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Wiharto, Wiharto. "CLINICAL DECISION SUPPORT SYSTEMS THEORY AND PRACTICE." Jurnal Teknosains 7, no. 2 (2018): 148. http://dx.doi.org/10.22146/teknosains.38641.
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Clinical Decision Support Systems Theory and Practice, adalah buku teks dalam seri Health Informatics yang membahas tentang sistem pendukung keputusan klinis. Pada buku ini terbagi menjadi dua kelompok bahasan, Pertama membahas tentang dasar dalam mengembangkan sistem Clinical Decision Supprot System (CDSS) dan evaluasinya. Kedua, Aplikasi CDSS dalam praktik klinis. Bahasan tersebut menjadikan pembaca dapat memperoleh gambaran tentang dasar-dasar yang diperlukan dalam membangun dan mengaplikasikan CDSS dalam praktik klinis. Secara rinci buku tersebut terbagi menjadi 11 Bab, yang dapat dikelompokkan menjadi 7 Bab tentang konsep dalam pengembangan CDSS, dan 4 Bab tentang aplikasi CDSS dalam praktik klinis.
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Conway, Nicholas, Karen A. Adamson, Scott G. Cunningham, et al. "Decision Support for Diabetes in Scotland: Implementation and Evaluation of a Clinical Decision Support System." Journal of Diabetes Science and Technology 12, no. 2 (2017): 381–88. http://dx.doi.org/10.1177/1932296817729489.
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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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Lu, Sheng-Chieh, Rebecca J. Brown, and Martin Michalowski. "A Clinical Decision Support System Design Framework for Nursing Practice." ACI Open 05, no. 02 (2021): e84-e93. http://dx.doi.org/10.1055/s-0041-1736470.
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Abstract Background As nurses increasingly engage in decision-making for patients, a unique opportunity exists to translate research into practice using clinical decision support systems (CDSSs). While research has shown that CDSS has led to improvements in patient outcomes and nursing workflow, the success rate of CDSS implementation in nursing is low. Further, the majority of CDSS for nursing are not designed to support the care of patients with comorbidity. Objectives The aim of the study is to conceptualize an evidence-based CDSS supporting complex patient care for nursing. Methods We conceptualized the CDSS through extracting scientific findings of CDSS design and development. To describe the CDSS, we developed a conceptual framework comprising the key components of the CDSS and the relationships between the components. We instantiated the framework in the context of a hypothetical clinical case. Results We present the conceptualized CDSS with a framework comprising six interrelated components and demonstrate how each component is implemented via a hypothetical clinical case. Conclusion The proposed framework provides a common architecture for CDSS development and bridges CDSS research findings and development. Next research steps include (1) working with clinical nurses to identify their knowledge resources for a particular disease to better articulate the knowledge base needed by a CDSS, (2) develop and deploy a CDSS in practice using the framework, and (3) evaluate the CDSS in the context of nursing care.
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Sharmi, Meenakshi, and Himanshu Aggarwal. "Methodologies of Legacy Clinical Decision Support System." International Journal of Computers in Clinical Practice 2, no. 2 (2017): 20–37. http://dx.doi.org/10.4018/ijccp.2017070102.
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Information technology playing a prominent role in the field of medical by incorporating the clinical decision support system (CDSS) in their routine practices. CDSS is a computer based interactive program to assist the physician to make the right decision at right time. Nowadays, clinical decision support systems are a dynamic research area in the field of computers, but the lack of understanding, as well as functions of the system, make adoption slow by physicians and patients. The literature review of this article focuses on the overview of legacy CDSS, the kind of methodologies and classifiers employed to prepare such a decision support system using a non-technical approach to the physician and the strategy-makers. This article provides understanding of the clinical decision support along with the gateway to physician, and to policy-makers to develop and deploy decision support systems as a healthcare service to make the quick, agile and right decision. Future directions to handle the uncertainties along with the challenges of clinical decision support systems are also enlightened in this study.
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António Ferreira Rodrigues Nogueira dos Santos, Marco, Hans Tygesen, Henrik Eriksson, and Johan Herlitz. "Clinical decision support system (CDSS) – effects on care quality." International Journal of Health Care Quality Assurance 27, no. 8 (2014): 707–18. http://dx.doi.org/10.1108/ijhcqa-01-2014-0010.
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Purpose – Despite their efficacy, some recommended therapies are underused. The purpose of this paper is to describe clinical decision support system (CDSS) development and its impact on clinical guideline adherence. Design/methodology/approach – A new CDSS was developed and introduced in a cardiac intensive care unit (CICU) in 2003, which provided physicians with patient-tailored reminders and permitted data export from electronic patient records into a national quality registry. To evaluate CDSS effects in the CICU, process indicators were compared to a control group using registry data. All CICUs were in the same region and only patients with acute coronary syndrome were included. Findings – CDSS introduction was associated with increases in guideline adherence, which ranged from 16 to 35 per cent, depending on the therapy. Statistically significant associations between guideline adherence and CDSS use remained over the five-year period after its introduction. During the same period, no relapses occurred in the intervention CICU. Practical implications – Guideline adherence and healthcare quality can be enhanced using CDSS. This study suggests that practitioners should turn to CDSS to improve healthcare quality. Originality/value – This paper describes and evaluates an intervention that successfully increased guideline adherence, which improved healthcare quality when the intervention CICU was compared to the control group.
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Kinney, William C. "Web-Based Clinical Decision Support System for Triage of Vestibular Patients." Otolaryngology–Head and Neck Surgery 128, no. 1 (2003): 48–53. http://dx.doi.org/10.1067/mhn.2003.33.
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OBJECTIVES: We sought to use a clinical decision support system (CDSS) to save costs and to improve scheduling of vestibular patients in an otolaryngology clinic. STUDY DESIGN AND SETTING: We conducted a concurrent review of 50 vestibular patients scheduled in the University of Missouri otolaryngology clinic with or without testing based on the outcome of a CDSS. The CDSS was implemented using Web-based technology. Charges incurred by the health care system through tests determined by the CDSS were compared with those incurred using the standard procedure of ordering hearing tests and electronystagmography for all patients. RESULTS: Thirty-nine tests were prescheduled using the CDSS. Twenty-five additional tests were ordered after the visit. The CDSS resulted in savings of $37,904.00 in charges to the health care system. The CDSS showed high specificity and variable sensitivity. CONCLUSION: A Web-based CDSS can be used to better manage and coordinate patient encounters. SIGNIFICANCE: One important reason to use a CDSS in health care management is to lower costs.
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Rahim, Nur Raidah, Sharifalillah Nordin, and Rosma Mohd Dom. "A Clinical Decision Support System based on Ontology and Causal Reasoning Models." Jurnal Intelek 14, no. 2 (2019): 187–97. http://dx.doi.org/10.24191/ji.v14i2.234.
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 Clinical decision support system (CDSS) is promising in assisting physicians for improving decision-making process and facilitates healthcare services. In medicine, causality has become the main concern
 throughout healthcare and decision-making. Causality is necessary for understanding all structures ofscientific reasoning and for providing a coherent and sufficient explanation for any event. However, thereare lack of existing CDSS that provide causal reasoning for the presented outcomes or decisions. Theseare necessary for showing reliability of the outcomes, and helping the physicians in making properdecisions. In this study, an ontology-based CDSS model is developed based on several key concepts andfeatures of causality and graphical modeling techniques. For the evaluation process, the Pellet reasoneris used to evaluate the consistency of the developed ontology model. In addition, an evaluation toolknown as Ontology Pitfall Scanner is used for validating the ontology model through pitfalls detection.The developed ontology-based CDSS model has potentials to be applied in clinical practice and helpingthe physicians in decision-making process.
 Keywords: clinical decision support system, ontology, causality, causal reasoning, graphical modeling
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Hak, Francini, Tiago Guimarães, and Manuel Santos. "Towards effective clinical decision support systems: A systematic review." PLOS ONE 17, no. 8 (2022): e0272846. http://dx.doi.org/10.1371/journal.pone.0272846.
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Background Clinical Decision Support Systems (CDSS) are used to assist the decision-making process in the healthcare field. Developing an effective CDSS is an arduous task that can take advantage from prior assessment of the most promising theories, techniques and methods used at the present time. Objective To identify the features of Clinical Decision Support Systems and provide an analysis of their effectiveness. Thus, two research questions were formulated: RQ1—What are the most common trend characteristics in a CDSS? RQ2—What is the maturity level of the CDSS based on the decision-making theory proposed by Simon? Methods AIS e-library, Decision Support Systems journal, Nature, PlosOne and PubMed were selected as information sources to conduct this systematic literature review. Studies from 2000 to 2020 were chosen covering search terms in CDSS, selected according to defined eligibility criteria. The data were extracted and managed in a worksheet, based on the defined criteria. PRISMA statements were used to report the systematic review. Results The outcomes showed that rule-based module was the most used approach regarding knowledge management and representation. The most common technological feature adopted by the CDSS were the recommendations and suggestions. 19,23% of studies adopt the type of system as a web-based application, and 51,92% are standalone CDSS. Temporal evolution was also possible to visualize. This study contributed to the development of a Maturity Staging Model, where it was possible to verify that most CDSS do not exceed level 2 of maturity. Conclusion The trend characteristics addressed in the revised CDSS were identified, compared to the four predefined groups. A maturity stage model was developed based on Simon’s decision-making theory, allowing to assess the level of maturity of the most common features of the CDSS. With the application of the model, it was noticed that the phases of choice and implementation are underrepresented. This constitutes the main gap in the development of an effective CDSS.
