Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2021 Apr 28.
Published in final edited form as: Int J Nurs Knowl. 2017 Jan 16;29(1):49–58. doi: 10.1111/2047-3095.12168

A Shovel-Ready Solution to Fill the Nursing Data Gap in the Interdisciplinary Clinical Picture

Gail M Keenan 1, Karen Dunn Lopez 2, Vanessa E C Sousa 3, Janet Stifter 4, Tamara G R Macieira 5, Andrew D Boyd 6, Yingwei Yao 7, T Heather Herdman 8, Sue Moorhead 9, Anna McDaniel 10, Diana J Wilkie 11
PMCID: PMC8080953  NIHMSID: NIHMS1649193  PMID: 28093877

Abstract

PURPOSE:

To critically evaluate 2014 American Academy of Nursing (AAN) call-to-action plan for generating interoperable nursing data.

DATA SOURCES:

Healthcare literature.

DATA SYNTHESIS:

AAN’s plan will not generate the nursing data needed to participate in big data science initiatives in the short term because Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine – Clinical Terms are not yet ripe for generating interoperable data. Well-tested viable alternatives exist.

CONCLUSIONS:

Authors present recommendations for revisions to AAN’s plan and an evidence-based alternative to generating interoperable nursing data in the near term. These revisions can ultimately lead to the proposed terminology goals of the AAN’s plan in the long term.

Keywords: Big data science, patient care planning, standardized nursing terminology

Purpose

Obtaining high-quality care data from the electronic health record (EHR) and applying big data analytics to it hold enormous potential for measuring and improving the contributions of each health discipline to patient outcomes. Until recently, understanding the impact of nursing care on patient outcomes within the context of the interdisciplinary team has been elusive due to the absence of feasible methods for capturing meaningful nursing care data. Omitting nursing data is a critical gap in clinical big data science. Care is not delivered in isolation by one profession. For example, a treatment prescribed by a physician may be more effective or increase chances of recovery when combined with a specific set of nursing interventions (Almasalha et al., 2013). In 2014, informatics leaders of the American Academy of Nursing (AAN) issued a call-to-action stating that the absence of high-quality nursing data in EHRs was a major barrier to big data science (Clancy et al., 2014). A central recommendation of the AAN leaders is the adoption of Systematized Nomenclature of Medicine – Clinical Terms (SNOMED-CT) and Logical Observation Identifiers Names and Codes (LOINC) (Figure 1), the proposed universal clinical terminologies for the United States. The AAN leaders, however, provide no directions for how to implement these terminologies that assures the production of sharable and comparable nursing data. Further, we found no evidence-based strategies in the literature showing how to produce sharable and comparable data amenable to big data science with SNOMED-CT and LOINC. In the absence of a common evidence-based strategy for implementing SNOMED-CT and LOINC into EHRs, organizations and vendors are naturally developing their own unique implementation strategies that limit the ability to share and compare data. The good news is there are shovel-ready evidence-based solutions available now to generate sharable and comparable nursing data that will simultaneously facilitate the growth of knowledge relevant to the effective use of SNOMED-CT and LOINC in the EHR. This article includes (1) a discussion of concerns about the 2014 AAN action plan, (2) a description of one evidence-based alternative that currently generates sharable and comparable nursing data, and (3) recommended revisions to the AAN plan that align with the exemplar alternative.

Figure 1.

Figure 1.

Open in a new tab

Overview of SNOMED-CT and LOINC Related to Nursing Terminologies According to the International Health Terminology Standards Development Organization (2016) (Matney, Dolin, Buhl, & Sheide, 2016; Regenstrief Institute, 2016; International Health Terminology Standards Development Organization, n.d.)

Background

The current emphasis on big data science in health care has led to an urgent need to ensure nursing is represented in all patient care data. The major reasons for the general absence of high-quality nursing data in the EHR are (1) the words, content, and timing of nursing documentation vary by institution; and (2) attempts to streamline this process have created disparate solutions and data that are not easily shared or compared (Keenan, Yakel, Dunn Lopez, Tschannen, & Ford, 2013). To remove these barriers, the AAN proposed the following five-point strategy:

(1) Advocate for the adoption of Systematized Nomenclature of Medicine – Clinical Terminology (SNOMED-CT) and Logical Observation Identifiers Names and Codes (LOINC) as national standards for clinical data, and link them with nursing terminologies through mappings; (2) Develop a strategy/campaign for educating front line nurses, students, and faculty on informatics competencies and the value of standardized nursing data; (3) Convene a consensus conference with leaders of the major nursing organizations and inter-professional stakeholders to educate them, hear their views, and ultimately speak in one voice; (4) Refresh and activate the American Nurses Association’s Nursing Information & Data Set Evaluation Center (NIDSEC) criteria to advance systems that represent and value nursing data; and (5) Continue bold participation in standards and EHR standards development to ensure a nursing voice. (Clancy et al., 2014)

