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Quality reporting for cervical cancer prevention is focused on patients with normal cervical cytology, and excludes patients with cytological abnormalities that may be at higher risk. The major obstacles for granular reporting are the complexity of surveillance guidelines and free-text data. We performed automated chart review to compare the cytology testing rates for patients with 'atypical squamous cells of undetermined significance' (ASCUS) cytology, with the rates for patients with normal cytology. We modeled the surveillance guidelines, and extracted information from free-text cytology reports, to perform this study on 28101 female patients. Our results show that patients with ASCUS cytology had significantly higher adherence rates (94.9%) than those for patients with normal cytology (90.4%). Overall our study indicates that the quality of care varies significantly between the high and average risk patients. Our study demonstrates the use of health information technology for higher granularity of reporting for cervical cytology testing.

We analyzed query logs from use of MEDLINEplus to answer the questions: Are consumers' health information needs stable over time? and To what extent do users' queries change over time? To determine log stability, we assessed an Overlap Rate (OR) defined as the number of unique queries common to two adjacent months divided by the total number of unique queries in those months. All exactly matching queries were considered as one unique query. We measured ORs for the top 10 and 100 unique queries of a month and compared these to ORs for the following month. Over ten months, users submitted 12,234,737 queries;only 2,179,571 (17.8%) were unique and these had a mean word count of 2.73 (S.D., 0.24); 121 of 137 (88.3%) unique queries each comprised of exactly matching search term(s) used at least 5000 times were of only one word. We could predict with 95% confidence that the monthly OR for the top 100 unique queries would lie between 67% - 87% when compared with the top 100 from the previous month. The mean month-to-month OR for top 10 queries was 62% (S.D., 20%) indicating significant variability;the lowest OR of 33% between the top 10 in Mar.compared to Apr. was likely due to "new" interest in information about SARS pneumonia in Apr. 2003.Consumers' health information needs are relatively stable and the 100 most common unique queries are about 77% the same from month to month. Website sponsors should provide a broad range of information about a relatively stable number of topics. Analyses of log similarity may identify media-induced, cyclical,or seasonal changes in areas of consumer interest.

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.

GELLO is a purpose-specific, object-oriented (OO) query and expression language [1]. 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.

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.

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.

Clinical guidelines developed at a national level are often difficult to use in specific-settings because of contextual factors. We have created a model for guideline representation that enables local adaptation of nationally developed guidelines. Guidelines are specified in a setting-independent manner and are then locally adapted by each practice. A guideline is represented hierarchically with the top level steps of the guideline specifying broad objectives and the lower level steps providing details of the recommendations. The axiomatic design framework provides a principled method for hierarchical design. The approach has been implemented by incorporation in the GLIF guideline modeling environment.

In a previous paper, we presented a proposed expansion of the National Guideline Clearing-house (NGC) classification1. We performed a preliminary evaluation of the classification based on 100 guidelines randomly selected from the NGC collection. We found that 89 of the 100 guidelines could be assigned to a single guideline category. To test inter-observer agreement, twenty guidelines were also categorized by a second investigator. Agreement was found to be 40-90% depending on the axis, which compares favorably with agreement among MeSH indexers (30-60%)2. We conclude that categorization is feasible. Further research is needed to clarify axes with poor inter-observer agreement.

GLIF, an execution model for clinical guidelines, was augmented by attributes from GEM, a document model. Our preliminary evaluation, based on an Alzheimers disease treatment guideline, shows that the merged model has enhanced capacity to relate decision and actions to explanations and the evidence upon which they are based.