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Nursing standards to support the electronic health record
ONLINE CONTENT
Nursing standards to support the electronic health record Bonnie L. Westra, PhD, RN Connie White Delaney, PhD, RN, FAAN, FACMI Debra Konicek, MSN, RN, BC Gail Keenan, PhD, RN
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Quality and low cost health care that is free of medical mistakes requires continuity of person-centric healthcare information across the life span and healthcare settings. Interoperable clinical information systems that rely on the use of multiple standards to support health information exchange and, in particular, nurse sensitive data, information, and knowledge are key components to support high quality, safe care. A 2004 Executive Order called for a National Health Information Network and the widespread adoption of electronic health records (EHRs) by 2014. While there are numerous standards influencing the exchange of health data, the primary focus of this article is to synthesize the state-of-the-art in nursing standardized terminologies to support the development, exchange, and communication of nursing data. Research exemplars are described for information systems to support nursing practice using standardized terminologies and secondary use of standardized nursing data from EHRs for knowledge development.
he Department of Health and Human Services’ framework for Strategic Action entitled “The Decade of Health Information Technology: Delivering Consumer-centric and Information-rich Health Care” was released in 2004.1 The vision articulated in this document is to use information technology for the purposes of avoiding medical mistakes, improving care, and reducing costs. Several efforts are underway to develop a Nationwide Health Information Network, which is the infrastructure to link electronic health data across different disciplines, healthcare settings, information systems, and geographic locations. This national effort also provides the leadership needed for the secure exchange of health information for connecting consumers, clinicians, and public health organizations to support individual and population health care. While there are numerous standards influencing the exchange of health data, the primary focus of this article is to synthesize the state-of-the-art in nursing standardized terminologies to support the development, exchange, and communication of nursing data. Research exemplars demonstrate methods of developing information systems to support nursing practice using standardized terminologies and secondary use of standardized nursing data from EHRs for knowledge development regarding the contribution of nursing to patient outcomes.
Bonnie L. Westra, PhD, RN, is an Assistant Professor, University of Minnesota School of Nursing, Robert Wood Johnson Executive Nurse Fellow, Minneapolis, MN. Connie White Delaney, PhD, RN, FAAN, FACMI, is a Professor & Dean, School of Nursing, University of Minnesota, Minneapolis, MN. Debra Konicek, MSN, RN, BC, is Director, Clinical Standards Initiatives, SNOMED Terminology Solutions, The College of American Pathologists, Northfield, IL. Gail Keenan, PhD, RN, is Associate Professor of Nursing/Director Nursing, Informatics Initiative, College of Nursing, University of Illinois, Chicago, IL. Corresponding author: Dr. Bonnie L. Westra, Robert Wood Johnson Executive Nurse Fellow, Assistant Professor, University of Minnesota School of Nursing, 5-160 Weaver Densford Hall, 306 Harvard St SE, Minneapolis, MN 55455. E-mail: [email protected]
WHAT ARE STANDARDS? Standards are agreed-upon ways to record and exchange data within and across information systems. There are standards everywhere—such as those to assure that any car where the speedometer indicates 55 miles per hour is, in fact, traveling at that speed. Other standards exist to communicate data and information about the weather, such as temperature, wind velocity, and humidity. Healthcare standards are also essential to represent, communicate, exchange, manage, and report data, information, and knowledge to support nursing practice. Standards are one mechanism to ensure validity of data. According to Bakken,2 three types of standards are
Nurs Outlook 2008;56:258-266. 0029-6554/08/$–see front matter Copyright © 2008 Mosby, Inc. All rights reserved. doi:10.1016/j.outlook.2008.06.005
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Figure 1. Nursing terminology development timeline.
most important for nursing. These include content, messaging, and confidentiality and security standards. Standardized terminologies are content standards, which include the terms that represent a focus of health concerns (diagnoses), interventions, and outcomes consistent with the scope of practice for nursing. To exchange data between information systems, there are messaging standards. For example, when 2 organizations use different information systems, data can be exchanged using messaging standards to assure that data entered into one system are consistent with the meaning when the same data are subsequently displayed in another system. The Health Insurance Portability and Accountability Act addresses confidentiality, privacy, and security standards. In this manuscript, the focus is on the first standard— the content standard—which includes concepts or terms that are used to document nursing practice. The content standard representing nursing data, information, and knowledge is essential to drive messaging standards. Without content, there is no need for messages to exchange data across information systems. These concepts are organized into nomenclatures, classifications, terminologies, and vocabularies. In this manuscript, the word “terminologies” is used in a generic way when referring to nomenclature, classification, vocabulary, and terminology. Additionally, “terminology” is used when discussing the representation of data, information, and knowledge used by nurses to document and communicate practice.
TERMINOLOGY STANDARDS Standardized terminologies have been in use since the 1850s when the International Classification of Diseases was developed to standardize the causes of death3; however, the development of standardized nursing terminologies is much newer. Initially, nursing’s interest was to develop a uniform terminology representing all of nursing practice, but the differences in practice and the major effort to accomplish this was daunting. The tactic changed to multiple specialty groups developing separate terminologies with the focus now on mapping among them. The timeline for development for nursing terminologies recognized by the American Nurses Association (ANA) is shown in Figure 1. The earliest terminology developed in the US was S
the North American Nursing Diagnoses Association (NANDA)4 nursing diagnoses, and the first funding for development of a standardized terminology, the Omaha System, occurred in 1975 by the Division of Nursing of the U.S. Department of Health and Human Services.5 The development of the Nursing Minimum Data Set (NMDS) in the mid-80’s,6 which identified the umbrella concepts for clinical nursing practice, produced a flurry of activity to specify terminologies for the nursing care elements of the NMDS—nursing diagnoses, interventions and outcomes. In 1989, recognizing a need to coordinate these developments, the ANA established the Steering Committee on Databases to develop criteria for recognition of nursing terminologies.7 Ten years later, in response to international activity related to content standards development, the ANA criteria were updated using the International Standards Organization (ISO) standards for terminologies. This extended the applicability of the ANA-recognized terminologies to one of international import. Two of the recognition criteria require that reliability and validity of the terminology must be established through research, as well as evidence of the utility of the terminology in practice. The value of ANA recognition of terminologies is that it represents a stringent review process by the profession. Recognition is required prior to ANA supporting these terminologies for public policy, such as the inclusion for interoperable electronic health records. Currently, the ANA recognizes 2 different minimum data sets and 10 distinct terminologies as shown in Table 1.8 There are 2 minimum data sets, 8 interface terminologies, and 2 reference terminologies that are currently recognized by ANA. Minimum data sets are a minimum, essential set of data elements with standardized definitions and codes collected for a specific purpose, such as describing clinical nursing practice or nursing management contextual data that influence care.9 Interface (point of care) terminologies include the actual terms/concepts readily used by nurses for describing and documenting the care of patients (individuals, families, and communities). Reference terminologies are used “behind the scenes” within clinical information systems to link concepts of similar meaning. These reference terminologies enable clinicians to use terms appropriate for their practice, such as perioperative nursing or home
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Table 1. ANA Recognized Terminologies and Data Element Sets8 Data Set/Terminology
Intended Setting
Minimum Data Sets Nursing Minimum Data Set (NMDS) Nursing Management Minimum Data Set (NMMDS) Interface Terminologies – Single Nursing Concepts NANDA International (NANDA-I) Nursing Interventions Classification (NIC) Nursing Outcomes Classification (NOC) ABC Codes Interface Terminologies – Multiple Nursing Concepts Clinical Care Classification (CCC) International Classification of Nursing Practice (ICNP) The Omaha System Perioperative Nursing Data Set
Reference Terminologies Logical Observation Identifiers Names and Codes (LOINC) Systematic Nomenclature of Medicine Clinical Terms (SNOMED-CT)
All Settings All Settings
Clinical Data Elements Nursing Administrative/Contextual Data Elements
All Settings All Settings All Settings Any Setting - Nursing and Other
Diagnoses Interventions Outcomes Interventions
Home Care All Settings Community-based Perioperative
Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes
Any Setting - Nursing and Other Any Setting - Nursing and Other
Minimum Data Sets According to the ANA,8 minimum data sets are used to identify and define the umbrella data elements to represent nursing rather than the specific terms used in daily practice. Minimum Data Sets are high level concepts that provide the structure for aggregating more granular terms for comparison across practice. The 2 nursing minimum data sets that are recognized by ANA are the NMDS6 and the Nursing Management Minimum Data Set (NMMDS).9 Together, these 2 data sets are useful when describing patient care and the context of care delivery as shown in Table 2 (Table 2 is available in the online version of this article at the Nursing Outlook Website: http://www.nursingoutlook.org). The NMDS includes a set of 16 data elements divided into 3 different categories: nursing care, patient or client demographics, and service.10 –12 The nursing care data elements include nursing diagnoses, interventions, and outcomes which are further specified by the interface and reference terminologies described later. The NMMDS includes 18 data elements to describe the context of nursing care in 3 categories: environment, V
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Outcome and Assessments Nursing and Other Diagnoses, Interventions, and Outcomes
nursing resources, and financial resources.10 –12 The value of using minimum data sets is building our knowledge of the nursing needs of patients, the contribution nursing makes, and the influence of management data for patient safety and outcomes. Since minimum data sets are higher level concepts, they support research that focuses on aggregating granular level nursing data for benchmarking the contribution of nursing care on patient safety and outcomes across information systems, healthcare settings, and geographical locations.
care, with mapping these terms through use of a reference terminology to communicate similar meaning across systems.