These recommendations have several strengths including supporting the need for nursing terminology and database standards. In addition, there is a laudable call for greater participation by the profession in the development of standards and EHRs that will impact nursing. The strategy, however, has several weaknesses. First, there is virtually no evidence in the literature that demonstrates how the use of SNOMED-CT and LOINC in EHRs (with mappings of nursing terminologies to them) produces data that are sharable and comparable for use in research involving big data. Second, in light of this lack of evidence, it is unclear how educating and hearing the views of stakeholders would offset the missing evidence.

Although the federal government has proposed SNOMED-CT and LOINC as the US clinical terminology standards (The Office of the National Coordinator for Heath Information Technology [ONC], 2015), there is a concerning paucity of available knowledge about how these systems perform in real-world healthcare settings. Indeed, in a recent comprehensive systematic review on standardized nursing terminologies (Tastan et al., 2014), the authors found no studies that demonstrated valid use of SNOMED-CT and LOINC in EHRs that produced sharable and comparable nursing data. In a broader scan of the literature, no studies were found that demonstrated how use of SNOMED-CT and LOINC produced interoperable (sharable and comparable) healthcare data; instead, many authors indicated a need for such studies.

We, however, found two studies in the literature that indicated potential problems with SNOMED-CT and LOINC. Kim, Hardiker, and Coenen (2014) reported that these terminologies contained incomplete and inaccurate content and a low concordance level in the interterminology mappings. In the second study (Thede & Schwirian, 2015), the authors found nurses to be dissatisfied with the usefulness of SNOMED-CT and LOINC and rated them poorly on a range of clinical tasks. For example, when asked to rate nine terminologies on their usefulness in patient care, the nurses rated SNOMED-CT and LOINC highest on the task of retrieving information on the same term(s). On all other vital tasks (e.g., organizing patient care, planning care, and generating appropriate outcomes), the nurses rated the nursing-specific terminologies (e.g., NANDA-I [nursing diagnoses classification], NIC [Nursing Intervention Classification], and NOC [Nursing Outcomes Classification]) higher in usefulness than SNOMED-CT and LOINC.

Given that there is limited evidence currently available about the effectiveness of SNOMED-CT and LOINC, we propose the use of alternative evidence-based methods that already produce high-quality sharable and comparable nursing data. Such methods can also serve as a foundation for developing valid strategies for using SNOMED-CT and LOINC to represent nursing in the EHR. One such alternative, the Hands-on Automated Nursing Data System (HANDS), is an extensively validated method and tool that captures high-quality sharable and comparable nursing data in EHRs. Its successful implementation in multiple hospitals and secondary data analysis of the nursing data collected has revealed its viability and enormous potential for including nursing in big data science in health care. To be clear, HANDS is not proposed as a replacement for SNOMED-CT and LOINC, but rather as a method for collecting sharable and comparable nursing data in the near term while continuously evolving it toward federal standard terminology goals. The next section presents a case study of the HANDS’s proposed alternative.

Case Study: HANDS

Initiation and Research

HANDS, an evidence-based method for producing high-quality interoperable nursing data, has been under development for nearly two decades. On a policy level, it aligns with the nation’s interoperability roadmap for health information technology (ONC, 2015). Through early research efforts, the team members found that implementation of nursing terminologies, though necessary, was insufficient to produce interoperable nursing data (Keenan et al., 2002). They learned that generating interoperable nursing data also requires consistency in (1) the use of the comprehensive disciplinary knowledge available in nursing classifications, (2) the database architecture, and (3) the supportive features and format of data entry at the user interface (Keenan et al., 2002). As a result, the HANDS team’s subsequent research efforts have been aimed at validating a method for collecting meaningful interoperable nursing data that can be used to predict outcomes and guide changes to the plan of care (POC) that will improve patient outcomes.

