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Electronic Health Records for Nursing Services Research Paper


This paper will examine the method to be applied in collecting data and analyzing the results. Since this study is focused on a specific region, the researcher will apply a research survey for the approach to gathering primary through qualitative data analysis. The choice of qualitative analysis was informed by the need to properly facilitate understanding of attributes related to the use of Electronic health records (EHR) to improve on the efficiency of service delivery among nurses (Barrett, 2017). EHR has revolutionized the way healthcare providers deliver care together with how patients experience it. Even though the system was meant for billing purposes, it has evolved to cover almost all facets of healthcare provision. Healthcare institutions now function efficiently by offering timely, quality, and convenient services (Cimino, 2013). From the research survey of thirty respondents, mainly supervisors and nurses, the research will analyze the results to identify the current policies, strategies, and challenges as part of Electronic health records (EHR) use and their effectiveness (Cimino, 2013). The application of this approach is necessary for facilitating the identification of different statistical patterns emerging from the collected data as related to the variables of study (Mason, 2017).

Research Design

The research design approach was semi-structured interviews targeting supervisors and nurses in health institutions who deal with Electronic health records (EHR) daily. The rationale for a qualitative research design was informed by the need to emphasize results supported by evidence gathered from the literature review to expand statistical accuracy (Sherif, 2018). Rather than focusing on solely factual matters, the surveys are necessary to understand and predict behavior patterns. The research survey was designed with all intensive purposes to represent the content from a literature review accurately and can address specific researcher needs (Timmins, 2015). This survey was based on the available research about the use of electronic health records by the nursing workforce with the purpose to derive options for enhancing and optimizing the system (Habibi-Koolaee, Safdari, & Bouraghi, 2015). Since the proposed study is dynamic, subjective, and focused, the researcher used a qualitative method to accommodate several tools of analysis and minimize the potential margin of error.

Internal and External Validity

The proposed primary survey selected reflects a verifiable, scientific, and clear approach for tracking any trend in different data sets. Based on these strengths, the sample size suggested is adequate and relevant to the scope and nature of the study. This means that the sample space is representational of space and purpose intervals as explained in the research design framework. The accurate sample space will also create room for a comprehensive data analysis in the complex qualitative research method proposed (Trommer, 2017). Thus, the researcher is empowered to test any internal or external bias for improving the accuracy and validity of the collected data. This means that the primary survey proposed will be a benchmark for the accurate and reliable representation of the sample population to strengthen any inference.

Survey Instrumentation: Research Questions

The researcher adopted five-item closed-ended questions that were relevant to the study objectives within the range of agree, agree, neutral, disagree, and strongly disagree. The other questions had a two-item format of [yes] and [no]. These questions were pretested for accuracy and relevance to the conceptual framework and aims of the entire research study (Bryman & Bell, 2015). The final survey questions were divided into four parts to capture the demographic data, current use of electronic health records, feedback, and satisfaction, and suggested improvements as summarized in tables 1, 2, 3, and 4.

Table 1. Closed-ended questions to capture the demographics of the sample population.

What is your age group?
  • 20-29yrs
  • 30-39yrs
  • 40-49yrs
  • 50-59yrs
  • 60-69yrs
  • 70yrs and over
What is your sex?
  • Male
  • Female
Indicate current job title or position: Indicate current nursing classification:
Number of years as a registered nurse: ___
Number of years in the current job position: ___
Indicate all levels of education received relevant to nursing/healthcare:
Have you received basic training for the use of EHR?
  • Yes
  • No
Indicate current practice setting and department:

Table 2: Closed-ended questions to capture data on the current use of electronic health records.

Current Use of EHR Strongly
Agree Neutral Disagree Strongly
Rating: 1 2 3 4 5
Do you use EHR daily?
Is the system easy to use?
Do you receive training and technical support on HER?
Is the software logical and easy to navigate in finding useful information?
The processing speed of EHR is fast.
Is the system easier to use than paper records?
I know which commands and prompts to use.
The EHR system commonly freezes or malfunctions.
The collection of patient data is helpful with EHR structure and reminders.
EHR displays relevant data on the screen.
Patient data collection or presentation is adequate in the level of detail.
EHR provides the necessary information to provide high-quality care to the patient.

