Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics


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Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics



Lynda R. Hardy / Bernadette Mazurek Melnyk



ABSTRACT



The integration of evidence-based practice (EBP) and informatics may be the result of an institutional culture change as technology and patient care continue to intersect. Melnyk and Fineout-Overholt (2019) describe EBP as a life-long problem-solving approach to the delivery of healthcare that integrates the best evidence from well-designed studies with a clinician’s expertise and a patient’s preferences and values. Evidence is the product of patient data, clinical expertise, and research. Informatics encompasses the ability to store, retrieve, and analyze data. Together, informatics supports the seven EBP steps, providing data and information to assist in accurate and appropriate decisionmaking to enhance patient care quality and safety while decreasing cost and burden. EBP and informatics are both supported by education, skills building, and competencies that provide a holistic approach to patient care.


INTRODUCTION



Evidence-based practice (EBP) provides the most effective and efficient patient care. Implementation of EBP by healthcare professionals results in higher quality healthcare, improved patient outcomes, care standardization, improved workflow, and reduced costs compared to care steeped in tradition or based on outdated policies and practices (McGinty & Anderson, 2008; Melnyk & FineoutOverholt, 2019). EBP provides a direct pathway to achieving the quadruple aim in healthcare (Beckett & Melnyk, 2018; Bodenheimer & Sinsky, 2014). Study findings support that implementation of EBP results in higher job satisfaction, increased group cohesion, and less job turnover (Krumholz, 2008; Melnyk, Fineout-Overholt, Giggleman, & Choy, 2017). Informatics provides five essential components that support EBP: standard terminology, digital sources of evidence, sharing data for better patient care, processes to support EBP in clinical situations, and informatics competencies (Bakken, 2001). The Institute of Medicine (now named the National Academy of Medicine) set a goal whereby 90% of healthcare decisions will be evidence-based by 2020 (McClellan, McGinnis, Nable, & Olsen, 2007). Efforts continue to further translate this intersection into implementation of EBP, in part, through a better understanding of all types of evidence.


In 2000, Sackett and colleagues defined EBP as the conscientious use of current best evidence in making decisions about patient care (Sackett, Straus, Richardson, Rosenberg, & Haynes, 2000). EBP has since been broadened and described as a problem-solving approach to the delivery of care that integrates the best evidence from welldesigned studies with a clinician’s expertise and a patient’s preferences and values (Melnyk & Fineout-Overholt, 2019). External evidence (i.e., findings from research) and internal evidence (i.e., evidence generated from outcomes management or quality improvement projects) should be used in evidence-based decision-making. The highest quality of care and best patient outcomes are achieved when clinicians deliver EBP in the context of caring supported by an EBP organizational culture and environment (Fig. 26.1).


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• FIGURE 26.1. EBP within a Culture and Environment that Supports Its Results in the Best Patient Outcomes. (Reproduced, with permission, from Melnyk, B. M., & Fineout-Overholt, E. (2003). The conceptual framework for healthcare. Rochester, New York: Advancing Research and Clinical Practice through Close Collaboration.)


SEVEN STEPS OF EVIDENCE-BASED PRACTICE



There are seven steps in the EBP process (Melnyk & Fineout-Overholt, 2019) (Table 26.1). Step 0, the first step in the EBP process, requires cultivation of a spirit of inquiry in clinicians and a culture of EBP to stimulate the essential clinical questions to improve patient care. Once a clinical question is generated, Step 1 in the EBP process involves formatting clinical questions into PICOT format (P = patient population, I = intervention or interest area, C = comparison intervention or group, O = outcome, and T = time). Formatting clinical questions in PICOT format is necessary to streamline the search for evidence to answer the question. An example of a PICOT question about a treatment is: In depressed adolescents (P), how does the delivery of cognitive behavior therapy in person (I) versus delivery of Web-based cognitive behavior therapy (C) affect depressive symptoms (O) three months after treatment (T).



TABLE 26.1. The Steps of the Evidence-Based Practice (EBP) Process


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Step 2 of the EBP process searches for evidence by entering each key word from the PICOT question into the database that is being searched (e.g., Medline, CINHAL) and then combining the search words together to reveal the studies that may answer the question. Reliable resources that should be used to find an answer to the PICOT question include systematic reviews, clinical practice guidelines, preappraised literature, and studies from peer-reviewed journals.


In Step 3 of EBP, a rapid critical appraisal of the studies from the search is conducted, followed by an evaluation and synthesis of the research evidence.


