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NURS FPX 6424 Assessment 4 Tool Kit for Critical Analysis of System Vulnerabilities, Data Validity Management, and System Analysis

nursfpx6424assessment / July 21, 2026

NURS FPX 6424 Assessment 4 Tool Kit for Critical Analysis of System Vulnerabilities, Data Validity Management, and System Analysis Student Name Capella University NURS-FPX6424 Data Mining to Advance Healthcare Professor Name Submission Date Tool Kit for Critical Analysis of System Vulnerabilities, Data Validity Management, and System Analysis An audit of potential system vulnerabilities of the medical-surgical unit (MSU) with 40 beds showed significant shortcomings in compliance with CDSS. It did not perform medication reconciliation, and the nurse was not well-versed in electronic health records. This toolkit includes evidence-based policies, guidelines, and actionable recommendations to address these vulnerabilities and enhance health informatics data to improve patient safety outcomes. Evidence-Based Policy The person who is responsible for complying with this policy is all (RN, charge RN, pharmacists, unit managers) registered nurses, charge nurses, pharmacists, and unit managers on the 40-bed medical-surgical unit. All staff will investigate all CDSS alerts, carry out medication reconciliation at each patient change of care, and report all of the near misses within 24 hours of occurrence. All told, these activities will help to reduce the number of medication-related errors if they are consistently carried out across all health care professions. At the start of January 2026, the medical-surgical unit had a rate of 8.2 errors for every 1,000 doses, had 54% compliance with the CDSS alerts, and had a total of 12 near-misses during the calendar month (January). The specific outcomes that will be measured for this policy are: error rates of ≤5.0, compliance with CDSS and medication reconciliation procedure ≥90%, no more than 4 near-misses reported per month, and nurses gaining their EHR competency level of ≥85%, which is based on the TIGER framework. All data gathered under this policy by the law is protected under HIPAA (Health Insurance Portability and Accountability Act) and can only be accessed by those with authorization. The Joint Commission NPSG 03.06.01 requires that nurses complete a medication reconciliation at each “transition of care. Medication reconciliation must be documented by the nurse at each “transition of care” per the Joint Commission NPSG 03.06.01. Nurses will be accountable for not properly completing this medication reconciliation or documenting an override of an alert by CDSS. As a consequence, data about students’ IQ collected under this policy will be used solely to inform quality improvement processes. Guidelines for Applying the Policy in Practice Within 15 minutes of an alert override/overrides the Registered Nurse is required to document their clinical reasoning about the override(s) in the CDSS. Staff will be required to have a 48-hour learning session if there is a shift that is more than a 30% override. At the end of each shift, charge nurses will discuss and record any override(s) that occurred during their shift with their unit manager. The medication reconciliation process must be performed at patient admission, discharge, and transfers, and it is built right into the workflow of the electronic health record (EHR) so that it is a required task for all staff to perform. Medication reconciliation is done within 2 hours of admission to the facility to check the accuracy of medications in the pharmacy. Any discrepancies will have to be resolved prior to giving a medication. Any event that is a near-miss medication is to be reported via the hazard reporting system within 24 hours. The average of unit near misses will be calculated monthly by the charge nurses, who will summarise this to the unit manager by the third business day of the next month. The unit manager, charge nurses, and pharmacists’ safety dashboard review meeting is held on the first Monday of every month to review how they are doing in relation to the service-wide benchmarks: number of medication errors, CDSS compliance, CDSS reconciliation completeness, near misses, competency of staff in using EHR, and CDSS override percentage. If an organisation does not meet any of its benchmarks, then action plans need to be created and put into place within 72 hours. Practical Recommendations All registered nurses need to attend a required orientation of two hours that includes active involvement in hands-on activities using an electronic health record (EHR) and procedures and policies for reconciliation and CDSS. Tip sheets defining end-user questions about EHRs are readily available in the nursing station, and quarterly, through team manager reviews, data is gathered from each unit to assess trends and outcomes. In addition to this information from these reviews, the units have 15 minutes each month for a safety huddle, which is completed during shift changes, whereby the units can discuss a few key performance indicators (KPIs). It outlines ways in which improvement could be achieved, and communicates any changes in protocol, ensuring that no time is lost and that all staff are aligned and accountable. The data monitoring is in four levels. Nurse staff supervisors will audit medication administration record (MAR) overrides on an ongoing, nightly basis and will immediately re-educate anyone who has exceeded the 30% override rate for administering medication. Once a month, the unit will consider how it is doing on each of the 6 metrics in its safety dashboard review meeting (SDRM) and reflect on these metrics in relation to the benchmark. The unit will have a policy review quarterly, to confirm the metrics and discuss any vulnerabilities that may have occurred. Unit data will be compared with the national data on an annual basis to assess the need for any changes in policy and/or EHRs. Specific Example: Medication Safety Initiative on the 40-Bed Medical-Surgical Unit There were three issues found in the EHR during an audit report in January 2026; they were related to an audit trail and dispensing from the EHR by a pharmacy. They were also much higher than the 30% Joint Commission’s benchmark, with an alert override percentage of 38%, lower than the benchmark on completing medication reconciliation processes with 61% (below the 90% completion rate expected by Joint Commission), and the lower than expected 48% of nurses who completed competency training with EHR. During the one-month period,