In the initial phase, a multidisciplinary team at the University of Michigan designed, pilot tested, and refined a single-user version of HANDS (Keenan, Johnson, & Maas, 1998–2001; Keenan et al., 2002; Keenan & Yakel, 2005). HANDS, version 1, was designed to streamline and standardize the documentation of the POC in a way that was both meaningful and useful to nurses. In addition, the database was built to ensure that the pieces of information and relationships among them entered by the nurse were preserved and retrievable from the data storage unit. Based on the 12-month, real-world pilot test, the version 1 of HANDS was updated and translated into a multiuser Web-based system that was deployed and further validated in a multisite implementation test (12–24 months) in four unique Midwest hospitals in the United States with 787 nurses using it in their daily work (Keenan, 2004–2008). The results of this study confirmed that it is not only feasible to collect the same POC data in the same way (standardized) across settings, but that nurses found the system appealing and useful to create and monitor individualized POCs for their patients during their hospitalization. Most importantly, implementation of HANDS provided evidence of the reliability, validity, and interoperability of the large volume of data collected and capability to easily analyze and compare data across the participating units and settings (Keenan et al., 2012, 2013; Keenan, Yakel, Tschannen, & Mandeville, 2008). The data collected included 357,522 POCs for 34,927 patients during 43,403 admissions entered by 787 unique nurses.

In two subsequent National Institutes of Health (NIH) funded studies (Keenan et al., 2011–2015; Wilkie, 2009–2011) the research team examined the end-of-life episodes (n = 1,593) of POCs gathered in the Agency for Healthcare Research and Quality (AHRQ) study to describe and evaluate the impact of nursing care on patient outcomes. These findings in turn are being converted into clinical nursing care plan decision support prototypes that demonstrate and validate the continuous learning capabilities of HANDS (Almasalha et al., 2013; Febretti et al., 2013; Yao et al., 2013). The topological and temporal structure of the HANDS data also enabled identification of potential info-markers for palliative care, provided insight on state of the end-of-life care, and generated best practice hypotheses to be tested in future studies (Yao et al., 2015). Nurse staffing data from HANDS also allowed computation of staffing statistics on multiple levels of granularity and multiple modalities: hospital, unit, patient episode, day and shift, and a study of their effects on patient outcomes (Stifter et al., in press). Besides standard statistical inference, association rule data mining has been applied to identify practice patterns (Lodhi et al., 2014), clustering and matching have been utilized for non-parametric control of patient risk factors (Stifter et al., in press), nearest-neighbor methods, support vector machine, decision tree algorithm, and naive Bayes methods have been successfully used for building predictive models of patients outcomes (Lodhi et al., 2015). Research is also underway to translate the evidence generated by the analysis of HANDS data and return it to the point of care as clinical decision support for nurses (Dunn Lopez et al., in press; Febretti et al., 2013; Sousa et al., in press).

Workflow and Content

The data are captured during routine care and stored within a self-contained Structural Query Language database module, which is connectable to any EHR system. The user interface is the same regardless of where it is implemented, supporting the ability to easily track patients’ clinical diagnoses, outcomes, and interventions across hospital and healthcare settings. The diagnoses, outcomes, and interventions are coded with the NANDA International (Herdman & Kamitsuru, 2014) terminology for nursing diagnosis, NOC (Moorhead, Johnson, Maas, & Swanson, 2012) terminology for nursing outcome assessment, and NIC (Bulechek, Butcher, Dochterman, & Wagner, 2012) terminology for nursing interventions, known as the NNN set of terminologies. These three classifications are recognized by the American Nurses Association (ANA,2012, June 4) and have a strong evidence-base supporting effective use (Anderson, Keenan, & Jones, 2009; Tastan et al., 2014). In sum, HANDS includes (1) a standardized set of data elements, (2) the use of ANA-recognized standardized nursing terminologies (classification knowledge and code for each term), (3) a common, user-friendly interface, (4) timed points of data entry that coincide with workflow, (5) relational database architecture that is compliant with the NIDSEC standards (ANA, 1997), (6) a standardized training program; and (7) the ability to personalize care and track progress toward outcomes at the individual patient level (Keenan et al., 2002).

Over the last two decades, HANDS has been developed, tested, and refined, and nursing care plan clinical decision support prototypes produced to help return evidence to nurses during routine workflow. Figure 2 includes examples of raw data and computable elements. The database architecture and content covers multiple nursing data standards such as Nursing Minimum Dataset (Werley, 1988; Werley & Lang, 1988), the Nursing Management Minimum Dataset (Huber, Schumacher, & Delaney, 1997), and the NIDSEC database standards (ANA, 1997) (see Figure 3 for example of database architecture). The main attributes (variables) collected are linked within the database to the patient’s care plan for each shift during a patient’s stay on a unit and include patient demographics, nurse demographics, nursing diagnoses, patient outcomes, medical interventions, and nurse ratings of patient progress at each handoff (Figure 2). The HANDS Ecosystem (Figure 3) depicts the phases of the research process that were used to conceptualize longitudinal development, testing, and validation of HANDS. As Figure 4 outlines, the HANDS macrolevel relational diagram includes tables for the nursing process variables (diagnosis, outcomes, and interventions) and their interrelationships. Figure 5 is a simple timeline of the key HANDS research studies and includes a brief description of each study, the findings, and the funding source. Finally, Figure 6 lists other relevant details about the features, functions, and impact of HANDS, including use of big data science approaches to understanding care at the end of life (Lodhi et al., 2014). Taken together, there is substantial evidence to support the implementation of a solution like HANDS to generate sharable and comparable nursing data for use in big data science research. Further, because the terms available in HANDS are linked to their corresponding SNOMED-CT codes, it is also possible to derive knowledge relevant to SNOMED-CT and LOINC. The following section provides recommendations for revising the initial AAN plan.

