Clinical Decision Support System Evaluation

Table of Contents

Selection of a Technology

Contemporary clinical information systems are designed to “to support safe, evidence-based, patient-centered care by examining patient-specific information, agency-specific information, and domain-specific information in the clinical context” (Shortliffe & Cimino, 2014, p. 496). The mentioned aspects of information are used for everyday decision-making by healthcare professionals. There are different types of clinical decisions necessary for successful care. First of all, the most important decision made in healthcare facilities is diagnosis. The task of a physician is to analyze the available information and provide the pathophysiologic explanation for the symptoms observed by the patient. Another decision that is equally complicated is diagnostic process. A healthcare professional decides on the questions to ask, the necessary tests or procedures to reveal the possible diagnosis, thus providing a background for diagnosis. Finally, decisions are made about management of patients, their condition, and treatment process. Traditionally, these decisions are made based on the experience and knowledge of a healthcare professional. However, such decisions are influenced by the human factor and result in wrong diagnoses followed by improper treatment and negative patient outcomes.

To improve the quality of care and increase its safety and efficiency, there is a need for technology that is able to manage patient data and empower clinical decisions. Clinical Decision Support System is one of technologies that can positively influence the mentioned healthcare factors. Health IT (2018) claim that CDSS “provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care” (para. 1). This system comprises diverse tools that embellish decisions made in the clinical workflow. Some of these tools are “computerized alerts and reminders to care providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support, and contextually relevant reference information” (HealthIT, 2018, para. 1). CDSS link knowledge base to patient data thus allowing generation of information and suggestions that empower diagnosing and generally improve the quality of care (Beeler, Bates, & Hug, 2014). Consequently, the purpose of this analysis is Clinical Decision Support System as a technology that has a potential to increase quality of care and improve patient outcomes, help to avoid errors and adverse events, and improve overall health care efficiency as well as patient satisfaction.

Assessment of Functionality

To fulfill its functions, every system should be properly designed and functional. In case of CDSS, its design and set of functions will depend on the clinical setting where the system is applied. Thus, it can be different for primary and acute care settings because decisions made there and the purposes of care are different. Yuan, Finley, Long, Mills, and Johnson (2013) claim that CDSS possesses significant tools that can enhance health care outcomes and decrease preventable medical adverse events. In fact, CDSS is frequently an integral component of Electronic Medical Record (EMR) implementation because CDSS is grounded on patient information extracted from EMR in addition to knowledge base. Nevertheless, the effectiveness of CDSS implementation depends on the context of healthcare facility and the system’s usability (Yuan et al., 2014).

One of the reasons for CDSS failures is its poor usability due to drawbacks in user-technology interface. In fact, usability design is one of the factors that significantly influence the success of CDSS implementation in healthcare settings (Yuan et al., 2014). A well-designed interface, on the contrary, can positively influence the functionality of the system. To assess the system, some of its functional elements have to be checked and evaluated. For example, one of such elements that contributes to CDSS usability is information organization (Febretti et al., 2014). The way information is presented in the application is directly related to its usability and implementation success. Also, navigation styles are important. A system that is easily navigated without a particular preparation is expected to be successfully implemented. In most of cases, time is critical for clinical decisions.

Therefore, availability of information and quick access to it due to good navigation are the factors that influence CDSS usability. One more element of CDSS contributing to its efficiency is generation of advice for patients in addition to supporting clinicians in their decisions. One of the crucial elements of this system as well as other systems and databases that contain personal information is preservation of privacy. Considering the fact that many CDS systems use data mining and cloud technology to get and store information, privacy should be one of the focuses (Liu, Lu, Ma, Chen, & Qin, 2016). Another element integral to CDSS is information accuracy. Since contemporary health care aims to achieve precision medicine, CDSS as one of its powerful tools should guarantee accuracy of data it contains and uses for supporting clinical decisions (Castaneda et al., 2015). Finally, a significant element of CDSS is its adjustability. It is important that the functions of software applied in the system could be adjusted to meet the needs of the facility units and correlate to the peculiarities of care provided there.

Elements Used for Evaluation

CDS is considered to be a complicated IT component. It demands computable biomedical knowledge, personal data, and a specialized mechanism that can combine these data and knowledge and generate information useful for clinicians and supporting the decision-making process (HealthIT, 2018). Currently, in conditions of information overload, decision-making in the process of care can be particularly complicated. CDSS is expected to filter, organize, and present information thus supporting the current workflow and empowering informed decisions as well as actions to provide better patient outcomes. These major purposes of CDSS can be better achieved in case the system has a well-designed user-technology interface. Doctors as well as other health care professionals are not IT experts and frequently awkward interfaces make them refuse to apply health technologies due to the lack of time.

