A Complex Systems Framework Approach Towards Multidisciplinary Tumor Boards

Multidisciplinary tumor boards (MTBs) are universally recommended and have been used in treatment of late stage cancers. Tumor boards have the advantage of including all the stakeholders in the decision making process and improving quality of care, however several studies have pointed to their lack of efficiency and tend to be lackluster while not producing the desired benefits for the participants. In this paper we present the design of a web based immersive framework for collaborative decision making that has the potential to improve several inefficiencies in conducting tumor boards and improve overall clinical outcomes. We present the design of our framework and use late stage cancer treatment as an example to explain its software components and its role in improving communication, treatment time and the overall decision making process. The framework which has been successfully used in other collaborative decision-making environments has the potential to transform how tumor boards could dramatically improve the quality of cancer care in the future

Barriers to Accrual and Enrollment in Brain Tumor Trials

Many factors contribute to the poor survival of malignant brain tumor patients, some of which are not easily remedied. However, one contributor to the lack of progress that may be modifiable is poor clinical trial accrual. Surveys of brain tumor patients and neuro-oncology providers suggest that clinicians do a poor job of discussing clinical trials with patients and referring patients for clinical trials. Yet, data from the Cancer Action Network of the American Cancer Society suggest that most eligible oncology patients asked to enroll on a clinical trial will agree to do so. To this end, the Society for Neuro-Oncology (SNO) in collaboration with the Response Assessment in Neuro-Oncology (RANO) Working Group, patient advocacy groups, clinical trial cooperative groups including the Adult Brain Tumor Consortium (ABTC), and other partners are working together with the intent to double clinical trial accrual over the next five years. Here we describe the factors contributing to poor clinical trial accrual in neuro-oncology and offer possible solutions

Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes

Clinical decision support tools (DST) promise improved healthcare outcomes by offering data-driven insights. While effective in lab settings, almost all DSTs have failed in practice. Empirical research diagnosed poor contextual fit as the cause. This paper describes the design and field evaluation of a radically new form of DST. It automatically generates slides for clinicians' decision meetings with subtly embedded machine prognostics. This design took inspiration from the notion of "Unremarkable Computing", that by augmenting the users' routines technology/AI can have significant importance for the users yet remain unobtrusive. Our field evaluation suggests clinicians are more likely to encounter and embrace such a DST. Drawing on their responses, we discuss the importance and intricacies of finding the right level of unremarkableness in DST design, and share lessons learned in prototyping critical AI systems as a situated experience

A Perspective on Challenges and Issues in Biomarker Development and Drug and Biomarker Codevelopment

A workshop sponsored by the National Cancer Institute and the US Food and Drug Administration addressed past lessons learned and ongoing challenges faced in biomarker development and drug and biomarker codevelopment. Participants agreed that critical decision points in the product life cycle depend on the level of understanding of the biology of the target and its interaction with the drug, the preanalytical and analytical factors affecting biomarker assay performance, and the clinical disease process. The more known about the biology and the greater the strength of association between an analytical signal and clinical result, the more efficient and less risky the development process will be. Rapid entry into clinical practice will only be achieved by using a rigorous scientific approach, including careful specimen collection and standardized and quality-controlled data collection. Early interaction with appropriate regulatory bodies will ensure studies are appropriately designed and biomarker test performance is well characterized

The Use of Multidisciplinary Care Teams in Diagnosing and Managing Care of Cancer Patients in Eastern Kentucky

The purpose of this casual comparative quantitative study addressed the lack of individual-level data on the effectiveness of multidisciplinary care teams for cancer patients in rural hospitals. The research questions sought to evaluate the strengths and weaknesses of multidisciplinary care teams for cancer patients in rural hospitals and the roadblocks for successful implementation. The study utilized the pragmatism paradigm to focus on the problem rather than the view of reality. This study was conducted with a fixed design using quantitative methods, specifically, casual comparative. This research worked within the framework of a well-established theory prevalent in the pertinent literature: Social Systems Theory. The actors in this casual comparative study included the healthcare organizations, Ackerville Regional Healthcare and Pinkerton Medical Center, administration, clinic managers, and medical teams. Independent variables included partnership, cooperation, and coordination within multidisciplinary cancer teams and the dependent variable was the provision of quality patient care. This study operated from a Biblical perspective. This study sought to fill gaps in the information of why this phenomenon persists. The results of a Kruskal-Wallis Test revealed statistical significance in multidisciplinary care teams between collaboration score (Kruskal-Wallis = 26.34, p \u3c .001), partnership score (Kruskal-Wallis = 37.67, p \u3c .001), and coordination score (Kruskal-Wallis = 24.95, p \u3c .001). Multidisciplinary teams support patient outcomes through coordination in ways that use the resources of time, tools, and skills more effectively

Key Lessons Learned from Moffitt's Molecular Tumor Board: The Clinical Genomics Action Committee Experience

The increasing practicality of genomic sequencing technology has led to its incorporation into routine clinical practice. Successful identification and targeting of driver genomic alterations that provide proliferative and survival advantages to tumor cells have led to approval and ongoing development of several targeted cancer therapies. Within many major cancer centers, molecular tumor boards are constituted to shepherd precision medicine into clinical practice

Distributed Knowledge Modeling and Integration of Model-Based Beliefs into the Clinical Decision-Making Process