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Farion, K., W. Michalowski, D. O’Sullivan, S. Rubin, D. Weiss, and S. Wilk. "Clinical Decision Support System for Point of Care Use." Methods of Information in Medicine 48, no. 04 (2009): 381–90. http://dx.doi.org/10.3414/me0574.
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Summary Objectives: The objective of this research was to design a clinical decision support system (CDSS) that supports heterogeneous clinical decision problems and runs on multiple computing platforms. Meeting this objective required a novel design to create an extendable and easy to maintain clinical CDSS for point of care support. The proposed solution was evaluated in a proof of concept implementation. Methods: Based on our earlier research with the design of a mobile CDSS for emergency triage we used ontology-driven design to represent essential components of a CDSS. Models of clinical decision problems were derived from the ontology and they were processed into executable applications during runtime. This allowed scaling applications’ functionality to the capabilities of computing platforms. A prototype of the system was implemented using the extended client-server architecture and Web services to distribute the functions of the system and to make it operational in limited connectivity conditions. Results: The proposed design provided a common framework that facilitated development of diversified clinical applications running seamlessly on a variety of computing platforms. It was prototyped for two clinical decision problems and settings (triage of acute pain in the emergency department and postoperative management of radical pros-tatectomy on the hospital ward) and implemented on two computing platforms – desktop and handheld computers. Conclusions: The requirement of the CDSS heterogeneity was satisfied with ontology-driven design. Processing of application models described with the help of ontological models allowed having a complex system running on multiple computing platforms with different capabilities. Finally, separation of models and runtime components contributed to improved extensibility and maintainability of the system.
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Stone, Erin G. "Unintended adverse consequences of a clinical decision support system: two cases." Journal of the American Medical Informatics Association 25, no. 5 (2017): 564–67. http://dx.doi.org/10.1093/jamia/ocx096.
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Abstract Many institutions have implemented clinical decision support systems (CDSSs). While CDSS research papers have focused on benefits of these systems, there is a smaller body of literature showing that CDSSs may also produce unintended adverse consequences (UACs). Detailed here are 2 cases of UACs resulting from a CDSS. Both of these cases were related to external systems that fed data into the CDSS. In the first case, lack of knowledge of data categorization in an external pharmacy system produced a UAC; in the second case, the change of a clinical laboratory instrument produced the UAC. CDSSs rely on data from many external systems. These systems are dynamic and may have changes in hardware, software, vendors, or processes. Such changes can affect the accuracy of CDSSs. These cases point to the need for the CDSS team to be familiar with these external systems. This team (manager and alert builders) should include members in specific clinical specialties with deep knowledge of these external systems.
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Alieva, A. S., E. I. Pavlyuk, E. M. Alborova, et al. "Clinical decision support system for lipid metabolism disorders: relevance and potential." Russian Journal of Cardiology 26, no. 6 (2021): 4539. http://dx.doi.org/10.15829/1560-4071-2021-4539.
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Current guidelines for the management of patients with dyslipidemia are well known and easily accessible. Despite this, according to research data based on actual clinical practice, selection of optimal tactics for managing patients with dyslipidemia often causes difficulties and leads to a failure to achieve the target levels. Tools such as clinical decision support system (CDSS) can help clinicians follow current clinical guidelines, taking into account the diversity of phenotypic profiles and side effects. This review highlights the effectiveness of CDSS implementation in medical practice as a means for making decisions in managing patients with dyslipidemia, as well as presents the algorithm for CDSS for lipid metabolism disorders created by specialists of the Almazov National Medical Research Center and the University of Milan.
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Meek, J., and L. Fathauer. "Initial implementation and evaluation of a Hepatitis C treatment clinical decision support system (CDSS)." Applied Clinical Informatics 03, no. 03 (2012): 337–48. http://dx.doi.org/10.4338/aci-2012-04-ra-0012.
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SummaryBackground: Clinician compliance with clinical guidelines in the treatment of patients with Hepatitis C (HCV) has been reported to be as low as 18.5%. Treatment is complex and patient compliance is often inconsistent thus, active clinician surveillance and support is essential to successful outcomes. A clinical decision support system (CDSS) embedded within an electronic health record can provide reminders, summarize key data, and facilitate coordination of care. To date, the literature is bereft of information describing the implementation and evaluation of a CDSS to support HCV treatment.Objective: The purpose of this case report is to describe the design, implementation, and initial evaluation of an HCV-specific CDSS while piloting data collection metrics and methods to be used in a larger study across multiple practices.Methods: The case report describes the design and implementation processes with preliminary reporting on impact of the CDSS on quality indicator completion by comparing the pre-CDSS group to the post-CDSS group.Results: The CDSS was successfully designed and implemented using an iterative, collaborative process. Pilot testing of the clinical outcomes of the CDSS revealed high rates of quality indicator completion in both the pre- and post-CDSS; although the post-CDSS group received a higher frequency of reminders (4.25 per patient) than the pre-CDSS group (.25 per patient).Conclusions: This case report documents the processes used to successfully design and implement an HCV CDSS. While the small sample size precludes generalizability of findings, results did positively demonstrate the feasibility of comparing quality indicator completion rates pre-CDSS and post-CDSS. It is recommended that future studies include a larger sample size across multiple providers with expanded outcomes measures related to patient outcomes, staff satisfaction with the CDSS, and time studies to evaluate efficiency and cost effectiveness of the CDSS.
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Lau, A. Y. S., G. Tsafnat, V. Sintchenko, F. Magrabi, and E. Coiera. "The Changing Nature of Clinical Decision Support Systems: a Focus on Consumers, Genomics, Public Health and Decision Safety." Yearbook of Medical Informatics 18, no. 01 (2009): 84–95. http://dx.doi.org/10.1055/s-0038-1638644.
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Summary Objectives To review the recent research literature in clinical decision support systems (CDSS). Methods A review of recent literature was undertaken, focussing on CDSS evaluation, consumers and public health, the impact of translational bioinformatics on CDSS design, and CDSS safety. Results In recent years, researchers have concentrated much less on the development of decision technologies, and have focussed more on the impact of CDSS in the clinical world. Recent work highlights that traditional process measures of CDSS effectiveness, such as document relevance are poor proxy measures for decision outcomes. Measuring the dynamics of decision making, for example via decision velocity, may produce a more accurate picture of effectiveness. Another trend is the broadening of user base for CDSS beyond front line clinicians. Consumers are now a major focus for biomedical informatics, as are public health officials, tasked with detecting and managing disease outbreaks at a health system, rather than individual patient level. Bioinformatics is also changing the nature of CDSS. Apart from personalisation of therapy recommendations, translational bioinformatics is creating new challenges in the interpretation of the meaning of genetic data. Finally, there is much recent interest in the safety and effectiveness of computerised physicianorderentry (CPOE) systems, given that prescribing and administration errors are a significant cause of morbidity and mortality. Of note, there is still much controversy surrounding the contention that poorly designed, implemented or used CDSS may actually lead to harm. Conclusions CDSS research remains an active and evolving area of research, as CDSS penetrate more widely beyond their traditional domain into consumer decision support, and as decisions become more complex, for example by involving sequence level genetic data.
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Laka, Mah, Adriana Milazzo, Drew Carter, and Tracy Merlin. "OP196 Clinical Decision Support Systems (CDSS) For Antibiotic Management: Factors Limiting Sustainable Digital Transformation." International Journal of Technology Assessment in Health Care 37, S1 (2021): 5. http://dx.doi.org/10.1017/s0266462321000763.
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IntroductionClinical decision support systems (CDSS) are being developed to support evidence-based antibiotic prescribing and reduce the risk of inappropriate or over-prescribing; however, adoption of CDSS into the health system is rarely sustained. We aimed to understand the implementation challenges at a macro (policymakers), meso (organizational) and micro-level (individual practices) to identify the drivers of CDSS non-adoption.MethodsWe have adopted a mixed-method study design which comprised of: (i) systematic review and meta-analysis to assess the impact of CDSS on appropriate antibiotic prescribing, (ii) Online survey of clinicians in Australia from hospitals and primary care to identify drivers of CDSS adoption and (iii) in-depth interviews with policymakers to evaluate policy-level challenges and opportunities to CDSS implementation.ResultsCDSS implementation can improve compliance with antibiotic prescribing guidelines, with a relative decrease in mortality, volume of antibiotic use and length of hospital stay. However, CDSS provision alone is not enough to achieve these benefits. Important predictors of clinicians’ perception regarding CDSS adoption include the seniority of clinical end-users (years), use of CDSS, and the care setting. Clinicians in primary care and those with significant clinical experience are less likely to use CDSS due to a lack of trust in the system, fear of comprising professional autonomy, and patients’ expectations. Lack of important policy considerations for CDSS integration into a multi-stakeholder healthcare system has limited the organizational capacity to foster change and align processes to support the innovation.ConclusionsThese results using multiple lines of evidence highlight the importance of a holistic approach when undertaking health technology management. There needs to be system-wide guidance that integrates individual, organizational and system-level factors when implementing CDSS so that effective antibiotic stewardship can be facilitated.
