Journal articles: 'Clinical support tools'

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Kosinski, Lawrence R. "Clinical Decision Support Tools." Clinical Gastroenterology and Hepatology 11, no. 7 (July 2013): 756–59. http://dx.doi.org/10.1016/j.cgh.2013.04.015.

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Sofianou, A., J. Kannry, D. M. Mann, T. G. McGinn, and L. J. McCullagh. "User Centered Clinical Decision Support Tools." Applied Clinical Informatics 05, no. 04 (2014): 1015–25. http://dx.doi.org/10.4338/aci-2014-05-ra-0048.

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Summary Background: Dissemination and adoption of clinical decision support (CDS) tools is a major initiative of the Affordable Care Act’s Meaningful Use program. Adoption of CDS tools is multipronged with personal, organizational, and clinical settings factoring into the successful utilization rates. Specifically, the diffusion of innovation theory implies that ‘early adopters’ are more inclined to use CDS tools and younger physicians tend to be ranked in this category. Objective: This study examined the differences in adoption of CDS tools across providers’ training level. Participants: From November 2010 to 2011, 168 residents and attendings from an academic medical institution were enrolled into a randomized controlled trial.Intervention: The intervention arm had access to the CDS tool through the electronic health record (EHR) system during strep and pneumonia patient visits. Main Measures: The EHR system recorded details on how intervention arm interacted with the CDS tool including acceptance of the initial CDS alert, completion of risk-score calculators and the signing of medication order sets. Using the EHR data, the study performed bivariate tests and general estimating equation (GEE) modeling to examine the differences in adoption of the CDS tool across residents and attendings. Key Results: The completion rates of the CDS calculator and medication order sets were higher amongst first year residents compared to all other training levels. Attendings were the less likely to accept the initial step of the CDS tool (29.3%) or complete the medication order sets (22.4%) that guided their prescription decisions, resulting in attendings ordering more antibiotics (37.1%) during an CDS encounter compared to residents. Conclusion: There is variation in adoption of CDS tools across training levels. Attendings tended to accept the tool less but ordered more medications. CDS tools should be tailored to clinicians’ training levels. Citation: McCullagh LJ, Sofianou A, Kannry J, Mann DM, McGinn TG. User centered clinical decision support tools: Adoption across clinician training level. Appl Clin Inf 2014; 5: 1015–1025http://dx.doi.org/10.4338/ACI-2014-05-RA-0048

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Modgil, S., and P. Hammond. "Decision support tools for clinical trial design." Artificial Intelligence in Medicine 27, no. 2 (February 2003): 181–200. http://dx.doi.org/10.1016/s0933-3657(02)00112-4.

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Stillman, Robert C. "Clinical Decision Support Tools Improving Cancer Care." Seminars in Oncology Nursing 34, no. 2 (May 2018): 158–67. http://dx.doi.org/10.1016/j.soncn.2018.03.007.

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Dighe, Anand S. "Clinical Decision Support: Tools, Strategies, and Emerging Technologies." Clinics in Laboratory Medicine 39, no. 2 (June 2019): i. http://dx.doi.org/10.1016/s0272-2712(19)30016-2.

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Sullivan, Frank, and Jeremy C. Wyatt. "How decision support tools help define clinical problems." BMJ 331, no. 7520 (October 6, 2005): 831–33. http://dx.doi.org/10.1136/bmj.331.7520.831.

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Jackups, Ronald. "Clinical Decision Support Tools for Microbiology Laboratory Testing." Clinical Microbiology Newsletter 42, no. 5 (March 2020): 35–44. http://dx.doi.org/10.1016/j.clinmicnews.2020.02.001.

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Bohen, Faye, and Ceri Woodrow. "Dynamic support database clinical support tool: inter-rater reliability." Advances in Mental Health and Intellectual Disabilities 14, no. 2 (February 3, 2020): 25–32. http://dx.doi.org/10.1108/amhid-09-2019-0027.

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Purpose The dynamic support database (DSD) clinical support tool structures the risk of admission rating for individuals with intellectual disabilities. This study aims to investigate inter-rater reliability between multi-disciplinary health care professionals within the North West of England. Design/methodology/approach A small-scale quantitative study investigated reliability between raters on the DSD clinical support tool. A data set of 60 rating tools for 30 individuals was used. Descriptive statistics and Kappa coefficient explored agreement. Findings The DSD clinical support tool was found to have strong inter-rater reliability between individual items and the differences between individual scores were spread suggesting variance found could not be attributed to specific questions. Strong inter-rater reliability was found in the overall ratings. Research limitations/implications Results suggest the DSD clinical support tool provides stratification for risk of admission ratings independent of who completes it. Future studies could investigate inter-rater reliability between organisations, i.e. health and social care professionals, and use a larger data sample to ensure generalisability. Replication of the study within child and adolescent services using the children’s DSD clinical support tool is also recommended. Originality/value The DSD clinical support tool has been implemented within the child and adult intellectual disability services across the North West. As more teams across England consider its implementation, the study provides reassurance that coding agreement is high, allowing for stratification for risk of admission independent of the rater.

