Chapter 6 History and Evolution of Nursing Informatics Objectives • Trace the evolution of nursing informatics from concept to specialty practice. • Relate nursing informatics metastructures, concepts, and tools to the knowledge work of nursing. • Explore the quest for consistent terminology in nursing and describe terminology approaches that accurately capture and codify the contributions of nursing to health care. • Explore the concept of nurses as knowledge workers. • Explore how nurses can create and derive clinical knowledge from information systems. Introduction • The information and knowledge informing the 21st century of healthcare delivery have been growing at an unprecedented pace in recent years. • Health service organizations, societies, and governments throughout the industrialized world are committed to ensuring that healthcare delivery is safer, knowledge based, cost-effective, seamless, and timely. Evolution of a Specialty • Nurses do the following: − Gather atomic-level data (e.g., blood pressure) − Aggregate data to derive information (e.g., impending shock) − Apply knowledge (e.g., lower head of bed to minimize potential effects of impending shock) What Is Nursing Informatics? • A specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice DIKW Paradigm • DIKW stands for data, information, knowledge, and wisdom. • Nursing informatics centers on these concepts. Metastructures of Nursing Informatics • Data: discrete entities that are described objectively without interpretation • Information: Data that are interpreted, organized, or structured • Knowledge: Information that is synthesized to identify and formalize relationships • Wisdom: Application of knowledge to the management and solution of human problems An Example of DIKW • Data: A patient’s vital signs • Information: A serial set of vital signs, placed into a context and used for longitudinal comparisons • Knowledge: Recognition of a pattern and identification of interventions • Wisdom: Accuracy of the synthesis of information and appropriate selection of interventions From Knowledge to Wisdom • Knowledge focuses on what is known. • Wisdom focuses on the appropriate application of that knowledge. • Example: − A knowledge base may include several options for managing an anxious family, while wisdom would guide the decisions about which of these options are most appropriate with a specific family. Wisdom in Informatics • The ability of the system to evaluate the documentation drawn from a health information system, and to adapt or change the system to improve the workflow of the clinical nurse • Nurses’ decision making is described as an array of decisions that include specific behaviors, as well as cognitive processes surrounding a cluster of issues. • Can any aspect of nursing wisdom be automated? Capturing and Codifying the Work of Nursing • Clinical outcomes can be further understood in the context of care environments. − Particularly implications related to availability of human and material resources to support care • Standardization of clinical inputs and outputs into EHRs will eventually provide a rich knowledge base to enhance practice and research and inform administrative and policy decisions. Useful Considerations • In EHRs, patient size may be recorded in either US or metric units. To compare patient data from multiple EHRs in different healthcare institutions to help predict the onset of Type II diabetes, these disparate measures will not translate well. • Other EHRs force data collection in coded database fields, and these data are more easily analyzed for trends than that same data recorded as free text. Structured Language as a Tool for Nursing Informatics • Nursing knowledge is gained by extracting data that specifically define nursing phenomena. • Many different languages and ways of organizing data, information, and knowledge create difficulties in mapping nursing phenomena. • The ANA has formalized the recognition of these languages/vocabularies through a review process of the Committee on Nursing Practice Information Infrastructure (CNPII). ANA-Recognized Terminologies • NANDA • NIC • CCC • Omaha System • NOC • NMMDS • PNDS SNOMED CT NMDS ICNP ABC Codes LOINC Structured Language as a Tool for Nursing Informatics • At a higher level, several resources have developed to facilitate interoperability between different types of systems, concepts, and nomenclatures. • The importance of developing universal languages and vocabularies cannot be understated. • The Informatics Nurse Specialist (INS) must attempt to envision the differing functions that may be used with the data, information, and knowledge that have been created. The Nurse as a Knowledge Worker • Has advanced formal education and is able to apply theoretical and analytical knowledge • Is a continuous learner • Is a specialist • Devotes 50% of work to searching and evaluating information • Is an innovator Demands