Capella University, MSN, NURS-FPX6424

NURS FPX 6424 Assessment 3 Proposal to Administration: Spreadsheet and Video Presentation

nursfpx6424assessment / July 21, 2026

NURS FPX 6424 Assessment 3 Proposal to Administration: Spreadsheet and Video Presentation Student Name Capella University NURS-FPX6424 Data Mining to Advance Healthcare Professor Name Submission Date Proposal to Administration: Spreadsheet and Video Presentation Greetings, esteemed administrators. My name is _________. I’d like to extend my Welcome to you for this presentation. This will consist of a spreadsheet and video presentation. The presentation will be presented using the data and the recommendations to medication safety concerns in a 40 bed medical/surgical unit. Studies have shown that the use of medication is one of the most common sources and preventable causes of harm to hospitalized patients. Such errors should be identified and corrected to be a key element of the quality of care issue. Signs also exist that if medication tools are used properly and training on medication tools is given to nurses, the amount of medication errors will substantially reduce. I have tracked Medication Error Rate (MER) for the past 6 months in my unit and have used EHR/ CDS in my unit. Analyzing What, Why, and How to Measure a Specific Quality Outcome The aim is to monitor number of errors induced for giving medicines in the medical surgical unit to assess level of care provision. This measurement was selected as the one to measure because it is directly related to the outcomes of medication errors in patient safety. It has long been known to be one of the major causes of iatrogenic harm in the acute care hospital setting. We looked at the initial error rate of the three months’ data for January to March 26 and found that it was at 8.2 errors per 1,000 doses, above the Institute for Safe Medication Practices (ISMP) error rate. This indicates that our medication administration system was ineffective in preventing errors in medication administration. We have since created an action plan to tackle problems with the error rate, with particular focus on carrying out more audits on the compliance of the nursing staff with the implementation of computerized clinical decision support systems (CDSSs). For each nursing staff member who has successfully completed medication reconciliation and each medication administration near miss; For nursing staff’s knowledge and skills in using electronic health records (EHRs). For example, during audited period, nurses frequently failed to check the CDSS alerts but did not do the medicine reconciliation at patient admission and at patient discharge, but rather gave the medicine to patients without first checking the CDSS alerts. My benchmarks are based on: ISMP 5.0 or fewer errors per 1,000 doses, CDSS alert compliance (90% or higher), completion of medication reconciliation (90% or higher) as specified in The Joint Commission NPSG.03.06.01, and 4 or fewer near miss events per month. The competency, set forth by the technology informatics guiding education reform (TIGER) framework, for a nurse’s competency with EHRs is set at 85% or higher. Finally, you should not have more than 30% of the alerts override. Audit logs in the EHR; CDSS audit logs, medication list reconciliation workflow logs, incident reporting system data and competency assessments of personnel. Together, this will provide us with a good idea of how our unit is functioning and what could be impacting it and causing it to have a better or worse performance. Evaluating Data Measures and Trends Data collected from January to June indicate progress is good on the medicine safety front. The rate of medication errors in Jan. was 8.2 errors/1,000 doses. It reduced its errors to the national level by June, to 5.0 errors per 1,000 doses, a decrease of 39% in errors after 6 months! The percentage of compliance with CDSS alerts was higher than 90%, and higher in June when compared to January at 98% versus 54% respectively. There was an increase in the rate of medication reconciliation from 61% (in January) to 92% (in June). The incidents of near misses decreased from 12 incidents per month on average to 3 for nurses, and the competency of nurses using the EHR improved from 48 percent to 88 percent (a target of 85 percent for competency for nurse use of EHR). There was a significant drop in the rate of overriding alerts, from 38% to 14% – this is well below the expected 30%. The higher the number of interventions, the better the metrics are. Improvements in metrics is correlated with implementation of interventions. Compared to nurses dropping the alerts, there was a significant increase in nurse EHR skills, nurse confidence and use of CDSS alerts to prevent medication errors. For each increase in CDSS adherence level the less errors were made, and the closer they were to the patient. Murthi said that the tools could be integrated into the workflow in the EHR, which would decrease fragmented care as well as decrease medication adverse events. This correlation, from month to month, is shown in Table 1. Table 1 Healthcare Outcome Trends by Month Month Medication Error Rate (per 1,000 doses) CDSS Alert Compliance (%) Medication Reconciliation Completion (%) Near-Miss Incidents Nurse EHR Competency (%) January 8.2 54 61 12 48 February 7.9 58 65 10 54 March 6.5 67 72 8 63 April 5.8 74 79 7 71 May 5.1 81 85 5 79 June 5 98 92 3 88 There are 6 rows (one for each month) for the months of January-June, in the year 2026. There are six metrics that are being monitored – each column corresponds to a selected metric. The data indicates that there is an overall increase in compliance, competency, and reconciliation, along with an overall decrease in error rates and near-misses. Data visualisation, like a spreadsheet or bar graph, helps healthcare teams to spot patterns faster and more easily and helps them to inform unit-level (evidence-based) decisions (Park et al., 2021). A bar graph (Figure 1) is used to visually represent the above-mentioned alignment. On the bar graph comparison, from January to June, there is clearly a trend that both compliance and competency bars are rising, and the bars