Figure 2.

Figure 2.

Open in a new tab

HANDS Model Data Elements. Main Raw Data Elements Captured With HANDS and Examples of Elements Computable From the Raw Elements. Copyright © 2014 HANDS Team, Reprinted With Permission.

Figure 3.

Figure 3.

Open in a new tab

The Hands-on Automated Nursing Data System (HANDS) Ecosystem. Production (left side); Environment Represents the People, Structures, Content, and Processes Involved in the Real-World Evaluation of HANDS. Development (Right Side) and Piloting Environment Includes the People, Structures, Content, Processes, Connection to the Production Environment, and Interrelationships Among Them That Build, Maintain, and Expand HANDS. Copyright © 2011 HANDS Team, Reprinted With Permission

Figure 4.

Figure 4.

Open in a new tab

Abridged HANDS Entity Relationship Diagram (ERD) Depicting the Tables and Relationships Among Them for Nursing Diagnoses, Nursing Outcomes, and Nursing Interventions Represented in This System With the NANDA-I, NOC, and NIC Terminologies. Unabridged ERD Comprises 89 Tables and 747 Variables. NANDA-I, North American Nursing Diagnosis-International; NIC, Nursing Interventions Classification; NOC, Nursing Outcomes Classification; POC, Plan-of-care. Copyright © 2014 HANDS Team, Reprinted With Permission

Figure 5.

Figure 5.

Open in a new tab

Timeline of HANDS Research and Development Studies. Copyright © 2014 HANDS Team, Reprinted With Permission

Figure 6.

Figure 6.

Open in a new tab

Features and Impact of HANDS. Copyright © 2014 HANDS Team, Reprinted With Permission

Recommended Revisions to the AAN’s Action Plan

In response to Clancy et al.’s (2014) call for action, alternative methods should be considered that can enable the generation of high-quality, interoperable nursing data. Although impressive efforts are underway to operationalize the call to action (Westra et al., 2015), advocacy for the use of evidence-based solutions to collect nursing data in the short term was not considered in the AAN Action Plan. This omission will significantly delay nursing’s participation in big data healthcare science when a practical and valid alternative is available now. The following section includes suggested revisions to the action plan that would encourage the adoption and use of evidence-based informatics methods to facilitate big data science in nursing.

Revised Action 1:

Advocate for the use of methods that are evidence based and have been shown to standardize and streamline nurse documentation and generate comparable and sharable nursing data.

AAN and other nursing leaders should put priority emphasis on supporting nursing information systems that meet stringent criteria for ensuring fully interoperable nursing data rather than on systems that lack such evidence. In the absence of supporting evidence, even for a declared national standard, costly and catastrophic results can occur. Thus, caution is recommended and the AAN and nursing leaders are encouraged to support methods that have demonstrated the capacity to achieve the desired data outcomes such as HANDS. It does not appear, from a recent systematic review of the standardized nursing terminology literature (Tastan et al., 2014), that there are other rigorously tested methods for collecting interoperable data. This gap, however, should not deter leaders from advocating for the use of evidence-based methods, especially since at least one evidence-based method is available.

Other nursing terminology systems are being utilized in clinical practice without published evidence that validates their method of collection. For example, we are aware through oral communication that the Perioperative Nursing Data Set (M. Golas, oral communication, March 2015) and Clinical Care Classification (V. Saba, oral communication, January 2014) are being used to collect standardized nursing data in practice, but there is no corroborating evidence in the literature. With regard to the Omaha System, there are a number of published studies in which this classification was used to code the data collected. Researchers, however, noted multiple problems associated with the extraction of the data, which compromised the depth and breadth of analysis achieved. These problems resulted from the variation in the way the Omaha System was integrated into different vendor systems (Westra, Oancea, Savik, & Marek, 2010). Further, Tastan et al. (2014) found no studies that demonstrated the validity and reliability of the processes used to implement the Omaha System into the vendor products. This simply means that full interoperability was not achieved or demonstrated with the data collected.