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Lipton, Jonathan, and Jan A. Hazelzet. "Clinical decision support systems: Important tools when appropriately used*." Pediatric Critical Care Medicine 10, no. 1 (January 2009): 128–29. http://dx.doi.org/10.1097/pcc.0b013e31819838f9.

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Walsh, Kieran. "What patients think of online clinical decision support tools." BMJ Simulation and Technology Enhanced Learning 4, no. 1 (February 17, 2017): 41–42. http://dx.doi.org/10.1136/bmjstel-2017-000198.

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Mould, DR, G. D'Haens, and RN Upton. "Clinical Decision Support Tools: The Evolution of a Revolution." Clinical Pharmacology & Therapeutics 99, no. 4 (February 15, 2016): 405–18. http://dx.doi.org/10.1002/cpt.334.

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Kaiafas, Kristen N. "Clinical Decision Support Tools and the COVID-19 Pandemic." Journal of Christian Nursing 37, no. 3 (July 2020): 192. http://dx.doi.org/10.1097/cnj.0000000000000737.

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Wright, A., and R. N. Shiffman. "Evidence-Based Clinical Decision Support." Yearbook of Medical Informatics 22, no. 01 (August 2013): 120–27. http://dx.doi.org/10.1055/s-0038-1638843.

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Summary Background: Clinical decision support (CDS) is a key tool for enabling evidence-based medicine and improving the quality of healthcare. However, effective CDS faces a variety of challenges, including those relating to knowledge synthesis, capture, transformation, localization and maintenance. If not properly addressed, these challenges can limit the effectiveness of CDS, and potentially risk inaccurate or inappropriate interventions to clinicians. Objectives: (1) To describe an approach to CDS development using evidence as a basis for clinical decision support systems that promote effective care; (2) To review recent evidence regarding the effectiveness of selected clinical decision support systems. Method: Review and analysis of recent literature with identification of trends and best practices. Results: The state-of-the-art in CDS has advanced significantly, and many recent trials have shown CDS to be effective, although the results are mixed overall. Issues related to knowledge capture and synthesis, problems in knowledge transformation at the interface between knowledge authors and CDS developers, and problems specific to local CDS design and implementation can interfere with CDS development. Best practices, tools and techniques to manage them are described. Conclusions: CDS, when used well, can be effective, but further research is needed for it to reach its full potential.

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Schubel, Laura, Lucy Stein, Ronald Romero, and Kristen Miller. "Mitigating Cardiovascular Risk Through User Informed Clinical Decision Support." Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 9, no. 1 (September 2020): 67–69. http://dx.doi.org/10.1177/2327857920091045.

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As the volume of data within the electronic health record (EHR) increases, there is an evident need for user-friendly and efficient clinical decision support tools developed to assist with patient assessment. Risk calculators, specifically for atherosclerotic cardiovascular disease (ASCVD), are examples of surveillance tools that intend to quantify and predict patient risk of suffering a cardiovascular event. However, despite reported frequent use by clinicians, risk calculators exist largely outside of the EHR, requiring external navigation and increasing the likelihood of user error. Using a mixed methods approach to development, the present research mitigates the challenges posed by external surveillance platforms and discusses the process of designing and optimizing a clinical tool intended to address ASCVD risk at the point of care. These methods ultimately resulted in a risk calculator with both provider- and patient-facing platforms, data autopopulating functionality, and customizable and flexible integration within the provider’s EHR workflow.

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Yan, Liang, Thomas Reese, and Scott D. Nelson. "A Narrative Review of Clinical Decision Support for Inpatient Clinical Pharmacists." Applied Clinical Informatics 12, no. 02 (March 2021): 199–207. http://dx.doi.org/10.1055/s-0041-1722916.

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Abstract Objective Increasingly, pharmacists provide team-based care that impacts patient care; however, the extent of recent clinical decision support (CDS), targeted to support the evolving roles of pharmacists, is unknown. Our objective was to evaluate the literature to understand the impact of clinical pharmacists using CDS. Methods We searched MEDLINE, EMBASE, and Cochrane Central for randomized controlled trials, nonrandomized trials, and quasi-experimental studies which evaluated CDS tools that were developed for inpatient pharmacists as a target user. The primary outcome of our analysis was the impact of CDS on patient safety, quality use of medication, and quality of care. Outcomes were scored as positive, negative, or neutral. The secondary outcome was the proportion of CDS developed for tasks other than medication order verification. Study quality was assessed using the Newcastle–Ottawa Scale. Results Of 4,365 potentially relevant articles, 15 were included. Five studies were randomized controlled trials. All included studies were rated as good quality. Of the studies evaluating inpatient pharmacists using a CDS tool, four showed significantly improved quality use of medications, four showed significantly improved patient safety, and three showed significantly improved quality of care. Six studies (40%) supported expanded roles of clinical pharmacists. Conclusion These results suggest that CDS can support clinical inpatient pharmacists in preventing medication errors and optimizing pharmacotherapy. Moreover, an increasing number of CDS tools have been developed for pharmacists' roles outside of order verification, whereby further supporting and establishing pharmacists as leaders in safe and effective pharmacotherapy.

