El e c t ronic health re c o rd s

Te rminology Use in Electro n i c Health Records: Basic Principles Tera J. Wa t k i n s R o b e rt E. Haskell Cynthia B. Lundberg Jane M. Bro k e l Marisa L. Wi l s o n Nicholas Hard i k e r El e c t ronic health re c o rd s (EHRs) are a necessary means to improving patient safety, quality, and evidence-based practice. Standa rdized clinical classification systems and terminologies pro v i d e the words and phrases needed to consistently define and document patient care. Consequently, term i- nologies and classifications are an essential ingredient of an EHR, and their selection must be driven by a clear understanding of requirements for their use (Rosenbloom, Miller, Johnson, Elkin, & Brown, 2008). Te rminology implementation includes developing evidence-based clinical content through plans of care and protocols, describing and documenting care, monitoring c a re through decision support, satisfying quality and re i m b u r s e- Tera J. Wa t k i n s, M S, R N, is Senior Solution D e s i g n e r, Cerner Corp o ration, Kansas City, M O. E l e c t ronic health records (EHRs) are a cost-saving and env i ro n m e n t - a l ly friendly means for documenting patient care and improv i n g patient safe t y, q u a l i t y, and evidence-based practice. S t a n d a rd i ze d clinical classification systems and terminologies are essential ingre - dients of the EHR. Their selection must be driven by a clear under - standing of requirements for their use and application. This art i cl e describes the principle uses of clinical information and motives fo r c o n s i s t e n cy in practice, and provides a distinction between cl a s s i f i - cation systems and reference terminologies for clinical settings. O b j e c t i v e s 2009 Society of Urologic Nurses and Associates U rologic Nurs i n g, p p. 3 2 1-3 2 7. Key Wo rd s : Nursing terminology, nursing classification, nursing process, clinical content standards, evidence-based practice, regulatory compliance, electronic health record (EHR). 1. Explain the essential elements of an electronic health re c o rd in the clinical setting. 2. Discuss policy and re g u l a t o ry compliance for the use of electro n- ic data storage devices in patient care. 3. Outline a framework for making the transition from paper to elect ronic health re c o rds storage. R o b e rt E. H a s k e l l, is a Software Architect, Siemens Corp o ration, Malve rn, PA. Cynthia B. L u n d b e rg, B S N, R N, is a Clinical I n fo rmatics Educator, The College of Amer - ican Pathologist SNOMED, Te rm i n o l o g y S o l u t i o n s, Deerfield, IL. Jane M. B ro k e l, P h D, R N, is an Assistant P r o fe s s o r, University of Iowa College of Nursing, Iowa City, IA. Marisa L. W i l s o n, D N S c, M H S c, R N - B C, i s an Assistant Professor, University of M a ryland School of Nursing, Baltimore, MD. ment re p o rting re q u i rements, and analyzing care for continuing i m p rovement. Only when re q u i rements for standard term i n o l o g y implementation are understood can an appropriate choice of clinical classification system and/or t e rminology be formed. Several clinical classification systems and terminologies exist t o d a y. A sampling is illustrated in Table 1. These and potentially other global, national, or local classifications and term i n o l o g i e s should be part of the pre l i m i n a ry consideration for EHR content and t e rminology implementation. Clinical Content The choice of content for a clinical application is a major strategic decision for all EHR implementations. Clinical content N i cholas Hard i k e r, P h D, R N, is a Senior Research Fellow, University of Salford, Greater Manchester, United Kingdom, and a P r o fessor (Adjunct), College of Nursing, U n i versity of Colora d o, Denve r, CO. Statement of Discl o s u r e : The authors reported no actual or potential conflict of interest in relation to this continuing nursing education art i c l e. N o t e : O b j e c t i ves and CNE Evaluation Fo rm appear on page 327. UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5 321

defines the material stru c t u re d into the EHR to help the system i n f o rm and guide clinical practice. Nurses are primary stakeholders in the choice and development of EHR content and are vital to ensure that content meets nursing information and knowledge needs. Additionally, information generated by clinical documentation can enable health care p roviders to improve the quality and efficiency of health care service delivery for all subjects of care (Hovenga, Garde, & Heard, 2005). Essential elements of clinical content for any EHR are the word s and phrases that populate the u s e r s view at the computer scre e n (Rosenbloom et al., 2008). These w o rds or phrases describe the assessments and interventions nurses need to document care and must be defined