Definition and Process
Clinical decision support system is a software that assists clinicians in effective decision-making in terms of treating a patient. CDSS is claimed to provide the necessary quality of care, reduce medical errors and avoid potential wrong drug prescription. In other words, this means that CDSS is focused on managing information in order to formulate clinical recommendations for the treatment of a patient based on the data available for the latter.
Evaluation of CDSS’s effectiveness and safety should be carried out at different stages – development, clinical research, state registration, clinical and economic analysis, assessment of medical technologies. Challenges arise because biological systems are very complex, and a clinical decision can require analyzing a considerable amount of potentially relevant data. For example, an evidence-based electronic system can potentially take into account symptoms, medical history, family history, and patient genetics, as well as historical and geographical epidemiological data and published clinical information regarding drug efficacy, when formulating recommendations for a patient’s treatment plan.
To date, the efficacy of the CDSS is ambiguous – there are both successes and failures. The main argument in favor of these systems is that they reduce the likelihood of medical error, which is a considerable threat to the life and health of patients in the United States. On the other hand, in my experience, interactions with such systems are not always convenient in a practical sense. When the doctor is under heavy workload, and the system continues to issue another stream of messages that are far from always worth attention, it happens that the clinician plays with the incoming information.
In my opinion, CDSS can indeed be an excellent technological solution. However, these should be made taking into account the doctor’s needs and be convenient to use to cope with their task and minimize the risks associated with the prescription of drugs and the diagnosis of diseases. Thus, I am confident that the system’s primary mission is the detection of potentially fatal risks. This paper aims to investigate the advantages and drawbacks of CSDD through a literature review.
My empirical observations find support in academic sources as well. Thus, Ancker et al. (2017) examine the effect of workload and its complexity on the comprehension and distinction of informative and informative system alerts. Using electronic health record data, researchers have managed to establish a concerning correlation. Indeed, clinicians’ cognitive overload often results in decreased ability to catch the informative value of repeated alerts. Thus, the staff became 30% more likely to ignore warnings, especially the repeated ones. Interestingly, this effect is attributed to the complexity of the patients. Therefore, if the doctor ignores the CDSS’s warnings in serious diagnoses, the sense in such a system is partially lost. The study also suggests that measures addressing optimization of alerts and repeated within-patient alerts may enhance the value produced by CDSS.
Another research article by Sutton et al. (2020) assesses both benefits and risks associated with clinical decision support systems. The paper elaborates, in particular, the issue of medical error in the US and the effect CDSS has in dealing with the problem. It turns out that up to 65% of inpatients are exposed to one or several potentially harmful drug-drug combinations.” (Sutton et al., 2020). Computerized provider order entry (CPOE) systems are now commonly installed into CDSSs, thus, minimizing the threat of wrongly prescribed drugs, incorrect dosing, etc.
Moreover, the article also underlines that CDSS is found to increase clinician’s adherence to established guidelines. However, at the same time, it mentions systems’ challenges that are mainly based on possible data inconsistency and gaps. Information is collected from various sources – empirical and theoretical, general and local – the generated recommendations may not always be reliable. Nevertheless, the doctor’s role is primarily to check the system’s suggestions for a potential threat leveling to a patient’s health, given the possible inaccuracy.
Furthermore, the paper indicates cost containment as another undoubtable advantage of CDSS. The system manages to decrease inpatient length-of-stay and suggests cheaper medicines alternatives. For example, the estimated savings of laboratory resourced possible dur to CDSS utilization is around 719 thousand dollars per year. Importantly, this characteristic of CDSS informs customers about less costly insurance options, thus making health services more available.
Evaluation and Summary of Articles
Luckily, the discussed articles consider CDSS from different angles and, in this sense, do not overlap. Ancher et al. (2017) highlights the psychological consequence of numerous alerts generated by the systems and concludes by giving practical policy recommendations on implementing CDSS more efficiently. As I mentioned in my evaluation of these systems, fatigue and work complexity prevent clinicians from paying enough attention to potentially life-saving warnings. Therefore, in my opinion, the research perfectly copes with the task by solving a significant problem associated with CDSS.
Finally, Sutton et al. (2020) conduct a more general overview of clinical decision support systems discussing the benefits and drawbacks. The article supports my comment regarding CDSS’s potential to solve medical errors while adequately recognizing some limitations the system holds. Overall, the literature review reflects my observations while deepening into root causes and policy recommendations for CDSS successful utilization.
Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., & Kaushal, R. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC medical informatics and decision making, 17(1), 1-9.
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.