Finally, mapping of new terms from the established nursing terminologies to SNOMED-CT and LOINC should continue, since this is an important step in building valid methods of collecting sharable and comparable nursing data with SNOMED-CT and LOINC (Bakken et al., 2001; Campbell, 1994). Direct use of SNOMED-CT and LOINC to populate nursing data fields in the EHR, however, should be strongly discouraged until there is convincing evidence that demonstrates such use achieves the desired outcomes.

Revised Action 2:

Use a method such as the HANDS system to educate nurses, students, and faculty on informatics competencies and the value of standardized nursing data.

Real-world experience with effective nursing documentation systems can help nurses, students, and faculty recognize the value of standardized nursing data. For example, HANDS, which includes standardized online training modules, could be used in national campaigns to increase awareness of the importance of standardized nursing data and evidence-based nursing knowledge in routine clinical decision-making. Existing paper and electronic nursing care plan documentation are often completed to meet medical record accreditation or risk requirements and as a result, are seen as a burden to nurses (Ehrenberg & Ehnfors, 2001; Hardey, Payne, & Coleman, 2000; Karkkainen & Eriksson, 2004; Karkkainen, Bondas, & Eriksson, 2005; Keenan et al., 2008; Stokke & Kalfoss, 1999; Urquhart, Currell, Grant, & Hardiker, 2009). When a system, however, is instead designed to improve nursing practice while also generating interoperable data, like HANDS, it will automatically produce valid and reliable nursing data amenable to big data science analytical methods (Keenan et al., 2012).

Revised Action 3:

Convene a conference of organizational and research leaders and interprofessional stakeholders to help develop and implement a plan for generating big data in nursing using an evidence-based clinical solution.

Without the support of nurse researchers, practice leaders, and interprofessional stakeholders to help motivate the adoption of solutions that have the capacity to generate nursing big data, the goal of nursing big data will remain elusive. Thus, it is important for professional leaders from key nursing associations such as the ANA and the AAN to collaborate with professional and interprofessional stakeholders (e.g., EHR vendors) including researchers who have already developed and applied big data science methods to the collection and analysis of nursing data.

Revised Actions 4 and 5:

Designate an official body to set priorities and desired outcomes of EHR standards applicable to nursing and support the participation of qualified nurses in the applicable standards groups.

Although the intent of actions 4 and 5 of Clancy et al.’s (2014) call-to-action is positive, more specificity is needed to direct ongoing EHR standards work applicable to nursing practice and the generation of interoperable nursing data. In addition, nurses who lack the appropriate expertise to represent nursing practice should not participate in standards development. Thus, an EHR standards committee should be created that can identify nursing content experts and help advance evidence-based EHR nursing standards. Although the NIDSEC standards need revision, changes should occur after a designated group of qualified experts have identified the priorities for EHR standards. Once these priorities are set, experts should be assigned to advocate for nursing in the applicable standards groups.

Conclusions

This article included a discussion of and suggested revisions to the action plan developed by the American Academy of Nursing (Clancy et al., 2014) that is focused on enabling nursing to participate in big data science soon. An exemplar of a well-tested evidence-based method (HANDS) for producing sharable and comparable data immediately was presented as an interim alternative to utilizing SNOMED-CT and LOINC directly to represent nursing in EHRs. The alternative was proposed as a viable short-term option in the absence of evidence demonstrating how SNOMED-CT and LOINC can be used to produce sharable and comparable clinical data. In sum, it is not necessary for nursing to wait for SNOMED CT and LOINC to be sufficiently ripened before participating in big data science initiatives when viable alternatives exist. It is therefore recommended that stakeholders take a closer look at what is possible today and weigh the down side of waiting for SNOMED CT and LOINC to be fully ripened. Since “big data science” has the enormous potential for discovering evidence and returning it to the point of care, it was argued that it is in nursing’s best interest to employ methods that will allow participation now.

Implications for Nursing Practice

Major stakeholders such as chief nursing officers, health information technology administrators, and EHR vendors are the nursing leaders faced with decisions about methods of collecting high-quality nursing data. A thoughtful review of the facts and recommendations presented in this article is certain to help these leaders carefully deliberate and decide on the option that is most likely to yield the desired outcomes relative to nursing data.

Acknowledgments.

The authors thank Veronica Angulo for clerical assistance and Eliana Steele for her thoughtful feedback. We wish to thank Debra McDonald whose advice and edits on this manuscript helped to dramatically simplify our ideas for readers who have limited knowledge of standardized nursing terminologies and informatics.

Conflict of Interest: The HANDS software that is discussed in this article is owned and distributed by HealthTeam IQ, LLC. Dr. Gail Keenan is the President and CEO of this company and has a current conflict of interest statement of explanation and management plan in place with the University of Florida. National funding for content noted in this article includes NIH NINR R01NR03437, P30NR01068-S1, and AHRQ R01HS015054.