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Hogan, A., J. Michel, A. R. Localio, D. Karavite, L. Song, M. J. Ramos, A. G. Fiks, S. Lorch, R. W. Grundmeier, and L. H. Utidjian. "Clinical Decision Support and Palivizumab." Applied Clinical Informatics 06, no. 04 (2015): 769–84. http://dx.doi.org/10.4338/aci-2015-08-ra-0096.

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SummaryBackground and Objectives: Palivizumab can reduce hospitalizations due to respiratory syncytial virus (RSV), but many eligible infants fail to receive the full 5-dose series. The efficacy of clinical decision support (CDS) in fostering palivizumab receipt has not been studied. We sought a comprehensive solution for identifying eligible patients and addressing barriers to palivizumab administration.Methods: We developed workflow and CDS tools targeting patient identification and palivizumab administration. We randomized 10 practices to receive palivizumab-focused CDS and 10 to receive comprehensive CDS for premature infants in a 3-year longitudinal cluster-randomized trial with 2 baseline and 1 intervention RSV seasons.Results: There were 356 children eligible to receive palivizumab, with 194 in the palivizumab-focused group and 162 in the comprehensive CDS group. The proportion of doses administered to children in the palivizumab-focused intervention group increased from 68.4% and 65.5% in the two baseline seasons to 84.7% in the intervention season. In the comprehensive intervention group, proportions of doses administered declined during the baseline seasons (from 71.9% to 62.4%) with partial recovery to 67.9% during the intervention season. The palivizumab-focused group improved by 19.2 percentage points in the intervention season compared to the prior baseline season (p < 0.001), while the comprehensive intervention group only improved 5.5 percentage points (p = 0.288). The difference in change between study groups was significant (p = 0.05).Conclusions: Workflow and CDS tools integrated in an EHR may increase the administration of palivizumab. The support focused on palivizumab, rather than comprehensive intervention, was more effective at improving palivizumab administration.

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Sun, J., S. Knoop, A. Shabo, B. Carmeli, D. Sow, T. Syed-Mahmood, W. Rapp, and M. S. Kohn. "IBM’s Health Analytics and Clinical Decision Support." Yearbook of Medical Informatics 23, no. 01 (August 2014): 154–62. http://dx.doi.org/10.15265/iy-2014-0002.

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Summary Objectives: This survey explores the role of big data and health analytics developed by IBM in supporting the transformation of healthcare by augmenting evidence-based decision-making. Methods: Some problems in healthcare and strategies for change are described. It is argued that change requires better decisions, which, in turn, require better use of the many kinds of healthcare information. Analytic resources that address each of the information challenges are described. Examples of the role of each of the resources are given. Results: There are powerful analytic tools that utilize the various kinds of big data in healthcare to help clinicians make more personalized, evidenced-based decisions. Such resources can extract relevant information and provide insights that clinicians can use to make evidence-supported decisions. There are early suggestions that these resources have clinical value. As with all analytic tools, they are limited by the amount and quality of data. Conclusion: Big data is an inevitable part of the future of healthcare. There is a compelling need to manage and use big data to make better decisions to support the transformation of healthcare to the personalized, evidence-supported model of the future. Cognitive computing resources are necessary to manage the challenges in employing big data in healthcare. Such tools have been and are being developed. The analytic resources, themselves, do not drive, but support healthcare transformation.

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Simpson, Matthew J., Jeanette M. Daly, Douglas H. Fernald, John M. Westfall, LeAnn C. Michaels, Barcey T. Levy, David L. Hahn, Lyle J. Fagnan, and Donald E. Nease. "How to Translate Self-Management Support Tools Into Clinical Practice." Journal of Patient-Centered Research and Reviews 5, no. 4 (October 29, 2018): 276–86. http://dx.doi.org/10.17294/2330-0698.1636.

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SCHNEIDER, MARY ELLEN. "Online Tools Offer Physicians Clinical Decision Support by Computer, PDA." Internal Medicine News 38, no. 23 (December 2005): 64. http://dx.doi.org/10.1016/s1097-8690(05)72495-1.

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Brouwers, Melissa C., Ivan D. Florez, Sheila A. McNair, Emily T. Vella, and Xioamei Yao. "Clinical Practice Guidelines: Tools to Support High Quality Patient Care." Seminars in Nuclear Medicine 49, no. 2 (March 2019): 145–52. http://dx.doi.org/10.1053/j.semnuclmed.2018.11.001.

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Wright, Deborah May, Cristin Gregor Print, and Arend Eric Hepburn Merrie. "Clinical decision support systems: should we rely on unvalidated tools?" ANZ Journal of Surgery 81, no. 5 (April 24, 2011): 314–17. http://dx.doi.org/10.1111/j.1445-2197.2011.05703.x.