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Interface Terminologies There are 8 interface terminologies that differ based on the intended use for nursing practice, the concepts included, and the organization of the concepts. Interface terminologies provide the granular level data to document nursing care and represent one or more of the nursing care data elements in the NMDS. Four of these terminologies address a single NMDS data element and 4 address multiple NMDS data elements. Three of these terminologies often are used together to plan and document nursing care13: the North American Nursing Diagnoses Association International (NANDA-I) nursing diagnoses,14 the Nursing Interventions Classification (NIC),15 and the Nursing Outcomes Classification O
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(NOC).16 The NANDA-I diagnoses were developed to address actual or potential problems in illness, wellness, and life that are the focus for intervening with patients.14 The NIC interventions focus on the behaviors used by nurses to improve patient outcomes. Interventions are treatments and include direct and indirect care for individuals, families, and communities.15 The intervention labels include a variety of associated activities, reflecting the actions documented by nursing. The NOC outcomes are used to develop goals and measure outcomes, which are defined as states, behaviors, or perceptions of individuals, families or communities that are sensitive to nursing interventions.16 Each outcome is associated with one of 19 different Likert scales to facilitate evaluation of patient care. The ABC codes17 also address a single NMDS data element to describe and bill interventions for advanced practice nurses, nurse midwives, and alternative care practitioners. The ABC Codes are complementary to other medical coding systems for billing and include a relative value unit which establishes the financial worth of an intervention or product. Four additional user interface terminologies each include all 3 NMDS nursing care data elements: nursing diagnoses, interventions, and outcomes. These are the Clinical Care Classification,18 the Omaha System,15 the Perioperative Nursing Data Set,19 and the International Classification of Nursing Practice (ICNP®).20 The Clinical Classification System initially was developed to predict resource needs and measure patient outcomes in homecare. Initially, it was named the Home Care Classification System, but was revised and renamed as the use was expanded to hospital settings.18 The Omaha System had roots in representing the terms used by nurses and other disciplines to document communitybased care such as homecare, hospice, public health, primary care nursing clinics, and parish nursing. More recently, it has been tested for usefulness in acute care settings.21 The Perioperative Nursing Data Set was developed to provide consistency in documentation of the nursing process within the perioperative setting.22 It includes a subset of NANDA nursing diagnoses, but specifies unique interventions and outcomes for perioperative nursing. These 3 terminologies were all developed within the United States but are expanding to be used globally. By contrast, the ICNP® was developed to use simple language that is culturally sensitive and useful in practice throughout the world.20 The ICNP® was initiated by International Council of Nursing to represent nursing practice in a way that is complementary to the classifications developed by the World Health Organization, such as the International Classification of Diseases. In summary, the interface terminologies are granular level terms or concepts for documenting nursing care. Some of these terminologies represent a single data element while others include multiple data elements S
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from the NMDS. In order for the terminologies to be recognized by ANA, each terminology has been developed using a variety of research methodologies for establishing reliability, validity, and usefulness for practice. The methodologies used varied across terminologies. Methods included chart reviews, expert consensus, concept analyses, content validation surveys, and inter-rater reliability studies. The NMDS provides the umbrella concepts for the development of the granular interface terminologies that provide the content in an EHR to represent nursing practice. The importance of multiple terminologies is the ability to select the appropriate ones based on the practice setting. In some cases, more than one terminology might be useful. Reference terminologies are a way of linking similar terms, and providing additional terms to support documentation and reuse of data for research.
Reference Terminologies Reference terminologies contain granular level data from multiple interface terminologies within a relational structure. Each provides a common structure and links multiple terminologies, including nursing as well as terms used by other disciplines. Reference terminologies are used in EHRs for exchange of data within and across information systems and to create queries that can combine data from multiple sources. For example, if a hospital uses the Perioperative Nursing Data Set in the perioperative setting, but uses NANDA diagnoses, the NIC interventions, and the NOC outcomes in combination to document the nursing process on a medical-surgical unit, a reference terminology can map and translate the terms between systems. The use of a reference terminology permits clinicians to use terms that fit their practice and share data across systems without reentry of data. The ANA recognizes 2 reference terminologies: Logical Observation Identifiers Names (LOINC®)22 and Systematized Nomenclature of Medicine—Clinical Terminology (SNOMED— CT®).23 The Regenstrief Institute developed LOINC® primarily to provide electronic exchange of laboratory data.23 Later, LOINC evolved to include additional types of data that represent clinical observations, linking data from various terminologies and assessment tools. Nursing terminologies included in LOINC® are the problems from the Omaha System and the Clinical Care Classification. More recently, the Nursing Management Minimum Data Set was approved for inclusion and the first 3 data elements will be published in the next LOINC® update.24 SNOMED—CT®23 is a comprehensive clinical terminology that is owned and distributed by The International Health Terminology Standards Development Organization (IHTSDO). The nursing content found within SNOMED—CT® enables greater standardization and exchange of data across systems, as well as reporting and retrieval of informa-
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tion specific to nursing. The ANA recognized nursing terminologies that are currently integrated within SNOMED—CT® include: the Clinical Care Classification, NANDA nursing diagnoses, the NIC interventions, the NOC outcomes, the Omaha System, and the Perioperative Nursing Data Set. Additionally, a collaborative agreement with the International Council of Nursing will address a strategy for mapping SNOMED—CT® and the ICNP®.
studies, investigators created their own tools to collect new data rather than conduct secondary analysis of EHR data. In other studies, the 3 NMDS data elements of nursing diagnoses, interventions, and outcomes across multiple agencies were not included, which limits the ability to understand the relationship of nursing care to outcomes. Additionally, no studies were found describing the NMDS data elements of problems, interventions, and outcomes across multiple agencies. Thus, 2 programs of research are discussed in depth as exemplars of designing information systems that incorporate standardized nursing terminologies, using terminologies to document care, and retrieving the clinical data for outcomes research. Keenan’s research uses a combination of NANDA diagnoses, NIC interventions, and NOC outcomes, whereas, Westra’s research focuses on use of the Omaha System.
RESEARCH IMPLICATIONS Nursing has a 30-year history of research to develop valid and reliable terminologies to describe nursing practice. There has been considerable research addressing the use of the terminologies to build nursing knowledge, including descriptive, predictive, and informatics research. For example, in 2 studies, homecare data were manually collected using the Omaha System25 and, in a second study, the NMDS nursing care elements (NANDA nursing diagnoses, NIC interventions) in one community hospital were abstracted to describe the complex nursing needs and interventions provided for large groups of patients.26 Nursing terminologies also were used in other studies27,28 demonstrating that the Omaha System nursing diagnoses significantly predicted outcomes of care which included discharge service and discharge condition status. Nursing diagnoses primarily have been used to predict resource utilization in Community Nursing Centers,29 home care,30 and hospital-based settings.31 Other studies have examined the effect of interventions on costs32 or the amount of care required based on the number of interventions for patients at risk for falls33,34 A newer area of informatics research is the external validation of mapping between terminologies for semantic comparability of terms that might be used in different information systems. Studies have been conducted for external validation of concepts mapped between NOC outcomes and SNOMED—CT,35 NIC interventions and SNOMED—CT,36 and mapping of ICNP with NANDA diagnoses, the Clinical Care Classification, The Omaha System and NIC interventions.37 The value of this research is that, not only does it provide evidence for interoperability of nursing data between information systems, but also the potential for abstracting and combining ERH data from terminologies across information systems for health trajectory research in the future. Previous research using standardized terminologies has contributed knowledge about terminology usefulness for describing nursing care, predicting outcomes and resource use, and interoperability between terminology systems. However, there are a number of limitations that need to be addressed in future studies. In the past, EHRs did not contain nursing terminologies in a way that could be exported electronically; therefore, manual abstractions from charts was required resulting in considerable cost to conduct the research. In other 262
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NANDA Diagnoses, NIC Interventions, and NOC Outcomes Research Program Keenan has conducted a series of studies to examine reliable, valid, and user-friendly methods of embedding nursing terminologies into an information system. Keenan’s initial study38 was to examine the reliability, validity, sensitivity, and usefulness of NOC outcomes in settings across the continuum of care in 3 Michigan sites (1 homecare and 2 ambulatory units). The data collected from the Michigan sites provided evidence of the reliability, validity, sensitivity, and usefulness of NOC in home care39,40 and ambulatory settings.41,42 While working on the NOC outcome study, it became apparent that implementing standardized terminologies was insufficient to create comparable nursing data. There was wide variation in the ways organizations were implementing nursing standardized terminologies. Therefore, studies were conducted to develop an information system—the Hands-on Automated Nursing Data System (HANDS)43,44—and subsequently test educational interventions to consistently document nursing care using standardized nursing terminologies.45 The HANDS tool was developed through the collaboration of a large group of scientists, clinicians, and technical experts whose goal was to create a care planning system that would look and function in a valid and reliable way while also being acceptable to users in all types of settings. The project used multiple methods to develop a data model and user interface that was simple and intuitive for practicing nurses. The HANDS tool integrated NANDA diagnosis, NIC intervention, and NOC outcome terminologies. Validation by software experts and focus groups of clinicians provided guidance with revisions with an overall conclusion that the HANDS software was easy to use. The HANDS tool was first used in the NOC outcome study45– 48 as a collection tool for research assistants who were trained and gathered care-planning data on all study patients in the Michigan research sites. When compared to a paper O
