Hypothesis-Driven Story Building: Counteracting Human Cognitive Biases to Improve Medical Diagnosis Support

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Autor/in	Zhu, Shizhuo
Titel	Hypothesis-Driven Story Building: Counteracting Human Cognitive Biases to Improve Medical Diagnosis Support
Quelle	(2010), (242 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, The Pennsylvania State University
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-1-3035-4636-5
Schlagwörter	Hochschulschrift; Dissertation; Medical Evaluation; Clinical Diagnosis; Decision Making; Bayesian Statistics; Information Seeking; Networks; Inferences; Attribution Theory; Probability; Internet; Web Sites; Guidelines; Patients; Vignettes; Nurses; Physicians; Control Groups; Experimental Groups; Accuracy; Heuristics; Correlation; Cognitive Processes; Efficiency; Pennsylvania + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Decision-making; Entscheidungsfindung; Informationserschließung; Inference; Inferenz; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Web-Design; Richtlinien; Patient; Physician; Doctor; Arzt; Heuristik; Korrelation; Cognitive process; Kognitiver Prozess; Effectiveness; Effektivität; Wirkungsgrad
Abstract	Clinical decision-making is challenging mainly because of two factors: (1) patient conditions are often complicated with partial and changing information; (2) people have cognitive biases in their decision-making and information-seeking. Consequentially, misdiagnoses and ineffective use of resources may happen. To better support clinical decision-making, a framework named Hypothesis-Driven Story Building (HDSB) was proposed to address the information challenges during clinical diagnosis. When given partial information, HDSB generates a list of hypotheses that can explain the current information and rank the hypotheses based on their likelihoods. If more information is needed, HDSB recommends a list of possible actions for information seeking, ranked in their effectiveness in differentiating the potential hypotheses. Whenever new information arrives, the HDSB framework updates the potential hypotheses accordingly. The HDSB framework was built based on Multi-Layer Bayesian Network (MLBN), which is an extension of standard Bayesian network with relational representation on each node, enabling the probabilistic causal inferences for relations based on variable bindings. In MLBN, different conditional probability tables can be defined for different variable bindings so that the Bayesian inferences can be specialized or personalized and abductive reasoning can be conducted. A web-based clinical diagnostic decision support prototype SRCAST-Diagnosis was developed based on the HDSB framework. In a given scenario, SRCAST-Diagnosis will display patient conditions, recommend differential diagnoses, and rank lab tests to the user. It was evaluated through a controlled experiment conducted at Hershey Medical Center. Participants including nurses, residents, and physicians were divided into a control group and an experimental group. Their actions (ordering lab tests and making diagnoses) were recorded. The result showed that SRCAST-Diagnosis can significantly improve the diagnosis accuracy and reduce the cost of resources overall, although the performances for different role players may vary. The data also showed that the tool helped more decision-makers who made the wrong initial diagnosis eventually find the correct diagnosis (counteracting anchoring heuristics) and helped them quickly figure out the correct diagnosis with significantly less resource cost (counteracting confirmation biases). It can be concluded that misdiagnosis and ineffective use of resources are associated with human cognitive biases, and a well-designed decision support system is able to improve diagnosis accuracy and resource efficiency by counteracting the cognitive biases. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01