with an appro p r i- ate standard term i n o l o g y. Howe v e r, clinical content is more than terminology. The terminology must be organized into compositions that are familiar to nurses and relevant to assessing and documenting care. In this fashion, the appropriate implementation of s t a n d a rd terminology can help t r a n s f o rmand assemble concepts within nurses minds into codes in computer databases (Park, Cho, & Byeun, 2007). The most important function of a standard clinical term i n o l o- gy is to define, consistently and re l i a b l y, medical and nursing concepts presented to users and re c o rded as standardized data in the EHR for multiple functions, t h e reby achieving semantic interoperability across multiple applications (Hovenga et al., 2005). Semantic interoperability is achieved when the meaning of t e rms shared across applications and their users is complete and unambiguous. Data captured for and during patient care have many potential applications, including clinical re s e a rch and public safety. There f o re, it seems reasonable that the same term i- nological standards would apply to health care data used by and Ta ble 1. Examples of Classifications and Terminologies Classifications (Interface Terminologies) I n t e rnational Classification of Diseases (National Center for Health Statistics [NCHS] & the Centers for Medicare and Medicaid Services [CMS], 2007) N A N DA-I (NANDA International, 2008) Nursing Interventions Classification (NIC) (Bulechek, Butcher, & D o c h t e rma n, 2008) Nursing Outcomes Classification (NOC) (Moorhead, Johnson, Maas, & S wanson, 2008) Clinical Care Classification (CCC) (Saba, 2006) The Omaha System (Martin, 2005) Pe ri o p e ra t i ve Nursing Data Set (Association of Pe ri o p e ra t i ve Registered N u r s e s, 2007) S o u r c e : Lundberg et al., 2008 R e ference Te r m i n o l o g y S y s t e m a t i zed Nomenclature of Medicine-Clinical Te rms (SNOMED CT) ( I n t e rnational Health Te rminology Standards Development Organization [IHTSD], 2009) I n t e rnational Classification for Nursing Practice (ICNP) (Intern a t i o n a l Council of Nurses, 2008) s h a red among clinicians, public health, and re s e a rch pro f e s s i o n- als (Richesson & Krischer, 2007). Clinical re s e a rch is important as a means to discover new evidence of best practice, which can subsequently be expressed and implemented as new clinical content in the EHR at the point of c a re. To ensure that clinical content and data are comparable a c ross institutions and for multiple purposes, existing standardized terminologies should be leveraged to help define clinical content during EHR system development, rather than each institution unnecessarily cre a t- ing its own set of term s. Many sources of broader clinical content exist today. Primary s o u rces are the independently authored clinical literature appraisals or the clinical practice guidelines available from org a n i- zations, such as the Society of U rologic Nurses and Associates (SUNA), and professional practice o rganizations, such as the American Nurses Association (ANA) and the Royal College of Nursing (RCN). These evidence-based s o u rces are essential for pro v i d i n g links to the guidelines stru c t u re d within plans of care, which follow the detail specifications of the guideline but use standard concepts with terms to codify the knowledge. For instance, the SUNA guideline for aseptic care of a urinary catheter contains an intervention called exchange catheter if infection [is] suspected (Rahn, 2008, p. 338). The guideline could be codified within the assessment parameters t h rough noticeable and defined indicators for an infection. The characteristics of f e v e r, pain, cloudy appearance, discolore d urine, or odor become defined assessments in a documentation guideline when translated into the EHR to support the nurse s documentation. Decision support rules can be p rogrammed into the system to s u p p o rt a notification to the nurse identifying patients with risk factors and/or when combinations of the above characteristics are documented using the standard i z e d t e rminology codes that have been applied consistently to these concepts. Sources of clinical content a re published nursing classification systems (Lundberg et al., 2008), which propose the basis of 322 UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5

