scholarly journals Mobile Decision Support Tool for Emergency Departments and Mass Casualty Incidents (EDIT): Initial Study (Preprint)

2018 ◽  
Author(s):  
Nicholas Boltin ◽  
Diego Valdes ◽  
Joan M. Culley ◽  
Homayoun Valafar
Keyword(s):  
Emergency Department ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Mass Casualty Incident ◽  
Mass Casualty ◽  
Patient Data ◽  
Chemical Exposures ◽  
System P ◽  
Collect Patient Data

BACKGROUND Chemical exposures pose a significant threat to life. A rapid assessment by first responders and emergency nurses is required to reduce death and disability. Currently, no informatics tools exist to process victims of chemical exposures efficiently. The surge of patients into a hospital emergency department during a mass casualty incident creates additional stress on an already overburdened system, potentially placing patients at risk and challenging staff to process patients for appropriate care and treatment efficacy. Traditional emergency department triage models are oversimplified during highly stressed mass casualty incident scenarios in which there is little margin for error. Emerging mobile technology could alleviate the burden placed on nurses by allowing the freedom to move about the emergency department and stay connected to a decision support system. OBJECTIVE This study aims to present and evaluate a new mobile tool for assisting emergency department personnel in patient management and triage during a chemical mass casualty incident. METHODS Over 500 volunteer nurses, students, and first responders were recruited for a study involving a simulated chemical mass casualty incident. During the exercise, a mobile application was used to collect patient data through a kiosk system. Nurses also received tablets where they could review patient information and choose recommendations from a decision support system. Data collected was analyzed on the efficiency of the app to obtain patient data and on nurse agreement with the decision support system. RESULTS Of the 296 participants, 96.3% (288/296) of the patients completed the kiosk system with an average time of 3 minutes, 22 seconds. Average time to complete the entire triage process was 5 minutes, 34 seconds. Analysis of the data also showed strong agreement among nurses regarding the app’s decision support system. Overall, nurses agreed with the system 91.6% (262/286) of the time when it came to choose an exposure level and 84.3% (241/286) of the time when selecting an action. CONCLUSIONS The app reliably demonstrated the ability to collect patient data through a self-service kiosk system thus reducing the burden on hospital resources. Also, the mobile technology allowed nurses the freedom to triage patients on the go while staying connected to a decision support system in which they felt would give reliable recommendations.

scholarly journals Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study (Preprint)

2020 ◽  
Author(s):  
Junsang Yoo ◽  
Jeonghoon Lee ◽  
Poong-Lyul Rhee ◽  
Dong Kyung Chang ◽  
Mira Kang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Broad Perspective

BACKGROUND Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. OBJECTIVE The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. METHODS This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. RESULTS During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. CONCLUSIONS In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

scholarly journals A Decision Support System to Enhance Self-Management of Low Back Pain: Protocol for the selfBACK Project (Preprint)

2017 ◽  
Author(s):  
Paul Jarle Mork ◽  
Kerstin Bach
Keyword(s):  
Physical Activity ◽  
Low Back Pain ◽  
Back Pain ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Patient Data ◽  
Self Management ◽  
Low Back ◽  
Design And Implementation

BACKGROUND Low back pain (LBP) is a leading cause of disability worldwide. Most patients with LBP encountered in primary care settings have nonspecific LBP, that is, pain with an unknown pathoanatomical cause. Self-management in the form of physical activity and strength and flexibility exercises along with patient education constitute the core components of the management of nonspecific LBP. However, the adherence to a self-management program is challenging for most patients, especially without feedback and reinforcement. Here we outline a protocol for the design and implementation of a decision support system (DSS), selfBACK, to be used by patients themselves to promote self-management of LBP. OBJECTIVE The main objective of the selfBACK project is to improve self-management of nonspecific LBP to prevent chronicity, recurrence and pain-related disability. This is achieved by utilizing computer technology to develop personalized self-management plans based on individual patient data. METHODS The decision support is conveyed to patients via a mobile phone app in the form of advice for self-management. Case-based reasoning (CBR), a technology that utilizes knowledge about previous cases along with data about the current patient case, is used to tailor the advice to the current patient, enabling a patient-centered intervention based on what has and has not been successful in previous patient cases. The data source for the CBR system comprises initial patient data collected by a Web-based questionnaire, weekly patient reports (eg, symptom progression), and a physical activity-detecting wristband. The effectiveness of the selfBACK DSS will be evaluated in a multinational, randomized controlled trial (RCT), targeting care-seeking patients with nonspecific LBP. A process evaluation will be carried out as an integral part of the RCT to document the implementation and patient experiences with selfBACK. RESULTS The selfBACK project was launched in January 2016 and will run until the end of 2020. The final version of the selfBACK DSS will be completed in 2018. The RCT will commence in February 2019 with pain-related disability at 3 months as the primary outcome. The trial results will be reported according to the CONSORT statement and the extended CONSORT-EHEALTH checklist. Exploitation of the results will be ongoing throughout the project period based on a business plan developed by the selfBACK consortium. Tailored digital support has been proposed as a promising approach to improve self-management of chronic disease. However, tailoring self-management advice according to the needs, motivation, symptoms, and progress of individual patients is a challenging task. Here we outline a protocol for the design and implementation of a stand-alone DSS based on the CBR technology with the potential to improve self-management of nonspecific LBP. CONCLUSIONS The selfBACK project will provide learning regarding the implementation and effectiveness of an app-based DSS for patients with nonspecific LBP. REGISTERED REPORT IDENTIFIER RR1-10.2196/9379