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Merijohn, George K., James D. Bader, Julie Frantsve-Hawley, and Krishna Aravamudhan. "Clinical Decision Support Chairside Tools for Evidence-Based Dental Practice." Journal of Evidence Based Dental Practice 8, no. 3 (September 2008): 119–32. http://dx.doi.org/10.1016/j.jebdp.2008.05.016.

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Yu, Feliciano, Thomas K. Houston, Midge N. Ray, Duriel Q. Garner, and Eta S. Berner. "Patterns of Use of Handheld Clinical Decision Support Tools in the Clinical Setting." Medical Decision Making 27, no. 6 (September 26, 2007): 744–53. http://dx.doi.org/10.1177/0272989x07305321.

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Knaus, M., L. McCullagh, A. Sofianou, L. Rosen, T. McGinn, J. Kannry, and D. Mann. "Measures of User experience in a Streptococcal pharyngitis and Pneumonia Clinical Decision Support Tools." Applied Clinical Informatics 05, no. 03 (2014): 824–35. http://dx.doi.org/10.4338/aci-2014-04-ra-0043.

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SummaryObjective: To understand clinician adoption of CDS tools as this may provide important insights for the implementation and dissemination of future CDS tools.Materials and Methods: Clinicians (n=168) at a large academic center were randomized into intervention and control arms to assess the impact of strep and pneumonia CDS tools. Intervention arm data were analyzed to examine provider adoption and clinical workflow. Electronic health record data were collected on trigger location, the use of each component and whether an antibiotic, other medication or test was ordered. Frequencies were tabulated and regression analyses were used to determine the association of tool component use and physician orders.Results: The CDS tool was triggered 586 times over the study period. Diagnosis was the most frequent workflow trigger of the CDS tool (57%) as compared to chief complaint (30%) and diagnosis/antibiotic combinations (13%). Conversely, chief complaint was associated with the highest rate (83%) of triggers leading to an initiation of the CDS tool (opening the risk prediction calculator). Similar patterns were noted for initiation of the CDS bundled ordered set and completion of the entire CDS tool pathway. Completion of risk prediction and bundled order set components were associated with lower rates of antibiotic prescribing (OR 0.5; CI 0.2-1.2 and OR 0.5; CI 0.3-0.9, respectively).Discussion: Different CDS trigger points in the clinician user workflow lead to substantial variation in downstream use of the CDS tool components. These variations were important as they were associated with significant differences in antibiotic ordering.Conclusions: These results highlight the importance of workflow integration and flexibility for CDS success.Citation: Mann D, Knaus M, McCullagh L, Sofianou A, Rosen L, McGinn T, Kannry J. Measures of user experience in a streptococcal pharyngitis and pneumonia clinical decision support tools. Appl Clin Inf 2014; 5: 824–835http://dx.doi.org/10.4338/ACI-2014-04-RA-0043

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Rudolf, Joseph W., and Anand S. Dighe. "Decision Support Tools within the Electronic Health Record." Clinics in Laboratory Medicine 39, no. 2 (June 2019): 197–213. http://dx.doi.org/10.1016/j.cll.2019.01.001.

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Baron, Jason M., Danielle E. Kurant, and Anand S. Dighe. "Machine Learning and Other Emerging Decision Support Tools." Clinics in Laboratory Medicine 39, no. 2 (June 2019): 319–31. http://dx.doi.org/10.1016/j.cll.2019.01.010.

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Evans, RW. "A critical perspective on the tools to support clinical decision making." Transfusion 36, no. 8 (August 1996): 671–73. http://dx.doi.org/10.1046/j.1537-2995.1996.36896374368.x.

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Litvin, Cara B., Kimberly S. Davis, William P. Moran, Patty J. Iverson, Yumin Zhao, and Jane Zapka. "The Use of Clinical Decision-Support Tools to Facilitate Geriatric Education." Journal of the American Geriatrics Society 60, no. 6 (May 29, 2012): 1145–49. http://dx.doi.org/10.1111/j.1532-5415.2012.03960.x.

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Zeier, Zane, Linda L. Carpenter, Ned H. Kalin, Carolyn I. Rodriguez, William M. McDonald, Alik S. Widge, and Charles B. Nemeroff. "Clinical Implementation of Pharmacogenetic Decision Support Tools for Antidepressant Drug Prescribing." American Journal of Psychiatry 175, no. 9 (September 2018): 873–86. http://dx.doi.org/10.1176/appi.ajp.2018.17111282.

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Rosenkrantz, Andrew B., and Ankur M. Doshi. "Continued Evolution of Clinical Decision Support Tools for Guiding Imaging Utilization." Academic Radiology 22, no. 4 (April 2015): 542–43. http://dx.doi.org/10.1016/j.acra.2014.12.009.