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Al-Jaghbeer, Mohammed, Dilhari Dealmeida, Andrew Bilderback, Richard Ambrosino, and John A. Kellum. "Clinical Decision Support for In-Hospital AKI." Journal of the American Society of Nephrology 29, no. 2 (2017): 654–60. http://dx.doi.org/10.1681/asn.2017070765.
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AKI carries a significant mortality and morbidity risk. Use of a clinical decision support system (CDSS) might improve outcomes. We conducted a multicenter, sequential period analysis of 528,108 patients without ESRD before admission, from October of 2012 to September of 2015, to determine whether use of a CDSS reduces hospital length of stay and in-hospital mortality for patients with AKI. We compared patients treated 12 months before (181,696) and 24 months after (346,412) implementation of the CDSS. Coprimary outcomes were hospital mortality and length of stay adjusted by demographics and comorbidities. AKI was diagnosed in 64,512 patients (12.2%). Crude mortality rate fell from 10.2% before to 9.4% after CDSS implementation (odds ratio, 0.91; 95% confidence interval [95% CI], 0.86 to 0.96; P=0.001) for patients with AKI but did not change in patients without AKI (from 1.5% to 1.4%). Mean hospital duration decreased from 9.3 to 9.0 days (P<0.001) for patients with AKI, with no change for patients without AKI. In multivariate mixed-effects models, the adjusted odds ratio (95% CI) was 0.76 (0.70 to 0.83) for mortality and 0.66 (0.61 to 0.72) for dialysis (P<0.001). Change in adjusted hospital length of stay was also significant (incidence rate ratio, 0.91; 95% CI, 0.89 to 0.92), decreasing from 7.2 to 6.0 days for patients with AKI. Results were robust to sensitivity analyses and were sustained for the duration of follow-up. Hence, implementation of a CDSS for AKI resulted in a small but sustained decrease in hospital mortality, dialysis use, and length of stay.
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Wright, Adam, Thu-Trang T. Hickman, Dustin McEvoy, et al. "Analysis of clinical decision support system malfunctions: a case series and survey." Journal of the American Medical Informatics Association 23, no. 6 (2016): 1068–76. http://dx.doi.org/10.1093/jamia/ocw005.
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Abstract Objective To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions. Materials and Methods We identified and investigated several CDSS malfunctions at Brigham and Women’s Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions. Results We identified four CDSS malfunctions at Brigham and Women’s Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually. Discussion CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate. Conclusion CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.
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Laka, Mah, Adriana Milazzo, and Tracy Merlin. "Factors That Impact the Adoption of Clinical Decision Support Systems (CDSS) for Antibiotic Management." International Journal of Environmental Research and Public Health 18, no. 4 (2021): 1901. http://dx.doi.org/10.3390/ijerph18041901.
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The study evaluated individual and setting-specific factors that moderate clinicians’ perception regarding use of clinical decision support systems (CDSS) for antibiotic management. A cross-sectional online survey examined clinicians’ perceptions about CDSS implementation for antibiotic management in Australia. Multivariable logistic regression determined the association between drivers of CDSS adoption and different moderators. Clinical experience, CDSS use and care setting were important predictors of clinicians’ perception concerning CDSS adoption. Compared to nonusers, CDSS users were less likely to lack confidence in CDSS (OR = 0.63, 95%, CI = 0.32, 0.94) and consider it a threat to professional autonomy (OR = 0.47, 95%, CI = 0.08, 0.83). Conversely, there was higher likelihood in experienced clinicians (>20 years) to distrust CDSS (OR = 1.58, 95%, CI = 1.08, 2.23) due to fear of comprising their clinical judgement (OR = 1.68, 95%, CI = 1.27, 2.85). In primary care, clinicians were more likely to perceive time constraints (OR = 1.96, 95%, CI = 1.04, 3.70) and patient preference (OR = 1.84, 95%, CI = 1.19, 2.78) as barriers to CDSS adoption for antibiotic prescribing. Our findings provide differentiated understanding of the CDSS implementation landscape by identifying different individual, organisational and system-level factors that influence system adoption. The individual and setting characteristics can help understand the variability in CDSS adoption for antibiotic management in different clinicians.
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Fan, Aihua, Di Lin, and Yu Tang. "Clinical Decision Support Systems for Comorbidity: Architecture, Algorithms, and Applications." International Journal of Telemedicine and Applications 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/1562919.
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In this paper, we present the design of a clinical decision support system (CDSS) for monitoring comorbid conditions. Specifically, we address the architecture of a CDSS by characterizing it from three layers and discuss the algorithms in each layer. Also we address the applications of CDSSs in a few real scenarios and analyze the accuracy of a CDSS in consideration of the potential conflicts when using multiple clinical practice guidelines concurrently. Finally, we compare the system performance in our design with that in the other design schemes. Our study shows that our proposed design can achieve a clinical decision in a shorter time than the other designs, while ensuring a high level of system accuracy.
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Rocha, Hermano Alexandre Lima, Irene Dankwa-Mullan, Sergio Ferreira Juacaba, et al. "Shared-decision making in prostate cancer with clinical decision-support." Journal of Clinical Oncology 37, no. 15_suppl (2019): e16576-e16576. http://dx.doi.org/10.1200/jco.2019.37.15_suppl.e16576.
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e16576 Background: Shared decision-making is the process of deliberately interacting with patients who wish to make informed value-based choices, when there are no indicated best treatment options. Given the wide variation in prostate cancer treatment options, clinical decision-support systems (CDSS) may effectively support treatment decisions for patients with challenging risk-benefit profiles. However, limited data are available regarding CDSS in shared decision making. This study aimed to assess the alignment of CDSS therapeutic options with treatment received through a shared decision process. Methods: We identified patients with prostate cancer (Gleason Groups 1-5) who were engaged in shared treatment decision making, (from August–September 2018) at the Instituto do Câncer do Ceará, Brazil. IBM Watson for Oncology (WfO), a CDSS was used for the study. Treatment decisions were compared with WfO options (active surveillance, clinical trial, chemotherapy [CT], hormone therapy [HT], radiation [RT], brachytherapy [brachy], surgery and systemic therapy with GnRH suppression) and categorized as concordant (equivalent), partially concordant (a partial match), or discordant. Results: Concordance between WfO and shared treatment decisions was observed in 54% (26/48) of patients, partial concordance in 15% (7/48) and discordance in 31% (15/48). Most frequent treatments were RT+HT combination therapy (25%) and prostatectomy (21%). 8/15 (53%) discordant cases were due to patient preference for treatment over active surveillance. Patient preference for treatment over active surveillance was the most common reason (53%) for discordance. Conclusions: Variation in prostate cancer treatment exists. CDSS therapy options may be useful in quantifying and modifying unwarranted variations in prostate cancer treatment. Future studies are important for understanding reasons for variations. [Table: see text]
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Abu-Kabeer, Tasneem, Mohammad Alshraideh, and Ferial Hayajneh. "Intelligence Clinical Decision Support System for Diabetes Management." WSEAS TRANSACTIONS ON COMPUTER RESEARCH 8 (May 20, 2020): 44–60. http://dx.doi.org/10.37394/232018.2020.8.8.
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Diabetes is the most common endocrine disease in all populations and all age groups. The diabetes patient should use correct therapy to live with this disease; there are several of important things to record about the patient and disease that help the doctors to make an optimal decision about the patient treatment. To improve the ability of the physicians, several tools have been proposed by the researchers for developing effective Clinical Decision Support System (CDSS), one of these tools is Artificial Neural Networks(ANN) that are computer paradigms that belong to the computational intelligence family. In this paper, a multilayer perceptron (MLP) feed-forward neural is used to develop a CDSS to determine the regimen type of diabetes management. The input layer of the system includes 25 input variables; the output layer contains one neuron that will produce a number that represents the treatment regimen. A Resilient backpropagation (Rprop) algorithm is used to train the system. In particular, a 10-fold cross-validation scheme was used, an 88.5% classification accuracy from the experiments made on data taken from 228 patient medical records suffering from diabetes (type II).
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Van den Hanenberg, Floor, Valentina Poetsema, Carolina JPW Keijsers, et al. "Improving Appropriate Prescribing For Geriatric Patients Using a Clinical Decision Support System." INNOVATIONS in pharmacy 13, no. 1 (2022): 18. http://dx.doi.org/10.24926/iip.v13i1.4514.
