Student Name Capella University NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner Prof. Name Date EHR Data Entry Protocols Purpose This policy and procedure aim to establish requirements for electronic documentation in the ambulatory health care record Electronic Health Record (EHR) for the organization. Following the principles below will help ensure accurate and effective documentation in the established EHR – iSalus that will serve patients well and facilitate communication and care coordination. This is considered best practice for reimbursement, risk management, care coordination, and communication among the healthcare team. Creating an electronic medical record that facilitates excellence in patient care meeting regulatory requirements, such as billing, clinical practice, necessary use, and standards of effective care, also serves as a legal record. It requires attention to detail and precise and accurate data entry. Legal, ethical, and billing compliance are no different from those governing traditional handwritten notes. However, there are fundamental differences between the EHR and paper records. EHRs have built-in support tools that can be helpful as well as problematic. The purpose of these protocols and standards is to facilitate an organizational standardized process for data entry and documentation within the facility’s electronic medical record (EMR) based on the following: NURS FPX 8030 Assessment 5 Creation of Policy or Procedure Electronic health records (EHRs) can improve patient safety through access to accurate and up-to-date patient information (Koppel et al., 2016). However, EHRs can also introduce new risks if not used correctly, such as errors in patient care resulting from poor data entry practices (Shim et al., 2019). To mitigate these risks and improve patient safety, we must implement strict data protocols in our healthcare organization. Supporting evidence from the literature suggests that implementing strict data entry protocols can help reduce the risk of errors in patient care (Bates & Gawande, 2017). A systematic review and meta-analysis of the impact of EHR adoption on patient safety found that the risk of errors increased when EHRs were not used properly but that implementing strict protocols for data entry and other interventions aimed at improving EHR usability could help to reduce the risk of errors (Xu et al., 2020). Clinical professionalism extends to the documentation of healthcare providers’ services – signing the clinical note implies that the provider takes full responsibility for the note’s content. Medical records serve to document the care provided and serve as legal documents. Entries in the EHR should be appropriate, concise, timely, relevant, and pertinent to the patient’s condition on the date the entry was made. NURS FPX 8030 Assessment 5 Creation of Policy or Procedure Revised PICO(T) question: In healthcare organizations using EHRs (P), how does the implementation of strict protocols for data entry (I) impact the risk of errors in patient care (O) compared to no intervention (C)? Is this intervention improving patient safety (T) over three months? It is necessary to act with the development of this policy to address the gap or problem in patient safety related to poor patient data entry practices in the organization’s EHR system. By implementing strict protocols for data entry, our healthcare organization can help reduce the risk of errors in patient care and improve patient safety. This policy has been developed based on the evidence from the literature review. It has been designed to address the identified patient safety issue in a systematic, evidence-based manner. Population Affected by the Policy The population affected by the policy on EHR data entry protocols includes healthcare providers and other users of the EHR, such as nurses, technicians, therapists, and other end users of the healthcare organization. Healthcare providers, such as doctors, nurses, and other clinical staff, will be responsible for implementing the policy and ensuring compliance with the established protocols for data entry. Patients will benefit from the policy through reduced risk of errors and adverse events related to EHR use. The policy applies to all healthcare providers and patients within our organization, regardless of age, gender, race, or other demographic factors. Definitions Electronic health record (EHR): An electronic version of a patient’s medical history, which includes information such as medications, allergies, diagnoses, and test results. EHRs manage and share patient information within healthcare organizations and can improve patient safety by providing access to accurate and up-to-date patient information (Koppel et al., 2016). Data entry: Entering information into an electronic system, such as an EHR. Data entry can involve transcribing information from paper records, updating information in the EHR, or entering new information into the system. Protocol: A set of rules or guidelines that outline how a particular process or procedure should be carried out. In the context of EHR data entry, protocols may include guidelines for verifying the accuracy of entered information, procedures for double-checking critical data points, and standards for