consistent clinical concepts to c a p t u re and assemble data into c a re plans to support practice. These sources also lend cre d i b i l i- ty to clinical applications for quality and re g u l a t o ry compliance, and are generally well accepted by clinicians. Policy and Regulatory Compliance A large portion of the information captured within the EHR for the patient is driven by re g u l a- tions and standards. Health care institutions are regularly under review by federal, state, accre d i t a- tion organizations, and national health services. For example, in the United States, the Center for M e d i c a re and Medicaid Serv i c e s (CMS) re q u i res that specific core measurable indicators be generated from collected electronic data. The occurrence of complications of care (such as never events ) will result in lost funding or no funding in the absence of documentation of pre-existing conditions. A d d i t i o n a l l y, to justify patient stays, commercial health plans and insurers demand patient information about pro c e- d u res and interventions pro v i d e d to patients. In recent years, health c a re institutions have contracted with payers for incentives known as pay-for- p e rf o rmance, which re q u i re data submissions on meas u reable indicators related to patient care with specific diagnosis groups, such as urinary tract infection. Better documented care i n c reases reimbursements. Thus, the use of standard term i n o l o g i e s to capture data within the EHR is beneficial. The Joint Commission, an a c c reditation organization, develops and evaluates health care o rganizations in the achievement of standards, such as for inform a- tion management and patient c a re. Patient care standard s re q u i re specific interd i s c i p l i n a ry patient assessments for priority p roblems, such as urinary re t e n- tion or incontinence, the plan for i n t e rmittent urinary catheterization, and teaching the patient to manage those problems. The inspector looks for the re c o rd e d documentation that the planned i n t e rventions were carried out and that the appropriate outcome was measured as part of the plan for the interd i s c i p l i n a ry team s readiness for discharge planning. Each health care institution has a plan for patient care serv i c e s, which includes the coord i n a t i o n of care within the interd i s c i p l i- n a ry team. The institutional policies and pro c e d u res dictate re q u i rements for documenting patient care. To the gre a t e s t extent possible, the data collected within implemented workflows should be leveraged to fulfill these documentation re q u i rements. When it is possible to re l y on standardized data elements to satisfy new or updated inform a- tion re q u i rements, the impact to c a regiving staff can be minimized (Hovenga et al., 2005). Less redundant data collection work is there f o re necessary. S t a n d a rd terminologies make this possible. Impact on Clinical Practice I n i t i a l l y, nurses will need to o rganize assessment data, and prioritize problem lists and plans of care within the EHR to document and to reuse data for the benefit of their patients. The intelligent filtering of incre a s e d amounts of data, accessible via the EHR, presents a new challenge for clinicians, who must now sort through and prioritize multiple findings presented to them (Berner & Moss, 2005; O s h e ro ff et al., 2007). Nursing theories, conceptual models, and empirical re s e a rch are anchore d using a broadly accepted nursing p rocess to address the patient s health within a variety of settings ( E ffkin, 2003; Fawcett, 2003). Nurses also collect an established standard minimum data set that reflects the nursing p rocess, which is efficiently and e ffectively re c o rded with stand a rd terms that document nursing care. The nursing pro c e s s p rovides some basic stru c t u re. H o w e v e r, features of EHRs, such as alerts for abnormal vital signs, critical laboratory values, and potential drug interactions, are also part of the new data, information, knowledge, and wisdom model today (Englehardt & Nelson, 2002; Graves & Corc o r a n, 1989; Staggers & Thompson, 2002). Prior work patterns based on paper, verbal exchange, and manual methods are being replaced with computerized systems. These are potentially less flexible because they re q u i re prescribed methods of data entry and p resentation (Weir et al., 2007). H o w e v e r, frameworks for care p rocess development are helpful skeletons on which to support common and reusable clinical concepts expressed in standard t e rm i n o l o g y. Recognized frameworks exist for planning a migration fro m paper to electronic documentation systems, as well as for integrating c a re processes into an EHR. M a r j o ry Gord o n s F u n c t i o n a l Health Patterns ( G o rdon, 1994) is an identified framework that a d d resses the scope of nursing c a re for organizing the content of EHRs. For example, F u n c t i o n a l Health Patterns p rovides a pro t o- type for organizing concepts for assessment, diagnosis, planning, i n t e rvening, and evaluating care. When applying this prototype, the p a t i e n t s human responses to disease, injury, or surg e ry are all valued within documentation. Nurses need to collect and evaluate i n f o rmation about how the patient moves, relates to others, and feels, as well as the patient s knowledge of his or her health situation and needs. Expressing what is important (valuing) and participating in decisions (choosing) are a part of the patient s perception, which used to be narrated in nursing notes. However, in electronic systems, it is necessary for this information to be collected and com- UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5 323

