Clinical Decision Support Systems: Pros and Cons

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Table of Contents

Introduction
Clinical Patient and Disease Scenario
Necessary Measures
Conclusion
References

Introduction

Pros	Cons
Increased physician efficiency. The interaction between the specialist and the Clinical Decision Support System (CDSS) increases productivity, makes work more accessible, and provides additional time for other tasks and responsibilities. This system will benefit young physicians who have not yet gained sufficient experience.	Negative impact on the physician. If the specialist is not correctly trained to work with such a system, it can result in professional burnout, insecurity, and erroneous conclusions that he is unqualified.
Storing large amounts of information. It is difficult for a physician to keep a colossal amount of formed medical knowledge in his head and constantly remember the small details. The volume of patient data is overgrowing, and it is more reliable to store it on a digital medium.	A long learning process. The CDSS is always lengthy, complicated, and requires retrospective studies. For the system’s proper operation and productive interaction between the doctor and the machine, long-term and high-quality personnel training are necessary.
Quick analysis. No doctor can analyze as much data in a short period as modern technology provides. In addition, the more data is accumulated, the more accurately the tool will work.	Shifting responsibility. It is necessary to understand that no artificial intelligence can solve all problems. The physician manages the treatment, bears the responsibility, and makes the final choice.

Clinical Decision Support Systems (CDSSs) and telemedicine and electronic health records are critical sectors in developing digital health. CDSS is designed to help various medical professionals make clinical and physician decisions (Sutton et al., 2020). However, it is essential to understand that machines cannot solve all problems. The doctor directs the treatment and makes the final choice. The clinical patient and disease scenario was created to explore this topic more deeply and consider the main points.

Clinical Patient and Disease Scenario

The case of the pandemic is still relevant today and, more importantly, not fully understood; new information is being discovered by scientists all the time. Currently, there are less data on the effect of COVID-19 coronavirus infection on pregnancy, childbirth, fetal state, and the postpartum period (Yang et al., 2020). This paper describes a clinical case of severe COVID-19 coronavirus infection in a thirty-year-old pregnant patient. She was transferred to an infectious diseases hospital from the maternity hospital on the fourth day of her illness with a referral diagnosis: twenty-nine-week pregnancy threatened preterm labor and right-sided pneumonia. The client complained of general weakness and infrequent cough; the patient’s medical history showed an increase in body temperature to 37.90 C four days ago. Pain in the lower abdomen appeared on the third day of the disease. The ambulance team hospitalized her in the maternity hospital, where she received symptomatic antibacterial therapy.

Necessary Measures

The aim of treatment is the patient’s recovery and preservation of pregnancy. The disease can develop acutely, with a reasonably rapid extensive damage to the respiratory tract (Tsang et al., 2021). Accordingly, it is necessary to act immediately in this case. It is required to examine the patient by an infectious disease doctor and an obstetrician-gynecologist, continue symptomatic therapy and make a chest CT scan. It is crucial to monitor the pregnancy, so an ultrasound scan should be done. If the disease worsens, the patient should be transferred to the ventilator. The CDSS has not collected much data on coronavirus now (McRae et al., 2020). Accordingly, this system is not a suitable option, as competent specialists need urgent action to help the patient.

Conclusion

CDSS is a developing field, but it is still understudied, and there is a long way to go before real successes are achieved. The medical staff needs to act rationally, think critically about the situation, and rely not on a general scenario but on the patient’s specific case. Every health care provider should understand that they have a personal responsibility to clients. Artificial intelligence is not always able to find the necessary solution quickly. Meanwhile, gathering information and putting it into the system is vital because it can improve patient outcomes.

References

McRae, M. P., Dapkins, I. P., Sharif, I., Anderman, J., Fenyo, D., Sinokrot, O., Kang, S. K., Christodoulides, N. J., Vurmaz, D., Simmons, G. W., Alcorn, T. M., Daoura, M. J., Gisburne, S., Zar, D., & McDevitt, J. T. (2020). Managing COVID-19 with a clinical decision support tool in a community health network: Algorithm development and validation.Journal of Medical Internet Research, 22(8).

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), 1-10.

Tsang, J. L., Binnie, A., & Fowler, R. A. (2021). Twenty articles that critical care clinicians should read about COVID-19.Intensive Care Medicine, 47(3), 337-341.

Yang, H., Wang, C., & Poon, L. C. (2020). Novel coronavirus infection and pregnancy.Ultrasound in Obstetrics & Gynecology, 55(4), 435-437.

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Clinical Decision Support Systems: Pros and Cons

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