An attempt was made to investigate the decision processes of subjects in a bivariate decision making task, similar to that facing a medical specialist who is required to classify a patient as belonging to one of a number of possible disease populations on the basis of the patient's scores of two predictor cues. It was felt that such tasks had been largely neglected in experimental psychology, where the tendency has been towards requiring subjects to learn relationships between continuous predictor variables and a continuous criterion, rather than between continuous predictor variables and a categorical criterion. When the relationship between the predictor variables is the same in both the populations to be discriminated, the best decision function is based on a linear combination of the cues (Fisher’s Linear Discriminant Function). It was found that the decisions of those subjects who learned to use the cues in a way which was at all valid in such situations, could be well approximated by a model which weighted the two cues equally in a linear combination and based it’s decisions on the result. When the relationship between the predictor variables differs from one population to the other, however, the best decision function becomes more complex, including terms in the squares and cross-products of the cues. It was felt that such situations are particularly relevant to medical decision making where clinicians have frequently claimed that the "pattern" of scores of a patient is important, not Just the individual scores on each cue. It was found that if differences in cue intercorrelation were large, then subjects seemed to inolude in their iii decision processes, some nonlinear term to take account of this fact. If, however, differences in cue intercorrelation were only moderate, or if the correlations involved were large hut negative, this seemed to go unnoticed by the subjects and did not lead to any reliance on nonlinear terms. The results show that previous findings in "real life" tasks, that decision making processes could be adequately represented as linear combinations of cues, may be due more to the linear nature of the tasks than to any predisposition towards linear processes on the part of human decision makers, and that the statistical properties of "real life" tasks must be more thoroughly investigated before it is assumed that they require nonlinear decision processes.

The efforts addressed in this thesis refer to applying nonlinear risk predictive techniques based on logistic regression to medical diagnostic test data. This study is motivated and pursued to address the following: 1. To extend logistic regression model of biostatistics to medical informatics 2. Computational preemptive and predictive testing to determine the probability of occurrence (p) of an event by fitting a data set to a (logit function) logistic curve: Finding upper and lower bounds on p based on stochastical considerations 3. Using the model developed on available (clinical) data to illustrate the bounds-limited performance of the prediction. Relevant analytical methods, computational efforts and simulated results are presented. Using the results compiled, the risk evaluation in medical diagnostics is discussed with real-world examples. Conclusions are enumerated and inferences are made with directions for future studies. / by Alice Horn Dupont. / Thesis (M.S.C.S.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

Background: Cancer treatment and management require complicated clinical decision making to provide the highest quality of care for an individual patient. This is facilitated in part with ever-increasing availability of medications and treatments but hindered due to barriers such as access to care, cost of medications, clinician knowledge, and patient preferences or clinical factors. Although guidelines for cancer treatment and many symptoms have been developed to inform clinical practice, implementation of these guidelines into practice is often delayed or does not occur. Informatics-based approaches, such as clinical decision support, may be an effective tool to improve guideline implementation by delivering patient-specific and evidence-based knowledge to the clinician at the point of care to allow shared decision making with a patient and their family. The large amount of data in the electronic health record can be utilized to develop, evaluate, and implement automated approaches; however, the quality of the data must first be examined and evaluated. Methods: This dissertation addresses gaps the literature about clinical decision making for cancer care delivery. Specifically, following an introduction and review of the literature for relevant topics to this dissertation, the researcher presents three studies. In Study One, the researcher explores the use of clinical decision support in cancer therapeutic decision making by conducting a systematic review of the literature. In Study Two, the researcher conducts a quantitative study to describe the rate of guideline concordant care provided for prevention of acute chemotherapy-induced nausea and vomiting (CINV) and to identify predictors of receiving guideline concordant care. In Study Three, the researcher conducts a mixed-methods study to evaluate the completeness, concordance, and heterogeneity of clinician documentation of CINV. The final chapter of this dissertation is comprised of key findings of each study, the strengths and limitations, clinical and research implications, and future research. Results: In Study One, the systematic review, the researcher identified ten studies that prospectively studied clinical decision support systems or tools in a cancer setting to guide therapeutic decision making. There was variability in these studies, including study design, outcomes measured, and results. There was a trend toward benefit, both in process and patient-specific outcomes. Importantly, few studies were integrated into the electronic health record. In Study Two, of 180 patients age 26 years or less, 36% received guideline concordant care as defined by pediatric or adult guidelines, as appropriate. Factors associated with receiving guideline concordant care included receiving a cisplatin-based regimen, being treated in adult oncology compared to pediatric oncology, and solid tumor diagnosis. In Study Three, of the 127 patient records reviewed for the documentation of chemotherapy-induced nausea and vomiting, 75% had prescriber assessment documented and 58% had nursing assessment documented. Of those who had documented assessments by both prescriber and nurse, 72% were in agreement of the presence/absence of chemotherapy-induced nausea and vomiting. After mapping the concept through the United Medical Language System and developing a post-coordinated expression to identify chemotherapy-induced nausea and vomiting in the text, 85% of prescriber documentation and 100% of nurse documentation could be correctly categorized as present/absent. Further descriptors of the symptoms, such as severity or temporality, however, were infrequently reported. Conclusion: In summary, this dissertation provides new knowledge about decision making in cancer care delivery. Specifically, in Study One the researcher describes that clinical decision support, one potential implementation strategy to improve guideline concordant care, is understudied or under published but a promising potential intervention. In Study Two, I identified factors that were associated with receipt of guideline concordant care for CINV, and these should be further explored to develop interventions. Finally, in Study Three, I report on the limitations of the data quality of CINV documentation in the electronic health record. Future work should focus on validating these results on a multi-institutional level.

