Informatics in the modern healthcare system is the mechanism that makes it possible to simplify the mode of operation of both junior and senior medical personnel. Various innovative systems that are designed to store, systematize, and transform certain information contribute to speeding up many procedures, for instance, control over the number of incoming patients, treatment dynamics, and other aspects of work. Today, this task is assigned to special decision support systems that function autonomously and do not require significant operating costs.
Such models used in the field of healthcare, as a rule, help medical employees to set up treatment and care regimes in accordance with specific parameters, for example, a diagnosis and concomitant factors of a particular disease.
As a result, credible work plans are drawn up, which makes it possible to avoid mistakes and calculate all the required intervention steps correctly. Such decision support systems in the healthcare sector have many examples in the academic literature where scholars describe the features of such models, consider their advantages and disadvantages, and study their impact on performance. The use of these systems in medical facilities allows employees to calculate all the steps of a certain intervention accurately and quickly and determine the potential outcomes of work based on the model used.
Those decision support systems that are generally used in the healthcare field perform special functions that are necessary exclusively for this area. Modern information technologies introduced in different areas are characterized by specific features and approaches to solving specific problems. In other words, decision support systems, for instance, for accountants will be significantly different from those used in medicine.
The analysis of the effectiveness of these models, their role in the treatment process, and the importance of improving performance can be conducted by using the relevant literature review. Various authors’ opinions regarding such systems may help to determine the advantages and disadvantages of introducing technologies in the healthcare sector and to identify those peculiarities that accompany this process.
Evidence from the Academic Literature
One of the varieties of systems that simplify the work of medical personnel is mentioned in the article “Clinical Decision Support Systems for Improving Diagnostic Accuracy and Achieving Precision Medicine” by Castaneda et al. (2015). The authors consider specific technologies that allow taking and recording patient data in relevant databases, thus avoiding the loss of valuable information (Castaneda et al., 2015).
Moreover, this storage method gives caregivers an opportunity to access any necessary data in the shortest possible time. Electronic health records are those tools that are utilized in different areas of medicine and are useful for both junior and senior employees. As the author’s remark, this mechanism of patient information storage helps to avoid medical and nursing errors, which is the crucial indicator of such systems’ effectiveness and their relevance in healthcare settings (Castaneda et al., 2015). Therefore, the value of the technologies of this type is emphasized, and their introduction into general medical practice is proposed.
Another decision support system is reviewed by Malhotra et al. (2017) who assess the relevance of such technology in the field of psychiatry. The main object of study is the mechanism for diagnosing and drawing up treatment plans for patients with mental disorders. According to the authors, their research results confirm the high accuracy of screening performed through using such a technological model (80–100%) and note that the application of this system increases the probability of treatment success significantly (Malhotra et al., 2017, p. 196).
As an analysis methodology, conversations with patients were conducted, and a medical team consisting of experienced professionals worked in parallel with the electronic system. As a result, the testing of the electronic model has proven that this screening and prediction method may be utilized in psychiatric departments. The accuracy of its indicators allows planning the entire course of treatment in detail, which makes such a system a useful and necessary mechanism.
The purpose of Martínez-Pérez et al. (2014) is to describe the role and importance of mobile decision support systems in healthcare through the literature review of those models that are mentioned in academic works. In accordance with the findings, the growth in the number of such systems in various medical settings has been observed over the past decade. Those applications that help nurses and doctors to compile specific treatment and care plans are the most relevant in such areas as emergency care, pharmacology, and pediatrics.
Quite rarely, these models are found in cases when complex diseases should be diagnosed and treated, for instance, endocrinological ailments, the illnesses of individual organs, and other complex cases. The variability of mobile applications is also mentioned, and the authors recommend using those systems that best fit specific goals and can cope with the tasks assigned (Martínez-Pérez et al., 2014). In other words, if the correct algorithms are utilized, the probability of positive work outcomes will be sufficiently high.
The use of electronic decision support systems is the subject of academic work by Moja et al. (2014). The authors study strategies for introducing these automated tools into the clinical process and consider the effects by comparing the indicators of standard and innovative methods of diagnosing and storing data (Moja et al., 2014). Also, various criteria are provided to confirm the validity of the study – participants, timeframes, the goals of work, and other aspects.
According to the results, Moja et al. (2014) argue that the introduction of electronic decision support systems does not bring significant benefits regarding the conditions of treatment and care. However, they simplify the activities of medical personnel due to the convenience of storing information. Prospects for such models are mentioned in the context of the auxiliary elements of nursing practice. Nevertheless, a full transition to electronic diagnosing is not recommended, and the role of experienced employees is considered crucial.
The focus of the study by Njie et al. (2015) is on assessing decision support systems for diagnosing and treating cardiovascular diseases. The productivity and effectiveness of innovative technological mechanisms are compared with the standard principles of care and treatment to obtain a comprehensive picture of such systems’ relevance in clinical settings. Based on the findings, the authors remark that decision support tools may be important components in medical practice and, in particular, in the treatment of diseases related to the cardiovascular system (Njie et al., 2015).