In Step 4, evidence is integrated with the clinician’s expertise and patient preferences and values to make a decision regarding whether a practice change should be made. Once a practice change is made based on the best evidence, outcomes should be measured to determine positive outcomes of the change (i.e., Step 5). Evaluation of outcomes is an essential step in EBP as it helps to determine if the EBP practice change was effective in positively impacting outcomes. The last step in the EBP process, Step 6, is disseminating the outcome of the practice change through presentation or publication so that others can benefit from the process.


EBP Competencies for Practicing Nurses and Advanced Practice Nurses


A set of EBP competencies for practicing nurses and advanced practice nurses (APNs) were recently developed in order to ensure delivery of the highest quality of safe clinical care (Melnyk et al., 2017). These competencies were first drafted by Bernadette Melnyk and Ellen Fineout-Overholt followed by a consensus process with five other national EBP experts, which resulted in 12 EBP competencies for practicing RNs and 11 additional EBP competencies for APNs (Melnyk, 2017). A national Delphi study was then conducted with EBP mentors throughout the United States to establish the final set of competencies. Total consensus was reached by the EBP mentors on the second round of the Delphi study, which resulted in the final set of 13 EBP competencies for RNs and 11 additional competencies for APNs (Melnyk, Gallagher-Ford, Long, & Fineout-Overholt, 2014) (see Table 26.2).



TABLE 26.2. Evidence-Based Practice Competencies for Practicing Registered Nurses and Advanced Practice Nurses


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Once developed, Melnyk and colleagues (2018) conducted a national study to determine the state of selfreported competency among 2344 practicing nurses and APNs throughout the United States. Findings from this study indicated that the nurses reported that they were not yet competent in meeting any of the 24 competencies. Master’s prepared nurses reported meeting one of the 24 competencies, which was asking clinical questions. Those nurses who were younger with higher levels of education reported higher competency. There were no differences in self-reported competency between nurses who were working in Magnet versus non-Magnet designated institutions (Melnyk et al., 2018). This study emphasizes the urgent need to provide EBP education and skills building to nurses across the United States in order to ensure the quality and safety of healthcare.


Healthcare systems should require that all nurses meet the EBP competencies. When onboarding new nurses, these competencies should be assessed, and nurses informed that they are expected to be competent in them within the first year of employment. If nurses are not meeting the competencies, they should be provided with EBP educational and skills building sessions. Five-day EBP immersions are offered by the Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare at The Ohio State University College of Nursing and at Johns Hopkins University. The EBP competencies also should be integrated into performance evaluations and clinical ladders. In addition, they should be used in academic programs to ensure that RN and APN graduates are competent in EBP when they enter real world practice settings. Leaders and managers also should be required to meet the competencies because if they do not “walk the talk” and role model EBP, it is unlikely that their staff will consistently deliver evidence-based care (Melnyk, 2017).


Barriers and Facilitators of Evidence-Based Practice


There are multiple barriers to advancing EBP in healthcare systems, including: (a) misperceptions by clinicians that it takes too much time, (b) lack of EBP knowledge and skills, (c) organizational cultures that do not support EBP, (d) lack of resources, including clinical decision support tools, (d) executive leaders and managers who do not model and/or support EBP, (e) lack of EBP mentors to work with point-of-care staff on implementing evidencebased care, (f ) inadequate access to databases by clinicians in order to track patient and system outcomes, and (g) negative attitudes toward research (McGinty & Anderson, 2008; Melnyk & Fineout-Overholt, 2019; Melnyk, FineoutOverholt, Gallagher-Ford, & Kaplan, 2012; Sittig, 1999).


Findings from studies also have established key facilitators of EBP that include: (a) strong beliefs about the value of EBP and the ability to implement it, (b) EBP knowledge and skills, (c) organizational cultures that support EBP, (d) EBP mentors who have in-depth knowledge and skills in evidencebased care as well as individual and organizational change, (e) administrative support, (f) clinical promotion systems that incorporate EBP competencies, and (g) EBP tools at the pointof-care, such as clinical decision support systems (Melnyk & Fineout-Overholt, 2019; Melnyk, Fineout-Overholt, & Mays, 2008; Newhouse, Dearholt, Poe, Pugh, & White, 2007).


