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NURSING ASSESSMENT AND PATIENT CONDITION: EVALUATION OF PRACTICE IN A SIMULATION ENVIRONMENT LADONNA CHRISTY, PHD(c), RN, CCRN-K, RN-BC, CHSE UNIVERSITY OF TEXAS CIZIK SCHOOL OF NURSING ABSTRACT Effective obtainment and documentation of the nursing physical assessment findings and vital signs serve as the foundation to identifying changes in any hospitalized patient’s clinical condition. The Rothman Index (RI), is a clinical algorithm that can identify early changes in a patient’s clinical condition, independent of needing a diagnosis (Rothman, Rothman, & Beals, 2013). Using high fidelity simulation, the purpose of this project is to identify the accuracy and variation of physical assessment findings among a group of nurses working in critical care areas and evaluate the impact of the variation on the RI score. Comment by Beauchamp, Jennifer E: Follow APA formatting for entire document and careful editing needed. Comment by Beauchamp, Jennifer E: But not the accuracy? As written appears you will only evaluate the variation and not the accuracy. Be sure throughout protocol to consistently include all variables you plan to study. The study will follow an observational, correlational design comparing the agreement of nursing assessment with the parameters set by the researcher and accuracy of findings documentation performed among a group of nursing staff in a controlled high-fidelity simulation environment. Inclusion criteria include nurses working in a community hospital’s mixed medical-surgical intensive care unit, emergency room, and progressive care (transitional care). Nurses will perform a full physical assessment using a high-fidelity simulator with predefined diagnoses. Level of agreement will be tested in nursing physical assessment, and documentation through the use of an objective structured clinical exam (OSCE). Data from this work will be used to design a future intervention study. Comment by Beauchamp, Jennifer E: Confusing since above for RI you state diagnosis not needed. Level of agreement seems to get at variation but not accuracy. Where is accuracy in this? Comment by Beauchamp, Jennifer E: Generic statement. SPECIFIC AIMS Effective obtainment and documentation of physical assessment findings and vital signs serve as the foundation to identifying changes in any hospitalized patient’s clinical condition. In recent years, advancements in technology allow decisional algorithms to be embedded in the electronic medical record (EMR); this can aid in identifying changes in a patient’s condition. Several early warning systems (EWS) were developed to identify changes in clinical condition; the Rothman Index (RI) is the only EWS that incorporates the nursing physical assessment (Rothman, Rothman, & Beals IV, 2013). The RI score is derived from several variables including vital signs and patient’s physical assessment parameters and generates a continuous real time score to monitor ongoing patient status. The intent of any EWS, to include the RI includes identifying changes in a patient’s clinical condition before they result in deterioration, and potentially arrest or death (Wengerter, Pei, Asuzu, & Davis, 2018). In this instance, the data used by the RI is largely dependent on elements of nursing physical assessment and clinical judgment. A gap exists in the literature, however, with respect to validating variation of the nursing assessment and documentation of the findings; these ultimately contribute to the RI score. In short, the focus of any change in a patient’s clinical condition starts a nurse’s physical assessment and documentation of the findings. High fidelity simulation is used in many professions to include aeronautics, military, and more recently healthcare practice (Aebersold, 2016). Submerging a participant in a clinically simulated environment has shown positive outcomes throughout the literature (Clapper, 2010; Dieckmann, Gaba, & Rall, 2007; Galloway, 2009). In health care, this technology can be applied to mimic a clinical scenario environment. Preprogramming a high-fidelity manikin (a lifelike full-sized manikin that duplicates patient conditions as programmed by the user) provides a novel opportunity to monitor the findings as a nurse performs a physical assessment and to review how – and how accurately and completely – the findings are communicated in the EMR. In a simulated environment, the investigator/teacher/observer can control for some confounders that may affect the outcome of the observations. These phenomena may help to identify nurses who have a gap in properly performing a physical assessment. In addition, in this setting the investigator can provide a static, non-dynamic patient scenario with set EWS algorithms to consistently observe if there is a variation in assessment and what effect that may or may not have on the corresponding score. In this project, the simulation environment will serve as the foundation to identify those barriers in practice that a nurse(s) may have based on their experience, training and socidemographics. The primary goal of this study is to identify the variation of physical assessment findings among a group of nurses working in critical care areas. It is assumed that nurses working in critical care settings (i.e. Intensive Care Unit [ICU], progressive care/step down/intermediate care units [IMU’s] and the emergency room [ER]) assess patients more frequently than in general medical-surgical areas. For this reason, this study will assess nurses working in higher acuity, critical care settings and to thereby create a foundation for