Documentation/standards, Nursing process/standards, Standardized nursing terminology



  1. Phillips, Toni DNP, RN-BC
  2. Baur, Karen DNP, MPH, NE-BC


The purpose of this quality improvement project was to conduct a scholarly assessment of the information collected within the nursing admission encounter and implement content revisions across three pilot medical surgical units. The guiding principles were to preserve regulatory information, identify nurse-sensitive data, and eliminate nonessential information. The goal was to decrease the number of clicks and time expended to document electronically an acute admission encounter by 20% and to project the number of hours returned to patient care as a result of decreasing computer clicks. A second goal was to quantify the projected costs of completing a nursing admission encounter. This quality improvement project leveraged nurse executive competencies to intersect the nursing process to develop a nursing documentation praxis. This author's praxis reduced nursing documentation burden in clicks by 29% and reduced time to document on an admission encounter by 34%. This restored the focus on nurse-patient interactions by returning 1016 hours per year to patient care activities, across three pilot units, as well as quantified the costs of completing a nursing admission assessment to utilize in future cost analysis of nursing tasks.


Article Content


The Health Information Technology for Economic and Clinical Health Act included a provision that incentivized the exchange of meaningful information across healthcare providers and systems through the adoption of technology.1 This legislation was a catalyst for the increase in the adoption of electronic healthcare records, yet nursing data that can be measured and compared remain invisible within electronic healthcare records and legislative initiatives.2 Electronic documentation burden has increased and resulted in nursing inefficiency,3 duplication of information,4 decreased nurse satisfaction,5 and potential risk to patient mortality, due to weakened nurse-patient interactions.6



In 2018, the Bureau of Labor Statistics stated that there were 2 951 960 registered nurses employed in the United States and 30.6% of those nurses were employed within hospitals.7 A total of 36 510 207 admissions occur yearly,8 producing the task of admitting a patient a frequent nursing activity. Within hospital reimbursement schedules, nursing relevance and intensity remain coupled within hospital room rates.9 Nurse executives are accountable to forecast staffing intensity within value-based reimbursement models, and in 2008, inpatient nursing labor costs were estimated at $216.7 billion per year.10 Therefore, nurse executive competencies were applied to seek knowledge of the intersection of the nursing process to define the relevance of nursing practice within a documentation praxis, shown in Figure 1. This author's praxis focuses on the integration of the nursing process within nursing documentation practices and is informed by nursing theory, evidenced-based practice, and quality improvement concepts.

Figure 1 - Click to enlarge in new windowFIGURE 1. Nursing praxis for evidence-based documentation.

The purpose of this quality improvement project was to conduct a scholarly assessment of the nursing admission content and implement content changes within three pilot medical surgical units. The objectives of this quality project were to decrease by 20% the amount of nursing time and clicks expended to document electronically an acute admission encounter and quantify the projected cost savings as a result of this quality improvement project. The overarching aims were to identify relevant standardized essential nursing admission information, restore the focus on nurse-patient interactions,11 and establish knowledge to measure nursing intensity within admission documentation activities.



The nursing theory that guided this scholarly project was the Deliberative Nursing Process.12 This theory emphasizes the importance of the nurse-patient interaction, which is an essential connection during the nursing admission assessment.12 The principles of the association of meaning to nursing practice require the identification of standardized essential information to include within a nursing documentation praxis. Several content areas of the current state of the nursing admission assessments reflected documented nursing actions that lack patient context or value. This is a core concept within Orlando's theory.12 Therefore, the Deliberative Nursing Process Theory provides a framework for this project's integration of standardized essential patient information gathered from nursing assessments. The elimination of extraneous nursing documentation such as the documentation in the electronic healthcare record of patient belongings fails to provide information that contributes to the care of the patient. As a result of this project, nursing documentation praxis was established to decrease nursing documentation burden and demonstrate evidence of interventions that return nursing time to nurse-patient interactions.13



The quality improvement model chosen was Kurt Lewin's change theory14 as the key concepts of unfreezing-change-refreezing was applicable to reviewing structure, process, and content within the current admission assessment as they traverse the nursing process concepts within this documentation praxis. The principles leveraged to inform local discussions and decisions regarding admission content were adopted from the work of the Transforming Documentation Workgroup15 and the American Nurses Association.16 Nursing admission content was reviewed by the facility expert panel of nurses appointed by nursing leadership within the shared governance documentation committee and further informed by national nursing modernization efforts during the unfreezing stage. These groups of nurses searched for evidenced-based knowledge on scales, tools, nursing terms, and observations to include or exclude within the nursing admission assessment encounter revisions.


