Bridging the gap between evidence and practice in asthma management by developing a mobile-based clinical decision support system for GINA asthma guideline

2020 ◽  
Author(s):  
Marsa Gholamzadeh ◽  
Hamidreza Abtahi ◽  
Shahideh Amini ◽  
Mehrnaz Asadi Gharabaghi
Keyword(s):  
Decision Support ◽  
Expert System ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision Making ◽  
Asthma Management ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers ◽  
Asthma Guideline

Abstract Background Physicians’ compliance with clinical practice guidelines (CPG) remains insufficient. Guideline-based clinical decision support systems (CDSSs) can be beneficial to address this challenge. The principal objective of this research is to translate the Global Initiative for Asthma guideline (GINA) into a mobile-based CDSS to improve its utilization as a clinical decision-making tool.Methods Designing and development of our expert system were conducted in an iterative and stepwise approach by the multidisciplinary expert team. Translating and extracting the embedded knowledge in GINA was done according to the Knowledge to Action framework. Next, extracted knowledge was converted to decision tree models to design the knowledge-base of the desired system. The accuracy and proficiency of the expert system were calculated based on the predefined scenarios. The expert system usability was evaluated by the think-aloud protocol and the GUIDES questionnaire.Results Based on the analysis of the GINA guideline, more than 220 rules and 336 knowledge statements were extracted. Our knowledge-based expert system was devised based on production rules. After modification with feedback from six experts, the system was developed in the Android platform. The overall accuracy and efficiency of our CDSS were 100% and 100%, respectively.Conclusion The ginasthma mobile-based CDSS was developed for android smartphones to improve the adherence of health care providers to GINA guideline with high accuracy and efficiency. Further investigation is needed to evaluate the efficacy of this app in real practice.

Clinical Decision Support Systems

2021 ◽  
pp. 1-7
Author(s):  
Andreas Teufel ◽  
Harald Binder
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Medical Knowledge ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Current Status ◽  
Care Providers

<b><i>Background:</i></b> By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge. <b><i>Summary and Key Messages:</i></b> Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

scholarly journals Evaluation of User-Interface Alert Displays for Clinical Decision Support Systems for Sepsis

2018 ◽  
Vol 38 (4) ◽  
pp. 46-54 ◽  
Author(s):  
Devida Long ◽  
Muge Capan ◽  
Susan Mascioli ◽  
Danielle Weldon ◽  
Ryan Arnold ◽  
...  
Keyword(s):  
Health Care ◽  
Decision Support ◽  
User Interface ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Care Providers

BACKGROUND Hospitals are increasingly turning to clinical decision support systems for sepsis, a life-threatening illness, to provide patient-specific assessments and recommendations to aid in evidence-based clinical decision-making. Lack of guidelines on how to present alerts has impeded optimization of alerts, specifically, effective ways to differentiate alerts while highlighting important pieces of information to create a universal standard for health care providers. OBJECTIVE To gain insight into clinical decision support systems–based alerts, specifically targeting nursing interventions for sepsis, with a focus on behaviors associated with and perceptions of alerts, as well as visual preferences. METHODS An interactive survey to display a novel user interface for clinical decision support systems for sepsis was developed and then administered to members of the nursing staff. RESULTS A total of 43 nurses participated in 2 interactive survey sessions. Participants preferred alerts that were based on an established treatment protocol, were presented in a pop-up format, and addressed the patient’s clinical condition rather than regulatory guidelines. CONCLUSIONS The results can be used in future research to optimize electronic medical record alerting and clinical practice workflow to support the efficient, effective, and timely delivery of high-quality care to patients with sepsis. The research also may advance the knowledge base of what information health care providers want and need to improve the health and safety of their patients.

scholarly journals Direct and Electronic Health Record Access to the Clinical Decision Support for Immunizations in the Minnesota Immunization Information System

2016 ◽  
Vol 8s2 ◽  
pp. BII.S40208
Author(s):  
Sripriya Rajamani ◽  
Aaron Bieringer ◽  
Stephanie Wallerius ◽  
Daniel Jensen ◽  
Tamara Winden ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Population Based ◽  
Direct Access ◽  
Care Providers ◽  
Study Objective ◽  
Vaccination Rates ◽  
Electronic Health