Contributor Information

Gail M. Keenan, College of Nursing, University of Florida, Gainesville, Florida.

Karen Dunn Lopez, College of Nursing, University of Illinois at Chicago, Chicago, Illinois.

Vanessa E. C. Sousa, College of Nursing, University of Illinois at Chicago, Chicago, Illinois.

Janet Stifter, American Organization of Nurse Executives, American Hospital Association, Chicago, Illinois.

Tamara G. R. Macieira, College of Nursing, University of Florida, Gainesville, Gainesville, Florida.

Andrew D. Boyd, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, Illinois.

Yingwei Yao, College of Nursing, University of Florida, Gainesville, Gainesville, Florida.

T. Heather Herdman, NANDA International and University of Wisconsin-Green Bay, Green Bay, Wisconsin.

Sue Moorhead, Nursing Classification Center, College of Nursing, University of Iowa, Iowa City, Iowa.

Anna McDaniel, College of Nursing, University of Florida, Gainesville, Gainesville, Florida.

Diana J. Wilkie, College of Nursing, University of Florida, Gainesville, Gainesville, Florida.

References

  1. Almasalha F, Xu D, Keenan GM, Khokhar A, Yao Y, Chen YC, … Wilkie DJ (2013). Data mining nursing care plans of end-of-life patients: A study to improve healthcare decision making. International Journal of Nursing Knowledge, 24(1), 15–24. doi: 10.1111/j.2047-3095.2012.01217.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Anderson CA, Keenan G, & Jones J (2009). Using bibliometrics to support your selection of a nursing terminology set. Computers, Informatics, Nursing, 27(2), 82–92. doi: 10.1097/NCN.0b013e3181972a24 [DOI] [PubMed] [Google Scholar]
  3. American Nurses Association. (2012, June 4). Professional standards: ANA recognized terminologies that support nursing practice. Retrieved from http://nursingworld.org/MainMenuCategories/ThePracticeofProfessionalNursing/NursingStandards/Recognized-Nursing-Practice-Terminologies.pdf [Google Scholar]
  4. Bakken S, Warren J, Lundberg C, Casey A, Correia C, Konicek D, & Zingo C (2001). An evaluation of the utility of the CEN categorical structure for nursing diagnoses as a terminology model for integrating nursing diagnosis concepts into SNOMED. Studies in Health Technology and Informatics, 84(Pt 1), 151–155. [PubMed] [Google Scholar]
  5. Bulechek G, Butcher H, Dochterman J, & Wagner C (Eds.). (2012). Nursing interventions classification (NIC) (6th ed.). St. Louis, MO: Elsevier. [Google Scholar]
  6. Campbell KE (1994). Terms used by nurses to describe patient problems: Can SNOMED III represent nursing concepts in the patient record? Journal of the American Medical Informatics Association, 1(1), 61–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Clancy TR, Bowles KH, Gelinas L, Androwich I, Delaney C, Matney S, … Westra B (2014). A call to action: Engage in big data science. Nursing Outlook, 62(1), 64–65. doi: 10.1016/j.outlook.2013.12.006 [DOI] [Google Scholar]
  8. Dunn Lopez K, Wilkie DJ, Yao Y, Sousa V, Febretti A, Stifter J, … Keenan G (2015). Nurses’ numeracy and graphical literacy: Informing studies of clinical decision. Journal of Nursing Care Quality, (31), 1+. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Ehrenberg A, & Ehnfors M (2001). The accuracy of patient records in Swedish nursing homes: Congruence of record content and nurses’ and patients’ descriptions. Scandinavian Journal of Caring Sciences, 15(4), 303–310. [DOI] [PubMed] [Google Scholar]
  10. Febretti A, Lopez K, Stifter J, Johnson A, Keenan G, & Wilkie DJ (2013). A component-based evaluation protocol for clinical decision support interfaces. In Marcus A (Ed.), Design, user experience, and usability: Design philosophy, methods, and tools (pp. 232–241). Berlin: Springer Berlin Heidelberg. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hardey M, Payne S, & Coleman P (2000). ‘Scraps’: Hidden nursing information and its influence on the delivery of care. Journal of Advanced Nursing, 32(1), 208–214. [DOI] [PubMed] [Google Scholar]
  12. Herdman TH, & Kamitsuru S (Eds.). (2014). NANDA international nursing diagnoses: Definitions & classification, 2015–2017. Oxford: Wiley Blackwell. [Google Scholar]
  13. Huber D, Schumacher L, & Delaney C (1997). Nursing Management Minimum Data Set (NMMDS). Journal of Nursing Administration, 27(4), 42–48. [DOI] [PubMed] [Google Scholar]