Nevertheless, the use of such technology should be supplemented with other essential components – team-based care, performance feedback mechanisms, and other crucial aspects of work. Therefore, the systems under consideration cannot be called a universal remedy that allows diagnosing and determining the optimal procedures for the treatment and care of patients with cardiovascular diseases.
Ranji, Rennke, and Wachter (2014) study the relevance of the application of decision support systems to prevent medical staff’s errors regarding adverse drug prescriptions. This topic is essential since improper treatment is fraught with dangerous consequences for both patients and the reputation of a particular healthcare institution. According to the authors, with the help of such high-tech systems, it is possible to check the presence or absence of allergies to certain drugs and calculate the appropriate doses of drugs (Ranji et al., 2014). Also, their utilization contributes to determining the optimal course of treatment.
The findings prove that those systems that target narrowly focused clinical tests, such as blood screening, are more effective than those that provide general support. Consequently, the introduction of such innovative models in clinical practice may allow eliminating errors associated with the incorrect prescription of medical drugs and their dosage.
Based on the presented pieces of evidence regarding the effectiveness, benefits, and usability of decision support systems in the field of healthcare, it is possible to make some conclusions. In particular, the fact is that the personnel of medical institutions can count on additional assistance provided by such technologies. The main argument in support of this conclusion is that these mechanisms speed up all the stages of the care process significantly, thereby increasing the productivity of interventions. Therefore, their utilization within almost all medical facilities is relevant.
Moreover, it is crucial to take into account that decision support systems cannot completely replace the activities of the medical staff, which makes them only auxiliary but not the basic tools of assistance. Control over the operations carried out through these mechanisms is still the task of the personnel of the clinical departments, and it is impossible to speak of people’s complete replacement with automatic equipment. The use of such systems opens up broader perspectives concerning the speed and convenience of the implementation of certain nursing and doctoral tasks. Therefore, equipping clinics with appropriate innovative technologies does not carry the threat of losing control over the treatment and care processes.
Despite the relatively fast processing of patient data, the use of decision support systems in healthcare cannot be considered the universal way of facilitating the medical staff’s activities. As Ranji et al. (2014) note, the introduction of such automated technologies in the treatment process is relevant only in those cases when deep and narrowly focused procedures are required.
It means that it is rational to purchase and implement these systems if complex diagnostic processes are carried out, for instance, the monitoring of the functioning of individual organs, the analysis of brain activity, and other screening tests. However, in general nursing and doctoral practice, decision support systems may be useless, except for the possibility of convenient data storage. Therefore, it is necessary to think in advance about the use of such technologies in order not to waste money on their maintenance.
Decision support systems used in the healthcare sphere allow the medical staff to increase the productivity and speed of work through effective assistance in performing a number of procedures. The analysis of various authors’ academic works proves that the use of these technologies is accompanied by different positive changes and successful patient outcomes. Despite some conventions, in particular, narrowly focused procedures carried out due to these systems, they are relevant in the clinical environment and should be introduced as auxiliary mechanisms. Both the junior and senior staff may benefit from their utilization; therefore, these technologies can certainly become innovative resources for treatment and care.
Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A.,… Suh, K. S. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(1), 4-19. Web.
Malhotra, S., Chakrabarti, S., Shah, R., Sharma, M., Sharma, K. P., Malhotra, A.,… Jassal, G. D. (2017). Telepsychiatry clinical decision support system used by non-psychiatrists in remote areas: Validity & reliability of the diagnostic module. The Indian Journal of Medical Research, 146(2), 196-204. Web.
Martínez-Pérez, B., de la Torre-Díez, I., López-Coronado, M., Sainz-De-Abajo, B., Robles, M., & García-Gómez, J. M. (2014). Mobile clinical decision support systems and applications: A literature and commercial review. Journal of Medical Systems, 38(1), 4-13. Web.
Moja, L., Kwag, K. H., Lytras, T., Bertizzolo, L., Brandt, L., Pecoraro, V.,… Iorio, A. (2014). Effectiveness of computerized decision support systems linked to electronic health records: A systematic review and meta-analysis. American Journal of Public Health, 104(12), e12-e22. Web.
Njie, G. J., Proia, K. K., Thota, A. B., Finnie, R. K., Hopkins, D. P., Banks, S. M.,… Kottke, T. E. (2015). Clinical decision support systems and prevention: A community guide cardiovascular disease systematic review. American Journal of Preventive Medicine, 49(5), 784-795. Web.
Ranji, S. R., Rennke, S., & Wachter, R. M. (2014). Computerised provider order entry combined with clinical decision support systems to improve medication safety: A narrative review. BMJ Quality & Safety, 23(9), 773-780. Web.