future research in all practice areas. By employing an immersive simulation experience using a high-fidelity mannequin, this research proposes to answer the following question: Does a variation exist within critical care nurses when performing and documenting a nursing physical assessment? How do variations in nursing physical assessment and documentation of the findings of early warning scores? What is known Timely identification of changes in a patient’s clinical status can help prevent patient deterioration (Moon, Cosgrove , Lea, Fairs, & Cressey, 2011). Zambas (2010) explains the importance of the nursing physical assessment as it relates to a patient’s clinical condition, asserting that the physical assessment completed by nursing staff is the first indicator of a patient’s ongoing status. Barriers to performing an adequate physical assessment include the culture of the unit/organization, technological constraints (based on human and environmental inputs), experience, prior training, confidence, role modeling, lack of trust concerning the nurse’s assessment and unit practice by specialty (providing more detail to a specific assessment related to the primary disorder) (Douglas et al., 2014). There are a few multi-dimensional scales available to predict clinical condition changes such as the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and Rothman Index (RI). Each of these algorithms provide indicator scores alerting the nurse that a patient may have a change in clinical condition (Kolic, Crane, McCartney, Perkins, & Taylor, 2015; Rothman et al., 2013; Subbe, Kruger, Rutherford, & Gemmel, 2001). Each EWS algorithm calculates a score within the electronic medical record (EMR) that include respiration rate, heart rate, oxygenation status, systolic blood pressure, urine output, temperature and neurological status (Mitsunaga et al., 2019). Variations in physical assessment may result in score alteration of any EWS which could lead to a delay in patient escalation if warranted. Adding to that, the RI is the one of the specific EWS algorithms that tracks changes in a patient’s condition in an ongoing fashion but relies heavily on the nursing assessment (Rothman et al., 2013). A decrease in the RI score, compared with the previous RI score, can indicate that there may be a need to further evaluate the patient. What is not known It is unknown how much variation exists in nursing assessment skills when performing a physical assessment. One qualitative study to assess for gaps in nursing practice resulted in the nurse’s top responses that included failure to recognize and respond to patient’s clinical assessment that led to deterioration in the patient’s condition (Jones & Johnstone, 2019). While several others indicated nurses may need increased training and education when performing a physical assessment (Douglas et al., 2014). As the physical assessment links the calculation of early warning scores, it is assumed that the assessment piece is vital to determining the need to escalate care of the patient. Within many inpatient hospital units, nurses may have a variety of educational degrees, variant level of experience, education, different cultural beliefs, and other variables that may influence their own personal practice. Any of these factors may cause variations in assessment skills (Chircop, Edgecombe, Hayward, Ducey-Gilbert, & Sheppard-Lemoine, 2013; Douglas et al., 2016, 2014). Minimal research has been completed on nursing physical assessment and documentation in a simulation environment; current research findings generally are of a qualitative nature. Among those identified, the primary focus is within the student population (Danielson, Venugopal, Mefford, & Clarke, 2019; Hatala et al., 2007; Khattab & Rawlings, 2001). Also, few articles exist identifying how much variation exist within a set population of nurses when performing a physical assessment using simulation technology (vital signs, physical assessment) (Boulet et al., 2003; Gharaibeh, Al-Smadi, Ashour, & Slater, 2019). What is needed/What will work The purpose of this study is to assess variation in the nursing physical assessment based on experience, education and training, and sociodemographics in a simulation environment. Due to the need to address nursing skill and practice to improve early recognition of deterioration, a simulation environment will allow the nurse to assess a patient under static conditions, using the skills he or she uses at the bedside. In addition, data will be correlated with findings related to how this may attribute to outcomes in EWS to include the RI (which relies heavily on nursing assessment). The long-term goal of this work is to better to identify practice gaps in nursing assessment and provide training, mentoring and education in future studies. The specific objective of this proposal is to perform inter-rater reliability testing through observing a group of critical care nursing staff with various demographic and experience parameters to identify variation of a physical assessment within a group of nurses. The central hypotheses focus on a group of nurses working in critical care units, positing that there exists no difference in each of the nurse’s performance of the nursing physical assessment and concurrent documentation. The rationale for the proposed research is that measuring the nursing assessment component in the predictive ability of clinical decision support technology, i.e. EWS is critical to quantifying changes in patient’s clinical condition. Specific Aims The study will be pursued with two aims. To test the central hypothesis and thereby obtain the overall objective, the following primary aim will be pursued: Aim 1. Identify sociodemographic, experience, and educational variables of the participant as it relates to the completion of a physical assessment. Hypothesis 1: More experienced nurses working in a critical care setting can perform a correct physical assessment when compared with nurses with less than two-year’s experience. 