Immunization information systems (IIS) are population-based and confidential computerized systems maintained by public health agencies containing individual data on immunizations from participating health care providers. IIS hold comprehensive vaccination histories given across providers and over time. An important aspect to IIS is the clinical decision support for immunizations (CDSi), consisting of vaccine forecasting algorithms to determine needed immunizations. The study objective was to analyze the CDSi presentation by IIS in Minnesota (Minnesota Immunization Information Connection [MIIC]) through direct access by IIS interface and by access through electronic health records (EHRs) to outline similarities and differences. The immunization data presented were similar across the three systems examined, but with varying ability to integrate data across MIIC and EHR, which impacts immunization data reconciliation. Study findings will lead to better understanding of immunization data display, clinical decision support, and user functionalities with the ultimate goal of promoting IIS CDSi to improve vaccination rates.

Mental Health Clinical Decision Support Exploiting Big Data

2019 ◽  
pp. 160-184 ◽  
Author(s):  
Jan Kalina
Keyword(s):  
Mental Health ◽  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision

The complexity of clinical decision-making is immensely increasing with the advent of big data with a clinical relevance. Clinical decision systems represent useful e-health tools applicable to various tasks within the clinical decision-making process. This chapter is devoted to basic principles of clinical decision support systems and their benefits for healthcare and patient safety. Big data is crucial input for clinical decision support systems and is helpful in the task to find the diagnosis, prognosis, and therapy. Statistical challenges of analyzing big data in psychiatry are overviewed, with a particular interest for psychiatry. Various barriers preventing telemedicine tools from expanding to the field of mental health are discussed. The development of decision support systems is claimed here to play a key role in the development of information-based medicine, particularly in psychiatry. Information technology will be ultimately able to combine various information sources including big data to present and enforce a holistic information-based approach to psychiatric care.

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

2020 ◽  
pp. 167-186
Author(s):  
Steven Walczak
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

scholarly journals Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis (Preprint)

2017 ◽  
Author(s):  
Saif Khairat ◽  
David Marc ◽  
William Crosby ◽  
Ali Al Sanousi
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Critical Review ◽  
Task Analysis ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
User Acceptance ◽  
Care Providers

BACKGROUND Clinical decision support systems (CDSSs) are an integral component of today’s health information technologies. They assist with interpretation, diagnosis, and treatment. A CDSS can be embedded throughout the patient safety continuum providing reminders, recommendations, and alerts to health care providers. Although CDSSs have been shown to reduce medical errors and improve patient outcomes, they have fallen short of their full potential. User acceptance has been identified as one of the potential reasons for this shortfall. OBJECTIVE The purpose of this paper was to conduct a critical review and task analysis of CDSS research and to develop a new framework for CDSS design in order to achieve user acceptance. METHODS A critical review of CDSS papers was conducted with a focus on user acceptance. To gain a greater understanding of the problems associated with CDSS acceptance, we conducted a task analysis to identify and describe the goals, user input, system output, knowledge requirements, and constraints from two different perspectives: the machine (ie, the CDSS engine) and the user (ie, the physician). RESULTS Favorability of CDSSs was based on user acceptance of clinical guidelines, reminders, alerts, and diagnostic suggestions. We propose two models: (1) the user acceptance and system adaptation design model, which includes optimizing CDSS design based on user needs/expectations, and (2) the input-process-output-engagemodel, which reveals to users the processes that govern CDSS outputs. CONCLUSIONS This research demonstrates that the incorporation of the proposed models will improve user acceptance to support the beneficial effects of CDSSs adoption. Ultimately, if a user does not accept technology, this not only poses a threat to the use of the technology but can also pose a threat to the health and well-being of patients.

