Biomedical subjects

Robert A Greenes

Publications and source records attributed to Robert A Greenes.

At least 19 recordsLinked to original sources

PubMed2004

Positive attitudes and failed queries: an exploration of the conundrums of consumer health information retrieval.

Several studies have found that consumers report a high level of satisfaction with the Internet as a health information resource. Belied by this positive attitude, however, are other studies reporting that consumers were often unsuccessful in searching for health information. In this paper, we present an interview and observation study in which we asked health consumers to search for health information on the Internet after first stating their search goals. Upon the conclusion of the session they were asked to evaluate their searches. We found that many consumers were unable to find satisfactory information when performing a specific query, while in general the group viewed health information retrieval (HIR) on the Internet in a positive light. We analyzed the observed search sessions to determine what factors accounted for the failure of specific searches and positive attitudes, and also discussed potential informatics solutions.

Adult↗

PubMed2004

GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines.

The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.

Artificial Intelligence↗

PubMed2004

Design and implementation of the GLIF3 guideline execution engine.

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

Database Management Systems↗

PubMed2004

Description and status update on GELLO: a proposed standardized object-oriented expression language for clinical decision support.

A major obstacle to sharing computable clinical knowledge is the lack of a common language for specifying expressions and criteria. Such a language could be used to specify decision criteria, formulae, and constraints on data and action. Al-though the Arden Syntax addresses this problem for clinical rules, its generalization to HL7's object-oriented data model is limited. The GELLO Expression language is an object-oriented language used for expressing logical conditions and computations in the GLIF3 (GuideLine Interchange Format, v. 3) guideline modeling language. It has been further developed under the auspices of the HL7 Clinical Decision Support Technical Committee, as a proposed HL7 standard., GELLO is based on the Object Constraint Language (OCL), because it is vendor-independent, object-oriented, and side-effect-free. GELLO expects an object-oriented data model. Although choice of model is arbitrary, standardization is facilitated by ensuring that the data model is compatible with the HL7 Reference Information Model (RIM).

Decision Making, Computer-Assisted↗

PubMed2004

Design of a standards-based external rules engine for decision support in a variety of application contexts: report of a feasibility study at Partners HealthCare System.

This project explored functional requirements for an institution-wide method, at Partners HealthCare, for interpreting clinical knowledge for decision support. Such knowledge is currently incorporated in a variety of clinical applications, yet the methods of representation and of execution vary and the ability to author/edit the rules by human experts is limited. We expanded on a 2002 "Knowledge Inventory" at Partners to evaluate feasibility of designing a single representation approach entailing: (a) exploration of specific needs of different applications, in terms of kinds of response required (synchronous/asynchronous, time criticality, etc.), context (e.g., implied patient, time frame, or episode), and kinds of actions to be triggered; (b) kind of representation of knowledge and feasibility of casting knowledge in the form of if em leader then statements; and (c) data and knowledge resources used (implied data model, and particular knowledge sources and terminology sources). The result of analysis was to design an architecture to accomplish this goal. We also did preliminary analysis of requirements for authoring for such a representation, and for implementation.

Artificial Intelligence↗

PubMed2004

Relationships among different subjective measurements of consumer health information retrieval performance.

BACKGROUND: Millions of consumers perform health information retrieval (HIR) online. To better understand the consumers' perspective on HIR performance, we conducted an observation and interview study of 97 health information consumers. METHODS: Consumers were asked to perform HIR tasks and we recorded their view regarding performance using several differ-ent subjective measurements: finding the desired information, usefulness of the information found, satisfaction with the information, and intention to continue searching. Statistical analysis was applied to verify if the multiple subjective measurements were redundant. RESULT: The measurements ranged from slight agreement to no agreement among them. A number of reasons were identified for this lack of agreement. CONCLUSION: Although related, the four subjective measurements of HIR performance are distinct from each other and carried different useful information

Consumer Behavior↗

PubMed2003

The InterMed approach to sharable computer-interpretable guidelines: a review.

InterMed is a collaboration among research groups from Stanford, Harvard, and Columbia Universities. The primary goal of InterMed has been to develop a sharable language that could serve as a standard for modeling computer-interpretable guidelines (CIGs). This language, called GuideLine Interchange Format (GLIF), has been developed in a collaborative manner and in an open process that has welcomed input from the larger community. The goals and experiences of the InterMed project and lessons that the authors have learned may contribute to the work of other researchers who are developing medical knowledge-based tools. The lessons described include (1) a work process for multi-institutional research and development that considers different viewpoints, (2) an evolutionary lifecycle process for developing medical knowledge representation formats, (3) the role of cognitive methodology to evaluate and assist in the evolutionary development process, (4) development of an architecture and (5) design principles for sharable medical knowledge representation formats, and (6) a process for standardization of a CIG modeling language.

