Clinical practice can be a challenging concept, particularly where expectations are high and the quality of healthcare outcomes have to match the patient expectations. In most cases, clinical decision making is reliant on the competency of the individual team members who have the responsibility of understanding patient conditions and analyzing those conditions to diagnose patients for better treatment. In the contemporary times however, new technologies have emerged to help clinicians make more efficient decisions with regards to patient testing needs, prescriptions and general disease management. Such technologies are referred to as Clinical Decision Support Systems (CDSS), one of which is IBM’s Truven Health Analytics that uses Micromedex software. Such systems help in integrating clinical workflows, offering care plan recommendations and providing assistance to clinicians during care. They are an easy way through which data mining is performed on previous patient records to examine patients’ medical history and to predict potential events. This paper presents a description of Truven Health Analytics, particularly its role in nursing and its contribution to evidence based practices in nursing.
Application in Nursing
CDSS systems help clinicians and nurses to offer patients the quality of care they need. They provide the nurses with a way of analyzing patient data and using that information in the formulation of diagnoses. These systems enable healthcare providers to offer preventive care through their use as reminders to give alerts about dangerous drug interactions in the patients. They also help in preventing unnecessary or redundant patient testing. For instance, when a patient is scheduled to undergo a test that was previously found inappropriate for the patient or one in which the result showed negative outcomes a short while before or one that is irrelevant to the patient symptoms. In this way, the systems lower the costs of healthcare service delivery and also increase the service efficiency. Additionally, some healthcare providers use such systems to flag improperly diagnosed patients. These may include patients who were missed in the diagnostic process, given wrong medication or even given wrong dosages of the right medication. Such errors are often included in the patient safety risks within a healthcare setting, and any system that improves the outcomes associated with these is good for evidence based nursing practice. The identified and corrected errors can be added to a healthcare facility’s bucket list and then included in the population health management reports (HPM), which are used as the facility improvement initiatives for a particular period.
According to Monica (2017), Truven Health Analytics is one of the technologies that provides healthcare providers with the opportunity to improve care delivery through the use of clinical decision making support. The technology offers hospitals and patients evidence-based clinical decision support. Patients are educated through Micromedex resources which have been designed for seamless integration into the existing hospital electronic health recording systems using standard application programming interfaces (APIs). The technology allows healthcare service providers to use CDSS in medication identification, disease diagnosis and management, and lab and testing information management from single or multiple sources. The information within a single hospital or facility can be shared with others and used in the improvement of patient care delivery. Currently the technology is in use in over 3,500 hospitals across the world and is recognized for its positive outcomes with regards to evidence-based care delivery.
The confirmation of the role of Truven Health Analytics in the delivery of evidence-based care is the prevalence of medication errors. Physicians, nurses and other healthcare providers are consistently seeking new technologies that promise individualized and consistent care delivery within the framework of organization specific values and healthcare practices. According to Monica (2017), such systems help in preventing certain negative outcomes in care delivery. For instance, medication errors occurring in the prescription and administration stages of healthcare delivery, have been reported as the third biggest cause of death in healthcare in the recent years. Clinicians can have access to the right information through systems that improve evidence-based clinical decision support, and the right information can help in preventing errors and reducing risks of patient safety violation. Truven is one such technology as it not only provides information to the healthcare providers but also to the patients. The need for checking and re-checking data during prescription and medication administration is eliminated through technologies such as Truven, which make the work easier and more automatic. According to Jia et al. (2016), such processes are part of the conventional evidence-based clinical practice.
Data Tracking, Monitoring, Storage and Trending
While using CDSS, data capturing and manipulation techniques are diverse and with various specific objectives. The Micromedex system used by Truven provides a series of data points for use in clinical decision support. IBM (2019) classifies the Micromedex software into four different functionality classes based on the data they collect and store. There is the Micromedex clinical knowledge, Micromedex patient connect, Micromedex pharmacy intervention and Micromedex clinical knowledge for the government. The four resources collect information on evidence-based clinical practice, patients, pharmacy interventions and government policies/ information regarding insurance and other general healthcare policies. Using the range of information provided by the Micromedex resources, Truven can provide evidence-based clinical decision making support spanning the entire healthcare service delivery process. This information is stored in the Truven data warehouse which is also an IBM feature.
The information in the data warehouse is accessible for use by federal agencies permitted by the specific healthcare facilities that manage the data; public healthcare employers who desire to help their employees improve the quality of healthcare delivery; Medicaid and government agencies that fund healthcare delivery; and health and human services organizations that handle patients on a one on one basis (IBM, 2017). The information can be distributed and used for the accomplishment of different healthcare practices based on research and artificial intelligence. The data in the system is indexed and can be obtained through specific search words that enable users to get quick access to specific information. Furthermore, data analytics is conducted and the findings stored within the data warehouse to boost clinical decision support. Such data is not subject to spatial constraints and can be used by healthcare providers and their patients wherever they are subject to the information privacy act (HIPAA).
IBM (2019) points out that the Micromedex resources foster delivery of high quality evidence based healthcare practices through the comprehensive information on drug dosing, diagnostic practices, toxicology, disease management and exposure at all levels of service delivery. The company promises clinical consistency in decision support and also provides information on how data in the Micromedex systems is monitored and controlled for greater efficiency. For instance, the resources in the systems frequently updated based on extensive research, and every source is referenced to ensure that it is based on reported evidence-based practice research. The software providers are also committed to providing consistent data that is free of errors and gaps. This commitment drives frequent and rigorous editorial process in which professional clinicians are involved to ensure that the data is not only accurate but also up to date (IBM, 2017). Such monitoring practices yield complete evidence that clinicians can use to make frequent clinical recommendations and to make informed clinical decisions.
