Clinical Decision Support System Discussion Paper

Clinical Decision Support System Discussion Paper

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The evolving landscape of healthcare is increasingly reliant on technological advancements, with Clinical Decision Support Systems (CDSS) emerging as pivotal tools in enhancing clinical decision-making processes. This annotated bibliography aims to explore a curated selection of articles delving into the multifaceted role of CDSS in supporting clinicians. As an integral component of healthcare informatics, CDSS offers insights, recommendations, and evidence-based guidance to practitioners, influencing diagnostic and treatment decisions. The annotated summaries will provide a nuanced understanding of the current literature, shedding light on the significance, challenges, and future prospects of CDSS in the dynamic realm of clinical decision support.

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Annotated Bibliography

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y

The study underscores the transformative impact of Computerized Clinical Decision Support Systems (CDSS) on contemporary healthcare. Since their inception in the 1980s, CDSS has evolved, becoming integral to electronic medical records and clinical workflows worldwide. While demonstrating success stories in improving outcomes and decision-making efficiencies, the study acknowledges lingering uncertainties. Despite proven efficacy in various use cases, there are identified pitfalls and potential harms associated with CDSS. The paper advocates for evidence-based strategies to minimize risks in CDSS design, implementation, and maintenance, emphasizing the need for a nuanced approach to harness the full potential of these systems in enhancing patient care and clinical processes. Clinical Decision Support System Discussion Paper

The article highlights valuable lessons learned from the application of Clinical Decision Support Systems (CDSS). One notable lesson is the importance of careful design and implementation to mitigate risks. For instance, the study emphasizes the need for alert fatigue management within CDSS. An example elucidating this lesson could be the reduction of irrelevant alerts, ensuring that healthcare providers are not overwhelmed with notifications, which could lead to oversight or desensitization. The article underscores the significance of continuous evaluation and refinement to optimize CDSS functionality, aligning with user needs and minimizing disruptions in clinical workflows for more effective and safer healthcare decision-making. Clinical Decision Support System Discussion Paper

Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T., & Shah, Z. (2020). The impact of clinical decision support systems (CDSS) on physicians: a scoping review. The Importance of Health Informatics in Public Health during a Pandemic, 470-473.

The study investigated the impact of Clinical Decision Support Systems (CDSSs) on physicians through a scoping review of 14 selected studies out of 300. Positive impacts of CDSS implementation include enhanced work efficiency, personalized care provision, improved knowledge and decision confidence, better prescribing behavior, and reduced unnecessary tests. However, negative effects were observed, including inefficient documentation, interruptions in patient-physician communication, and an increase in unnecessary referrals. Overall, the use of CDSSs demonstrated substantial improvements in clinical outcomes, efficiency, and decision-making processes, but challenges such as communication disruptions and documentation issues should be considered. Clinical Decision Support System Discussion Paper

The study highlights valuable lessons from the application of Clinical Decision Support Systems (CDSSs). One key lesson is the positive impact on work efficiency and decision-making, exemplified by reduced unnecessary laboratory and imaging tests. However, the study also underscores potential drawbacks, such as inefficient documentation and disruptions in patient-physician communication. An example of a lesson learned is the need for a balanced implementation that maximizes CDSS benefits while addressing challenges to ensure seamless integration into clinical practice. This emphasizes the importance of considering both positive and negative outcomes to optimize the application of CDSSs in healthcare settings. Clinical Decision Support System Discussion Paper

Javeed, A., Ali, L., Mohammed Seid, A., Ali, A., Khan, D., & Imrana, Y. (2022). A clinical decision support system (cdss) for unbiased prediction of caesarean section based on features extraction and optimized classification. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/1901735

The study focuses on improving the accuracy of Clinical Decision Support Systems (CDSS) for predicting the necessity of caesarean section (CS) in pregnant women. The novel CDSS, named ROSE-PCA-RF, employs random oversampling to address minority class issues, utilizes principal component analysis for efficient feature extraction, and applies a random forest (RF) model for classification. Compared to a conventional RF model, ROSE-PCA-RF demonstrates a 4.5% improvement in performance with reduced time complexity. The proposed model achieves an impressive 96.29% accuracy on training data and enhances testing data accuracy to 97.12%, showcasing its potential for efficient CS prediction.

