scholarly journals Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China (Preprint)

2020 ◽  
Author(s):  
Mengchun Gong ◽  
Li Liu ◽  
Xin Sun ◽  
Yue Yang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mobile Devices ◽  
Clinical Decision Support ◽  
Syndromic Surveillance ◽  
Improve Patient Care ◽  
Clinical Effectiveness ◽  
Clinical Decision ◽  
Effective Control ◽  
Multiple Sources ◽  
Related Data

BACKGROUND Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. OBJECTIVE The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. METHODS The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. RESULTS HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. CONCLUSIONS The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

scholarly journals Cloud-Based System for Effective Surveillance and Control of COVID-19: Useful Experiences From Hubei, China

10.2196/18948 ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. e18948 ◽  
Author(s):  
Mengchun Gong ◽  
Li Liu ◽  
Xin Sun ◽  
Yue Yang ◽  
Shuang Wang ◽  
...  
Keyword(s):  
Decision Support ◽  
Mobile Devices ◽  
Clinical Decision Support ◽  
Syndromic Surveillance ◽  
Improve Patient Care ◽  
Clinical Effectiveness ◽  
Clinical Decision ◽  
Effective Control ◽  
Multiple Sources ◽  
Related Data

Background Coronavirus disease (COVID-19) has been an unprecedented challenge to the global health care system. Tools that can improve the focus of surveillance efforts and clinical decision support are of paramount importance. Objective The aim of this study was to illustrate how new medical informatics technologies may enable effective control of the pandemic through the development and successful 72-hour deployment of the Honghu Hybrid System (HHS) for COVID-19 in the city of Honghu in Hubei, China. Methods The HHS was designed for the collection, integration, standardization, and analysis of COVID-19-related data from multiple sources, which includes a case reporting system, diagnostic labs, electronic medical records, and social media on mobile devices. Results HHS supports four main features: syndromic surveillance on mobile devices, policy-making decision support, clinical decision support and prioritization of resources, and follow-up of discharged patients. The syndromic surveillance component in HHS covered over 95% of the population of over 900,000 people and provided near real time evidence for the control of epidemic emergencies. The clinical decision support component in HHS was also provided to improve patient care and prioritize the limited medical resources. However, the statistical methods still require further evaluations to confirm clinical effectiveness and appropriateness of disposition assigned in this study, which warrants further investigation. Conclusions The facilitating factors and challenges are discussed to provide useful insights to other cities to build suitable solutions based on cloud technologies. The HHS for COVID-19 was shown to be feasible and effective in this real-world field study, and has the potential to be migrated.

scholarly journals Development and Implementation of a Subcutaneous Insulin Clinical Decision Support Tool for Hospitalized Patients

2018 ◽  
Vol 13 (3) ◽  
pp. 522-532 ◽  
Author(s):  
Nestoras Mathioudakis ◽  
Rebecca Jeun ◽  
Gerald Godwin ◽  
Annette Perschke ◽  
Swaytha Yalamanchi ◽  
...  
Keyword(s):  
Decision Support ◽  
Critically Ill ◽  
Clinical Decision Support ◽  
Clinical Effectiveness ◽  
Clinical Decision ◽  
Healthcare Research ◽  
Hospitalized Patients ◽  
Insulin Regimen ◽  
Subcutaneous Insulin ◽  
Support Tool

Background: Insulin is one of the highest risk medications used in hospitalized patients. Multiple complex factors must be considered in determining a safe and effective insulin regimen. We sought to develop a computerized clinical decision support (CDS) tool to assist hospital-based clinicians in insulin management. Methods: Adapting existing clinical practice guidelines for inpatient glucose management, a design team selected, configured, and implemented a CDS tool to guide subcutaneous insulin dosing in non–critically ill hospitalized patients at two academic medical centers that use the EpicCare® electronic medical record (EMR). The Agency for Healthcare Research and Quality (AHRQ) best practices in CDS design and implementation were followed. Results: A CDS tool was developed in the form of an EpicCare SmartForm, which generates an insulin regimen by integrating information about the patient’s body weight, diabetes type, home and hospital insulin requirements, and nutritional status. Total daily recommended insulin doses are distributed into respective basal and nutritional doses with a tailored correctional insulin scale. Preimplementation, several approaches were used to communicate this new tool to clinicians, including emails, lectures, and videos. Postimplementation, a support team was available to address user technical issues. Feedback from stakeholders has been used to continuously refine the tool. Inclusion of the programming in the EMR vendor’s community library has allowed dissemination of the tool outside our institution. Conclusions: We have developed an EMR-based tool to guide SQ insulin dosing in non–critically ill hospitalized patients. Further studies are needed to evaluate adoption and clinical effectiveness of this intervention.

scholarly journals Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017

2018 ◽  
Vol 27 (01) ◽  
pp. 016-024 ◽  
Author(s):  
Prabhu Shankar ◽  
Nick Anderson
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Policy Development ◽  
Public Awareness ◽  
Clinical Decision ◽  
Data Sources ◽  
Computational Techniques ◽  
Scientific Conference ◽  
Multiple Sources ◽  
Commercial Applications

Introduction: Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. Objective: Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. Methods: Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. Results: CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related “Big Data” sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of “app” ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. Conclusion: Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.

scholarly journals Clinical Decision Support May Link Multiple Domains to Improve Patient Care: Viewpoint