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version, data collection time was reduced by as much as 80%, 25–30 minutes to 5–10 minutes, using the HANDS tool.43,44 Next, the prototype was expanded to include rules of use and training, and then tested in a medical specialty intensive care unit under real-time conditions.46 – 48 The pilot provided preliminary evidence of the utility, user acceptance, and reliability and validity of the data entered. Moreover, the methods used proved to be effective in providing a multi-faceted understanding of the impact of a system like HANDS on clinical practice. The results of the evaluation were utilized to refine the method and create a web-based version of HANDS that included an improved interface, training plan, and rules of use. Building on all of the previous work, the next step was to test the HANDS plan of care method in multiple sites. A multi-site “real-time” study of the HANDS tool, training, and rules of use was initiated in 2005 and is nearing completion.49 The main research questions address the use of standardized terminologies to promote continuity of care in the “hand-off” of patients from one nurse to another. Specifically, the research questions were: (1) can standardization of the HANDS tool, training, and rules of use (HANDS Method) be maintained across diverse settings supporting a common understanding of care, and (2) is there evidence that users in all types of settings find the HANDS Method acceptable and useful in day-to-day practice? More than 700 nurses from 8 units in 4 diverse settings participated in this study. The findings thus far provide multi-dimensional evidence that the standardization of the documentation process for care planning using nursing terminologies in an information system can be maintained across diverse settings when incorporated with training and rules of use. Moreover, nurses found the combination of a tool, rules of use, and training was significantly more useful than their previous care planning methods, and nurses were significantly more satisfied with the standardized terminologies (i.e., NANDA diagnoses, NIC interventions, and NOC outcomes) than at baseline.45,46 Interrater reliabilities were conducted for current and expected NOC outcome ratings. The interrater reliabilities for current outcome ratings were within 1 point 86% of the time, and 94% were within 1 point for the expected outcome rating. Short written exams were used to test nurses’ understanding of the definitions for the NANDA, NIC, and NOC terms frequently used on the units. The mean reliability scores for matching terms and definitions at 3 points in time ranged from .73–.82. The compliance with updating care plans at the handoff from one nurse to another was 90%. Collectively, the evidence is compelling and suggests that utilization of a technology-supported plan of care method like HANDS is not only acceptable and useful to users but also can produce data that is valid and reliable for multiple uses. S
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Keenan and colleagues report the current research directions include refining methods like HANDS to improve effective use specifically to: (1) standardize clinician handoffs, (2) serve as the multidisciplinary central plan of care, and (3) deliver evidence automatically generated from the HANDS in a usable format, at the right time. Knowledge gained is being used to design, test, and refine the HANDS tool and methods to support all members of the interdisciplinary team. Finally, extensive work has already taken place to create the algorithms that will automatically convert the data collected with the HANDS Method into evidence that is automatically returned to the point of care. A series of automatic reports have already been created and tested to examine questions of interest to the front-end users and administrators. Additional work is needed and will be pursued to convert the results into evidence that is automatically returned to the point of care, at the right time, in a usable format. In conclusion, the research to support development of an information tool and methods of using it provides an example of a research agenda that has moved the standardized terminologies from sets of raw terms and measures to a solution for systematically supporting nursing practice and generating valid and reliable data to continuously improve nursing practice. This work provides essential knowledge for integration of standardized terminologies that will support the creation of valid and reliable secondary data for future outcomes and safety research.
Omaha System Program of Research After 12 years of developing an EHR for homecare that incorporates both the Omaha System and the Outcome and Assessment Information Set (OASIS)50,51 OASIS is Centers for Medicare and Medicaid Services’ (CMS) standardized assessment for all Medicare and Medicaid skilled patients receiving homecare services. The Omaha System and the Outcome and Assessment Information Set is important because it is used for Medicare payment, public reporting of outcomes, and certification of agencies. Westra’s research focuses on the secondary use of EHR data for predicting outcomes for older adults receiving homecare services. The first study was conducted reusing the Omaha System data and comparing it with the CMS reported outcome for improvement in pain.52 Agency staff abstracted the Omaha System data from their EHR and compared outcomes between the Omaha System problem of pain and the OASIS pain outcome for 133 patients. Based on chart reviews, the Omaha System pain outcome was identified by the nurse managers as more accurately reflecting the patients’ pain status compared with documentation of the OASIS data. In-service education sessions were conducted with staff to share the findings and develop consistent guidelines for documentation of OASIS, followed by monthly monitoring. A compari-
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son of the CMS outcome reports demonstrated improved documentation of pain management from 45% in Year 1 to 64.5% in Year 2. Findings from this study support the value of the Omaha System for conducting quality improvement studies. The purpose of a follow-up study was to test the feasibility of integrating Omaha System data across 15 homecare agencies and 2 different information systems (vendors) to describe problems, interventions, and outcomes.53 Patients were selected if they had received services in 2004. There were 2901 patients who were primarily older adults (81.9% ⬎ 65 years of age), recently discharged from an inpatient facility (75%), and living alone or with an elderly spouse (76.4%). Patients had a variety of payers and varied in their length of stay, with 79.2% receiving services for ⬍ 4 months. The median number of problems per patient ranged from 2–7, most frequently representing the domain of physiological problems (60%) or healthrelated problems (35%) such as nutrition or medication management. All Omaha System problems were used at least once except for human sexuality. The 5 most frequent problems included: neuro-musculoskeletal function, integument, pain, prescribed medication regimen, and circulation. There were 536 819 interventions documented with surveillance performed the most frequently (47%), followed by teaching (22%), treatments (19%), and case management (6%). The outcomes, which represent a change from admission to discharge, demonstrated that the majority of clients improved in knowledge (66.8%), behavior (64.0%), and status (64.0%). The conclusion of this study is that data could be aggregated and compared for the Omaha System across agencies and vendors. This work supports the ability to exchange data as well as conduct research across agencies and vendors. A subsequent study focused on discovering factors for predicting hospitalization of older adults using a combination of OASIS data and Omaha System interventions.54 Hospitalization of homecare patients has remained at 28% nationally regardless of various efforts to reduce this rate, making this a priority outcome to reduce costs and improve the quality of life for older adults.55 Episodes of care were created using a start and an end of care OASIS assessment. Omaha System interventions were linked to episodes based on the visit date. When using logistic regression for predicting hospitalization, the results were not clinically meaningful; this was thought to be due to the heterogeneity of the patients. Therefore, latent class analysis was used for clustering patients into clinically meaningful groups. Latent class analysis is a multivariate technique of clustering similar types of patients based on a combination of variables. Based on testing a combination of variables predictive of hospitalization in prior studies, 4 latent classes were identified by activity of daily living scores, who provides caregiving assistance, 264
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oral medication management, and primary diagnoses. Within each of these classes, different variables were predictive of hospitalization, either as protective or risk factors. For instance, in class IV patients, 100% were identified as “cardiac or circulatory problems.” For these patients, any score ⬎ 0 on the Charlson Index, a fair or poor prognosis, mild to severe pain, moderate to dependent status for instrumental activities of daily living, needing setup assistance with equipment, and bowel incontinence were predictive of hospitalization. Each of these factors increased the odds of hospitalization by almost twice compared to patients who did not experience these problems, except for assistance with equipment, which increased the likelihood of hospitalization by 4 times compared with no help needed with equipment. The nursing intervention of teaching about the disease process decreased hospitalization about half compared with no teaching in this area. A low frequency of medication administration and high amounts of teaching about disease increased hospitalization by 2–3 times compared to patients not receiving these interventions. The high frequency of teaching likely is related to increasing severity of disease. Further analysis of this data is still under investigation. The results of this study demonstrates the ability to reuse EHR data that includes standardized nursing terminologies represented by the Omaha System in combination with other data to discover new insights for hospitalization of homecare patients and develop evidence-based intervention to decrease costly outcomes. The ability to merge data also supports the ability for future exchange across systems and agencies.
