There has been much advancement in nursing informatics education, practice, and research since Scholes and Barber (1980) proposed the groundbreaking definition of nursing informatics as “the application of computer technology to all fields of nursing: nursing service, nurse education, and nursing research” (p. 73). Although many different definitions have since been proposed, they all emphasize the role of computers and novel software and devices as supports for nursing. As in real life, the definitions include advancements in technology, science, knowledge, information structures and processes, and connection with patients and other care providers (Staggers & Thompson, 2002). Although it is not directly mentioned in the definitions, informatics is connected to innovations, which are ideas, practices, or means of developing a new focus or target within healthcare. Innovations are adopted by a range of people, from innovators to laggards, which all can benefit from the implementation of informatics (European Commission, 2012a). When speaking about nursing informatics, researchers often mention technological innovations that take the form of products (e.g., devices or tools) or processes (e.g., telehealth). Less often, innovations are described as social innovations involving the development, adoption, and integration of new practices intended to change methods in healthcare. However, both types of innovations are needed in nursing practice. The World Health Organization highlighted the importance of using technology to support continuity and care coordination and the need for research and innovation to successfully implement predictive risk tools, decision support tools, algorithms, and guidelines intended to coordinate care and achieve the greatest effect on nursing practice (WHO, 2018a). The use of information technology, including applications and tools for processing, sharing, and storing information as well as connecting data at the personal or administrative level, in healthcare settings is usually referred to as health information technology (HIT) or eHealth (electronic health) (European Commission, 2012b). Many terms have been used to describe this phenomenon. Digitalization, which is most often used in strategical, steering, or policy texts, is the most recently developed term but the most incoherently applied. The complexity of the term is evident when one considers the history of documenting patient care. In the previous decades, it was crucial that computers were used for nursing notes, and so these notes were transferred from analog to digital form with the use of electronic health records (EHRs). The transfer of notes from analog to digital form improved the quality of documentation by increasing the readability of the notes and providing a structure that made it easier to find information. However, this process was not truly focused on digitalization; digitalization aims to create value from the use of new, advanced technologies by exploiting digital networks’ dynamics and the giant digital flow of information, but nursing information systems could not process notes. The Committee on Data Standards for Patient Safety classifies EHR systems based on their core functionalities (e.g., delivery of healthcare services, care management, and support processes), which can be classified as subfunctionalities based on users’ needs (D’Agostino et al., 2018). Core functionalities are also classified based on the administrative processes (e.g., billing and reimbursement) they support, which again may be classified into subfunctionalities based on users’ needs (Tang, 2003). The lack of software or functionality for information systems remains the greatest obstacle to information flow in nursing; when data cannot be accessed in a timely manner or the same data is recorded many times in various records, severe safety concerns, difficulties in decision-making, deficiencies in information exchange, and frustration in work processes may arise. Digitalization is connected to the interoperability of information systems or how various devices share, use, and produce information. In the healthcare context, interoperability refers to the ability of two or more healthcare providers to exchange and utilize the information with precoordination and context such that the information can be used to improve patient care. Thus, it is the key to seamless care, service, and data flow. In terms of digitalization, interoperability is often attached to technical interoperability, but in healthcare (e.g., standards, terminologies), organizational (e.g., structures, roles, responsibilities, policies, agreements) and legal (e.g., institutional arrangements, acts, degree, orders) contexts, interoperability must create real value using new, advanced technologies (European Union, 2017). Interoperability guidelines for nursing practice have effectively improved not only cooperation between care providers but also care coordination and patient outcomes by ensuring data sharing and fluid information flow, which facilitate patient safety and high-quality care. Already, Florence Nightingale inspired nurses to stress guidelines for practice and ensure high-quality care. In her day, notes on nursing care and statistical methods of generating reports to correlate patient outcomes