Computer Systems↗

PubMed2003

A meta-data model for knowledge in decision support systems.

Clinical decision support such as alerts, reminders and guidance are driven by rules often distributed among a variety of applications in a healthcare information system. Due to the increasing size of rule bases, there is a growing need to manage this dispersed knowledge in an integrated environment. A system for management of executable clinical knowledge such as rules should (1) assist in the development and maintenance of rules throughout the rules' life-cycles, (2) support search and retrieval of rules in the knowledge base (e.g., rules for diabetes, rules created by a particular individual), and (3) facilitate the analyses of rules in the knowledge base (e.g., identify rules not updated in the last year). In order to create such a clinical knowledge management system it is necessary to model the meta-data of rules. There have been efforts to document meta-data about rules within the Arden Syntax Medical Logical Modules' project. However, the maintenance and library categories in that project allow mainly free-text information about a rule. We have created a comprehensive meta-data structure and taxonomy for describing clinical rules that supports the features of a knowledge management system. We also tested this model using a representative set of rules.

Artificial Intelligence↗

PubMed2003

A method for subdividing clinical guidelines into process modules with associated triggers and objectives to facilitate implementation.

Representation of multi-step clinical guidelines (CG) and their implementation in computerized decision support (DS) systems are complex and logistically challenging tasks. However, many simple rules based on CGs (e.g., medical logic modules), have been successfully implemented through a few popular DS models (e.g., prevention reminders, order entry systems). To facilitate mapping of CGs to practical DS models, we propose an empirical method for sub-dividing CGs into modules according to the locus in a clinical process flow model where implementation would be most effective (e.g., post-encounter provider order entry). We further propose a classification of triggers and objectives for CG modules that provides a framework for a DS system to implement the module Successful application of the method to ten diverse CGs in the outpatient setting is described.

Decision Support Systems, Clinical↗

PubMed2003

Applying axiomatic design methodology for guideline revision.

We are investigating the use of axiomatic design (AD) as a principled approach to the revision of guidelines. AD models guidelines in a modular and hierarchical manner and captures interactions be-tween modules. To test this approach we applied AD to encode segments of three guidelines and their revised versions. Guideline encodings for the original versions were modified to incorporate changes made in the revised documents. The results indicate that AD is a promising approach for guideline modeling.

Decision Support Systems, Clinical↗

PubMed2003

GELLO: an object-oriented query and expression language for clinical decision support.

GELLO is a purpose-specific, object-oriented (OO) query and expression language. GELLO is the result of a concerted effort of the Decision Systems Group (DSG) working with the HL7 Clinical Decision Support Technical Committee (CDSTC) to provide the HL7 community with a common format for data encoding and manipulation. GELLO will soon be submitted for ballot to the HL7 CDSTC for consideration as a standard.

Decision Making, Computer-Assisted↗

PubMed2003

Developing a shared agenda for health care systems safety and quality.

High quality, computer-interpretable, patient-specific knowledge at the point of need is essential, as we seek to incorporate decision support and other approaches in clinical information systems to achieve safety and increased quality of health care. This gives rise to the need for shared, standards-based approaches to representing the knowledge and tools for management of it.

Cooperative Behavior↗

PubMed2002

Representation primitives, process models and patient data in computer-interpretable clinical practice guidelines: a literature review of guideline representation models.

Representation of clinical practice guidelines in a computer-interpretable format is a critical issue for guideline development, implementation, and evaluation. We studied 11 types of guideline representation models that can be used to encode guidelines in computer-interpretable formats. We have consistently found in all reviewed models that primitives for representation of actions and decisions are necessary components of a guideline representation model. Patient states and execution states are important concepts that closely relate to each other. Scheduling constraints on representation primitives can be modeled as sequences, concurrences, alternatives, and loops in a guideline's application process. Nesting of guidelines provides multiple views to a guideline with different granularities. Integration of guidelines with electronic medical records can be facilitated by the introduction of a formal model for patient data. Data collection, decision, patient state, and intervention constitute four basic types of primitives in a guideline's logic flow. Decisions clarify our understanding on a patient's clinical state, while interventions lead to the change from one patient state to another.

Artificial Intelligence↗

PubMed2002

Using the critical incident technique to define a minimal data set for requirements elicitation in public health.

