PURPOSE Tumor boards, clinical practice guidelines, and cancer registries are intertwined cancer care quality instruments. Standardized structured reporting has been proposed as a solution to improve clinical documentation, while facilitating data reuse for secondary purposes. This study describes the implementation and evaluation of a national standard for tumor board reporting for breast cancer on the basis of the clinical practice guideline and the potential for reusing clinical data for the Netherlands Cancer Registry (NCR). METHODS Previously, a national information standard for breast cancer was derived from the corresponding Dutch clinical practice guideline. Using data items from the information standard, we developed three different tumor board forms: preoperative, postoperative, and postneoadjuvant-postoperative. The forms were implemented in Amphia Hospital’s electronic health record. Quality of clinical documentation and workload before and after implementation were compared. RESULTS Both draft and final tumor board reports were collected from 27 and 31 patients in baseline and effect measurements, respectively. Completeness of final reports increased from 39.5% to 45.4% ( P = .04). The workload for tumor board preparation and discussion did not change significantly. Standardized tumor board reports included 50% (61/122) of the data items carried in the NCR. An automated process was developed to upload information captured in tumor board reports to the NCR database. CONCLUSION This study shows implementation of a national standard for tumor board reports improves quality of clinical documentation, without increasing clinical workload. Simultaneously, our work enables data reuse for secondary purposes like cancer registration.

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.

Background Clinical decision-making by multidisciplinary teams (MDTs) is getting more complex as treatment advice for the individual patient must be based on an increasing amount of patient and tumor characteristics, and scientific evidence on treatment efficacy. Clinical decision support systems (CDSSs) can make an important contribution to assist but also optimize MDT decision-making. However, implementation of CDSSs in clinical practice is challenging. Aim & methods The aim of our study is to set up a CDSSs implementation model for multidisciplinary decision-making in solid cancer. It is based on a scoping review of the currently reported CDSSs for MDT decision-making in solid cancers with identification of reported barriers and facilitators for implementation of these CDSSs. For this we systematically searched the Cochrane Library, MEDLINE (accessed through PubMed) and Scopus up to September 1st 2021. Results Of the 710 screened abstracts, 38 papers met the inclusion criteria (table 1). Sixteen different CDSSs were identified. For implementation of CDSSs, 87 barriers and 73 facilitators were reported. The reported barriers could be categorized in the same categories as those of the facilitators (a factor can be reported as a barrier if the factor is not addressed well, and as a facilitator if the factor is properly addressed). The most frequently reported barriers for CDSS implementation for MDT decision making mainly concerned CDSS maintenance (e.g. not incorporating guideline updates), loco-regional feasibility of the CDDS recommendation (e.g. no access to diagnostics or treatment), validity, not incorporating patient preference in decision making, data accuracy, noncoverage of certain patient subpopulations, lack of an information standard, usability, data availability and no interoperability of the CDSS with the electronic health record. The most frequently reported facilitators included, besides the categories as mentioned above, the category shared decision making (reporting of alternative treatment options) and technical skills (involvement of a computer scientist). Table 2 shows the most frequently reported categories of barriers and facilitators, and scores for each included study the number of reported barriers (B) and facilitators (F) in each category. Conclusion Based on the identified barriers and facilitators, we developed a CDSS implementation model to guide more successful CDSS integration in the clinical workflow to support MDTs (the model will be shown at the congress). The usability of this theoretical model should be explored in future studies. Table 1. Characteristics of 38 included articles Table 2. Overview of the most frequently reported categories of barriers and facilitators. For each included study the number of reported barriers (B) and facilitatiors (F) are scored for each category. Citation Format: Mathijs P. Hendriks, Kees C. Ebben, Janine A. Van Til, Agnes Jager, Sabine Siesling. Clinical decision support systems for multidisciplinary team decision-making in patients with solid cancer: an implementation model based on a scoping review [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P4-07-22.

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