to environmental conditions were utilized. The process of developing quality measures from research involves several phases, beginning with the translation of evidence to clinical practice guidelines, which are the key components in the provision of high-quality nursing care. Further, these guidelines can be used to develop quality measures and define the parameters for measuring quality. Parameters are needed to create the best indicators for both quality and outcome measures. In many cases, the model proposed by Donabedian (1992), which focuses on the structure, process, and outcome measures, has increased the rigor of measures of nursing quality. Also, several indicators of nursing quality have been defined. One such indicator is the relation of patient processes and outcomes and the structure of nurse staffing. Today, innovative means, such as electronic databases and registers, are used to quantify this relation more efficiently and effectively. Also, several data sources are used to improve the quality of nursing. For example, administrative data from hospital registries (indicators of structure and process) and patient records (indicators of outcomes) as well as qualitative data from surveys or interviews with patients involving various tools and technologies (indicators of structure, process, and outcomes) may be used. Such means of measuring care quality are evolving (Heslop & Lu, 2014). In particular, face-toface encounters are transformed into virtual spaces through digital care pathways. Also, patients are gaining an active role as producers of their health-related data and users of information from repositories accessed with personal devices such as smartphones or tablets (Alsahafi & Gay, 2018). The use of data analytics has transformed the requirements of data structures. Structured nursing data based on standardized nursing terminology has enabled data to be reused for several purposes. Narrative data can be analyzed with several methods, such as data or text mining and natural language processing (Kivekäs et al., 2016). These methods depend not only on data structures but also legislation, such as European General Data Protection Regulation (European Union, 2016). Data reuse is always connected to confidentiality, patient privacy, and data security; and in statistical processes, anonymity must be guaranteed utilizing authentication. The rapid development of technology makes it possible for innovations to transform healthcare practices. Innovations can take the form of replicable products, processes, or structures that satisfy a specific need (Varkey, Horne, & Bennet, 2008). Products typically consist of technology or digital services, such as a software application or medical device. A process, such as a digital care path, changes how care is delivered using technology. Structural innovations usually affect the internal and external infrastructure of healthcare organizations, and they require significant system-wide changes and adoption of new digital solutions. However, not all developments are innovative; multiple features determine the degree of innovativeness, including newness, availability, the degree of advancement of clinical practice with proven outcomes, use and usability, the supporting environment, other context factors, and stakeholder perspectives (Hübner, 2015). Nursing informatics innovations are expected to improve the effectiveness, safety, timeliness, patient/family-centeredness, and efficiency of care as well as patients’ access to services, improving the quality of care (Agency for Healthcare Research and Quality, 2014). However, these innovations might unintentionally have unexpected or negative impacts on patients, professionals, and organizations (Rigby & Ammenwerth, 2016). Therefore, the effects of new nursing informatics innovations need to be carefully evaluated with actual evidence, such as research or health data obtained from EHRs or other real-world sources. The adoption of nursing informatics innovations must be based on evidence and careful consideration of expected and possibly unintended outcomes. Traditionally, the effectiveness of interventions has been the main focus of evidence-based practice. Nowadays, however, it relies on much more. Evidence-based healthcare refers to decisionmaking, including that regarding the implementation of health informatics innovations in practice that considers the feasibility, appropriateness, meaningfulness, and effectiveness of healthcare practices (Jordan, Lockwood, Munn, & Aromataris, 2019). Feasibility is defined as the extent to which innovation is physically, culturally, or financially practical or possible within a given context. Appropriateness refers to the fit of an innovation in the context in which care is provided. Meaningfulness relates to the personal experiences, opinions, values, thoughts, beliefs, and interpretations of those using an innovation, such as patients. Evidence about the meaningfulness of innovation may improve the understanding of whether patients are likely to perceive innovations as positive or negative and whether the changes will be accepted (Fig. 42.1). • FIGURE 42.1. Sources of evidence for nursing informatics innovations. The overarching principle driving evaluation and regulation of any nursing informatics intervention