Clinical Decision Support and Information Systems

Table of Contents

Introduction

With the ongoing integration of technology into medicine, a computer system has been designed to aid health professionals in decision-making at any given point. The clinical decision support (CDS) systems is an integral part of a net of IT systems that improves the quality of patient care, prevents human error, and allows for the use of electronic health records (EHRs). The purpose of CDS tools is to provide background information from both medical literature databases and patient-specific files.

That information is analyzed and filtered to present possible guidelines and solutions based on which a professional make an informed decision. It documents evidence and research which enhances staff performance and provides information for patients as well. The CDS provides mechanical efficiency in certain processes helping trim excess costs for the healthcare organizations (Byrne et al., 2014, p.1-2). As a system, the CDS has been empirically proven to present numerous benefits. However, there are obstacles both technological and human which hinder full implementation.

Topic Example

A case study was conducted at Duke University Hospital to see the effects of CDS implementation in nursing practice regarding fall risk and prevention amongst patients. A literature review was inconclusive on the effects of the systems but noticed a trend that nurses communicated with patients first, only later to enter health records and confirm the diagnosis with CDS. The project’s aim was to improve documentation of risk assessments and plans of care, assess staff reactions to the computerized system, and consequently improve clinical statistics of patient falls.

The staff was trained, and a system was implemented. The focus groups were specific units where the compliance rate for documentation was below acceptable. The results were mixed, with risk assessment documentation improving in most units during and post-implementation of CDS. However, safety reports and fall prevention plans statistically remained the same, with some high-risk group documentation declining in compliance post-CDS (Lytle et al., 2015).

A similar study was conducted in a large dental practice to examine EHR-based CDS tools in practice. A positive correlation was determined with the positive work environment and decreased stress. The statistical improvement was seen in workplace training and utilization of instruments. The programs CAMBRA and PENUMBRA focused on caries and disease management by risk assessment. The staff voiced positive feedback as they now had more information to understand the science behind various tools and processes (Mertz, Wides, & White, 2017).

The development of CDS systems has grown recently with federal initiatives such as the “Meaningful Use” program and the American Recovery and Reinvestment Act which encourage implementation of information technology into medicine. However, most healthcare facilities lack any CDS and rely solely on EHR software. Most often EHR comes with a basic CDS function, but it is not as efficient as a specialty developed system (Berner & La Lande, 2013, p. 8).

Due to the complexity of these IT systems, the problem of fractalization and inoperability often comes up when trying to combine and establish infrastructure for the system. Copious amounts of information that originates from the CDS data mining for relevant research puts a strain on the computer network. Further technological imperfections exist such as improper or overlapping alerts, improper diagnostics, and redundant data entry. The primary hindrance to the widespread adoption of CDS systems is a disruption to the workflow and communication in the hospital environment which severely reduces the quality of patient care (Byrne et al., 2014).

Personal Experience

My recent experience with CDS occurred when visiting the emergency room with a family member. It was a medium-sized hospital in a health system network of a city. The patient was experiencing severe numbness and a tingling sensation in the leg below the knee for more than 36 hours, making it nearly impossible to walk. The emergency room staff was quick to proceed in gathering the patient’s personal information and entering it into an administrative system.

Basic medical parameters such as weight, temperature, and blood pressure were also collected. After being assigned to a room, the patient was laid down and hooked up to a heart monitor to await care. Blood samples were taken for analysis. Later, a nurse arrived and turned on the computer module in the room, which immediately loaded all the information previously given. Following, the patient was asked a long series of questions detailing the medical history, prescription medications, and experienced symptoms. The program that the nurse was using seemed complex but broken down for simple use so the nurse could systematically enter the information with preset lists and button combinations.

After the questioning had ended, the nurse exited the room to later return with another practitioner. They held a paper copy of a computer-generated report which contained all the given information along with charts, graphs, and bloodwork results. As the healthcare professionals communicated with the patient, they constantly addressed and checked the report as the course of action was determined. The CDS generated possible outcomes that they could explore to determine the cause of the symptoms. It was determined to conduct an ultrasound in the area to check for blood clots, and an X-Ray was done to check for bone damage.

While up to this point the CDS proved extremely efficient in gathering and synthesizing this tremendous amount of information on the EHR profile of the patient, it could only provide guidelines. After the options provided by the system were completed, finding nothing, the emergency room staff seemed helpless. They could not offer any other solution and were offering hospitalization to the patient. It leads to show that the reliance on CDS may be hindering critical thinking and exploring solutions outside those provided by the program. In a way, they completed their duty in offering primary care and ensuring nothing was directly endangering the patient’s life, but more initiative could have been shown in attempting to find a solution and better communicate with the patient.

Conclusion

Clinical decision support is an integrated digital technology that seeks to provide guidance in decision-making to medical professionals when treating a patient. It synthesizes and filters the flow of information from medical databases and patient history to create workable solutions. It has the possibility of aiding with administrative tasks and other healthcare technologies. While the system is efficient, it has not reached widespread implementation due to factors related to cost, workflow, and fractalization of IT systems. However, as shown by example in empirical studies as well as real-world scenarios, the system has moderate success in improving the quality of care. Especially, this is shown in providing filtered information in an age where the medical practice has become heavily evidence-based.

The fundamental insight to take from this research is to focus on training and learning the CDS system if it is integrated into the workplace. The whole premise is based on information being input and shared, in a way that does not drastically disrupt the workflow of the healthcare organization. Along with the uniqueness of each workplace, medical professionals should also understand that a system is a support tool rather than a failproof diagnostic and treatment machine. The combination of rational reasoning and accurate information will allow for successful nursing practice.

References

Berner E., & La Lande T. (2013). Overview of clinical decision support systems. In Berner E. (Eds.), Clinical decision support systems theory and practice (pp. 1-17). New York, United States: Springer-Verlag.

Byrne, C., Sherry, D., Mercincavage, L., Johnston, D., Pan, E., & Schiff, G. (2014). Key lessons in clinical decision support implementation (Tech. No. HHSP23320095649WC). Web.

Lytle, K., Short, N., Richesson, R., & Horvath, M. (2015). Clinical decision support for nurses. CIN: Computers, Informatics, Nursing, 33(12), 530-537. Web.

Mertz, E., Wides, C., & White, J. (2017). Clinician attitudes, skills, motivations and experience following the implementation of clinical decision support tools in a large dental practice. Journal of Evidence Based Dental Practice, 17(1), 1-12. Web.

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