1a. Recruitment strategies will include the need to obtain nurses in different levels of practice experience, for this reason, nurses with all levels of experience will be eligible. 1b. Data will be analyzed using nurse demographics (age, race, certifications, experience in current position, primary language, birthplace, years worked in United States, etc.) Aim 2. Test agreement in nursing physical assessment and documentation using high-fidelity simulation environment. Hypothesis 2: The variation in nursing assessment and documentation among registered nurses will be no greater than ± 2 standard deviations from the mean when assessing the same patient under the same conditions (using a pre-programmed high-fidelity mannequin). 2a. Reliability identifies the reproducibility of the clinicians’ practice. The focus of the testing will be in a simulated environment with 2 different simulated patients. One patient will be preprogrammed to have respiratory complications, another patient will be programmed to have cardiac complications. The simulator’s monitor will have vital signs displayed for the nurse to document. The nurse participant will complete 2 full patient assessments, one on a high fidelity simulator, one in a virtual reality environment, and document finding s in the EMR. 2b. A priori criterion of ICC of r ≥ .80 (95% CI) will be set to establish reliability. An intraclass correlation between .075 and .90 indicate “good” reliability (Koo & Li, 2016). 2c. The Bland-Altman method compares the measurements’ differences to the mean of the measurements. Level of agreement will be measured among assessment variables with a priori criterion of 2-5 SD from the mean. Significance As a large cohort of the population in the United States continues to thrive well beyond life expectancy, nursing practice must be empirically consistent and methodical to discriminate an accurate picture of the patient’s current condition (National Council on Aging, 2019). A gap exists within the known consistency of nursing assessment between each clinician and how these variables affect EWS algorithms. This study addresses the foundational components of identifying changes in a patient’s clinical condition through the variables associated with the nurse population (such as experience, education and sociodemographics). In turn, EWS algorithms such as the NEWS, MEWS and RI RI are reflective upon a physical assessment (Alam et al., 2014; Finlay, Rothman, & Smith, 2014). Any errors in correctly assessing the patient results in a less than accurate score, thus reliability testing will assist in identifying trends in documentation and prepare for the next phase of research involving educational design to ensure consistency. This is vital as the benefit of having accurate real time information about a patient’s condition can improve their treatment modality, decrease time of stay and effect post discharge outcomes (Downey, Tahir, Randell, Brown, & Jayne, 2017). Upon completion of proposed research, identifying variables that may differentiate assessment skills and documentation in nursing populations will aid in determining how this element effects EWS. Reliability testing completion will further identify whether gaps actually exist within how nurse clinicians assess and document patient findings and thereby affecting the outcome of various scoring instruments. This phenomenon is significant in that it adds to science the relevance of applicability of EWS instruments that are meaningful to practice and patient care. This project will also guide future research to assess and implement measures through education and training that address barriers to performance and documentation of nursing assessment. Innovation From an innovative standpoint, this proposed project incorporates an environment entirely of simulation with a set of predesigned patients. This project supports an innovative design thought the following: Use of high-fidelity manikins in a simulated patient environment. Using high fidelity manikins, two scenarios would be programmed for the nurse to perform a physical assessment, and complete documentation in the electronic medical record (EMR). Each manikin will be programmed to display a set of vital signs and have key indicators of patient condition changes that are visible and/or stated by the simulator. The two programmed scenarios will include two common diagnosis that are seen preemptively before a patient decline: respiratory distress and active chest pain. Each clinician participating in the study will be blinded to the each of the patient’s diagnosis but will be given a brief report before performing the patient that led up to the patient’s admission. The nurse will be allowed a total of 30 minutes to perform a complete physical assessment, and document in the EMR. The participants will be instructed not to discuss any patient scenarios and or elements of the simulation environment. Use of state-of-the-art hand-held technology/software to document simulated findings. The results will be documented on a hand help pad or laptop in a simulated version of the organization’s EMR. The RI score will automatically be calculated once the vital signs and physical