municated in a more stru c t u re d and standardized way, there b y c reating one complete, up-to-date, and accurate source of inform a- tion for all providers. The patient s knowledge level and decisionmaking capacity may not be documented unless the EHR is designed to accommodate a more holistic description of the patient. One challenge in the use of electronic records is mapping how nursing process documentation is captured and used during nurses daily care. Outside of basic nursing care and evaluation processes, as well as broadly respected policies of care org a n i- zations, modern tools are available that allow for process mapping of an entire health care t e a m s management and exchange of data. Process mapping is a tool that illustrates when and how data are documented and capt u red within the EHR and how data are managed by all disciplines. This interdisciplinary a p p roach may involve complex s o f t w a re applications that re q u i re detailed input of particular tasks and interactions that intersect with multiple providers and points in time. A d d i t i o n a l l y, when an institution improves care processes, a g roup of clinicians might be asked to participate in the redesign of a better process (Brokel & Harr i s o n, 2009). This process mapping e ff o rt is valuable in training nurses about their steps in the nursing p rocess while documenting care in an EHR. The process map is often updated to model all future EHR changes and upgrades. P rocess maps can be leveraged to p redict the impact of potential p rocess changes to staffing ratios, c a re quality, and cost. Standard t e rminology is a core re q u i re m e n t for process mapping to consistently re p resent activities and decision points that cross disciplines. Basic Principles of Implementation To build on prior work that has been done in the area of nursing terminologies and evidencebased practice, nurses must now concentrate on using a process to i d e n t i f y, document, implement, manage, and govern the nursing knowledge domain, as well as contribute to the development of relevant international standard s (Hovenga et al., 2005). Designing content consistent with an established standard should pro v i d e i n c reased likelihood of intero p e r- ability between providers and institutions in the long term. Although consistent implementation of terminology standards will not guarantee that future re q u i re m e n t s will be met, it does help maximize i n t e rnal consistency and quality and quantity of retrievable data, and provides essential inform a t i c s i n f r a s t ru c t u re as a primary building block for evidence-based practice (Bakken, 2001). This consistent application of t e rms is achieved by data stand a rdization, which refers to the p rocess of identifying unique concepts that have single meaning and are unambiguous. Synonymous terms are allowed to accommodate local and individual pre f- e rences, but ambiguous terms are not. Ambiguity is a common terminology and data problem. For example, what does the term incontinence mean? It may seem straightforw a rd unless you have an application that uses the t e rm incontinence in multiple contexts. Does it mean simply that a person lacks control of their ability to hold urine? Does it exclude fecal incontinence in certain contexts? Is there a diff e rence between s t ress incontinence and general incontinence when it comes to t reatment plans and interv e n t i o n s to care for the patient? Are these t e rms synonymous? One must be clear about what is implied in these circumstances to obtain the p roper data from the system for decision support rules, re p o rt s, and views for the bedside clinic i a n. Another common problem is applying terms that overlap in meaning. For example, the term nausea with vomiting may be added to a system s data catalogue, when nausea/vomiting/ diarrhea already exists. The question for the clinician is which concept truly re p resents what is happening with the patient when the patient is assessed for nausea without vomiting? A query to find patients who had nausea would re q u i re that each of these discre t e data items be selected individually to extract data that re p re s e n t what was assessed. Even then, t h e re would be no way to easily separate those patients with nausea but not vomiting, unless a concept of nausea alone was presented and documented at the point-of-care. The goal is to retrieve only