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Purpose: Polypharmacy is a known risk factor for potentially inappropriate prescribing. Recently there is an increasing interest in clinical decision support systems (CDSS) to improve prescribing. The objective of this study was to evaluate the impact of a CDSS, with the START-STOPP criteria as main content in the setting of a geriatric ward. Endpoints were 1) appropriateness of prescribing and 2) acceptance rate of recommendations. Methods: This prospective study comparing the use of a CDSS with usual care involved patients admitted to geriatric wards in two teaching hospitals in the Netherlands. Patients were included from January to May 2017. The medications of 64 patients in the first six weeks was assessed according to the current standard, whereas the medications of 61 patients in the second six weeks were also assessed by using a CDSS. Medication appropriateness was assessed with the Medication Appropriateness Index (MAI). Results: The medications of 125 patients (median age 83 years) were reviewed. In both the usual care group and the intervention group MAI scores decreased significantly from admission to discharge (within group analyses, p<0.001). This effect was significantly larger in the intervention group (p<0.05). MAI scores at discharge in the usual care group and the intervention group were respectively 9.95±6.70 and 7.26±5.07. The CDSS generated 193 recommendations, of which 71 concerned START criteria, 45 STOPP criteria, and 77 potential interactions. Overall, 31.6% of the recommendations were accepted. Conclusion: This study shows that a CDSS to improve prescribing has additional value in the setting of a geriatric ward. Almost one third of the software-generated recommendations were interpreted as clinically relevant and accepted, on average one per patient.
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Vemula, Ridhima, Uli Chettipally, Mamata Kene, et al. "Optimizing Clinical Decision Support in the Electronic Health Record." Applied Clinical Informatics 07, no. 03 (2016): 883–98. http://dx.doi.org/10.4338/aci-2016-05-ra-0073.
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SummaryAdoption of clinical decision support (CDS) tools by clinicians is often limited by workflow barriers. We sought to assess characteristics associated with clinician use of an electronic health record-embedded clinical decision support system (CDSS).In a prospective study on emergency department (ED) activation of a CDSS tool across 14 hospitals between 9/1/14 to 4/30/15, the CDSS was deployed at 10 active sites with an on-site champion, education sessions, iterative feedback, and up to 3 gift cards/clinician as an incentive. The tool was also deployed at 4 passive sites that received only an introductory educational session. Activation of the CDSS – which calculated the Pulmonary Embolism Severity Index (PESI) score and provided guidance – and associated clinical data were collected prospectively. We used multivariable logistic regression with random effects at provider/facility levels to assess the association between activation of the CDSS tool and characteristics at: 1) patient level (PESI score), 2) provider level (demographics and clinical load at time of activation opportunity), and 3) facility level (active vs. passive site, facility ED volume, and ED acuity at time of activation opportunity).Out of 662 eligible patient encounters, the CDSS was activated in 55%: active sites: 68% (346/512); passive sites 13% (20/150). In bivariate analysis, active sites had an increase in activation rates based on the number of prior gift cards the physician had received (96% if 3 prior cards versus 60% if 0, p<0.0001). At passive sites, physicians < age 40 had higher rates of activation (p=0.03). In multivariable analysis, active site status, low ED volume at the time of diagnosis and PESI scores I or II (compared to III or higher) were associated with higher likelihood of CDSS activation.Performing on-site tool promotion significantly increased odds of CDSS activation. Optimizing CDSS adoption requires active education.Citation: Ballard DW, Vemula R, Chettipally UK, Kene MV, Mark DG, Elms AK, Lin JS, Reed ME, Huang J, Rauchwerger AS, Vinson DR, for the KP CREST Network Investigators. Optimizing clinical decision support in the electronic health record – clinical characteristics associated with the use of a decision tool for disposition of ED patients with pulmonary embolism.
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Ravikumar, K. E., Kathy MacLaughlin, Marianne Scheitel, et al. "Improving the Accuracy of a Clinical Decision Support System for Cervical Cancer Screening and Surveillance." Applied Clinical Informatics 09, no. 01 (2018): 062–71. http://dx.doi.org/10.1055/s-0037-1617451.
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Background Clinical decision support systems (CDSS) for cervical cancer prevention are generally limited to identifying patients who are overdue for their next routine/next screening, and they do not provide recommendations for follow-up of abnormal results. We previously developed a CDSS to automatically provide follow-up recommendations based on the American Society of Colposcopy and Cervical Pathology (ASCCP) guidelines for women with both previously normal and abnormal test results leveraging information available in the electronic medical record (EMR). Objective Enhance the CDSS by improving its accuracy and incorporating changes to reflect the latest revision of the guidelines. Methods After making enhancements to the CDSS, we evaluated the performance of the clinical recommendations on 393 patients selected through stratified sampling from a set of 3,704 patients in a nonclinical setting. We performed chart review of individual patient's record to evaluate the performance of the system. An expert clinician assisted by a resident manually reviewed the recommendation made by the system and verified whether the recommendations were as per the ASCCP guidelines. Results The recommendation accuracy of the enhanced CDSS improved to 93%, which is a substantial improvement over the 84% reported previously. A detailed analysis of errors is presented in this article. We fixed the errors identified in this evaluation that were amenable to correction to further improve the accuracy of the system. The source code of the updated CDSS is available at https://github.com/ohnlp/MayoNlpPapCdss. Conclusion We made substantial enhancements to our earlier prototype CDSS with the updated ASCCP guidelines and performed a thorough evaluation in a nonclinical setting to improve the accuracy of the CDSS. The CDSS will be further refined as it is utilized in the practice.
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Kim, Min, Frederick Thum, Laura Rivera, et al. "Usability Evaluation of a Clinical Decision Support System for Geriatric ED Pain Treatment." Applied Clinical Informatics 07, no. 01 (2016): 128–42. http://dx.doi.org/10.4338/aci-2015-08-ra-0108.
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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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Suwanvecho, Suthida, Harit Suwanrusme, Tanawat Jirakulaporn, et al. "Comparison of an oncology clinical decision-support system’s recommendations with actual treatment decisions." Journal of the American Medical Informatics Association 28, no. 4 (2021): 832–38. http://dx.doi.org/10.1093/jamia/ocaa334.
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Abstract Objective IBM(R) Watson for Oncology (WfO) is a clinical decision-support system (CDSS) that provides evidence-informed therapeutic options to cancer-treating clinicians. A panel of experienced oncologists compared CDSS treatment options to treatment decisions made by clinicians to characterize the quality of CDSS therapeutic options and decisions made in practice. Methods This study included patients treated between 1/2017 and 7/2018 for breast, colon, lung, and rectal cancers at Bumrungrad International Hospital (BIH), Thailand. Treatments selected by clinicians were paired with therapeutic options presented by the CDSS and coded to mask the origin of options presented. The panel rated the acceptability of each treatment in the pair by consensus, with acceptability defined as compliant with BIH’s institutional practices. Descriptive statistics characterized the study population and treatment-decision evaluations by cancer type and stage. Results Nearly 60% (187) of 313 treatment pairs for breast, lung, colon, and rectal cancers were identical or equally acceptable, with 70% (219) of WfO therapeutic options identical to, or acceptable alternatives to, BIH therapy. In 30% of cases (94), 1 or both treatment options were rated as unacceptable. Of 32 cases where both WfO and BIH options were acceptable, WfO was preferred in 18 cases and BIH in 14 cases. Colorectal cancers exhibited the highest proportion of identical or equally acceptable treatments; stage IV cancers demonstrated the lowest. Conclusion This study demonstrates that a system designed in the US to support, rather than replace, cancer-treating clinicians provides therapeutic options which are generally consistent with recommendations from oncologists outside the US.
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Melton, Brittany, Alan Zillich, Jason Saleem, Alissa Russ, James Tisdale, and Brian Overholser. "Iterative Development and Evaluation of a Pharmacogenomic-Guided Clinical Decision Support System for Warfarin Dosing." Applied Clinical Informatics 07, no. 04 (2016): 1088–106. http://dx.doi.org/10.4338/aci-2016-05-ra-0081.
