Methods and tools to support the computerisation of clinical
practice guidelines: a short introduction.
In a paper commissioned by OpenClinical
in 2002, Yuval Shahar describes the need for guideline-based
decision support tools as part of a fully integrated healthcare
information system infrastructure:
"There is a clear need for effective guideline-support
tools at the point of care and at the point of critiquing,
which will relieve the current information overload on both
care providers and administrators. To be effective, these
tools need to be grounded in the patient's record, must use
standard medical vocabularies, should have clear semantics,
must facilitate knowledge maintenance and sharing, and need
to be sufficiently expressive to explicitly capture the design
rational (process and outcome intentions) of the guideline's
author, while leaving flexibility at application time to the
attending physician and their own preferred methods"
for specifying electronic guidelines
A number of methods to support the computerisation of guidelines
have been or are being developed by the Health Informatics community.
Guideline-based decision support systems aim to enable the latest
clinical knowledge to be accessible and usable at the point
of care and so make significant contributions to quality and
safety in healthcare.
A number of these methods are based on guideline models which
are capable of formalising medical knowledge as electronic applications
that can be executed (or enacted) to generate patient-specific
recommendations for clinical decisions and actions. Such methods
employ different representation formalisms and computational
techniques, for example:
A study, co-ordinated by Mor Peleg of Haifa University and Samson Tu of Stanford University,
carried out in 2002 to compare six computer-interpretable guideline models
(Asbru, EON, GLIF, GUIDE, PRODIGY and PROforma). Full details of the paper,
published in JAMIA in 2002, can be found in this section.
Other current methods for representing clinical knowledge
take a different approach by structuring guidelines on the
basis of XML guideline document models. Methods in this category
include GEM, Stepper and HGML.
The DeGeL (Digital electronic Guideline Library) infrastructure
of methods and tools represents a hybrid approach: it is capable
of supporting both markup and formal, executable methods of
representating clinical guidelines.
This section of
OpenClinical aims to provide information on all methods and
tools designed to support the specification of electronic
clinical guideline applications for use in healthcare.
Mor Peleg. Guideline and Workflow Models.
In: Medical Decision-Making: Computational Approaches to Achieving Healthcare Quality and Safety,
Robert A. Greenes (ed.), Elsevier/Academic Press, 2006.
Clinical guidelines aim to improve quality of care, decrease unjustified practice variations, and save costs. In order for guidelines to affect clinicians' behavior, they should provide patient-specific decision support during patient encounters. Specifying guidelines in computer-interpretable guideline (CIG) formalisms that could provide automatic inference based on patient data may achieve this goal. The knowledge contained in guidelines is difficult to formalize due to the fact that despite efforts made to improve the quality of narrative guidelines, evidence-based recommendations are often incomplete and vague, and do not constitute a full care process. Several methodologies have been developed to support the transition from narrative guidelines into CIG implementations. They include (1) methodologies for marking-up narrative guideline elements in order to assess a guideline's quality and completeness and map it to CIG formalisms and (2) CIG formalisms. Many CIG formalisms exist, differing in their goals, computation model, the elements used to structure guideline knowledge, and the degree to which they support workflow integration. Specifying a narrative guideline as a CIG is a difficult task, yet the resulting application cannot be easily shared by different institutions and software systems. Therefore, sharing encoded knowledge is a challenging goal. The specification of standard methods to support such sharing is a major focus in the field. The road to achieving wide-spread use of guideline-based decision-support systems is long and difficult. This chapter reviews the current state-of-the-art in guideline-based decision support research and considers likely future directions that can be taken to reach the ultimate goal.
Mulyar N, van der Aalst WM, Peleg M.
A Pattern-based Analysis of Clinical Computer-Interpretable Guideline Modeling Languages.
