scholarly journals XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study

10.2196/17512 ◽  
2020 ◽  
Vol 4 (10) ◽  
pp. e17512
Author(s):  
Ever Augusto Torres Silva ◽  
Sebastian Uribe ◽  
Jack Smith ◽  
Ivan Felipe Luna Gomez ◽  
Jose Fernando Florez-Arango
Keyword(s):  
Decision Support ◽  
Antenatal Care ◽  
Clinical Decision Support ◽  
Web Application ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structure Design ◽  
Mobile App ◽  
Clinical Workflow

Background Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. Objective This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. Methods We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. Results In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. Conclusions Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

scholarly journals XML Data and Knowledge-Encoding Structure for a Web-Based and Mobile Antenatal Clinical Decision Support System: Development Study (Preprint)

2019 ◽  
Author(s):  
Ever Augusto Torres Silva ◽  
Sebastian Uribe ◽  
Jack Smith ◽  
Ivan Felipe Luna Gomez ◽  
Jose Fernando Florez-Arango
Keyword(s):  
Decision Support ◽  
Antenatal Care ◽  
Clinical Decision Support ◽  
Web Application ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structure Design ◽  
Mobile App ◽  
Clinical Workflow

BACKGROUND Displeasure with the functionality of clinical decision support systems (CDSSs) is considered the primary challenge in CDSS development. A major difficulty in CDSS design is matching the functionality to the desired and actual clinical workflow. Computer-interpretable guidelines (CIGs) are used to formalize medical knowledge in clinical practice guidelines (CPGs) in a computable language. However, existing CIG frameworks require a specific interpreter for each CIG language, hindering the ease of implementation and interoperability. OBJECTIVE This paper aims to describe a different approach to the representation of clinical knowledge and data. We intended to change the clinician’s perception of a CDSS with sufficient expressivity of the representation while maintaining a small communication and software footprint for both a web application and a mobile app. This approach was originally intended to create a readable and minimal syntax for a web CDSS and future mobile app for antenatal care guidelines with improved human-computer interaction and enhanced usability by aligning the system behavior with clinical workflow. METHODS We designed and implemented an architecture design for our CDSS, which uses the model-view-controller (MVC) architecture and a knowledge engine in the MVC architecture based on XML. The knowledge engine design also integrated the requirement of matching clinical care workflow that was desired in the CDSS. For this component of the design task, we used a work ontology analysis of the CPGs for antenatal care in our particular target clinical settings. RESULTS In comparison to other common CIGs used for CDSSs, our XML approach can be used to take advantage of the flexible format of XML to facilitate the electronic sharing of structured data. More importantly, we can take advantage of its flexibility to standardize CIG structure design in a low-level specification language that is ubiquitous, universal, computationally efficient, integrable with web technologies, and human readable. CONCLUSIONS Our knowledge representation framework incorporates fundamental elements of other CIGs used in CDSSs in medicine and proved adequate to encode a number of antenatal health care CPGs and their associated clinical workflows. The framework appears general enough to be used with other CPGs in medicine. XML proved to be a language expressive enough to describe planning problems in a computable form and restrictive and expressive enough to implement in a clinical system. It can also be effective for mobile apps, where intermittent communication requires a small footprint and an autonomous app. This approach can be used to incorporate overlapping capabilities of more specialized CIGs in medicine.

scholarly journals Assessing the Safety of Custom Web-Based Clinical Decision Support Systems in Electronic Health Records: A Case Study

2019 ◽  
Vol 10 (02) ◽  
pp. 237-246 ◽  
Author(s):  
Jeritt Thayer ◽  
Jeffrey Miller ◽  
Alexander Fiks ◽  
Linda Tague ◽  
Robert Grundmeier
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
High Volume ◽  
Clinical Decision ◽  
Clinical Workflow ◽  
System Integrity ◽  
Electronic Health