information that is complete, reliable, and with one m e a n i n g. Data on the user screen display should provide the clinician with natural language expre s- sions that are common at the p o i n t - o f - c a re. This is accomplished through the application of synonyms that have equivalent meaning to the underlying clinical concepts, which in turn are linked behind the scenes to a s t a n d a rdized terminology code m o re useful for re s e a rch, analysis, and clinical decision support. For example, weakness, a synonym of asthenia in SNOMED C T, could be implemented at the p o i n t - o f - c a re to document the t rue complaint expressed by the patient and the more commonly understood clinical term that can be re c o rded by other ancillary team members. Since the system recognizes the same code for either term, the EHR can understand them interchangeably for re p o rting and decision support purposes. This is all invisible to c a regiving staff. P a rticular types of data collected in an EHR are more likely to have re q u i rements tied to administrative coding systems. In the United States, clinicians will typically re q u i re re l i a b l e diagnosis coding to ICD codes to manage reimbursement for their 324 UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5

s e rvices. Similar considerations a re present for pro c e d u res done in laboratories and radiologic and surg e ry units worldwide. These coding system re q u i rements will vary widely fro m c o u n t ry to country. Furt h e r, federal level recommendations are becoming more common, with some countries and cert i f i c a t i o n g roups governing the implementation of terminology standard s, and even potentially basing funding on an institution s compliance with such re c o m m e n d a- tions. When content needs and p rocesses are defined, data are s t a n d a rdized, and secondary data re q u i rements are anticipated, terminology use is then optimized. Content and term i n o l o g y c o m p rehensiveness, understanding the org a n i z a t i o n s business and clinical re q u i rements, and alignment with the clinician s n e c e s s a ry administrative and s t a n d a rd coding systems are the keys to determining which clinical terminologies will best meet your institution s needs. As previously mentioned and illustrated in Table 1, two basic types of coding systems exist today for EHRs: the classification system and the stru c t u re d re f e rence term i n o l o g y. Classification systems are stru c t u re d systems of terms and definitions that are grouped together based on re s e a rch and common characteristics of origin, composition, s t ru c t u re, or function (Coenen, McNeil, Bakken, Bickford, & Wa rren, 2001). The disease classifications serve data aggre g a t i o n purposes, such as morbidity/ m o rtality re p o rting, comparisons to inform countries about the health of their populations, patient outcomes re s e a rch to m e a s u re care quality and eff e c- tiveness, communication between caregivers via shared term s and meanings, and re i m b u r s e- ment. Although classification systems serve important purposes for defining re s e a rc h - b a s e d linking of concepts, their stru c- t u res lack some ability to enable the enhanced query capabilities that a re f e rence terminology provides by including more synonyms. Reference term i n o l o g i e s, such as SNOMED CT (Lundberg et al., 2008; Park, Lu, Konicek, & Delaney, 2007) and ICNP (Coenen, 2003), provide machinereadable ways of indexing, storing, retrieving, and aggreg a t i n g clinical data across specialties and sites of care. The implementation of re f e rence term i n o l o g i e s within EHRs supports intero p e r- able data extraction, analysis, and messaging capabilities so i n f o rmation can be shared with much greater detail than a classification system is intended to p rovide (Coenen, Marin, Park, & Bakken, 2001). R e f e rence terminologies also d i rectly integrate specialty classification systems. For instance, SNOMED CT and ICNP have both integrated nursing classification systems so that the concepts within those classification systems are also included within their term i- nology (Park, Lu et al., 2007). This is sometimes re f e rred to as a harmonized or comprehensive term i- n o l o g y, which simply means that t h e re is one source of inform a t i o n that contains concepts defined by many authors. Both SNOMED CT and ICNP have worked with NANDA-I to ensure that all nursing diagnoses in that classification a re included within the SNOMED CT and ICNP term i n o l o g i e s. C o n c l u s i o n Health care organizations operate in a dynamic enviro n- ment and must be able to implement information or knowledge (such as evidence-based clinical or best practice guidelines as re q u i red), communicate intern a l- ly and extern a l l y, and apply