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Memedovich, K. A., D. Grigat, L. Dowsett, D. Lorenzetti, J. E. Andruchow, A. D. McRae, E. S. Lang, and F. Clement. "MP39: Characteristics of clinical decision support tools that impact physician behaviour: a systematic review and meta-analysis." CJEM 20, S1 (May 2018): S55. http://dx.doi.org/10.1017/cem.2018.193.

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Introduction: Clinical decision support (CDS) has been implemented in many clinical settings in order to improve decision-making. Their potential to improve diagnostic accuracy and reduce unnecessary testing is well documented; however, their effectiveness in impacting physician practice in real world implementations has been limited by poor physician adherence. The objective of this systematic review and meta-regression was to establish the effectiveness of CDS tools on adherence and identify which characteristics of CDS tools increase physician use of and adherence. Methods: A systematic review and meta-analysis was conducted. MEDLINE, EMBASE, PsychINFO, the Cochrane Central Register of Controlled Trials and Cochrane Database of Systematic Reviews were searched from inception to June 2017. Included studies examined CDS in a hospital setting, reported on physician adherence to or use of CDS, utilized a comparative study design, and reported primary data. All tool type was classified based on the Cochrane Effective Practice and Organization of Care (EPOC) classifications. Studies were stratified based on study design (RCT vs. observational). Meta-regression was completed to assess the different effect of characteristics of the tool (e.g. whether the tool was mandatory or voluntary, EPOC classifications). Results: A total of 3,359 candidate articles were identified. Seventy-two met inclusion criteria, of which 46 reported outcomes appropriate for meta-regression (5 RCTs and 41 observational studies). Overall, a trend of increased CDS use was found (pooled RCT OR: 1.36 [95% CI: 0.97-1.89]; pooled observational OR: 2.12 [95% CI: 1.75-2.56]).When type of tool is considered, clinical practice guidelines were superior compared to other interventions (p=.150). Reminders (p=.473) and educational interventions (p=.489) were less successful than other interventions. Multi-modal tools were not more successful that single interventions (p=.810). Lastly, voluntary tools may be supperior to than mandatory tools (p=.148). None of these results are statistically significant. Conclusion: CDS tools accompanied by a planned intervention increases physician utilization and adherence to the tool. Meta-regression found that clinical practice guidelines had the biggest impact on physician adherence although not statistically significant. Further research is required to understand the most effective intervention to maximize physician utilization of CDS tools.

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Moreno-Conde, Alberto, Tony Austin, Jesús Moreno-Conde, Carlos L. Parra-Calderón, and Dipak Kalra. "Evaluation of clinical information modeling tools." Journal of the American Medical Informatics Association 23, no. 6 (June 7, 2016): 1127–35. http://dx.doi.org/10.1093/jamia/ocw018.

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Abstract Objective Clinical information models are formal specifications for representing the structure and semantics of the clinical content within electronic health record systems. This research aims to define, test, and validate evaluation metrics for software tools designed to support the processes associated with the definition, management, and implementation of these models. Methodology The proposed framework builds on previous research that focused on obtaining agreement on the essential requirements in this area. A set of 50 conformance criteria were defined based on the 20 functional requirements agreed by that consensus and applied to evaluate the currently available tools. Results Of the 11 initiative developing tools for clinical information modeling identified, 9 were evaluated according to their performance on the evaluation metrics. Results show that functionalities related to management of data types, specifications, metadata, and terminology or ontology bindings have a good level of adoption. Improvements can be made in other areas focused on information modeling and associated processes. Other criteria related to displaying semantic relationships between concepts and communication with terminology servers had low levels of adoption. Conclusions The proposed evaluation metrics were successfully tested and validated against a representative sample of existing tools. The results identify the need to improve tool support for information modeling and software development processes, especially in those areas related to governance, clinician involvement, and optimizing the technical validation of testing processes. This research confirmed the potential of these evaluation metrics to support decision makers in identifying the most appropriate tool for their organization. OBJECTIVO Los Modelos de Información Clínica son especificaciones para representar la estructura y características semánticas del contenido clínico en los sistemas de Historia Clínica Electrónica. Esta investigación define, prueba y valida un marco para la evaluación de herramientas informáticas diseñadas para dar soporte en la en los procesos de definición, gestión e implementación de estos modelos. METODOLOGIA El marco de evaluación propuesto se basa en una investigación previa para obtener consenso en la definición de requisitos esenciales en esta área. A partir de los 20 requisitos funcionales acordados, un conjunto de 50 criterios de conformidad fueron definidos y aplicados en la evaluación de las herramientas existentes. RESULTADOS Un total de 9 de las 11 iniciativas identificadas desarrollando herramientas para el modelado de información clínica fueron evaluadas. Los resultados muestran que las funcionalidades relacionadas con la gestión de tipos de datos, especificaciones, metadatos y mapeo con terminologías u ontologías tienen un buen nivel de adopción. Se identifican posibles mejoras en áreas relacionadas con los procesos de modelado de información. Otros criterios relacionados con presentar las relaciones semánticas entre conceptos y la comunicación con servidores de terminología tienen un bajo nivel de adopción. CONCLUSIONES El marco de evaluación propuesto fue probado y validado satisfactoriamente contra un conjunto representativo de las herramientas existentes. Los resultados identifican la necesidad de mejorar el soporte de herramientas a los procesos de modelado de información y desarrollo de software, especialmente en las áreas relacionadas con gobernanza, participación de profesionales clínicos y la optimización de la validación técnica en los procesos de pruebas técnicas. Esta investigación ha confirmado el potencial de este marco de evaluación para dar soporte a los usuarios en la toma de decisiones sobre que herramienta es más apropiadas para su organización.

