Introduction
In the present day, clinical decision support systems (CDSSs) already play a crucial role in quality health care delivery in medical facilities. At the same time, due to the emergence of new information technologies and the increasing complexity of treatment processes, the necessity for more developed electronic clinical decision support tools appears. Initially, clinical decision support (CDS) was a part of the electronic health records (EHR) limited by drug interactions and medication reminders, however, its further expansion requires more functions. Thus, an advanced technological system for the generation of large amounts of data that will allow health care providers, including DNP-prepared nurses, to make informed decisions in their daily work should be developed. This paper aims to define particular requirements for a potential electronic clinical decision support tool to be used by DNP-prepared nurses in their daily work. These features should allow the system to ensure the improvement of health care providers’ performance and the quality of health care delivery.
Requirements for a Clinical Decision Support Tool
First of all, it goes without saying that the decision support tool’s requirements should be determined by its functions and benefits for the end-users. In general, the tool should be used for the improvement of health care delivery due to the accessibility of patient information, informed-decision-making on the basis of individual patient characteristics and existing knowledge, better tracking, avoidance of medical errors, and trustworthy communication across health care teams and with patients (Akbar et al., 2021; Sutton et al., 2020). Moreover, according to Mujumdar and Jeffcoat (2022), “CDS tools can decrease administrative and documentation burden, relieve cognitive burdens, synthesize and share treatment options, provide input from clinical guidelines, and employ artificial intelligence (AI), machine learning (ML), and predictive analytics for patient outcomes and price transparency” (para. 14). At the same time, the implementation of the tool cannot occur in isolation – in other words, it should address all stages of data cycle, including its search, collection, analysis, acting, and evaluation (Rios-Zertuche et al., 2020). In addition, all stakeholders should be aware of their responsibilities in this process to ensure positive outcomes in improving patients’ health and saving their lives.
Meanwhile, electronic decision support tools cannot replace health care professionals’ cognitive activities essential in problem-solving process and care planning in nursing practice. In order to evaluate a patient’s needs and make appropriate decisions related to treatment and care, nurses apply clinical judgment and critical thinking following five sequential steps, including “assessment, problem identification, planning, intervention, and evaluation” (Akbar et al., 2021, p. 2502). Thus, taking into consideration the fact that the implementation of an electronic tool requires time and proficiency while information is continuously changing, this system should support nurses’ decision-making based on their skills, knowledge, and experience without replacing it to avoid clinical errors.
On the basis of the analysis of modern clinical settings and the purposes of decision support in them, several requirements for an electronic tool may be identified. First of all, it should incorporate multiple elements and functions to optimize and facilitate nurses’ performance, and this possibility is supported by the fact that in the present day, “tools are limited in scope only by developers’ and users’ imaginations” (Mujumdar & Jeffcoat, 2022, para. 12). Thus, a developing tool should have a large and constantly updating database of medical knowledge. In particular, clinical decision support tools currently have libraries that summarize thousands of topics, diagnoses, peer-reviewed articles from medical journals, leaflets, studies, medical calculators, and guidelines. Summaries, along with grade recommendations and rating scales, are continuously curated and updated if necessary to ensure the quality of evidence-based support.
At the same time, the tool should be connected to the EHR system of a medical facility. First of all, it is necessary to make an accurate diagnosis, develop a plan of health care delivery, and monitor changes making updates on the basis of existing literature and a patient’s individual characteristics and conditions (Sutton et al., 2020). In addition, using EHRs, the tool will include reminders and prompts for users’ assistance in helping patients and implementing evidence-based recommendations. Using patient data, DNP nurses will ensure the provision of safe and time-sensitive health care delivery (Sutton et al., 2020). Moreover, as data in EHRs is constantly updated, it may be used for the evaluation of current strategies and health care providers’ adherence to quality standards and existing guidelines. Other elements of an electronic tool may include artificial intelligence that will support human decision-making by the analysis of all available data and the creation of potential treatment plans and wearable technologies for tracking vital signals and data collection in a comfortable and accessible manner.
Electronic clinical decision support tools should serve as a link between patients and health care providers, helping to establish relationships between them on the basis of mutual respect and trust as well. In other words, patients should have an opportunity to access the EHRs, ask questions about their health conditions, and receive comprehensive answers. In addition, an electronic tool should collect data from all departments to keep all members of multi-disciplined teams well informed. In this case, it should have a user-friendly interface and design to be understandable by both patients and health care professionals. In addition, comfortable use should be determined by the tool’s compatibility with the majority of modern computer systems and mobile devices.
Finally, the development of an electronic tool should reduce the most considerable threat associated with data security and optimization in order to be safe and efficient. In particular, it should be effectively controlled, monitored, and updated by a medical facility’s IT department to ensure that patients’ private information is inaccessible to cybercriminals and protected in the case of an emergency that may lead to data loss. In addition, the tool should be easily checked and maintained to avoid technological bugs and data gaps. The continuous update of tool settings should also be organized to prevent alert fatigue and related misunderstanding and errors caused by a large number of unnecessary notifications.
Conclusion
Electronic clinical decision support tools play a crucial role in modern clinical settings as they allow to improve the quality of health care delivery and health care providers’ performance. They help ensure patient safety, reduce unnecessary or repeated activities, and minimize the risk of medical errors due to uncoordinated duties, lack of information, or negligence. At the same time, there are several requirements of an efficient electronic tool that should be considered. To assist educated decision-making based on patient characteristics and current knowledge, it should first integrate a medical facility’s EHR system and a database containing thousands of peer-reviewed resources. In addition, it should be comprehensible and user-friendly to meet the expectations of both patients and clinicians. Finally, the tool should be easily controlled, monitored, maintained, and updated by IT specialists to ensure data safety and the optimization of all settings for appropriate functionality.
References
Akbar, S., Lyell, D., & Magrabi, F. (2021). Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes. Journal of the American Medical Informatics Association, 28(11), 2502-2513. Web.
Mujumdar, V., & Jeffcoat, H. (2022). How clinical decision support tools can be used to support modern care delivery. Bulletin of the American College of Surgeons, 107(9). Web.
Rios-Zertuche, D., Gonzalez-Marmol, A., Millán-Velasco, F., Schwarzbauer, K., & Tristao, I. (2020). Implementing electronic decision-support tools to strengthen healthcare network data-driven decision-making. Archives of Public Health, 78(1), 1-11. Web.
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(17), 1-10. Web.