J Am Med Inform Assoc. 2007 Aug 21;
OBJECTIVE Languages used to specify computer interpretable guidelines (CIGs) differ in their approaches to addressing particular modeling challenges. The main goals of this paper are: 1) to examine the expressive power of CIG modeling languages; and 2) to define the differences, from the control-flow perspective, between process languages in workflow management systems and modeling languages used to design clinical guidelines. DESIGN The pattern-based analysis was applied to guideline modeling languages Asbru, EON, GLIF, and PROforma. We focused on control-flow and left other perspectives out of consideration. MEASUREMENTS We evaluated the selected CIG modeling languages and identified their degree of support of 43 control-flow patterns. We used a set of explicitly defined evaluation criteria to determine whether each pattern is supported directly, indirectly or not at all. RESULTS PROforma offers direct support for 22 of 43 patterns, Asbru 20, GLIF 17, and EON 11. All four directly support. Basic Control-flow patterns, Cancellation patterns, and some Advance Branching and Synchronization patterns. None support Multiple Instances patterns. They offer varying levels of support for Synchronizing Merge patterns and State-based patterns. Some support a few scenarios not covered by the 43 control-flow patterns. CONCLUSION CIG modeling languages are remarkably close to traditional workflow languages from the control-flow perspective, but cover many fewer workflow patterns. CIG languages offer some flexibility that supports modeling of complex decisions and provide ways for modeling some decisions not covered by workflow management systems. Workflow management systems may be suitable for clinical guideline applications.
Peleg M, Tu S, Bury J, Ciccarese
P, Fox J, Greenes RA, Hall R, Johnson PD, Jones N, Kumar
A, Miksch S, Quaglini S, Seyfang A, Shortliffe EH, Stefanelli
M. Comparing computer-interpretable guideline models:
a case-study approach. J Am Med Inform Assoc. 2003 Jan-Feb;10(1):52-68.
documentation on OC]
|" OBJECTIVES: Many groups are developing
computer-interpretable clinical guidelines (CIGs) for
use during clinical encounters. CIGs use "Task-Network
Models" for representation but differ in their approaches
to addressing particular modeling challenges. We have
studied similarities and differences between CIGs in order
to identify issues that must be resolved before a consensus
on a set of common components can be developed. DESIGN:
We compared six models: Asbru, EON, GLIF, GUIDE, PRODIGY,
and PROforma. Collaborators from groups that created these
models represented, in their own formalisms, portions
of two guidelines: the American College of Physicians-American
Society of Internal Medicine's guideline for managing
chronic cough and the Sixth Report of the Joint National
Committee on Prevention, Detection, Evaluation, and Treatment
of High Blood Pressure. MEASUREMENTS: We compared the
models according to eight components that capture the
structure of CIGs. The components enable modelers to encode
guidelines as plans that organize decision and action
tasks in networks. They also enable the encoded guidelines
to be linked with patient data-a key requirement for enabling
patient-specific decision support. RESULTS: We found consensus
on many components, including plan organization, expression
language, conceptual medical record model, medical concept
model, and data abstractions. Differences were most apparent
in underlying decision models, goal representation, use
of scenarios, and structured medical actions. CONCLUSION:
We identified guideline components that the CIG community could adopt as standards. Some of the participants are
pursuing standardization of these components under the
auspices of HL7. "
de Clercq PA, Blom JA, Korsten
HH, Hasman A. Approaches for creating computer-interpretable
guidelines that facilitate decision support. Artif Intell
Med. 2004 May;31(1):1-27.
|" During the last decade, studies
have shown the benefits of using clinical guidelines in
the practice of medicine. Although the importance of these
guidelines is widely recognized, health care organizations
typically pay more attention to guideline development
than to guideline implementation for routine use in daily
care. However, studies have shown that clinicians are
often not familiar with written guidelines and do not
apply them appropriately during the actual care process.
Implementing guidelines in computer-based decision support
systems promises to improve the acceptance and application
of guidelines in daily practice because the actions and
observations of health care workers are monitored and
advice is generated whenever a guideline is not followed.
Such implementations are increasingly applied in diverse
areas such as policy development, utilization management,
education, clinical trials, and workflow facilitation.
Many parties are developing computer-based guidelines
as well as decision support systems that incorporate these
guidelines. This paper reviews generic approaches for
developing and implementing computer-based guidelines
that facilitate decision support. It addresses guideline
representation, acquisition, verification and execution
aspects. The paper describes five approaches (the Arden
Syntax, GuideLine Interchange Format (GLIF), PROforma,
Asbru and EON), after the approaches are compared and
Wang D, Peleg M, Tu SW, Boxwala
AA, Greenes RA, Patel VL, Shortliffe EH. Representation
primitives, process models and patient data in computer-interpretable
clinical practice guidelines: a literature review of
guideline representation models. Int J Med Inform. 2002.
|"Representation of clinical practice
guidelines in a computer interpretable format is a critical
issue for guideline development, implementation and evaluation.