10.2196/20265 ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. e20265
Author(s):  
David Kao ◽  
Cynthia Larson ◽  
Dana Fletcher ◽  
Kris Stegner
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Team Approach ◽  
Electronic Health Record Data ◽  
Target User ◽  
Precision Level ◽  
Improve Patient

Integrating clinical decision support (CDS) across the continuum of population-, encounter-, and precision-level care domains may improve hospital and clinic workflow efficiency. Due to the diversity and volume of electronic health record data, complexity of medical and operational knowledge, and specifics of target user workflows, the development and implementation of comprehensive CDS is challenging. Additionally, many providers have an incomplete understanding of the full capabilities of current CDS to potentially improve the quality and efficiency of care delivery. These varied requirements necessitate a multidisciplinary team approach to CDS development for successful integration. Here, we present a practical overview of current and evolving applications of CDS approaches in a large academic setting and discuss the successes and challenges. We demonstrate that implementing CDS tools in the context of linked population-, encounter-, and precision-level care provides an opportunity to integrate complex algorithms at each level into a unified mechanism to improve patient management.

scholarly journals The Value of Monitoring Clinical Decision Support Interventions

2018 ◽  
Vol 09 (01) ◽  
pp. 163-173 ◽  
Author(s):  
Eileen Yoshida ◽  
Shirley Fei ◽  
Karen Bavuso ◽  
Charles Lagor ◽  
Saverio Maviglia
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
User Feedback ◽  
Detection Algorithms ◽  
Robust Testing ◽  
Improve Patient ◽  
Careful Design ◽  
Systematic Program

Background Well-functioning clinical decision support (CDS) can facilitate provider workflow, improve patient care, promote better outcomes, and reduce costs. However, poorly functioning CDS may lead to alert fatigue, cause providers to ignore important CDS interventions, and increase provider dissatisfaction. Objective The purpose of this article is to describe one institution's experience in implementing a program to create and maintain properly functioning CDS by systematically monitoring CDS firing rates and patterns. Methods Four types of CDS monitoring activities were implemented as part of the CDS lifecycle. One type of monitoring occurs prior to releasing active CDS, while the other types occur at different points after CDS activation. Results Two hundred and forty-eight CDS interventions were monitored over a 2-year period. The rate of detecting a malfunction or significant opportunity for improvement was 37% during preactivation and 18% during immediate postactivation monitoring. Monitoring also informed the process of responding to user feedback about alerts. Finally, an automated alert detection tool identified 128 instances of alert pattern change over the same period. A subset of cases was evaluated by knowledge engineers to identify true and false positives, the results of which were used to optimize the tool's pattern detection algorithms. Conclusion CDS monitoring can identify malfunctions and/or significant improvement opportunities even after careful design and robust testing. CDS monitoring provides information when responding to user feedback. Ongoing, continuous, and automated monitoring can detect malfunctions in real time, before users report problems. Therefore, CDS monitoring should be part of any systematic program of implementing and maintaining CDS.

scholarly journals Usability Evaluation of a Clinical Decision Support System for Geriatric ED Pain Treatment

2016 ◽  
Vol 07 (01) ◽  
pp. 128-142 ◽  
Author(s):  
Min Kim ◽  
Frederick Thum ◽  
Laura Rivera ◽  
Rosemary Beato ◽  
Carolyn Song ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Video Analysis ◽  
Pain Treatment ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Improve Patient Care ◽  
Clinical Decision ◽  
Usability Evaluation ◽  
Pain Care

SummaryOlder adults are at risk for inadequate emergency department (ED) pain care. Unrelieved acute pain is associated with poor outcomes. Clinical decision support systems (CDSS) hold promise to improve patient care, but CDSS quality varies widely, particularly when usability evaluation is not employed.To conduct an iterative usability and redesign process of a novel geriatric abdominal pain care CDSS. We hypothesized this process would result in the creation of more usable and favorable pain care interventions.Thirteen emergency physicians familiar with the Electronic Health Record (EHR) in use at the study site were recruited. Over a 10-week period, 17 1-hour usability test sessions were conducted across 3 rounds of testing. Participants were given 3 patient scenarios and provided simulated clinical care using the EHR, while interacting with the CDSS interventions. Quantitative System Usability Scores (SUS), favorability scores and qualitative narrative feedback were collected for each session. Using a multi-step review process by an interdisciplinary team, positive and negative usability issues in effectiveness, efficiency, and satisfaction were considered, prioritized and incorporated in the iterative redesign process of the CDSS. Video analysis was used to determine the appropriateness of the CDS appearances during simulated clinical care.Over the 3 rounds of usability evaluations and subsequent redesign processes, mean SUS progressively improved from 74.8 to 81.2 to 88.9; mean favorability scores improved from 3.23 to 4.29 (1 worst, 5 best). Video analysis revealed that, in the course of the iterative redesign processes, rates of physicians’ acknowledgment of CDS interventions increased, however most rates of desired actions by physicians (such as more frequent pain score updates) decreased.The iterative usability redesign process was instrumental in improving the usability of the CDSS; if implemented in practice, it could improve geriatric pain care. The usability evaluation process led to improved acknowledgement and favorability. Incorporating usability testing when designing CDSS interventions for studies may be effective to enhance clinician use.

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