ensuring the completeness and timeliness of data entry. Patient safety: The absence of preventable harm to a patient during the provision of healthcare services (World Health Organization, 2018). Patient safety is a critical issue in healthcare and can be impacted by factors such as errors in patient care, adverse events, and medical mistakes (Bates & Gawande, 2017). Policy Statement Our healthcare organization is committed to improving patient safety and is implementing a policy on EHR data entry protocols to reduce the risk of errors in patient care. The policy will be implemented within the next three months and applied to all healthcare providers and patients within our organization. The goals of the policy are to ensure that all data entered into the EHR is accurate, complete, and up-to-date and to provide clear guidelines for data entry to reduce the risk of errors. By implementing this policy, we aim to improve patient safety and the quality of care we provide to our patients. The behavioral health program’s policy is that all users of the organization attend mandatory compliance training within 30 days of hire annually. When critical updates to the EHR system are made, they may require additional training. Procedure Develop training based on policy and procedure:  The office manager/practice manager will

Student Name Capella University NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner Prof. Name Date Critical Appraisal of Evidence-Based Literature on Diagnostic Errors The accurate diagnosis of medical conditions is a fundamental responsibility for healthcare providers. However, errors in diagnosis, including missed, incorrect, or delayed diagnoses, can lead to adverse outcomes (Abimanyi-Ochom et al., 2019). The research on diagnostic errors faces challenges in defining, detecting, preventing, and discussing these errors. Furthermore, effectively measuring diagnostic errors remains elusive, with limited sources of valid and reliable data. Such errors contribute to elevated healthcare costs, resulting from negative health outcomes, income loss, decreased productivity, and, in extreme cases, loss of life (Abimanyi-Ochom et al., 2019). Erosion of trust in the healthcare system can lead to dissatisfaction among patients and healthcare professionals. Therefore, there is a compelling need for effective interventions to mitigate diagnostic errors in clinical settings. PICOT Question  Among adult patients in acute or ambulatory care settings (P), the presence of a clinical decision support system in a hospital (I), compared with its absence (C), can enhance diagnostic processes to reduce diagnostic errors (O), within 24 months of implementation (T). Critical Appraisal Tool  The JBI Checklist for Systematic Reviews will be employed as the critical appraisal tool for evaluating articles in this study. This tool ensures the methodological quality of the studies and assesses the extent to which bias has been addressed in their design, conduct, and analysis. Given that the selected studies are largely systematic reviews, the JBI Checklist is deemed appropriate for its ability to provide robust evidence across various research questions. Annotated Bibliography Abimanyi-Ochom, J., et al. (2019). Strategies to reduce diagnostic errors: a systematic review. BMC Medical Informatics and Decision Making, 19(1), 1-14. [https://doi.org/10.1186/s12911-019-0901-1] This study explores communication and audit strategies to reduce diagnostic errors, emphasizing technology-based interventions like clinical decision support systems. The research recommends trigger algorithms, including computer-based systems and alerts, to prevent delays in diagnosis and improve accuracy. Ronicke, S., et al. (2019). Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet Journal of Rare Diseases, 14(1), 1-12. [https://doi.org/10.1186/s13023-019-1040-6] This study investigates the diagnostic decision support system Ada DX, showing its potential to suggest accurate rare disease diagnoses early in the course of cases. The Checklist for Case-Control Studies ensures the methodological quality of the study, supporting the use of clinical decision support systems in diagnostic improvement. Fernandes, M., et al. (2020). Clinical decision support systems for triage in the emergency department using intelligent systems: a review. Artificial Intelligence in Medicine, 102, 101762. [https://doi.org/10.1016/j.artmed.2019.101762] NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature This paper reviews the contributions of intelligent clinical decision support systems to emergency department care. The study underscores the benefits of these systems in triage improvement, critical care prediction, and reduced misdiagnosis, supporting the potential of CDSS in reducing diagnostic errors. Ford, E., et al. (2021). Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners. BMC Medical Informatics and Decision Making, 21(1), 1-13. [https://doi.org/10.1186/s12911-021-01557-z] This qualitative study explores the features and contexts of clinical decision support system use, providing insights into barriers and facilitators. It emphasizes coproduction with general practitioners, clear clinical pathways, and adequate training to improve CDSS implementation. Proposed Intervention  Various interventions have been proposed for preventing diagnostic errors, with clinical decision support systems (CDSS) standing out as effective. Studies demonstrate that CDSS can significantly reduce misdiagnosis and delayed diagnosis, particularly in rare disease cases. Conclusion  Diagnostic errors, including missed, wrong, and delayed diagnoses, pose significant risks to patient well-being. Limited research on diagnostic errors necessitates effective interventions. This study recommends the implementation of CDSS, supported by evidence indicating its efficacy in reducing diagnostic errors and ensuring patient safety. References Abimanyi-Ochom, J., Bohingamu Mudiyanselage, S., Catchpool, M., Firipis, M., Wanni Arachchige Dona, S., & Watts, J. J. (2019). Strategies to reduce diagnostic errors: A systematic review. BMC Medical Informatics and Decision Making, 19(1), 1-14. [https://doi.org/10.1186/s12911-019-0901-1] Fernandes, M., Vieira, S. M., Leite, F., Palos, C., Finkelstein, S., & Sousa, J. M. (2020). Clinical decision support systems for triage in the emergency department using intelligent systems: A review. Artificial Intelligence in Medicine, 102, 101762. [https://doi.org/10.1016/j.artmed.2019.101762] Ford, E., Edelman, N., Somers, L., Shrewsbury, D., Lopez Levy, M., Van Marwijk, H., Curcin, V., & Porat, T. (2021). Barriers and facilitators to the adoption of electronic clinical decision support systems: A qualitative interview study with UK general practitioners. BMC Medical Informatics and Decision Making, 21(1), 1-13. [https://doi.org/10.1186/s12911-021-01557-z] Ronicke, S., Hirsch, M. C., Türk, E., Larionov, K., Tientcheu, D., & Wagner, A. D. (2019). Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet Journal of Rare Diseases, 14(1), 1-12. [https://doi.org/10.1186/s13023-019-1040-6] NURS FPX 8030 Assessment 3 Critical Appraisal of Evidence-Based Literature Scott, I. A., & Crock, C. (2020). Diagnostic error: Incidence, impacts, causes, and preventive strategies. Medical Journal of Australia, 213(7), 302-305. [https://doi.org/10.5694/mja2.50771] Soufi, M. D., Samad-Soltani, T., Vahdati, S. S., & Rezaei-Hachesu, P. (2018). Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic. International Journal of Medical Informatics, 114, 35-44. [https://doi.org/10.1016/j.ijmedinf.2018.03.008] Trinkley, K. E., Blakeslee, W. W., Matlock, D. D., Kao, D. P., Van Matre, A. G., Harrison, R., Larson, C. L., Kostman, N., Nelson, J. A., Lin , C. T., & Malone, D. C. (2019). Clinician preferences for computerized clinical decision support for medications in primary care: A focus group study. BMJ Health & Care Informatics, 26(1), 0. [https://doi.org/10.1136/bmjhci-2019-000015] Willmen, T., Völkel, L., Ronicke, S., Hirsch, M. C., Kaufeld, J., Rychlik, R. P., & Wagner, A. D. (2021). Health economic benefits through the use of diagnostic support systems and expert knowledge. BMC Health Services Research, 21(1), 1-11. [https://doi.org/10.1186/s12913-021-06926-y]

Student Name Capella University NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner Prof. Name Date Evidenced-Based Literature Search and Organization Staffing shortages have been a persistent issue in the healthcare sector, raising concerns about patient safety. The repercussions of inadequate staffing extend to various patient safety issues, including increased rates of patient falls, catheter-associated urinary tract infections (CAUTIs), hospital-acquired pressure injuries (HAPIs), and reduced patient satisfaction scores. Inadequate staffing not only impacts patient care but may also lead nursing staff to forgo certain tasks due to time constraints. Addressing staffing challenges is crucial for healthcare, especially in its impact on nurses. This paper aims to gather evidence on the effectiveness of maintaining adequate staffing ratios and explores whether it results in improved patient satisfaction scores within a one-year timeframe. The formulated PICOT question is as follows: In hospital patients, how do safe staffing ratios of 1:6, compared to unsafe staffing ratios of less than 1:6, affect patient safety? P (Population): NursesI (Intervention): Safe staffing ratios 1:6C (Comparison): Unsafe staffing ratios less than 1:6O (Outcome): Patient satisfaction scores and reduction of patient safety issuesT (Time): 6 months Search for Literature The initial step in the literature search process involves identifying a problem affecting nurses. In this case, the focus is on staffing ratios and their daily impact on nurses and patients. A PICOT question was formulated, and relevant keywords were identified to create a strategic search plan. The chosen keywords, including nurses, nursing ratios, patient satisfaction, patient safety, and six months, were utilized in a database search. Two key search engines, CINAHL Complete and Nursing & Allied Health, were employed, using an advanced search with truncation and Boolean connectors. Inclusion/Exclusion Criteria The inclusion criteria stipulated that selected articles must be peer-reviewed and not older than five years, with most articles within a three-year timeframe (2019 onwards). Full-text availability online ensured ease of access. Exclusion criteria encompassed articles older than five