Mapping Patient Data to Colorectal Cancer Clinical Algorithms for Personalized Guideline-Based Treatment.
Journal
Applied clinical informatics
ISSN: 1869-0327
Titre abrégé: Appl Clin Inform
Pays: Germany
ID NLM: 101537732
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
entrez:
19
3
2020
pubmed:
19
3
2020
medline:
12
1
2021
Statut:
ppublish
Résumé
Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. In this context, evidence-based guidelines positively impact the quality of treatment processes for cancer patients. However, evidence of their impact on real-world patient care remains unclear. To ensure the success of clinical guidelines, a fast and clear provision of knowledge at the point of care is essential. The objectives of this study are to model machine-readable clinical algorithms for colon carcinoma and rectal carcinoma annotated by Unified Medical Language System (UMLS) based on clinical guidelines and the development of an open-source workflow system for mapping clinical algorithms with patient-specific information to identify patient's position on the treatment algorithm for guideline-based therapy recommendations. This study qualitatively assesses the therapy decision of clinical algorithms as part of a clinical pathway. The solution uses rule-based clinical algorithms, which were developed based on the corresponding guidelines. These algorithms are executed on a newly developed open-source workflow system and are visualized at the point of care. The aim of this approach is to create clinical algorithms based on an established business process standard, the Business Process Model and Notation (BPMN), which is annotated by UMLS terminologies. The gold standard for the validation process was set by manual extraction of clinical datasets from 86 rectal cancer patients and 89 colon cancer patients. Using this approach, the algorithm achieved a precision value of 87.64% for colon cancer and 84.70% for rectal cancer with recall values of 87.64 and 83.72%, respectively. The results indicate that the automatic positioning of a patient on the decision pathway is possible with tumor stages that have a less complex clinical algorithm with fewer decision points reaching a higher accuracy than complex stages.
Sections du résumé
BACKGROUND
Colorectal cancer is the most commonly occurring cancer in Germany, and the second and third most commonly diagnosed cancer in women and men, respectively. In this context, evidence-based guidelines positively impact the quality of treatment processes for cancer patients. However, evidence of their impact on real-world patient care remains unclear. To ensure the success of clinical guidelines, a fast and clear provision of knowledge at the point of care is essential.
OBJECTIVES
The objectives of this study are to model machine-readable clinical algorithms for colon carcinoma and rectal carcinoma annotated by Unified Medical Language System (UMLS) based on clinical guidelines and the development of an open-source workflow system for mapping clinical algorithms with patient-specific information to identify patient's position on the treatment algorithm for guideline-based therapy recommendations.
METHODS
This study qualitatively assesses the therapy decision of clinical algorithms as part of a clinical pathway. The solution uses rule-based clinical algorithms, which were developed based on the corresponding guidelines. These algorithms are executed on a newly developed open-source workflow system and are visualized at the point of care. The aim of this approach is to create clinical algorithms based on an established business process standard, the Business Process Model and Notation (BPMN), which is annotated by UMLS terminologies. The gold standard for the validation process was set by manual extraction of clinical datasets from 86 rectal cancer patients and 89 colon cancer patients.
RESULTS
Using this approach, the algorithm achieved a precision value of 87.64% for colon cancer and 84.70% for rectal cancer with recall values of 87.64 and 83.72%, respectively.
CONCLUSION
The results indicate that the automatic positioning of a patient on the decision pathway is possible with tumor stages that have a less complex clinical algorithm with fewer decision points reaching a higher accuracy than complex stages.
Identifiants
pubmed: 32187632
doi: 10.1055/s-0040-1705105
pmc: PMC7080556
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
200-209Informations de copyright
Georg Thieme Verlag KG Stuttgart · New York.
Déclaration de conflit d'intérêts
None declared.
Références
Br J Cancer. 2012 May 22;106(11):1875-80
pubmed: 22555397
Langenbecks Arch Surg. 2012 Jun;397(5):755-61
pubmed: 22362053
Appl Clin Inform. 2017 Feb 15;8(1):162-179
pubmed: 28197619
Appl Clin Inform. 2016 Jul 06;7(3):633-45
pubmed: 27452661
Stud Health Technol Inform. 2008;136:353-8
pubmed: 18487756
J Med Syst. 2018 Aug 29;42(10):181
pubmed: 30155797
Health Technol Assess. 2004 Feb;8(6):iii-iv, 1-72
pubmed: 14960256
J Am Med Inform Assoc. 1996 Nov-Dec;3(6):399-409
pubmed: 8930856
AMIA Annu Symp Proc. 2007 Oct 11;:533-7
pubmed: 18693893
Stud Health Technol Inform. 2013;186:73-7
pubmed: 23542971
BMJ. 2005 Apr 2;330(7494):765
pubmed: 15767266
Appl Clin Inform. 2015 Feb 04;6(1):56-74
pubmed: 25848413
Internist (Berl). 2006 Jul;47(7):690, 692-7
pubmed: 16763795