Name
Abstract | ||
|---|---|---|
GLIF, an execution model for clinical guidelines, was augmented by attributes from GEM, a document model. Our preliminary evaluation, based on an Alz- heimer's disease treatment guideline, shows that the merged model has enhanced capacity to relate deci- sion and actions to explanations and the evidence upon which they are based. Background: The Guideline Elements Markup (GEM) model (1) was developed at Y ale as a way of tagging elements of narrative guidelines, to enable them to be more precisely accessed and retrieved. Many of these elements relate to the nature of the process used to develop the guideline, kinds of ev i- dence, and explanations and references for recom- mendations. The GuideLine Interchange Format (GLIF) (2) is a modeling approach developed at Ha r- vard, Stanford, and Columbia (the "InterMed Col- laboratory") aimed at creating a shared guidelines representation that is computer interpretable, in order to integrate guidelines into clinical systems, and de- liver patient-specific recommendations at the point of care. GEM uses XML tags to identify various ele- ments in a guideline document; hence it is a doc u- ment model. The GLIF representation of a guideline is based on an object-oriented model of classes of guideline steps that support execution. It was our hypothesis that GLIF would benefit from the inclusion of GEM elements that provide links to portions of the narrative document providing back- ground information on which various decision steps and actions are based; we therefore extended the GLIF model by adding GEM elements to it. We evaluated the resulting model informally by applying it to an Alzheimer's disease treatment guideline (3). Methods: We mapped GEM elements to GLIF class definitions, by finding those that were equivalent, or adding new attributes to GLIF classes representing relevant GEM documentation descriptors, or in some cases adding new classes to the GLIF model. We used both the Together/J UML tool and the Protégé knowledge-modeling tool (4) to represent the merged GLIF-GEM ontology. While the UML tool was used to output a report of the ontology, including class diagrams and documentation, Protégé was used to encode the Alzheimer guideline by creating instances of the ontology classes. We also marked the same guideline with the GEM Cutter tool (5) to ensure that we did not miss any GEM elements. We tabulated the frequencies of occurrence of the various GEM elements in the encoding of t he Alzheimer guideline using the GLIF-GEM ontology. Results: In developing the merged ontology, we mapped 8 GEM classes and about 80 elements to the GLIF class definitions, by modifying 7 of the existing GLIF classes and by creating 10 new classes, where no such constructs were present in GLIF. The only GEM class that was not mapped was Knowledge Components because the GLIF model contains the same or similar attributes and corresponding func- tions. Of all the mapped classes, Evidence was most used (50 times) when encoding the Alzheimer's guideline. A total of 288 new information items in the Alzheimer guideline were incorporated by incl u- sion of GEM elements. Discussion: The GLIF and GEM models are com- plementary, in that an execution model would benefit from links to the background information on which various decision steps and actions are based; and |
Year | Venue | Keywords |
|---|---|---|
2001 | JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION | point of care,bioinformatics,class diagram,biomedical research,col |
Field | DocType | Issue |
Data science,Information retrieval,Computer science,Document model,Execution model,Guideline | Conference | SUPnan |
ISSN | Citations | PageRank |
1067-5027 | 1 | 0.71 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James Q. Yin | 1 | 1 | 1.05 |
| Mor Peleg | 2 | 1135 | 110.07 |
| Aziz A. Boxwala | 3 | 585 | 72.72 |
| Robert Greenes | 4 | 644 | 106.18 |