The integration of clinical decision support system (CDSS) in the healthcare is of great significance. In fact, medical agencies have realized that the fusion of CDSS in clinics promotes the healthcare staff’s activities by assisting professionals in making an accurate diagnosis when assessing various ailments and symptoms (Khalifa, 2014). These therapeutic systems have also essentially developed from being dependable programs mostly utilized in scientific and technical institutions to being an evolving network infrastructure that holds digital workflow, electronic medical record, and vast patient care solutions (Khalifa, 2014). Therefore, the application of CDSS in medical industry assists the healthcare practitioners to make sound medical decisions. The current paper outlines the relevance of CDSS in the medical center, notable CDSS types used in this healthcare setting, the CDSS structure, and its success determinants with the aim of promoting overall clinical practices. In addition, it highlights the various key cases of CDSS implementation in the field.
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Relevant Literature
Most recent therapeutic studies have shown that the application of CDSS in the clinical industry is designed to provide clinical decision aid. With this analysis, the CDSS specifies the computer systems that are developed with the fundamental purpose of assisting healthcare specialists, such as doctors and nurses, to make life-saving medical decisions for the visiting patients (Khalifa, 2014). For instance, from the medical literature, the Association of the Medical American Colleges (AAMC) has massively benefited from the recent utilization of CDSS. With the deployment of this clinical application, this agency anticipates a decrease of approximately 130, 600 medical practitioners by 2025 because the CDSS will support all the healthcare-related decisions (Castaneda et al., 2015). Thereupon, the application of CDSS at the infirmary centers will boost the overall performance of medical care provider, improve patient’s quality care, and provide useful clinical decisions.
The CDSS guarantees to abate the existing time constraints among physicians. Predominantly, since the implementation of the Affordable Care Act in America, approximately 46% of the interviewed emergency medical doctors reported a growing number of visiting patients in healthcare emergency rooms (Castaneda et al., 2015). From this survey, the patients turn out increased due to the inclusion of CDSS in their clinical institutions. Thus, these systems synthesized accurate medical data from individual patients and applied the relevant knowledge when diagnosing them.
Discussion
The Relevance of CDSS in Medical Industry
Providing up-to-date therapeutic information to the physicians, patients, and other care persons is the primary relevance of CDSS in the hospital settings. This computer system provides timely medical data to the healthcare practitioners in order to make appropriate decisions about the patient’s health and care delivery accordingly. Since determining the most accurate evidence-based insight can be challenging in clinical centers, mainly when the time is crucial, the deployment of CDSS can notify the healthcare providers on the detected symptoms, chronic diseases and proper drug prescriptions that were neglected during the physical examination (Musen, Middleton, & Greenes, 2014). Therefore, the CDSS administers accurate medicinal information to the clinicians so that they can make reliable health care decisions.
Enhancing productivity and abating medication errors are other relevance of CDSS in the medical industry. Principally, the National Academy of Medicine reports that approximately $ 17 billion is disbursed yearly on ineffective patients care because of poor diagnosising (Castaneda et al., 2015). Therefore, if physicians can use the CDSS to accurately recognize the correct prescription, medical diagnosis, and physical lab evaluations, they can request for the correct tests and make appropriate medical orders. This procedure will save ample time, reduce the risk of medication flaws, and minimize the extra costs for the patients and the emergency departments (ED). Thus, the utilization of CDSS in the infirmary facilitates healthcare productivity and eliminates patient’s disruptions at the ED.
Types of CDSS Utilized in Healthcare
Information management tools (IMTs) are a primary category of CDSS in healthcare. These systems are developed to provide sophisticated platforms for saving and recovering the medical knowledge. They also offer the clinician an opportunity of assessing this expertise and modifying it with personal information that may be required later for resolving the associated clinical problems (Musen et al., 2014). It should be noted that the IMTs provide useful medical data and insight that are required by the physician. Therefore, the clinician is left with the duty of interpreting the generated data along with determining the appropriate information necessary to fix the medical issue.
At the same time, focusing attention tools (FATs) are CDSS systems that provide a mechanism for alerting physicians when abnormal medical values are detected. They offer possible lists of assessments for these abnormalities to a clinical system that notifies the healthcare providers about a potential medical prescription. These tools are programmed to alert the care provider about diagnoses or problems that might have been neglected (Musen et al., 2014). Subsequently, the FATs utilize simple algorithms to output lists of common responses for that particular abnormality.
Patient-specific recommendation tools (PSRTs) are another noteworthy category of CDSS. These systems are programmed to provide therapeutic decisions to an individual patient based on his/her previous clinical history. Similarly to FATs, the PSRTs relies on the simple logical code to reach their intended action (De la Rosa Algarin, 2011). Thus, the PSRTs adopt decision assumption, cost-benefit analysis strategy, and a rough set mechanism to determine the appropriate clinical advice for solving the patient’s problems using their previous medical data.