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SummaryObjective Pharmacogenomic-guided dosing has the potential to improve patient outcomes but its implementation has been met with clinical challenges. Our objective was to develop and evaluate a clinical decision support system (CDSS) for pharmacogenomic-guided warfarin dosing designed for physicians and pharmacists.Methods Twelve physicians and pharmacists completed 6 prescribing tasks using simulated patient scenarios in two iterations (development and validation phases) of a newly developed pharmacogenomic-driven CDSS prototype. For each scenario, usability was measured via efficiency, recorded as time to task completion, and participants’ perceived satisfaction which were compared using Kruskal-Wallis and Mann Whitney U tests, respectively. Debrief interviews were conducted and qualitatively analyzed. Usability findings from the first (i.e. development) iteration were incorporated into the CDSS design for the second (i.e. validation) iteration.Results During the CDSS validation iteration, participants took more time to complete tasks with a median (IQR) of 183 (124–247) seconds versus 101 (73.5–197) seconds in the development iteration (p=0.01). This increase in time on task was due to the increase in time spent in the CDSS corresponding to several design changes. Efficiency differences that were observed between pharmacists and physicians in the development iteration were eliminated in the validation iteration. The increased use of the CDSS corresponded to a greater acceptance of CDSS recommended doses in the validation iteration (4% in the first iteration vs. 37.5% in the second iteration, p<0.001). Overall satisfaction did not change statistically between the iterations but the qualitative analysis revealed greater trust in the second prototype.Conclusions A pharmacogenomic-guided CDSS has been developed using warfarin as the test drug. The final CDSS prototype was trusted by prescribers and significantly increased the time using the tool and acceptance of the recommended doses. This study is an important step toward incorporating pharmacogenomics into CDSS design for clinical testing.Citation: Melton BL, Zillich AJ, Saleem JJ, Russ AL, Tisdale JE, Overholser BR. Iterative development and evaluation of a pharmacogenomic-guided clinical decision support system for warfarin dosing.
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Xu, Fengrui, Martín-J. Sepúlveda, Zefei Jiang, et al. "Effect of an Artificial Intelligence Clinical Decision Support System on Treatment Decisions for Complex Breast Cancer." JCO Clinical Cancer Informatics, no. 4 (October 2020): 824–38. http://dx.doi.org/10.1200/cci.20.00018.
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PURPOSE To examine the impact of a clinical decision support system (CDSS) on breast cancer treatment decisions and adherence to National Comprehensive Cancer Center (NCCN) guidelines. PATIENTS AND METHODS A cross-sectional observational study was conducted involving 1,977 patients at high risk for recurrent or metastatic breast cancer from the Chinese Society of Clinical Oncology. Ten oncologists provided blinded treatment recommendations for an average of 198 patients before and after viewing therapeutic options offered by the CDSS. Univariable and bivariable analyses of treatment changes were performed, and multivariable logistic regressions were estimated to examine the effects of physician experience (years), patient age, and receptor subtype/TNM stage. RESULTS Treatment decisions changed in 105 (5%) of 1,977 patients and were concentrated in those with hormone receptor (HR)–positive disease or stage IV disease in the first-line therapy setting (73% and 58%, respectively). Logistic regressions showed that decision changes were more likely in those with HR-positive cancer (odds ratio [OR], 1.58; P < .05) and less likely in those with stage IIA (OR, 0.29; P < .05) or IIIA cancer (OR, 0.08; P < .01). Reasons cited for changes included consideration of the CDSS therapeutic options (63% of patients), patient factors highlighted by the tool (23%), and the decision logic of the tool (13%). Patient age and oncologist experience were not associated with decision changes. Adherence to NCCN treatment guidelines increased slightly after using the CDSS (0.5%; P = .003). CONCLUSION Use of an artificial intelligence–based CDSS had a significant impact on treatment decisions and NCCN guideline adherence in HR-positive breast cancers. Although cases of stage IV disease in the first-line therapy setting were also more likely to be changed, the effect was not statistically significant ( P = .22). Additional research on decision impact, patient-physician communication, learning, and clinical outcomes is needed to establish the overall value of the technology.
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Robles, Noemí, Carme Carrion i Ribas, Montserrat Pàmias, et al. "PP166 A Mobile Clinical Decision Support System for Autism Spectrum Disorder." International Journal of Technology Assessment in Health Care 35, S1 (2019): 68–69. http://dx.doi.org/10.1017/s0266462319002654.
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IntroductioneHealth is a new approach for managing several health conditions, but up to now not so many interventions have shown their efficacy/effectiveness. The AUTAPP Project tries to add knowledge in eHealth interventions targeted to Mental Health disorders, specifically Autism Spectrum Disorder (ASD) management that requires complex interventions that integrate different psychosocial interventions. AUTAPP aims to develop an evidence based Clinical Decision Support System (CDSS) using mobile technology for improving the decision process on psychosocial therapies in ASD. This study aimed to identify recommendations on which the algorithm of the CDSS will be developed.MethodsA systematic review (November 2009-November 2018) was carried out to identify the existing scientific evidence published in relation to the effectiveness of: (i) early detection protocols; (ii) assessment tools; (iii) existing non-pharmacological therapies. Main databases were consulted (PubMed, Cochrane Library, PsychoInfo). Articles were reviewed by two independent reviewers. The quality of included publications and recommendations were assessed according to SIGN criteria.ResultsA total number of 147 publications were included (477 identified): 96 for non-pharmacological therapies, 33 for assessment tools and eighteen for early detection. Regarding early detection and assessment, 12 recommendations were identified and six obtained the highest level (A), such as the convenience of multidisciplinary diagnosis teams and the usefulness of the Modified Checklist for Autism in Toddlers (M-CHAT) for ASD confirmation. For non-pharmacological therapies, 16 recommendations were collected. Those with higher levels of recommendations were family, environmental and educational (three As and one B). Interventions with lower levels of recommendation (C) were interventions which exercise, computers and neurological approaches.ConclusionsThis systematic review allows both to identify gaps and opportunities in psychosocial interventions research and be the base for the CDSS algorithm. In the future professionals, careers and people diagnosed with ASD will validate the mobile CDSS.
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Hosseini, Azamossadat, Farkhondeh Asadi, and Leila Akramian Arani. "Development of a Knowledge-based Clinical Decision Support System for Multiple Sclerosis Diagnosis." Journal of Medicine and Life 13, no. 4 (2020): 612–23. http://dx.doi.org/10.25122/jml-2020-0182.
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The diagnosis of multiple sclerosis (MS) is difficult considering its complexity, variety in signs and symptoms, and its similarity to the signs and symptoms of other neurological diseases. The purpose of this study is to design and develop a clinical decision support system (CDSS) to help physicians diagnose MS with a relapsing-remitting phenotype. The CDSS software was developed in four stages: requirement analysis, system design, system development, and system evaluation. The Rational Rose and SQL Server were used to design the object-oriented conceptual model and develop the database. The C sharp programming language and the Visual Studio programming environment were used to develop the software. To evaluate the efficiency and applicability of the software, the data of 130 medical records of patients aged 20 to 40 between 2017 and 2019 were used along with the Nilsson standard questionnaire. SPSS Statistics was also used to analyze the data. For MS diagnosis, CDSS had a sensitivity, specificity and accuracy of 1, 0.97 and 0.99, respectively, and the area under the ROC curve was 0.98. The agreement rate of kappa coefficient (κ) between software diagnosis and physician’s diagnosis was 0.98. The average score of software users was 98.33%, 96.65%, and 96.9% regarding the ease of learning, memorability, and satisfaction, respectively. Therefore, the applicability of the CDSS for MS diagnosis was confirmed by the neurologists. The evaluation findings show that CDSS can help physicians in the accurate and timely diagnosis of MS by using the rule-based method.
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Kaufman, D., S. Bakken, L. M. Currie, and B. Sheehan. "Cognitive Analysis of Decision Support for Antibiotic Ordering in a Neonatal Intensive Care Unit." Applied Clinical Informatics 03, no. 01 (2012): 105–23. http://dx.doi.org/10.4338/aci-2011-10-ra-0060.
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SummaryBackground: Clinical decision support systems (CDSS) are a method used to support prescribing accuracy when deployed within a computerized provider order entry system (CPOE). Divergence from using CDSS is exemplified by high alert override rates. Excessive cognitive load imposed by the CDSS may help to explain such high rates. Objectives: The aim of this study was to describe the cognitive impact of a CPOE-integrated CDSS by categorizing system use problems according to the type of mental processing required to resolve them.Methods: A qualitative, descriptive design was used employing two methods; a cognitive walk-through and a think-aloud protocol. Data analysis was guided by Norman’s Theory of Action and a theory of cognitive distances which is an extension to Norman’s theory.Results: The most frequently occurring source of excess cognitive effort was poor information timing. Information presented by the CDSS was often presented after clinicians required the information for decision making. Additional sources of effort included use of language that was not clear to the user, vague icons, and lack of cues to guide users through tasks.Conclusions: Lack of coordination between clinician’s task-related thought processes and those presented by a CDSS results in excessive cognitive work required to use the system. This can lead to alert overrides and user errors. Close attention to user’s cognitive processes as they carry out clinical tasks prior to CDSS development may provide key information for system design that supports clinical tasks and reduces cognitive effort.
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Walter Costa, Maria Beatriz, Mark Wernsdorfer, Alexander Kehrer, et al. "The Clinical Decision Support System AMPEL for Laboratory Diagnostics: Implementation and Technical Evaluation." JMIR Medical Informatics 9, no. 6 (2021): e20407. http://dx.doi.org/10.2196/20407.
