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Design and implementation of a platform for configuring clinical dynamic safety checklist applications

Frontiers of Information Technology & Electronic Engineering 2018年第7期19卷 937-946页

作者： Shan NAN Xu-dong LU Pieter VAN GORP Hendrikus H.M.KORSTEN Richard VDOVJAK Uzay KAYMAK Hui-long DUAN Department of Biomedical Engineering Zhejiang University Hangzhou 310027 China School of Industrial Engineering Eindhoven University of Technology Eindhoven 5600 MB the Netherlands Department of Aneasthiaology and Intensive-Care Catharina Ziekenhuis Eindhoven 5623 E J the Netherlands Philips Research Eindhoven Eindhoven 5656 AE the Netherlands

In recent years, it has been demonstrated that checklists can improve patient safety significantly. To facilitate the effective use of checklists in daily practice, both the medical community and the informatics community propose to implement checklists in dynamic checklist applications that can be integrated into the clinical workflow and that is specific to the patient context. However, it is difficult to develop such applications because they are tightly intertwined with the content of specific checklists. We propose a platform that enables access to dynamic checklist applications by configuring the infrastructures provided in the platform. Then, the applications can be developed without time-consuming programming work. We define a number of design criteria regarding point of care and clinical processes by analyzing the existing checklist applications and the lessons learned from ***, by applying rule-based clinical decision support and workflow management technologies, we design technical mechanisms to satisfy the design criteria. A dynamic checklist application platform is designed based on these mechanisms. Finally, we build a platform in various design cycle iterations, driven by multiple clinical cases. By applying the platform, we develop nine comprehensive dynamic checklist applications with 242 dynamic *** results demonstrate both the feasibility and the overall generic nature of the proposed approach. We propose a novel platform for configuring dynamic checklist applications. This platform satisfies the general requirements and can be easily configured to satisfy different scenarios in which safety checklists are used.

关键词： Checklist Workflow clinical decision support Process management Patient safety

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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health

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Computers, Materials & Continua 2023年第5期75卷 4553-4571页

作者： Md.Moddassir Alam Md Mottahir Alam Muhammad Moinuddin Mohammad Tauheed Ahmad Jabir Hakami Anis Ahmad Chaudhary Asif Irshad Khan Tauheed Khan Mohd Department of Health Information Management and Technology University of Hafr Al-BatinSaudi Arabia Department of Electrical and Computer Engineering Faculty of EngineeringKing AbdulazizJeddah21589Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdul Aziz UniversityJeddahSaudi Arabia College of Medicine King Khalid UniversityAbhaSaudi Arabia Department of Physics College of ScienceJazan UniversityP.O.Box.114Jazan45142Saudi Arabia Department of Biology College of ScienceImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11623Saudi Arabia Computer Science Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Math and Computer Science Augustana CollegeIL61201United States

Artificial Intelligence(AI)is finding increasing application in healthcare *** learning systems are utilized for monitoring patient health through the use of IoT sensor,which keep track of the physiological state by way of various health ***,early detection of any disease or derangement can aid doctors in saving patients’***,there are some challenges associated with predicting health status using the common algorithms,such as time requirements,chances of errors,and improper *** propose an Artificial Krill Herd based on the Random Forest(AKHRF)technique for monitoring patients’health and eliciting an optimal prescription based on their health *** begin with,various patient datasets were collected and trained into the system using IoT *** a result,the framework developed includes four processes:preprocessing,feature extraction,classification,and result ***,preprocessing removes errors,noise,and missing values from the dataset,whereas feature extraction extracts the relevant ***,in the classification layer,we updated the fitness function of the krill herd to classify the patient’s health status and also generate a *** found that the results fromthe proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity,specificity,Area under the Curve(AUC),accuracy,precision,recall,and F-measure.

关键词： Healthcare system health monitoring clinical decision support internet of things artificial intelligence machine learning diagnosis

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A linked data-based approach for clinical treatment selecting support

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Journal of Management Analytics 2014年第4期1卷 301-316页

作者： Lihong Jiang Ling Li Hongming Cai Hua Liu Jingyuan Hu Cheng Xie School of Software Shanghai Jiao Tong UniversityShanghaiChina College of Business&Public Administration Old Dominion UniversityNorfolkUSA Renji Hospital Shanghai Jiao Tong UniversityShanghaiChina

Treatment plan selection is a complex process because it sometimes needs sufficient experience and clinical *** it is even harder for doctors to select an appropriate treatment plan for certain patients since doctors might encounter difficulties in obtaining the right information and analyzing the diverse clinical *** order to improve the effectiveness of clinical decision making in complicated information system environments,we first propose a linked data-based approach for treatment plan *** approach integrates the patients’clinical records in hospitals with open linked data sources out of ***,based on the linked data net,treatment plan selection is carried on aided by similar historical therapy ***,we reorganize the electronic medical records of 97 colon cancer patients using the linked data model and count the similarity of these records to help treatment *** experiment shows the usability of our method in supporting clinical decisions.

关键词： linked data cancer treatment semantic web case-based reasoning clinical decision support

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Towards automated calculation of evidence-based clinical scores

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World Journal of Methodology 2017年第1期7卷 16-24页

作者： Christopher A Aakre Mikhail A Dziadzko Vitaly Herasevich Division of General Internal Medicine Department of Internal MedicineMayo Clinic Department of Anesthesiology Mayo Clinic Multidisciplinary Epidemiology and Trans- lational Research in Intensive Care Mayo Clinic

AIM To determine clinical scores important for automated calculation in the inpatient *** A modified Delphi methodology was used to create consensus of important clinical scores for inpatient practice. A list of 176 externally validated clinical scores were identified from freely available internet-based services frequently used by clinicians. Scores were categorized based on pertinent specialty and a customized survey was created for each clinician specialty group. Clinicians were asked to rank each score based on importance of automated calculation to their clinical practice in three categories-"not important", "nice to have", or "very important". Surveys were solicited via specialty-group listserv over a 3-mo interval. Respondents must have been practicing physicians with more than 20% clinical time spent in the inpatient setting. Within each specialty, consensus was established for any clinical score with greater than 70% of responses in a single category and a minimum of 10 responses. Logistic regression was performed to determine predictors of automation *** Seventy-nine divided by one hundred and forty-four(54.9%) surveys were completed and 72/144(50%) surveys were completed by eligible respondents. Only the critical care and internal medicine specialties surpassed the 10-respondent threshold(14 respondents each). For internists, 2/110(1.8%) of scores were "very important" and 73/110(66.4%) were "nice to have". For intensivists, no scores were "very important" and 26/76(34.2%) were "nice to have". Only the number of medical history(OR = 2.34; 95%CI: 1.26-4.67; P clinical scores used by internists was predictive of desire for automation. CONCLUSION Few clinical scores were deemed "very important" for automated calculation. Future efforts towards score calculator automation should focus on technically feasible "nice to have" scores.

关键词： Automation clinical prediction rule decision support techniques clinical decision support

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