Table 3. Closed-ended questions to capture the feedback and satisfaction in the use of electronic health records.

Current Use of EHR Strongly
Agree Neutral Disagree Strongly
Rating: 1 2 3 4 5
How satisfied are you with the current EHR system adopted by the organization?
How efficient is the system’s integration?
Are you satisfied with the system usability?
Do you believe that the EHR system is friendly or useful for nurses?
Do think the system meets clinical nursing needs?
Are there organizational standards in place for the use of EHR?
Do you believe EHR is a limited concept?
Does the organization provide an adequate amount and quality of training?
Is EHR compatible with your current organization and clinical practice?
Is EHR compatible with other health technology or information systems used by the organization?
Do you feel forced to use EHR?
Would you use manual systems instead of EHR?
Does EHR improve the quality of care?

Table 4. Closed-ended questions to capture data on proposed improvements.

Current Use of EHR Strongly
Agree Neutral Disagree Strongly
Rating: 1 2 3 4 5
Do you believe EHR functionality should be improved?
Does the HER system ask to collect or display irrelevant data?
Should the EHR user interface be improved?
Does EHR need interoperability improvements with other health information or basic electronic hardware?
Should the EHR interface be more dynamic and intuitive?
Does EHR need increased speed and connectivity?
Is there a need for better EHR integration into the clinical workflow?
Should EHR provide broader access to medical literature?
Should EHR include logistical aspects such as prescription management or appointment scheduling?
What other recommendations would you have for the enhancement of EHR use in the organization?

The researcher also used a probing question to capture personal insight, especially in the answers that were entirely based on personal opinion (Mason, 2017). With the intent of capturing the insider perspective, the surveys were focused within the electronic health records section. This means that the closed-ended questions were adequate in exploring the what, how, and why aspects of the research topic.

The setting, Sample Population, and Pre-testing

The researcher began by compiling relevant themes captured in the literature review on electronic health records best practices and current state in the targeted health institution (Timmins, 2015). This was followed by a compilation and probing of the survey questions. The researcher then pre-tested the questions by having a short survey of five randomly selected respondents to confirm their reaction and relevance of the proposed questions (Sherif, 2018). The responses from the pilot study were used to generate the final list of questions. The setting for conducting the survey was mainly at health institution X. The sample consisted of thirty respondents drawn from officials and nurses, mainly from the health records department. This sample consists of experienced persons who understand the use of electronic health records besides being directly involved in decision-making and or policy implementation. Moreover, the sample group has an expansive knowledge of the procedures being implemented through electronic health records management to address issues associated with improved services and general efficiency (Bryman & Bell, 2015). The thirty sample respondents currently work in the same field and understand the functional interaction within electronic health records usage. Conducting surveys for top management echelons of the X institution ensured that inferences made by the researcher could be compared to policy structuring and context of the same (Mason, 2017). The researcher used the Bowen formula proposed in 1972 to generate an adequate sample size and guarantee dependability as summarized below (Irwin, 2015). Specifically, the researcher proposed a sample space of 30 respondents.

n=N/ (1+N (e2))


  • n = sample size
  • N= Target population
  • e= Degree of freedom
  • n=30/ (1+30*0.052)
  • n=30/1.075
  • n= 27.9

As expounded by the Central Limit Theorem, any sample size of 25 and above is adequate for ensuring that the generated X-bar has a normal distribution for all the samples, despite the size of a population or other relevant dynamics (García-Herrero & Xu, 2016). In this case, the generated sample size of 27.9 is not only adequate but falls in the range recommended by the Central Limit Theorem. The use of snowball random sampling was informed by the need to capture wide insight and evenly cover the entire health institution (Mitchell, 2015). Moreover, this method of sampling ensures that a trend or pattern in data collection is followed for adequate results (Saunders, Lewis, & Thornhill, 2016). Also, the use of a random sampling survey is relatively affordable since the researcher will simply drop the forms and collect them after a week when they are duly filled. This means that the researcher will not waste time interviewing each respondent.