The introduction of computer-based information systems (ISs) in public health provides enhanced possibilities for service improvements and hence also for improvement of the population's health. Not least, new communication systems can help in the socialization and integration process needed between the different professions and geographical regions. Therefore, development of ISs that truly support public health practices require that technical, cognitive, and social issues be taken into consideration. A notable problem is to capture 'voices' of all potential users, 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 study is to develop a minimal data set for capturing users' voices on problems experienced by public health professionals in their daily work and opinions about how these problems can be solved. The issues of concern thus captured can be used both as the basis for formulating the requirements of ISs for public health professionals and to create an understanding of the use context. Further, the data can help in directing the design to the features most important for the users.

Humans↗

PubMed2002

Future of medical knowledge management and decision support.

Attempts to predict the future are typically off the mark. Beyond the challenges of forecasting the stock market or the weather, dramatic instances of notoriously inaccurate prognostications have been those by the US patent office in the late 1800s about the future of inventions, by Thomas Watson in the 1930s about the market for large computers, and by Bill Gates in the early 1990s about the significance of the Internet. When one seeks to make predictions about health care, one finds that, beyond the usual uncertainties regarding the future, additional impediments to forecasting are the discontinuities introduced by advances in biomedical science and technology, the impact of information technology, and the reorganizations and realignments attending various approaches to health care delivery and finance. Changes in all three contributing areas themselves can be measured in "PSPYs", or paradigm shifts per year. Despite these risks in forecasting, I believe that certain trends are sufficiently clear that I am willing to venture a few predictions. Further, the predictions I wish to make suggest a goal for the future that can be achieved, if we can align the prevailing political, financial, biomedical, and technical forces toward that end. Thus, in a sense this is a call to action, to shape the future rather than just let it happen. This chapter seeks to lay out the direction we are heading in knowledge management and decision support, and to delineate an information technology framework that appears desirable. I believe the framework to be discussed is of importance to the health care-related knowledge management and decision making activities of the consumer and patient, the health care provider, and health care delivery organizations and insurers. The approach is also relevant to the other dimensions of academic health care institution activities, notably the conduct of research and the processes of education and learning.

Biomedical Technology↗

PubMed2002

Using a neural network with flow cytometry histograms to recognize cell surface protein binding patterns.

Flow cytometric systems are being used increasingly in all branches of biological science including medicine. To develop analytic tools for identifying unknown molecules such as the antibodies that recognize different structure in the identical antigens, we explored use of a neural network in flow cytometry data comparison. Peak locations were extracted from flow cytometry histograms and we used the Marquardt backpropagation neural networks to recognize identical or similar binding patterns between antibodies and antigens based on the peak locations. The neural network showed 93.8% to 99.6% correct classification rates for identical or similar molecules. This suggests that the neural network technique can be useful in flow cytometry histogram data analysis.

Antibodies, Monoclonal↗

PubMed2002

Support for guideline development through error classification and constraint checking.

Clinical guidelines aim to eliminate clinician errors, reduce practice variation, and promote best medical practices. Computer-interpretable guidelines (CIGs) can deliver patient-specific advice during clinical encounters, which makes them more likely to affect clinician behavior than narrative guidelines. To reduce the number of errors that are introduced while developing narrative guidelines and CIGs, we studied the process used by the ACP-ASIM to develop clinical algorithms from narrative guidelines. We analyzed how changes progressed between subsequent versions of an algorithm and between a narrative guideline and its derived clinical algorithm. We recommend procedures that could limit the number of errors produced when generating clinical algorithms. In addition, we developed a tool for authoring CIGs in GLIF3 format and validating their syntax, data type matches, cardinality constraints, and structural integrity constraints. We used this tool to author guidelines and to check them for errors.

Algorithms↗

PubMed2002

Matching of flow-cytometry histograms using information theory in feature space.

Flow cytometry is a widely available technique for analyzing cell-surface protein expression. Data obtained from flow cytometry is frequently used to produce fluorescence intensity histograms. Comparison of histograms can be useful in the identification of unknown molecules and in the analysis of protein expression. In this study, we examined the combination of a new smoothing technique called SiZer with information theory to measure the difference between cytometry histograms. SiZer provides cross-bandwidth smoothing and allowed analysis in feature space. The new methods were tested on a panel of monoclonal antibodies raised against proteins expressed on peripheral blood lymphocytes and compared with previous methods. The findings suggest that comparing information content of histograms in feature space is effective and efficient for identifying antibodies with similar cell-surface binding patterns.

Animals↗