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Background Laboratory results are of central importance for clinical decision making. The time span between availability and review of results by clinicians is crucial to patient care. Clinical decision support systems (CDSS) are computational tools that can identify critical values automatically and help decrease treatment delay. Objective With this work, we aimed to implement and evaluate a CDSS that supports health care professionals and improves patient safety. In addition to our experiences, we also describe its main components in a general manner to make it applicable to a wide range of medical institutions and to empower colleagues to implement a similar system in their facilities. Methods Technical requirements must be taken into account before implementing a CDSS that performs laboratory diagnostics (labCDSS). These can be planned within the functional components of a reactive software agent, a computational framework for such a CDSS. Results We present AMPEL (Analysis and Reporting System for the Improvement of Patient Safety through Real-Time Integration of Laboratory Findings), a labCDSS that notifies health care professionals if a life-threatening medical condition is detected. We developed and implemented AMPEL at a university hospital and regional hospitals in Germany (University of Leipzig Medical Center and the Muldental Clinics in Grimma and Wurzen). It currently runs 5 different algorithms in parallel: hypokalemia, hypercalcemia, hyponatremia, hyperlactatemia, and acute kidney injury. Conclusions AMPEL enables continuous surveillance of patients. The system is constantly being evaluated and extended and has the capacity for many more algorithms. We hope to encourage colleagues from other institutions to design and implement similar CDSS using the theory, specifications, and experiences described in this work.
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Sherimon, Vinu, P. C. Sherimon, Rahul V. Nair, et al. "eCOVID19 – Development of Ontology-based Clinical Decision Support System for COVID-19." Frontiers in Health Informatics 11, no. 1 (2022): 101. http://dx.doi.org/10.30699/fhi.v11i1.339.
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Introduction: Humankind is passing through a period of significant instability and a worldwide health catastrophe that has never been seen before. COVID-19 spread over the world at an unprecedented rate. In this context, we undertook a rapid research project in the Sultanate of Oman. We developed ecovid19 application, an ontology-based clinical decision support system (CDSS) with teleconference capability for easy, fast diagnosis and treatment for primary health centers/Satellite Clinics of the Royal Oman Police (ROP) of Sultanate of Oman.Materials and Methods: The domain knowledge and clinical guidelines are represented using ontology. Ontology is one of the most powerful methods for formally encoding medical knowledge. The primary data was from the ROP hospital's medical team, while the secondary data came from articles published in reputable journals. The application includes a COVID-19 Symptom checker for the public users with a text interface and an AI-based voice interface and is available in English and Arabic. Based on the given information, the symptom checker provides recommendations to the user. The suspected cases will be directed to the nearby clinic if the risk of infection is high. Based on the patient's current medical condition in the clinic, the CDSS will make suitable suggestions to triage staff, doctors, radiologists, and lab technicians on procedures and medicines. We used Teachable Machine to create a TensorFlow model for the analysis of X-rays. Our CDSS also has a WebRTC (Web Real-Time Communication system) based teleconferencing option for communicating with expert clinicians if the patient develops difficulties or if expert opinion is requested.Results: The ROP hospital's specialized doctors tested our CDSS, and the user interfaces were changed based on their suggestions and recommendations. The team put numerous types of test cases to assess the clinical efficacy. Precision, sensitivity (recall), specificity, and accuracy were adequate in predicting the various categories of patient instances.Conclusion: The proposed CDSS has the potential to significantly improve the quality of care provided to Oman's citizens. It can also be tailored to fit other terrifying pandemics.
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Olakotan, Olufisayo Olusegun, and Maryati Mohd Yusof. "The appropriateness of clinical decision support systems alerts in supporting clinical workflows: A systematic review." Health Informatics Journal 27, no. 2 (2021): 146045822110075. http://dx.doi.org/10.1177/14604582211007536.
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A CDSS generates a high number of inappropriate alerts that interrupt the clinical workflow. As a result, clinicians silence, disable, or ignore alerts, thereby undermining patient safety. Therefore, the effectiveness and appropriateness of CDSS alerts need to be evaluated. A systematic review was carried out to identify the factors that affect CDSS alert appropriateness in supporting clinical workflow. Seven electronic databases (PubMed, Scopus, ACM, Science Direct, IEEE, Ovid Medline, and Ebscohost) were searched for English language articles published between 1997 and 2018. Seventy six papers met the inclusion criteria, of which 26, 24, 15, and 11 papers are retrospective cohort, qualitative, quantitative, and mixed-method studies, respectively. The review highlights various factors influencing the appropriateness and efficiencies of CDSS alerts. These factors are categorized into technology, human, organization, and process aspects using a combination of approaches, including socio-technical framework, five rights of CDSS, and Lean. Most CDSS alerts were not properly designed based on human factor methods and principles, explaining high alert overrides in clinical practices. The identified factors and recommendations from the review may offer valuable insights into how CDSS alerts can be designed appropriately to support clinical workflow.
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Imlawi, Jehad. "Clinical Decision Support Systems’ Usage Continuance Intentions by Health Care Providers in Jordan: Toward an Integrated Model." International Journal of Online and Biomedical Engineering (iJOE) 19, no. 02 (2023): 111–33. http://dx.doi.org/10.3991/ijoe.v19i02.37239.
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Health organizations in Jordan has just started adopting a nationwide health information system [Hakeem] including useful tools such as the clinical decision support system [CDSS]. Adopting CDSS by health care providers is not mandatory; However, the fruitful results of these tools can only be gained after adopted by the health care providers, and when they have the intentions to continue use it in the future.
 The current study proposes a model that integrates factors from tow important theories of technology acceptance; Technology Acceptance Model [TAM], and Information Systems Success Model [ISSM] to predict the health care providers’ usage continuance intentions of CDSS in future. The study also checks if gender, experience, and CDSS alerts’ frequency has any moderation effects on the proposed research model. To assess the research model, data were collected from 218 participants via an online survey.
 The proposed model has strongly predicted the CDSS usage continuance intentions [R2=0.486]. However, the moderators; gender, experience, and CDSS alerts’ frequency, partially moderate the proposed relationships. Conclusions: This research extends the growing literature on health information systems' adoption by building an integrated model that integrates factors from two well-established technology acceptance models, TAM and ISSM. The findings proved a significant impact of ISSM's factors [system quality, information quality, and satisfaction] on CDSS usage continuance intentions.
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Han, Peijin, Sang Ho Lee, Kazumasa Noro, et al. "Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy." JCO Clinical Cancer Informatics, no. 5 (August 2021): 944–52. http://dx.doi.org/10.1200/cci.20.00189.
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PURPOSE Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients. MATERIALS AND METHODS CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared. RESULTS CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82). CONCLUSION Machine learning–based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.
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Robles, Noemí, Carme Carrion i Ribas, and Marta Aymerich. "VP29 Designing A Mobile Clinical Decision Support System For Dementia." International Journal of Technology Assessment in Health Care 35, S1 (2019): 83. http://dx.doi.org/10.1017/s0266462319003039.
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IntroductionE-health offers the opportunity of supporting the management of several diseases, but most of these tools are far from being based on scientific evidence and demonstrating their effectiveness and efficacy. The PSICODEM Project aims to develop a mobile personalized clinical decision support system (CDSS) based on evidence for contributing to e-health interventions addressed to the management of dementia that require not only a pharmacological approach but also psychosocial interventions for improving patients’ quality of life and reducing emotional, cognitive and behavioral symptoms. The present communication focuses on the identification of the evidence on which the CDSS algorithm will be developed.MethodsThree systematic reviews were carried out in order to identify the existing scientific evidence published in relation to the effectiveness of behavioral, emotional and cognitive therapies addressing dementia (January 2009 to December 2017). The main databases were consulted (PubMed, Cochrane Library, PsychoInfo) and only randomized control trials (RCT) were considered. Articles were reviewed by two independent reviewers. The quality of the selected publications was assessed according to the SIGN criteria.ResultsForty-seven RCTs were selected for cognitive therapies, thirty-two for emotional ones and fifteen for behavioral interventions. Those therapies with more support of evidence were skills training for cognitive therapies and reminiscence interventions for emotional interventions; however, in behavioral interventions a variety of therapeutically approaches were found. Wide differences were found between studies in terms of types and levels of dementia, forms of intervention (number, length and frequency of sessions) and outcome measures.ConclusionsIn-depth analysis of evidence will allow the identification of those interventions more appropriate for each patient according to their symptoms and level of dementia. According to this evidence, the mobile CDSS algorithm will be developed. Additionally, these findings point out the gaps in psychosocial intervention research.
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Hermsen, Elizabeth D., Trevor C. VanSchooneveld, Harlan Sayles, and Mark E. Rupp. "Implementation of a Clinical Decision Support System for Antimicrobial Stewardship." Infection Control & Hospital Epidemiology 33, no. 4 (2012): 412–15. http://dx.doi.org/10.1086/664762.
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Clinical decision support systems (CDSSs) for antimicrobial stewardship require considerable human resources and financial investments. This pre-/postimplementation study evaluated the effect of a CDSS on performance of prospective audit with intervention and feedback and demonstrated an increase in interventions and recommendation acceptance countered by a substantial number of non-actionable alerts.