scholarly journals Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study

10.2196/23351 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23351 ◽  
Author(s):  
Junsang Yoo ◽  
Jeonghoon Lee ◽  
Poong-Lyul Rhee ◽  
Dong Kyung Chang ◽  
Mira Kang ◽  
...  
Keyword(s):  
Risk Factors ◽  
Emergency Department ◽  
Logistic Regression ◽  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Broad Perspective

Background Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. Objective The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. Methods This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. Results During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. Conclusions In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type.

Kontinuierliche Planung von Logistikflächen/Continuous planning of logistic areas within factories

2020 ◽  
Vol 110 (04) ◽  
pp. 195-200
Author(s):  
Michael Teucke ◽  
Marius Veigt ◽  
Hendrik Engbers ◽  
Malte Klose ◽  
Michael Freitag
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Continuous Monitoring ◽  
Software Tools ◽  
System P ◽  
And Storage

Da Logistikflächen für innerstädtische Fabriken nur begrenzt verfügbar sind, ist deren bestmögliche Nutzung bedeutsam. Der Einsatz von Softwarewerkzeugen ist in der Neuplanung von Logistikflächen gängige Praxis. Die effiziente Nutzung bestehender Flächen in Anbetracht geänderter Anforderungen wird aber selten kontinuierlich überprüft. Der Beitrag zeigt, wie eine kontinuierliche planerische Restrukturierung von Logistikflächen durch ein digitales Assistenzsystem unterstützt werden kann.   Due to limited available space in urban production plants, the best possible use of logistic and storage areas is very important. The use of software tools is common practice for the planning of new logistics areas. However, continuous monitoring of the efficient use of existing areas due to changing requirements is only rarely implemented. This article describes how a continuous restructuring planning of logistics areas can be supported by a decision support system.

scholarly journals Using trigger tools to identify triage errors by ambulance dispatch nurses in Sweden: an observational study

BMJ Open ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. e035004 ◽  
Author(s):  
Douglas Spangler ◽  
Lennart Edmark ◽  
Ulrika Winblad ◽  
Jessica Colldén-Benneck ◽  
Helena Borg ◽  
...  
Keyword(s):  
Primary Care ◽  
Emergency Department ◽  
Decision Support ◽  
Observational Study ◽  
Decision Support System ◽  
Support System ◽  
Emergency Care ◽  
Incident Reporting Systems ◽  
Dispatch Centre ◽  
Reporting Systems

ObjectivesThis study aimed to assess whether trigger tools were useful identifying triage errors among patients referred to non-emergency care by emergency medical dispatch nurses, and to describe the characteristics of these patients.DesignAn observational study of patients referred by dispatch nurses to non-emergency care.SettingDispatch centres in two Swedish regions.ParticipantsA total of 1089 adult patients directed to non-emergency care by dispatch nurses between October 2016 and February 2017. 53% were female and the median age was 61 years.Primary and secondary outcome measuresThe primary outcome was a visit to an emergency department within 7 days of contact with the dispatch centre. Secondary outcomes were (1) visits related to the primary contact with the dispatch centre, (2) provision of care above the primary level (ie, interventions not available at a typical local primary care centre) and (3) admission to hospital in-patient care.ResultsOf 1089 included patients, 260 (24%) visited an emergency department within 7 days. Of these, 209 (80%) were related to the dispatch centre contact, 143 (55%) received interventions above the primary care level and 99 (38%) were admitted to in-patient care. Elderly (65+) patients (OR 1.45, 95% CI 1.05 to 1.98) and patients referred onwards to other healthcare providers (OR 1.58, 95% CI 1.15 to 2.19) had higher likelihoods of visiting an emergency department. Six avoidable patient harms were identified, none of which were captured by existing incident reporting systems, and all of which would have received an ambulance if the decision support system had been strictly adhered to.ConclusionThe use of these patient outcomes in the framework of a Global Trigger Tool-based review can identify patient harms missed by incident reporting systems in the context of emergency medical dispatching. Increased compliance with the decision support system has the potential to improve patient safety.

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