System for Context-Specific Visualization of Clinical Practice Guidelines (GuLiNav): Concept and Software Implementation.
clinical
clinical decision support system
clinical practice guideline
computer-assisted decision making
decision making
decision support techniques
eHealth
electronic health
guideline representation
software
support systems
workflow
workflow control patterns
Journal
JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394
Informations de publication
Date de publication:
22 Jun 2022
22 Jun 2022
Historique:
received:
17
02
2021
accepted:
17
03
2022
revised:
14
06
2021
entrez:
22
6
2022
pubmed:
23
6
2022
medline:
23
6
2022
Statut:
epublish
Résumé
Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model. Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient's current treatment context. We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine. We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians. The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.
Sections du résumé
BACKGROUND
BACKGROUND
Clinical decision support systems often adopt and operationalize existing clinical practice guidelines leading to higher guideline availability, increased guideline adherence, and data integration. Most of these systems use an internal state-based model of a clinical practice guideline to derive recommendations but do not provide the user with comprehensive insight into the model.
OBJECTIVE
OBJECTIVE
Here we present a novel approach based on dynamic guideline visualization that incorporates the individual patient's current treatment context.
METHODS
METHODS
We derived multiple requirements to be fulfilled by such an enhanced guideline visualization. Using business process and model notation as the representation format for computer-interpretable guidelines, a combination of graph-based representation and logical inferences is adopted for guideline processing. A context-specific guideline visualization is inferred using a business rules engine.
RESULTS
RESULTS
We implemented and piloted an algorithmic approach for guideline interpretation and processing. As a result of this interpretation, a context-specific guideline is derived and visualized. Our implementation can be used as a software library but also provides a representational state transfer interface. Spring, Camunda, and Drools served as the main frameworks for implementation. A formative usability evaluation of a demonstrator tool that uses the visualization yielded high acceptance among clinicians.
CONCLUSIONS
CONCLUSIONS
The novel guideline processing and visualization concept proved to be technically feasible. The approach addresses known problems of guideline-based clinical decision support systems. Further research is necessary to evaluate the applicability of the approach in specific medical use cases.
Identifiants
pubmed: 35731571
pii: v6i6e28013
doi: 10.2196/28013
pmc: PMC9260532
doi:
Types de publication
Journal Article
Langues
eng
Pagination
e28013Informations de copyright
©Jonas Fortmann, Marlene Lutz, Cord Spreckelsen. Originally published in JMIR Formative Research (https://formative.jmir.org), 22.06.2022.
Références
Methods Inf Med. 2004;43(4):413-26
pubmed: 15472756
BMC Med Inform Decis Mak. 2006 Feb 01;6:6
pubmed: 16451720
BMJ. 2013 Feb 14;346:f657
pubmed: 23412440
Int J Med Inform. 2015 Nov;84(11):901-11
pubmed: 26343972
BMJ Open. 2020 Feb 10;10(2):e033391
pubmed: 32047014
BMC Med Inform Decis Mak. 2016 Feb 10;16:20
pubmed: 26863868
J Biomed Inform. 2013 Aug;46(4):744-63
pubmed: 23806274
J Biomed Inform. 2004 Oct;37(5):325-44
pubmed: 15488747
Stud Health Technol Inform. 2018;248:17-24
pubmed: 29726414
J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30
pubmed: 12925543
Int J Med Inform. 2008 Oct;77(10):641-9
pubmed: 18353713
Artif Intell Med. 2006 Oct;38(2):115-35
pubmed: 16343873
Artif Intell Med. 2010 May;49(1):11-31
pubmed: 20303245
J Am Med Inform Assoc. 2007 Nov-Dec;14(6):781-7
pubmed: 17712087
J Am Med Inform Assoc. 2007 Sep-Oct;14(5):589-98
pubmed: 17600098
Yale J Biol Med. 2014 Jun 06;87(2):187-97
pubmed: 24910564
Comput Methods Programs Biomed. 2012 May;106(2):114-25
pubmed: 22206864
J Biomed Inform. 2018 Feb;78:134-143
pubmed: 29246790
Artif Intell Med. 2018 Nov;92:7-9
pubmed: 26254699
Artif Intell Med. 2008 May;43(1):17-34
pubmed: 18442899
Stud Health Technol Inform. 2014;205:43-7
pubmed: 25160142
Stud Health Technol Inform. 2004;101:162-6
pubmed: 15537221
J Biomed Inform. 2004 Jun;37(3):147-61
pubmed: 15196480
Qual Saf Health Care. 2009 Oct;18(5):385-92
pubmed: 19812102
Med Inform Internet Med. 2000 Oct-Dec;25(4):247-63
pubmed: 11198187
Lancet. 1993 Nov 27;342(8883):1317-22
pubmed: 7901634
Open Med Inform J. 2010;4:255-77
pubmed: 21611137
Stud Health Technol Inform. 2019 Sep 3;267:118-125
pubmed: 31483263