What is it about?

Healthcare systems are under pressure from an aging population, rising costs, and increasingly complex conditions and treatments. Although data are determined to play a bigger role in how doctors diagnose and prescribe treatments, they struggle due to a lack of time and an abundance of structured (such as numerical lab results) and unstructured (such as textual patient conversation reports) information. To address this challenge, we introduce MediCoSpace, a visual decision-support tool for more efficient doctor-patient consultations. The tool links patient reports to past and present diagnoses, diseases, drugs, and treatments, both for the current patient and other patients in comparable situations. MediCoSpace uses textual medical data and deep-learning supported text analysis to facilitate a visual discovery process. The tool is evaluated with five medical doctors. The results show that MediCoSpace facilitates a promising, yet complex way to discover unlikely relations and thus suggests a path toward the research and development of interactive visual tools to provide physicians with more holistic diagnoses and personalized, dynamic treatments for patients.

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Why is it important?

While current tools made by researchers make important steps, the diagnosis and creation of non-trivial treatment plans are more difficult than more trivial ones. To our knowledge, no medical decision-support system addresses relationships between diseases, drugs, and treatments combined with advanced search support within the patient’s history and across similar patients, and links this back to patient reports to discover and leverage possibly overlooked relations.

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This page is a summary of: MediCoSpace : Visual Decision-Support for Doctor-Patient Consultations using Medical Concept Spaces from EHRs, ACM Transactions on Management Information Systems, January 2023, ACM (Association for Computing Machinery), DOI: 10.1145/3564275.
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