assessment data have been entered. At the end of 30 minutes the PI will be given 10 minutes prepare for the next scenario, and the next participant will begin the assessment and documentation process. This is a novel approach to identify differences in nursing assessment that may or may not affect the outcome of EWS (in this case, the RI). A simulated environment will be adapted for the iPad or laptop that will mirror the EMR and be able to download variables into SPSS. In addition to identifying variation in physical assessment practices, this project seeks to examine the following: Do more experienced nurses perform a physical assessment correctly in a simulated environment? Do more highly educated nurses perform physical assessments correctly in a simulated environment? Do cultural variation influence assessment practices? Is there any difference in performing physical assessments in a high fidelity versus a virtual reality setting? Given these variables the opportunity exists to identify trends in assessment and documentation which may indicate how well a population of nurse clinicians document and assess patients. APPROACH Theoretical Framework Simulation is an art form that is experiential in nature, and can be used as a method to evaluate and observe behavior, patterns, and choices (Jeffries, 2012). The National League of Nursing (NLN)/ Jeffries Theory Simulation Framework will be used to guide the design and implementation of the study. The five concepts of the theory include facilitator, participant, educational practices, outcomes, and simulation design characteristics (Jeffries, 2016). Jeffries (2005) assert there may not exist a need to use all five concepts; the goal of the framework is to provide a contextual basis for the concepts of relevancy to studies (p.97). Classical test theory will be used as a guide to establish psychometric properties of the assessment and simulation as it is one of the more commonly used theories in health research when evaluating measurements (DeVellis R. , 2006). The classical test theory assert that there will exist some random error in measurement; the observed score is equal to the true score plus the random error (Carmines & Zeller, 1979). Measurements Comment by Beauchamp, Jennifer E: Need to address every measured used within your study- not other studies. Patient Physical Assessment The systems physical assessment is a standard approach to collect data about patients’ baseline and ongoing condition. This approach is used by healthcare professions educational entities (e.g. schools of medicine and nursing). A systems OSCE physical assessment form will be created using the variables in the electronic medical record. The RI determines a clinical condition score based on the integration of 26 variables that include vital signs, nursing assessment input, laboratory values, and cardiac rhythm (Rothman et al.,, 2013). The RI follows two algorithms, one algorithm with laboratory values and one without laboratory values – when there is a lapse in labs for more than 48 hours. Variables of a calculated Rothman score include: vital signs (temperature, systolic/diastolic blood pressure, pulse oximetry, respiration rate and heart rate), Head to toe nursing assessment (cardiac, respiratory, gastrointestinal, genitourinary, neurological, skin, safety, peripheral vascular, food/nutrition, psychosocial and musculoskeletal), Braden score, laboratory tests (creatinine, sodium, chloride, potassium, blood urea nitrogen, white blood cells, and hemoglobin), and cardiac rhythm (sinus rhythm, sinus bradycardia, sinus tachycardia, atrial fibrillation, atrial flutter, heart block, junctional rhythm, paced, ventricular fibrillation). If a patient does not meet the normal parameters of each body system, then the score of “fail” is given. A score of “pass” is given if all indicators in each body system meet normal parameters. All elements must be met as defined by each system in order to calculate an RI score. Rothman Index Algorithm The RI is a continuous measure of a patient’s condition that is independent of the patient’s diagnosis. RI determines a clinical condition score based on the integration of 26 variables that include vital signs, nursing assessments, laboratory values, and cardiac rhythm (Rothman, Rothman, & Beals IV, 2013). The RI follows 2 algorithms, 1 algorithm calculated with laboratory values, and one algorithm calculated without laboratory values, with a lapse in labs for more than 48 hours. Non laboratory value scoring algorithm is dependent upon of 42% vital signs, 11% cardiac rhythms and 47% nursing assessment (Rothman, Rothman, & Beals IV, 2013). The integration of laboratory values yields a RI score that is dependent upon 35% of vital signs, 34% of nursing assessments, and 31% of lab results. In both instances the nursing assessment and vital signs account for the majority of the scoring algorithm. Evidence of validity show an AUC of ≥0.92 when matched against categories of discharge and an AUC of ≥.93 against 24-hour mortality in a multiple site hospital study of over 170,000 patients in acute and critical care settings. Design Comment by Beauchamp, Jennifer E: Largely needs more detail and/or clarification. Reviewing IRB approved protocol templates might help. The study will follow an observational, correlational design comparing the agreement of nursing assessment with the parameters set by the researcher and accuracy of findings documentation performed among a group of nursing staff in a controlled high-fidelity simulation environment. Comment by Beauchamp, Jennifer E: This seems to get at accuracy and