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Biomedical subjects

Aziz A Boxwala

Publications and source records attributed to Aziz A Boxwala.

14 recordsLinked to original sources

Organization and representation of patient safety data: current status and issues around generalizability and scalability.

Recent reports have identified medical errors as a significant cause of morbidity and mortality among patients. A variety of approaches have been implemented to identify errors and their causes. These approaches include retrospective reporting and investigation of errors and adverse events and prospective analyses for identifying hazardous situations. The above approaches, along with other sources, contribute to data that are used to analyze patient safety risks. A variety of data structures and terminologies have been created to represent the information contained in these sources of patient safety data. Whereas many representations may be well suited to the particular safety application for which they were developed, such application-specific and often organization-specific representations limit the sharability of patient safety data. The result is that aggregation and comparison of safety data across organizations, practice domains, and applications is difficult at best. A common reference data model and a broadly applicable terminology for patient safety data are needed to aggregate safety data at the regional and national level and conduct large-scale studies of patient safety risks and interventions.

Humans↗

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↗

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↗

A methodology for modular representation of guidelines.

Computer-based clinical practice guidelines often need to be modified when medical knowledge evolves or when guidelines are implemented in a local setting with specific constraints and preferences. To enable easy modifications to guidelines and maintain their integrity, we have developed a methodology for modular representation of guidelines. Using this approach, we create guidelines in a hierarchical and modular manner. We use the Axiomatic Design methodology to facilitate the development of independent modules. Design matrices capture the interactions among modules. The design matrices can be used during guideline modification to create a change process and to enable identification of other modules that are affected by a change to a module. We implemented this modular knowledge representation approach by incorporating it into the Guideline Interchange Format (GLIF) language. We applied this approach to encode parts of three outdated guidelines released during 2000-2001, and we revised these designs to model updated releases of the guideline. Qualitative and quantitative metrics were developed to assess the types of changes made to the encoded guidelines.

Adult↗

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↗

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↗

Coverage of patient safety terms in the UMLS metathesaurus.

The integration and large-scale analyses of medical error databases would be greatly facilitated by the use of a standard terminology. We investigated the availability in the UMLS metathesaurus of concepts that are required for coding patient safety data. Terms from three proprietary patient safety terminologies were mapped to the concepts in UMLS by an automated mapping program developed by us. From these candidate mappings, the concept that matched its corresponding term was selected manually. The reliability of the mapping procedure was verified by manually searching for terms in the UMLS Knowledge Source Server. Matching concepts in UMLS were identified for less than 27% of the terms in the study dataset. The matching rates of terms that describe the type of error and the causes of errors were even lower. The lack of such terms in the existing standard terminologies underscores the need for development of a standard patient safety terminology.

Humans↗

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↗

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↗

Building an application framework for integrative genomics.

The accelerated pace of biological research and the current availability of whole-genome data sets provides significant new sources of functional insight. We designed an architecture and framework for software to query and explore such data in an orderly and iterative fashion. The architecture is intended to provide an extensible platform for developing web based bioinformatics applications and to offer a flexible and end-user-extensible software environment to explore and integrate disparate biological data sources. This will enable the user to explore existing relationships and discover new functional relationships among these data.

Computational Biology↗

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↗

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↗

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↗

Providing context-sensitive decision-support based on WHO guidelines.

A decision support system was developed implementing the WHO guideline for diarrhea management. The decision-support system is integrated into a medical records application on a handheld computer. The system will be used by primary health care workers in rural India. The guideline was encoded as a set of chained rules in CLIPS format. To enhance adherence to guidelines, we use a model based on a context-adapted guideline to provide decision support at the point of care in a particular setting. The purpose of the system is to tailor the recommendations based on the patient's condition and the local factors such as resource availability in order to create feasible uniformity in a practice across different providers of care.

Computers, Handheld↗