Data Collection and Coding

The researcher made an official survey request to the respondents and expounded to them the nature and scope of the study using a sample four-section sample questionnaire (Sherif, 2018). The activities of the survey process and their meaning were explained to give the respondent enough time for preparing responses. This means that the policy practices and opinions of the respondents were the research subject matter. The researcher ensured that the responses were captured and transcribed to describe the research questions in the context of the use of electronic health records, feedback and satisfaction, and suggested improvements (Mason, 2017). Moreover, the researcher took field notes to highlight impressions for a more focused and continuous evaluation of the responses. Each section of the survey questions was transcribed independently to enable the researcher to highlight critical points and identify a trend (Bryman & Bell, 2015). Through a proactive and holistic approach, the researcher will be in a position to grasp all the primary themes from the survey. During coding, the researcher intends to create cross-tabulation as a draw line for carrying out comparative analysis in the correlations between different data sets. For instance, the researcher will do a regression analysis to establish the existing correlation between dependent and independent variables of the study. The coding process will also involve the generation of figures, charts, and tabulation of the raw and analyzed data (Guiso, Sapienza, & Zingales, 2015). In performing regression analysis, the researcher will use different software such as Excel, reviews, or Google Docs on a need basis. The researcher has set the confidence interval at 99% as explained in the equation below.

Sample statistic + Z value * standard error / √n

b1 = 7.1175 ± 2.57 * 0.9631 / √133

= 7.1175 ± 2.57 * 0.9631 / 11.5326

= 7.1175 ±0.2146

= 6.9029 ≤ b1 ≤ 7.3321

At 95%

b1 = 7.1175 ± 1.96 * 0.9631 / √133

= 7.1175 ± 1.96 * 0.9631 / 11.5326

= 7.1175 ± 0.1635

= 6.954 ≤ b1 ≤ 7.281

At 90%

b1 = 7.1175 ± 1.64 * 0.9631 / √133

= 7.1175 ± 1.64 * 0.9631 / 11.5326

= 7.1175 ± 0.1368

= 6.981 ≤ b1 ≤ 7.254

Based on the above calculations, the generated confidential intervals are 6.9029 ≤ b1 ≤ 7.3321 of 99%, 6.954 ≤ b1 ≤ 7.281 of 95%, and 6.981 ≤ b1 ≤ 7.254 of 90%. These results indicate that the estimated confidential intervals rise as levels decreases and vice versa (Denscombe, 2015).

Data Analysis

Qualitative data analysis was performed by the researcher based on the survey results that were tabulated to identify a common trend (Bryman & Bell, 2015). Kothari (2013) notes that “application of the ANOVA is focused on quantifying the existing variance in different sets of data by disintegrating the differences existing in the sets for each transcribed group” (p. 56). This means that the researcher will use the ANOVA tool to perform a comprehensive data analysis to establish a trend. According to Hughes and Malcolm (2013), the use of ANOVA as an analysis instrument is important in establishing the variation in the mean of different sets of data by disintegrating the variances. Within the proposed study, this statistical tool will be used to quantify any observable statistical variances in the mean of different data sets (Nguyen, Bellucci, & Nguyen, 2014).

The researcher organized the collected data into demographic, feedback, and satisfaction, and suggested improvements. This set of data was selected because it would facilitate a proactive analysis of the use of electronic health records in improving the efficiency and sustainability of quality service (Sherif, 2018). For instance, demographic data were used to analyze the dependability and uniformity of the respondents. Moreover, the feedback and satisfaction and suggested improvements sets of data were collected to examine and establish the current trend and future projections. The published data from the literature review indicated that the use of electronic health records has increased steadily in the last ten years, besides improving efficiency and quality of service (Mason, 2017). The rationale for using different data sets in the analysis was informed by the segregated nature of data from the literature review.

Reliability and Validity

The researcher intends to ensure validity and reliability through the provision of sequential, clear, and detailed data collection, transcription, and analysis. Moreover, the proposed qualitative research design is adequate in ensuring that data transcription and analysis is consistent with the structure of the research questions (Bansal et al., 2017). Also, the analytical framework and comprehensive theoretical construct performed in the literate review section are detailed to facilitate the transition in analysis and drawing of inferences to create meaningful parallelism. The researcher is also well-trained in proper research skills.

