Skip Navigation LinksHome > June 2011 - Volume 29 - Issue 6 > Strengths and Limitations of the Electronic Health Record fo...
CIN: Computers, Informatics, Nursing:
doi: 10.1097/NCN.0b013e3181fc4139
Feature Article

Strengths and Limitations of the Electronic Health Record for Documenting Clinical Events


Free Access
Article Outline
Collapse Box

Author Information

Author Affiliations: College of Nursing, University of Colorado, Denver (Dr Carrington); College of Nursing, University of Arizona, Tucson (Dr Effken).

This research was supported by the Department of Veteran's Affairs.

Portions of the data presented in this article were reported at the Nursing Informatics Work Group Tutorial at the American Medical Informatics Association Meeting, November 2008.

Corresponding author: Jane M. Carrington, PhD, RN, College of Nursing, University of Colorado, Denver, Mail Stop C288-19, 13120 E 19th Ave, Room 4227, PO Box 6511, Aurora, CO 80045 (

Collapse Box


The purpose of this research was to compare nurses' perceptions of the strengths and limitations of the electronic health record with and without nursing languages for documenting and retrieving patient information regarding a clinical event. The effectiveness of the electronic health record to facilitate nurse-to-nurse communication is not well understood. Furthermore, little is known how nurse-to-nurse communication influences patient safety and failure-to-rescue events. This qualitative study used a descriptive design in which open-ended, semistructured interviews were conducted with 37 registered nurses. Qualitative content analysis produced 260 thematic units from which five categories emerged: usability, legibility, communication, workarounds, and collaboration. Nurses perceived aspects of usability as strengths (retrievability) and limitations (lack of efficiency and barriers) of the electronic health record. Furthermore, within the category communication, lack of relevance of the documentation was also viewed as a limitation by the nurses. Nurses suggested that they be involved in electronic health record decisions and that hospitals try to reduce the identified barriers to electronic health record use.

The IOM has challenged the healthcare system to implement electronic health records (EHRs) to facilitate accurate and timely documentation, including evaluating the effectiveness of care, describing patients' responses to therapy, communicating patient status, and meeting legal documentation requirements.1-5 Nurses are one of the most frequent EHR user groups because they must enter information at least once a shift on each of their assigned patients. When a patient's condition changes (a clinical event), nurses must document information pertinent to the event. The purpose of the research was to explore nurses' perceptions of the strengths and limitations of the EHR, with and without nursing languages for documenting patient information related to a clinical event.

Back to Top | Article Outline


Several studies have evaluated the usefulness of the EHR. Electronic health records have been associated with reduced nurse documentation time,6-8 improved legibility, more frequent documentation, and fewer documentation errors than paper-based systems.9,10 On the other hand, a variety of problems have been identified that limit the usefulness of the EHR, specifically, the mismatch between a linear EHR system and a nonlinear work environment,11 discrepancies between care provided and care documented,12 limited useful information that can be retrieved,12 and barriers to effective interdisciplinary communication and collaboration induced by discipline-specific areas for documentation (eg, case management, nutrition, and physical therapy).8 In these studies, respondents (including nurses) were asked their overall impression of the usefulness of the EHR; however, the question was not asked in relation to any specific event.

Nurse-to-nurse communication has been defined for this study as the process in which patient information is transferred from one nurse to the next nurse related to a clinical event to continue care using the EHR. How nurse-to-nurse communication influences patient safety or "failure to rescue" or patient deaths associated with treatment13 has not been fully explored. Precursors to failure-to-rescue events have included high patient-to-nurse staffing ratios, chaotic work environment, and ineffective nurse-physician collaboration.14-17 The relationships between EHRs and nurse-to-nurse communication and therefore to patient safety and failure to rescue are not well understood.

Information Theory as described by Shannon18 consists of a communication system (sender, device, and receiver) and four key elements: entropy, probability, redundancy, and noise. In Information Theory, entropy is a measure of uncertainty as to content of the message. With higher entropy (ie, uncertainty), the content of the message is less predictable.19-22 Information reduces entropy by limiting the possible content of messages. Probability is a measure of predictability that allows the receiver to act on the information received without presuming more than what is transmitted by the sender or failing to use all that is transmitted.20

Redundant (ie, repetitious) messages are frequent in all types of communication and may prevent errors in understanding through duplication.20,21 Within the documentation system, redundancy is inherent (ie, the nurse documents a patient assessment, adds a nursing progress note, and summarizes these during the verbal change of shift report). The nurse reading or hearing the information (the receiving nurse) has multiple opportunities for the patient's status to be clarified or reinforced. Redundancy, then, is associated with increased information (decreased entropy).

