Dataset Information

A meta-model for computer executable dynamic clinical safety checklists.

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.

SUBMITTER: Nan S

PROVIDER: S-EPMC5727863 | biostudies-other | 2017 Dec

REPOSITORIES: biostudies-other

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A meta-model for computer executable dynamic clinical safety checklists.

Nan Shan S Van Gorp Pieter P Lu Xudong X Kaymak Uzay U Korsten Hendrikus H Vdovjak Richard R Duan Huilong H

BMC medical informatics and decision making 20171212 1

<h4>Background</h4>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 ...[more]

PMID: 29233155

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Project description:PurposeThe present systematic review aimed to investigate the accuracy of zygomatic implant (ZI) placement using dynamic computer-aided surgery (d-CAIS), static computer-aided surgery (s-CAIS), and a free-hand approach in patients with severe atrophic edentulous maxilla and/or deficient maxilla.MethodsElectronic and manual literature searches until May 2023 were performed in the PubMed/Medline, Scopus, Cochrane Library, and Web of Science databases. Clinical trials and cadaver studies were selected. The primary outcome was planned/placed deviation. Secondary outcomes were to evaluate the survival of ZI and surgical complications. Random-effects meta-analyses were conducted and meta-regression was utilized to compare fiducial registration amounts for d-CAIS and the different designs of s-CAIS.ResultsA total of 14 studies with 511 ZIs were included (Nobel Biocare: 274, Southern Implant: 42, SIN Implant: 16, non-mentioned: 179). The pooled mean ZI deviations from the d-CAIS group were 1.81 mm (95% CI: 1.34-2.29) at the entry point and 2.95 mm (95% CI: 1.66-4.24) at the apex point, and angular deviations were 3.49 degrees (95% CI: 2.04-4.93). The pooled mean ZI deviations from the s-CAIS group were 1.19 mm (95% CI: 0.83-1.54) at the entry point and 1.80 mm (95% CI: 1.10-2.50) at the apex point, and angular deviations were 2.15 degrees (95% CI: 1.43-2.88). The pooled mean ZI deviations from the free-hand group were 2.04 mm (95% CI: 1.69-2.39) at the entry point and 3.23 mm (95% CI: 2.34-4.12) at the apex point, and angular deviations were 4.92 degrees (95% CI: 3.86-5.98). There was strong evidence of differences in the average entry, apex, and angular deviation between the navigation, surgical guide, and free-hand groups (p < 0.01). A significant inverse correlation was observed between the number of fiducial screws and the planned/placed deviation regarding entry, apex, and angular measurements.ConclusionUsing d-CAIS and modified s-CAIS for ZI surgery has shown clinically acceptable outcomes regarding average entry, apex, and angular deviations. The maximal deviation values were predominantly observed in the conventional s-CAIS. Surgeons should be mindful of potential deviations and complications regardless of the decision making in different guide approaches.

Project description:IntroductionIndocyanine green (ICG) quantification and assessment by machine learning (ML) could discriminate tissue types through perfusion characterisation, including delineation of malignancy. Here, we detail the important challenges overcome before effective clinical validation of such capability in a prospective patient series of quantitative fluorescence angiograms regarding primary and secondary colorectal neoplasia.MethodsICG perfusion videos from 50 patients (37 with benign (13) and malignant (24) rectal tumours and 13 with colorectal liver metastases) of between 2- and 15-min duration following intravenously administered ICG were formally studied (clinicaltrials.gov: NCT04220242). Video quality with respect to interpretative ML reliability was studied observing practical, technical and technological aspects of fluorescence signal acquisition. Investigated parameters included ICG dosing and administration, distance-intensity fluorescent signal variation, tissue and camera movement (including real-time camera tracking) as well as sampling issues with user-selected digital tissue biopsy. Attenuating strategies for the identified problems were developed, applied and evaluated. ML methods to classify extracted data, including datasets with interrupted time-series lengths with inference simulated data were also evaluated.ResultsDefinable, remediable challenges arose across both rectal and liver cohorts. Varying ICG dose by tissue type was identified as an important feature of real-time fluorescence quantification. Multi-region sampling within a lesion mitigated representation issues whilst distance-intensity relationships, as well as movement-instability issues, were demonstrated and ameliorated with post-processing techniques including normalisation and smoothing of extracted time-fluorescence curves. ML methods (automated feature extraction and classification) enabled ML algorithms glean excellent pathological categorisation results (AUC-ROC > 0.9, 37 rectal lesions) with imputation proving a robust method of compensation for interrupted time-series data with duration discrepancies.ConclusionPurposeful clinical and data-processing protocols enable powerful pathological characterisation with existing clinical systems. Video analysis as shown can inform iterative and definitive clinical validation studies on how to close the translation gap between research applications and real-world, real-time clinical utility.

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A meta-model for computer executable dynamic clinical safety checklists.

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A meta-model for computer executable dynamic clinical safety checklists.

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