CONCLUSION The importance of standards, particularly nursing terminology content standards, is a high priority with the increasing emphasis on using electronic health records that are interoperable, support the exchange of data between information systems, and create secondary data for research. Nursing has a long history of developing standardized terminologies for processing and managing health information. The profession is now in the process of moving from development of terminologies to demonstrating their usefulness in practice to support documentation, interoperability, and reuse of data for research. Nursing minimum data sets have guided the development of more granular terminologies and provide a way of aggregating data for comparison within and across organizations and information systems. The multiple interface terminologies recognized by ANA provide useful terms for documenting nursing care and conducting research to describe nursing care, and predict resource and outcomes. Reference terminologies provide a way of linking nursing concepts. Informatics research has been useful for external validation of mapping termiO
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nologies into a reference terminology for exchange and aggregation of nursing data. The next phase of knowledge generation now is in progress with studies demonstrating methods for embedding terminologies into information systems, and reuse of the data for conducting descriptive, correlational, and predictive modeling to build the knowledge in nursing. If nursing is to be visible as essential providers of health care, then the use of standardized terminologies for interoperable EHRs is critical to support patient care across settings and demonstrate the contribution of nursing. REFERENCES 1. Thompson TG, Brailer DJ. The decade of health information technology: Delivering consumer-centric and informationrich health. Available at: http://www.hhs.gov/healthit/. Accessed November 2, 2007. 2. Bakken S. Informatics for patient safety: A nursing research perspective. Annu Rev Nurs Res 2006;24:219-54. 3. World Health Organization. International Classification of Diseases (ICD). Available at: http://www.who.int/classifications/ icd/en/. Accessed November 2, 2007. 4. NANDA. NANDA: History & Historical Highlights 1973 Through 1998. Available at: http://www.nanda.org/html/ history1.html. Accessed 11/02, 2007. 5. Martin KS. The Omaha System: A Key to Practice, Documentation, and Information Management. St. Louis: Elsevier Saunders; 2005. 6. Werley HH, Lang NM, eds. Identification of the Nursing Minimum Data Set. New York: Springer Publishing Company; 1988. 7. Coenen A, McNeil B, Bakken S, Bickford C, Warren JJ. Toward comparable nursing data: American Nurses Association criteria for data sets, classification systems, and nomenclatures. Comput Nurs 2001;19:240-8. 8. American Nurses Association. ANA Recognized Terminologies and Data Element Sets. Available at:http://nursingworld. org/npii/terminologies.htm. Accessed July 5, 2007. 9. Werley HH, Devine EC, Zorn CR, Ryan P, Westra BL. The Nursing Minimum Data Set: Abstraction tool for standardized, comparable, essential data. Am J Public Health 1991; 81:421-6. 10. Huber D, Delaney C. The American Organization Of Nurse Executives (AONE) research column. The Nursing Management Minimum Data Set. Appl Nurs Res 1997;10:164-5. 11. Huber D, Schumacher L, Delaney C. Nursing Management Minimum Data Set (NMMDS). J Nurs Adm 1997;27:42-8. 12. Huber DG, Delaney C, Crossley J, Mehmert M, Ellerbe S. A Nursing Management Minimum Data Set: Significance and development. J Nurs Adm 1992;22:35-40. 13. Johnson M, Bulechek G, Butcher H, et al, eds. NANDA, NOC, and NIC Linkages: Nursing Diagnoses, Outcomes, & Interventions. 2nd ed. St. Louis: Mosby; 2006. 14. NANDA International, ed. Nursing Diagnoses: Definitions and Classification, 2007-2008. Philadelphia: NANDA International; 2007. 15. Dochterman JM, Bulechek GM, eds. Nursing Intervention Classification (NIC). 4th ed. St. Louis: Mosby; 2004.
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Westra et al 16. Moorhead S, Johnson J, Maas M, eds. Nursing Outcomes Classification (NOC). 3rd ed. St. Louis: Mosby; 2004. 17. ABC Coding Solutions. ABC Coding Manual for Integrative Healthcare. Albuquerque, NM: ABC Coding Solutions Alternative Link; 2007. 18. Saba VK. Clinical Care Classification (CCC) System Manual: A Guide to Nursing Documentation. New York: Springer; 2007. 19. Beyea SC, ed. Perioperative Nursing Data Set: The Perioperative Nursing Vocabulary 2nd ed. Denver: AORN; 2002. 20. International Council of Nurses. ICNP® Catalogues. Available at: http://www.icn.ch/icnp_catalogues.htm. Accessed December 8, 2007. 21. Bowles KH. Use of the Omaha System in research. In: Martin KS, ed. The Omaha System: A Key to Practice, Documentation, and Information Management. 2nd ed. St. Louis, MO: Elsevier Saunders; 2005:105-33. 22. Saxion E. Interview. Multidisciplinary terminology: Logical Observation Identifiers Names And Codes (LOINC). . . Susan Matney, RN, MS. Online J Nurs Inform 2006;10:2. 23. College of American Pathologists. SNOMED Clinical Terms® User Guide. Available at: http://www.cap.org/apps/ docs/snomed/documents/snomed_ct_user_guide.pdf. Accessed December 8, 2007. 24. Subramanian A, Westra BL, Matney S, Wilson PS, Delaney CW, Huff S. Integrating the Nursing Management Minimum Data Set Into The Logical Observation Identifier Names and Codes System. In process. 25. Martin KS, Scheet NJ, Stegman MR. Home health clients: Characteristics, outcomes of care, and nursing interventions. Am J Public Health 1993;83:1730-4. 26. Park M, Delaney C, Maas M, Reed D. Using a nursing minimum data set with older patients with dementia in an acute care setting. J Adv Nurs 2004;47:329-39. 27. Marek KD. Analysis of the relationships among nursing diagnoses and other selected patient factors, nursing interventions and other measures of utilization, and outcomes in home health care. University of Wisconsin, Milwaukee, PhD Dissertation. 1992. 28. Helberg JL. Patients’ status at home care discharge. Image J Nurs Scholarsh 1993;25:93-9. 29. Coenen A, Marek KD, Lundeen SP. Using nursing diagnoses to explain utilization in a community nursing center. Res Nurs Health 1996;19:441-5. 30. Marek KD. Nursing diagnoses and home care nursing utilization. Public Health Nurs 1996;13:195-200. 31. Halloran E, Halloran DC. Exploring the DRG/nursing equation. Am J Nurs 1985;85:1093-5. 32. Titler M, Dochterman J, Picone DM, et al. Cost of hospital care for elderly at risk of falling. Nurs Econ 2005;23:290306. 33. Titler M, Dochterman J, Kim T, et al. Cost of care for seniors hospitalized for hip fracture and related procedures. Nurs Outlook 2007;55:5-14. 34. Dochterman J, Titler M, Wang J, et al. Describing use of nursing interventions for three groups of patients. J Nurs Scholarsh 2005;37:57-66. 35. Lu D, Park H, Ucharattana P, Konicek D, Delaney C. Nursing Outcomes Classification (NOC) in SNOMED CT: A cross-mapping validation. Int J Nurs Terminol Classif 2006;17:43-4.
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46. Keenan G, Yakel E, Tschannen D, Mandeville M. Documentation and the nurse care planning process. In Hughes R, ed. Patient safety and quality: An evidence based handbook for nurses. AHRQ Publication No. 08-0043. Rockville, MD: Agency for Healthcare Research and Quality; 2008: Chapter 49, http://www.ahrq.gov/qual/nurseshdbk/docs/KeenanG_ DNCPP.pdf. Accessed July 9, 2008. 47. Keenan G, Yakel, E. Promoting safe nursing care by bringing visibility to the disciplinary aspects of interdisciplinary care. AMIA Annual Symposium Proceedings 2005: 385-9. 48. Keenan G, Yakel E, Marriott D. HANDS: A revitalized technology supported care planning method to improve nursing handoffs. Proceedings 9th International Conference on Nursing Informatics. 2006. 49. Keenan G. (2004-2008). HIT (Health Information Technology) support for safe nursing. 1 R01 HS015054-01- HHS PHS, Agency of Health Research and Quality (AHRQ). 50. Westra B, Raup G. Computerized charting: An essential tool for survival. Caring 1995;14:57-61. 51. Westra B, Solomon D. The Omaha System: Bridging home care and technology. Online J Nurs Inform 1999;3. 52. Westra BL, Solomon D, Ashley DM. Use of the Omaha System data to validate Medicare required outcomes in home care. Journal of Healthcare Information Management 2006; 20:88-94. 53. Westra BL, Oancea CS, Savik K, Marek KD. The feasibility of integrating the Omaha System data across homecare agencies and vendors. CIN. Submitted. 54. Westra BL, Savik K, Holmes JH, Oancea C, Choromanski L. Factors influencing hospitalization for homecare patients. In Process. 55. Briggs. Briggs® national quality improvement/hospitalization reduction study. West Des Moines, IA: Briggs Corporation; 2006. Available at: http://www.nahc.org/pdf_ apps/BriggsStudy.pdf. Accessed November 2, 2007.
36. Park H, Lu D, Konicek D, Delaney C. Nursing Interventions Classification in systematized nomenclature of medicine clinical terms: A cross-mapping validation. Cin Comput Inform Nurs 2007;25:198-210. 37. Hyun S, Park HA. Cross-mapping the ICNP with NANDA, HHCC, Omaha System and NIC for unified nursing language system development. Int Nurs Rev 2002;49:99-110. 38. Johnson M, Maas M, Moorhead S. Evaluation of nursingsensitive outcomes measures. 1997;#1RO1NR03437-0, National Institute of Nursing Research. 39. Keenan G, Stocker J, Barakauskas V, et al. Establishing the validity, reliability, and sensitivity of NOC in home care settings. Journal of Nursing Measurement 2003;11:135-55. 40. Keenan G, Stocker J, Barkauskas V, Treder M, Heath, C. Toward integrating a common nursing data set in home care to facilitate monitoring outcomes across settings. Journal of Nursing Measurement 2003:11:157-69. 41. Keenan G, Barkauskas V, Johnson M, Maas M, Moorhead S, Reed D. Establishing the validity, reliability, and sensitivity of NOC in an adult care nurse practitioner setting. Outcomes Manage 2003;7:74-83. 42. Keenan G, Stocker J, Barkauskas V, Treder M, Heath C. Toward collecting a standardized nursing data set across the continuum: Case of adult care nurse practitioner setting. Outcomes Manage 2003;7:113-20. 43. Keenan G, Falan S, Heath C, Treder M. Establishing competency in the use of North American Nursing Diagnosis Association, Nursing Outcomes Classification, And Nursing Interventions Classification terminology. J Nurs Meas 2003; 11:183-98. 44. Keenan GM, Stocker JR, GeoThomas AT, Soparkar NR, Barkauskas VH, Lee JL. The HANDS project: Studying and refining the automated collection of a cross-setting clinical data set. CIN: Comput Inform Nurs 2002;20:89-100. 45. Keenan G, Tschannen D, Wesley M. Standardized terminologies can transform Practice. Journal of Nursing Administration 2008:38:103-6.