In addition, regulatory, local, and national nursing policies were reviewed to assess inclusion or exclusion of applicable content. The design of the electronic templates was reviewed to seek and implement functionality, which reduced redundancy in clicks, and identify nursing content as the source of truth for information to decrease redundancy in duplicative documentation. The content within the admission assessment was changed, and refreezing of nursing content occurred to leverage information to assess changes in content.


Point-of-care training was conducted on all three shifts prior to unfreezing the revised admission assessment. One pilot unit at a time implemented the changes to support staff questions during the implementation. Future scholarship will evolve around the repetition of this process to evolve new or absent relevant standardized nursing content and to determine associations between standardized nursing documentation, patient outcomes, and nursing intensity. The overarching quality improvement initiative is to return nurses' time to patient care and surveillance as opposed to time spent on irrelevant nursing documentation.



The literature search reviewed 53 studies, of which 34 studies provided the foundational evidence to inform this praxis to reduce nursing documentation burden and measure the costs of completing a nursing admission assessment. Of these 34 studies, 20 were qualitative studies and 14 were quantitative studies. Qualitative studies summarized evidence that supports the integration of the nursing process within the documentation praxis and quantitative studies informed the value of critical elements within nursing data to impact patient outcomes and measure nursing intensity. Search terms included praxis, nursing minimum data set, standardized patient care data, and electronic documentation burden.


One level V systematic review17 quantified the evidence on nursing documentation practices and provided documentation recommendations that promote standardization of content and expression of nursing practice. In addition, a second level V systematic review analyzed structured patient care content as it impacts primary and secondary use of electronic documentation.18 The synthesis of this information provided project insight into the content review and changes, to identify essential nursing content as the source of truth for information such as with mental health or infectious disease screenings. The literature provided guidance and synthesis of knowledge as a result of the findings of repetitive themes, such as lack of standardization in nursing content,19 poor designs that lack integration into nursing workflows,20 and absence of the identification of nursing costs as a by-product of nursing documentation.21 The examination of these common themes from the literature review informed the framework for the nursing documentation praxis, as well as the project design and aims.



Project participants were volunteers and the admission data collections were dependent upon when admissions occurred on the three pilot medical surgical units. Analysis of 3 months of admission data demonstrated that 80% of admissions occur between the hours of 12 noon and 8 PM, which guided the collection of time measurement data. Nurse participants included licensed registered nurses on the three pilot units; staff who floated to these units were excluded.


Voluntary participation in the time studies was solicited to measure the amount of time to complete an admission assessment. The number of nurses who participated in the baseline time studies consisted of 20 registered nurses, with the average years in nursing consisting of 10 years. The number of nurses who participated in the postintervention admission assessment time studies was 18, with the average years in nursing consisting of 9.4 years.


The project was submitted to the University of Alabama's Institutional Review Board and was determined to be a system change, quality improvement project. University guidelines and course objectives were adhered to within the project. In addition, review of the project prior to implementation was conducted by the facilities scholarly review board and determined to be a quality improvement project. Finally, review and approval from local labor unions was received prior to project initiation as required by facility regulations.



Examining the quality and quantity of information within the nursing electronic admission assessment, hours returned to patient care as a result of the revised admission assessment, and projected costs to complete an admission assessment was the focus of the data collection for this project. The quality of information was examined from the preintervention admission assessment as described in the quality improvement section to determine content changes and identification of terms and/or observations that would provide nursing value to the care of patients.22 The quality of nursing admission assessment content was further reviewed by facility shared governance committees in comparison with evidenced-based content decisions from national nursing modernization initiatives to inform assessment revisions. In addition, policy, regulatory elements, and operational metrics were reviewed to identify key areas necessary for compliance. Interprofessional consulting was done to obtain advisement on content and usability from clinical informatics, health information management, hospitalists, quality safety and value, nurse educators, nurse managers, and nursing leadership. This collaboration resulted in a comprehensive analysis and identification of elimination of content redundancy and provided opportunities to identify the source of truth for information. This permits the reuse of information across administrative processes or clinical documentation to eliminate redundant or nonessential documentation data elements.


