The Value of Diagnostic Software and Doctors\u27 Decision Making

The prevalence of medical misdiagnosis has remained high despite the adoption of diagnostic software. This ongoing controversy about the role of technology in mitigating the problem of misdiagnosis centers on the question of whether diagnostic software does reduce the incidence of misdiagnosis if properly relied upon by physicians. The purpose of this quantitative, cross-sectional study based on planned behavior theory was to measure doctors\u27 opinions of diagnostic technology\u27s medical utility. Recruitment e-mails were sent to 3,100 AMA-accredited physicians through their database that yielded a sample of 99 physicians for the study. One-sample t tests and, where appropriate because of non-normal data, one-sample Wilcoxon signed-rank tests were conducted on the data to address the following key research questions on whether diagnostic software decreases misdiagnosis in healthcare versus unassisted human diagnostic method, if physicians use diagnostic software frequently enough to decrease misdiagnosis in healthcare, and if liability concerns prevent physicians from using diagnostic software. It was found that in the opinion of those surveyed (a) diagnostic software was likely to result in fewer misdiagnoses in healthcare than unassisted human diagnostic methods, (b) when speaking for themselves, physicians thought they used diagnostic software frequently enough to decrease misdiagnoses, and (c) physicians agreed they were not prevented from using diagnostic software because of liability concerns. The study\u27s social significance is the affirmation of diagnostic software\u27s usefulness: Policy and technology stakeholders can use this finding to speed the adoption of diagnostic software, leading to a reduction in the socially costly problem of misdiagnosis

Identifying Non-Sublattice Equivalence Classes Induced by an Attribute Reduction in FCA

The detection of redundant or irrelevant variables (attributes) in datasets becomes essential in different frameworks, such as in Formal Concept Analysis (FCA). However, removing such variables can have some impact on the concept lattice, which is closely related to the algebraic structure of the obtained quotient set and their classes. This paper studies the algebraic structure of the induced equivalence classes and characterizes those classes that are convex sublattices of the original concept lattice. Particular attention is given to the reductions removing FCA's unnecessary attributes. The obtained results will be useful to other complementary reduction techniques, such as the recently introduced procedure based on local congruences

Metacognition and Decision-Making Style in Clinical Narratives

Clinical decision-making has high-stakes outcomes for both physicians and patients, yet little research has attempted to model and automatically annotate such decision-making. The dual process model (Evans, 2008) posits two types of decision-making, which may be ordered on a continuum from intuitive to analytical (Hammond, 1981). Training clinicians to recognize decision-making style and select the most appropriate mode of reasoning for a particular context may help reduce diagnostic error (Norman, 2009). This study makes preliminary steps towards detection of decision style, based on an annotated dataset of image-based clinical reasoning in which speech data were collected from physicians as they inspected images of dermatological cases and moved towards diagnosis (Hochberg et al., 2014a). A classifier was developed based on lexical, speech, disfluency, physician demographic, cognitive, and diagnostic difficulty features to categorize diagnostic narratives as intuitive vs. analytical; the model improved on the baseline by over 30%. The introduced computational model provides construct validity for the dual process theory. Eventually, such modeling may be incorporated into instructional systems that teach clinicians to become more effective decision makers. In addition, metacognition, or self-assessment and self-management of cognitive processes, has been shown beneficial to decision-making (Batha & Carroll, 2007; Ewell-Kumar, 1999). This study measured physicians\u27 metacognitive awareness, an online component of metacognition, based on the confidence-accuracy relationship, and also exploited the corpus annotation of decision style to derive decision metrics. These metrics were used to examine the relationships between decision style, metacognitive awareness, expertise, case difficulty, and diagnostic accuracy. Based on statistical analyses, intuitive reasoning was associated with greater diagnostic accuracy, with an advantage for expert physicians. Case difficulty was associated with greater user of analytical decision-making, while metacognitive awareness was linked to decreased diagnostic accuracy. These results offer a springboard for further research on the interactions between decision style, metacognitive awareness, physician and case characteristics, and diagnostic accuracy

Using fuzzy-trace theory to understand and improve health judgments, decisions, and behaviors: A literature review.