Cultivating a Culture That Supports and Sustains Evidence-based Practice


In order to cultivate a culture and environment that supports and sustains EBP, an organization must provide system-wide support for evidence-based care. This support begins with a vision, philosophy and mission that incorporate EBP as a key component, which are made visible to all throughout the organization. High-level administration and nurse managers must not only “buy-in” to this vision, but also model EBP themselves, as much of how clinicians perform is learned through observation of their key leaders and managers. Further, it is essential for leaders and managers to create and sustain an EBP culture and environment so that evidence-based care can consistently be the norm in the organization. Adequate resources and supports must be provided to clinicians that enhance their ability to provide evidence-based care. Examples of resources and supports are identified in Table 26.3.



TABLE 26.3. Supports and Resources for Clinicians to Enhance Ability to Provide Evidence-Based Care


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ROLE OF TECHNOLOGY IN EVIDENCE-BASED PRACTICE



Health Information Technology and Health Informatics in Clinical Practice


The power of data is within the grasp of healthcare providers. These data are focal points for the Institute of Medicine’s Crossing the quality chasm while aiming at the quadruple aim of patient care quality, safety, cost, and provider burden (Institute of Medicine, 2001). Today’s nurses and other healthcare providers are learning the value of data for decision-making and providing the right information to the right person at the right time. The role of technology and informatics continues to add to the patient care toolbox but not without challenges such as developing and using effective risk assessment models, standardizing user interface and functioning, developing and implementing decision support related to safety, and establishing cultural norms and legal frameworks for sharing patient information (Sittig et al., 2018).


Health information technology (HIT) is a clinician’s tool for EBP providing patient-centered decision-making, and increased patient quality and safety, and connecting patients to community and other educational programs to increase health literacy. It is the integration of provider and patient health information that is stored, shared, and analyzed. HIT includes the electronic health record (EHR), patient portals or personal health records, and electronic prescribing (E-prescribing). It has narrowed the digital distance between patient and provider to facilitate healthrelated decision-making. The aggregation of patient-related data provides rich information leading to increased knowledge and ultimately wisdom related to patient care. HIT acceptance has not been an easy road as healthcare providers exit their paper-related comfort zones and morph into a digital environment or ecosystem that provides dashboards, visualization tools, and, in some cases—confusion. The transition from paper to digital HIT is becoming more facile as the increase in nursing education teaches technology and informatics fundamentals and how data provide the evidence to change practice and policy (Health IT, n.d.).


Health informatics is defined by the National Library of Medicine as “the interdisciplinary study of the design, development, adoption and application of IT-based innovations in healthcare services delivery, management and planning” (Health Informatics, n.d.).


Nursing informatics, a component of health informatics, integrates nursing science with computer and information science to manage the transfer of data to wisdom in patient care. Figure 26-2 provides an overview of the complexity and interdisciplinary nature of informatics in health and patient care. The magnitude of this interdisciplinary approach provides a framework for a learning health system model where clinicians, patients, and the clinical and digital environments intersect to create and improve upon evidence associated with patient care. The coupling of nursing informatics and EBP suggests the integration of an empathic, informed, and patient-centered method of practice (Simpson, 2006).


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• FIGURE 26.2. The Umbrella of Health Informatics. (Reproduced, with permission, from Hardy, L.R. (2018). The Umbrella of Health Informatics. Figure created by L Hardy.)


Informatics Competencies


Healthcare systems recognize the need for communication between information technology and healthcare providers for the provision of safe, quality care (Elkind, 2009). Today’s nurse is capable of understanding technology with the abundance of smart phones and tablets but understanding a digital health information system requires additional finesse. The electronic health care world integrates E-prescribing, tele-health, patient portals, and online appointment scheduling (Powell & Myers, 2018). Healthcare providers must integrate and synchronize their understanding of clinical and technical information to support safe, effective patient care, and ensure efficient workflow (Sensmeir & Ivory, 2018).


Today’s educational environment has adopted the mantra that data is power in-patient care by assuring that nursing provides adequate informatics training for all practice levels. The need for informatics competencies in nursing has been well documented nationally and internationally (Pordeli, 2018); Westra & Delaney, 2008).


Quality and Safety Education for Nurses (QSEN)


The Quality and Safety Education for Nurses (QSEN) project adopted the Institute of Medicine (2003) competencies for nursing addressing six major areas, including informatics. QSEN faculty defined pre-licensure and graduate quality and safety nursing competencies to increase knowledge, skills, and attitudes in nursing pre-licensure and graduate programs (Institute of Medicine, 2003; Cronenwett et al., 2007). The basic premises for informatics competencies are provided in Table 26.4.



