The COVID-19 global pandemic has overwhelmed health services with large numbers of patients presenting to hospital, requiring immediate triage and diagnosis. Complications include acute respiratory distress syndrome, myocarditis, septic shock, and multiple organ failure. Point of care ultrasound is recommended for critical care triage and monitoring in COVID-19 by specialist critical care societies, however current guidance has mainly been published in webinar format, not a comprehensive review. Important limitations of point of care ultrasound include inter-rater variability and subjectivity in interpretation of imaging findings, as well as infection control concerns. A practical approach to clinical integration of point of care ultrasound findings in COVID-19 patients is presented to enhance consistency in critical care decision making, and relevant infection control guidelines and operator precautions are discussed, based on a narrative review of the literature.
Humans ; COVID-19/complications* ; SARS-CoV-2 ; Point-of-Care Systems ; Decision Support Systems, Clinical ; Ultrasonography

Humans ; Decision Support Systems, Clinical ; Rare Diseases/therapy*

Clinical decision support systems are interactive computer systems for situational decision making and can improve decision efficiency and safety of care. We investigated the role of these systems in enhancing prehospital care. This narrative review included full-text articles published since 2000 that were available in databases/e-journals including Web of Science, PubMed, Science Direct, and Google Scholar. Search keywords included “clinical decision support system,” “decision support system,” “decision support tools,” “prehospital care,” and “emergency medical services.” Non-journal articles were excluded. We revealed 14 relevant studies that used such a support system in prehospital emergency medical service. Owing to the dynamic nature of emergency situations, decision timing is critical. Four key factors demonstrated the ability of clinical decision support systems to improve decision-making, reduce errors, and improve the safety of prehospital emergency activity: computer-based, offer support as a natural part of the workflow, provide decision support in the time and place of decision making, and offer practical advice. The use of clinical decision support systems in prehospital care resulted in accurate diagnoses, improved patient triage and patient outcomes, and reduction of prehospital time. By improving emergency management and rescue operations, the quality of prehospital care will be enhanced.
Computer Systems ; Decision Making ; Decision Support Systems, Clinical ; Diagnosis ; Emergencies ; Emergency Medical Services ; Humans ; Triage

Obesity-related clinical decision support tools in electronic health records (EHRs) can improve pediatric care, but the degree of adoption of these tools is unknown. DocStyles 2015 survey data from US pediatric healthcare providers (n = 1,156) were analyzed. Multivariable logistic regression identified provider characteristics associated with three EHR functionalities: automatically calculating body mass index (BMI) percentile (AUTO), displaying BMI trajectory (DISPLAY), and flagging abnormal BMIs (FLAG). Most providers had EHRs (88%). Of those with EHRs, 90% reporting having AUTO, 62% DISPLAY, and 54% FLAG functionalities. Only provider age was associated with all three functionalities. Compared to providers aged > 54 years, providers < 40 years had greater odds for: AUTO (adjusted odds ratio [aOR], 3.0; 95% confidence interval [CI], 1.58–5.70), DISPLAY (aOR, 2.07; 95% CI, 1.38–3.12), and FLAG (aOR, 1.67; 95% CI, 1.14–2.44). Future investigations can elucidate causes of lower adoption of EHR functions that display growth trajectories and flag abnormal BMIs.
Adolescent ; Body Mass Index ; Decision Support Systems, Clinical ; Delivery of Health Care ; Electronic Health Records ; Health Personnel ; Humans ; Logistic Models ; Odds Ratio ; Pediatric Obesity

OBJECTIVES: Mobile health (m-Health) technologies may provide an appropriate follow-up support service for patient groups with post-treatment conditions. While previous studies have introduced m-Health methods for patient care, a smart system that may provide follow-up communication and decision support remains limited to the management of a few specific types of diseases. This paper introduces an m-Health solution in the current climate of increased demand for electronic information exchange. METHODS: Adopting a novel design science research approach, we developed an innovative solution model for post-treatment follow-up decision support interaction for use by patients and physicians and then evaluated it by using convergent interviewing and focus group methods. RESULTS: The cloud-based solution was positively evaluated as supporting physicians and service providers in providing post-treatment follow-up services. Our framework provides a model as an artifact for extending care service systems to inform better follow-up interaction and decision-making. CONCLUSIONS: The study confirmed the perceived value and utility of the proposed Clinical Decision Support artifact indicating that it is promising and has potential to contribute and facilitate appropriate interactions and support for healthcare professionals for future follow-up operationalization. While the prototype was developed and tested in a developing country context, where the availability of doctors is limited for public healthcare, it was anticipated that the prototype would be user-friendly, easy to use, and suitable for post-treatment follow-up through mobility in remote locations.
Artifacts ; Climate ; Decision Support Systems, Clinical ; Delivery of Health Care ; Developing Countries ; Focus Groups ; Follow-Up Studies ; Humans ; Patient Care ; Telemedicine

