A Methodology for Bi-Directional Knowledge-Based Assessment of Compliance to Continuous Application of Clinical Guidelines

• URL: http://arxiv.org/abs/2103.07789v1
• Date: Sat, 13 Mar 2021 20:43:45 GMT
• Title: A Methodology for Bi-Directional Knowledge-Based Assessment of Compliance to Continuous Application of Clinical Guidelines
• Authors: Avner Hatsek and Yuval Shahar
• Abstract summary: We introduce a new approach for automated guideline-based quality assessment of the care process. The BiKBAC method assesses the degree of compliance when applying clinical guidelines. The DiscovErr system was evaluated in a separate study in the type 2 diabetes management domain.
• Score: 1.52292571922932
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clinicians often do not sufficiently adhere to evidence-based clinical guidelines in a manner sensitive to the context of each patient. It is important to detect such deviations, typically including redundant or missing actions, even when the detection is performed retrospectively, so as to inform both the attending clinician and policy makers. Furthermore, it would be beneficial to detect such deviations in a manner proportional to the level of the deviation, and not to simply use arbitrary cut-off values. In this study, we introduce a new approach for automated guideline-based quality assessment of the care process, the bidirectional knowledge-based assessment of compliance (BiKBAC) method. Our BiKBAC methodology assesses the degree of compliance when applying clinical guidelines, with respect to multiple different aspects of the guideline (e.g., the guideline's process and outcome objectives). The assessment is performed through a highly detailed, automated quality-assessment retrospective analysis, which compares a formal representation of the guideline and of its process and outcome intentions (we use the Asbru language for that purpose) with the longitudinal electronic medical record of its continuous application over a significant time period, using both a top-down and a bottom-up approach, which we explain in detail. Partial matches of the data to the process and to the outcome objectives are resolved using fuzzy temporal logic. We also introduce the DiscovErr system, which implements the BiKBAC approach, and present its detailed architecture. The DiscovErr system was evaluated in a separate study in the type 2 diabetes management domain, by comparing its performance to a panel of three clinicians, with highly encouraging results with respect to the completeness and correctness of its comments.

Related papers

• Deep Reinforcement Learning for Personalized Diagnostic Decision Pathways Using Electronic Health Records: A Comparative Study on Anemia and Systemic Lupus Erythematosus [1.7965876401882177]
  We formulate the task of diagnosis as a sequential decision-making problem. We study the use of Deep Reinforcement Learning (DRL) algorithms to learn the optimal sequence of actions to perform. We develop two clinical use cases: Anemia diagnosis and Systemic Lupus Erythematosus diagnosis.
  arXiv  Detail & Related papers  (2024-04-09T00:07:16Z)
• EndoOOD: Uncertainty-aware Out-of-distribution Detection in Capsule Endoscopy Diagnosis [11.82953216903558]
  Wireless capsule endoscopy (WCE) is a non-invasive diagnostic procedure that enables visualization of the gastrointestinal (GI) tract. Deep learning-based methods have shown effectiveness in disease screening using WCE data. Existing capsule endoscopy classification methods mostly rely on pre-defined categories.
  arXiv  Detail & Related papers  (2024-02-18T06:54:51Z)
• Towards Unifying Anatomy Segmentation: Automated Generation of a Full-body CT Dataset via Knowledge Aggregation and Anatomical Guidelines [113.08940153125616]
  We generate a dataset of whole-body CT scans with $142$ voxel-level labels for 533 volumes providing comprehensive anatomical coverage. Our proposed procedure does not rely on manual annotation during the label aggregation stage. We release our trained unified anatomical segmentation model capable of predicting $142$ anatomical structures on CT data.
  arXiv  Detail & Related papers  (2023-07-25T09:48:13Z)
• Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes [50.8044927215346]
  We consider a comorbidity risk prediction scenario and focus on contexts regarding the patients clinical state. We employ several state-of-the-art LLMs to present contexts around risk prediction model inferences and evaluate their acceptability. Our paper is one of the first end-to-end analyses identifying the feasibility and benefits of contextual explanations in a real-world clinical use case.
  arXiv  Detail & Related papers  (2023-02-11T18:07:11Z)
• Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy [43.55994393060723]
  gaitXplorer is a visual analytics approach for the classification of CP-related gait patterns. It integrates Grad-CAM, a well-established explainable artificial intelligence algorithm, for explanations of machine learning classifications.
  arXiv  Detail & Related papers  (2022-08-10T09:21:28Z)
• Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network [51.44506007844284]
  We propose a shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection surgery. We first devise an algorithm to automatically generate relation keypoint heatmaps, which intuitively represent the prior knowledge of spatial relations among landmarks. We then develop two complementary regularization schemes to progressively incorporate the prior knowledge into the training process.
  arXiv  Detail & Related papers  (2021-11-08T07:57:30Z)
• Semi-Supervised Variational Reasoning for Medical Dialogue Generation [70.838542865384]
  Two key characteristics are relevant for medical dialogue generation: patient states and physician actions. We propose an end-to-end variational reasoning approach to medical dialogue generation. A physician policy network composed of an action-classifier and two reasoning detectors is proposed for augmented reasoning ability.
  arXiv  Detail & Related papers  (2021-05-13T04:14:35Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)
• Inheritance-guided Hierarchical Assignment for Clinical Automatic Diagnosis [50.15205065710629]
  Clinical diagnosis, which aims to assign diagnosis codes for a patient based on the clinical note, plays an essential role in clinical decision-making. We propose a novel framework to combine the inheritance-guided hierarchical assignment and co-occurrence graph propagation for clinical automatic diagnosis.
  arXiv  Detail & Related papers  (2021-01-27T13:16:51Z)
• AI Driven Knowledge Extraction from Clinical Practice Guidelines: Turning Research into Practice [2.803896166632835]
  Clinical Practice Guidelines (CPGs) represent the foremost methodology for sharing state-of-the-art research findings in the healthcare domain with medical practitioners. However, extracting relevant knowledge from the plethora of CPGs is not feasible for already burdened healthcare professionals. This research presents a novel methodology for knowledge extraction from CPGs to reduce the gap and turn the latest research findings into clinical practice.
  arXiv  Detail & Related papers  (2020-12-10T07:23:02Z)
• Health improvement framework for planning actionable treatment process using surrogate Bayesian model [1.2468700211588881]
  This study proposes a novel framework to plan treatment processes in a data-driven manner. A key point of the framework is the evaluation of the "actionability" for personal health improvements.
  arXiv  Detail & Related papers  (2020-10-30T06:02:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.