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Table 2. Nursing Minimum Data Sets Nursing Minimum Data Set6
Nursing Management Minimum Data Set13
Nursing Care Elements 1. Nursing Diagnosis 2. Nursing Intervention 3. Nursing Outcome 4. Intensity of Nursing Care Patient Demographics 5. Personal Identification 6. Date of Birth 7. Sex 8. Race/Ethnicity 9. Residence Service Items 10. Unique Facility or Service Agency Number 11. Unique Health Record Number of Patient or Client 12. Unique Number of Principal RN Provider 13. Episode Admission or Encounter Date 14. Discharge or Termination Date 15. Disposition of Patient or Client 16. Expected Payer for Most of This Bill
Environment 1. Unit/Service Unique Identifier 2. Type of Nursing Delivery Unit/Service 3. Patient/Client Population 4. Volume of Nursing Delivery Unit/Service 5. Nursing Delivery Unit/Service Accreditation 6. Decisional Participation 7. Unit/Service Complexity 8. Patient/Client Accessibility 9. Method of Care Delivery 10. Complexity of Clinical Decision Making Nurse Resources 11. Manager Demographic Profile 12. Nursing Staff & Client Care Support Personnel 13. Nursing Care Staff Demographic Profile 14. Nursing Care Staff Satisfaction Financial Resources 15. Payer Type 16. Reimbursement 17. Nursing Delivery Unit/Service Budget 18. Expenses
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Nursing standards to support the electronic health record Bonnie L. Westra, PhD, RN Connie White Delaney, PhD, RN, FAAN, FACMI Debra Konicek, MSN, RN, BC Gail Keenan, PhD, RN
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Quality and low cost health care that is free of medical mistakes requires continuity of person-centric healthcare information across the life span and healthcare settings. Interoperable clinical information systems that rely on the use of multiple standards to support health information exchange and, in particular, nurse sensitive data, information, and knowledge are key components to support high quality, safe care. A 2004 Executive Order called for a National Health Information Network and the widespread adoption of electronic health records (EHRs) by 2014. While there are numerous standards influencing the exchange of health data, the primary focus of this article is to synthesize the state-of-the-art in nursing standardized terminologies to support the development, exchange, and communication of nursing data. Research exemplars are described for information systems to support nursing practice using standardized terminologies and secondary use of standardized nursing data from EHRs for knowledge development.
he Department of Health and Human Services’ framework for Strategic Action entitled “The Decade of Health Information Technology: Delivering Consumer-centric and Information-rich Health Care” was released in 2004.1 The vision articulated in this document is to use information technology for the purposes of avoiding medical mistakes, improving care, and reducing costs. Several efforts are underway to develop a Nationwide Health Information Network, which is the infrastructure to link electronic health data across different disciplines, healthcare settings, information systems, and geographic locations. This national effort also provides the leadership needed for the secure exchange of health information for connecting consumers, clinicians, and public health organizations to support individual and population health care. While there are numerous standards influencing the exchange of health data, the primary focus of this article is to synthesize the state-of-the-art in nursing standardized terminologies to support the development, exchange, and communication of nursing data. Research exemplars demonstrate methods of developing information systems to support nursing practice using standardized terminologies and secondary use of standardized nursing data from EHRs for knowledge development regarding the contribution of nursing to patient outcomes.
Bonnie L. Westra, PhD, RN, is an Assistant Professor, University of Minnesota School of Nursing, Robert Wood Johnson Executive Nurse Fellow, Minneapolis, MN. Connie White Delaney, PhD, RN, FAAN, FACMI, is a Professor & Dean, School of Nursing, University of Minnesota, Minneapolis, MN. Debra Konicek, MSN, RN, BC, is Director, Clinical Standards Initiatives, SNOMED Terminology Solutions, The College of American Pathologists, Northfield, IL. Gail Keenan, PhD, RN, is Associate Professor of Nursing/Director Nursing, Informatics Initiative, College of Nursing, University of Illinois, Chicago, IL. Corresponding author: Dr. Bonnie L. Westra, Robert Wood Johnson Executive Nurse Fellow, Assistant Professor, University of Minnesota School of Nursing, 5-160 Weaver Densford Hall, 306 Harvard St SE, Minneapolis, MN 55455. E-mail: [email protected]
WHAT ARE STANDARDS? Standards are agreed-upon ways to record and exchange data within and across information systems. There are standards everywhere—such as those to assure that any car where the speedometer indicates 55 miles per hour is, in fact, traveling at that speed. Other standards exist to communicate data and information about the weather, such as temperature, wind velocity, and humidity. Healthcare standards are also essential to represent, communicate, exchange, manage, and report data, information, and knowledge to support nursing practice. Standards are one mechanism to ensure validity of data. According to Bakken,2 three types of standards are
Nurs Outlook 2008;56:258-266. 0029-6554/08/$–see front matter Copyright © 2008 Mosby, Inc. All rights reserved. doi:10.1016/j.outlook.2008.06.005
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Figure 1. Nursing terminology development timeline.
most important for nursing. These include content, messaging, and confidentiality and security standards. Standardized terminologies are content standards, which include the terms that represent a focus of health concerns (diagnoses), interventions, and outcomes consistent with the scope of practice for nursing. To exchange data between information systems, there are messaging standards. For example, when 2 organizations use different information systems, data can be exchanged using messaging standards to assure that data entered into one system are consistent with the meaning when the same data are subsequently displayed in another system. The Health Insurance Portability and Accountability Act addresses confidentiality, privacy, and security standards. In this manuscript, the focus is on the first standard— the content standard—which includes concepts or terms that are used to document nursing practice. The content standard representing nursing data, information, and knowledge is essential to drive messaging standards. Without content, there is no need for messages to exchange data across information systems. These concepts are organized into nomenclatures, classifications, terminologies, and vocabularies. In this manuscript, the word “terminologies” is used in a generic way when referring to nomenclature, classification, vocabulary, and terminology. Additionally, “terminology” is used when discussing the representation of data, information, and knowledge used by nurses to document and communicate practice.
TERMINOLOGY STANDARDS Standardized terminologies have been in use since the 1850s when the International Classification of Diseases was developed to standardize the causes of death3; however, the development of standardized nursing terminologies is much newer. Initially, nursing’s interest was to develop a uniform terminology representing all of nursing practice, but the differences in practice and the major effort to accomplish this was daunting. The tactic changed to multiple specialty groups developing separate terminologies with the focus now on mapping among them. The timeline for development for nursing terminologies recognized by the American Nurses Association (ANA) is shown in Figure 1. The earliest terminology developed in the US was S
the North American Nursing Diagnoses Association (NANDA)4 nursing diagnoses, and the first funding for development of a standardized terminology, the Omaha System, occurred in 1975 by the Division of Nursing of the U.S. Department of Health and Human Services.5 The development of the Nursing Minimum Data Set (NMDS) in the mid-80’s,6 which identified the umbrella concepts for clinical nursing practice, produced a flurry of activity to specify terminologies for the nursing care elements of the NMDS—nursing diagnoses, interventions and outcomes. In 1989, recognizing a need to coordinate these developments, the ANA established the Steering Committee on Databases to develop criteria for recognition of nursing terminologies.7 Ten years later, in response to international activity related to content standards development, the ANA criteria were updated using the International Standards Organization (ISO) standards for terminologies. This extended the applicability of the ANA-recognized terminologies to one of international import. Two of the recognition criteria require that reliability and validity of the terminology must be established through research, as well as evidence of the utility of the terminology in practice. The value of ANA recognition of terminologies is that it represents a stringent review process by the profession. Recognition is required prior to ANA supporting these terminologies for public policy, such as the inclusion for interoperable electronic health records. Currently, the ANA recognizes 2 different minimum data sets and 10 distinct terminologies as shown in Table 1.8 There are 2 minimum data sets, 8 interface terminologies, and 2 reference terminologies that are currently recognized by ANA. Minimum data sets are a minimum, essential set of data elements with standardized definitions and codes collected for a specific purpose, such as describing clinical nursing practice or nursing management contextual data that influence care.9 Interface (point of care) terminologies include the actual terms/concepts readily used by nurses for describing and documenting the care of patients (individuals, families, and communities). Reference terminologies are used “behind the scenes” within clinical information systems to link concepts of similar meaning. These reference terminologies enable clinicians to use terms appropriate for their practice, such as perioperative nursing or home
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Table 1. ANA Recognized Terminologies and Data Element Sets8 Data Set/Terminology
Intended Setting
Minimum Data Sets Nursing Minimum Data Set (NMDS) Nursing Management Minimum Data Set (NMMDS) Interface Terminologies – Single Nursing Concepts NANDA International (NANDA-I) Nursing Interventions Classification (NIC) Nursing Outcomes Classification (NOC) ABC Codes Interface Terminologies – Multiple Nursing Concepts Clinical Care Classification (CCC) International Classification of Nursing Practice (ICNP) The Omaha System Perioperative Nursing Data Set
Reference Terminologies Logical Observation Identifiers Names and Codes (LOINC) Systematic Nomenclature of Medicine Clinical Terms (SNOMED-CT)
All Settings All Settings
Clinical Data Elements Nursing Administrative/Contextual Data Elements
All Settings All Settings All Settings Any Setting - Nursing and Other
Diagnoses Interventions Outcomes Interventions
Home Care All Settings Community-based Perioperative
Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes Diagnoses, Interventions, Outcomes
Any Setting - Nursing and Other Any Setting - Nursing and Other
Minimum Data Sets According to the ANA,8 minimum data sets are used to identify and define the umbrella data elements to represent nursing rather than the specific terms used in daily practice. Minimum Data Sets are high level concepts that provide the structure for aggregating more granular terms for comparison across practice. The 2 nursing minimum data sets that are recognized by ANA are the NMDS6 and the Nursing Management Minimum Data Set (NMMDS).9 Together, these 2 data sets are useful when describing patient care and the context of care delivery as shown in Table 2 (Table 2 is available in the online version of this article at the Nursing Outlook Website: http://www.nursingoutlook.org). The NMDS includes a set of 16 data elements divided into 3 different categories: nursing care, patient or client demographics, and service.10 –12 The nursing care data elements include nursing diagnoses, interventions, and outcomes which are further specified by the interface and reference terminologies described later. The NMMDS includes 18 data elements to describe the context of nursing care in 3 categories: environment, V
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Outcome and Assessments Nursing and Other Diagnoses, Interventions, and Outcomes
nursing resources, and financial resources.10 –12 The value of using minimum data sets is building our knowledge of the nursing needs of patients, the contribution nursing makes, and the influence of management data for patient safety and outcomes. Since minimum data sets are higher level concepts, they support research that focuses on aggregating granular level nursing data for benchmarking the contribution of nursing care on patient safety and outcomes across information systems, healthcare settings, and geographical locations.