Capella University, MSN, NURS-FPX6424

NURS FPX 6424 Assessment 2 Conference Poster Presentation

nursfpx6424assessment / July 21, 2026

NURS FPX 6424 Assessment 2 Conference Poster Presentation Student Name Capella University NURS-FPX6424 Data Mining to Advance Healthcare Professor Name Submission Date Abstract Leveraging EHR Data and the TIGER Informatics Model: A Nursing-Led Strategy to Reduce Medication Errors by Standardizing Clinical Decision Support Across a 40-Bed Medical-Surgical Unit Medications are one of the largest threats to the health care of patients in the United States. An estimated 1.5 million patients suffer from the effects of medication errors every year, and there are many unnecessary deaths as a result of medication errors (Webster, 2022). The QI initiative was shared at the 2026 American Nursing Association (ANA) Annual Conference in Webster, 2022. It was after a 22% rise in medication error incident reporting was detected using EHR Audit Logs and Pharmacy reconciliation that this was achieved. The identified gaps were: inconsistent EHR safety data use of clinical decision support systems (CDSS), an incomplete medication reconciliation at clinical transition points, and decreased nurse EHR safety data competency. Some research indicates that the alerts for adverse drug events can reduce ADEs when nurses respond to the alerts when they are triggered by CDSS. Medication reconciliation – some of the workflows that have been studied have been shown to reduce hand-off errors. Moreover, real-time dashboards can support care coordinators in early detection of at-risk situations (Tsai et al., 2022). The intervention “TIGER” was holistic and had three stages in terms of the three pillars of the TIGER model: computer literacy, information literacy, and data management. A common method to identify prescribed medication/dosage out of target range and/or an adult patient prescribed medications for a non-USFDA-approved indication. The medication reconciliation module was incorporated into the admission and discharge processes. A new unit-level safety dashboard was created that provides an hourly data refresh update of a variety of alerts, medication reconciliation completion, and near-miss rates. Nursing staff were re-educated anytime the override rate at night was more than 30% after the medication administration records (MARS) were audited at night. A re-education for the nursing staff was provided within 48 hours of the nightly override rate being more than 30% after medication administration records (MARS) were audited at night. Thirty-eight percent (38%) fewer medication errors were found in Outcomes; 44% more adherence to the clinical decision support system (CDSS); and 31% less discrepancy in medication reconciliation in Outcomes within 60 days following implementation. This indicates that the competency of data management, alert override monitoring, and nursing practice competency involving informatics in nursing is a necessity to further improve the outcome of healthcare delivery in terms of medication safety. Keywords: Technology, such as electronic health records (EHRs), can reduce the risk of medication errors if HIT systems are considered from the drug administration perspective by the nurse as well. Adjusting the nurse’s perspective of HIT systems to include the drug administration point of view can help lower the risk of medication errors when using electronic health records (EHRs). Step-By-Step Instructions to write NURS FPX 6424 Assessment 2 Contact us today for expert step-by-step instructions to complete NURS FPX 6424 Assessment 2 with confidence. References for NURS FPX 6424 Assessment 2 Abraham, J., King, C. R., Lavanya