Data Management and Clinical Informatics

Data Management and Clinical Informatics

Jane M. Brokel


The information and knowledge age has pushed decision support toward greater sharing and exchanging of patient and population data. In the past 40 years, society has seen the widespread adoption of wireless personal computers, smart phones with database and digital applications and multi-messaging capabilities, global positioning systems, and satellite and cable networks for real-time broadcasting and contiguous communication. Information can now be transmitted or exchanged across the world, immediately, in a variety of formats. Information technology has changed the way people work, play, learn, manage their personal lives, and view the world. Consequently, information and evidence-based knowledge databases and applications have become a commodity to be bought, sold, and managed.

The business of health care information technologies is evolving rapidly. Management of the health care industry and care delivery relies extensively on the device capture, collection, and analysis of data. Data about the patient, provider, outcomes, and processes of care delivery are collected from many individuals practicing in different specialties and must be standardized, integrated, coordinated, and managed. Moreover, widespread demand to use these data for performance measurement and reporting to accountable care customers, regulators, and accrediting/certification bodies comes at a time when incentive payments to providers and health care institutions is linked with patient outcome measures (Centers for Medicare & Medicaid Services [CMS], 2011; Petersen et al., 2006). Reimbursement for health care services can be increased, decreased, or denied based on the patient’s response to treatment. In 2008, Medicare stopped paying for eight hospital-acquired patient conditions deemed preventable, including objects left in the patient during surgery, urinary tract infections, and pressure ulcers. Redesigned payment for more hospital-acquired complications is proposed (Fuller et al., 2009). Regulatory and governmental agencies require the collection of data to measure performance (e.g., The Joint Commission [TJC]), the organization of these data into specific formats (e.g., Medicare/Medicaid), and adequate protections to ensure the confidentiality of these data (e.g., Health Insurance Portability and Accountability Act [HIPAA]). To meet these demands, administrators need data that can be compared across multiple settings, both geographically and clinically.


Health information technology (HIT) applications in nursing services arise from the intersection of three areas: nursing administration, clinical informatics, and effectiveness research, including research on client outcomes. The technologies are tools for downloading, collecting, organizing, and analyzing vast amounts of complex data; and providing clinical decision support. By having these data in an accessible format, nursing leaders, managers, and administrator are better able to make informed decisions regarding the organization and delivery of patient care. When information cannot be accessed in a timely manner, leaders are forced to make critical decisions without considering key elements or facts.

The domain of technology and informatics combines the sciences of engineering, computers, and information with the cognitive health sciences. Nursing informatics is a specialty that integrates nursing science with computer and information sciences to manage and communicate data, information, knowledge, and wisdom in nursing practice. Nursing informatics supports consumers, patients, nurses, and other providers in their decision making in all roles and settings. This support is accomplished through the use of information structures, information processes, and information technology (American Nurses Association [ANA], 2008).

Effectiveness research applies epidemiological methods to large databases to study relationships among health care problems, interventions, outcomes, and costs. These methods can be used to identify alternatives and their effects and reveal associations with different patient characteristics and intervening variables (Ozbolt, 1991).

Health information management (HIM), management information system (MIS), and biomedical technicians provide integrated services to automate and support clinicians’ and managers’ decision-making processes. These services include downloading, collecting, storing, retrieving, and processing collective sets of data through the use of networking technology and applications to locate and aggregate the data from an integrated data repository. A management information system (MIS) is an integrated system for collecting, storing, retrieving, and processing a collective set of data; the data are queried from repository storage for direct use and application in the process of directing and controlling resources and for measurement, comparison, and evaluation of the achievement of specific management objectives. Clinical information systems (CISs) capture clinical data to support more efficient and effective decision making and clinical care delivery (Ward et al., 2006). The health information exchange (HIE) is defined as the electronic movement of health-related information among organizations according to nationally recognized standards (U.S. Department of Health and Human Services [USDHHS], 2008, p. 6). The HIE is a process within either a state health information network or a regional health information organization (RHIO), often for a geographic area. Today MIS and HIM departments work with clinical informatics roles (i.e., nurse informaticians) to organize and process information and provide accessible knowledge resource databases (e.g., drug and nursing evidence-based databases) to guide and support decisions during patient care workflow; to monitor patient safety, satisfaction, and quality of care; to manage human resources, physical resources, fiscal resources; and more recently evidence-based knowledge resources (Hannah et al., 2006; Osheroff, 2009; Sewell & Thede, 2013). The 10 characteristics identified by Austin in 1979 for a good MIS are that it is (1) informative, (2) relevant, (3) sensitive, (4) unbiased, (5) comprehensive, (6) timely, (7) action-oriented, (8) uniform, (9) performance-targeted, and (10) cost-effective; these still hold true today. An example of a component of an MIS is a nursing workload management system (NWMS), also called a patient classification system (PCS). These systems automate the collection of patient acuity data to calculate the number of patient care hours needed to provide care to the same group of patients (Hannah et al., 2006). An example of a component of an HIM is a continuity of care document for the health information exchange network. Both departments are capable of extracting nurses’ documentation from an electronic health records repository to support reports for analysis and to exchange nursing data with other organizations.