Unfortunately, information is not always transmitted accurately. Occasionally, the message becomes distorted, making it difficult to read and interpret. This distortion may be due to noise, which results when the message either does not reach the receiver (a lost message) or reaches the receiver but is unclear because of distortion.21 Noise, then, results in decreased information (increased entropy).

For this research, Information Theory tenets were operationalized in the following manner: the sender was the documenting nurse, EHR the communication device, and the receiving nurse was the recipient of the message. The strengths of the EHR that nurses identified were treated as redundancy and the limitations as noise.

Nurses were asked to identify the strengths and limitations of the EHR for documenting a recent clinical event, in an attempt to focus their evaluative comments more specifically on EHR functionality. A clinical event was defined as an unexpected event or change in patient condition that did not result in a patient transfer and was not associated with a nursing protocol.23

Back to Top | Article Outline


Setting and Sample

Interviews took place in two urban Arizona hospitals (sites A and B) from December 2007 to January 2008. Both sites used an EHR system. Permission for the study was obtained from the University of Arizona's institutional review board, as well as from the research sites. Participants were RNs who were employed full time on their respective units (medical, surgical, and telemetry), had experience using the electronic nursing documentation system for at least 3 months, understood and spoke English, and had either documented care for a patient who experienced a clinical event or had assumed care for a patient who had experienced a clinical event prior to their shift within the past 24 hours.

Back to Top | Article Outline
Design and Procedures

The researcher regularly visited the nursing units and asked the charge nurse or other staff nurses if a clinical event had occurred. Once a clinical event occurred, the researcher recruited the documenting nurse and/or receiving nurse. The documenting nurse (nurse caring for the patient at the time of the clinical event) and receiving nurse (nurse who resumed care of the patient) were recruited. A convenience sample of nurses was recruited (ie, the documenting and receiving nurse for the same clinical event were not required). Within 24 hours of the clinical event, the participants selected a secluded room within the hospital and a time before or after their shifts to be interviewed. Interviews lasted between 20 and 30 minutes and were digitally recorded, transcribed, and reviewed for accuracy. Interview questions were semistructured (Table 1) and constructed based on the nurse's role as a documenting or receiving nurse.

Table 1
Table 1
Image Tools
Back to Top | Article Outline
Data Analysis

For this study, content analysis and descriptive statistics were used. Thematic units were inductively organized into categories and subcategories using qualitative content analysis, and frequencies were tabulated.24-26 A panel of three judges was used to test the reliability of the coding scheme and category definitions. Each judge was sent a list of 17 thematic units (large-enough sample of thematic units determined by the researcher) with a list of categories and definitions, along with directions on how to indicate agreement.23 Agreement was calculated as a percentage for each individual judge and then calculated again for all three judges as an aggregate.23 This process was repeated with modifications in categories and definitions until acceptable agreement was reached at 0.72.27

Back to Top | Article Outline


Nurse Characteristics

Thirty-seven nurses met the inclusion criteria (19 at site A and 18 at site B). Documenting and receiving nurses were evenly distributed across sites: site A = 10 documenting and nine receiving nurses; site B = 10 documenting and eight receiving nurses. The sample included two men and 17 women at site A, and six men and 12 women at site B. Nursing experience varied by site. Nurses at site B had more nursing experience, 1 to 30 years (mean, 12.1 years), than nurses at site A, 1 to 27 years (mean, 7.7 years). Nurses at site B also had more experience working on their nursing units than those at site A, 1 to 10 years (mean, 2.9 years) and 1 to 3 years (mean, 1.9 years), respectively. Furthermore, site B also exceeded site A in experience using the EHR, 1 to 16 years (mean, 5.3 years) and 1 to 6 years (mean, 3.9 years), respectively. Nurses at sites A and B had comparable years of experience working in a hospital, 0.4 to 30 years (mean, 7.9 years) and 1 to 30 years (mean, 10.3 years), respectively.

Back to Top | Article Outline
Clinical Events

Twenty-one clinical events, of which 14 were unique, were reported within a 10-day period at each site, 10 at site A and 11 at site B. The clinical events are reported here using the nurses' own language. The most frequent event was change in mental status (two events at each site). A drop in hemoglobin and hematocrit occurred twice at site B, and hypoxic events occurred twice at site A. Patient falls and not being medicated for pain occurred twice, once at each site. Nine clinical events occurred, one at either site. Four additional single occurring clinical events were reported at site A: unstable congestive heart failure, small bowel obstruction, constipation, and distended abdomen. Intravenous line too small for blood administration, patient was emotional, patient has fever, blood pressure was low, and seizure activity each occurred once at site B.