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Walsh, Kieran. "Infectious disease outbreaks: how online clinical decision support could help." BMJ Simulation and Technology Enhanced Learning 5, no. 4 (July 21, 2018): 218–20. http://dx.doi.org/10.1136/bmjstel-2018-000368.

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This paper describes an evaluation of how doctors might use an online clinical decision support tool to improve the care that they would provide to patients with infectious disease and what features they would expect in such a clinical decision support tool. Semistructured interviews were conducted by telephone with doctors to evaluate the utility of a clinical decision support tool in helping them to improve the care that they would provide to patients with infectious disease and to assess the features that they would value in such a tool. The doctors were primarily interested in how they could use the tool to improve care. They were short of time and so needed to be able to access the content that they needed really quickly. They expected content that was both evidence based and current, and they used a range of devices to access the content. They used desktops, laptops, mobiles and sometimes mobile apps. Doctors view the utility of clinical decision support in the management of rare infectious diseases from a number of perspectives. However, they primarily see utility in the tools as a result of their capacity to improve clinical practice in infectious diseases.

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Warokka, Ari. "Digital Marketing Support and Business Development Using Online Marketing Tools: An Experimental Analysis." International Journal of Psychosocial Rehabilitation 24, no. 1 (January 20, 2020): 1181–88. http://dx.doi.org/10.37200/ijpr/v24i1/pr200219.

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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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Burdick, Timothy, and Rodger Kessler. "Development and use of a clinical decision support tool for behavioral health screening in primary care clinics." Applied Clinical Informatics 08, no. 02 (April 2017): 412–29. http://dx.doi.org/10.4338/aci-2016-04-ra-0068.

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SummaryObjective: Screening, brief intervention, and referral for treatment (SBIRT) for behavioral health (BH) is a key clinical process. SBIRT tools in electronic health records (EHR) are infrequent and rarely studied. Our goals were 1) to design and implement SBIRT using clinical decision support (CDS) in a commercial EHR; and 2) to conduct a pragmatic evaluation of the impact of the tools on clinical outcomes.Methods: A multidisciplinary team designed SBIRT workflows and CDS tools. We analyzed the outcomes using a retrospective descriptive convenience cohort with age-matched comparison group. Data extracted from the EHR were evaluated using descriptive statistics.Results: There were 2 outcomes studied: 1) development and use of new BH screening tools and workflows; and 2) the results of use of those tools by a convenience sample of 866 encounters. The EHR tools developed included a flowsheet for documenting screens for 3 domains (depression, alcohol use, and prescription misuse); and 5 alerts with clinical recommendations based on screening; and reminders for annual screening. Positive screen rate was 21% (≥1 domain) with 60% of those positive for depression. Screening was rarely positive in 2 domains (11%), and never positive in 3 domains. Positive and negative screens led to higher rates of documentation of brief intervention (BI) compared with a matched sample who did not receive screening, including changes in psychotropic medications, updated BH terms on the problem list, or referral for BH intervention. Clinical process outcomes changed even when screening was negative.Conclusions: Modified workflows for BH screening and CDS tools with clinical recommendations can be deployed in the EHR. Using SBIRT tools changed clinical process metrics even when screening was negative, perhaps due to conversations about BH not captured in the screening flowsheet. Although there are limitations to the study, results support ongoing investigation.Citation: Burdick TE, Kessler RS. Development and use of a clinical decision support tool for behavioral health screening in primary care clinics. Appl Clin Inform 2017; 8: 412–429 https://doi.org/10.4338/ACI-2016-04-RA-0068

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Richardson, Safiya, David Feldstein, Thomas McGinn, Linda S. Park, Sundas Khan, Rachel Hess, Paul D. Smith, Rebecca Grochow Mishuris, Lauren McCullagh, and Devin Mann. "Live Usability Testing of Two Complex Clinical Decision Support Tools: Observational Study." JMIR Human Factors 6, no. 2 (April 15, 2019): e12471. http://dx.doi.org/10.2196/12471.

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New, Timothy David. "Clinical decision support tools in A&E nursing: a preliminary study." Nursing Standard 14, no. 34 (May 10, 2000): 32–39. http://dx.doi.org/10.7748/ns2000.05.14.34.32.c2836.