In the current health care industry, the National Institute for Standards and Testing (NIST) raised concerns about the information infrastructure that is needed to allow cross-enterprise document sharing, messaging profiles, medical device communication, a nationwide health information exchange network, patient identification matching, and continuity of care document specifications or semantic interoperability of patient data (NIST, 2011). Automated CISs are used for the device capture (e.g., monitors, ventilators) and electronic documentation of clinical data related to the direct care of clients and managing care processes. CISs organize clinical data and trend clinical parameters and results for display, scan and check medications for interactions and errors before administration, and can provide a summary of the client’s story from nursing documentation. Nursing documentation systems include structured entry using drop-down menus, checklists, and computerized ordering/planning for scheduled care interventions. When completed, the documentation is often used within clinical decision support logic to automate actions for communication, add problems or risk diagnoses to the problem list, or elicit intervention reminder messages for evidence-based practice (Brokel et al., 2011). CISs also allow unstructured narrative documentation that is not included in the structured portion of the system (Moss et al., 2007). Integrating these data with other knowledge database systems can facilitate safe medication administration practice and adherence to evidence-based practice protocols (Brokel, 2007). Information collected through the use of CISs is integrated with financial and patient management systems within large data warehouses and queried to evaluate the effectiveness of nursing care and track adverse events.


Nursing’s data needs fall into four domains: (1) client care, (2) provider competencies and staffing, (3) administration of care and sustainability of the organization, and (4) knowledge-based research for evidence-based practice. The first three are distinct areas for work-flow processes, whereas research, the fourth domain, interacts with all of the other three. The four areas and the sources for the data are as follows:

1. Client: Longitudinal client care/clinical care and its evaluation, clinical findings, and client outcomes. Source: the client’s health care record, their personal health record, patient-provider messages, and information from the health information exchange network.

2. Provider: Professional data, role responsibilities (i.e., competencies, skills), caregiver outcomes, and decision-maker variables. Source: personnel records, national data banks, and documentation links to client records.

3. Administrative: Management and resource oversight, organization statistics, system outcomes, contextual variables, and comparative targets. Source: administrative, fiscal, population, registry, and regulatory performance data.

4. Research: Knowledge base development and comparative effectiveness with phenotype data dictionaries (Pathak et al., 2011). Source: existing and newly gathered data, relational databases, and common data elements from emerging exchange networks.

Table 26-1 displays examples of outcomes and variables to be measured in relation to the three distinct domains of nursing’s data needs. For example, in the client domain, the cost and continuity of care for the client are important because data are now shared among providers within the HIE to manage care. In the provider domain, professional skills/knowledge and intensity of nursing care are variables that may be measured to monitor variability and control workforce capacity. The quality and type of services is dependent on the competencies of the professional workforce, and nurse administrators need data to prepare a plan for strengthening the quality and capacity of not only the nursing workforce but also other needs, such as for mental health services (Institute of Medicine [IOM], 2006, 2011).

The collection and analysis of data are critical to propel health services research. Data analysis is aimed at cost, safety, quality, and effectiveness outcomes. Collecting and extracting data that describe the processes and outcomes of nursing care electronically has provided evidence for the design of care protocols and delivery models (Horn & Gassaway, 2010). The formal process of using these patient data for providing this evidence is termed practice-based evidence (DeJong, 2007). This comparative effectiveness research framework analyzes a comprehensive set of patient, treatment, and outcome variables to identify treatments associated with better outcomes while controlling for patient differences (Horn & Gassaway, 2010). Although both are used to inform the delivery of practice with evidence, in reality, practice-based evidence and evidence-based practice are derived from different sources. Deriving evidence for informing practice from research is termed evidence-based practice, whereas informing practice from the analysis of patient data collected during the delivery of care is termed practice-based evidence. However, this contribution rests on structuring the input logically and providing a level of accuracy and completeness to ensure valid and reliable output. Explicit data definitions, valid linkage between datasets, and well-defined coding of input are essential in securing meaningful and usable output. The aggregation of consistent meaningful information over time and how daily workflow affects the quality of information are especially important to uniform datasets. Using practice-based evidence requires the compilation of clinical data into a clinical data repository. This compilation may also be called a data warehouse or data repository. Data are stored longitudinally over multiple episodes of care. These data are accessed to provide continuity of care to the individual patient, extracted to measure care effectiveness and productivity, queried to provide evidence for care delivery, and analyzed to inform public policy.