new or existing knowledge and p rocess information to facilitate e fficient and effective manager and clinician decision making (Hovenga et al., 2005). This investment in content and pro c e s s development will be a necessary cost for many organizations. S o u rces of clinical content, such as re f e rence texts, classifications systems, laboratory and drug guides, and critical appraisal, are often leveraged to contain costs and provide for a more practical basis of content for clinical users, which can later be enhanced or i m p roved upon. As with any legal stance on practice, nurses will need and want to know the sourc e of professional practice guidelines to provide a credible basis for the clinical documentation system and the legal re c o rd of patient c a re. Using valid and reliable content sources, terminology stand a rds and collaborative pro c e s s models that are agreed upon, these re s o u rces and guidelines can be translated into the EHR in the most efficient way possible. The standardized data within the clinical workflow will then provide the foundation for re s e a rc h and the increasingly complex and vital quality and regulatory re p o rting. Perhaps most import a n t l y, it will provide continuing and evolving processes to support the bedside clinician. It is ultimately the re s p o n s i- bility of each health institution to establish its own needs for clinical documentation standards, re p o rting re q u i rements, and decision support priorities, as well as evaluate the content, pro c e s s e s, and terminologies to satisfy these re q u i rements. However, consistency of data, and ultimately i n t e ro p e r a b i l i t y, are necessary to s e rve patient-centered care, where health care information exists with many providers. R e s o u rce guidance is necess a ry for health care institutions to l e a rn of solutions that leverage i n d u s t ry standards and offer education that promotes the bro a d e r implementation of the standard s outlined in this article. Key re s o u rces, such as software providers, educators, content developers, and classification/t e rm i n o l- ogy developers, all have vital ro l e s to play in the implementation of the EHR. Software providers have UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5 325

the responsibility to package quality solutions for their customers. Educators have an obligation to demonstrate and study standard s implementations. Content developers have the responsibility to link to s t a n d a rd classification systems and t e rminologies. Finally, classification and terminology developers have the responsibility to analyze and understand concept meanings and pro g ress terms with the science. Regardless, all must collaborate to achieve the desired consist e n c y, reuse, and intero p e r a b i l i t y re q u i red to achieve these goals for applicability in the clinical setting. R e f e re n c e s Association of PeriOperative Registere d Nurses (AORN). (2007). P e r i o p e r a t i v e nursing data set: The perioperative nursing vocabulary. Denver, CO: AORN, Incorporated. Bakken, S. (2001). An informatics infras t ru c t u re is essential for evidencebased practice. Journal of the American Medical Informatics Association, 8, 199-201. B e rn e r, E.S., & Moss, J. (2005). Inform a t i c s challenges for the impending patient i n f o rma tion explosion. J o u rnal of the American Medical Inform a t i c s Association, 12, 614-617. B rokel, J.M., & Harrison, M.I. (2009). Redesigning care processes using an e l e c t ronic health re c o rd: A system s experience. The Joint Commission Journal on Quality and Patient S a f e t y, 35(2), 82-92. Bulechek, G.M., Butcher, H., & D o c h t e rman, J.M. (2008). N u r s i n g i n t e rventions classification (5th ed.). St. Louis, MO: Mosby/Elsevier. Coenen, A. (2003). The International Classification for Nursing Practice (ICNP). programme: Advancing a unifying framework for nursing. Online Journal of Issues in Nursing. Retrieved March 1, 2009, from h t t p :// w w w. n u r s i n g w o r l d. o rg / M a i n M e n u C a t e g o r i e s / A N A M a r k e t p l a c e / A N A P e r i o d i c a l s / O J I N / Ta b l e o f C o n t e n t s / Vo l u m e 82003 / N o 2 M a y 2003 / A rt i c l e s P re v i o u s To p i c s / T h e I n t e rn a t i o n a l C l a s s i f i c a t i o n f o r N u r s i n g P r a c t i c e. a s p x Coenen, A., Marin, H.F., Park, H., & Bakken, S. (2001). Collaborative e ff o rts for re p resenting nursing concepts in computer-based systems: I n t e rnational perspectives. J o u rnal of the American Medical Inform a t i c s Association, 8, 202-211. Coenen, A., McNeil, B., Bakken, S., B i c k f o rd, C., & Wa rren, J.J. (2001). To w a rd comparable nursing data: American Nurses Association criteria for data sets, classification systems, and nomenclatures. Computers in Nursing, 