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Leveraging workflow control patterns in the domain of clinical practice guidelines

Overview of attention for article published in BMC Medical Informatics and Decision Making, February 2016
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Title
Leveraging workflow control patterns in the domain of clinical practice guidelines
Published in
BMC Medical Informatics and Decision Making, February 2016
DOI 10.1186/s12911-016-0253-z
Pubmed ID
Authors

Katharina Kaiser, Mar Marcos

Abstract

Clinical practice guidelines (CPGs) include recommendations describing appropriate care for the management of patients with a specific clinical condition. A number of representation languages have been developed to support executable CPGs, with associated authoring/editing tools. Even with tool assistance, authoring of CPG models is a labor-intensive task. We aim at facilitating the early stages of CPG modeling task. In this context, we propose to support the authoring of CPG models based on a set of suitable procedural patterns described in an implementation-independent notation that can be then semi-automatically transformed into one of the alternative executable CPG languages. We have started with the workflow control patterns which have been identified in the fields of workflow systems and business process management. We have analyzed the suitability of these patterns by means of a qualitative analysis of CPG texts. Following our analysis we have implemented a selection of workflow patterns in the Asbru and PROforma CPG languages. As implementation-independent notation for the description of patterns we have chosen BPMN 2.0. Finally, we have developed XSLT transformations to convert the BPMN 2.0 version of the patterns into the Asbru and PROforma languages. We showed that although a significant number of workflow control patterns are suitable to describe CPG procedural knowledge, not all of them are applicable in the context of CPGs due to their focus on single-patient care. Moreover, CPGs may require additional patterns not included in the set of workflow control patterns. We also showed that nearly all the CPG-suitable patterns can be conveniently implemented in the Asbru and PROforma languages. Finally, we demonstrated that individual patterns can be semi-automatically transformed from a process specification in BPMN 2.0 to executable implementations in these languages. We propose a pattern and transformation-based approach for the development of CPG models. Such an approach can form the basis of a valid framework for the authoring of CPG models. The identification of adequate patterns and the implementation of transformations to convert patterns from a process specification into different executable implementations are the first necessary steps for our approach.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 43 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Hungary 1 2%
United States 1 2%
Austria 1 2%
Unknown 40 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Student > Master 8 19%
Researcher 3 7%
Professor > Associate Professor 3 7%
Student > Bachelor 3 7%
Other 11 26%
Unknown 7 16%
Readers by discipline Count As %
Computer Science 16 37%
Medicine and Dentistry 6 14%
Business, Management and Accounting 3 7%
Economics, Econometrics and Finance 2 5%
Arts and Humanities 1 2%
Other 6 14%
Unknown 9 21%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 February 2016.
All research outputs
#14,707,467
of 22,844,985 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,216
of 1,991 outputs
Outputs of similar age
#220,964
of 400,522 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 31 outputs
Altmetric has tracked 22,844,985 research outputs across all sources so far. This one is in the 35th percentile – i.e., 35% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,991 research outputs from this source. They receive a mean Attention Score of 4.9. This one is in the 38th percentile – i.e., 38% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 400,522 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.