Assumptions in the Study

The first assumption was that the collected data will be nearly normal, in terms of the trend for present use, feedback and satisfaction, and suggested improvements (Mason, 2017). The same trend is assumed for responses from surveys since the targeted respondents are directly involved in the usage and management of electronic health records. Specifically, the research will overly rely on the primary data to come up with scientific inferences (Smith, 2017). Another assumption is that the respondents will give accurate information that can be transcribed into a data set for establishing any existing trend (Bryman & Bell, 2015).

Limitations of the Study

Since substantial data was collected from the qualitative survey, there is a possibility of bias besides inaccurate results on the usage and management of electronic health records. The sample space only consisted of thirty respondents, thus, the research might end up with common approaches or views to the questions (Bryman & Bell, 2015). Under qualitative data, the collection of accurate demographic data was difficult since some respondents might opt not to give some information. Therefore, the researcher may not be able to compare the demographic traits of the respondents.

Delimitations of the Study

The researcher will subject each response in qualitative data to transcription to ensure consistency and dependability. For instance, each response to the four sections of the survey form by a respondent will be coded to reduce generalization bias (Bryman & Bell, 2015). Moreover, the researcher has adequate training on professionalism and will remain neutral in presenting results. Thematic and content analyses will also be closely observed to ensure that findings fall within the context of the study (Sherif, 2018).


The researcher used a qualitative research design approach to integrate effective data analysis. This design involved a direct survey of a sample space of thirty respondents who use or manage electronic health records. The small sample space might result in biases from limited insight. However, the research will probe and code each question to guarantee consistency and accuracy in presentation and analysis. The rationale for selecting a qualitative research design was informed by the need to establish the current insight in usage, feedback, and satisfaction, and suggested improvements in electronic health records.


Bansal, H., Eldridge, J., Haider, A., Knowles, R., Murray, M., Sehmer, L., & Turner, D. (2017). Shorter interviews, longer surveys. International Journal of Market Research, 59(2), 221-238.

Barrett, A. K. (2017). Electronic health record (EHR) organizational change: Explaining resistance through profession, organizational experience, and HER communication quality. Health Communication, 33(4), 496-506.

Blaxter, L., Hughes, C., & Malcolm, T. (2013). How to research. Berkshire, UK: Open University Press.

Bryman, A., & Bell, E. (2015). Business research methods (4th ed.). Oxford, UK: Oxford University Press.

Cimino, J. J. (2013). Improving the electronic health record: Getting what we wished for. JAMA: The Journal of the American Medical Association, 309(10), 10-34.

Denscombe, M. (2015). Ethics: Ground rules for good research. Buckingham, UK: Open University.

Habibi-Koolaee, M., Safdari, R., & Bouraghi, H. (2015). Nurses readiness and electronic health records. Acta Informatica Medica, 23(2), 105–107.

Kothari, R. (2013). Research methodology: Methods and techniques (3rd ed.). New Delhi, India: New Age International.

Mason, J. (2017). Qualitative researching (3rd ed.). London, UK: SAGE.

Mitchell, J. (2015). Electronic documentation: assessmentof newly graduated nurses’ competencyand confidence levels. Online Journal of Nursing Informatics, 19(2), 1-7.

Nguyen, L., Bellucci, E., & Nguyen, L. T. (2014). Electronic health records implementation: An evaluation of information system impact and contingency factors. International Journal of Medical Informatics, 83(11), 779-796.

Saunders, M., Lewis, P., & Thornhill A. (2016). Research methods for business students (7th ed.). Harlow, UK: Pearson Education.

Sherif, V. (2018). Evaluating pre-existing qualitative research data for secondary analysis. Forum: Qualitative Social Research, 19(2), 56-67.

Smith, S. (2017). Practical tourism research (2nd ed.). New York, NY: CABI.

Timmins, F. (2015). Surveys and questionnaires in nursing research. Nursing Standard, 29(42), 42-50.

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