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Beattie, James W. "Web-Based PDA Downloads for Clinical Practice Guidelines and Decision Support Tools." Medical Reference Services Quarterly 22, no. 4 (November 18, 2003): 57–64. http://dx.doi.org/10.1300/j115v22n04_06.

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Clauson, Kevin A., Hyla H. Polen, Amy S. Peak, Wallace A. Marsh, and Sandra L. DiScala. "Clinical Decision Support Tools: Personal Digital Assistant versus Online Dietary Supplement Databases." Annals of Pharmacotherapy 42, no. 11 (October 28, 2008): 1592–99. http://dx.doi.org/10.1345/aph.1l297.

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Background: Clinical decision support tools (CDSTs) on personal digital assistants (PDAs) and online databases assist healthcare practitioners who make decisions about dietary supplements. Objective: To assess and compare the content of PDA dietary supplement databases and their online counterparts used as CDSTs. Methods: A total of 102 question-and-answer pairs were developed within 10 weighted categories of the most clinically relevant aspects of dietary supplement therapy. PDA versions of AltMedDex. Lexi-Natural, Natural Medicines Comprehensive Database, and Natural Standard and their online counterparts were assessed by scope (percent of correct answers present), completeness (3-point scale), ease of use, and a composite score integrating all 3 criteria. Descriptive statistics and inferential statistics, including a χ2 test, Scheffé's multiple comparison test, McNemar's test, and the Wilcoxon signed rank test were used to analyze data. Results: The scope scores for PDA databases were: Natural Medicines Comprehensive Database 84.3%, Natural Standard 58.8%, Lexi-Natural 50.0%, and AltMedDex 36.3%, with Natural Medicines Comprehensive Database statistically superior (p < 0.01). Completeness scores were; Natural Medicines Comprehensive Database 78.4%, Natural Standard 51.0%, Lexi-Natural 43.5%, and AltMedDex 29.7%. Lexi-Natural was superior in ease of use (p < 0.01). Composite scores for PDA databases were: Natural Medicines Comprehensive Database 79.3, Natural Standard 53.0, Lexi-Natural 48.0, and AltMedDex 32.5, with Natural Medicines Comprehensive Database superior (p < 0.01). There was no difference between the scope for PDA and online database pairs with Lexi-Natural (50.0% and 53.9%, respectively) or Natural Medicines Comprehensive Database (84.3% and 84.3%, respectively) (p > 0.05), whereas differences existed for AltMedDex (36.3% vs 74.5%, respectively) and Natural Standard (50.8% vs 80.4%, respectively) (p < 0.01). For composite scores, AltMedDex and Natural Standard online were better than their PDA counterparts (p < 0.01). Conclusions: Natural Medicines Comprehensive Database achieved significantly higher scope, completeness, and composite scores compared with other dietary supplement PDA CDSTs in this study. There was no difference between the PDA and online databases for Lexi-Natural and Natural Medicines Comprehensive Database, whereas online versions of AltMedDex and Natural Standard were significantly better than their PDA counterparts.

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KOSSMAN, SUSAN P., LEIGH ANN BONNEY, and MYOUNG JIN KIM. "Electronic Health Record Tools’ Support of Nurses’ Clinical Judgment and Team Communication." CIN: Computers, Informatics, Nursing 31, no. 11 (November 2013): 539–44. http://dx.doi.org/10.1097/01.ncn.0000432122.79452.7b.

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&NA;. "Electronic Health Record Tools’ Support of Nurses’ Clinical Judgment and Team Communication." CIN: Computers, Informatics, Nursing 31, no. 11 (November 2013): 545–46. http://dx.doi.org/10.1097/01.ncn.0000438396.60521.fc.

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Kranzler, Amy, Sandra S. Pimentel, and Amanda Zayde. "Scaffolding and Support Beams: Clinical and Administrative Tools for Emerging Adult Programs." Evidence-Based Practice in Child and Adolescent Mental Health 4, no. 2 (December 20, 2018): 122–40. http://dx.doi.org/10.1080/23794925.2018.1551092.

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Freundlich, Robert E., Jonathan P. Wanderer, and Jesse M. Ehrenfeld. "Clinical Decision Support Tools Need to Improve More Than Just Process Outcomes." Anesthesiology 129, no. 3 (September 1, 2018): 614. http://dx.doi.org/10.1097/aln.0000000000002349.

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Weisskopf, Michael, Guido Bucklar, and Jürg Blaser. "Tools in a clinical information system supporting clinical trials at a Swiss University Hospital." Clinical Trials 11, no. 6 (August 12, 2014): 673–80. http://dx.doi.org/10.1177/1740774514546702.

