Essay Example on Clinical Support Systems: Enhancing Healthcare Quality & Efficiency

Introduction

Typically, the Clinical Support Systems (CDS) have been currently hailed for their capability of minimizing errors in the medical sector. The physicians have critically considered the implementation of CDS systems in their practice to increase healthcare quality and efficiency. The CDS systems have been evolving and have provided varieties of assistance tools. These assistance tools offered by the CDS systems include; physician reminder alerts, automated patient, documentation templates, condition-specific order strategies, clinical guidelines, and diagnostic support. The primary goal of the modern CDS systems is assisting the clinicians at the point of care to analyze and come to a diagnosis based on the patients' data. Currently, the CDS systems have been categorized into two different types which include data mining systems and knowledge-based systems. According to Hogarty, Mackey, and Hewitt, (2019), each type is evaluated according to its specific merits and the needs of the medical operation.

Is your time best spent reading someone else’s essay? Get a 100% original essay FROM A CERTIFIED WRITER!

Save 25%

Typically, the data mining systems are configured to carry out the examination of patients' medical history, which should be combined with the trusted clinical researches. This type of CDS is imperative since it foresees the potentiality of incidences. These incidences may vary from drugs interaction, analytical results to illness, and the indicators of disease. On the other end, the knowledge-based system is essential in the application of reasoning standards to enhance analyzing the clinical information. In this sector, the input data undergoes analyzation against its rules which therefore permits the program to show the outcomes, whether negative or positive. In understanding the current state of the CDS systems, the systems have facilitated accuracy in diagnosis. Due to the implementation of the CDS in medical facilities, the healthcare providers are now able to timely give efficient screenings on adversative drug events and other preventable diseases (Hogarty, Mackey & Hewitt 2019). The CDS systems have proved their capability in addressing all the areas simultaneously; improve the patients' conveniences, promote efficiency, and theoretically lower the medical operations costs. Notably, in complicated tasks, for example, diagnostic decision making, the system has a primary goal of assisting the clinicians. In the currents CDS systems' state, the system offers a suggestion or suggestions although the health provider is needed to screen the data, run suggestions review then come to a conclusion whether to act and the type of action to be taken.

How are CDS Systems Integrated with the EHR?

Typically, in integrating the clinical decision support (CDS) systems with the electronic health record (EHR) system, the facility workflow integration requires procedural and policies enhancement which includes retraining of all personnel on a variety of data collection procedures and more data entry fields. More so, in implementing CDS systems with EHR, the electronic health record requires some adjustments which will allow accommodation of additional areas where the clinical decisions support can report. Notably, the integration process is much complicated and cumbersome, whereby there may rise interoperability software conflicts that could lead to data loss, overlook, or alteration (Zorc et al., 2018). Because CDSS are computer systems which are made with a design of impacting clinicians' decision making on their patients, it is therefore clear that integrating the EHR and CDSS will be of many benefits. For CDSS to be productive and successful, significant planning is required by the healthcare organizations to implement a CDS system which is integrated with EHR fully.

How do CDS Systems Support the Patient Care Continuum?

The CDS systems support patient care continuum by reducing diagnostic errors. Typically, the patient care continuum comprises integrating mechanisms and services whereby diagnostic errors are commonly faced. The clinical decision support tools, when implemented correctly, they help in reducing patient safety events. The diagnostic errors are encountered across the care-continuum as a patient moves through multiple points of potential failure. In this case, the CDS tools are used to synthesize massive volumes of data which enable a large amount of work to be done within the shortest time possible (Connolly & Magowan 2018). Besides, the system ensures that the key details are well preserved and do not escape the attentions of the busy clinical staffs during their work. The healthcare providers use the new way of thinking, fresh perspective, and data-driven insights on their day to day activities which facilitate in making a patient diagnosis. In this case, the healthcare providers can reduce claim frequency, promote the safety of the patients, and improve the diagnostic accuracy.

Current Initiatives that Impact these Systems

The current information technology initiatives which impact the CDS systems include Electronic Health/Medical Records, Medical Archiving Systems, Backup (disk-based and online storage), Infrastructure for Health Information Systems, Storage Consolidation, and Virtualization and Securing Electronic Protected Health Information. Also, American Medical Colleges (AAMC) has highly facilitated impacts on the CDS systems. The American teaching hospitals have professional physicians as well as health medical scientists in combination with the advanced technology. The AAMC, therefore, impacts the implementation and use of the CDS systems in healthcare facilities profoundly.

How are CDS Systems Advancing?

In general, the CDS systems are advancing in each of the six axes. These six axes include users, implementation and integration, architecture and technology, inference, knowledge, and data. Key advancements have been done in the structuring and encoding of the standardized data, and also there is increased data availability, developed CDS systems knowledge, and an improved capability in sharing the artifacts knowledge. Moreover, these systems are advancing by improving the CDS implementation methods and their integration into the clinical workflow. Eventually, there has also been an explosion of inferring and analyzing processes from the clinical data. The evolution of architectures and information technology has been initiated and improved the application of CDS. Notably, the CDS has dramatically advanced for the last 25 years as cited in Hogarty, Mackey, and Hewitt (2019), hence for the next 25 years, it is expected to improve more dramatically. The clinical encounter between patients and clinicians will highly be supported by wide cognitive aides of varieties, thus ensuring the diagnosis supports, health maintenance, surveillance and prevention, care coordination, and treatments.

The Impacts that the CDS Systems are having on Patient Care

The Clinical Decision Support systems have impacted patient care in three main ways, according to Connolly and Magowan (2018). First, the CDS systems have highly improved the safety of the patients. In this case, the electronic decision support systems ensure the patients' safety through reducing the errors in which are commonly faced in medication. The systems minimize adverse events, improves the drug, and test ordering. Secondly, CDS systems have impacted patient care by improving care quality. The system has increased the clinicians' availability for direct patient care and enhanced clinical documentation as well as patients' satisfaction. It has also facilitated increased applications of clinical guidelines and pathways and the use of clinical evidence which is up-to-date. Thirdly, the CDS system has led to health care delivery efficiency through reduced cost. In this sector, the CDS systems have facilitated faster processing of orders, reduced test duplications, and minimized adverse events. Furthermore, the system has led to a change in drug prescribing pattern, thus favoring cheaper but with equal active generic brands.

References

Connolly, T. M., & Magowan, B. (2018). A Clinical Decision Support System for Maternity Risk Assessment Developed for NHS Scotland. Progressing Aspects in Pediatrics and Neonatology, 1(5), 92-95.

Hogarty, D. T., Mackey, D. A., & Hewitt, A. W. (2019). Current state and future prospects of artificial intelligence in ophthalmology: a review. Clinical & experimental ophthalmology, 47(1), 128-139.

Zorc, J., Shelov, E., Lavelle, J., Halkyard, K., Schast, A., Keren, R., & Luan, X. (2018). Leveraging Web & EHR Technology to Bring Evidence-based Care to the Bedside and Reduce Variation.