Development and Implementation of Clinical Decision Algorithms for Oncology.

Decision-making in healthcare often relies on narrative guidelines; however, these instruments are poorly accessible for supporting clinical decision-making. This study explores the application of rule-based decision logic in algorithmic modeling, emphasizing its great potential in clinical decision support and research. Integrating rule-based algorithms with existing information systems and real-world data poses a serious challenge. Integrating decision algorithms with information standards increases their effectiveness across various applications. This study outlines a method for constructing clinical decision trees (CDTs), highlighting their transparency and interpretability, using information standards as a design principle. We use the digitization of the Dutch breast cancer guideline through CDTs as a case study to exemplify their versatility and practical significance. The process step 'primary treatment' has been successfully translated from the narrative guidelines format to the anticipated ted computational format.

Development and Implementation of Standardized, Structured Clinical Reporting for Oncology.

Reuse of clinical data within the healthcare process and for secondary purposes is particularly valuable. This study emphasizes the crucial role of Standardized, Structured Reports (SSRs) in supporting continuity of care while also advancing reusability of data, decision support functionalities, and accommodating future developments. Integrating SSRs with existing information systems poses a serious challenge. The integration of SSRs with information standards enhances their utility in diverse applications. The significance of SSRs is further highlighted by their seamless integration into healthcare processes, and development and implementation is supported by various available applications. This research contributes to the evolution of medical informatics by emphasizing the importance of collaborative efforts in standardized, structured reporting, all aimed at enhancing patient care.

Development and Implementation of Semantic Information Standards for Oncology.

Data exchange in oncological healthcare is hindered by insufficient standardization agreements. An Information Standard comprises agreements facilitating accurate communication of care information with the necessary quality and timeliness. We introduce a structured approach to designing, implementing, and maintaining semantic information standards for oncology, supporting information use across medical scenarios. It consists of an element dataset organized into three tiers, ensuring comprehensive documentation and reliable information exchange. These agreements enhance health data interoperability and system functionality, governed by semantic standardization. Together with communication standards, they empower healthcare professionals with extensive medical records and grant patients control over their health data. Consequently, a high-quality semantic information standard supports both providers and patients, and is adequate during development and manageable during maintenance.

A novel method for continuous measurements of clinical practice guideline adherence.

Introduction: Clinical practice guidelines (hereafter 'guidelines') are crucial in providing evidence-based recommendations for physicians and multidisciplinary teams to make informed decisions regarding diagnostics and treatment in various diseases, including cancer. While guideline implementation has been shown to reduce (unwanted) variability and improve outcome of care, monitoring of adherence to guidelines remains challenging. Real-world data collected from cancer registries can provide a continuous source for monitoring adherence levels. In this work, we describe a novel structured approach to guideline evaluation using real-world data that enables continuous monitoring. This method was applied to endometrial cancer patients in the Netherlands and implemented through a prototype web-based dashboard that enables interactive usage and supports various analyses. Method: The guideline under study was parsed into clinical decision trees (CDTs) and an information standard was drawn up. A dataset from the Netherlands Cancer Registry (NCR) was used and data items from both instruments were mapped. By comparing guideline recommendations with real-world data an adherence classification was determined. The developed prototype can be used to identify and prioritize potential topics for guideline updates. Results: CDTs revealed 68 data items for recording in an information standard. Thirty-two data items from the NCR were mapped onto information standard data items. Four CDTs could sufficiently be populated with NCR data. Conclusion: The developed methodology can evaluate a guideline to identify potential improvements in recommendations and the success of the implementation strategy. In addition, it is able to identify patient and disease characteristics that influence decision-making in clinical practice. The method supports a cyclical process of developing, implementing and evaluating guidelines and can be scaled to other diseases and settings. It contributes to a learning healthcare cycle that integrates real-world data with external knowledge.

Guideline-Based Algorithmic Recommendations Versus Multidisciplinary Team Advice for Gynecologic Oncology.

Evidence-based clinical decision making in oncology is challenging. Multi-disciplinary team (MDTs) meetings are organized to consider different diagnostic and treatment options. MDT advice are often based on clinical practice guideline recommendations which can be extensive and ambiguous, making it difficult to implement in clinical practice. To address this issue, guideline-based algorithms have been developed. These are applicable in clinical practice and enable accurate guideline adherence evaluation. This ongoing study aims to determine the optimal decision-making approach for different subpopulations of patients with high-incidence gynecological cancers.