variability between nurses but how does it tie into the scoring algorithm? Setting. The study will take place in a community hospital in Houston, Texas. Population. The population of participants will be solicited from the mixed medical-surgical ICU, ER and IMU. Since the frequency of assessment is higher in the critical care areas than in the medical-surgical acute care areas, due to patient ratio and acuity, the assumption is that the critical care nurse will appropriately and consistently assess and document a physical assessment and vital signs. Comment by Beauchamp, Jennifer E: One might also argue that since frequency is higher this may negatively impact assessment/documentation since busier. Sample. A convenience sample of nurses who work in the three areas will be obtained. Nurses will be included if they: 0-30+ years of experience, hold a registered nurse license to practice in the state of Texas from an associate or baccalaureate degree program, have little or no experience using the Rothman Index, and work on the floor in a day/night shift or weekend capacity. All employment statuses will be applicable for the study (full-time, part-time, per-diem, contract and other). Nurses will be excluded if they hold a licensed vocational nurse certification to practice in the state of Texas, work primarily in an area other than the medical-surgical ICU and PCU’s. A large effect size of 0.5, power of .80 and p value of 0.05 yield a sample size of 26 participants. Comment by Beauchamp, Jennifer E: How will each aspect of the criteria be determined (procedures)? Comment by Beauchamp, Jennifer E: State the 3 areas. Procedure for Data Collection A flyer will be developed that provides an overview of the study, criteria for participation, and contact information of the principal investigator (PI). This information will be posted in the employee lounge in the units of interest, an email will be sent out, and project will be discussed in unit meetings to solicit participants. Nursing staff who want to participate in the study can voluntarily contact the principal investigator to set a time and date for consent and a detailed overview of the requirements. At the agreed upon time and date, the principal investigator will meet with the potential participant, explain the purpose of the study, requirements for participation and to answer any questions the potential participant may have. Once the respondent agrees to participate in the study, two copies of the informed consent will be signed by investigator and participant (one copy will be given to the participant and the other retained by the investigator). The nurse will then pick a time to perform the testing and will be scheduled by the PI. The nurse participant will then fill out a sociodemographic questionnaire (See Figure 1-questionnaire). A 20.00 gift card will be given to participants after the completion of the session. Data will be collected in the simulation environment tentatively over the course of one month. Comment by Beauchamp, Jennifer E: How will email addresses be obtained? Comment by Beauchamp, Jennifer E: You need procedures/details for how all data will be collected and recorded. Largely, you only provide recruitment procedures. Also what steps will you take to ensure data quality and control? Data Analysis Plan To test the central hypothesis and thereby obtain the overall objective, the focus of the project will include testing the level of agreement and interrater reliability of the performance of nursing physical assessment. The project will use the RI score of a patient who has not had any laboratory values for more the 48 hours (following the RI scoring index with a lapse in laboratory values for more than 48 hour). Within this algorithm, the RI score is heavily dependent on the nurse’s physical assessment. As this project will be identifying variation in physical assessment, the vital signs, and cardiac rhythm (ECG) will be provided to the participant before the beginning of the simulation. A handoff pre brief report and an orientation to the manikin and simulation environment will be given to the participant before he/she is allowed to begin the assessment (see appendix). The participant will be allowed to ask questions about the simulation environment before being allowed to begin. The following hypothesis will be tested: Hypothesis 1: The variation in nursing assessment and documentation among registered nurses will be no greater than ± 2 standard deviations from the mean when assessing the same patient under the same conditions (using a pre-programmed high-fidelity mannequin). Testing agreement in nursing physical assessment, and documentation will be conducted through observation using an OSCE in a high-fidelity simulation environment. Each participant will be randomized and selected to complete one of the two scenarios. A total of 12 body systems will be assessed (see table 2, coding sheet). The variables in each of the body systems must be all correct to output the accurate score (a deviation from one variable in the assessment results in s “fail” per the body system in the algorithm) (Rothman et al., 2013). A correct assessment will be documented as 001; an incorrect assessment of the system will be documented as 002 for both scenarios. At the end of the scenario, the score will be provided to the PI and reviewed against the score of the participant. Once the nurse completes the assessment and documentation session, the PI will provide a follow-up questionnaire addressing questions about the simulation and assessment component. Each participant will have 30 minutes to complete