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Antoniadi, Anna Markella, Miriam Galvin, Mark Heverin, Lan Wei, Orla Hardiman, and Catherine Mooney. "A Clinical Decision Support System for the Prediction of Quality of Life in ALS." Journal of Personalized Medicine 12, no. 3 (2022): 435. http://dx.doi.org/10.3390/jpm12030435.
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Amyotrophic Lateral Sclerosis (ALS), also known as Motor Neuron Disease (MND), is a rare and fatal neurodegenerative disease. As ALS is currently incurable, the aim of the treatment is mainly to alleviate symptoms and improve quality of life (QoL). We designed a prototype Clinical Decision Support System (CDSS) to alert clinicians when a person with ALS is experiencing low QoL in order to inform and personalise the support they receive. Explainability is important for the success of a CDSS and its acceptance by healthcare professionals. The aim of this work isto announce our prototype (C-ALS), supported by a first short evaluation of its explainability. Given the lack of similar studies and systems, this work is a valid proof-of-concept that will lead to future work. We developed a CDSS that was evaluated by members of the team of healthcare professionals that provide care to people with ALS in the ALS/MND Multidisciplinary Clinic in Dublin, Ireland. We conducted a user study where participants were asked to review the CDSS and complete a short survey with a focus on explainability. Healthcare professionals demonstrated some uncertainty in understanding the system’s output. Based on their feedback, we altered the explanation provided in the updated version of our CDSS. C-ALS provides local explanations of its predictions in a post-hoc manner, using SHAP (SHapley Additive exPlanations). The CDSS predicts the risk of low QoL in the form of a probability, a bar plot shows the feature importance for the specific prediction, along with some verbal guidelines on how to interpret the results. Additionally, we provide the option of a global explanation of the system’s function in the form of a bar plot showing the average importance of each feature. C-ALS is available online for academic use.
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Losik, D. V., S. N. Kozlova, Yu S. Krivosheev, et al. "Retrospective analysis of clinical decision support system use in patients with hypertension and atrial fibrillation (INTELLECT)." Russian Journal of Cardiology 26, no. 4 (2021): 4406. http://dx.doi.org/10.15829/1560-4071-2021-4406.
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Aim. To evaluate the relationship between the clinical decision support system use (CDSS) and adherence to clinical guidelines.Materials and methods. Medical records of 300 patients with atrial fibrillation and hypertension from the electronic medical database of the Almazov National Medical Research Center were analyzed. Demographic and clinical data, as well as information on anticoagulant, antiarrhythmic and antihypertensive prescriptions were analyzed. The primary endpoint was adherence of prescribed treatment to current clinical guidelines for each of the three therapies. Firstly, a group of independent clinical experts assessed primary endpoint for retrospective prescriptions. Secondly, new prescriptions were simulated by another group of clinical experts using CDSS and blinded to previous therapy. Primary endpoint at the second step was analysed by independent experts. We compared adherence to relevant clinical guidelines with and without use of CDSS. Additionally, we analyzed predictors of failing to meet the current recommendations in the retrospective records.Results. Out of 300 patients, only 291 (97%) had all characteristics and were included in the analysis. In 26 patients (18%), all three treatment strategies were in accordance with current clinical guidelines. Anticoagulant therapy was adherent to the guidelines in 92% of cases. Experts who used CDSS were 15% (95% confidence interval [CI], 10-21%) more likely to prescribe novel oral anticoagulants and 14% (95% CI, 10-19%) less likely to prescribe warfarin compared to baseline. Antiarrhythmic therapy was adherent to the guidelines in 69% of cases. When the CDSS platform was applied, experts were 14% (95% CI 4-19%) more likely to prefer antiarrhythmic drug (AAD) monotherapy and 32% (95% CI 26-37%) more often prescribed radiofrequency ablation (RFA) of left atrium. At baseline, antihypertensive therapy combinations were adherent clinical guidelines in 28% of cases. The use of the CDSS platform by experts was significantly associated with an increase in the frequency of prescribing dual and triple antihypertensive therapy.Conclusion. CDSS use is associated with improved adherence to current clinical guidelines. Prospective randomized trials are needed to evaluate the CDSS effectiveness in the prevention of cardiovascular events.
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Chernov, Anton A., E. B. Kleymenova, Dmitry A. Sychev, et al. "Implementation of clinical decision support system for anticoagulant prescribing for patients with deep vein thrombosis." Annals of the Russian academy of medical sciences 75, no. 1 (2020): 69–76. http://dx.doi.org/10.15690/vramn1252.
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Background: Physicians adherence to recommendations for appropriate antithrombotic therapy of venous thromboembolism (VTE) can reduce the risk of recurrent VTE, pulmonary hypertension, bleeding and other adverse events. Clinical decision support systems (CDSS) are shown to increase physicians adherence to clinical guidelines.
 Aims: To assess effectivenes and safety of CDSS for anticoagulant prescribing for inhospital patients with VTE.
 Methods: A prospective cohort study was conducted in a Moscow general hospital from 06.30.2017 to 06.23.2018 to compare physicians compliance with clinical guidelines for DVT anticoagulant therapy, the rate of drug errors and direct costs of anticoagulant therapy before and after CDSS implementation (55 patients in control group and 49 in experimental group).
 Results: The rate of anticoagulant prescribing for patients with DVT did not alter significantly after CDSS implementation (96% compared with 91% before CDSS), but physicians compliance with recommendations on anticoagulant dosage increased from 32.7% to 73.5% (p = 0.0003) with corresponding decrease in the rate of anticoagulant prescribing errors from 1.35 to 0.65 per 1 patient (p = 0.0005). The length of stay and hemorrhagic complication rate did not differ between control and experimental groups. LMWH replacement with new oral anticoagulants has reduced the cost of anticoagulant therapy for 1 patient from 11.800 rubles (IQR = 7000) to 5.430 rubles (IQR = 5700) (p 0.005).
 Conclusions: СDSS can increase physicians adherence to recommended anticoagulant therapy for patients with DVT: to prevent unreasonable under-/overdosing or prolongation of anticoagulant therapy. CDSS for DVT drug therapy can be economically feasible.
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Chien, Shuo-Chen, Ya-Lin Chen, Chia-Hui Chien, et al. "Alerts in Clinical Decision Support Systems (CDSS): A Bibliometric Review and Content Analysis." Healthcare 10, no. 4 (2022): 601. http://dx.doi.org/10.3390/healthcare10040601.
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A clinical decision support system (CDSS) informs or generates medical recommendations for healthcare practitioners. An alert is the most common way for a CDSS to interact with practitioners. Research about alerts in CDSS has proliferated over the past ten years. The research trend is ongoing with new emerging terms and focus. Bibliometric analysis is ideal for researchers to understand the research trend and future directions. Influential articles, institutes, countries, authors, and commonly used keywords were analyzed to grasp a comprehensive view on our topic, alerts in CDSS. Articles published between 2011 and 2021 were extracted from the Web of Science database. There were 728 articles included for bibliometric analysis, among which 24 papers were selected for content analysis. Our analysis shows that the research direction has shifted from patient safety to system utility, implying the importance of alert usability to be clinically impactful. Finally, we conclude with future research directions such as the optimization of alert mechanisms and comprehensiveness to enhance alert appropriateness and to reduce alert fatigue.
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Lamy, J. B., and J. Bouaud. "A Medical Informatics Perspective on Clinical Decision Support Systems." Yearbook of Medical Informatics 22, no. 01 (2013): 128–31. http://dx.doi.org/10.1055/s-0038-1638844.
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Summary Objective: To summarize excellent research and to select best papers published in 2012 in the field of computer-based decision support in healthcare. Methods: A bibliographic search focused on clinical decision support systems (CDSSs) and computer provider order entry was performed, followed by a double-blind literature review. Results: The review process yielded six papers, illustrating various aspects of clinical decision support. The first paper is a systematic review of CDSS intervention trials in real settings, and considers different types of possible outcomes. It emphasizes the heterogeneity of studies and confirms that CDSSs can improve process measures but that evidence lacks for other types of outcomes, especially clinical or economic. Four other papers tackle the safety of drug prescribing and show that CDSSs can be efficient in reducing prescription errors. The sixth paper exemplifies the growing role of ontological resources which can be used for several applications including decision support. Conclusions: CDSS research has to be continuously developed and assessed. The wide variety of systems and of interventions limits the understanding of factors of success of CDSS implementations. A standardization in the characterization of CDSSs and of intervention trial reporting will help to overcome this obstacle.
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Romero-Aroca, Pedro, Raquel Verges, Najlaa Maarof, et al. "Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain." BMJ Open Ophthalmology 7, no. 1 (2022): e000974. http://dx.doi.org/10.1136/bmjophth-2022-000974.
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ObjectiveThe aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme.Methods and analysisThe sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine–albumin ratio and glomerular filtration.ResultsThe mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine–albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and β error of 0.0179.ConclusionOur CDSS for predicting DR was successful when applied to a real population.
