Nurses ' Usage Of Clinical Decision Support Systems

It ensures that clinical decisions are based on the most up to date information and promotes care that is safe and well organized. Clinical decision support system CDSS helps healthcare providers make clinical decisions. Healthcare professionals use a CDSS to prepare a diagnosis and to review the diagnosis to improve final results. The clinical system can help prevent errors, which can range from disease symptoms to drug interaction. The clinician would input the information and wait for the CDSS to output the “right” choice and the clinician would simply act on that output. In addition, the CDSS has a number of important benefits, including improved efficiency, cost benefits, and provider and patient satisfaction, avoidance of errors and adverse events, increased quality of care and enhanced health outcomes.

Nursing informatics and technology are quickly becoming the hot buzz words for nursing in the twenty-first century. While performing research for this specific paper, the observations of how far technology has come from its inception is mind boggling. When looking back to the mid 1990’s every patient had paper charting. Nurses manually charted vital signs, nursing notes, treatments and all orders were manually written in the chart. The patient’s name, insurance information, and billing items were stored electronically. Fast forward twenty plus years and everything nurses do with, for or to a patient is filed electronically. This file today is known as the electronic health record (EHR) (Lavin, Harper, & Barr, 2015). This paper will be delving into the history of nursing informatics and technology, the pros and cons for nurses and what will be the big picture for informatics and technology in nursing today and in the future. Nursing informatics and the technology that has evolved over time are changing and quickly affecting how nurses treat, communicate, plan and document everything that they do for their patients.

An interview with an Assistant Professor at Duke University Health System in the Department of Medicine, Maestro Care Provider Champion and Clinical Content Architect. This physician works to incorporate clinical decision support tools into the electronic health record at Duke Health System. He manages the best practice advisory committee that may provide a way to deploy alerts to clinicians at the point of care. Alerts with order sets and recommended actions are created and updated to notify providers of current patient care guidelines or patient safety concerns.

Alert fatigue and using a clinical decision support system (CDS) in an electronic health record is a growing concern in health care. Although alerts and warnings in an EHR are well intended, the volume of alerts EHR end-users receive is surprising. The Agency for Healthcare Research and Quality (AHRQ) estimates that in some EHR end-users have the potential to receive over 100 CDS alerts per day (Agency for Healthcare Research and Quality [AHRQ], 2015, p. 1). This cause’s alert fatigue when the end-users become desentized to the alerts and even the most important alerts become meaningless.

Wager, K. A., Lee, F. W., & Glaser, J. P. (2009). Health care information systems, a practical approach for health care management. (2nd ed.). Jossey-Bass Inc Pub.

Clinical decision support is a system designed with capabilities to enhance physician and other health care provider in the clinical decision task. It enable the physician to have more knowledge of the patient that they are provided with care, more advance knowledge of the type of illness that the patient is going through so that appropriate clinical decision would be included in the patient treatment plan (PTP) ("What is Clinical Decision Support (CDS)? | Policy Researchers & Implementers | HealthIT.gov," 2013)

Health informatics has successfully captured the attention of clinical and public health leaders around the nation as they realize its potential to solve problems, cut cost and enhance patient experience. As discussed in class, The American Reinvestment and Recovery Act (ARRA) of 2009 initiated a program designed to equip hospitals and medical practices around the country with electronic health record systems. Known as the Meaningful Use program, it has provided financial encouragements to health care organizations to install these computerized systems. This act has resulted in a huge increase of electronic health records (EHR) companies and has generated countless jobs for healthcare data analysts and related IT positions.

Romano, 2011). This survey was performed using 20 quality indicators to evaluate the relationship between the Electronic Health Records, Computerized Physician Order Entry, Health information Technology, and Health Information Exchange systems and Clinical Decision Support using a multivariate logistic regression model that can be observed in table 1.

For policymaking, regulations and strategies, clinical decision support (CDS) 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. CDS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include 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, among other tools.

The data used in our work was gathered from nine medical-surgical units located in four unique Midwestern hospitals for a period of a three years (2005-2008). The Hands-On Automated Nursing Data System (HANDS) [43] was used for this purpose. The HANDS is an EHR system that is designed explicitly to record nursing care given to the patients using standardized terminologies. The registered nurses (RNs) from these units used standardized terminologies in HANDS to document all the patient problems, outcomes and different interventions at each shift change. To confirm that the RN made the accurate diagnosis, HANDS provided all the appropriate attributes of each NANDA-I diagnosis through decision support icons. This support facilitated the validity of the RN making the appropriate diagnosis. Previous research findings have shown

The value of Clinical Decision Support Systems is having additional avenues monitoring patient’s data input. The Clinical Decision Support Systems are offering notifications of patient’s record data to specified department or medical personal.

The role of nurses, relative to an EHR interfacing with CDSS, is clinical documentation, interpret other clinical data, utilize the interpretation of the clinical documentation into plan of care, and simultaneously identify patterns of outcomes for given plans with similar clinical condition of patients. CDSS alleviates stress for less experienced nurses with limited knowledge of clinical care patterns, and helps them arrive at appropriate decisions for care. Thus, acceptance and use of CDSS is more likely to occur in nursing fields where clinical care patterns often vary, or with areas employing less experienced nursing professionals. Alert fatigue occurs with overusing repeated algorithms for common clinical care decision paths; therefore,

Reminders are generated by the Clinical Reminder System (CRS) and inform the provider of recommended actions in regards to tests that should be performed, vaccinations, and also have the ability to discuss the pros and cons of treatment plans (Zheng, n.d.). CRS is an evidence adaptive decision support system which supplies decision aids with a knowledge base which is constantly adapting to new research and evidence based medicine (Zheng, n.d.). CRS manages four chronic health conditions which are diabetes, asthma, hyperlipidemia, and hypertension. There are also four preventative care areas such as cervical cancer, breast cancer, influenza, and pneumonia (Zheng, n.d.). CRS is also real time with a wide diversity of patient data from other hospital systems, which include laboratory test results, vital signs, and disease diagnoses.

Clinical decision-support systems (CDSS) apply best-known medical knowledge to patient data for the purpose of generating case-specific decision-support advice. CDSS forms the cornerstone of health informatics research and practice. It is an embedded concept in almost all major clinical information systems and plays an instrumental role in helping health care achieve its ultimate goal: providing high quality patient care while, at the same time, assuring patient safety and reducing costs. This computer based systems designed to impact clinician decision making about individual patients at the point in time that these decisions are made. If used properly, CDSS have the potential to change the way medicine has been taught and

Technology can assist healthcare workers on every clinical and administrative level to use information more effectively in clinical decision-making for patients, and in implementing strategic goals within an organization.