The quantity of data entry points was obtained through manual counting of the total number of possible data entry points within the preadmission and revised postassessment documentation template. In addition, nursing quantity was measured by a review of each content item to identify the total number of possible mouse clicks depending upon template design.23 Total hours to complete mouse clicks was calculated pre and post admission assessment revision based on three mouse clicks24 and multiplied by the average number of admission encounters, recognizing that wireless access points, age of equipment, and bandwidth can impact functionality.


The average number of admission encounters for 3 months was used in the calculation of the projected hours to complete an admission assessment. Time measurement to complete the admission assessment was captured through direct observation of 20 nurses from the start through completion of the admission assessment across the three pilot units over a 2-month period.25 Computer connectivity and nurse interruptions were deducted from the overall time captured. The average admissions were multiplied by the average time to complete an admission assessment, and this figure was used to project the number of hours per year for registered nurses to complete an admission assessment, as shown in Table 1. This analysis was done with the admission assessments to use for comparative analysis with pre and post admission assessment revisions as well as to assign costs to the nursing admission activity.

Table 1 - Click to enlarge in new windowTable 1 Documentation Efficiency and Cost Analysis Pre and Post Admission Assessment Revisions

Nursing cost projections for completing an admission assessment were determined by using several data points. Forecasted nursing costs were measured by obtaining baseline salary data, absent of fringe benefits, to determine an average hourly salary rate for each pilot unit. The total amount of hours to complete an admission assessment each year was multiplied by the hourly rate. The projected yearly nursing costs associated with the activity of a nursing admission assessment were assigned using these data points, as shown in Table 1.



An average of 453 admissions occurred in 3 months within the three pilot medical surgical units. Preintervention admission assessment findings were analyzed and a total of 1175 possible data elements and 1094 mouse clicks existed within the admission assessment. Preintervention, an average of 31.71 minutes was recorded to complete the current admission assessment. The postintervention revised admission assessment contained a total of 835 possible data elements and 744 mouse clicks, which included core content related to regulatory, core assessment information and screening tools such as alcohol, tobacco, and mental health. This resulted in a decrease of 340 possible data elements and a decrease of 350 clicks for a total of 29% decrease in electronic documentation burden and was supported in the literature. In addition, an average of 20.30 minutes was recorded to complete the postintervention revised admission assessment. This resulted in a 35% decrease in time spent electronically documenting a nursing admission assessment encounter.


The time to complete the preintervention admission assessment per year was 580 hours for pilot unit 1, 1215 hours for pilot unit 2, and 1065 hours for pilot unit 3. The total time to complete the preintervention admission assessment was 2860 hours per year across the three pilot units. The time to complete the postintervention revised admission assessment was 357 hours for pilot unit 1, 778 hours for pilot unit 2, and 709 hours for pilot unit 3 per year. The total time to complete the postintervention revised admission assessment was 1844 hours per year across all three pilot units. This resulted in a total of 1016 hours that could be eliminated from electronic documentation and assigned back to patient care and surveillance.


The yearly estimated costs to complete the preintervention admission assessment are $311 437 for pilot unit 1, $661 020 for pilot unit 2, and $543 596 for pilot unit 3. Preintervention, the yearly cost across all three units to perform a nursing admission assessment was $1 516 053. The yearly estimated costs to complete the postintervention revised admission assessment are $191 970 for pilot unit 1, $422 930 for pilot unit 2, and $362 040 for pilot unit 3. The projected yearly cost savings of $539 114 could be demonstrated across three pilot units with the adoption of the postintervention revised admission assessment.


Nursing value statements during direct observation were valuable contributions to the findings. Statements such as "I do not know why we have to ask this question again" or "How does this information contribute to nursing care" were collated during the direct observation and measurement of the admission assessments pre and post revision of the nursing admission assessment. Post intervention, nurses stated "this should have been changed years ago" and "thank you for listening." Although this information was not a part of the project aims, it was valuable in providing insight for the revised design and desired functionality of the admission assessment as expressed by nurses at the point of the care. This information is of value for nursing leadership to evaluate nurse satisfaction within nursing documentation. The nursing value statements, were briefed within shared governance and leadership meetings to highlight nurse perceptions of the admission assessment and to be proactive with resolving connectivity issues with the use of the computers on wheels.