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The study underscores key lessons from implementing the CDSS for predicting caesarean section necessity. One lesson is the significance of addressing class imbalances, evident in the successful application of the ROSE technique to eliminate minority class issues. Additionally, the use of principal component analysis (PCA) for feature extraction highlights the importance of streamlining data for enhanced model efficiency. The adoption of a random forest (RF) model, fine-tuned through a grid search algorithm, exemplifies the need for meticulous model optimization. Overall, the lessons emphasize the importance of tailored techniques, like ROSE-PCA-RF, to improve CDSS accuracy and efficiency in predicting medical interventions such as caesarean sections. Clinical Decision Support System Discussion Paper

Cuvelier, E., Robert, L., Musy, E., Rousseliere, C., Marcilly, R., Gautier, S., … & Décaudin, B. (2021). The clinical pharmacist’s role in enhancing the relevance of a clinical decision support system. International journal of medical informatics, 155, 104568. https://doi.org/10.1016/j.ijmedinf.2021.104568

This retrospective study investigates the impact of involving a clinical pharmacist in the development and management of a Clinical Decision Support System (CDSS) to enhance prescription reviews. The CDSS, when overseen by a pharmacist, resulted in a significant reduction in technically invalid alerts (13%). Pharmaceutically relevant alerts, leading to Pharmaceutical Interventions (PIs), were identified in 24.6% of cases. Noteworthy causes of pharmaceutical irrelevance included CDSS specificity issues (70.8%). Ultimately, 64.6% of suggested PIs were accepted by physicians. The study suggests that integrating a clinical pharmacist into CDSS development improves system efficiency by reducing irrelevant alerts and may enhance patient care. Clinical Decision Support System Discussion Paper

The research highlights key insights gained from integrating clinical pharmacists into CDSS development. A notable takeaway is the impact of CDSS specificity on pharmaceutical relevance, exemplified by a significant percentage (70.8%) of cases. To elaborate, insufficient specificity in recommending therapeutic changes can lead to irrelevant alerts, contributing to alert fatigue. This underscores the necessity of fine-tuning CDSS algorithms to align with clinical intricacies, ensuring alerts are contextually meaningful. The study implies that valuable lessons for CDSS enhancement involve refining specificity to enhance pharmaceutical relevance, ultimately optimizing the system’s contribution to informed clinical decision-making.

Recap

In conclusion, this annotated bibliography provides a comprehensive exploration of Clinical Decision Support Systems (CDSS) in healthcare. The selected articles emphasize the transformative impact of CDSS on clinical decision-making, addressing benefits, risks, and strategies for success. Lessons learned include the importance of careful design to mitigate risks, the need for balanced implementation considering positive and negative impacts, and tailored techniques for enhanced accuracy, as demonstrated in predicting caesarean sections. Integrating clinical pharmacists into CDSS development emerges as a key strategy to optimize system efficiency and pharmaceutical relevance. This synthesis contributes valuable insights to the evolving landscape of healthcare informatics and CDSS utilization. Clinical Decision Support System Discussion Paper

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References

Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), 17. https://doi.org/10.1038/s41746-020-0221-y

Muhiyaddin, R., Abd-Alrazaq, A. A., Househ, M., Alam, T., & Shah, Z. (2020). The impact of clinical decision support systems (CDSS) on physicians: a scoping review. The Importance of Health Informatics in Public Health during a Pandemic, 470-473.

Javeed, A., Ali, L., Mohammed Seid, A., Ali, A., Khan, D., & Imrana, Y. (2022). A clinical decision support system (cdss) for unbiased prediction of caesarean section based on features extraction and optimized classification. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/1901735

Cuvelier, E., Robert, L., Musy, E., Rousseliere, C., Marcilly, R., Gautier, S., … & Décaudin, B. (2021). The clinical pharmacist’s role in enhancing the relevance of a clinical decision support system. International journal of medical informatics, 155, 104568. https://doi.org/10.1016/j.ijmedinf.2021.104568 Clinical Decision Support System Discussion Paper

To Prepare:

Review the Resources and reflect on the impact of clinical systems on outcomes and efficiencies within the context of nursing practice and healthcare delivery.
Conduct a search for recent (within the last 5 years) research focused on the application of clinical systems. The research should provide evidence to support the use of one type of clinical system to improve outcomes and/or efficiencies, such as “the use of personal health records or portals to support patients newly diagnosed with diabetes.”
Identify and select 4 peer-reviewed research articles from your research.

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The Assignment: (4-5 pages not including the title and reference page)

In a 4- to 5-page paper, synthesize the peer-reviewed research you reviewed. Format your Assignment as an Annotated Bibliography. Be sure to address the following:

Identify the 4 peer-reviewed research articles you reviewed, citing each in APA format.
Include an introduction explaining the purpose of the paper.
Summarize each study, explaining the improvement to outcomes, efficiencies, and lessons learned from the application of the clinical system each peer-reviewed article described. Be specific and provide examples.
In your conclusion, synthesize the findings from the 4 peer-reviewed research articles.
Use APA format and include a title page Clinical Decision Support System Discussion Paper