years, non-peer-reviewed articles, and those not set in a hospital setting. Patients with mental health problems, cognitive delay, or dementia were excluded due to challenges in obtaining accurate data and feedback from these populations. Research Articles Five relevant articles were selected based on the inclusion criteria: These articles, meeting the inclusion criteria, provide recent, reliable, and peer-reviewed information relevant to nursing staffing ratios and patient satisfaction. A PRISMA diagram (see Appendix 1) was utilized to organize and present the results of the literature search, reflecting the identification and selection of studies via databases and registers. References Al Muharaaq, E. H., Alallah, S. M., Alkhayrat, S. A., & Jahlan, A. G. (2022). An overview of missed nursing care and its predictors in Saudi Arabia: A cross-sectional study. Nursing Research and Practice, 2022. https://doi.org/10.1155/2022/4971890 Disch, J., & Finis, N. (2022). Rethinking nursing productivity: A review of the literature and interviews with thought leaders. Nursing Economics, 40(2), 59-71. Fildes, C., Munt, R., & Chamberlain, D. (2022). Impact of dual intensive care unit and rapid response team nursing roles on service delivery in the intensive care unit. Critical Care Nurse, 42(5), 23-31. https://doi.org/10.4037/ccn2022540 NURS FPX 8030 Assessment 2 Evidenced-Based Literature Search and Organization Kowalski, M. O., Basile, C., Bersick, E., Cole, D. A., McClure, D. E., & Weaver, S. H. (2020). What do nurses need to practice effectively in the hospital environment? An integrative review with implications for nurse leaders. Worldviews on Evidence-Based Nursing, 17(1), 60-70. https://doi.org/10.1111/wvn.12401 Van Den Oetelaar, W. F. J. M., Roelen, C. A. M., Grolman, W., Stellato, R. K., & Willem, v. R. (2021). Exploring the relation between modelled and perceived workload of nurses and related job demands, job resources, and personal resources; a longitudinal study. PLoS One, 16(2). https://doi.org/10.1371/journal.pone.0246658 Appendix 1: PRISMA Diagram NURS FPX 8030 Assessment 2 Evidenced-Based Literature Search and Organization

Student Name Capella University NURS-FPX 8030 Evidence-Based Practice Process for the Nursing Doctoral Learner Prof. Name Date PRESENTATION OUTLINE Medication or Drug Error as a Patient Safety Issue at Healthy Elite Metropolitan Medical Center Objectives: PATIENT SAFETY ISSUE: Medication/Drug Errors Medication errors rank as the third leading cause of death in the United States (Ferrah et al., 2017). One in seven patients in healthcare organizations falls victim to medication errors. Key medical errors include technical errors, delayed diagnosis, medication errors, inadequate post-procedure monitoring, and failure to act on test results. PATIENT SAFETY ISSUE: Medication Error at Healthy Elite Metropolitan Medical Center Medication errors attributed to poor communication, administration of incorrect dosages, negligence by healthcare staff, and electronic medical record failures. INTERNAL EVIDENCE OF MEDICATION/DRUG ERROR Medication errors at Health Elite Metropolitan Medical Center contribute to increased lawsuits, patient deaths, and healthcare service costs. The organization incurred a loss of over $17.4 million in lawsuits within the last 12 months. Medication errors resulted in the layoff of over 20 healthcare workers, impacting healthcare service delivery. INTERNAL EVIDENCE OF MEDICATION/DRUG ERROR EXTERNAL EVIDENCE OF MEDICATION/DRUG ERROR Medication errors are a common patient safety concern globally (Mulac et al., 2021; Ferrah et al., 2017). Research indicates a 19% prevalence of medication errors in over 36 US healthcare organizations (Mulac et al., 2021). Causes include unauthorized medication administration (4%), omission errors (43%), and wrong dosage administration (17%). ORGANIZATIONAL PRIORITY FOR INTERVENTION Medication/drug errors significantly impact patient health, organizational operations, and community health. Consequences include severe physical, emotional, and psychological injuries, financial burdens, reduced community trust, and potential caregiver shortages. Creating awareness among patients can reduce errors, and community concern arises from the loss of loved ones and caregiver shortages. QUALITY IMPROVEMENT PROJECT AND PATIENT OUTCOME Quality improvement interventions: REFERENCES Ferrah, N., Lovell, J. J., & Ibrahim, J. E. (2017). Systematic review of the prevalence of medication errors resulting in hospitalization and death of nursing home residents. Journal of the American Geriatrics Society, 65(2), 433-442. Hines, S., Kynoch, K., & Khalil, H. (2018). Effectiveness of interventions to prevent medication errors: an umbrella systematic review protocol. JBI Evidence Synthesis, 16(2), 291-296. Mulac, A., Taxis, K., Hagesaether, E., & Granas, A. G. (2021). Severe and fatal medication errors in hospitals: findings from the Norwegian Incident Reporting System. European Journal of Hospital Pharmacy, 28(e1), e56-e61. Wheeler, A. J., Scahill, S., Hopcroft, D., & Stapleton, H. (2018). Reducing medication errors at transitions of care is everyone’s business. Australian prescriber, 41(3), 73. NURS FPX 8030 Assessment 1 Building the Case for Healthcare Improvement