OP199 From Pilot Studies To System-Wide Innovation: Challenges And Opportunities For Clinical Decision Support Systems (CDSS) Implementation In Australia

2021 ◽  
Vol 37 (S1) ◽  
pp. 5-6
Author(s):  
Mah Laka ◽  
Adriana Milazzo ◽  
Drew Carter ◽  
Tracy Merlin
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Clinical Decision Making ◽  
Support Systems ◽  
Digital Health ◽  
National Level ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Policy Framework

IntroductionThe clinical data is increasing at a considerably higher rate than the capacity of the healthcare system and clinicians to manage this data. Digital tools such as clinical decision support systems (CDSS) provide opportunities for evidence-based patient care by intelligently filtering and presenting the information required for clinical decision making at the point of care. Despite the success of pilot projects, CDSS have had limited implementation in broader health systems. We aimed to identify challenges faced by policymakers for CDSS implementation and to provide policy recommendations.MethodsWe conducted eleven semi-structured interviews with Australian policymakers from state and national committees involved in digital health activities. The data were analyzed using reflexive thematic analysis to identify policy priorities.ResultsOur findings indicate that fragmentation of care processes and structures in the digital health ecosystem is one of the main impediments to delivering coordinated care using CDSS. Five themes for policy action were identified: (i) establishing a shared conceptual framework for user-centered design of CDSS that is aligned with stakeholders’ priorities, (ii) maintaining the right balance between the customization and standardization of systems, (iii) developing mutually agreed semantic interoperability standards at the local, state and national level, allowing generation and exchange of information across the health system without changing its context and meaning, (iv) reorienting organizational structures to build capacity to foster change, and (v) developing collaborative care models to avoid conflicting interests between stakeholders.ConclusionsFindings highlight the importance of developing system-wide guidance to establish a clear vision for CDSS implementation and alignment of organizational processes across all levels of health care. There is a need to build a shared policy framework for modelling the innovative activities such as CDSS implementation across the digital health landscape which minimizes the operational and strategic fragmentation of different organizations.

scholarly journals Economic Value of Data and Analytics for Health Care Providers: Hermeneutic Systematic Literature Review

10.2196/23315 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23315
Author(s):  
Philip von Wedel ◽  
Christian Hagist
Keyword(s):  
Health Care ◽  
Decision Support ◽  
Economic Impact ◽  
Clinical Decision Support ◽  
Health Care Providers ◽  
Clinical Decision ◽  
Economic Benefits ◽  
Economic Effects ◽  
Care Providers ◽  
Advanced Analytics

Background The benefits of data and analytics for health care systems and single providers is an increasingly investigated field in digital health literature. Electronic health records (EHR), for example, can improve quality of care. Emerging analytics tools based on artificial intelligence show the potential to assist physicians in day-to-day workflows. Yet, single health care providers also need information regarding the economic impact when deciding on potential adoption of these tools. Objective This paper examines the question of whether data and analytics provide economic advantages or disadvantages for health care providers. The goal is to provide a comprehensive overview including a variety of technologies beyond computer-based patient records. Ultimately, findings are also intended to determine whether economic barriers for adoption by providers could exist. Methods A systematic literature search of the PubMed and Google Scholar online databases was conducted, following the hermeneutic methodology that encourages iterative search and interpretation cycles. After applying inclusion and exclusion criteria to 165 initially identified studies, 50 were included for qualitative synthesis and topic-based clustering. Results The review identified 5 major technology categories, namely EHRs (n=30), computerized clinical decision support (n=8), advanced analytics (n=5), business analytics (n=5), and telemedicine (n=2). Overall, 62% (31/50) of the reviewed studies indicated a positive economic impact for providers either via direct cost or revenue effects or via indirect efficiency or productivity improvements. When differentiating between categories, however, an ambiguous picture emerged for EHR, whereas analytics technologies like computerized clinical decision support and advanced analytics predominantly showed economic benefits. Conclusions The research question of whether data and analytics create economic benefits for health care providers cannot be answered uniformly. The results indicate ambiguous effects for EHRs, here representing data, and mainly positive effects for the significantly less studied analytics field. The mixed results regarding EHRs can create an economic barrier for adoption by providers. This barrier can translate into a bottleneck to positive economic effects of analytics technologies relying on EHR data. Ultimately, more research on economic effects of technologies other than EHRs is needed to generate a more reliable evidence base.

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