This study examines the decision-making activities and communicative activities of two groups participating in a simulated medical emergency activity: the control group (CG) using a traditional whiteboard and the experimental group (EG) using a structured interactive whiteboard. The two groups differ in that the EG has a structured template to annotate and share their arguments with each other. Data analysis of the decision-making activities focused on planning, data collecting, managing, and interpreting patient data. Data analysis of the communicative activities focused on informative, argumentative, elicitative, responsive, and directive acts. In the early stage of decision-making the EG spent significantly more time interpreting the situation and less time managing the patient than the CG; in the later stage the EG spent significantly more time managing the patient but less time interpreting the situation. No significant results were found in communicative activities due to low cell frequencies of the utterances. Qualitative results indicated that shared visualizations can disambiguate and clarify verbal interactions and promote productive argumentation and negotiation activities. Shared cognition facilitates the construction of shared situation models and joint problem spaces which lead to better decision making and problem solving.

No description available.

published_or_final_version / Public Health / Master / Master of Public Health

Thesis (PhD)--Macquarie University, Division of Linguistics & Psychology, Department of Linguistics, 2003. / Bibliography: p. 397-424. / Introduction -- Models of shared decision-making in medicine -- Framing the study -- The analytic goals of modelling agency -- The context of treatment decision-making in HIV -- Agency and alignment -- Study conclusions and implications. / The quality of doctor-patient communication has been shown to influence treatment uptake, adherence and effectiveness in HIV medicine and elsewhere. Increasingly, it is considered essential that doctors and patients jointly participate in decisions concerning treatment. There is a growing body of literature describing joint decisionmaking and suggesting guidelines for its practice. Few of these studies, however, relate their descriptions of medical decision-making as a social process to the ways in which patterns of verbal interaction realize or foreclose on joint decision-making. -- Dominant models of medical decision-making view shared decision-making as a midpoint between enlightened paternalism and informed choice. Based on a corpus of HIV consultations audio-recorded in Sydney in the late 1990s, this thesis argues that it can be better modelled as a particular type of social process, which differs across a number of dimensions from other styles of medical decision-making, specifiable as contextual parameters of meaning. The thesis then identifies ways in which specific discursive practices realize these contextual parameters. -- A major component of the thesis focuses on agency, and a model is presented in the form of a socio-semantic network, drawing on work by van Leeuwen (1996) and others, which relates a range of grammatical features, not only transitivity patterns, to ways of construing social agency. The thesis then considers the way in which doctors and patients mobilise these and other resources for bringing together potentially conflicting points of view in framing and articulating treatment decisions. Here I draw on notions of mutual alignment (e.g., Goffman 1981) but expand the analysis of what is aligned to account for speakers' implicit discourse orientation, as well as more overt markers. -- Findings emphasise the relationship between representing and enacting agentive roles; the importance of doctors and patients mutually projecting each other's voices; and the variable and iterative character of shared decision-making. The research demonstrates how doctors and patients negotiate a complex, interactionally and symbolically mediated agency, and shows that patients often take the lead in developing more collaborative decision-making practice. There are still institutionally and socially determined limits to the degree of control patients may exercise within the consultation, many of which are of course well founded. / Mode of access: World Wide Web. / xvii, 533, [22] p. ill

The implantable cardioverter defibrillator (ICD) is an electronic medical device that was invented by Dr. Michael Mirowski and his team in 1980. The purpose of the ICD, which is implanted in a person's chest, is to sense and shock the heart when detecting a lethal cardiac arrhythmia into a rhythm that can sustain life. While the ICD saves lives, it also has the potential to deliver painful shocks when it is activated. The ICD was initially inserted in people who had survived a sudden cardiac arrest; the device is now being implanted in older adults with heart failure and no known history of cardiac arrhythmias. When talking with patients and personal family members who had an ICD, it was unclear what influenced their decision to have an ICD implanted. Understanding the experience of decision-making for older adults who had an ICD has added to nursing knowledge, practice, and education when working with people who had an ICD inserted. To understand the lived experience, the researcher conducted a phenomenological research study, guided by the theoretical lens of Paterson and Zderad's (1976/1988) humanistic nursing and analyzed the data as outlined by Giorgi (2009). The results of the study indicated the participants' lived experience of decision-making for older adults who had an implantable cardioverter defibrillator inserted was influenced by the following : trust in their physician's decision; accepting the device was necessary; the decision was easy to make; and hope and desire to live longer. / by Louise A. Lucas. / Thesis (Ph.D.)--Florida Atlantic University, 2011. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2011. Mode of access: World Wide Web.