PURPOSE: The purpose of this study was to develop predictive models for pressure ulcer incidence using electronic health record (EHR) data and to compare their predictive validity performance indicators with that of the Braden Scale used in the study hospital. METHODS: A retrospective case-control study was conducted in a tertiary teaching hospital in Korea. Data of 202 pressure ulcer patients and 14,705 non-pressure ulcer patients admitted between January 2015 and May 2016 were extracted from the EHRs. Three predictive models for pressure ulcer incidence were developed using logistic regression, Cox proportional hazards regression, and decision tree modeling. The predictive validity performance indicators of the three models were compared with those of the Braden Scale. RESULTS: The logistic regression model was most efficient with a high area under the receiver operating characteristics curve (AUC) estimate of 0.97, followed by the decision tree model (AUC 0.95), Cox proportional hazards regression model (AUC 0.95), and the Braden Scale (AUC 0.82). Decreased mobility was the most significant factor in the logistic regression and Cox proportional hazards models, and the endotracheal tube was the most important factor in the decision tree model. CONCLUSION: Predictive validity performance indicators of the Braden Scale were lower than those of the logistic regression, Cox proportional hazards regression, and decision tree models. The models developed in this study can be used to develop a clinical decision support system that automatically assesses risk for pressure ulcers to aid nurses.
Case-Control Studies ; Data Mining ; Decision Support Systems, Clinical ; Decision Trees ; Electronic Health Records ; Hospitals, Teaching ; Humans ; Incidence ; Korea ; Logistic Models ; Patient Safety ; Pressure Ulcer ; Proportional Hazards Models ; Retrospective Studies ; ROC Curve ; Ulcer

OBJECTIVES: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. METHODS: Based on our search and exclusion criteria, we reviewed 13 studies. RESULTS: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. CONCLUSION: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.
Artificial Intelligence ; Autism Spectrum Disorder ; Autistic Disorder ; Behavior Observation Techniques ; Decision Support Systems, Clinical ; Delivery of Health Care ; Diagnosis ; Mass Screening ; Methods ; Sensitivity and Specificity

Recent advances in new technologies such as artificial intelligence, big data, and virtual reality have led to significant innovations in various industries. Artificial intelligence, particularly in applications using deep learning algorithms, has shown performance superior to that of humans in several contexts. Accordingly, many researchers and companies have tried to apply artificial intelligence to the healthcare system, with applications including image interpretation, voice recognition, clinical decision support, risk prediction, drug discovery, medical robotics, and workflow improvement. However, several important technical, ethical, and social barriers must be overcome, such as overfitting, lack of interpretability, privacy, security, and safety. Doctors should be prepared to play a key role in applying artificial intelligence through the full course of development, validation, clinical performance, and monitoring.
Artificial Intelligence ; Decision Support Systems, Clinical ; Delivery of Health Care ; Drug Discovery ; Humans ; Learning ; Machine Learning ; Privacy ; Robotics ; Voice

OBJECTIVES: The objective of this study was to investigate the clinical decision support (CDS) functions and digitalization of clinical documents of Electronic Medical Record (EMR) systems in Korea. This exploratory study was conducted focusing on current status of EMR systems. METHODS: This study used a nationwide survey on EMR systems conducted from July 25, 2018 to September 30, 2018 in Korea. The unit of analysis was hospitals. Respondents of the survey were mainly medical recorders or staff members in departments of health insurance claims or information technology. This study analyzed data acquired from 132 hospitals that participated in the survey. RESULTS: This study found that approximately 80% of clinical documents were digitalized in both general and small hospitals. The percentages of general and small hospitals with 100% paperless medical charts were 33.7% and 38.2%, respectively. The EMR systems of general hospitals are more likely to have CDS functions of warnings regarding drug dosage, reminders of clinical schedules, and clinical guidelines compared to those of small hospitals; this difference was statistically significant. For the lists of digitalized clinical documents, almost 93% of EMR systems in general hospitals have the inpatient progress note, operation records, and discharge summary notes digitalized. CONCLUSIONS: EMRs are becoming increasingly important. This study found that the functions and digital documentation of EMR systems still have a large gap, which should be improved and made more sophisticated. We hope that the results of this study will contribute to the development of more sophisticated EMR systems.
Appointments and Schedules ; Decision Support Systems, Clinical ; Electronic Health Records ; Health Information Exchange ; Hope ; Hospitals, General ; Humans ; Inpatients ; Insurance, Health ; Korea ; Medical Informatics ; Medical Records ; Medical Records Systems, Computerized ; Surveys and Questionnaires

OBJECTIVE: The aims of this study were to develop a mobile app-based clinical decision support system (CDSS) for implementation of Korean clinical imaging guidelines (K-CIGs) and to assess future developments therein. MATERIALS AND METHODS: K-CIGs were implemented in the form of a web-based application (http://cdss.or.kr/). The app containing K-CIGs consists of 53 information databases, including 10 medical subspecialties and 119 guidelines, developed by the Korean Society of Radiology (KSR) between 2015 and 2017. An email survey consisting of 18 questions on the implementation of K-CIGs and the mobile app-based CDSS was distributed to 43 members of the guideline working group (expert members of the KSR and Korean Academy of Oral and Maxillofacial Radiology) and 23 members of the consultant group (clinical experts belonging to related medical societies) to gauge opinion on the future developmental direction of K-CIGs. RESULTS: The web-based mobile app can be downloaded from the Google Play Store. Detailed information on the grade of recommendation, evidence level, and radiation dose for each imaging modality in the K-CIGs can be accessed via the home page and side menus. In total, 32 of the 66 experts contacted completed the survey (response rate, 45%). Twenty-four of the 32 respondents were from the working group and eight were from the consulting group. Most (93.8%) of the respondents agreed on the need for ongoing development and implementation of K-CIGs. CONCLUSION: This study describes the mobile app-based CDSS designed for implementation of K-CIGs in Korea. The results will allow physicians to have easy access to the K-CIGs and encourage appropriate use of imaging modalities.
Consultants ; Decision Support Systems, Clinical ; Electronic Mail ; Humans ; Korea ; Mobile Applications ; Surveys and Questionnaires