Literature DB >> 32029957

A framework for automated conflict detection and resolution in medical guidelines.

J Bowles1, M B Caminati1, S Cha2, J Mendoza1.   

Abstract

Common chronic conditions are routinely treated following standardised procedures known as clinical guidelines. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.
© 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Clinical guidelines; Formal methods; Isabelle/HOL; SMT solvers; Theorem provers

Year:  2019        PMID: 32029957      PMCID: PMC6993806          DOI: 10.1016/j.scico.2019.07.002

Source DB:  PubMed          Journal:  Sci Comput Program        ISSN: 0167-6423            Impact factor:   0.863


  8 in total

1.  Analyzing interactions on combining multiple clinical guidelines.

Authors:  Veruska Zamborlini; Marcos da Silveira; Cedric Pruski; Annette Ten Teije; Edwin Geleijn; Marike van der Leeden; Martijn Stuiver; Frank van Harmelen
Journal:  Artif Intell Med       Date:  2017-04-11       Impact factor: 5.326

Review 2.  Computer technologies to integrate medical treatments to manage multimorbidity.

Authors:  David Riaño; Wilfrido Ortega
Journal:  J Biomed Inform       Date:  2017-09-21       Impact factor: 6.317

3.  Clinical practice guidelines and quality of care for older patients with multiple comorbid diseases: implications for pay for performance.

Authors:  Cynthia M Boyd; Jonathan Darer; Chad Boult; Linda P Fried; Lisa Boult; Albert W Wu
Journal:  JAMA       Date:  2005-08-10       Impact factor: 56.272

4.  Mining time dependency patterns in clinical pathways.

Authors:  F Lin; S Chou; S Pan; Y Chen
Journal:  Int J Med Inform       Date:  2001-06       Impact factor: 4.046

5.  Guidelines for people not for diseases: the challenges of applying UK clinical guidelines to people with multimorbidity.

Authors:  Lloyd D Hughes; Marion E T McMurdo; Bruce Guthrie
Journal:  Age Ageing       Date:  2012-08-21       Impact factor: 10.668

Review 6.  Adoption of clinical decision support in multimorbidity: a systematic review.

Authors:  Paolo Fraccaro; Mercedes Arguello Casteleiro; John Ainsworth; Iain Buchan
Journal:  JMIR Med Inform       Date:  2015-01-07

7.  Modelling and performance analysis of clinical pathways using the stochastic process algebra PEPA.

Authors:  Xian Yang; Rui Han; Yike Guo; Jeremy Bradley; Benita Cox; Robert Dickinson; Richard Kitney
Journal:  BMC Bioinformatics       Date:  2012-09-07       Impact factor: 3.169

Review 8.  The use of computer-interpretable clinical guidelines to manage care complexities of patients with multimorbid conditions: A review.

Authors:  Eda Bilici; George Despotou; Theodoros N Arvanitis
Journal:  Digit Health       Date:  2018-10-03
  8 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.