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Structure of CDSS in the Medical Industry
The patient data forms the basic structure of the CDSS. It is a relevant information source that physicians utilize along with their medicinal expertise to determine on the appropriate diagnoses. Importantly, the CDSS should be able to obtain and verify data in a manner that is consistent with the clinicians and secure it to maintain patient data confidentiality. With this option, the data acquisition must incorporate the speech recognition ability, graphics and compatible healthcare data that meets physician’s care requirements (Dinevski, Bele, Sarenac, Rajkovic, & Sustersic, 2011). For example, the patient’s data can be gathered from e-health record, e-medical record (EMR), and medical data repository.
The medical knowledge axes are the next necessary structure of the CDSS. This medicinal expertise falls into two essential categories, including low-level and high-level intelligence. With this approach, the low-level expertise constitutes the awareness of body structure and functions, potential ailments and their medical treatments. The high-level knowledge is obtained from the healthcare experience and provides the physicians with the capability to implement correct medical choices (De la Rosa Algarin, 2011). Therefore, the medical knowledge must be modeled, extracted, represented and cogitated comprehensively in order to achieve remarkable success from the CDSS implementation.
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The system performance axis for CDSS sustains performance’s gold standard. When the efficiency of this system is validated, the formal clinicians will accurately evaluate the program’s generated instructions with the conventional medical standards (Musen et al., 2014). This fundamental mechanism for verifying the system performance is represented in clinical diagnostic programs, especially in cases where radiology, surgery or even postmortem data is utilized as a useful gold standard. Subsequently, the CDSS uses accurate patient’s data and clinical knowledge to guarantee the overall system performance.
Additionally, the workflow integration axis merges the knowledge-based computer mechanisms into applications intended to save, retrieve, and modify the patient’s information. Since hospitals utilize vast computers for conducting various healthcare operations, their integration mechanism affects the system performance. This challenge is attributed to the network structure and the existing user interface (UI) of the CDSS (Dinevski et al., 2011). Therefore, the CDSS must meet the recommended requirements for data input, the network strength and UI to integrate efficiently into the clinical workflow.
The Success Determinants of CDSS
The advice provided by the CDSS should be systematic to enhance clinician’s overall workflow. This decision must be knowledge-based and digitally supported because, in systems where the physicians were needed to search for advice manually, such CDSS have not produced favorable medical results. Notably, the decision support should be issued at the exact time and place of decision making (Khalifa, 2014). For instance, if the physician must interfere with the regular routine of patient care in order to evacuate to a different workstation or adopts sophisticated startup medical steps, the system will fail to provide adequate decisions for patient care. Thus, the CDSS system should be integrated into the clinical workflow instead of being a mere stand-alone application.
Contemporary Examples of CDSS Implementation in the Medical Industry
The Athena CDSS provides clinical instructions for treating hypertension. The program’s algorithms offer support for blood pressure management and offer medical advice on the appropriate therapy choice that should be adopted to medicate hypertension. The Athena knowledge base focuses on the patient’s medical fitness, risk analysis, blood pressure margins, recommendation strategies, and morbid states for the ailing patient (Dinevski et al., 2011). Therefore, Athena provides drug prescription suggestions for controlling the detected hypertension.
Isabel and Diagnosis Pro are other critical examples of the modern CDSS. It is a web-based application that provides clinical advice support for the patient care. Isabel is suitable for geriatrics medication and infant treatment. It generates immediate potential lists of diagnoses from the EMR for the given clinical prescription such as symptoms, test results, alerts, and assessments and provides instructions for an appropriate drug administration (De la Rosa Algarin, 2011). Diagnosis Pro allows the user to enter specific patient findings and the system produces a stratified list of medical diagnosis from the data repository. Hence, the utilization of Isabel and Diagnosis Pro promotes clinician advice and boost patient quality care.
Summary and Conclusions
The utilization of CDSS in the medical agency is essential because it assists the physicians to make effective decisions about the medical diagnosis when examining different illnesses. The system provides updated medicinal information to the healthcare practitioners and patients. It also enhances healthcare productivity and reduces the patient’s inconveniences at the clinic along with abating the dangers of medication errors. Principally, the CDSS incorporates fundamental categories that are used to offer clinical guidelines such as IMTs, FATs, and PSRTs. The leading structure of this system is the patient data, the medical knowledge axis, and the system performance module. Therefore, with the proper application of these structures, the CDSS guarantees profound success in providing accurate medical decisions as witnessed from Athena, Isabel and Diagnosis Pro recent applications.
Managerial Implications of CDSS in the Medical Industry
The healthcare management should ensure that the incoming patients are engaged in the care that is administered to them. These patients should not feel neglected when receiving medication from healthcare practitioners. Most importantly, the management must ensure that the CDSS system is comprehensively integrated into the clinical workflow to sustain program usability (Dinevski et al., 2011). This strategy will prevent various issues, such as user-system interaction, input methods, and system performance related problems among others. With the help of the network administrators, the management should also assess the network strength, speed, and the quality of service to improve the overall CDSS performance (Khalifa, 2014). Hence, the security mechanism of CDSS must also be strengthened by deploying logical access controls to prevent unauthorized access to the clinical data and possible hacker infringement.
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