is to generate and synthesize evidence demonstrating that the use of a product, process, or structure is not only safe but also has benefits regarding the health or healthcare of the targeted individuals, patients, professionals, or society. Research evidence is obtained from original research studies and systematic reviews. These studies and reviews must apply a variety of methodologies to achieve high-quality care. However, systematic reviews that meet rigorous methodological standards (e.g., those proposed by the Cochrane Collaboration and Joanna Briggs Institute) and synthesize research in the given context are said to provide the best evidence, regardless of methodology. The goal of systematic reviews is to make recommendations regarding decision-making (in this case, in the context of healthcare). However, the methodologies applied in systematic reviews have changed over time. Increasingly, these reviews are used to answer a broad range of questions regarding the health of societies and consider randomized controlled trials as well as other forms of research, such as qualitative studies of the meaningfulness of innovation. The Joanna Briggs Institute regards the results of high-quality research studies based on any methodological tradition as more credible than anecdotes or personal opinions (Jordan et al., 2019). As implementing health informatics innovations in practice is complex, evidence from multiple perspectives is needed to understand the factors that affect such innovations. Implementation should be encouraged only for health informatics innovations that have been proven to be feasible, appropriate, effective, and meaningful in the healthcare context. It is essential to secure sufficient research evidence as well as real-world data (RWD) so that nursing informatics innovation will provide benefits for patients, health professionals, and organizations and any negative effects of these innovations will be minimized. RWD is a term used to describe data related to patients’ health status and the delivery of healthcare. It is collected from a variety of sources other than randomized clinical trials, such as retrospective and prospective studies, registries, claims databases, electronic patient records, biobanks, social media, chat rooms, and patient communities (Miani et al., 2014). High-quality RWD can be used to support decisions regarding the implementation of nursing informatics innovations. Real-world evidence (RWE) is obtained in clinical settings, and it concerns the usage and potential benefits of risks of nursing informatics innovations according to the analysis of RWD. It is especially necessary for contexts characterized by new technologies and a lack of available research data. Technology enables various types of data to be transformed into knowledge through automated processes. It also provides access to accurate information and knowledge, which are required for the implementation of nursing informatics innovations for high-quality patient care (Moen & Mæland Knudsen, 2013). In the future, it will be possible to combine and process data from different registries and sources utilizing data analytics to produce RWE in order to monitor the outcomes of care. Together with research evidence, RWE will serve as the basis for nursing informatics innovations. The WHO (2017) proposed that health data must be complete, timely, consistent, reliable, and accurate to ensure its quality. Data quality is an important issue in patient data management. The importance of the quality of patient data, the possibilities of use and reuse of that data, and measuring its effects and preferences have been highlighted in the eHealth strategies of Nordic countries (Vehko, Ruotsalainen, & Hyppönen, 2019). The quality of data is a key factor in the evaluation of the use of electronic patient record (EPR) data for patient care as well as for secondary use of data, such as for research, statistics, treatment method development, and administrative purposes (Weiskopf & Weng, 2013; Meystre et al., 2017). The quality of notes in the EPR system affects the quality of patient care and patient safety (Jylhä, 2017; Palojoki, 2017). Specifically, there is a risk that inaccurate nursing diagnoses can lead to implementation of inappropriate interventions or misinterpretation of related outcomes. Also, missing and inadequate documentation can distort research results and prevent further development of patient care and data reuse (Sanson, Vellone, Kangasniemi, Alvaro, & D’Agostino, 2017; Sollie, Sijmons, Helsper, & Numans, 2017). Even though the value of high-quality health data and reuse of that data has been recognized for decades and in several contexts, there still exist many problems regarding this worldwide for both nursing and medical documentation. Requiring uniform data structures enables betterquality data for patient care management and secondary use, but concrete developments in the implementation of these structures are quite rare (Saranto et al., 2014; McCormick et al., 2015; O’Brien, Weaver, Settergren, Hook, & Ivory, 2015, Meystre et al., 2017, Vuokko, MäkeläBengs, Hyppönen, Lindqvist, & Doupi, 2017). Worldwide, there are over 20 million practicing nurses and midwives who document daily patient care (WHO, 2018b). Thus, it is important to