The following elements are expected to contribute to usability of CDSS and can be measured to assess its effectiveness. The first element is adjustability. Thus, the system should allow adjusting the functions depending on the current needs and the user preferences. The best possible variant is a CDSS designed especially for the unit or facility with the consideration of needs and workflow peculiarities (Yuan et al., 2013). The second significant element is CDSS integration with EHR because the system uses personal patient data from electronic records (Castaneda et al., 2015). The third element to consider in the analysis is effectiveness of alerts that are provided by the system to prevent some complications or support the process of treatment (Beeler et al., 2014).

Definition of Elements and Description of Evaluation

It is necessary to define the selected elements of CDSS technology and provide peculiarities of their measurement and evaluation. Thus, adjustability as an element of the system implies an opportunity to alter the system and select its functions which depend on the peculiarities of the unit where the system is implemented and preferences of healthcare professionals who use it. Adjustability can be assessed through a survey conducted among the staff members of the facility where CDSS is introduced. They should react to the question if the system is easily adjustable to meet the needs of their unit, needs of patients, and their professional claims. Another factor to evaluate adjustability is its correspondence to the workflow I the unit. The survey results will be analyzed with the use of descriptive statistics to reveal if the technology is adjustable.

Another important element of CDSS to consider is its integration with EHR. This integration is the demand of time because it allows the system to extract personal patient data from electronic records and analyze them using the knowledge databases (Castaneda et al., 2015). It can be defined as an opportunity for CDSS to use EHR data and process them to generate clinical decision support information. In fact, CDSS can be treated as “tools that incorporate established clinical knowledge and updated patient information to enhance patient care” (Castaneda et al., 2015, p. 6). The integration can be measured by evaluating relevance of patient data extracted by CDSS from EHR. High rate of patient data and history inclusion would mean that integration is successful.

The last element to define and measure is the effectiveness of alerts generated by CDSS to inform care providers on important or dangerous patient issues. Alerts in the context of CDSS implementation are warnings of the system regarding the patient’s condition that needs attention of a healthcare professional (Beeler et al., 2014). The alerts produced by CDSS should be assessed from the point of view of productivity meaning the correlation between the general number of alerts and the situations that needed professional’s interference. Another indicator to consider here is the frequency of alerts. According to Beeler et al. (2014), they should not be too frequent because of the risk of alert fatigue.

Proposal for Improvement

Based on the evaluation of CDSS elements, some suggestions for improvement can be provided. First of all, CDSS can be effective in case it is easy to use and its application does not demand extra time from healthcare professionals. To achieve this simplicity, the interface of the technology should be functional and adjustable. The technology should be available not only to doctors or nursing staff but to other professionals involved in the process of care as well as patients. Consequently, the interface has to be adjustable to the needs of every group and meet the workflow peculiarities (Yuan et al., 2013). As for EHR integration, it can be suggested that CDSS and EHR were implemented simultaneously. The use of EHR data in addition to evidence-based guidance are expected to provide diagnostic accuracy (Castaneda et al., 2015). Finally, the frequency of alerts should be regulated depending on the clinical setting where CDSS is implemented. Thus, in acute care they should be more frequent while in long-term care their frequency can be reduced. However, the most appropriate solution to achieve the efficiency in CDSS implementation is its design specifically for a healthcare facility with consideration of its peculiarities.

References

Beeler, P., Bates, D., & Hug, B. (2014). Clinical decision support systems. Swiss Medical Weekly, 144(14073), 1-7. Web.

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(4), 1-16. Web.

Febretti, A., Lopez, K., Stifter, J., Johnson, A., Keenan, G., & Wilkie, D. (2014). Evaluating a clinical decision support interface for end-of-life nurse care. Proceedings of the Extended Abstracts of the 32Nd Annual ACM Conference on Human Factors in Computing Systems – CHI EA ’14, pp. 1633-1638. Web.

Health IT. (2018). Web.

Liu, X., Lu, R., Ma, J., Chen, L., & Qin, B. (2016). Privacy-preserving patient-centric Clinical Decision Support System on Naïve Bayesian classification. IEEE Journal Of Biomedical And Health Informatics, 20(2), 655-668. Web.

Shortliffe, E. H., & Cimino, J. J. (Eds.). (2014). Biomedical informatics: Computer applications in health care and biomedicine (4th ed.). London, UK: Springer-Verlag.

Yuan, M., Finley, G., Long, J., Mills, C., & Johnson, R. (2013). Evaluation of user interface and workflow design of a bedside nursing Clinical Decision Support System. Interactive Journal of Medical Research, 2(1), e4. Web.

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