Background With the widespread adoption of vendor-supplied electronic health record (EHR) systems, clinical decision support (CDS) customization efforts beyond those anticipated by the vendor may require the use of technologies external to the EHR such as web services. Pursuing such customizations, however, is not without risk. Validating the expected behavior of a customized CDS system in the high-volume, complex environment of the live EHR is a challenging problem. Objective This article identifies technology failures that impacted clinical care related to web service-based advanced custom CDS systems embedded in the complex sociotechnical context of a production EHR. Methods In an academic health system’s primary care network, we performed an inventory of incidents between January 1, 2008 and December 31, 2016 related to a customized CDS system and performed a targeted review of changes in the CDS source code. Additional feedback on the root cause of individual incidents was obtained through interviews with members of the CDS project teams. Results We identified five CDS malfunctions that impaired clinical workflow. The mechanisms for these failures are mapped to four characteristics of well-behaved applications: (1) system integrity; (2) data integrity; (3) reliability; and (4) scalability. Over the 9-year period, two malfunctions of the customized CDS significantly impaired clinical workflow for a total of 5 hours. Lesser impacts—loss of individual features with straightforward workarounds—arose from three malfunctions, which affected users on 53 days. Discussion Advanced customization of EHRs for the purpose of CDS can present significant risks to clinical workflow. Conclusion This case study highlights that advanced customization of CDS within a commercial EHR may support care for complex patient populations, but ongoing monitoring and support is required to ensure its safe use.

scholarly journals Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges

2017 ◽  
Vol 25 (3) ◽  
pp. 1091-1104 ◽  
Author(s):  
Mirza Mansoor Baig ◽  
Hamid GholamHosseini ◽  
Aasia A Moqeem ◽  
Farhaan Mirza ◽  
Maria Lindén
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Hospital Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Smartphone Applications ◽  
Clinical Workflow ◽  
Healthcare Applications ◽  
Wide Range

Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians’ acceptability, as well as the low impact on the medical professionals’ decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.

scholarly journals AI in Healthcare practice: opportunities and challenges for clinical decision support systems

2019 ◽  
Author(s):  
David R. Millen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Language Processing ◽  
Support Systems ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Lessons Learned ◽  
Healthcare Applications

In the past few years there has been great optimism about the potential benefits of incorporating AI (cognitive) capabilities into healthcare products and services. Indeed, progress in Natural Language Processing (NLP) has made electronic health records both more accessible and comprehensible, advances in image processing algorithms has helped to early identify tumors, and large datasets with new discovery services can help with breakthrough insights in life sciences and drug discovery. Importantly, new AI-based solutions are embedded in the sociotechnical systems of clinical care and within complex regulatory environments and globally diverse cultural frameworks. In this talk, I will present several case studies of novel AI – based healthcare applications that have been introduced in recent years and share lessons learned along the way. Particular focus will be on design research challenges for healthcare products, including understanding complex workflows within clinical settings and highly specialized and diverse mental modals, and understanding multiple stakeholders and interdependent participants. Design considerations and emerging opportunities for AI-based clinical decision support systems will also be shared.

scholarly journals A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

2020 ◽  
Vol 27 (12) ◽  
pp. 1968-1976
Author(s):  
Anna Ostropolets ◽  
Linying Zhang ◽  
George Hripcsak
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Scoping Review ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
The United States ◽  
Quality Outcomes ◽  
New Knowledge

Abstract Objective A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. Materials and Methods PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles’ references. The details of design, implementation and evaluation of included CDSSs were extracted. Results Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. Conclusions We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

Using Social Networks to Obtain Medical Diagnosis

2013 ◽  
pp. 306-320 ◽  
Author(s):  
Gandhi Samuel Hernández-Chan ◽  
Alejandro Rodríguez-González ◽  
Ricardo Colomo-Palacios
Keyword(s):  
Social Networks ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Web Sites ◽  
Medical Diagnosis ◽  
Web Application ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
General Information ◽  
Social Web

Web 2.0 Applications have gained much power and usability in the last years. A particular case is medicine Web sites, like forums, wikis, and others. In most cases, these sites provide general information without making contact with the physicians. On other side, the CDSS (Clinical Decision Support Systems) are very useful applications, and many of them are ontology based. In this chapter, the authors propose a social Web application that allows patients to make contact with their physicians through a CDSS list of signs. This application combines social Web, CDSS, and Web services.

scholarly journals Unveiling the Black Box of Diagnostic and Clinical Decision Support Systems for Antenatal Care: Realist Evaluation

10.2196/11468 ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. e11468 ◽  
Author(s):  
Ibukun-Oluwa Omolade Abejirinde ◽  
Marjolein Zweekhorst ◽  
Azucena Bardají ◽  
Rudolf Abugnaba-Abanga ◽  
Norbert Apentibadek ◽  
...  
Keyword(s):  
Decision Support ◽  
Antenatal Care ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Realist Evaluation ◽  
Black Box

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.

