A Semantic Extraction and Sentimental Assessment of Risk Factors (SESARF): An NLP Approach for Precision Medicine: A Medical Decision Support Tool for Early Diagnosis from Clinical Notes

2017 ◽  
Author(s):  
Susan Sabra ◽  
Khalid Mahmood ◽  
Mazen Alobaidi
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Early Diagnosis ◽  
Precision Medicine ◽  
Decision Support Tool ◽  
Medical Decision ◽  
Support Tool ◽  
Clinical Notes ◽  
Medical Decision Support

41 Enhancing Burn Medical Care During a Disaster Using a Novel Augmented-Reality Application

2021 ◽  
Vol 42 (Supplement_1) ◽  
pp. S31-S31
Author(s):  
Sena Veazey ◽  
Maria SerioMelvin ◽  
David E Luellen ◽  
Angela Samosorn ◽  
Alexandria Helms ◽  
...  
Keyword(s):  
Decision Support ◽  
Augmented Reality ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Burn Injuries ◽  
Medical Decision ◽  
Support Tool ◽  
Burn Care ◽  
Clinical Decision Support Tool

Abstract Introduction In disaster or mass casualty situations, access to remote burn care experts, communication, or resources may be limited. Furthermore, burn injuries are complex and require substantial training and knowledge beyond basic clinical care. Development and use of decision support (DS) technologies may provide a solution for addressing this need. Devices capable of delivering burn management recommendations can enhance the provider’s ability to make decisions and perform interventions in complex care settings. When coupled with merging augmented reality (AR) technologies these tools may provide additional capabilities to enhance medical decision-making, visualization, and workflow when managing burns. For this project, we developed a novel AR-based application with enhanced integrated clinical practice guidelines (CPGs) to manage large burn injuries for use in different environments, such as disasters. Methods We identified an AR system that met our requirements to include portability, infrared camera, gesture and voice control, hands-free control, head-mounted display, and customized application development abilities. Our goal was to adapt burn CPGs to make use of AR concepts as part of an AR-enabled burn clinical decision support system supporting four sub-applications to assist users with specific interventional tasks relevant to burn care. We integrated relevant CPGs and a media library with photos and videos as additional references. Results We successfully developed a clinical decision support tool that integrates burn CPGs with enhanced capabilities utilizing AR technology. The main interface allows input of patient demographics and injuries with step-by-step guidelines that follow typical burn management care and workflow. There are four sub-applications to assist with these tasks, which include: 1) semi-automated burn wound mapping to calculate total body surface area; 2) hourly burn fluid titration and recommendations for resuscitation; 3) medication calculator for accurate dosing in preparation for procedures and 4) escharotomy instructor with holographic overlays. Conclusions We developed a novel AR-based clinical decision support tool for management of burn injuries. Development included adaptation of CPGs into a format to guide the user through burn management using AR concepts. The application will be tested in a prospective research study to determine the effectiveness, timeliness, and performance of subjects using this AR-software compared to standard of care. We fully expect that the tool will reduce cognitive workload and errors, ensuring safety and proper adherence to guidelines.

Impact of a clinical decision support tool targeting QT-prolonging medications

2020 ◽  
Vol 77 (Supplement_4) ◽  
pp. S111-S117
Author(s):  
Katie Chernoby ◽  
Michael F Lucey ◽  
Carrie L Hartner ◽  
Michelle Dehoorne ◽  
Stephanie B Edwin
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Adverse Events ◽  
Clinical Decision Support ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Study Groups ◽  
Pharmacist Intervention ◽  
Support Tool ◽  
The Impact

Abstract Purpose To evaluate the impact of a newly implemented clinical decision support (CDS) tool targeting QT interval–prolonging medications on order verification and provider interventions. Methods A multicenter, retrospective quasi-experimental study was conducted to evaluate provider response to CDS alerts triggered during ordering of QT-prolonging medications for adult patients. The primary outcome was the proportion of orders triggering QTc alerts that were continued without intervention during a specified preimplementation phase (n = 49) and during a postimplementation phase (n = 100). Patient risk factors for QTc prolongation, provider alert response, and interventions to reduce the risk of QTc-associated adverse events were evaluated. Results The rate of order continuation without intervention was 82% in the preimplementation phase and 37% in the postimplementation phase, representing an 55% reduction in continued verified orders following implementation of the QT-focused CDS tool. Most alerts were initially responded to by the prescriber, with pharmacist intervention needed in only 33% of cases. There were no significant differences in patient QTc-related risk factors between the 2 study groups (P = 0.11); the postimplementation group had a higher proportion of patients using at least 2 QTc-prolonging medications (48%, compared to 26% in the preimplementation group; P = 0.02). Conclusion Implementation of the CDS tool was associated with a reduction in the proportion of orders continued without intervention in patients at high risk for QTc-related adverse events.

scholarly journals A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Seyed Abbas Mahmoodi ◽  
Kamal Mirzaie ◽  
Maryam Sadat Mahmoodi ◽  
Seyed Mostafa Mahmoudi
Keyword(s):  
Risk Factors ◽  
Gastric Cancer ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Healthcare Professionals ◽  
Fuzzy Cognitive Maps ◽  
Medical Decision ◽  
Risk Level ◽  
Medical Decision Support

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

Electronic Intervention to Improve Structured Cancer Stage Data Capture

2016 ◽  
Vol 12 (10) ◽  
pp. e949-e956 ◽  
Author(s):  
Michael Cecchini ◽  
Kim Framski ◽  
Patricia Lazette ◽  
Teresita Vega ◽  
Michael Strait ◽  
...  
Keyword(s):  
Decision Support ◽  
Best Practice ◽  
Decision Support Tool ◽  
Cancer Staging ◽  
Free Text ◽  
Cancer Stage ◽  
Support Tool ◽  
Clinical Notes ◽  
Practice Advisory ◽  
Electronic Intervention

Purpose: Cancer staging is critical for prognostication, treatment planning, and determining clinical trial eligibility. Electronic health records (EHRs) have structured staging modules, but physician use is inconsistent. Typically, stage is entered as unstructured free text in clinical notes and cannot easily be used for reporting. Methods: We created an Epic Best Practice Advisory (BPA) decision support tool that requires physicians to enter cancer stage in a structured module. If certain conditions are met, the BPA is triggered as a hard stop, and the physician cannot chart until staging is complete or a reason for not staging is selected. We used Plan, Do, Study, Act methodology to inform the intervention and compared preexisting staging rates to rates at 4, 8, and 12 months postintervention. Results: For 12 months before BPA implementation, 1,480 of 5,222 (28%) patients had cancer stage structured within the Epic problem list. From 1 to 4 months after the BPA 2,057 of 1,788 (115%) cases were staged in Epic. In the 5- to 8-month period after the BPA, 1,057 of 1,893 (56%) cases were staged, and 9 to 12 months after the BPA 1,082 of 1,817 (60%) were staged. Conclusion: Electronic decision support improves the rate of structured cancer staging at our institution. The staging rates between 56% and 60% for the 5- to 8-month and 9- to 12-month periods likely reflect accurate postintervention staging rates, whereas the initial 115% rate for 1 to 4 months is inflated by providers staging cancers diagnosed before the BPA.

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