Cost Effectiveness of a Clinical Decision Support System Based on the Recommendations of the European Society of Cardiology and Other Societies for the Management of Hypercholesterolemia

Cobos, Albert; Vilaseca, Jordi; Asenjo, César; Pedro-Botet, J; Sánchez, Emília; Val, Alicia; Torremadé, Eva; Espinosa, Cristina; Bergoñón, Salvador

doi:10.2165/00115677-200513060-00007

Cited by 27 publications

(59 citation statements)

References 32 publications

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“…Varonen et al (2008), in a study group of physicians on implementing CDSSs; Robertson et al (2011);Hor et al (2010), in a study of the attitudes and preparedness towards clinical decision support in e-Prescribing; and Lai, Macmillan, Daudelin & Kent (2006), in their study of the potential of training to increase acceptance and use of CDSS for medical diagnosis; all noted that obscure workflow issues and the ability to incorporate the system within the normal flow of practice acted as barriers to clinician use. Cobos et al (2005), in a study of the cost effectiveness of a CDSS for the management of hypercholesterolemia; pointed out the lack of agreements with the system created an impediment to use. Subramanian et al (2007), in their physician order entry system using CDSS, included the loss of productivity of the clinician as a barrier to use of the system.…”

Section: Performance Expectancy Barriers To Cdss Adoption In Clinicalmentioning

confidence: 99%

“…Poor computer skills (Leslie, et al, 2006); (Toth-Pal, Wardh, Strender, & Nilsson, 2008) Economic constraints/ finance and resources (Kazemi, et al, 2009); (Macy, Skelly, Shiffman, & Flynn, 2005); (Egger, Epstein, Macario, Pearl, & Grunwald, 2008); (Peek , et al, 2011); (Subramanian, et al, 2007); (Robertson, et al, 2011); (Cobos, et al, 2005) Provider discontinuity/support (Trafton, et al, 2010); (Egger, Epstein, Macario, Pearl, & Grunwald, 2008) Poor customer support (Trafton, et al, 2010) Prior bad experience (Varonen, Kortteisto, & Kaila, 2008); (Peek , et al, 2011); (Cobos, et al, 2005) Lack of motivation/incentives (Peek , et al, 2011);…”

Section: Conditionsmentioning

confidence: 99%

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Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Devaraj¹,

Sharma²,

Fausto³

et al. 2014

JBAR

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The objective of the study was to identify potential barriers and facilitators to improve clinical practice using computer-based Clinical Decision Support System (CDSS). Studies published since 2000 were found using PubMed database, PsychInfo, CINAHL, EBSCOhost database, and Google scholar. Twenty-six relevant publications were examined. Thirty-five unique barriers and twenty-five unique facilitators were identified in the literature as important determinants of CDSS's adoption in clinical practice. The list of barriers and facilitators collected from each study were then organized under the four dimensions of The Unified Theory of Acceptance and Use of Technology (UTAUT) model: performance expectancy, effort expectancy, social influence, and facilitating conditions. Some of the important barriers to CDSS use include; lack of time or time constraints, economic constraints (e.g., finance and resources), lack of knowledge of system or content, reluctance to use system in front of patients, obscure workflow issues, less authenticity or reliability of information, lack of agreement with the system, and physician or user attitude toward the system. The study contributes immensely to the literature by identifying the important barriers and facilitators of CDSS.

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Section: Performance Expectancy Barriers To Cdss Adoption In Clinicalmentioning

confidence: 99%

Section: Conditionsmentioning

confidence: 99%

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Devaraj¹,

Sharma²,

Fausto³

et al. 2014

JBAR

View full text Add to dashboard Cite

show abstract

“…In a similar study, Cobos et al (2005) found that the use and adoption of CDSS in clinical practice "was as effective as usual care and induced important savings in the management of hypercholesterolemia" (p. 431). The above two studies contribute to our understanding about the need to implement CDSS in clinical practice so as to support the efficiency of healthcare delivery.…”

Section: Efficiency Of Healthcare Deliverymentioning

confidence: 96%

Impacts and Risks of Adopting Clinical Decision Support Systems

Bonney¹

2011

Efficient Decision Support Systems - Practice and Challenges in Biomedical Related Domain

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“…Twenty-six studies were from the U.S., 24,25,28,29,31,34,36,39,44,45,47,51–54,56,58,63,65,67,80–82,84,85,87 with the remaining studies conducted in Canada (five studies) 41,48,61,66,83 ; Europe (11 studies) 26,27,30,33,37,38,42,46,50,67,68 ; Australia (two studies) 32,60 ; and New Zealand (one study). 43 Most studies implemented CDSSs in outpatient practices; two studies 54,55 were in a hospital setting.…”

Section: Evidence Synthesismentioning

confidence: 99%

Clinical Decision Support Systems and Prevention

Njie

Proia

Thota

et al. 2015

American Journal of Preventive Medicine

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Context Clinical decision support systems (CDSSs) can help clinicians assess cardiovascular disease (CVD) risk and manage CVD risk factors by providing tailored assessments and treatment recommendations based on individual patient data. The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors, practices for CVD-related preventive care services such as clinical tests and prescribed treatments, and management of CVD risk factors. Evidence acquisition An existing systematic review (search period, January 1975–January 2011) of CDSSs for any condition was initially identified. Studies of CDSSs that focused on CVD prevention in that review were combined with studies identified through an updated search (January 2011–October 2012). Data analysis was conducted in 2013. Evidence synthesis A total of 45 studies qualified for inclusion in the review. Improvements were seen for recommended screening and other preventive care services completed by clinicians, recommended clinical tests completed by clinicians, and recommended treatments prescribed by clinicians (median increases of 3.8, 4.0, and 2.0 percentage points, respectively). Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure, total and low-density lipoprotein cholesterol, and hemoglobin A1C levels. Conclusions CDSSs are effective in improving clinician practices related to screening and other preventive care services, clinical tests, and treatments. However, more evidence is needed from implementation of CDSSs within the broad context of comprehensive service delivery aimed at reducing CVD risk and CVD-related morbidity and mortality.

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Cost Effectiveness of a Clinical Decision Support System Based on the Recommendations of the European Society of Cardiology and Other Societies for the Management of Hypercholesterolemia

Cited by 27 publications

References 32 publications

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Barriers and Facilitators to Clinical Decision Support Systems Adoption: A Systematic Review

Impacts and Risks of Adopting Clinical Decision Support Systems

Clinical Decision Support Systems and Prevention

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