TABLE 26.4. QSEN Competencies—Graduate Level


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Technology Informatics Guiding Education Reform (TIGER Initiative)


The TIGER initiative, while not specifically “competencies,” provides a basic understanding of health informatics in the context of joining informatics and technology. It was originally a nursing-based grassroots initiative begun in 2006. TIGER was supported by more than 70 organizations that included the American Medical Informatics Association (AMIA), Healthcare Information and Management Systems Society (HIMSS), and grants from the Robert Wood Johnson Foundation. Its focus was to prepare the healthcare workforce for the use of technology to improve patient care. TIGER is currently under the oversight of HIMSS within the interdisciplinary framework of clinical informatics.


QSEN and TIGER share a patient-center approach to improving healthcare quality and patient safety through the grounding of informatics in EBP.


DECISION SUPPORT AND EVIDENCEBASED PRACTICE



Decision Support Systems


Decision support systems in healthcare analyze data assisting healthcare providers in decision-making to improve patient care. They focus on providing the right information to the right person at the right time and place (Sims et al., 2001).


Decision support systems (DSS) “are automated tools designed to support decision making activities and improve the decision–making process and decision outcomes. Such systems are intended to use the enormous amounts of data that exist in information systems to facilitate decision processes” (Androwich & Kraft, 2011, p. 427). Clinical decision support systems (CDSS) are systems designed “to support healthcare providers in making decisions about the delivery and management of patient care” (Androwich & Kraft, 2011, p. 427). They have “the potential to improve the patient safety and outcomes for specific patient populations, as well as compliance with clinical guidelines and standards of practice and regulatory requirements” (Androwich & Kraft, 2011, p. 427).


Data-driven clinical decision support systems are adaptive systems designed to incorporate large heterogeneous healthcare data sets theoretically driven to provide practice-based evidence and adapt as data change supporting the learning health system concept (Dagliati et al., 2018; Zhang, Guo, Han, & Li, 2016).


Data Standardization


Clinical data are context dependent. They involve patients in terms of their complaints, signs, symptoms, lab values, and other pertinent information. Other contextual components include data stored in other information systems such as registries, clinical trial documents, and other clinical information databases (Office of the Healthcare Coordinator for Health Information Technology, 2017). Therefore, when considering data standardization, context is important (Schulz, Stegwee, & Chronaki, 2019). Data standardization in nursing began with the Nursing Minimum Data Set (NMDS) defining the minimum amount of data nurses should collect for patient care (Werley, Devine, Zorn, Ryan, & Westra, 1991). Capturing these data provided access to comparable data on local, regional, national, and international levels to determine healthcare trends supporting EBP improvement. Huber and colleagues improved upon the NMDS incorporating additional terms and practices for nursing administrators (Huber, Schumacher, & Delaney, 1997). What the NMDS lacked was definition related to data nomenclature. Methods to standard the approach to healthcare data collection has been implemented with the development of Systematized Nomenclature of Medicine (SNOMED) International. SNOMED International, founded in 2007, creates standard health terminologies for use across and within EHRs and healthcare systems. SNOMED’s focus is to develop, maintain, promote, and enable the uptake and correct use of terminology worldwide. The NMDS and NMMDS have been incorporated into SNOMED.


The use of standardized clinical terminology is necessary for evidence to be both computable and interoperable within and between healthcare systems. Standardized nursing terminology is required for quantifiable and retrievable data. Data within EHRs must be made machine readable (in a form that a computer can process) to allow for decision support systems to process the data into information and allow that data to be visualized for nurse use in EBPs. Healthcare has often measured quantity of care but has morphed into the understanding that the patient-care value and quality are important and our ability to measure quality requires updating. Terminology standardization is necessary to determine patient-care quality; without standardization, the ability to measure outcomes is nonexistent, thereby obviating evidence for practice (Porter, Larsson, & Lee, 2016).


Some EHR systems have incorporated machine readable nursing data while others must rely on natural language processing (NLP) to provide information related to nursing care. NLP is a component of artificial intelligence (the ability for computers to learn from what they read) helping computers understand, interpret, and manipulate human language. NLP draws from computer science and computational linguistics to translate human language into a form that the computer can read and interpret.


Standard terminology is important to allow interoperability and data sharing resulting in better analytics to support EBP. Eight of the 13 recommendations in the IOM’s Crossing the quality chasm are related to collecting, aggregating, and using healthcare data to improve healthcare quality. Other data-related recommendations include the need for workforce training, cost-related reimbursement models, and the ability to utilize data for quality care. Examples of nursing classifications used today are listed in Table 26.5.



TABLE 26.5. Examples of Classification Systems and Medical Classifications

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Jul 29, 2021 | Posted by in NURSING | Comments Off on Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics
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