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Background: Issues concerning inadequate source data of clinical trials rank second in the most common findings by regulatory authorities. The increasing use of electronic clinical information systems by healthcare providers offers an opportunity to facilitate and improve the conduct of clinical trials and the source documentation. We report on a number of tools implemented into the clinical information system of a university hospital to support clinical research. Methods: In 2011/2012, a set of tools was developed in the clinical information system of the University Hospital Zurich to support clinical research, including (1) a trial registry for documenting metadata on the clinical trials conducted at the hospital, (2) a patient–trial–assignment–tool to tag patients in the electronic medical charts as participants of specific trials, (3) medical record templates for the documentation of study visits and trial-related procedures, (4) online queries on trials and trial participants, (5) access to the electronic medical records for clinical monitors, (6) an alerting tool to notify of hospital admissions of trial participants, (7) queries to identify potentially eligible patients in the planning phase as trial feasibility checks and during the trial as recruitment support, and (8) order sets to facilitate the complete and accurate performance of study visit procedures. Results: The number of approximately 100 new registrations per year in the voluntary trial registry in the clinical information system now matches the numbers of the existing mandatory trial registry of the hospital. Likewise, the yearly numbers of patients tagged as trial participants as well as the use of the standardized trial record templates increased to 2408 documented trial enrolments and 190 reports generated/month in the year 2013. Accounts for 32 clinical monitors have been established in the first 2 years monitoring a total of 49 trials in 16 clinical departments. A total of 15 months after adding the optional feature of hospital admission alerts of trial participants, 107 running trials have activated this option, including 48 out of 97 studies (49.5%) registered in the year 2013, generating approximately 85 alerts per month. Conclusions: The popularity of the presented tools in the clinical information system illustrates their potential to facilitate the conduct of clinical trials. The tools also allow for enhanced transparency on trials conducted at the hospital. Future studies on monitoring and inspection findings will have to evaluate their impact on quality and safety.

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Ostropolets, Anna, Linying Zhang, and George Hripcsak. "A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time." Journal of the American Medical Informatics Association 27, no. 12 (October 29, 2020): 1968–76. http://dx.doi.org/10.1093/jamia/ocaa200.

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

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Bouaud, J., and V. Koutkias. "Computerized Clinical Decision Support: Contributions from 2015." Yearbook of Medical Informatics 25, no. 01 (August 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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Reeves, J. Jeffery, Hannah M. Hollandsworth, Francesca J. Torriani, Randy Taplitz, Shira Abeles, Ming Tai-Seale, Marlene Millen, Brian J. Clay, and Christopher A. Longhurst. "Rapid response to COVID-19: health informatics support for outbreak management in an academic health system." Journal of the American Medical Informatics Association 27, no. 6 (April 27, 2020): 853–59. http://dx.doi.org/10.1093/jamia/ocaa037.

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Abstract Objective To describe the implementation of technological support important for optimizing clinical management of the COVID-19 pandemic. Materials and Methods Our health system has confirmed prior and current cases of COVID-19. An Incident Command Center was established early in the crisis and helped identify electronic health record (EHR)-based tools to support clinical care. Results We outline the design and implementation of EHR-based rapid screening processes, laboratory testing, clinical decision support, reporting tools, and patient-facing technology related to COVID-19. Discussion The EHR is a useful tool to enable rapid deployment of standardized processes. UC San Diego Health built multiple COVID-19-specific tools to support outbreak management, including scripted triaging, electronic check-in, standard ordering and documentation, secure messaging, real-time data analytics, and telemedicine capabilities. Challenges included the need to frequently adjust build to meet rapidly evolving requirements, communication, and adoption, and to coordinate the needs of multiple stakeholders while maintaining high-quality, prepandemic medical care. Conclusion The EHR is an essential tool in supporting the clinical needs of a health system managing the COVID-19 pandemic.

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Henry, N. Lynn, Philippe L. Bedard, and Angela DeMichele. "Standard and Genomic Tools for Decision Support in Breast Cancer Treatment." American Society of Clinical Oncology Educational Book, no. 37 (May 2017): 106–15. http://dx.doi.org/10.1200/edbk_175617.

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Over the past few decades, comprehensive characterization of the cancer genome has elucidated pathways that drive cancer and mechanisms of resistance to therapy and provided important insights for development of new therapies. These advances have resulted in the development of prognostic and predictive tools for use in clinical settings, which can assist clinicians and patients in making informed decisions about the benefits of established therapies. In early-stage breast cancer, multiparameter genomic assays are now available for decision making about the duration of adjuvant endocrine therapy and the use of adjuvant chemotherapy. Similarly, in metastatic disease, there are multiple commercially available next-generation sequencing options for identifying genetic alterations in tumors that may be targeted with a drug. Although these tools hold great promise for providing precision medicine, it can be difficult for the treating physician to evaluate their clinical utility and appropriately select tools for individual clinical situations. This review summarizes the currently available genomic tools in breast cancer, the data underlying their clinical validity and utility, and how they can be used in conjunction with standard clinicopathologic data for making adjuvant and metastatic treatment decisions.

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Vemula, Ridhima, Uli Chettipally, Mamata Kene, Dustin Mark, Andrew Elms, James Lin, Mary Reed, et al. "Optimizing Clinical Decision Support in the Electronic Health Record." Applied Clinical Informatics 07, no. 03 (July 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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Journal articles on the topic 'Clinical support tools'

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