  14. International Health Terminology Standards Development Organization. (n.d.). What is SNOMED CT? Retrieved from www.ihtsdo.org/snomed-ct/what-is-snomed-ct
  15. Karkkainen O, Bondas T, & Eriksson K (2005). Documentation of individualized patient care: A qualitative metasynthesis. Nursing Ethics, 12(2), 123–132. [DOI] [PubMed] [Google Scholar]
  16. Karkkainen O, & Eriksson K (2004). Structuring the documentation of nursing care on the basis of a theoretical process model. Scandinavian Journal of Caring Sciences, 18(2), 229–236. doi: 10.1111/j.1471-6712.2004.00274.x [DOI] [PubMed] [Google Scholar]
  17. Keenan GM (2004–2008). Health information technology support for safe nursing care: Agency for health research and quality. U.S. Department of Health and Human Services, Agency for Healthcare Research and Quality (AHRQ) R01 HS015054. [Google Scholar]
  18. Keenan GM, Johnson M, & Maas M (1998–2001). Evaluation of nursing-sensitive outcomes measures, University of Iowa, National Institutes of Health/National Institute for Nursing Research. [Google Scholar]
  19. Keenan GM, Stocker JR, Geo-Thomas AT, Soparkar NR, Barkauskas VH, & Lee JL (2002). The HANDS project: Studying and refining the automated collection of a cross-setting clinical data set. Computers, Informatics, Nursing, 20(3), 89–100. doi: 10.1097/00024665-200205000-00008 [DOI] [PubMed] [Google Scholar]
  20. Keenan GM, Wilkie DJ, Yao YW, Khokhar A, Ansari R, Johnson A, & Dunn Lopez K (2011–2015). Describing, contrasting, and visualizing end-of-life care in the 21st century, National Institutes of Health/National Institute of Nursing Research. [Google Scholar]
  21. Keenan G, & Yakel E (2005). Promoting safe nursing care by bringing visibility to the disciplinary aspects of interdisciplinary care. American Medical Informatics Association Annual Symposium Proceedings, 2005, 385–389. Washington, DC. Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1560582/. [PMC free article] [PubMed] [Google Scholar]
  22. Keenan G, Yakel E, Dunn Lopez K, Tschannen D, & Ford YB (2013). Challenges to nurses’ efforts of retrieving, documenting, and communicating patient care information. Journal of American Medical Informatics Association, 20(2), 245–251. doi: 10.1136/amiajnl-2012-000894 [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Keenan GM, Yakel E, Tschannen D, & Mandeville M (2008). Documentation and the nurse care planning process. In Hughes RG (Ed.), Patient safety and quality: An evidence-based handbook for nurses. (AHRQ Publication No. 08-0043), Rockville, MD: Agency for Healthcare Research and Quality, Retrieved from http://www.ahrq.gov/qual/nurseshdbk/docs/KeenanG_DNCPP.pdf. [PubMed] [Google Scholar]
  24. Keenan GM, Yakel E, Yao Y, Xu D, Szalacha L, Tschannen D, … Wilkie DJ (2012). Maintaining a consistent big picture: Meaningful use of a Web-based POC EHR system. International Journal of Nursing Knowledge, 23(3), 119–133. doi: 10.1111/j.2047-3095.2012.01215.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Kim T, Hardiker N, & Coenen A (2014). Inter-terminology mapping of nursing problems. Journal of Biomedical Informatics, 49, 213–220. doi: 10.1016/j.jbi.2014.03.001 [DOI] [PubMed] [Google Scholar]
  26. Lodhi MK, Cheema U, Stifter J, Wilkie DJ, Keenan GM, Ansari R, & Khokhar A (2014). Hospitalized end of life patients’ death anxiety captured from a structured electronic record: Difference by patient and nurse characteristics. Research in Gerontological Nursing, 7(5), 224–234. doi: 10.3928/19404921-20140818-01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lodhi MK, Stifter J, Yao Y, Ansari R, Keenan GM, Wilkie DJ, & Khokhar AA (2015). Predictive Modeling for End-of-Life Pain Outcome Using Electronic Health Records. In Advances in Data Mining: Applications and Theoretical Aspects (pp. 56–68). 15th Industrial Conference, ICDM 2015, Hamburg, Germany. Proceedings (July 11th–24th) http://www.springer.com/978-3-319-20909-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Matney SA, Dolin G, Buhl L, & Sheide A (2016). Communicating nursing care using the health level seven consolidated clinical document architecture release 2 care plan. CIN: Computers, Informatics, Nursing, 34(3), 128–136. doi: 10.1097/CIN.0000000000000214 [DOI] [PubMed] [Google Scholar]