Back to Top | Article Outline

Five categories (usability, legibility, communication, workaround, and collaboration) emerged from the 260 thematic units identified.23 The categories were defined based on common informatics terminology and their definitions. Usability was defined as "suitable or congruent, easy, or comfortable for the needs and purpose of the user." The category, legibility, contained thematic units that were consistent with "capability of being read." Communication was defined as the process of information exchange being correct, organized, and systematic. Workarounds described strategies to bypass or avoid an undesirable feature in a system. Collaboration described participation in decision making. Several categories (eg, usability, communication, and workarounds) were large categories and were further organized into subcategories. Legibility and collaboration categories contained fewer thematic units and could not be further organized into subcategories.

Using an empirical content analysis approach, frequencies were calculated by adding the number of themes (t) cited per nurse and the number of nurses who cited each theme (n) for each category and subcategory (Table 2). These frequency calculations, however, limited insight into the strength of the thematic units to answer the research question. For example, seven nurses cited 13 thematic units in the difficult-to-use subcategory. From this example, redundancy was evident, whereby themes were repeated by one or many participants past the point of saturation.28

Table 2
Table 2
Image Tools

There was no clear way to determine whether the number of thematic units (t) or the number of nurses reporting the theme (n) was more important in determining the strength of the thematic units. Therefore, a "degree of redundancy" (DOR) calculation was performed (adapted from Miles and Huberman29,30) to better understand the importance of the themes to the nurses in this study. The DOR proportions the number of thematic units and nurses citing the thematic unit and the sample size. The DOR is calculated using a two-step process. First, the ratio of the number of thematic units (t) cited to the number of nurses citing (n) the thematic unit (t / n) is determined. Second, a proportion of the number of nurses citing (n) and the sample size (N) is then calculated (n / N). These two values are then multiplied to equal the DOR (t / n × n / N = DOR). The ratio was calculated to resolve the issue of redundancy or repeated thematic units in the data and provides numeracy or insight into the potential meaning to the perceptions of the nurses. Degree-of-redundancy scores ranged from 0.08 to 1.84 (Table 2), with the lower scores indicating a weak thematic representation and higher scores indicating a strong thematic representation.

Usability included six subcategories, with DOR scores ranging from 0.35 to 1.84 (Table 2). Communication and workaround each had three subcategories, with DOR scores ranging from 0.29 to 0.57 for communication and 0.11 to 0.19 for workaround. Using a DOR score of 0.50 or greater as a cutoff to represent strength of categories and subcategories, legibility, workaround, and collaboration were the weakest categories (DOR score, <0.50). The strongest themes related to usability, with the subcategories barriers (DOR score, 1.84), retrievability (DOR score, 0.99), and lack of efficiency (DOR score, 0.89) rated highest. The next strongest theme was related to communication, because of the subcategory lack of relevance (DOR score, 0.57).

Back to Top | Article Outline
Site A

Table 3 lists the categories, subcategories, and DOR scores for site A. The DOR for categories and subcategories from site A ranged from 0.10 to 2.26. The usability category consisted of six subcategories, with DOR scores ranging from 0.26 to 2.26. Communication consisted of three subcategories, with DOR scores ranging from 0.42 to 0.73 with the strongest subcategory lack of relevance; workaround had three subcategories, with DOR scores ranging from 0.10 to 0.21. Collaboration had a DOR score of 0.16. The weakest categories at site A were legibility, workaround, and collaboration. The strongest category was usability with subcategories barriers, lack of efficiency, and difficult to use (DOR scores, 2.26, 0.84, and 0.53, respectively). The next strongest category was communication because of the DOR score of 0.73 for the subcategory lack of relevance.

Table 3
Table 3
Image Tools
Back to Top | Article Outline
Site B

Categories, subcategories, and DOR scores for site B, which used the same EHR, are listed in Table 4. The range of DOR for categories and subcategories at site B ranged from 0.17 to 1.61. Like site A, usability was the largest category, containing six subcategories, with DOR scores from 0.62 to 1.61. The DOR score for legibility was 0.78; those for communication ranged from 0.17 to 0.39, and that for workaround, 0.28. The weakest categories were workaround and communication. The strongest category was usability, with the strongest subcategories retrievability and barriers (DOR scores, 1.61 and 1.38, respectively). Collaboration did not emerge as a category at this site.