of Nursing Informatics • A significant amount of knowledge and nonknowledge work − Knowledge work includes such duties as interpreting trends in laboratories and symptoms. − Nonknowledge work includes such tasks as calling the laboratory to check on laboratory results or making beds. • Nurses, on a daily basis, rely on their extensive clinical information and specialized knowledge to implement and evaluate the processes and outcomes related to patient care. Four Tasks Associated with Human Information Processing • Data gathering • Information use • Creative application of knowledge to clinical practice • Generation of new knowledge Four Roles Nurses Take on as Knowledge Workers • Data gatherer • Information user • Knowledge user • Knowledge builder • Nurses require different types of decision support processes to support their knowledge needs. Decision Support Systems (DSSs) (1 of 2) • DSSs are typically rule-based, using a specified knowledge base and a set of rules to analyze data and information and provide recommendations. Decision Support Systems (DSSs) (2 of 2) • Most DSS tools available for nursing practice are simplistic and in early development. • Typically, DSSs include such tools as: − Computerized alerts and reminders − Clinical guidelines − Online information retrieval − Clinical order sets and protocols − Online access to organizational policies and procedures Clinical Information Systems (CISs) • Informational elements can include specifics about individuals’ multicultural practices and beliefs. • Example: − A client voices concerns about her prescribed dietary treatment and requests a female care provider. − With a query to the CIS for the client’s history and sociocultural background, the nurse understands that these requests derive from the patient’s religious and cultural background. − The nurse makes a notation to highlight and carry this information forward in the EHR for any future admissions. Capture of Multifaceted Data and Information in CISs • Biometrics (e.g., facial recognition, security) • Voice and video recordings (e.g., client interviews and observations) • Voice-to-text files (e.g., voice recognition for documentation) • Medical devices (e.g., infusion pumps, ventilators) • Bar-code and radio-frequency identification (RFID) technologies (e.g., medication administration) • Telehomecare monitoring (e.g., for use in diabetes and other chronic disease management) Advances in CISs • Nurses will become generators of new knowledge by virtue of designs that embed machine learning and case-based reasoning methods within their core functionality. • This functionality will become possible only with national and international adoption of standardized nursing language. Expert Systems • Expert systems that implement knowledge automatically are becoming available. • The expert system differs from DSS tools by not requiring human interaction. • Example: − An insulin pump that senses the patient’s blood glucose level and automatically administers insulin based on those data The Future (1 of 2) • Technology continues to evolve with a rapidity and unfolding that is rich with promise and potential peril. • It is anticipated that computing power will be capable of aggregating and transforming multidimensional data and information sources into the CIS. • With these rich repositories, further opportunities will allow us to: − Enhance the training of health professionals − Advance the design and application of clinical decision support − Deliver care that is informed by the most current evidence − Engage with individuals and families in ways yet unimagined The Future (2 of 2) • The future of nursing informatics promises increasing informatics concepts and solutions in mainstream nursing and healthcare practices. • As informatics solutions become as common a tool as the stethoscope, each nurse may be considered, in part, an informatics nurse. • New materials and concepts will evolve in the future. Summary • Contemplate a future without being limited by the world of practice as it is known today. • Information technology is not a panacea for all of the challenges found in health care, but it will provide the nursing profession with an unprecedented capacity to generate and disseminate new knowledge at rapid speed. Thought-Provoking Questions (1 of 2) • How is the concept of wisdom in nursing informatics like or unlike professional nursing judgment? • How can a single agreed-upon model of terminology use (with linkages to a single terminology) help to integrate knowledge into routine clinical practice? • Can you create examples of how expert systems (not decision support but true expert systems) can be used to support nursing practice? Thought-Provoking Questions (2 of 2) • How would you incorporate the data-to-wisdom continuum into a job description for a nurse? • What are the possibilities to accelerate the generation and uptake of new nursing knowledge?
