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Mendeley readers
Attention Score in Context
| Title |
A meta-model for computer executable dynamic clinical safety checklists
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, December 2017
|
| DOI | 10.1186/s12911-017-0551-0 |
| Pubmed ID | |
| Authors |
Shan Nan, Pieter Van Gorp, Xudong Lu, Uzay Kaymak, Hendrikus Korsten, Richard Vdovjak, Huilong Duan |
| Abstract |
Safety checklist is a type of cognitive tool enforcing short term memory of medical workers with the purpose of reducing medical errors caused by overlook and ignorance. To facilitate the daily use of safety checklists, computerized systems embedded in the clinical workflow and adapted to patient-context are increasingly developed. However, the current hard-coded approach of implementing checklists in these systems increase the cognitive efforts of clinical experts and coding efforts for informaticists. This is due to the lack of a formal representation format that is both understandable by clinical experts and executable by computer programs. We developed a dynamic checklist meta-model with a three-step approach. Dynamic checklist modeling requirements were extracted by performing a domain analysis. Then, existing modeling approaches and tools were investigated with the purpose of reusing these languages. Finally, the meta-model was developed by eliciting domain concepts and their hierarchies. The feasibility of using the meta-model was validated by two case studies. The meta-model was mapped to specific modeling languages according to the requirements of hospitals. Using the proposed meta-model, a comprehensive coronary artery bypass graft peri-operative checklist set and a percutaneous coronary intervention peri-operative checklist set have been developed in a Dutch hospital and a Chinese hospital, respectively. The result shows that it is feasible to use the meta-model to facilitate the modeling and execution of dynamic checklists. We proposed a novel meta-model for the dynamic checklist with the purpose of facilitating creating dynamic checklists. The meta-model is a framework of reusing existing modeling languages and tools to model dynamic checklists. The feasibility of using the meta-model is validated by implementing a use case in the system. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 51 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 11 | 22% |
| Student > Bachelor | 6 | 12% |
| Student > Ph. D. Student | 5 | 10% |
| Researcher | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Other | 6 | 12% |
| Unknown | 16 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 16% |
| Nursing and Health Professions | 4 | 8% |
| Business, Management and Accounting | 4 | 8% |
| Engineering | 4 | 8% |
| Medicine and Dentistry | 4 | 8% |
| Other | 6 | 12% |
| Unknown | 21 | 41% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 03 January 2019.
All research outputs
#16,642,310
of 24,484,013 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,383
of 2,084 outputs
Outputs of similar age
#276,715
of 448,539 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#23
of 29 outputs
Altmetric has tracked 24,484,013 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,084 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 24th percentile – i.e., 24% 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 448,539 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.