care, with mapping these terms through use of a reference terminology to communicate similar meaning across systems.
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Interface Terminologies There are 8 interface terminologies that differ based on the intended use for nursing practice, the concepts included, and the organization of the concepts. Interface terminologies provide the granular level data to document nursing care and represent one or more of the nursing care data elements in the NMDS. Four of these terminologies address a single NMDS data element and 4 address multiple NMDS data elements. Three of these terminologies often are used together to plan and document nursing care13: the North American Nursing Diagnoses Association International (NANDA-I) nursing diagnoses,14 the Nursing Interventions Classification (NIC),15 and the Nursing Outcomes Classification O
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(NOC).16 The NANDA-I diagnoses were developed to address actual or potential problems in illness, wellness, and life that are the focus for intervening with patients.14 The NIC interventions focus on the behaviors used by nurses to improve patient outcomes. Interventions are treatments and include direct and indirect care for individuals, families, and communities.15 The intervention labels include a variety of associated activities, reflecting the actions documented by nursing. The NOC outcomes are used to develop goals and measure outcomes, which are defined as states, behaviors, or perceptions of individuals, families or communities that are sensitive to nursing interventions.16 Each outcome is associated with one of 19 different Likert scales to facilitate evaluation of patient care. The ABC codes17 also address a single NMDS data element to describe and bill interventions for advanced practice nurses, nurse midwives, and alternative care practitioners. The ABC Codes are complementary to other medical coding systems for billing and include a relative value unit which establishes the financial worth of an intervention or product. Four additional user interface terminologies each include all 3 NMDS nursing care data elements: nursing diagnoses, interventions, and outcomes. These are the Clinical Care Classification,18 the Omaha System,15 the Perioperative Nursing Data Set,19 and the International Classification of Nursing Practice (ICNP®).20 The Clinical Classification System initially was developed to predict resource needs and measure patient outcomes in homecare. Initially, it was named the Home Care Classification System, but was revised and renamed as the use was expanded to hospital settings.18 The Omaha System had roots in representing the terms used by nurses and other disciplines to document communitybased care such as homecare, hospice, public health, primary care nursing clinics, and parish nursing. More recently, it has been tested for usefulness in acute care settings.21 The Perioperative Nursing Data Set was developed to provide consistency in documentation of the nursing process within the perioperative setting.22 It includes a subset of NANDA nursing diagnoses, but specifies unique interventions and outcomes for perioperative nursing. These 3 terminologies were all developed within the United States but are expanding to be used globally. By contrast, the ICNP® was developed to use simple language that is culturally sensitive and useful in practice throughout the world.20 The ICNP® was initiated by International Council of Nursing to represent nursing practice in a way that is complementary to the classifications developed by the World Health Organization, such as the International Classification of Diseases. In summary, the interface terminologies are granular level terms or concepts for documenting nursing care. Some of these terminologies represent a single data element while others include multiple data elements S
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from the NMDS. In order for the terminologies to be recognized by ANA, each terminology has been developed using a variety of research methodologies for establishing reliability, validity, and usefulness for practice. The methodologies used varied across terminologies. Methods included chart reviews, expert consensus, concept analyses, content validation surveys, and inter-rater reliability studies. The NMDS provides the umbrella concepts for the development of the granular interface terminologies that provide the content in an EHR to represent nursing practice. The importance of multiple terminologies is the ability to select the appropriate ones based on the practice setting. In some cases, more than one terminology might be useful. Reference terminologies are a way of linking similar terms, and providing additional terms to support documentation and reuse of data for research.
Reference Terminologies Reference terminologies contain granular level data from multiple interface terminologies within a relational structure. Each provides a common structure and links multiple terminologies, including nursing as well as terms used by other disciplines. Reference terminologies are used in EHRs for exchange of data within and across information systems and to create queries that can combine data from multiple sources. For example, if a hospital uses the Perioperative Nursing Data Set in the perioperative setting, but uses NANDA diagnoses, the NIC interventions, and the NOC outcomes in combination to document the nursing process on a medical-surgical unit, a reference terminology can map and translate the terms between systems. The use of a reference terminology permits clinicians to use terms that fit their practice and share data across systems without reentry of data. The ANA recognizes 2 reference terminologies: Logical Observation Identifiers Names (LOINC®)22 and Systematized Nomenclature of Medicine—Clinical Terminology (SNOMED— CT®).23 The Regenstrief Institute developed LOINC® primarily to provide electronic exchange of laboratory data.23 Later, LOINC evolved to include additional types of data that represent clinical observations, linking data from various terminologies and assessment tools. Nursing terminologies included in LOINC® are the problems from the Omaha System and the Clinical Care Classification. More recently, the Nursing Management Minimum Data Set was approved for inclusion and the first 3 data elements will be published in the next LOINC® update.24 SNOMED—CT®23 is a comprehensive clinical terminology that is owned and distributed by The International Health Terminology Standards Development Organization (IHTSDO). The nursing content found within SNOMED—CT® enables greater standardization and exchange of data across systems, as well as reporting and retrieval of informa-
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tion specific to nursing. The ANA recognized nursing terminologies that are currently integrated within SNOMED—CT® include: the Clinical Care Classification, NANDA nursing diagnoses, the NIC interventions, the NOC outcomes, the Omaha System, and the Perioperative Nursing Data Set. Additionally, a collaborative agreement with the International Council of Nursing will address a strategy for mapping SNOMED—CT® and the ICNP®.
studies, investigators created their own tools to collect new data rather than conduct secondary analysis of EHR data. In other studies, the 3 NMDS data elements of nursing diagnoses, interventions, and outcomes across multiple agencies were not included, which limits the ability to understand the relationship of nursing care to outcomes. Additionally, no studies were found describing the NMDS data elements of problems, interventions, and outcomes across multiple agencies. Thus, 2 programs of research are discussed in depth as exemplars of designing information systems that incorporate standardized nursing terminologies, using terminologies to document care, and retrieving the clinical data for outcomes research. Keenan’s research uses a combination of NANDA diagnoses, NIC interventions, and NOC outcomes, whereas, Westra’s research focuses on use of the Omaha System.