Recognized by the American Nurses Association (ANA) as a nursing specialty in 1992, informatics was one of the fastest growing practice areas in health care. As defined by ANA (2008) the practice of nursing informatics views the relationship of data, information, knowledge, and wisdom as a continuum with increasing complexity and interrelations as nurses aggregate and apply them in decision making (Englebardt & Nelson, 2002, p. 12). Data are defined as discrete, objective entities, without interpretation; information is data that are structured, organized, or interpreted; and knowledge is information that is synthesized with identified relationships and meaning. Wisdom is appropriate use of knowledge in managing and solving patient problems, risks, and needs for health enhancement (i.e., nursing diagnoses). Wisdom is knowing when and how to apply the evidence-based knowledge with client information (Englebardt & Nelson, 2002), which nurses exercise through critical thinking and clinical reasoning skills.

Nursing informatics specialists assist practitioners by providing information and evidence-based knowledge to support clinical decision making and delivery of safe patient care. Although these specialists may not be directly involved with care delivery, their effort is integrally related to reengineering workflow for clinical and administrative practice. Nursing informatics specialists participate in analysis, design, and implementation of information and communication systems; effectiveness and informatics research; and education of nurses in informatics and information technology through the Technology and Informatics Guiding Education Reform initiative. More recently, nursing informatics specialists represent nurses at the policy table in building better interoperable frameworks for care coordination and delivery through optimizing processes and technology usability (Sensmeier, 2011).

The first master’s degree in nursing informatics was offered by the University of Maryland in 1989. In 1992, that same university followed with the first doctoral program in nursing informatics. Now, programs in nursing informatics can be found throughout the United States. These programs offer a variety of educational options, including master’s degrees, post-master’s certificates, and doctoral degrees. Nurses prepared at the master’s level in nursing informatics are titled informatics nursing specialists (INSs) (Hannah et al., 2006). Nurses prepared at the baccalaureate or master’s level can obtain certification in nursing informatics from the American Nurses Credentialing Center (ANCC).


In 1965, El Camino Hospital in Mountain View, California, was one of the first to attempt to develop an electronic health record (EHR). Along with Technicon Medical Information Systems and Lockheed Missiles and Space Company, an information system was created that communicated physicians’ orders, retrieved laboratory results, and supported the documentation of nursing care (Staggers et al., 2001). The development of early information systems designed to support an EHR were confined to large tertiary care centers and federal agencies such as the U.S. Department of Veterans Affairs (VA) and the National Institutes of Health (NIH). The high cost of these systems provided little incentive for most health care institutions to change. The U.S. Department of Health and Human Services (USDHHS) funded programs that stimulated EHR implementations across eligible providers in ambulatory settings and critical access hospitals through incentives from CMS, regional training curriculums, and regional extension centers staffed with HIT skilled workers (USDHHS, 2010).

The shift from a retrospective fee-for-service and managed care to accountable care organization financial structure for the payment of medical services changes how partners develop informational, technical, financial, and professional capabilities that allow rewarding savings for coordinated longitudinal population-based care (Fisher et al., 2011). Currently, patient data are of interest not only to health care providers and accountable care organizations but also to governmental payers, who want to ensure that specific data and information are present and distributed to patients or public health entities, thus providing incentives when achieving meaningful use indicators (CMS, 2010). These data are also analyzed by health care providers to ensure that patient needs are transpiring in the most efficient and cost-effective manner with these required indicators. An indicator such as the quality of the patient problem list can meet the demands of the final rule, but it needs to also inform the providers using a clinicians’ vernacular of patient conditions, co-morbidities, or long-term risks (e.g., hyperlipidemia, diabetes type 2, risk for falls) (Ochylski et al., 2012).



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Aug 7, 2016 | Posted by in NURSING | Comments Off on Data Management and Clinical Informatics

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