Table 4
Table 4
Image Tools
Back to Top | Article Outline


Five life-threatening complications have been identified as precursors to failure-to-rescue events (pneumonia, shock or cardiac arrest, upper gastrointestinal tract bleeding, sepsis, or deep venous thrombosis).31 Four clinical events or derivatives of life-threatening complications were identified in this study within a narrow time margin (changes in mental status, drop in hematocrit and hemoglobin, hypoxia, and pain) and were among the top five most frequently identified events in this study. This finding suggests that clinical events are frequently occurring and should be regarded as precursors to failure-to-rescue events.

Usability was the largest category for both sites, accounting for 180 (70%) of the total thematic units (n = 260). However, the sites differed on the strengths and limitations of the categories. At both sites, for example, there was 100% agreement among nurses that the EHR posed barriers. This was the only limitation of the EHR identified by all the nurses. This finding is consistent with those of other studies reporting that the EHR inhibited multidisciplinary communication11 and was not "user friendly."32

Little is known about how workarounds and barriers influence nurse-to-nurse communication using the EHR, with and without nursing languages. Despite the strength of the subcategory barriers (usability), workarounds did not emerge as a strong category in this study. This finding suggests that the created workarounds have not bypassed all the identified barriers. There is a need to further identify the events that constitute "barriers" to the nurse's ability to communicate using the EHR. The barriers subcategory included more thematic units than any other category, and 100% of the participants contributed thematic units to that category. Barriers and their associated workarounds must be better understood for the EHR to be an effective means of communication.

Lack of EHR efficiency was reported by 50% of the nurses at each site. This finding was supported by DORs of greater than 0.80 at both sites. These findings are interesting because one would expect that nurses with greater experience with the EHR (site B) would find the system more efficient. One possible explanation for this finding is that additional barriers exist for which successful workarounds have yet to be created.

Nurses also differed between sites in regard to the identified strengths and limitations of the EHR. For example, the usability subcategory, difficult to use, had a DOR score of 0.53 at site A, but a DOR score of 0.17 at site B. This was an unexpected finding as site B has nursing languages (nursing diagnoses [NANDA], nursing interventions [NIC], and nursing outcomes [NOC]) embedded in the EHR. The EHR without embedded nursing languages has been reported as easier to use than the EHR with nursing languages.32-36 One would have expected therefore that nurses at site A would have reported lower scores than nurses at site B. It is unclear whether site A experienced greater difficulty using the EHR because of the lack of nursing languages or because the nurses were less experienced and/or had less experience with the EHR. This disparity also may be explained by the fact that this study had nurses focus on a clinical event. Previous work did not limit their scope of nursing documentation, nor did they compare EHR with and without nursing languages.

Nurses at site B found retrievability (DOR score, 1.61) to be a strength of the documentation system. The existing literature has conflicting results concerning information retrievability and the EHR. In one study, nurses reported that the EHR provides sufficient clear and relevant information32; in another study, nurses reported that the EHR did not facilitate retrieval of patient information.12 In neither study did the EHR include nursing languages. These disparate findings may be related to a number of factors. Perhaps because the current study focused on a clinical event rather than on overall impressions of the EHR, nurses were able to more clearly identify a situation in which retrieval of information was made easier. In addition, the use of nursing languages at site B or their greater familiarity with the EHR may have contributed to the differences in findings between sites.

The communication category emerged as a limitation primarily at site A. At that site, 31% of nurses identified lack of relevance of documentation (DOR score, 0.73), whereas at site B, there was only weak (DOR score, 0.39) thematic representation for this subcategory, with 22% of nurses reporting a lack of relevance. This finding may suggest that, when compared with documentation using "natural" or medical languages (EHR without nursing languages), EHR with nursing languages may increase the relevance of the documentation by reducing ambiguity.36,37 Further research will be needed to test this hypothesis.

No nurse cited reduced documentation time or fewer errors as a strength of the EHR, which is inconsistent with the literature.6,9,10 However, the cited studies were comparing electronic with paper-based documentation systems.

These data suggest that nurses perceive retrievability (usability) as a strength of the EHR or characteristic of the EHR that enhances communication (redundancy). Barriers and lack of efficiency (usability) and lack of relevance (communication) were perceived limitations of the EHR impeding communication (noise). Nurses using the EHR without nursing languages did not perceive the EHR as having elements of redundancy, only noise: barriers, lack of efficiency, and difficult to use (usability) and lack of relevance (communication). Nurses using the EHR with nursing languages, however, perceived retrievability and ease of use (usability) and legibility as elements of redundancy or strengths of the documentation system and barriers and lack of efficiency (usability) as limitations or noise.