19, 2 4 0-2 4 6. E ffken, J.A. (2003). An organizing framework for nursing inform a t i c s re s e a rch. Computers, Inform a t i c s, Nursing, 21, 3 1 6-3 2 3. E n g l e h a rdt, S.P., & Nelson, R. (2002). Health care informatics: An interd i s - c i p l i n a ry appro a c h. St. Louis: Mosby. Fawcett, J. (2003). Analyzing and evalua - tion of contemporary nursing knowl - edge: Nursing models and theories. Philadelphia: FA Davis Co. G o rdon, M. (1994). Nursing diagnosis: P rocess and application ( 3 rd ed.). St. Louis, MO: Mosby. Graves, J.R., & Corcoran, S. (1989).The study of nursing informatics. I m a g e, 2 1, 2 2 7-2 3 1. Hovenga, E., Garde, S., & Heard, S. (2005). Nursing constraint models for elect ronic health re c o rds: A vision for domain knowledge govern a n c e. I n t e rnational Journal of Medical I n f o rmatics, 74, 886-898. I n t e rnational Council of Nurses (ICNP). (2008). I n t e rnational Classification of Nursing Practice (ICNP ), R e t r i e v e d F e b ru a ry 6, 2009, from http://www. i c n. c h / i c n p. h t m I n t e rnational Health Te rminology Standard s Development Organization (IHTSDO). ( 2 0 0 9 ). Systematized Nomenclature of Medicine Clinical Te rms (SNOMED CT), Retrieved March 1, 2009, fro m h t t p : / / w w w. i h t s d o. o rg / s n o m e d - c t / L u n d b e rg, C., Wa rren, J., Brokel, J., Bulechek, G., Butcher, H., Mart i n, K., et al. (2008). Selecting a stand a rdized terminology for the elect ronic health re c o rd that reveals the impact of nursing on patient care. Online Journal of Nursing Infor - matics, 12(2). Retrieved March 1, 2009, from http://ojni.org / 1 2 _ 2 / l u n d b e rg. p d f M a rtin, K.S. (2005). The Omaha System: A key to practice, documentation and information management ( 2 n d ed.). St. Louis, MO: Mosby/Elsevier. M o o rhead, S., Johnson, M., Maas, M., & Swanson, E. (2008). Nursing out - comes classification (4th ed.). St. Louis, MO: Mosby/Elsevier. NANDA International. (2008). N u r s i n g diagnoses: Definition and classifica - tion, 2009-2011. Boston: Wiley Blackw e l l. U rologic Nursing Editorial Board Statements of Discl o s u r e National Center for Health Statistics (NCHS) and the Centers for Medicare and Medicaid Services (CMS). (2007). I n t e rnational classification of diseases 9th clinical modifications. Retrieved Febru a ry 6, 2009, fro m h t t p : / / i c d 9 c m. c h r i s e n d re s.c o m / i c d 9 c m / O s h e ro ff, J.A., Teich, J.M., Middleton, B., Steen, E.B., Wright, A., & Detmer, D.E. (2007). A roadmap for national action on clinical decision support. J o u rn a l of the American Medical Inform a t i c s Association, 14, 141-145. Park, H., Cho, I., & Byeun, N. (2007). Modeling a terminology-based elect ronic nursing re c o rd system: An object-oriented approach. Inter - national Journal of Medical I n f o rmatics, 76, 735-746. Park, H., Lu, D., Konicek, D., & Delaney, C. (2007). Nursing interventions classification in systematized nomenclature of medicine clinical terms: A cro s s - mapping validation. Computers, I n f o rmatics, Nursing, 25, 1 9 8-2 0 8. Rahn, D.D. (2008). Urinary tract infections: Contemporary management. U rologic Nursing, 28(5), 333-341. Richesson, R.L. & Krischer, J. (2007). Data s t a n d a rds in clinical re s e a rch: Gaps, overlaps, challenges and future dire c- tions. Journal of the American Medical Informatics Association. 14, 6 8 7-6 9 6. Rosenbloom, S.T., Miller, R.A., Johnson, K.B., Elkin, P.L., & Brown, S.H. (2008). A model for evaluating interface terminologies. J o u rnal of the American Medical Informatics Association, 15, 6 5-7 6. Saba, V. (2006). Clinical care classifica - tion system manual. New Yo r k : Springer Publishing Co. Staggers, N., & Thompson, C.B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. J o u rnal of the American Medical Inform a t i c s Association, 9, 2 5 5-2 6 1. We i r, C.R., Nebeker, J.R., Hicken, B.L., Campo, R., Drews, F., & LeBar, B. (2007). A cognitive task analysis of i n f o rmation management strategies in a computerized provider order entry e n v i ronment. J o u rnal of the American Medical Informatics Association, 1 4(1), 65-75. In accordance with ANCC-COA gove rning rules Urologic Nursing E d i t o rial Board statements of disclosure are published with each CNE offe ri n g. The statements of disclosure fo r this offe ring are published below. K aye K. G a i n e s, M S, A R N P, C U N P, disclosed that she is on the Speake r s Bureau fo r P f i ze r, Inc., and Nova rtis Oncology. Susanne A. Q u a l l i ch, A N P - B C, N P - C, C U N P, disclosed that she is on the Consultants Bureau for Coloplast. All other U rologic Nurs i n g E d i t o rial Board members reported no actual or potential conflict of interest in relation to this continuing nursing education art i c l e. 326 UROLOGIC NURSING / September-October 2009 / Volume 29 Number 5