Truven Health Analytics provide clinical information to various healthcare departments. This information is used by different healthcare providers in different ways. As such, all information trending targets the specific people that use the data. For instance, information on clinical practice is trended to emergency department clinicians, nurses, medical libraries and students; information on government policies is trended to clinical administrators and financiers; information on pharmacy up dates is trended to pharmacists and clinical physicians; information on patients is confined to healthcare facilities; and general clinical information is provided to all populations that can access the system (IBM, 2017). In this way, data access is not only limited to those in need of the data, but the system also ensures that all members are able to adhere to the general data use practices.
Uses of the Information Technology
Truven Health Analytics can be used by nurses, nurse researchers, nurse informaticists and nurse leaders to define patient safety extensively. Patient safety is considered to be a function of various factors including patient centered care and organization based factors such as staffing adequacy and competency, staff empowerment and information availability. Each of these factors is significantly impacted by the use of clinical decision support systems such as Truven, and can be enhanced through such systems. For instance, Truven Health Analytics promotes patient centered care through focus on patient information as stored via the Micromedex clinical resources. Specific patient information can be used by nurses, nurse informaticists and nurse researchers to explore drug interactions, conduct further research on those interactions and provide other healthcare practitioners with information that fosters sufficient staffing skills. The information in these systems not only help the various nursing-linked groups to understand general practices in nursing and how those practices foster patient safety in regards to outcomes.
According to Pazokian & Borhani (2017), staffing adequacy and competency also play a crucial role in determining patient safety outcomes in healthcare facility contexts. For instance, the outlined technologies provide nurses with high volumes of evidence based practice information, which can be translated into the practice context to promote patient safety through outcomes such as reduced medication errors, improved patient education efficiency, control of environmental factors that affect patient safety and personnel safety control (Cassano, 2014). The information can be used to achieve lower rates of re-infection within the healthcare setting and also to increase the efficiency of disease prevention management. Moreover, the information can also be used to identify facility equipment gaps which have the probability of impeding patient safety, and to raise concern about the same. In these ways, the systems promote patient safety outcomes.
Enhancing the Technology through Participation
Nurses play a significant role in healthcare delivery. As such, they are well versed with various practice elements including nurse participation and challenges, treatment procedures and organization specific cultures. This implies that compared to clinicians, physicians and other healthcare practitioners, nurses have the capacity to deliver complete information in the design and testing phase of technology systems use in healthcare. They experience first-hand the impacts of various treatment approaches, and the existing gaps in healthcare practice. They also understand the dynamic flow of patient care as well as the imperative interactions between various healthcare providers (Weckman & Janzen, 2009). It has also been confirmed that nurses play an important role in ensuring technology success when they participate in the planning, design and testing phases of the technology. Through their inputs particularly on the organizational process and documentation practices, nurses can create a big difference in the outcomes realized through technology use in healthcare.
For this to be achieved however, the change implementation process has to involve the nurses from the word go. Active participation in technology design and testing begins from the recognition of the need for that technology, which subsequently promotes active participation technology training and distribution to all teams. They can be involved through shared decision making, provision of input into the system design and efficiency expectations and evaluation of the technologies to establish whether the expected healthcare improvements have been achieved through the technology. Weckman and Janzen (2009) explain the rationale behind nurse participation in healthcare technology development, with the conclusion that failure to involve nurses at the initial stages of technology development can impact negatively on its implementation and efficiency.
Healthcare related technologies have improved the quality and safety of patients in various healthcare contexts. Technologies such Truven Health Analytics provide healthcare practitioners with the opportunity to improve patient safety outcomes through improved access to information on pharmacy, diagnosis and general clinical practice. This technology can be used to promote patient safety as long as there is active participation of nurses from the design through the testing and implementation phases.
Cassano, C. (2014). The right balance – technology and patient care. Online Journal of Nursing Informatics, 18(3). Retrieved from www.himss.org/right-balance-technology-and-patient-care
IBM (2017). IBM Micromedex clinical knowledge suite. IBM Watson Health. Retrieved from truvenhealth.com/products/micromedex/product-suites/clinical-knowledge
Jia, P., Zhang, L., Cheng, J., Zhao, P., Zhang, P., & Zhang, M. (2016). The effects of clinical decision support systems on medication safety: An overview. PLoS One, 11(12). Retrieved from www.ncbi.nlm.nih.gov/pmc/articles/PMC5157990/
Monica, K. (2017, April 7). Top Clinical Decision Support System (CDSS) companies by ambulatory, inpatient settings. Retrieved from ehrintelligence.com/news/top-clinical-decision-support-system-cdss-companies-by-ambulatory-inpatient
Pazokian, M., & Borhani, F. (2017). Nurses’ perspectives on factors affecting patient safety: A qualitative study. Evidence Based Care Journal, 7(3), 76-81. Retrieved from ebcj.mums.ac.ir/article_9382.html
Weckman, H., & Janzen, S., (2009, May 31). The critical nature of early nursing involvement for introducing new technologies. OJIN: The Online Journal of Issues in Nursing, 14(2), Manuscript 2. Retrieved from ojin.nursingworld.org/MainMenuCategories/ANAMarketplace/ANAPeriodicals/OJIN/TableofContents/Vol142009/No2May09/Nursing-Involvement-and-Technology.html