Fuzzy-trace theory is a dual-process model of memory, reasoning, judgment, and decision making that contrasts with traditional expectancy-value approaches. We review the literature applying fuzzy-trace theory to health with three aims: evaluating whether the theory’s basic distinctions have been validated empirically in the domain of health; determining whether these distinctions are useful in assessing, explaining, and predicting health-related psychological processes; and determining whether the theory can be used to improve health judgments, decisions, or behaviors, especially in comparison to other approaches

Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers

Predicting the Effectiveness of Medical Interventions

This dissertation explores several conceptual and methodological features of medical science that influence our ability to accurately predict medical effectiveness. Making reliable predictions about the effectiveness of medical treatments is crucial to mitigating death and disease and improving individual and population health, yet generating such predictions is fraught with difficulties. Each chapter deals with a unique challenge to predictions of medical effectiveness. In Chapter 1, I describe and analyze the principles underlying three prominent approaches to physical disease classification—the etiological, symptom-based, and pathophysiological models—and suggest a broadly pragmatic approach whereby appropriate classifications depend on the goal in question. In line with this, I argue that particular features of the pathophysiological model, such as its focus on disease mechanisms, make it most relevant for predicting medical effectiveness. Chapter 2 explores the debate between those who argue that statistical evidence is sufficient for inferring medical effectiveness and those who argue that we require both statistical and mechanistic evidence. I focus on the question of how mechanistic and statistical evidence can be integrated. I highlight some of the challenges facing formal techniques, such as Bayesian networks, and use Toulmin’s model of argumentation to offer a complementary model of evidence amalgamation, which allows for the systematic integration of statistical and mechanistic evidence. In Chapter 3, I focus on p-hacking, an application of analytic techniques that may lead to exaggerated experimental results. I use philosophical tools from decision theory to illustrate how severe the effects of p-hacking can be. While it is typically considered epistemically questionable and practically harmful, I appeal to the argument from inductive risk to defend the view that there are some contexts in which p-hacking may be warranted. Chapter 4 draws attention to a particular set of biases plaguing medical research: Meta-biases. I argue that biases of this type, such as publication bias and sponsorship bias, lead to exaggerated clinical trial results. I then offer a framework, the bias dynamics model, that corrects for the influence of meta-biases on estimations of medical effectiveness. In Chapter 5, I argue against the prominent view that AI models are not explainable by showing how four familiar accounts of scientific explanation can be applied to neural networks. The confusion about explaining AI models is due to the conflation of ‘explainability’, ‘understandability’, and ‘interpretability’. To remedy this, I offer a novel account of AI-interpretability, according to which an interpretation is something one does to an explanation with the explicit aim of producing another, more understandable, explanation.The Oppenheimer Memorial Trust Department of History and Philosophy of Science, Cambridge Universit

A STUDY OF PREVENTABLE HOSPITAL UTILIZATION AMONG MEDICAID-INSURED PEDIATRIC PATIENTS IN NORTH CAROLINA’S FEDERALLY QUALIFIED HEALTH CENTERS

Objective. The goal of this research is to evaluate preventable hospital utilization among Medicaid-insured federally qualified health center (FQHC) patients in North Carolina and to determine organizational factors associated with preventable hospital use. Methods. Using 2013-2015 Medicaid claims data, we applied instrumental variable analysis using two-stage residual inclusion to account for differential patient selection into FQHCs and estimated the association of FQHC use on preventable hospital utilization. Because there is no “gold standard” in performance classification, we applied three different methodologies to rank FQHC organizations according to their relative rates of preventable hospital use and estimated an overall performance ranking that incorporated the results of the three statistical approaches. Finally, we estimated patient-level regression models with FQHC fixed effects and ran organization-level configurational comparative analyses to identify organizational characteristics associated with preventable hospital utilization. Results. Across all model specifications in this study sample, we found that FQHC patients had a significantly higher probability of preventable hospital utilization when compared to patients accessing primary care services from non-FQHC providers. We identified variation in the absolute rankings of FQHC organizations across performance classification methodologies, but the organizations comprising the top- and bottom-performance quartiles remained consistent. We demonstrated that the geometric mean could be used to estimate an overall performance ranking across methodologies. Finally, we found that patients utilizing FQHCs with a broader scope of non-medical services and more of certain non-medical services staff were more likely to experience preventable hospital use even after controlling for patient characteristics. However, these results were associated with significant limitations. Conclusions. The differential effect of FQHC use may be driven by higher emergency department utilization among FQHC patients, as this comprised the majority of hospital use among pediatric asthma patients in this study. Patients using FQHCs with a broader scope of non-medical services and more of certain types of non-medical services staff were more likely to have preventable hospital utilization, but these organization-level factors do not reflect patient-level utilization of services. Children may be accessing non-medical services in FQHCs less frequently than adults, for example. Future research should incorporate FQHCs’ electronic health record data and qualitative interviews to best identify organization structures and processes associated with performance. This research also underscores the need for policymakers and payers to incorporate encounter-level data on non-medical services in claims submissions in order to better measure the effect of non-medical services on health care costs, utilization and outcomes across all provider types.Doctor of Philosoph