Argumentation in Decision Support for Medical Care Planning for Patients and Clinicians.

Developing a care plan for a patient requires an understanding of interactions and dependencies between procedures, and of their possible outcomes for an individual patient, and it requires the planner to keep track of this information as the proposed plan evolves. This is difficult even for experienced clinicians, but increasingly patients are expected (and expect) to participate. We describe an argumentation-based planning support system designed to ameliorate the cognitive load imposed by the planning and communication elements of such tasks. An initial evaluation study in the field of genetic counseling produced promising results. The approach may provide a general aid for clinicians and patients in visualizing, customizing, evaluating and communicating about care plans

Theoretical surgery: a new specialty in operative medicine

Theoretical surgery is defined as a nonoperative decision analysis and clinical and basic research supporting system for surgery. It developed to meet the needs of academic surgeons to coordinate communication with basic science disciplines. This article summarizes the development of this idea at the University of Marburg where theoretical surgery has reached departmental and institutional proportions. Its objectives and methods are described. Central to its operation are permanent working teams of 2 clinical surgeons, 1 basic scientist (theoretical surgeon), 1-2 technicians, and 1-2 students focusing on one problem in a joint interdisciplinary manner. Decision analysis with classification methods and the creation of decision trees and algorithms are central to the operation of this experiment. Lessons learned from this academic experiment and the accomplishments during the past 20 years are summarized on 3 levels of efficacy: performance, changing strategies, and outcome

A canonical theory of dynamic decision-making

Decision-making behavior is studied in many very different fields, from medicine and eco- nomics to psychology and neuroscience, with major contributions from mathematics and statistics, computer science, AI, and other technical disciplines. However the conceptual- ization of what decision-making is and methods for studying it vary greatly and this has resulted in fragmentation of the field. A theory that can accommodate various perspectives may facilitate interdisciplinary working. We present such a theory in which decision-making is articulated as a set of canonical functions that are sufficiently general to accommodate diverse viewpoints, yet sufficiently precise that they can be instantiated in different ways for specific theoretical or practical purposes. The canons cover the whole decision cycle, from the framing of a decision based on the goals, beliefs, and background knowledge of the decision-maker to the formulation of decision options, establishing preferences over them, and making commitments. Commitments can lead to the initiation of new decisions and any step in the cycle can incorporate reasoning about previous decisions and the rationales for them, and lead to revising or abandoning existing commitments. The theory situates decision-making with respect to other high-level cognitive capabilities like problem solving, planning, and collaborative decision-making. The canonical approach is assessed in three domains: cognitive and neuropsychology, artificial intelligence, and decision engineering