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Background The landscape of clinical decision support systems (CDSSs) is evolving to include increasingly sophisticated data-driven methods, such as machine learning, to provide clinicians with predictions about patients’ risk for negative outcomes or their likely responses to treatments (predictive CDSSs). However, trust in predictive CDSSs has shown to challenge clinician adoption of these tools, precluding the ability to positively impact patient outcomes. This is particularly salient in the hospital setting where clinician time is scarce, and predictive CDSSs have the potential to decrease preventable mortality. Many have advised that clinicians should be involved in the development, implementation, and evaluation of predictive CDSSs to increase translation from development to adoption. Yet, little is known about the prevalence of clinician involvement or the factors that influence clinicians’ trust in predictive CDSSs for the hospital setting. The specific aims of this dissertation were: (a) to survey the literature on predictive CDSSs for the hospital setting to describe the prevalence and methods of clinician involvement throughout stages of system design, (b) to identify and characterize factors that influence clinicians’ trust in predictive CDSSs for in-hospital deterioration, and (c) to explore the use of a trust conceptual framework for incorporating clinician expertise into machine learning model development for predicting rapid response activation among hospitalized non-ICU patients using electronic health record (EHR) data. Methods To address the first aim (presented in Chapter Two), a scoping review was conducted to summarize the state of the science of clinician (nurse, physician, physician assistant, nurse practitioner) involvement in predictive CDSS design, with a specific focus on systems using machine learning methods with EHR data for in-hospital decision-making. To address the second aim (presented in Chapter Three), semi-structured interviews with nurses and prescribing providers (i.e., physicians, physicians assistants, nurse practitioners) were conducted and analyzed inductively and deductively (using the Human-Computer Trust conceptual framework) to identify factors that influence trust in predictive CDSSs, using an implemented predictive CDSS for in-hospital deterioration as a grounding example. Finally, to address the third aim (presented in Chapter Four), clinician expertise was elicited in the form of model specifications (requirements, insights, preferences) for facilitating factors shown to influence trust in predictive CDSSs, as guided by the Human-Computer Trust conceptual framework. Specifications included: (a) importance ranking of input features, (b) preference for a more sensitive or specific model, (c) acceptable false positive and negative rates, and (d) prediction lead time. Specifications informed development and evaluation of machine learning models predicting rapid response activation using retrospective EHR data. Results The scoping review identified 80 studies. Seventy-six studies described developing a machine learning model for a predictive CDSS, 28% of which described involving clinicians during development. Clinician involvement during development was categorized as: (a) determining clinical relevance/correctness, (b) feature selection, (c) data preprocessing, and (d) serving as a gold standard. Only five studies described implemented predictive CDSSs and no studies described systems in routine use. The qualitative investigation with 17 clinicians (9 prescribing providers, 8 nurses) confirmed that the Human-Computer Trust concepts of perceived understandability and perceived technical competence are factors that influence hospital clinicians’ trust in predictive CDSSs and further characterized these factors (i.e., themes). This study also identified three additional themes influencing trust: (a) actionability, (b) evidence, and (c) equitability, and found that clinicians’ needs for explanations of machine learning models and the impact of discordant predictions may vary according to the extent to which clinicians rely on the predictive CDSS for decision-making. Only two of 28 categories/sub-categories and one theme emerged uniquely to nurses or prescribing providers. Finally, the third study elicited model specifications from fifteen total clinicians. Not all clinicians answered all questions. Vital sign frequency was ranked the most important feature category on average (n = 8 clinicians), the most frequently preferred prediction lead time was shift-change/8-12 hours (n = 9 clinicians), most preferred a more specific than sensitive model (71%; n = 7 clinicians), the average acceptable false positive rate was 42% (n = 9 clinicians), the average acceptable false negative rate was 29% (n = 6 clinicians). These specifications informed development and testing of four machine learning classification models (ridge regression, decision trees, random forest, and XGBoost). 249,676 patient admissions from 2015–2018 at a large northeastern hospital system were modeled to predict whether or not patients would have a rapid response within the 12-hour shift. The random forest classifier met clinician’s average acceptable false positive (27.7%) and negative rates (28.9%) and was marginally more specific (72.2%) than sensitive (71.1%) on a holdout test set. Conclusions Studies do not routinely report clinician involvement in model development of predictive CDSSs for the hospital setting and publications on implementation considerably lag those on development. Nurses and prescribing providers described largely shared experiences of trust in predictive CDSSs. Clinicians’ reliance on the predictive CDSS for decision-making within the target clinical workflow should be considered when aiming to facilitate trust. Incorporating clinician expertise into model development for the purpose of facilitating trust is feasible. Future research is needed on the impact of clinician involvement on trust, clinicians’ personal attributes that influence trust, and explanation design. Increased education for clinicians about predictive CDSSs is recommended.