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Bouaud, J., and V. Koutkias. "Computerized Clinical Decision Support: Contributions from 2015." Yearbook of Medical Informatics 25, no. 01 (2016): 170–77. http://dx.doi.org/10.15265/iy-2016-055.
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Summary Objective: To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. Method: A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection. Results: Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians’ decisions. Conclusions: While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate the benefits that they promise.
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Abu-Hanna, A., and B. Nannings. "Characterizing Decision Support Telemedicine Systems." Methods of Information in Medicine 45, no. 05 (2006): 523–27. http://dx.doi.org/10.1055/s-0038-1634113.
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Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.
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Michalowski, W., D. O’Sullivan, K. Farion, et al. "A Task-based Support Architecture for Developing Point-of-care Clinical Decision Support Systems for the Emergency Department." Methods of Information in Medicine 52, no. 01 (2013): 18–32. http://dx.doi.org/10.3414/me11-01-0099.
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SummaryObjectives: The purpose of this study was to create a task-based support architecture for developing clinical decision support systems (CDSSs) that assist physicians in making decisions at the point-of-care in the emergency department (ED). The backbone of the proposed architecture was established by a task-based emergency workflow model for a patient-physician encounter.Methods: The architecture was designed according to an agent-oriented paradigm. Specifically, we used the O-MaSE (Organization-based Multi-agent System Engineering) method that allows for iterative translation of functional requirements into architectural components (e.g., agents). The agent-oriented paradigm was extended with ontology-driven design to implement ontological models representing knowledge required by specific agents to operate.Results: The task-based architecture allows for the creation of a CDSS that is aligned with the task-based emergency workflow model. It facilitates decoupling of executable components (agents) from embedded domain knowledge (ontological models), thus supporting their interoperability, sharing, and reuse. The generic architecture was implemented as a pilot system, MET3-AE – a CDSS to help with the management of pediatric asthma exacerbation in the ED. The system was evaluated in a hospital ED.Conclusions: The architecture allows for the creation of a CDSS that integrates support for all tasks from the task-based emergency workflow model, and interacts with hospital information systems. Proposed architecture also allows for reusing and sharing system components and knowledge across disease-specific CDSSs.
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Papandreou, Panos, Charalampia Amerikanou, Chara Vezou, Aristea Gioxari, Andriana C. Kaliora, and Maria Skouroliakou. "Improving Adherence to the Mediterranean Diet in Early Pregnancy Using a Clinical Decision Support System; A Randomised Controlled Clinical Trial." Nutrients 15, no. 2 (2023): 432. http://dx.doi.org/10.3390/nu15020432.
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Prenatal health is important for both mother and child. Additionally, the offspring’s development is affected by the mother’s diet. The aim of this study was to assess whether a Clinical Decision Support System (CDSS) can improve adherence to the Mediterranean diet in early pregnancy and whether this change is accompanied by changes in nutritional status and psychological parameters. We designed a three month randomised controlled clinical trial which was applied to 40 healthy pregnant women (20 in the CDSS and 20 in the control group). Medical history, biochemical, anthropometric measurements, dietary, and a psychological distress assessment were applied before and at the end of the intervention. Pregnant women in the CDSS group experienced a greater increase in adherence to the Mediterranean diet, as assessed via MedDietScore, in the first trimester of their pregnancy compared to women in the control group (p < 0.01). Furthermore, an improved nutritional status was observed in pregnant women who were supported by CDSS. Anxiety and depression levels showed a greater reduction in the CDSS group compared to the control group (p = 0.048). In conclusion, support by a CDSS during the first trimester of pregnancy may be beneficial for the nutritional status of the mother, as well as for her anxiety and depression status.
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Chang, Chi-Chang, and Chuen-Sheng Cheng. "A structural design of clinical decision support system for chronic diseases risk management." Open Medicine 2, no. 2 (2007): 129–39. http://dx.doi.org/10.2478/s11536-007-0021-7.
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AbstractIn clinical decision making, the event of primary interest is recurrent, so that for a given unit the event could be observed more than once during the study. In general, the successive times between failures of human physiological systems are not necessarily identically distributed. However, if any critical deterioration is detected, then the decision of when to take thei ntervention, given the costs of diagnosis and therapeutics, is of fundamental importance This paper develops a possible structural design of clinical decision support system (CDSS) by considering the sensitivity analysis as well as the optimal prior and posterior decisions for chronic diseases risk management. Indeed, Bayesian inference of a nonhomogeneous Poisson process with three different failure models (linear, exponential, and power law) were considered, and the effects of the scale factor and the aging rate of these models were investigated. In addition, we illustrate our method with an analysis of data from a trial of immunotherapy in the treatment of chronic granulomatous disease. The proposed structural design of CDSS facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert’s opinions and the sampling information which will furnish decision makers with valuable support for quality clinical decision making.
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Barcelo, Carla Fernandez, Elena Calvo-Cidoncha, and Laura Sampietro-colom. "PP151 VALIDATE Methodology On A Medication-Related Clinical Decision Support System: Holistic Assessment For Optimal Technology Adoption." International Journal of Technology Assessment in Health Care 38, S1 (2022): S89. http://dx.doi.org/10.1017/s026646232200263x.
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IntroductionIn the past decade, health technology assessment (HTA) has narrowed its scope to analyses of mainly clinical and economic benefits. Technology challenges in the 21st century emphasize the need for holistic assessments to obtain accurate recommendations for decision-making, as in HTA’s foundations. Using the VALues In Doing Assessments of health TEchnologies (VALIDATE) methodology for complex technologies provides a deeper understanding of problems through analysis of stakeholders’ views, allowing for more comprehensive HTAs. This study aimed to assess a pharmaceutical clinical decision support system (CDSS) using VALIDATE.MethodsSemi-structured interviews with different stakeholders were conducted in the following domains: problem definition (medication error [ME] occurrence and prevention); judgement of solution (existing preventive methods and previous experiences of the CDSS); background theories (future impact and personal beliefs); and barriers to and facilitators of implementation. The following individuals were interviewed: medical informatic specialists (n=3), pharmacists (n=2), nurses (n=2), physicians (n=2), CDSS company representatives (n=1), electronic health record developer (n=1), and health consultancy firm representatives (n=1). Content analysis was used to integrate and analyze the data.ResultsThe multistakeholder interviews identified various barriers to the acceptance and implementation of a pharmaceutical CDSS that were different from those reported in the literature. These included: (i) occurrence of ME (no traceability of medication taken or poor patient medication empowerment); (ii) perception of current level of MEs (huge improvement from ten years ago); (iii) perception of technology as a tool to prevent ME (not enough if only implemented at one point of care); (iv) previous experiences with a CDSS (low rates of development of CDSSs are due to medication prescriptions being digitalized last in hospitals); (v) CDSS metrics (input data should be measured to control CDSS performance); and (vi) other barriers.ConclusionsIncluding facts and stakeholders’ values in problem definition and the scoping of health technologies is essential for the proper conduct of HTAs. Incorporating views from multiple stakeholders when scoping the assessment of health technologies brings additional values to literature findings, resulting in a more holistic evaluation. The lack of multistakeholder scoping can lead to inaccurate information and result in wrong decisions about if, when, and how to adopt a CDSS.
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Coiera, Enrico, and Huong Ly Tong. "Replication studies in the clinical decision support literature–frequency, fidelity, and impact." Journal of the American Medical Informatics Association 28, no. 9 (2021): 1815–25. http://dx.doi.org/10.1093/jamia/ocab049.
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Abstract Objective To assess the frequency, fidelity, and impact of replication studies in the clinical decision support system (CDSS) literature. Materials and Methods A PRISMA-compliant review identified CDSS replications across 28 health and biomedical informatics journals. Included articles were assessed for fidelity to the original study using 5 categories: Identical, Substitutable, In-class, Augmented, and Out-of-class; and 7 IMPISCO domains: Investigators (I), Method (M), Population (P), Intervention (I), Setting (S), Comparator (C), and Outcome (O). A fidelity score and heat map were generated using the ratings. Results From 4063 publications matching search criteria for CDSS research, only 12/4063 (0.3%) were ultimately identified as replications. Six articles replicated but could not reproduce the results of the Han et al (2005) CPOE study showing mortality increase and, over time, changed from truth testing to generalizing this result. Other replications successfully tested variants of CDSS technology (2/12) or validated measurement instruments (4/12). Discussion A replication rate of 3 in a thousand studies is low even by the low rates in other disciplines. Several new reporting methods were developed for this study, including the IMPISCO framework, fidelity scores, and fidelity heat maps. A reporting structure for clearly identifying replication research is also proposed. Conclusion There is an urgent need to better characterize which core CDSS principles require replication, identify past replication data, and conduct missing replication studies. Attention to replication should improve the efficiency and effectiveness of CDSS research and avoiding potentially harmful trial and error technology deployment.