discuss and harmonize how patient data are documented. The patient care process is the core of healthcare; other administrative processes, such as information management, financial management, human resource management, and education, are intended to support it. Each day, a huge amount of data is generated by healthcare professionals and imported into databases during different phases of the patient care process, including the care planning, intervention implementation, and outcome evaluation phases (Westra, Pruinelli, & Delaney, 2015; Westra et al., 2017). The nursing process model is the standard of nursing documentation and nursing care plan. It enables data recording and sharing in different electronic medical record systems (Müller-Staub, de Graaf-Waar, & Paans, 2016). The need to develop standardized nursing terminology to ensure the comparability and ability to disseminate nursing data is well recognized (Westra, Bauman, Delaney, Lundberg, & Petersen, 2008; McCormick et al., 2015). Specifically, standardized models and terminologies for nursing documentation are required to generate valid data that can be reused (Liyanage et al., 2015; Whittenburg & Meetim, 2016). Also, data with standardized terminology supports evidence-based decision-making and facilitates the assessment of nursing care and outcomes (Saranto et al., 2014; Müller-Staub et al., 2016). Concepts related to unified nursing documentation are referred to as nursing content standards, and they may take the form of a data set, code set, terminology, dictionary, language, nomenclature, classification, vocabulary, or taxonomy (Cimino, 1998; Saba & Taylor, 2007; Westra et al., 2008). In particular, researchers across the world have developed terminologies to structure nursing documentation. Crossmapping and coordination across nursing classifications make it possible to evaluate the comparability of utilized content and concepts and promote shared use of various nursing classifications while avoiding redundancy in information (Lu, Park, Ucharattana, Konicek, & Delaney, 2006; Park, Lu, Konicek, & Delaney, 2007; Kim, Hardiker, & Coenen, 2014). In Finland, over the past decade, a standardized model of nursing documentation was developed as part of a national health information technology project intended to define the core components of the national EHR. This model is based on the nursing process for decision-making, core data regarding nursing (i.e., the Finnish Nursing Minimum Data Set, FNMDS), and the standardized nursing terminology to be used in care plans, summaries, and notes according to the Finnish Care Classification (FinCC). The FNMDS includes nursing diagnoses, interventions, outcomes, intensity data, and discharge summaries (Kinnunen, Ensio, & Liljamo, 2011; Kinnunen et al., 2014; Liljamo, Kinnunen, & Saranto, 2020). According to a recent survey of Finnish nurses, they are very competent regarding electronic documentation that complies with the national core structures (Kinnunen et al., 2019a). FinCC is based on the Clinical Care Classification (CCC), formerly called the Home Health Care Classification (HHCC), which was developed by Dr. Virginia Saba. Following the HHCC, the CCC has a three-level hierarchical format (Saba, 2007, 2012; Saba & Taylor, 2007; Ensio, Kinnunen, & Mykkänen, 2012). For international users, the CCC has been translated into various languages, including Chinese, Dutch, Finnish, German, Korean, Norwegian, Portuguese, Slovene, Spanish, Taiwanese, and Turkish (Saba, 2012). The FinCC consists of the Finnish Classification of Nursing Diagnoses (FiCND), the Finnish Classification of Nursing Interventions (FiCNI), and the Finnish Classification of Nursing Outcomes (FiCNO). Development and cultural validation of the FinCC started at the beginning of 2000 when nursing records were grouped, analyzed, and mapped with the HHCC (Saba, 2007, 2012; Saba & Taylor, 2007; Ensio et al., 2012). Concurrent with the development of the CCC, work to develop the FinCC continued in Finland as part of several national projects. The first versions of the FiCND and FiCNI were accepted in 2007 into the Finnish National Code Server, which is organized by the National Institute of Health and Welfare (Ikonen, Tanttu, Hoffren, & Mäkilä, 2007). To date, FinCC is the only nursing terminology in Finland to be accepted into the Code Server and thus remains freely available to all vendors (Kalliokuusi & Eerola, 2014). Finland’s national documentation model, which included the FinCC, was further developed from 2005 to 2009 through national projects. In 2008, the University of Eastern Finland (UEF) became responsible for the maintenance and development of the FinCC (Ensio, Saranto, Ikonen, & Iivari, 2006; Ikonen et al., 2007; Tanttu & Rusi, 2007). During a special documentation project conducted from 2008 to 2012 in cooperation with nursing education and nursing practice representatives, the competencies needed to use the model were defined by educational institutions and various healthcare facilities (Rajalahti, Heinonen, & Saranto, 2014). In 2010, the model was translated into Swedish for Swedish-speaking areas in Finland. Today, FinCC is widely used in specialized and primary healthcare in Finland. Both the FiCND and the FiCNI have 17 components (Fig. 42.2). Each component has a