Clinical Decision Support Systems

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Clinical Decision Support Systems

Table of Contents

Introduction
Sources Analysis
Conclusion
References

Introduction

Clinical decision support (CDS) systems are among the most significant technological advancements in the healthcare sector. These tools are meant to help caregivers come up with directives and work to achieve better outcomes for all stakeholders involved, like reducing costs and making work more efficient and care better. CDS systems were first used in the 1980s and have seen tremendous growth since then. The purpose of the paper is to provide sufficient evidence on the impact CDSS has had on healthcare settings.

Sources Analysis

Gold, R., Larson, A. E., Sperl-Hillen, J. M., Boston, D., Sheppler, C. R., Heintzman, J., McMullen, C., Middendorf, M., Appana, D., Thirumalai, V., Romer, A., Bava, J., Davis, J. V., Yosuf, N., Hauschildt, J., Scott, K., Moore, S., & OConnor, P. J. (2022). Effect of clinical decision support at community health centers on the risk of cardiovascular disease: A cluster randomized clinical trial. JAMA Network Open, 5(2), e2146519. Web.

One of the articles devoted to CDS systems is the study by Gold et al. (2022). The researchers put forward the idea that improving the control mechanisms through the application of an appropriate decision-support program could have a positive impact on the outcomes of patients with cardiovascular disease (Gold et al., 2022). As a mechanism for testing the hypothesis, the two groups were organized in which 70 clinics were included in accordance with the random principle of selection. The control group included healthcare facilities with a traditional approach to treatment, while in the intervention group, clinics were selected using CDS systems equipped with the option of making recommendations to specialists.

Given the findings of this study, the authors found a direct correlation between the use of CDSS and improved patient outcomes (Gold et al., 2022). As a remark worthy of note, one should pay attention to the fact that the use of appropriate instruments was not associated with improved quality of care for the general population. At the same time, patients at risk of complications indicated an improvement in well-being. Thus, the CDSS technology avails the benefits of providing caregivers with immediate data access. This means that practitioners are able to make fast decisions based on the best practices. This research is useful for medical professionals looking to implement CDSS in their practice. Based on this study, one can conclude that utilizing appropriate systems in cardiology departments is valid for practitioners to contribute to improved care outcomes in patients with severe health risks.

Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65-69. Web.

In their study, Hannun et al. (2019) set the goal of designing a deep neural network (DNN) to interpret computerized electrocardiogram (ECG) data and see how it could facilitate the care process. The team used their system to classify 12 rhythm classes from over 90,000 ECGs and validated the results against a test dataset developed by a group of cardiologists. The researchers found that their system achieved an accuracy level similar to those of trained and board-certified practicing cardiologists (Hannun et al., 2019). The system achieved a sensitivity above the average of practicing cardiologists, indicating that it could perform better than the average practitioner.

The results of this study show that these systems could classify a wide array of distinct arrhythmias with high accuracy, much like that of trained professionals. What remains is for the system to be tested in clinical settings. As a result, by following the relevant measurements, physicians can avoid potential errors in ECG interpretations and expect to increase work efficiency through an expanded list of priority cases. The findings are useful to medical professionals, particularly those specializing in cardiovascular diseases. The study proves that applying advanced CDC systems is a favorable risk prevention practice in the treatment of heart disease, but more research is needed to test the validity of these tools in other settings.

Jacob, V., Thota, A. B., Chattopadhyay, S. K., Njie, G. J., Proia, K. K., Hopkins, D. P., Ross, M. N., Pronk, N. P., & Clymer, J. M. (2017). Cost and economic benefit of clinical decision support systems for cardiovascular disease prevention: A community guide systematic review. Journal of the American Medical Informatics Association, 24(3), 669-676. Web.

To analyze the costs and benefits of using CDSS systems in cardiovascular disease (CVD) prevention, Jacob et al. (2017) conducted their research. The article reviewed the literature on CDSS application in CVD care between 1976 and 2015 (Jacob et al., 2017). The team had difficulty determining the costs related to CDSS acquisition and operation because of significant heterogeneity. Some of the studies discussed demonstrated a positive correlation between applying CDSS and reducing healthcare costs. At the same time, several works discussed showed controversial results and incomplete conclusions, which called into question the belief in the financial benefit of using CDSS.

The uniformity of reporting data was not achieved, which caused the insufficient confirmation of the fact that it was possible to reduce costs through the use of the instruments under consideration. One of the reasons was this paucity of data in the reviewed papers, which precluded a definitive verdict on the financial benefits of CDSS. Thus, the study showed that theoretically, such systems could be applied as cost reduction instruments, but from a practical standpoint, no valid confirmation was received. The article is useful to CDSS researchers and practitioners looking to implement such tools in their work. Future research should try to solve or minimize the uniformity issue.

Rossom, R. C., Crain, A. L., OConnor, P. J., Waring, S. C., Hooker, S. A., Ohnsorg, K., Taran, A., Kopski, K. M., & Sperl-Hillen, J. M. (2022). Effect of clinical decision support on cardiovascular risk among adults with bipolar disorder, schizoaffective disorder, or schizophrenia. JAMA Network Open, 5(3), e220202. Web.

To evaluate the role of CDS systems from the perspective of enhancing favorable outcomes for cardiovascular patients with mental disorders, Rossom et al. (2022) conducted a special study. Bipolar disorder, schizoaffective disorder, and schizophrenia are classified as serious mental illnesses (SMI), and cardiovascular disease is confirmed to be the main cause of death in adults with SMI (Rossom et al., 2022). To prove the value of CDSS in addressing the aforementioned issues, Rossom et al. (2022) recruited 76 clinics caring for targeted patients. While considering the individual characteristics of the participants involved and the data obtained during the application of the corresponding system, recommendations were made to patients and physicians.

The researchers found that the systems reduced the rate of increase in total modifiable risk by 4% (Rossom et al., 2022, p. e220202). However, the changes in modifiable risk factors were negligible. This means that they found CDSSs reduced the chances of patients developing more risks, but the systems did not result in the modification of any given risk. CDSSs systems should thus be used early in the care of adults with SMI. Although the systems did not modify any CVD risk, they reduced the chances of patients developing more, which speaks to their utility. This article is useful for caregivers looking to implement CDSSs in primary care settings for SMI adult patients. More research should be conducted to target a larger sample and obtain more credible test results.

Conclusion

Clinical decision support systems came with the potential to revolutionize healthcare as they make it easier to access the critical knowledge needed to make the usually complex clinical decisions. The studies featured in the annotated bibliography show that these systems enhance the quality of care, improve patient and caregiver outcomes, and minimize costs. However, there are several associated challenges, pitfalls, and risks. The design and implementation of CDSSs are not standardized, these systems are expensive, errors are still possible when providers make mistakes or try to work around the systems, and sometimes they can be featured in the wrong use cases.

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Introduction

Sources Analysis

Conclusion

References