information systems in public health provides new possibilities for improvements in public health services and hence also of population's health. However, development of information systems that truly supports public health practices requires that technical, cognitive, and social issues be taken into consideration. In requirements engineering for public health, a notable problem is that of capturing all aspects of the future user's voices, i.e., the viewpoints of different public health practitioners. Failing to capture these voices will result in inefficient or even useless systems. The aim of this paper is to report a requirements-engineering instrument to describe problems in the daily work of public health professionals. The issues of concern thus captured can be used as the basis for formulating the requirements of information systems for public health professionals.

eCare and eHealth: the Internet meets health care.

Increasing use of the Internet by consumers in general is being reflected in greater reliance on this medium for health information and health care. Patients are consulting the Web for medical information and resources from databases that are generally accessible to the public as well as physicians. In addition, interactions between physicians and patients via e-mail present an opportunity for greater efficiency in medical practice. Though physicians have been somewhat reluctant to embrace this modality, it offers significant opportunities for enhancing patient care without undue liability. Issues of confidentiality and security of medical information are also discussed.

Methods of cognitive analysis to support the design and evaluation of biomedical systems: the case of clinical practice guidelines.

This article provides a theoretical and methodological framework for the use of cognitive analysis to support the representation of biomedical knowledge and the design of clinical systems, using clinical-practice guidelines (CPGs) as an example. We propose that propositional and semantic analyses, when used as part of the system-development process, can improve the validity, usability, and comprehension of the resulting biomedical applications. The framework we propose is based on a large body of research on the study of how people mentally represent information and subsequently use it for problem solving. This research encompasses many areas of psychology, but the more important ones are the study of memory and the study of comprehension. Of particular relevance is research devoted to investigating the comprehension and memory of language, expressed verbally or in text. In addition, research on how contextual variables affect performance is informative because these psychological processes are influenced by situational variables (e.g., setting, culture). One important factor limiting the acceptance and use of clinical-practice guidelines (CPGs) may be the mismatch between a guideline's recommended actions and the physician-user's mental models of what seems appropriate in a given case. Furthermore, CPGs can be semantically complex, often composed of elaborate collections of prescribed procedures with logical gaps or contradictions that can promote ambiguity and hence frustration on the part of those who attempt to use them. An improved understanding of the semantics and structure of CPGs may help to improve such matching, and ultimately the comprehensibility and usability of CPGs. Cognitive methods of analysis can help guideline designers and system builders throughout the development process, from the conceptual design of a computer-based system to its implementation phases. By studying how guideline creators and developers represent guidelines, both mentally and in text, and how end-users understand and make decisions with such guidelines, we can inform the development of technologies that seek to improve the match between the representations of experts and practitioners. We urge informaticians to recognize the potential relevance of cognitive analysis methods and to begin more extensive experimentation with the their use in biomedical informatics research.

Problems and challenges in patient information retrieval: a descriptive study.

Many patients now turn to the Web for health care information. However, a lack of domain knowledge and unfamiliarity with medical vocabulary and concepts restrict their ability to successfully obtain information they seek. The purpose of this descriptive study was to identify and classify the problems a patient encounters while performing information retrieval tasks on the Web, and the challenges it poses to informatics research. In this study, we observed patients performing various retrieval tasks, and measured the effectiveness of, satisfaction with, and usefulness of the results. Our study showed that patient information retrieval often failed to produce successful results due to a variety of problems. We propose a classification of patient IR problems based on our observations.

Using features of Arden Syntax with object-oriented medical data models for guideline modeling.

Computer-interpretable guidelines (CIGs) can deliver patient-specific decision support at the point of care. CIGs base their recommendations on eligibility and decision criteria that relate medical concepts to patient data. CIG models use expression languages for specifying these criteria, and define models for medical data to which the expressions can refer. In developing version 3 of the GuideLine Interchange Format (GLIF3), we used existing standards as the medical data model and expression language. We investigated the object-oriented HL7 Reference Information Model (RIM) as a default data model. We developed an expression language, called GEL, based on Arden Syntax's logic grammar. Together with other GLIF constructs, GEL reconciles incompatibilities between the data models of Arden Syntax and the HL7 RIM. These incompatibilities include Arden's lack of support for complex data types and time intervals, and the mismatch between Arden's single primary time and multiple time attributes of the HL7 RIM.

Molecular identification using flow cytometry histograms and information theory.

Flow cytometry is a common technique for quantitatively measuring the expression of individual molecules on cells. The molecular expression is represented by a frequency histogram of fluorescence intensity. For flow cytometry to be used as a knowledge discovery tool to identify unknown molecules, histogram comparison is a major limitation. Many traditional comparison methods do not provide adequate assessment of histogram similarity and molecular relatedness. We have explored a new approach applying information theory to histogram comparison, and tested it with histograms from 14 antibodies over 3 cell types. The information theory approach was able to improve over traditional methods by recognizing various non-random correlations between histograms in addition to similarity and providing a quantitative assessment of similarity beyond hypothesis testing of identity.

Guideline classification to assist modeling, authoring, implementation and retrieval.

The National Guideline Clearinghouse (NGC) and its guideline classification system are significant contributions to the study of clinical practice guidelines (CPGs) and their incorporation into routine clinical care. The NGC classification system is primarily designed to support guideline retrieval. We believe that a guideline classification system should also support identification of features that relate to incorporation of executable CPGs into computer-based applications for sharing and delivering guideline-based advice. We have developed a proposed expansion of the NGC guideline classification for this purpose. The axes of the proposed scheme have implications for designing formal models and structures for representing and authoring CPGs. This scheme also has implications for future research.

GLIF3: the evolution of a guideline representation format.

The Guideline Interchange Format (GLIF) is a language for structured representation of guidelines. It was developed to facilitate sharing clinical guidelines. GLIF version 2 enabled modeling a guideline as a flowchart of structured steps, representing clinical actions and decisions. However, the attributes of structured constructs were defined as text strings that could not be parsed, and such guidelines could not be used for computer-based execution that requires automatic inference. GLIF3 is a new version of GLIF designed to support computer-based execution. GLIF3 builds upon the framework set by GLIF2 but augments it by introducing several new constructs and extending GLIF2 constructs to allow a more formal definition of decision criteria, action specifications and patient data. GLIF3 enables guideline encoding at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that can be incorporated into particular institutional information systems.