Mixed-initiative Decision Support for Care Planning Based on Clinical Practice Guidelines

2001 ◽  
Vol 10 (3-4) ◽  
pp. 429-448 ◽  
Author(s):  
Gleb Beliakov ◽  
James Warren ◽  
Joseph Noone
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Care Planning ◽  
Mixed Initiative

scholarly journals Comparing a Mobile Decision Support System Versus the Use of Printed Materials for the Implementation of an Evidence-Based Recommendation: Protocol for a Qualitative Evaluation (Preprint)

2018 ◽  
Author(s):  
Jhon Camacho ◽  
Ana María Medina Ch. ◽  
Zach Landis-Lewis ◽  
Gerald Douglas ◽  
Richard Boyce
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Mobile App ◽  
Second Phase ◽  
Evidence Based ◽  
Clinical Algorithm ◽  
Qualitative Evidence ◽  
Printed Materials

BACKGROUND The distribution of printed materials is the most frequently used strategy to disseminate and implement clinical practice guidelines, although several studies have shown that the effectiveness of this approach is modest at best. Nevertheless, there is insufficient evidence to support the use of other strategies. Recent research has shown that the use of computerized decision support presents a promising approach to address some aspects of this problem. OBJECTIVE The aim of this study is to provide qualitative evidence on the potential effect of mobile decision support systems to facilitate the implementation of evidence-based recommendations included in clinical practice guidelines. METHODS We will conduct a qualitative study with two arms to compare the experience of primary care physicians while they try to implement an evidence-based recommendation in their clinical practice. In the first arm, we will provide participants with a printout of the guideline article containing the recommendation, while in the second arm, we will provide participants with a mobile app developed after formalizing the recommendation text into a clinical algorithm. Data will be collected using semistructured and open interviews to explore aspects of behavioral change and technology acceptance involved in the implementation process. The analysis will be comprised of two phases. During the first phase, we will conduct a template analysis to identify barriers and facilitators in each scenario. Then, during the second phase, we will contrast the findings from each arm to propose hypotheses about the potential impact of the system. RESULTS We have formalized the narrative in the recommendation into a clinical algorithm and have developed a mobile app. Data collection is expected to occur during 2018, with the first phase of analysis running in parallel. The second phase is scheduled to conclude in July 2019. CONCLUSIONS Our study will further the understanding of the role of mobile decision support systems in the implementation of clinical practice guidelines. Furthermore, we will provide qualitative evidence to aid decisions made by low- and middle-income countries’ ministries of health about investments in these technologies.

scholarly journals MP39: Characteristics of clinical decision support tools that impact physician behaviour: a systematic review and meta-analysis

CJEM ◽  
2018 ◽  
Vol 20 (S1) ◽  
pp. S55-S55
Author(s):  
K. A. Memedovich ◽  
D. Grigat ◽  
L. Dowsett ◽  
D. Lorenzetti ◽  
J. E. Andruchow ◽  
...  
Keyword(s):  
Systematic Review ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Meta Analysis ◽  
Clinical Decision ◽  
Meta Regression ◽  
Physician Adherence

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.

Formalizing clinical practice guideline for clinical decision support systems

2016 ◽  
Vol 23 (2) ◽  
pp. 146-156 ◽  
Author(s):  
Wessam Gad El-Rab ◽  
Osmar R Zaïane ◽  
Mohammad El-Hajj
Keyword(s):  
Clinical Practice ◽  
Decision Support ◽  
Clinical Decision Support ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Clinical Practice Guideline ◽  
Practice Guideline ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Clinical Knowledge

Clinical practice guidelines are valuable sources of clinical knowledge for healthcare professionals. However, the passive dissemination of clinical practice guidelines like publishing in medical journals is ineffective in changing clinical practice behaviour. In this work, we proposed a framework to help adopting an active clinical practice guideline dissemination approach by automatically extracting clinical knowledge from clinical practice guidelines into a clinical decision support system–friendly format. The proposed framework is intended to help human modellers by automating some of the manual formalization activities in order to minimize their manual effort. We evaluated our framework using all recommendations from two clinical practice guidelines produced by the Scottish Intercollegiate Guidelines Network: the ‘Management of lung cancer’ clinical practice guideline and the ‘Management of chronic pain’ clinical practice guideline. We conclude that the proposed framework can be effectively used to formalize drug and procedure recommendation in clinical contexts.

Intention-based Expressions in GASTINE

2009 ◽  
Vol 48 (04) ◽  
pp. 391-396 ◽  
Author(s):  
P. de Clercq ◽  
J. van den Herik ◽  
A. Hasman ◽  
A. Latoszek-Berendsen
Keyword(s):  
Heart Failure ◽  
Clinical Practice ◽  
Decision Support ◽  
Clinical Practice Guidelines ◽  
Support System ◽  
Practice Guidelines ◽  
Plan Recognition ◽  
Current Implementation ◽  
Guideline System ◽  
Insight Into

Summary Objectives: 1) To evaluate the design of the framework for computerized intention-based clinical practice guidelines; 2) to implement runtime features such as plan recognition and backtracking. Method: To evaluate the design, we implemented the heart failure guideline into GASTINE, a tool for representing and executing intention-based clinical guidelines. Result: Description of the current implementation of intention-based expressions in GASTINE and analysis of some generic shortcomings. Explanation of how these shortcomings are addressed. Presentation of how plan recognition and backtracking work and how they improve the system. Conclusion: The improved guideline system is rather flexible, i.e., it allows deviations from the guideline as long as they are in the spirit of the guideline. The recognition of actions as intended by the users facilitates a flexible decision support system. The intentions are used to explain why certain actions were suggested. Therefore it is assumed that showing the intention behind suggested actions provides a better insight into why these actions are advised.

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