Ontology driven clinical decision support for early diagnostic recommendations

Diagnostic error is a significant problem in medicine and a major cause of concern for patients and clinicians and is associated with moderate to severe harm to patients. Diagnostic errors are a primary cause of clinical negligence and can result in malpractice claims. Cognitive errors caused by biases such as premature closure and confirmation bias have been identified as major cause of diagnostic error. Researchers have identified several strategies to reduce diagnostic error arising from cognitive factors. This includes considering alternatives, reducing reliance on memory, providing access to clear and well-organized information. Clinical Decision Support Systems (CDSSs) have been shown to reduce diagnostic errors. Clinical guidelines improve consistency of care and can potentially improve healthcare efficiency. They can alert clinicians to diagnostic tests and procedures that have the greatest evidence and provide the greatest benefit. Clinical guidelines can be used to streamline clinical decision making and provide the knowledge base for guideline based CDSSs and clinical alert systems. Clinical guidelines can potentially improve diagnostic decision making by improving information gathering. Argumentation is an emerging area for dealing with unstructured evidence in domains such as healthcare that are characterized by uncertainty. The knowledge needed to support decision making is expressed in the form of arguments. Argumentation has certain advantages over other decision support reasoning methods. This includes the ability to function with incomplete information, the ability to capture domain knowledge in an easy manner, using non-monotonic logic to support defeasible reasoning and providing recommendations in a manner that can be easily explained to clinicians. Argumentation is therefore a suitable method for generating early diagnostic recommendations. Argumentation-based CDSSs have been developed in a wide variety of clinical domains. However, the impact of an argumentation-based diagnostic Clinical Decision Support System (CDSS) has not been evaluated yet. The first part of this thesis evaluates the impact of guideline recommendations and an argumentation-based diagnostic CDSS on clinician information gathering and diagnostic decision making. In addition, the impact of guideline recommendations on management decision making was evaluated. The study found that argumentation is a viable method for generating diagnostic recommendations that can potentially help reduce diagnostic error. The study showed that guideline recommendations do have a positive impact on information gathering of optometrists and can potentially help optometrists in asking the right questions and performing tests as per current standards of care. Guideline recommendations were found to have a positive impact on management decision making. The CDSS is dependent on quality of data that is entered into the system. Faulty interpretation of data can lead the clinician to enter wrong data and cause the CDSS to provide wrong recommendations. Current generation argumentation-based CDSSs and other diagnostic decision support systems have problems with semantic interoperability that prevents them from using data from the web. The clinician and CDSS is limited to information collected during a clinical encounter and cannot access information on the web that could be relevant to a patient. This is due to the distributed nature of medical information and lack of semantic interoperability between healthcare systems. Current argumentation-based decision support applications require specialized tools for modelling and execution and this prevents widespread use and adoption of these tools especially when these tools require additional training and licensing arrangements. Semantic web and linked data technologies have been developed to overcome problems with semantic interoperability on the web. Ontology-based diagnostic CDSS applications have been developed using semantic web technology to overcome problems with semantic interoperability of healthcare data in decision support applications. However, these models have problems with expressiveness, requiring specialized software and algorithms for generating diagnostic recommendations. The second part of this thesis describes the development of an argumentation-based ontology driven diagnostic model and CDSS that can execute this model to generate ranked diagnostic recommendations. This novel model called the Disease-Symptom Model combines strengths of argumentation with strengths of semantic web technology. The model allows the domain expert to model arguments favouring and negating a diagnosis using OWL/RDF language. The model uses a simple weighting scheme that represents the degree of support of each argument within the model. The model uses SPARQL to sum weights and produce a ranked diagnostic recommendation. The model can provide justifications for each recommendation in a manner that clinicians can easily understand. CDSS prototypes that can execute this ontology model to generate diagnostic recommendations were developed. The decision support prototypes demonstrated the ability to use a wide variety of data and access remote data sources using linked data technologies to generate recommendations. The thesis was able to demonstrate the development of an argumentation-based ontology driven diagnostic decision support model and decision support system that can integrate information from a variety of sources to generate diagnostic recommendations. This decision support application was developed without the use of specialized software and tools for modelling and execution, while using a simple modelling method. The third part of this thesis details evaluation of the Disease-Symptom model across all stages of a clinical encounter by comparing the performance of the model with clinicians. The evaluation showed that the Disease-Symptom Model can provide a ranked diagnostic recommendation in early stages of the clinical encounter that is comparable to clinicians. The diagnostic performance can be improved in the early stages using linked data technologies to incorporate more information into the decision making. With limited information, depending on the type of case, the performance of the Disease-Symptom Model will vary. As more information is collected during the clinical encounter the decision support application can provide recommendations that is comparable to clinicians recruited for the study. The evaluation showed that even with a simple weighting and summation method used in the Disease- Symptom Model the diagnostic ranking was comparable to dentists. With limited information in the early stages of the clinical encounter the Disease-Symptom Model was able to provide an accurately ranked diagnostic recommendation validating the model and methods used in this thesis

Using argumentation theory to identify the challenges of shared decision-making when the doctor and the patient have a difference of opinion

This paper aims to identify the challenges in the implementation of shared decision-making (SDM) when the doctor and the patient have a difference of opinion. It analyses the preconditions of the resolution of this difference of opinion by using an analytical and normative framework known in the field of argumentation theory as the ideal model of critical discussion. This analysis highlights the communication skills and attitudes that both doctors and patients must apply in a dispute resolution-oriented communication. Questions arise over the methods of empowerment of doctors and patients in these skills and attitudes as the preconditions of SDM. Overall, the paper highlights aspects in which research is needed to design appropriate programmes of training, education and support in order to equip doctors and patients with the means to successfully engage in shared decision-making

‘We don't have recipes; we just have loads of ingredients’: explanations of evidence and clinical decision making by speech and language therapists

Rationale, aims and objectives: Research findings consistently suggest that speech and language therapists (SLTs) are failing to draw effectively on research-based evidence to guide clinical practice. This study aimed to examine what constitutes the reasoning provided by SLTs for treatment choices and whether science plays a part in those decisions. Method: This study, based in Ireland, reports on the qualitative phase of a mixed-methods study, which examined attitudes underpinning treatment choices and the therapy process. SLTs were recruited from community, hospital and disability work settings via SLT managers who acted as gatekeepers. A total of three focus groups were run. Data were transcribed, anonymized and analysed using thematic analysis. Results: In total, 48 participants took part in the focus groups. The majority of participants were female, represented senior grades and had basic professional qualifications. Three key themes were identified: practice imperfect; practice as grounded and growing; and critical practice. Findings show that treatment decisions are scaffolded primarily on practice evidence. The uniqueness of each patient results in dynamic and pragmatic practice, constraining the application of unmodified therapies. Conclusion: The findings emerging from the data reflect the complexities and paradoxes of clinical practice as described by SLTs. Practice is pivoted on both the patient and clinician, through their membership of groups and as individuals. Scientific thinking is a component of decision making; a tool with which to approach the various ingredients and the dynamic nature of clinical practice. However, these scientific elements do not necessarily reflect evidence-based practice as typically constructed