different number of main categories and subcategories. The content of the FinCC was revised based on user feedback in 2004 (Ensio et al., 2006), 2007 and 2010 (Kinnunen et al., 2011), and 2018 (Kinnunen et al., 2019b). The experts involved in the FinCC represent different healthcare organizations, including the THL, the Association of Finnish Local and Regional Authorities, and the UEF. The expert group supervises the development of terminology, networking with users and researchers, and continuous evaluation and validation of the FinCC. • FIGURE 42.2. Components of Finnish Care Classification (FinCC), version 4.0. The most recent update to FinCC was implemented from 2018 to 2019. The first phase of the process included searching for evidence from, for example, clinical guidelines, other nations’ guidelines, laws, regulations, and scientific papers. This update aimed to increase the utilization of different scales (e.g., pain scales, wound scales, malnutrition risk scales) and evidence-based research for the development of terminology. First, a draft of version 4.0 of FinCC was developed (Fig. 42.2). Second, an e-questionnaire including 34 pages of statements concerning the 17 components of FiCND and FiCNI and all main categories and subcategories was sent to healthcare organizations (n=34) and universities of applied sciences (n=14) in order to assess how well the new version of the FinCC complied with actual nursing practices as well as its practicality and understandability. A Likert-type scale ranging from 1 to 5 (i.e., totally disagree to totally agree) was used to assess the understandability and practicality of the main and subcategories. Also, participants could freely comment after each statement. The respondents included nurses, nursing lecturers, senior nurses, senior nursing officers, and nursing students. The mean practicality and understandability of the components of the FiCND and FiCNI were 4.1–4.9. Also, the comments raised several questions, which led to consultation with different experts. Third, the update process included expert validation of terminology and then acceptance of the terminology into the Finnish National Code Server. The new version of the FinCC has been launched in the Autumn of 2019, after which it has been freely available to all vendors. The user guide book has also been published in Finnish, and in Swedish, and will be published in English in the Autumn 2020 (Kinnunen et al., 2019b). When a nurse regularly uses the Finnish national documentation model to record daily patient data, a standardized, holistic care process involving data collection, nursing diagnosis, planning and implementation of patient care, and evaluation of outcomes is followed. Also, final assessment using clinical reasoning and documentation of the whole care process is important for planning appropriate interventions for patients (ISO, 2014). According to the Finnish national guidelines, which are based on legislation, nursing summaries have been handled by Finland’s Kanta services since 2011. In principle, this means that nursing summaries are stored in the national eArchive and can be viewed from different patient record systems and any healthcare facility, regardless of the patient’s or doctor’s/nurse’s location (Fig. 42.3). The structure of nursing summaries is based on the above-mentioned national core data set (i.e., they include nursing diagnoses, nursing interventions, nursing outcomes, and patient care intensity; Kinnunen et al., 2014; Liljamo et al., 2020). Also, these summaries utilize the standardized terminology adopted by all healthcare organizations (Kuusisto, 2018). • FIGURE 42.3. Kanta services. Structured data provides many possibilities for data reuse (Saranto & Kinnunen, 2009; Kinnunen et al., 2014; Meystre et al., 2017). Structured data using a nursing terminology was shown to be valuable in data mining, and it made appropriate visible areas for documentation and development (Table 42.1). Data mining facilitates knowledge discovery from databases (Kivekäs et al., 2016; Kinnunen, Kivekäs, Paananen, Kälviäinen, & Saranto, 2016). For managerial purposes, such as allocation of nursing resources, the data profiles of documented nursing data provide information and increased visibility of the whole patient care process, how the planned care was implemented, and patient outcomes for care needs (Mykkänen, Miettinen, & Saranto, 2016). Concerning the reuse of structured nursing data, auditing nursing documentation is very important for achieving unified, high-quality documentation, which is, in turn, connected to high-quality care (Mykkänen, Saranto, & Miettinen, 2012). TABLE 42.1. Summary of Outcomes Achieved from Mining Structured Nursing Data for Date Reuse
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Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents
INTRODUCTION
REQUIREMENTS FOR IMPROVING THE QUALITY OF PATIENT CARE
DEFINING AND ANALYZING QUALITY
EVIDENCE-BASED HEALTH INFORMATICS INNOVATIONS TO ENSURE HIGH-QUALITY CARE
USE OF NURSING DATA AS EVIDENCE FOR HIGH-QUALITY PATIENT CARE
Importance of and Requirements for Data Quality
Development and Validation of National Nursing Terminology
Utilization of Structured Nursing Data for Better Patient Outcomes
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