Das Treffen komplexer medizinischer Entscheidungen wird durch die stetig steigende Menge an zu berücksichtigenden Informationen zunehmend komplexer. Dieser Umstand ist vor allem auf die Verfügbarkeit von immer präziseren diagnostischen Methoden zur Charakterisierung der Patienten zurückzuführen (z.B. genetische oder molekulare Faktoren). Hiermit einher geht die Entwicklung neuartiger Behandlungsstrategien und Wirkstoffe sowie die damit verbundenen Evidenzen aus klinischen Studien und Leitlinien. Dieser Umstand stellt die behandelnden Ärztinnen und Ärzte vor neuartige Herausforderungen im Hinblick auf die Berücksichtigung aller relevanten Faktoren im Kontext der klinischen Entscheidungsfindung. Moderne IT-Systeme können einen wesentlichen Beitrag leisten, um die klinischen Experten weitreichend zu unterstützen. Diese Assistenz reicht dabei von Anwendungen zur Vorverarbeitung von Daten für eine Reduktion der damit verbundenen Komplexität bis hin zur systemgestützten Evaluation aller notwendigen Patientendaten für eine therapeutischen Entscheidungsunterstützung. Möglich werden diese Funktionen durch die formale Abbildung von medizinischem Fachwissen in Form einer komplexen Wissensbasis, welche die kognitiven Prozesse im Entscheidungsprozess adaptiert. Entsprechend werden an den Prozess der IT-konformen Wissensabbildung erhöhte Anforderungen bezüglich der Validität und Signifikanz der enthaltenen Informationen gestellt. In den ersten beiden Kapiteln dieser Arbeit wurden zunächst wichtige methodische Grundlagen im Kontext der strukturierten Abbildung von Wissen sowie dessen Nutzung für die klinische Entscheidungsunterstützung erläutert. Hierbei wurden die inhaltlichen Kernthemen weiterhin im Rahmen eines State of the Art mit bestehenden Ansätzen abgeglichen, um den neuartigen Charakter der vorgestellten Lösungen herauszustellen. Als innovativer Kern wurde zunächst die Konzeption und Umsetzung eines neuartigen Ansatzes zur Fusion von fragmentierten Wissensbausteinen auf der formalen Grundlage von Bayes-Netzen vorgestellt. Hierfür wurde eine neuartige Datenstruktur unter Verwendung des JSON Graph Formats erarbeitet. Durch die Entwicklung von qualifizierten Methoden zum Umgang mit den formalen Kriterien eines Bayes-Netz wurden weiterhin Lösungen aufgezeigt, welche einen automatischen Fusionsprozess durch einen eigens hierfür entwickelten Algorithmus ermöglichen. Eine prototypische und funktionale Plattform zur strukturierten und assistierten Integration von Wissen sowie zur Erzeugung valider Bayes-Netze als Resultat der Fusion wurde unter Verwendung eines Blockchain Datenspeichers implementiert und in einer Nutzerstudie gemäß ISONORM 9241/110-S evaluiert. Aufbauend auf dieser technologischen Plattform wurden im Anschluss zwei eigenständige Entscheidungsunterstützungssysteme vorgestellt, welche relevante Anwendungsfälle im Kontext der HNO-Onkologie adressieren. Dies ist zum einen ein System zur personalisierten Bewertung von klinischen Laborwerten im Kontext einer Radiochemotherapie und zum anderen ein in Form eines Dashboard implementiertes Systems zur effektiveren Informationskommunikation innerhalb des Tumor Board. Beide Konzepte wurden hierbei zunächst im Rahmen einer initialen Nutzerstudie auf Relevanz geprüft, um eine nutzerzentrische Umsetzung zu gewährleisten. Aufgrund des zentralen Fokus dieser Arbeit auf den Bereich der klinischen Entscheidungsunterstützung, werden an zahlreichen Stellen sowohl kritische als auch optimistische Aspekte der damit verbundenen praktischen Lösungen diskutiert.:1 Introduction 1.1 Motivation and Clinical Setting 1.2 Objectives 1.3 Thesis Outline 2 State of the Art 2.1 Medical Knowledge Modeling 2.2 Knowledge Fusion 2.3 Clinical Decision Support Systems 2.4 Clinical Information Access 3 Fundamentals 3.1 Evidence-Based Medicine 3.1.1 Literature-Based Evidence 3.1.2 Practice-Based Evidence 3.1.3 Patient-Directed Evidence 3.2 Knowledge Representation Formats 3.2.1 Logic-Based Representation 3.2.2 Procedural Representation 3.2.3 Network or Graph-Based Representation 3.3 Knowledge-Based Clinical Decision Support 3.4 Conditional Probability and Bayesian Networks 3.5 Clinical Reasoning 3.5.1 Deterministic Reasoning 3.5.2 Probabilistic Reasoning 3.6 Knowledge Fusion of Bayesian Networks 4 Block-Based Collaborative Knowledge Modeling 4.1 Data Model 4.1.1 Belief Structure 4.1.2 Conditional Probabilities 4.1.3 Metadata 4.2 Constraint-Based Automatic Knowledge Fusion 4.2.1 Fusion of the Bayesian Network Structures 4.2.2 Fusion of the Conditional Probability Tables 4.3 Blockchain-Based Belief Storage and Retrieval 4.3.1 Blockchain Characteristics 4.3.2 Relevance for Belief Management 5 Selected CDS Applications for Clinical Practice 5.1 Distributed Knowledge Modeling Platform 5.1.1 Requirement Analysis 5.1.2 System Architecture 5.1.3 System Evaluation 5.1.4 Limitations of the Proposed Solution 5.2 Personalization of Laboratory Findings 5.2.1 Requirement Analysis 5.2.2 System Architecture 5.2.3 Limitations of the Proposed Solution 5.3 Dashboard for Collaborative Decision-Making in the Tumor Board 5.3.1 Requirement Analysis 5.3.2 System Architecture 5.3.3 Limitations of the Proposed Solution 6 Discussion 6.1 Goal Achievements 6.2 Contributions and Conclusion 7 Bibliograph