the session. To allow for sufficient time to complete the simulation, documentation and post follow-up survey, participants will be scheduled every 2 hours, for an 8-hour day (in which a total of 4-5 participants will be scheduled each day). Hypothesis 2: More experienced nurses working in a critical care setting can perform an accurate physical assessment versus nurses with less than one-year experience. To assess variables that may affect the outcome of the physical assessment, a sociodemographic questionnaire will be given to the participant to complete during the consent. Variables that will be assessed will include age, race, primary language, years as a nurse, years of experience in the unit currently working, years of experience in a US hospital, certification held (type and date of initial certification), educational level, and employment status. The principal investigator will assign a 2-digit code to all patient information that will serve as an identifier. Only the principal investigator will have access to the identifiers and keep the codes. The study team will not have access to the identifiers or the codes. All data will be entered through the UTHealth REDCAP data base. Hypothesis 3: No relationship exists between any variation in nursing assessment and the RI score. Each of the assessment stations will be reviewed for accuracy and correlated with an RI score. Statistical Analysis. Data will be analyzed using SPSS version 25.0 (Armonk, NY: IBM). Sociodemographics and follow-up questionnaire will be assessed through descriptive statistics. Inter-rater reliability will be assessed using a one- and two-way ANOVA with a priori criterion intra-class correlation coefficient (95% CI, ICC). A priori criterion will be set at ICC of r ≥ 0.8. Significance level will be set at 0.05. The Bland- Altman method will be used to measure the level of agreement between the assessment and documentation from the nurse participants. A priori criteria of the following variables will be established: Nursing Assessment: · Neurological System = ± 2 standard deviations (SD) from the mean · Cardiac System = ± 2 standard deviations (SD) from the mean · Integumentary System = ± 2 standard deviations (SD) from the mean · Respiratory System = ± 2 standard deviations (SD) from the mean · Gastrointestinal System = ± 2 standard deviations (SD) from the mean Pearson’s correlation will be used to correlate any variation in assessments and the RI score. The follow up questionnaire will include an open-ended question to obtain feedback about the simulation experience. The question will be reviewed line by line and categorized into themes. POTENTIAL PITFALLS AND LIMITATIONS AND ALTERNATIVE STRATEGIES Pitfalls to this project may include the inability to obtain an adequate sample size, equipment and challenges that may affect the outcome of the assessment. In order to address equipment challenges, the scenarios will be loaded on the simulator and a trial run will be performed at least 24 prior to the start of the session. Dues to the observational nature of the study, causation cannot be identified. This study will set the groundwork for future studies in a simulation environment that will identify factors related to nursing practice. Comment by Beauchamp, Jennifer E: What does this mean? Comment by Beauchamp, Jennifer E: Makes it sound as though you are testing simulation and not EWS. HUMAN SUBJECTS 18 4 There are no direct personal benefits to participating in the study. The findings from this study may benefit future educational efforts to ensure training and observation of consistent practice to improve patient care. While no risks are anticipated, participants may experience anxiety because they are invited to participate in performing a physical assessment that will be reviewed for research purposes. This may cause some anxiety in the participant and/or participants may become tired during the session. Considerations (e.g., participants taking breaks or re-scheduling the session if they wish to continue at a later date) will be offered by the principal investigator to address these concerns. Participants will be offered reassurance about confidentiality regarding their completion of assessment and concurrent documentation. Participants may end participation at any time during the study. Risks and benefits are clearly outlined in the Participant Information Sheet (figure 2) which will be given to each potential participant at recruitment. Comment by Beauchamp, Jennifer E: Tie into EWS. Comment by Beauchamp, Jennifer E: Will PHI be collected? Comment by Beauchamp, Jennifer E: Needs to be detailed under data management above. Study Timeline Description 2019 2020 Comment by Beauchamp, Jennifer E: What occurs during the empty months? December January February March April May June July August September October November December January Study Design XX IRB Application XX Recruitment and Data Collection XX Data Analysis and Result Review XX Manuscript Preparation XX Publication and Abstract Submission XX Follow Up Study Proposals XX References: Aebersold, M. (2016). The history of simulation and its impact on the future. AACN Advanced Critical Care, 27(1), 56–61. https://doi.org/10.4037/aacnacc2016436 Boulet, J. R., Murray, D., Kras, J., Woodhouse, J., McAllister, J., & Ziv, A. (2003). Reliability and Validity of a Simulation-based Acute Care Skills Assessment for Medical Students and Residents. Anesthesiology, 99(6), 1270–1280. https://doi.org/10.1097/00000542-200312000-00007 Chircop, A., Edgecombe, N., Hayward, K., Ducey-Gilbert, C., & Sheppard-Lemoine, D. (2013). Evaluating the Integration of Cultural Competence Skills Into Health and Physical Assessment Tools: A Survey of Canadian Schools of Nursing. 