RESEARCH IMPLICATIONS Nursing has a 30-year history of research to develop valid and reliable terminologies to describe nursing practice. There has been considerable research addressing the use of the terminologies to build nursing knowledge, including descriptive, predictive, and informatics research. For example, in 2 studies, homecare data were manually collected using the Omaha System25 and, in a second study, the NMDS nursing care elements (NANDA nursing diagnoses, NIC interventions) in one community hospital were abstracted to describe the complex nursing needs and interventions provided for large groups of patients.26 Nursing terminologies also were used in other studies27,28 demonstrating that the Omaha System nursing diagnoses significantly predicted outcomes of care which included discharge service and discharge condition status. Nursing diagnoses primarily have been used to predict resource utilization in Community Nursing Centers,29 home care,30 and hospital-based settings.31 Other studies have examined the effect of interventions on costs32 or the amount of care required based on the number of interventions for patients at risk for falls33,34 A newer area of informatics research is the external validation of mapping between terminologies for semantic comparability of terms that might be used in different information systems. Studies have been conducted for external validation of concepts mapped between NOC outcomes and SNOMED—CT,35 NIC interventions and SNOMED—CT,36 and mapping of ICNP with NANDA diagnoses, the Clinical Care Classification, The Omaha System and NIC interventions.37 The value of this research is that, not only does it provide evidence for interoperability of nursing data between information systems, but also the potential for abstracting and combining ERH data from terminologies across information systems for health trajectory research in the future. Previous research using standardized terminologies has contributed knowledge about terminology usefulness for describing nursing care, predicting outcomes and resource use, and interoperability between terminology systems. However, there are a number of limitations that need to be addressed in future studies. In the past, EHRs did not contain nursing terminologies in a way that could be exported electronically; therefore, manual abstractions from charts was required resulting in considerable cost to conduct the research. In other 262
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NANDA Diagnoses, NIC Interventions, and NOC Outcomes Research Program Keenan has conducted a series of studies to examine reliable, valid, and user-friendly methods of embedding nursing terminologies into an information system. Keenan’s initial study38 was to examine the reliability, validity, sensitivity, and usefulness of NOC outcomes in settings across the continuum of care in 3 Michigan sites (1 homecare and 2 ambulatory units). The data collected from the Michigan sites provided evidence of the reliability, validity, sensitivity, and usefulness of NOC in home care39,40 and ambulatory settings.41,42 While working on the NOC outcome study, it became apparent that implementing standardized terminologies was insufficient to create comparable nursing data. There was wide variation in the ways organizations were implementing nursing standardized terminologies. Therefore, studies were conducted to develop an information system—the Hands-on Automated Nursing Data System (HANDS)43,44—and subsequently test educational interventions to consistently document nursing care using standardized nursing terminologies.45 The HANDS tool was developed through the collaboration of a large group of scientists, clinicians, and technical experts whose goal was to create a care planning system that would look and function in a valid and reliable way while also being acceptable to users in all types of settings. The project used multiple methods to develop a data model and user interface that was simple and intuitive for practicing nurses. The HANDS tool integrated NANDA diagnosis, NIC intervention, and NOC outcome terminologies. Validation by software experts and focus groups of clinicians provided guidance with revisions with an overall conclusion that the HANDS software was easy to use. The HANDS tool was first used in the NOC outcome study45– 48 as a collection tool for research assistants who were trained and gathered care-planning data on all study patients in the Michigan research sites. When compared to a paper O
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version, data collection time was reduced by as much as 80%, 25–30 minutes to 5–10 minutes, using the HANDS tool.43,44 Next, the prototype was expanded to include rules of use and training, and then tested in a medical specialty intensive care unit under real-time conditions.46 – 48 The pilot provided preliminary evidence of the utility, user acceptance, and reliability and validity of the data entered. Moreover, the methods used proved to be effective in providing a multi-faceted understanding of the impact of a system like HANDS on clinical practice. The results of the evaluation were utilized to refine the method and create a web-based version of HANDS that included an improved interface, training plan, and rules of use. Building on all of the previous work, the next step was to test the HANDS plan of care method in multiple sites. A multi-site “real-time” study of the HANDS tool, training, and rules of use was initiated in 2005 and is nearing completion.49 The main research questions address the use of standardized terminologies to promote continuity of care in the “hand-off” of patients from one nurse to another. Specifically, the research questions were: (1) can standardization of the HANDS tool, training, and rules of use (HANDS Method) be maintained across diverse settings supporting a common understanding of care, and (2) is there evidence that users in all types of settings find the HANDS Method acceptable and useful in day-to-day practice? More than 700 nurses from 8 units in 4 diverse settings participated in this study. The findings thus far provide multi-dimensional evidence that the standardization of the documentation process for care planning using nursing terminologies in an information system can be maintained across diverse settings when incorporated with training and rules of use. Moreover, nurses found the combination of a tool, rules of use, and training was significantly more useful than their previous care planning methods, and nurses were significantly more satisfied with the standardized terminologies (i.e., NANDA diagnoses, NIC interventions, and NOC outcomes) than at baseline.45,46 Interrater reliabilities were conducted for current and expected NOC outcome ratings. The interrater reliabilities for current outcome ratings were within 1 point 86% of the time, and 94% were within 1 point for the expected outcome rating. Short written exams were used to test nurses’ understanding of the definitions for the NANDA, NIC, and NOC terms frequently used on the units. The mean reliability scores for matching terms and definitions at 3 points in time ranged from .73–.82. The compliance with updating care plans at the handoff from one nurse to another was 90%. Collectively, the evidence is compelling and suggests that utilization of a technology-supported plan of care method like HANDS is not only acceptable and useful to users but also can produce data that is valid and reliable for multiple uses. S
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Keenan and colleagues report the current research directions include refining methods like HANDS to improve effective use specifically to: (1) standardize clinician handoffs, (2) serve as the multidisciplinary central plan of care, and (3) deliver evidence automatically generated from the HANDS in a usable format, at the right time. Knowledge gained is being used to design, test, and refine the HANDS tool and methods to support all members of the interdisciplinary team. Finally, extensive work has already taken place to create the algorithms that will automatically convert the data collected with the HANDS Method into evidence that is automatically returned to the point of care. A series of automatic reports have already been created and tested to examine questions of interest to the front-end users and administrators. Additional work is needed and will be pursued to convert the results into evidence that is automatically returned to the point of care, at the right time, in a usable format. In conclusion, the research to support development of an information tool and methods of using it provides an example of a research agenda that has moved the standardized terminologies from sets of raw terms and measures to a solution for systematically supporting nursing practice and generating valid and reliable data to continuously improve nursing practice. This work provides essential knowledge for integration of standardized terminologies that will support the creation of valid and reliable secondary data for future outcomes and safety research.
Omaha System Program of Research After 12 years of developing an EHR for homecare that incorporates both the Omaha System and the Outcome and Assessment Information Set (OASIS)50,51 OASIS is Centers for Medicare and Medicaid Services’ (CMS) standardized assessment for all Medicare and Medicaid skilled patients receiving homecare services. The Omaha System and the Outcome and Assessment Information Set is important because it is used for Medicare payment, public reporting of outcomes, and certification of agencies. Westra’s research focuses on the secondary use of EHR data for predicting outcomes for older adults receiving homecare services. The first study was conducted reusing the Omaha System data and comparing it with the CMS reported outcome for improvement in pain.52 Agency staff abstracted the Omaha System data from their EHR and compared outcomes between the Omaha System problem of pain and the OASIS pain outcome for 133 patients. Based on chart reviews, the Omaha System pain outcome was identified by the nurse managers as more accurately reflecting the patients’ pain status compared with documentation of the OASIS data. In-service education sessions were conducted with staff to share the findings and develop consistent guidelines for documentation of OASIS, followed by monthly monitoring. A compari-
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son of the CMS outcome reports demonstrated improved documentation of pain management from 45% in Year 1 to 64.5% in Year 2. Findings from this study support the value of the Omaha System for conducting quality improvement studies. The purpose of a follow-up study was to test the feasibility of integrating Omaha System data across 15 homecare agencies and 2 different information systems (vendors) to describe problems, interventions, and outcomes.53 Patients were selected if they had received services in 2004. There were 2901 patients who were primarily older adults (81.9% ⬎ 65 years of age), recently discharged from an inpatient facility (75%), and living alone or with an elderly spouse (76.4%). Patients had a variety of payers and varied in their length of stay, with 79.2% receiving services for ⬍ 4 months. The median number of problems per patient ranged from 2–7, most frequently representing the domain of physiological problems (60%) or healthrelated problems (35%) such as nutrition or medication management. All Omaha System problems were used at least once except for human sexuality. The 5 most frequent problems included: neuro-musculoskeletal function, integument, pain, prescribed medication regimen, and circulation. There were 536 819 interventions documented with surveillance performed the most frequently (47%), followed by teaching (22%), treatments (19%), and case management (6%). The outcomes, which represent a change from admission to discharge, demonstrated that the majority of clients improved in knowledge (66.8%), behavior (64.0%), and status (64.0%). The conclusion of this study is that data could be aggregated and compared for the Omaha System across agencies and vendors. This work supports the ability to exchange data as well as conduct research across agencies and vendors. A subsequent study focused on discovering factors for predicting hospitalization of older adults using a combination of OASIS data and Omaha System interventions.54 Hospitalization of homecare patients has remained at 28% nationally regardless of various efforts to reduce this rate, making this a priority outcome to reduce costs and improve the quality of life for older adults.55 Episodes of care were created using a start and an end of care OASIS assessment. Omaha System interventions were linked to episodes based on the visit date. When using logistic regression for predicting hospitalization, the results were not clinically meaningful; this was thought to be due to the heterogeneity of the patients. Therefore, latent class analysis was used for clustering patients into clinically meaningful groups. Latent class analysis is a multivariate technique of clustering similar types of patients based on a combination of variables. Based on testing a combination of variables predictive of hospitalization in prior studies, 4 latent classes were identified by activity of daily living scores, who provides caregiving assistance, 264
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oral medication management, and primary diagnoses. Within each of these classes, different variables were predictive of hospitalization, either as protective or risk factors. For instance, in class IV patients, 100% were identified as “cardiac or circulatory problems.” For these patients, any score ⬎ 0 on the Charlson Index, a fair or poor prognosis, mild to severe pain, moderate to dependent status for instrumental activities of daily living, needing setup assistance with equipment, and bowel incontinence were predictive of hospitalization. Each of these factors increased the odds of hospitalization by almost twice compared to patients who did not experience these problems, except for assistance with equipment, which increased the likelihood of hospitalization by 4 times compared with no help needed with equipment. The nursing intervention of teaching about the disease process decreased hospitalization about half compared with no teaching in this area. A low frequency of medication administration and high amounts of teaching about disease increased hospitalization by 2–3 times compared to patients not receiving these interventions. The high frequency of teaching likely is related to increasing severity of disease. Further analysis of this data is still under investigation. The results of this study demonstrates the ability to reuse EHR data that includes standardized nursing terminologies represented by the Omaha System in combination with other data to discover new insights for hospitalization of homecare patients and develop evidence-based intervention to decrease costly outcomes. The ability to merge data also supports the ability for future exchange across systems and agencies.
