Mapping Patient Data to Colorectal Cancer Clinical Algorithms for Personalized Guideline-Based Treatment

Abstract

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.

PCaGuard: A Software Platform to Support Optimal Management of Prostate Cancer

“…In the era of colorectal cancer, Becker et al have evaluated an open-source workflow system based on business process model and notation (BPMN) with unified medical language system (UMLS) for colorectal cancer screening. 38 Since electronic health records (EHRs) contain more and more data, researchers develop methods to extract characteristics either from data 39,40 or scanned and other outside documents contained in EHRs. 41 In the market, data management systems available clinical, such as REDCap, OpenClinica, and eClinicalOS, but these systems are either commercial or not suitable for this project because the whole workflow is time consuming, error prone, and requires the integration of different components.…”

Uptake of a Cervical Cancer Clinical Decision Support Tool: A Mixed-Methods Study

“…5 The use of clinical decision support (CDS) tools in electronic health records (EHRs) might help improve rates of such follow-up care. Such tools have been shown to support clinical teams' adherence to care guidelines in some settings, [6][7][8][9][10][11][12][13][14][15][16][17] including the provision of CC follow-up care. 9,13,18 None of these studies evaluating CDS for CC follow-up assessed tool adoption, but instead estimated or demonstrated the benefit of these tools in improving CC follow-up care.…”

A process mining approach for clinical guidelines compliance: real-world application in rectal cancer

“…For example, to investigate surveillance process of melanoma patients, clinical guidelines were represented with BPMN (Business Process Modeling Notation) and time boxing ( 19 , 24 ). Furthermore, BPMN was used to check conformity to guideline-based therapy recommendations in colorectal cancer ( 25 ).…”