The major outcomes of this project were to decrease electronic documentation time by 20% within an admission encounter, define nursing cost attributed to documenting an admission assessment, and restore the nursing focus to patient care interactions. Documentation clicks were decreased by 29%; projected yearly cost savings of $539 114 were demonstrated with the adoption of the postintervention revised admission assessment, but most importantly, 1016 hours could be utilized for patient care as opposed to electronic documentation. One advantage of direct observation of nursing assessment to capture time was the ability to measure precisely the time to complete an admission assessment and the ability to capture nurse recommendations for design and content inclusion for planned admission assessment revisions as a part of their workflow. For example, nurses expressed that "drop down boxes in their current electronic health record system results in extra clicks," but if they "could have terminology arranged horizontally," they would only have to click once to select options reflective of their assessment. Limiting drop lists to 10 or fewer added to end user satisfaction.


In addition, the nurses suggested placing an option at the beginning of the documentation tool that stated, "unable to obtain due to behavior or medical condition." This permitted them to pause the assessment and reassess at a later time the clinical appropriateness to complete this content within a nursing admission assessment. Self-reported time could have been utilized, but the validity of information could be questioned. Lastly, capturing electronic start and stop times from the electronic health record system was not available at this time, and if this was a possible option, idle time from nurse interruptions is often a limiting factor to these data.


The analysis cost of nursing time spent on electronic documentation activities can inform staffing models and hospital costs and identify essential registered nurse information to promote safe effective patient care outcomes. Data were pulled for admissions from the months of February, March, and April. The value of this analysis is to document the frequency of admissions to attribute a cost for the nursing admission activity to measure nursing activities for future scholarly analysis and continue to build evidence to remove nursing from the hospital room rate. In the future, analyzing nursing admission content as it affects patient care and mortality while the patient is hospitalized could provide meaningful real-time analysis of the value of nursing.



One limitation of the direct timed observation was the dependency on when admissions occur during the writer's surveillance. This strategy limited the sample size. Preintervention admission assessment sample size was 20 and was limited to three pilot units within a medical surgical practice and requires replication with a larger sample size. This was a result of synchronous computer limitations with capturing time measurement to exclude nurse interruptions. Computer issues and nurse interruptions result in pauses within documentation by the nurse and prevent correct time measurement to complete the admission assessment when computer logs are used for time measurement without having the metadata to explain phenomena.


The population was restricted to adult patients older than 21 years admitted to medical surgical units; therefore, further analysis of specialty content would need to occur before diffusion to pediatric, inpatient mental health, oncology, or intensive care units. National nursing content was not finalized at the time of this project; therefore, postcomparison of terms and observations will need to be revisited and reviewed to deploy across the enterprise. This limited the integration of standardized national content as it was not always available for inclusion within this project. Finally, the geographical location of the facility is in a seasonal location, and therefore, admission numbers could be inflated when compared with June, July, and August. This circumstance could result in data fluctuations regarding admission averages depending upon the time of year as this facility was located in a seasonal region of the country.



Establishing nursing praxis for electronic documentation provided a framework for the author to seek new meaning to understanding documentation burden within an acute care medical surgical assessment encounter using the nursing process as the foundational framework.26 Nursing has the potential to identify their relevance within quality outcomes, as a by-product of evidenced-based nursing documentation data sets. Nursing knowledge generated from this project promoted documentation praxis for replication with other nursing documentation phenomena and provided insight to methodologies to examine nursing content within electronic medical records. Building upon this praxis to discover new meaning in nursing documentation phenomena could bring evidence and measurement into nursing practice. Further research is needed to determine the outcomes associated with an admission assessment to correlate the nursing admission assessment value to patient care, length of stay or patient mortality.




1. United States, Congress, House. Health Information Technology for Economic and Clinical Health (HITECH) Act, Title XIII of Division A and Title IV of Division B of the American Recovery and Reinvestment Act of 2009 (ARRA), Pub. L. No. 111-5, 123 Stat. 226 passed 17 Feb. 2009. [Context Link]


2. Westra B, Latimer G, Matney S, et al. National action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. JAMA. 2015;22: 600-607. doi:. [Context Link]


3. Karp E, Freeman R, Simpson K, Simpson A. Changes in efficiency and quality of nursing electronic health record documentation after implementation of an admission patient history essential data set. Computers, Informatics, Nursing. 2019;37(5): 1-6. doi:. [Context Link]


4. O'Brien A, Weaver C, Settergren T, Hook M, Ivory N. EHR documentation the hype and the hope for improving nursing satisfaction and quality outcomes. Nursing Administration Quarterly. 2015;39(4): 333-339. doi:. [Context Link]