We studied eleven 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."
| Greenes RA, Peleg M, Boxwala A, Tu
S, Patel V, Shortliffe EH. Sharable computer-based clinical
practice guidelines: rationale, obstacles, approaches,
and prospects. Medinfo 2001;10(Pt 1):201-5.
- SMI] [Paper
|" 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. "
| S. W. Tu & M. A. Musen. Representation
Formalisms and Computational Methods for Modeling Guideline-Based
Patient Care. First European Workshop on Computer-based
Support for Clinical Guidelines and Protocols, Leipzig,
Germany, 125-142. 2000.
|" This paper (1) presents a set
of patient-care tasks for which a computer-interpretable
representation of clinical practice guideline and protocols
can provide assistance, (2) surveys the formalisms and
computational methods that have been proposed in relation
to these tasks, and (3) describes a multi-method paradigm
that we have been developing in the EON project to support
these patient-care tasks. "
| D. A. Wang, M. Peleg, S. Tu, E. H.
Shortliffe, & R. A. Greenes Representation of Clinical
Practice Guidelines For Computer-based Implementations
Medinfo, London, UK, 2001.
|" 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 models. Typical
primitives for these models include decisions, actions,
patient states and execution states. We also find temporal
constraints and nesting to be important aspects of guideline
structure representation. Integration of guidelines with
electronic medical records is facilitated by the introduction
of formal models of patient data. Patient states and execution
states are closely related to one another. Interrelationship
among data collection, decisions, patient states and interventions
in a guideline's logic flow varies in different guideline
representation models. "
| P Elkin, M Peleg, R Lacson et al.
Toward Standardization of Electronic Guidelines. MD Computing,
Vol. 17, No. 6, 2000, pp. 39-44.
|" We describe approaches to electronic
guideline representation and classify them into 3 groups:
rule-based, decision analysis, markup, and multi-step
guidelines modeled as a hierarchical set of nested guideline
tasks. We also describe the relationships and influences
among the various representation methods and present current
trends and future directions in the field of electronic
guideline representation. "
Tu SW, Campbell J, Musen MA.
The structure of guideline recommendations: a synthesis.
AMIA Annu Symp Proc. 2003;:679-83.
We propose that recommendations in a clinical guideline can be structured either as collections of decisions that are to be applied in specific situations or as processes that specify activities that take place over time. We formalize them as "recommendation sets" consisting of either Activity Graphs that represent guideline-directed processes or Decision Maps that represent atemporal recommendations or recommendations involving decisions made at one time point. We model guideline processes as specializations of workflow processes and provide possible computational models for decision maps. We evaluate the proposed formalism by showing how various guideline-modeling methodologies, including GLIF, EON, PRODIGY3, and Medical Logic Modules can be mapped into the proposed structures. The generality of the formalism makes it a candidate for standardizing the structure of recommendations for computer-interpretable guidelines.
|references: standards and computer-interpretable guidelines
Biondich PG, Downs SM, Carroll AE, Shiffman RN, McDonald CJ.
Collaboration between the medical informatics community and guideline authors: fostering HIT standard development that matters.
AMIA Annu Symp Proc. 2006;:36-40.
Clinical guideline authors, health information technology (HIT) standards development organizations, and information system implementers all work to improve the processes of healthcare, but have long functioned independently towards realizing these goals. This has led to clinical standards of care that often poorly align with the functional and technical HIT standards developed to realize them.We describe the shortcomings and inefficiencies inherent in this current process and introduce two national initiatives that attempt to unite these communities. The mission of these two initiatives is to create examples of unambiguous, decidable, and executable clinical guidelines which both utilize and inform HIT terminology and logical expression standards. All of the products of this work aim to facilitate enterprise-wide guideline implementation and create a rising tide which lifts all ships.
|Entry on OpenClinical: 2002
Last main update: 29 August 2005