  29. Moorhead S, Johnson M, Maas M, & Swanson E (Eds.). (2012). Nursing outcomes classification (NOC) (5th ed.). St. Louis, MO: Elsevier. [Google Scholar]
  30. Nursing Information & Data Set Evaluation Center & American Nurses Association. (1997). NIDSEC standards and scoring guidelines. Washington, DC: American Nurses Pub. [Google Scholar]
  31. The Office of the National Coordinator for Heath Information Technology (ONC). (2015). Connecting health and care for the nation: A shared nationwide interoperability roadmap draft version 1.0. Retrieved from http://www.healthit.gov/sites/default/files/nationwide-interoperability-roadmap-draft-version-1.0.pdf.
  32. Regenstrief Institute. (n.d.). About LOINC. Retrieved from https://loinc.org/background
  33. Sousa V, Dunn Lopez K, Febretti A, Stifter, Yao Y, Johnson A, Wilkie D, Keenan GM (2015). Use of simulation to study nurses’ acceptance and non-acceptance of clinical decision support suggestions. Computers, Informatics, Nursing, (33), 465–472. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Stifter J, Yao Y, Lodhi MK, Dunn Lopez K, Khokhar A, Wilkie DJ, Keenan GM (2015). Nurse continuity and hospital–acquired pressure ulcers: A comparative analysis using an electronic health record “big data” set. Nursing Research, 64(5), 361–371. DOI: 10.1097/NNR.0000000000000112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Stokke TA, & Kalfoss MH (1999). Structure and content in Norwegian nursing care documentation. Scandinavian Journal of Caring Sciences, 13(1), 18–25. [PubMed] [Google Scholar]
  36. Tastan S, Linch GCF, Keenan GM, Stifter J, McKinney D, Fahey L, … Wilkie DJ (2014). Evidence for the existing ANA recognized standardized nursing terminologies: A systematic review. International Journal of the Nursing Studies, 51(8), 1160–1170. doi: 10.1016/j.ijnurstu.2013.12.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Thede LQ, & Schwirian PM (2015). Informatics: The standardized nursing terminologies: A national survey of nurses’ experience and attitudes-SURVEY II: Participants’ perception of the helpfulness of standardized nursing terminologies in clinical care. Online Journal of Issues in Nursing, 20(1), 17. doi: 10.3912/OJIN.Vol20No01InfoCol01 [DOI] [PubMed] [Google Scholar]
  38. Urquhart C, Currell R, Grant MJ, & Hardiker NR (2009). Nursing record systems: Effects on nursing practice and healthcare outcomes. Cochrane Database Systematic Review, (1), CD002099. doi: 10.1002/14651858.CD002099.pub2 [DOI] [PubMed] [Google Scholar]
  39. Werley HH (1988). Introduction to the nursing minimum dataset. In Werley HH & Lang N (Eds.), Identification of the nursing minimum dataset (pp. 1–15). New York: Springer. [Google Scholar]
  40. Werley HH, & Lang N (1988). The consensually derived nursing minimum data set: Elements and definitions. In Werley HH, & Lang N (Eds.), Identification of the nursing minimum dataset (pp. 402–413). New York: Springer. [Google Scholar]
  41. Westra BL, Latimer GE, Matney SA, Park JI, Sensmeier J, Simpson RL, & Delaney CW (2015). A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. Journal of American Medical Informatics Association, 22(3), 600–607. doi: 10.1093/jamia/ocu011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Westra BL, Oancea C, Savik K, & Marek KD (2010). The feasibility of integrating the Omaha system data across home care agencies and vendors. Computers, Informatics, Nursing, 28(3), 162–171. doi: 10.1097/NCN.0b013e3181d7812c [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Wilkie DJ, Keenan GM, Ansari R, Khokhar A, Yao Y, & Johnson A (2009–2011). Center for End-of-Life Transition Research (CEoLTR), supplement, National Institutes of Health, National Institute of Nursing Research. [Google Scholar]
  44. Yao Y, Keenan G, Al-Masalha F, Lopez KD, Khokar A, Johnson A, … Wilkie DJ (2013). Current state of pain care for hospitalized patients at end of life. American Journal of Hospice and Palliative Care, 30(2), 128–136. doi: 10.1177/1049909112444458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yao Y, Stifter J, Ezenwa MO, Lodhi M, Khokhar A, Ansari R, Keenan GM & Wilkie DJ (2015). Infomarkers for transition to goals consistent with palliative care in dying patients. Palliative & supportive care, 25, 1–8. PMID: 25711431 DOI: S1478951515000103. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES