Using guideline-based clinical decision support in oncological multidisciplinary team meetings: A prospective, multicenter concordance study

Abstract

Background Multidisciplinary team meetings formulate guideline-based individual treatment plans based on patient and disease characteristics and motivate reasons for deviation. Clinical decision trees could support multidisciplinary teams to adhere more accurately to guidelines. Every clinical decision tree is tailored to a specific decision moment in a care pathway and is composed of patient and disease characteristics leading to a guideline recommendation. This study investigated 1) the concordance between multidisciplinary team and clinical decision tree recommendations and 2) the completeness of patient and disease characteristics available during multidisciplinary team meetings to apply clinical decision trees such that it results in a guideline recommendation. Methods This prospective, multicenter, observational concordance study evaluated 17 selected clinical decision trees, based on the prevailing Dutch guidelines for breast, colorectal and prostate cancer. In cases with sufficient data concordance between multidisciplinary team and clinical decision tree recommendations was classified as concordant, conditional concordant (multidisciplinary team specified a prerequisite for the recommendation) and non-concordant. Results Fifty-nine multidisciplinary team meetings were attended in 8 different hospitals, and 355 cases were included. For 296 cases (83.4%), all patient data were available for providing an unconditional clinical decision tree recommendation. In 59 cases (16.6%) insufficient data was available resulting in provisional clinical decision tree recommendations. From the 296 successfully generated clinical decision tree recommendations, the multidisciplinary team recommendations were concordant in 249 (84.1%) cases, conditional concordant in 24 (8.1%) cases and non-concordant in 23 (7.8%) cases of which in 7 (2.4%) cases the reason for deviation from the clinical decision tree generated guideline recommendation was not motivated. Conclusion The observed concordance of recommendations between multidisciplinary teams and clinical decision trees and data completeness during multidisciplinary team meetings in this study indicate a potential role for implementation of clinical decision trees to support multidisciplinary team decision-making.

Use and accuracy of decision support systems using artificial intelligence for tumor diseases: a systematic review and meta-analysis

“…Five studies had a very small number of cases (< 100) ( 20 , 24 , 25 , 27 , 40 ) while the other studies had a relatively large number of included cases(> 1000) ( 16 – 18 , 36 , 43 ). Three studies examined multiple tumor entities, and included only a small number of participants in the subgroups ( 21 , 38 , 44 ).…”

“…The most common treatment decisions reviewed in this study were for breast cancer, with the MDM and CDSS evaluated in multiple studies. Colorectal cancer was the subject of eight studies ( 21 , 23 , 25 , 37 – 39 , 41 , 44 ), followed by lung cancer with seven studies ( 17 , 21 , 22 , 30 , 31 , 35 , 38 ). Gastric cancer was reviewed in three studies ( 21 , 24 , 29 ), while cervical ( 21 , 32 ) and prostate cancers ( 33 , 44 ) were each the focus of two studies.…”

“…Colorectal cancer was the subject of eight studies ( 21 , 23 , 25 , 37 – 39 , 41 , 44 ), followed by lung cancer with seven studies ( 17 , 21 , 22 , 30 , 31 , 35 , 38 ). Gastric cancer was reviewed in three studies ( 21 , 24 , 29 ), while cervical ( 21 , 32 ) and prostate cancers ( 33 , 44 ) were each the focus of two studies. Thyroid cancer was examined in two studies ( 26 , 40 ), and there was one study each for ovarian cancer ( 21 ), basal cell carcinoma ( 42 ), and hepatocellular carcinoma (HCC) ( 28 ).…”

Towards Agility in Breast Cancer Treatment Principles as Adopted from Agile Software Engineering

“… 95 Recently, clinical decision trees have been used as a supportive tool for MDTs in their decision-making. 96 MDT participation and decision-making is increased by allocating smaller caseloads, recording higher attendance at meetings, having leadership support, supportive regulations, participating in strong and continuous communication, identifying clearly involved roles and responsibilities, and considering the patient’s holistic needs. 76 …”

Improved guideline adherence in oncology through clinical decision-support systems: still hindered by current health IT infrastructures?

“…CDSSs are increasingly acknowledged for their potential to improve clinical decision-making [38]. CDSSs incorporate medical knowledge from, for example, evidence-based clinical practice guidelines and deliver specific recommendations on the basis of individual patient data in real time as care is being delivered [39,40,41 ▪▪ ]. Basically, the rules laid down in traditional guidelines are transferred to and encoded by CDSSs that then integrate medical knowledge with patient data mostly provided by electronic health records (EHR) [42 ▪▪ ,43 ▪▪ ,38].…”