5. Kerr N. 'Creating a protective picture': a grounded theory of RN decision making when using a charting-by-exception documentation system. Medsurg Nursing. 2013;22(2): 110-118.[Context Link]


6. Rothman M, Solinger A, Rothman S, Finlay G. Clinical implications and validity of nursing assessments: a longitudinal measure of patient condition from analysis of the electronic medical record. BMJ. 2012;2(4): 1-6. doi:. [Context Link]


7. Bureau of Labor Statistics. Occupational employment and wages. US Department of Labor. May 2018.[Context Link]


8. American Hospital Association. Fast facts on U.S. hospitals. January 2019).[Context Link]


9. Welton JM, Harper EM. Nursing care value-based financial models. Nursing Economics. 2015;33(1): 14-25. [Context Link]


10. Welton JM. Hospital nursing workforce costs, wages, occupational mix, and resource utilization. Journal of Nursing Administration. 2011;41(7/8): 309-314. doi:. [Context Link]


11. Bose E, Maganti S, Bowles K, Brueshoff B, Monson K. Machine learning methods for identifying critical data elements in nursing documentation. Nursing Research. 2019;68(1): 65-72. doi:. [Context Link]


12. Orlando IJ. The nursing situation in relation to principles of practice. In: Orlando IJ, ed. The Dynamic Nurse-Patient Relationship: Function, Process, and Principles. New York, NY: G.P. Putnam; 1961: 31-61. [Context Link]


13. Gonzalo A. Ida Jean Orlando's Deliberative Nursing Process Theory. Nurseslabs. 2014.[Context Link]


14. Lewin K. Frontiers in group dynamics: concepts, method and reality in social science; social equilibria and social change. Human Relations; Studies Towards the Integration of the Social Sciences. 1947;1(1): 5-41. doi:. [Context Link]


15. Transforming Documentation Workgroup (WG) & Admission History Cross-WG Task Force. Nursing Knowledge Big Data Science Conference. 2019.[Context Link]


16. American Nurses Association. ANA's Principles for Nursing Documentation Guidance for Registered Nurses. Silver Spring, MD: American Nurses Association; 2010. [Context Link]


17. Slade S. Nursing record systems/documentation [evidence summary]. The Joanna Briggs Institute EBP Database, JBI@Ovid. JBI59. 2017. [Context Link]


18. Vuokko R, Makela-Bengs P, Hypponen H, Lindqvist M, Doupi P. Impacts of structuring the electronic health record: results of a systematic literature review from the perspective of secondary use of patient data. International Journal of Medical Informatics. 2017;97: 293-303. doi:. [Context Link]


19. Matney S, Settergren T, Carrington J, Richesson R, Sheide A, Westra B. Standardizing physiologic assessment data to enable big data analytics. Western Journal of Nursing Research. 2017;39(1): 63-77. doi:. [Context Link]


20. Dykes PC, Wantland D, Whittenburg L, Lipsitz S, Saba VK. A pilot study to explore the feasibility of using the clinical care classification system for developing a reliable costing method for nursing services. AMIA Annual Symposium Proceedings. 2013;364-371.[Context Link]


21. Gardner C, Pearce P. Customization of electronic medical record templates to improve end-user satisfaction. Computers, Informatics, Nursing: CIN. 2013;31(3): 115-121. doi:. [Context Link]


22. Moon L, Clancy G, Welton J, Harper E. Nursing value user stories a value measurement method for linking nurse contributions to patient outcomes. Computers, Informatics, Nursing. 2019;37(3): 161-170. doi:. [Context Link]


23. Hill R, Sears M, Melanson S. 4000 Clicks: a productivity analysis of electronic medical records in a community hospital. American Journal of Emergency Medicine. 2013;31: 1591-1594. doi:. [Context Link]


24. Chen L, Uta G, Lijo I, et al. Racing against the clock: internal medicine residents' time spent on electronic health records. Journal of Graduate Medical Education. 2016;8: 39-44. doi:. [Context Link]


25. Lopetegui M, Yen P, Lai A, Jeffries J, Embi P, Payne P. Time motion studies in healthcare: what are we talking about. Journal of Biomedical Informatics. 2014;49: 292-299. doi:. [Context Link]


26. Penney W, Warelow PJ. Understanding the prattle of praxis. Nursing Inquiry. 1999;6(4): 259-268. doi:. [Context Link]