Back to Top | Article Outline


Although the sample size was appropriate for the research methods used, further research is needed with a more controlled and larger sample size to better generalize results. Interview questions did not ask the age of the participants, how they currently use computers, or if they had previously used a paper documentation system. These questions may have provided greater insight into the participants and assisted in better understanding their perspectives of the EHR.

Back to Top | Article Outline


In this study, a clinical event or potential precursor to a failure-to-rescue event was used to elicit nurses' perceptions of the strengths and limitations of the EHR when documenting or retrieving patient information associated with a clinical event. The DOR was used to determine the strength of a thematic unit. All of the nurses reported that the EHR created barriers to documentation. There were, however, clear differences between sites. Nurses at site A identified no strengths of the documentation system, only limitations in usability (barriers, lack of efficiency, and difficult to use) and communication (lack of relevance). By contrast, nurses at site B identified two categories of EHR strengths: legibility and usability (with subcategories of retrievability and ease of use). It is unclear if the differences between sites related to the use of nursing language, years of experience as a nurse, or experience with the EHR. The disparities in the nurses' perceptions of the EHR with and without languages suggest that additional research is needed to better understand the use of nursing languages to communicate patient status associated with a clinical event.

There have been numerous studies regarding the use of the EHR by nurses, but there are still many areas that need further exploration. Nursing languages have been recognized as important for nursing documentation; however, it is still unclear how much they increase ease of use of the EHR. Furthermore, barriers are a significant issue for nurses using the EHR, yet little is known how barriers and workarounds influence nurse-to-nurse communication. Our understanding of nurses' perceptions of the EHR might be enhanced through exploration of nurse experiences using the EHR when compared with paper documentation systems.

By focusing on the documentation of a specific event, we believe we were better able to identify the nurses' perception of the EHR as a communication system. These results can potentially aid in our understanding of nurse-to-nurse communication of patient status associated with clinical events. These findings can also guide the development of EHR systems because nurses are key users. Understanding how nurse-to-nurse communication is enhanced or limited by EHRS may contribute to increased patient safety and ultimately to a reduction in failure-to-rescue events.

Back to Top | Article Outline


The authors thank Suzanne Lareau, MS, RN, FAAN, for her significant contributions during the preparation of this article.

Back to Top | Article Outline


1. Institute of Medicine. Crossing the Quality Chasm. Washington, DC: National Academies Press; 2001.

2. An Bord Altranais. Recording Clinical Practice Guidance to Nurses and Midwives. 1st ed. Dublin, Ireland: An Bord Altranais; 2002.

3. Edelstein J. A study of nursing documentation. Nurs Manage. 1990;21(11):40-46.

4. Nahm R, Poston I. Measurement of the effects of an integrated, point-of-care computer system on quality of nursing documentation and patient satisfaction. Comput Nurs. 2000;18(5):220-229.

5. Newfield JS. Documentation: focusing on better rather than more. Home Healthc Manage Pract. 2006;18(3):247-249.

6. Dennis KE, Sweeney PM, Macdonald LP, Morse NA. Point of care technology: impact on people and paperwork. Nurs Econ. 1993;11(4):229-248.

7. Hammond J, Johnson HM, Varas R, Ward CG. A qualitative comparison of paper flowsheets vs. a computer-based clinical information system. Chest. 1991;99:155-157.

8. Korst LM, Eusebio-Angeja AC, Chamorro T, Aydin CE, Gregory KD. Nursing documentation time during implementation of an electronic medical record. J Nurs Adm. 2003;33(1):24-30.

9. Pabst MK, Scherubel JC, Minnick AF. The impact of computerized documentation on nurses' use of time. Comput Nurs. 1996;14(1):25-30.

10. Smith K, Smith V, Krugman M, Oman K. Evaluating the impact of computerized clinical documentation. Comput Inform Nurs. 2005;23(5):132-138.

11. Ash JS, Berg M, Coiera E. Some unintended consequences of information technology in health care: the nature of patient care information system-related errors. J Am Med Inform Assoc. 2004;11:104-112.

12. Darbyshire P. 'Rage against the machine?': Nurses and midwives experiences of using computerized patient information systems for clinical information. J Clin Nurs. 2004;13:17-25.