Framework for Evaluating Appropriateness of Clinical Decision Support Systems Alert in Supporting Clinical Workflow (Preprint)

2019 ◽  
Author(s):  
Maryati Yusof ◽  
Olufisayo Olakotan ◽  
Sharifa Ezat Puteh
Keyword(s):  
Patient Safety ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Process Analysis ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Structured Interview ◽  
Clinical Workflow

BACKGROUND Clinical decision support systems (CDSS) generate excessive alerts that lead to alert fatigue and override. Alert overrides have resulted in patient death and medical errors. CDSS with its alert function can also disrupt clinical workflow. Therefore, inefficient clinical processes that contribute to the misfit between CDSS alert and workflows must be evaluated. Evaluation findings can serve as input to the process redesign. Redesigning clinical processes can enhance CDSS alert appropriateness and subsequently improve patient safety. OBJECTIVE The paper presents a proposed framework for evaluating CDSS appropriateness in supporting clinical workflow. The paper also discusses the preliminary results of the framework validation. METHODS A subjectivist, qualitative case study evaluation was conducted at a 620-bed public teaching hospital using semi-structured interview, observation, and document analysis methods to investigate the features and functions of alert appropriateness and workflow related issues. The current state map for medication prescription process was also modelled to identify problems pertinent to alert appropriateness. RESULTS The main findings showed that CDSS is not well designed to fit into clinical workflow due to several influencing factors including technology (system design and implementation), human (information analysis and acquisition), organization (clinical tasks, organizational policies & procedures) and process (process analysis, redesign, implementation, monitoring and improvement) impeding the use of CDSS with its alert function. CONCLUSIONS CDSS alerts should be integrated into clinical workflows due to their potentials in enhancing patient safety. Process improvement methods such as Lean can be used to enhance the appropriateness of CDSS alerts by identifying inefficient clinical processes that impede the fit of CDSS alerts with clinical workflow. The validated framework can be used to address alert and workflow related problems in any healthcare setting.

scholarly journals Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System

2017 ◽  
Vol 12 (2) ◽  
pp. 381-388 ◽  
Author(s):  
Nicholas Conway ◽  
Karen A. Adamson ◽  
Scott G. Cunningham ◽  
Alistair Emslie Smith ◽  
Peter Nyberg ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Care Delivery ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Workflow ◽  
Long Term Conditions ◽  
Process Measures ◽  
Patient Questionnaires

Background: Automated clinical decision support systems (CDSS) are associated with improvements in health care delivery to those with long-term conditions, including diabetes. A CDSS was introduced to two Scottish regions (combined diabetes population ~30 000) via a national diabetes electronic health record. This study aims to describe users’ reactions to the CDSS and to quantify impact on clinical processes and outcomes over two improvement cycles: December 2013 to February 2014 and August 2014 to November 2014. Methods: Feedback was sought via patient questionnaires, health care professional (HCP) focus groups, and questionnaires. Multivariable regression was used to analyze HCP SCI-Diabetes usage (with respect to CDSS message presence/absence) and case-control comparison of clinical processes/outcomes. Cases were patients whose HCP received a CDSS messages during the study period. Closely matched controls were selected from regions outside the study, following similar clinical practice (without CDSS). Clinical process measures were screening rates for diabetes-related complications. Clinical outcomes included HbA1c at 1 year. Results: The CDSS had no adverse impact on consultations. HCPs were generally positive toward CDSS and used it within normal clinical workflow. CDSS messages were generated for 5692 cases, matched to 10 667 controls. Following clinic, the probability of patients being appropriately screened for complications more than doubled for most measures. Mean HbA1c improved in cases and controls but more so in cases (–2.3 mmol/mol [–0.2%] versus –1.1 [–0.1%], P = .003). Discussion and Conclusions: The CDSS was well received; associated with improved efficiencies in working practices; and large improvements in guideline adherence. These evidence-based, early interventions can significantly reduce costly and devastating complications.

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