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Assessing the relationship between cardiac physical examination technique and accurate bedside diagnosis during an objective structured clinical examination (OSCE). Academic Medicine, 82(10 SUPPL.), 26–29. https://doi.org/10.1097/ACM.0b013e31814002f1 Jeffries, P. R. (2005). A framework for designing, implementing, and evaluating: simulations used as teaching strategies in nursing. Nursing Education Perspectives, 26(2), 96–103. Jeffries, P. R. (2012). Simulation in nursing education: From conceptualization to evaluation. New York, NY: National League for Nursing. Jeffries, P. R. (Ed.). (2016). The NLN Jeffries Simulation Theory. New York: National League for Nursing. Jones, A., & Johnstone, M. J. (2019). Managing gaps in the continuity of nursing care to enhance patient safety. Collegian, 26(1), 151–157. https://doi.org/10.1016/j.colegn.2018.06.006 Khattab, A. D., & Rawlings, B. (2001). Assessing nurse practitioner students using a modified objective structured clinical examination (OSCE). Nurse Education Today, 21(7), 541–550. https://doi.org/10.1054/nedt.2001.0590 Kolic, I., Crane, S., McCartney, S., Perkins, Z., & Taylor, A. (2015). Factors affecting response to National Early Warning Score (NEWS). Resuscitation, 90, 85–90. https://doi.org/10.1016/j.resuscitation.2015.02.009 Koo, T. K., & Li, M. Y. (2016). A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine, 15(2), 155–163. https://doi.org/10.1016/j.jcm.2016.02.012 Polit, D. F., Beck, C. T., & Owen, S. V. (2008). The Content Validity Index: Are You Sure You Know What’s Being Reported? Critique and Recommendations. Research in Nursing & Health, 31(4), 341–354. https://doi.org/10.1002/nur Rothman, M. J., Rothman, S. I., & Beals, J. (2013). Development and validation of a continuous measure of patient condition using the Electronic Medical Record. Journal of Biomedical Informatics, 46(5), 837–848. https://doi.org/10.1016/j.jbi.2013.06.011 Subbe, C. P., Kruger, M., Rutherford, P., & Gemmel, L. (2001). Validation of a modified Early Warning Score in medical admissions. QJM : Monthly Journal of the Association of Physicians, 94(10), 521–526. https://doi.org/10.1093/qjmed/94.10.521 Zambas, S. I. (2010). Purpose of the systematic physical assessment in everyday practice: Critique of a “sacred cow.” Journal of Nursing Education, 49(6), 305–310. https://doi.org/10.3928/01484834-20100224-03 APPENDIX Comment by Beauchamp, Jennifer E: Text should refer to each table. But not sure why this is reported. Will you stratify your data analysis based on certain demographics? Will you use purposive sampling to obtain diversity? Table 1. Nurse Demographics in Texas by age group Younger than 25 Age 25-34 Age 35-44 Age 45-54 Age 55-64 Over the age of 65 Ethnicity Male Female Male Female Male Female Male Female Male Female Male Female Caucasian 6690 603 4480 5498 42307 5931 36649 5675 39953 4111 22355 1947 African American 652 57 7563 911 10763 1508 8018 1209 4796 648 2708 157 American Indian 56 10 348 35 283 50 246 41 179 48 80 14 Asian 375 60 5721 1544 6289 1314 6753 1403 3463 397 1441 111 Hispanic 94 16 1813 387 6098 1223 5183 1317 3261 706 1652 246 Other 1305 250 10131 2307 6147 1354 3338 776 1154 228 304 38 Table II. Nurse Demographics by Degree by counties in Houston County Diploma Associates Baccalaureate in Nursing Baccalaureate in other Master’s in nursing Master in Other Field Doctorate in Nursing Doctorate in another Field RN/PN unknown Harris 1369 10352 21742 3 5011 5 501 21 12 Fort Bend 679 2494 8622 1 2244 2 202 5 0 Montgomery 230 2458 3436 2 796 0 72 0 1 Brazoria 163 1908 4032 0 1121 0 97 2 0 Galveston 171 2454 2966 0 768 0 102 4 2 Liberty 7 306 181 0 37 0 4 0 0 Waller 9 101 174 0 33 0 1 0 0 Chambers 11 232 220 0 46 0 8 0 2 PARTICIPANT INFORMATION SHEET Comment by Beauchamp, Jennifer E: Not all aspects of inclusion criteria included so how will you determine those aspects? Name________________________ Date____________________ Comment by Beauchamp, Jennifer E: How will PHI be managed? Unit_________________________ Classification of Unit (circle one) ER ICU IMU Please answer the following questions to the best of your knowledge. I. EDUCATION a. What is the highest level of education you have completed? (Check all that apply) i. Associate Degree in Nursing ii. Associate degree other than nursing iii. Baccalaureate in Nursing iv. Baccalaureate other than Nursing v. Master’s degree in nursing vi. Master’s Degree other than Nursing vii. Doctor of Nursing Practice (DNP) viii. Doctor of Philosophy (PhD) in Nursing ix. Doctorate other than Nursing II. EMPLOYMENT STATUS a. Full time b. Part time c. Per Diem (PRN) III. Ethnicity a. White b. Hispanic or Latino c. Black or African American d. Native American or American Indian e. Asian / Pacific Islander f. Other IV. PRIMARY LANGUAGE a. English b. Other (Please Specify)________________________________ V. YEARS OF EXPERIENCE AS A NURSE a. Less than one year b. 1-2 years’ experience c. 3-5 years’ experience d. 5-10 years’ experience e. Greater than ten years’ experience (please specify number of years) _____________ VI. YEARS OF EXPERIENCE IN THE UNIT CURRENTLY WORKING a. Less than one year b. 1-2 years’ experience c. 3-5 years’ experience d. 5-10 years’ experience e. Greater than ten years’ experience (please specify number of years) _____________ VII. YEARS OF EXPERIENCE IN A US HOSPITAL a. Less than one year b. 1-2 years’ experience c. 3-5 years’ experience d. 5-10 years’ experience e. Greater than ten years’ experience (please specify number of years) _____________ VIII. CERTIFICATION HELD (TYPE AND DATE OF INITIAL CERTIFICATION) Please specify your certification____________________________________________ Please specify the initial date of certification___________________________________ IX. AGE a. What is your age ____________________ Figure 1. Demographic Assessment Table 2. PI Coding sheet Comment by Beauchamp, Jennifer E: This procedure is unclear in the text above. Body System Variables assessed Scenario 1 abnormalities (Chest Pain) Scenario 2 abnormalities (Respiratory insufficiency) Code Assigned if all variables are correct Code assigned if 1 or more variables are incorrect Pain Pain standard Without pain or VAS (visual analogue pain scale) <4 or experiencing chronic pain that is managed effectively. Neurological Generalized pain of 5. No radiation. Upset stomach. Pain on inspiration Diminished breath sounds on all right sided lobes 001 002 Cardiac Pulse regular, rate 60–100 BPM, skin warm and dry. Blood pressure less than 140/90 and no symptoms of hypotension. Pulse 144 beats per minute. Blood pressure 100/58 Pulse 126 Blood Pressure 134/92 001 002 Respiratory Resp. 12–24/min at rest, quiet and regular. Bilateral breath sounds clear. Nail beds and mucous membranes pink. Sputum clear, if present. RR 26 RR 28, noted difficulty breathing 001 002 Gastrointestinal Abdomen soft and non-tender. Bowel sounds present. No nausea or vomiting. Continent. Bowel pattern normal as observed or stated None None 001 002 Genitourinary Voids without difficulty. Continent. Urine clear, yellow to amber as observed or stated. Urinary catheter patent if present None None 001 002 Neurological Alert, oriented to person, place, time, and situation. Speech is coherent None None 001 002 Skin Skin clean, dry and intact with no reddened areas. Patient is alert, cooperative and able to reposition self independently. Braden scale >15 Diaphoretic None 001 002 Safety/falls risk Safety/fall risk factors not present. Patient is not a risk to self or others. 001 002 Peripheral Vascular Extremities are normal or pink and warm. Peripheral pulses palpable. Capillary refill <3 s. No edema, numbness or tingling. standard Bounding pulses 001 002 Food/Nutrition No difficulty with chewing, swallowing or manual dexterity. Patient consuming >50% of daily diet ordered as observed or stated none Eaten 25% of breakfast 001 002 Psychosocial Behavior appropriate to situation. Expressed concerns and fears being addressed. Adequate support system. None None 001 002 Musculoskeletal Independently able to move all extremities and perform functional activities as observed or stated (includes assistive devices) None Moves with pain 001 002 Figure 2. Participant Information Sheet(adapted from the UTH Committee for the Protection of Human subjects informed consent template) (see attached) Comment by Beauchamp, Jennifer E: Where? NURSING ASSESSMENT AND PATIENT CONDITION: E VALUATION OF PRACTICE IN A SIMULATION ENVIRON M E NT L ADONNA CHRISTY , PHD( c), RN , CCRN - K , RN - BC , CHSE UNIVERSITY OF TEXAS CIZIK SCHOOL OF NURSING NURSING ASSESSMENT AND PATIENT CONDITION: EVALUATION OF PRACTICE IN A SIMULATION ENVIRONMENT LADONNA CHRISTY, PHD(c), RN, CCRN-K, RN-BC, CHSE UNIVERSITY OF TEXAS CIZIK SCHOOL OF NURSING

Sheet1 Name Date Unit (Classification Code) Education (Code) Employment Status Ethnicity Primary Language Year of Experience as A Nurse Years of Experience in the Unit Currently Working Year of experience in a US Hospital Certification Type Certification Date Age What Area Do you work in Other Body System Scenario 1 Body System Scenario 2 Pain Scenario 1 Pain Scenario 2 Cardiac Scenario 1 Cardia Scenario 2 Respiratory Scenario 1 Respiratory Scenario 2 Gastrointestinal Scenario 1 Gastrointestinal Scenario 2 Genitourinary Scenario 1 Genitourinary Scenario 2 Neurological Scenario 1 Neurogical Scenario 2 Skin Scenario 1 Skin Scenario 2 Safety Scenario 1 Safety Scenario 2 Peripheral Vascular Scenario 1 Peripheral Vascular Scenario 2 Food/Nutrition Scenario 1 Food/Nutrition Scenario 2 Psychosocial Scenario 1 Psychosocial Scenario 2 Musculoskeletal Scenario 1 Musculoskeletal Scenario 2

Data Analysis Plan 1. Present summary statistics table with summary of demographic variables and performance related variables. 2. Reliability analysis using Cronbach’s alpha and intra-class correlation for all performance measures. 3. If consistent from the reliability analysis, create overall performance score or performance scores in subgroups. One ca also look into scores separately for scenario 1 and scenario 2. 4. Use general linear models with performance measure as the dependent variable and all sociodemographic variables as independent variables to find their effects on the performance. Exclude the variables, which are not significant. 5. Perform analysis of variance for performance measure for critical care nurses and medical surgical nurses with other socio-demographic variables identified in the previous step as covariates (or confounding factors). 6. Similarly, look into the effects of experience. Use interaction effects in analysis of variance to check if the effect of experience is different for medical surgical nurses to the critical care nurses. 7. For Aim 2, we have already done reliability analysis in 2 above. One can also check the level of agreement in assessment between the nurses with different methods like Bland-Alman etc. 8. One can also look into differences between scenarios, between different assessments etc.

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