13. Silber JH, Williams SV, Krakauer H, Schwartz JS. Hospital and patient characteristics associated with death after surgery: a study of adverse occurrence and failure to rescue. Med Care. 1992;30(7):615-629.

14. Aiken LH, Clarke SP, Sloane DM, Sochalski JA, Silber JH. Hospital nurse staffing and patient mortality, nurse burn-out, and job dissatisfaction. J Am Med Assoc. 2002;288(16):1987-1993.

15. Boyle SM. Nursing unit characteristics and patient outcomes. Nurs Econ. 2004;22(3):111-119.

16. Clark SP, Aiken LH. Failure to rescue. Am J Nurs. 2003;103(1):42-47.

17. Manojlovich M, Talsma A. Identifying nursing processes to reduce failure to rescue. J Nurs Adm. 2007;37(11):504-509.

18. Shannon CE. The mathematical theory of communication. In: Shannon CE, Weaver W. The Mathematical Theory of Communication. Chicago, IL: University of Illinois Press; 1967:31-125.

19. Campbell J. Grammatical Man: Information, Entropy, Language, and Life. New York, NY: Simon & Schuster, Inc; 1982.

20. Clover TM, Thomas JA. Elements of Information Theory. 2nd ed. Hoboken: NJ: John Wiley & Sons, Inc., Publication; 2006.

21. Pierce JR. An Introduction to Information Theory Symbols, Signals, and Noise. 2nd ed. New York: Dover Publications; 1980.

22. Reza FM. An Introduction to Information Theory. New York: Dover Publications; 1994.

23. Carrington JM. The effectiveness of the electronic health record with standardized nursing languages for communicating patient status related to a clinical event. Diss Abstr Int. 2008;69(03):AAT3297974.

24. Downe-Wanboldt B. Content analysis: method, applications, and issues. Health Care Women Int. 1992;13:313-321.

25. Krippendorff K. Content Analysis: An Introduction to Its Methodology. 2nd ed. Newbury Park, CA: Sage Publications; 1980.

26. Krippendorff K. Content Analysis: An Introduction to Its Methodology. Thousand Oaks, CA: Sage Publications; 2004.

27. Goodwin LD, Goodwin WL. Statistical techniques in "AERJ" articles, 1979-1983: the preparation of graduate students to read the educational research literature. Educ Res. 1985;14(2):5-11.

28. Lincoln Y, Guba E. Naturalistic Inquiry. Newbury Park, CA: Sage Publications; 1985.

29. Miles MB, Huberman AM. Qualitative Data Analysis A Sourcebook of New Methods. Beverly Hills, CA: Sage Publications; 1984.

30. Miles MB, Huberman AM. An Expanded Sourcebook Qualitative Data Analysis. 2nd ed. Thousand Oaks, CA: Sage Publications; 1994.

31. Needleman J, Buerhaus P, Mattke S, Stewart M, Zelevinsky K. Nurse-staffing levels and the quality of care in hospitals. N Engl J Med. 2002;346(22):1715-1722.

32. Otieno G, Toymana H, Asonuma M, Kanai-Pak M, Naitoch K. Nurses' views on the use, quality and user satisfaction with electronic medical records: questionnaire development. J Adv Nurs. 2007;60(2):209-219.

33. Campbell J, Carpenter P, Sneiderman C, Cohn S, Chute C, Warren J. Phase II evaluation of clinical coding schemes: completeness, taxonomy, mapping, definitions, and clarity. J Am Med Inform Assoc. 1997;4:238-251.

34. Harris M, Graves J, Solbrig H, Elkin P, Chute C. Embedded structures and representation of nursing knowledge. J Am Med Inform Assoc. 2000;7:539-549.

35. Henry S, Holzemer W, Reilly C, Campbell K. Terms used by nurses to describe patient problems. J Am Med Inform Assoc. 1994;1:61-74.

36. Henry S, Warren J, Lange L, Button P. A review of major nursing vocabularies and the extent to which they have the characteristics required for implementation in computer-based systems. J Am Med Inform Assoc. 1998;5:321-328.

37. Kim H, Harris M, Savova G, Chute C. Content coverage of SNOMED-CT toward the ICU nursing flowsheets and the acuity indicators. Stud Health Technol Inform. 2006;122:722-726.


Electronic health record; Information Theory; Nursing documentation; Nursing languages

© 2011 Lippincott Williams & Wilkins, Inc.



Article Tools



Article Level Metrics

Search for Similar Articles
You may search for similar articles that contain these same keywords or you may modify the keyword list to augment your search.