CONCLUSION The importance of standards, particularly nursing terminology content standards, is a high priority with the increasing emphasis on using electronic health records that are interoperable, support the exchange of data between information systems, and create secondary data for research. Nursing has a long history of developing standardized terminologies for processing and managing health information. The profession is now in the process of moving from development of terminologies to demonstrating their usefulness in practice to support documentation, interoperability, and reuse of data for research. Nursing minimum data sets have guided the development of more granular terminologies and provide a way of aggregating data for comparison within and across organizations and information systems. The multiple interface terminologies recognized by ANA provide useful terms for documenting nursing care and conducting research to describe nursing care, and predict resource and outcomes. Reference terminologies provide a way of linking nursing concepts. Informatics research has been useful for external validation of mapping termiO
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nologies into a reference terminology for exchange and aggregation of nursing data. The next phase of knowledge generation now is in progress with studies demonstrating methods for embedding terminologies into information systems, and reuse of the data for conducting descriptive, correlational, and predictive modeling to build the knowledge in nursing. If nursing is to be visible as essential providers of health care, then the use of standardized terminologies for interoperable EHRs is critical to support patient care across settings and demonstrate the contribution of nursing. REFERENCES 1. Thompson TG, Brailer DJ. The decade of health information technology: Delivering consumer-centric and informationrich health. Available at: http://www.hhs.gov/healthit/. Accessed November 2, 2007. 2. Bakken S. Informatics for patient safety: A nursing research perspective. Annu Rev Nurs Res 2006;24:219-54. 3. World Health Organization. International Classification of Diseases (ICD). Available at: http://www.who.int/classifications/ icd/en/. Accessed November 2, 2007. 4. NANDA. NANDA: History & Historical Highlights 1973 Through 1998. Available at: http://www.nanda.org/html/ history1.html. Accessed 11/02, 2007. 5. Martin KS. The Omaha System: A Key to Practice, Documentation, and Information Management. St. Louis: Elsevier Saunders; 2005. 6. Werley HH, Lang NM, eds. Identification of the Nursing Minimum Data Set. New York: Springer Publishing Company; 1988. 7. Coenen A, McNeil B, Bakken S, Bickford C, Warren JJ. Toward comparable nursing data: American Nurses Association criteria for data sets, classification systems, and nomenclatures. Comput Nurs 2001;19:240-8. 8. American Nurses Association. ANA Recognized Terminologies and Data Element Sets. Available at:http://nursingworld. org/npii/terminologies.htm. Accessed July 5, 2007. 9. Werley HH, Devine EC, Zorn CR, Ryan P, Westra BL. The Nursing Minimum Data Set: Abstraction tool for standardized, comparable, essential data. Am J Public Health 1991; 81:421-6. 10. Huber D, Delaney C. The American Organization Of Nurse Executives (AONE) research column. The Nursing Management Minimum Data Set. Appl Nurs Res 1997;10:164-5. 11. Huber D, Schumacher L, Delaney C. Nursing Management Minimum Data Set (NMMDS). J Nurs Adm 1997;27:42-8. 12. Huber DG, Delaney C, Crossley J, Mehmert M, Ellerbe S. A Nursing Management Minimum Data Set: Significance and development. J Nurs Adm 1992;22:35-40. 13. Johnson M, Bulechek G, Butcher H, et al, eds. NANDA, NOC, and NIC Linkages: Nursing Diagnoses, Outcomes, & Interventions. 2nd ed. St. Louis: Mosby; 2006. 14. NANDA International, ed. Nursing Diagnoses: Definitions and Classification, 2007-2008. Philadelphia: NANDA International; 2007. 15. Dochterman JM, Bulechek GM, eds. Nursing Intervention Classification (NIC). 4th ed. St. Louis: Mosby; 2004.
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46. Keenan G, Yakel E, Tschannen D, Mandeville M. Documentation and the nurse care planning process. In Hughes R, ed. Patient safety and quality: An evidence based handbook for nurses. AHRQ Publication No. 08-0043. Rockville, MD: Agency for Healthcare Research and Quality; 2008: Chapter 49, http://www.ahrq.gov/qual/nurseshdbk/docs/KeenanG_ DNCPP.pdf. Accessed July 9, 2008. 47. Keenan G, Yakel, E. Promoting safe nursing care by bringing visibility to the disciplinary aspects of interdisciplinary care. AMIA Annual Symposium Proceedings 2005: 385-9. 48. Keenan G, Yakel E, Marriott D. HANDS: A revitalized technology supported care planning method to improve nursing handoffs. Proceedings 9th International Conference on Nursing Informatics. 2006. 49. Keenan G. (2004-2008). HIT (Health Information Technology) support for safe nursing. 1 R01 HS015054-01- HHS PHS, Agency of Health Research and Quality (AHRQ). 50. Westra B, Raup G. Computerized charting: An essential tool for survival. Caring 1995;14:57-61. 51. Westra B, Solomon D. The Omaha System: Bridging home care and technology. Online J Nurs Inform 1999;3. 52. Westra BL, Solomon D, Ashley DM. Use of the Omaha System data to validate Medicare required outcomes in home care. Journal of Healthcare Information Management 2006; 20:88-94. 53. Westra BL, Oancea CS, Savik K, Marek KD. The feasibility of integrating the Omaha System data across homecare agencies and vendors. CIN. Submitted. 54. Westra BL, Savik K, Holmes JH, Oancea C, Choromanski L. Factors influencing hospitalization for homecare patients. In Process. 55. Briggs. Briggs® national quality improvement/hospitalization reduction study. West Des Moines, IA: Briggs Corporation; 2006. Available at: http://www.nahc.org/pdf_ apps/BriggsStudy.pdf. Accessed November 2, 2007.
36. Park H, Lu D, Konicek D, Delaney C. Nursing Interventions Classification in systematized nomenclature of medicine clinical terms: A cross-mapping validation. Cin Comput Inform Nurs 2007;25:198-210. 37. Hyun S, Park HA. Cross-mapping the ICNP with NANDA, HHCC, Omaha System and NIC for unified nursing language system development. Int Nurs Rev 2002;49:99-110. 38. Johnson M, Maas M, Moorhead S. Evaluation of nursingsensitive outcomes measures. 1997;#1RO1NR03437-0, National Institute of Nursing Research. 39. Keenan G, Stocker J, Barakauskas V, et al. Establishing the validity, reliability, and sensitivity of NOC in home care settings. Journal of Nursing Measurement 2003;11:135-55. 40. Keenan G, Stocker J, Barkauskas V, Treder M, Heath, C. Toward integrating a common nursing data set in home care to facilitate monitoring outcomes across settings. Journal of Nursing Measurement 2003:11:157-69. 41. Keenan G, Barkauskas V, Johnson M, Maas M, Moorhead S, Reed D. Establishing the validity, reliability, and sensitivity of NOC in an adult care nurse practitioner setting. Outcomes Manage 2003;7:74-83. 42. Keenan G, Stocker J, Barkauskas V, Treder M, Heath C. Toward collecting a standardized nursing data set across the continuum: Case of adult care nurse practitioner setting. Outcomes Manage 2003;7:113-20. 43. Keenan G, Falan S, Heath C, Treder M. Establishing competency in the use of North American Nursing Diagnosis Association, Nursing Outcomes Classification, And Nursing Interventions Classification terminology. J Nurs Meas 2003; 11:183-98. 44. Keenan GM, Stocker JR, GeoThomas AT, Soparkar NR, Barkauskas VH, Lee JL. The HANDS project: Studying and refining the automated collection of a cross-setting clinical data set. CIN: Comput Inform Nurs 2002;20:89-100. 45. Keenan G, Tschannen D, Wesley M. Standardized terminologies can transform Practice. Journal of Nursing Administration 2008:38:103-6.
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Table 2. Nursing Minimum Data Sets Nursing Minimum Data Set6
Nursing Management Minimum Data Set13
Nursing Care Elements 1. Nursing Diagnosis 2. Nursing Intervention 3. Nursing Outcome 4. Intensity of Nursing Care Patient Demographics 5. Personal Identification 6. Date of Birth 7. Sex 8. Race/Ethnicity 9. Residence Service Items 10. Unique Facility or Service Agency Number 11. Unique Health Record Number of Patient or Client 12. Unique Number of Principal RN Provider 13. Episode Admission or Encounter Date 14. Discharge or Termination Date 15. Disposition of Patient or Client 16. Expected Payer for Most of This Bill
Environment 1. Unit/Service Unique Identifier 2. Type of Nursing Delivery Unit/Service 3. Patient/Client Population 4. Volume of Nursing Delivery Unit/Service 5. Nursing Delivery Unit/Service Accreditation 6. Decisional Participation 7. Unit/Service Complexity 8. Patient/Client Accessibility 9. Method of Care Delivery 10. Complexity of Clinical Decision Making Nurse Resources 11. Manager Demographic Profile 12. Nursing Staff & Client Care Support Personnel 13. Nursing Care Staff Demographic Profile 14. Nursing Care Staff Satisfaction Financial Resources 15. Payer Type 16. Reimbursement 17. Nursing Delivery Unit/Service Budget 18. Expenses
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