Pedamallu, Light, M., & Henrichs, B. (2024). Effect of standardized EHR-integrated handoff report on intraoperative communication outcomes. Journal of the American Medical Informatics Association, 31(10), 2356–2368. https://doi.org/10.1093/jamia/ocae204 Reid, L., Button, D., Breaden, K., & Brommeyer, M. (2026). Nursing informatics and undergraduate nursing curricula: A scoping review. Nursing Reports, 16(2), 42. https://doi.org/10.3390/nursrep16020042 Syrowatka, A., Motala, A., Lawson, E., & Shekelle, P. (2024, February). Computerized clinical decision support to prevent medication errors and adverse drug events: Rapid review. PubMed; Agency for Healthcare Research and Quality (US). https://www.ncbi.nlm.nih.gov/books/NBK600580/ Tsai, W.-C., Liu, C.-F., Lin, H.-J., Hsu, C.-C., Ma, Y.-S., Chen, C.-J., Huang, C.-C., & Chen, C.-C. (2022). Design and implementation of a comprehensive AI dashboard for real-time prediction of adverse prognosis of ED patients. Healthcare, 10(8), 1498. https://doi.org/10.3390/healthcare10081498 Webster, C. S. (2022). Existing knowledge of medication error must be better translated into improved patient safety. Frontiers in Medicine, 9(9). https://doi.org/10.3389/fmed.2022.870587 Appendix for NURS FPX 6424 Assessment 2 Appendix: ANA-NY 2026 Annual Conference Abstract Submission Requirements Source: American Nurses Association – New York (ANA-NY). (2025). Call for abstracts for ANA-NY’s 2026 Annual Conference. https://anany.org/?view=article&id=317:call-for-abstracts-for-ana-nys-2026-annual-conference&catid=11 Abstract Format Used Background: Description of the problem and its significance to clinical practice Purpose: Goals and objectives of the project Methods: Design, setting, sampling, measures, and analysis approach Results: Summarized findings with statistical analysis where appropriate Conclusions/Implications: Main outcomes and implications for clinical practice, including limitations and future recommendations References Best Capella professors to choose from for NURS-FPX6424 Class Buddy Wiltcher, EdD, MSN, APRN, FNP-C JacQualine Abbe, MSN, DNP (FAQs) related to NURS FPX 6424 Assessment 2 Question 1: What is NURS FPX 6424 Assessment 2 about? Answer 1: A nursing-led QI abstract applying the TIGER informatics model and EHR data to reduce medication errors.

Capella University, MSN, NURS-FPX6424

NURS FPX 6424 Assessment 1 MSN Practicum Conference Call Template

nursfpx6424assessment / July 21, 2026

NURS FPX 6424 Assessment 1 MSN Practicum Conference Call Template Student Name Capella University NURS-FPX6424 Data Mining to Advance Healthcare Professor Name Submission Date MSN Practicum Conference Call Template Date: _______________ Attending: Student, Professor, and Preceptor Meeting objectives: What is going well in your practicum? What are some of the struggles you have? What are your objectives for practicum? On 27 May 2026, there was a Zoom meeting with the student, professor, and preceptor. The professor explained that the MSN Practicum Handbook was updated and that students are now able to complete the 50 hours of practicum virtually and 50 hours in person. She explained that Assessment 1 is only a brief synopsis of the discussion at the meeting and said that the above information does not need to be repeated, although it may be in table format. After the preceptor’s departure, the professor directed the student on how to change the time zone and gave the student a copy of the updated handbook for future use, as well as advised the student to do Assessment 5 first before Assessments 2 and 3 to ensure the practicum hours were submitted on time. Step-By-Step Instructions to write NURS FPX 6424 Assessment 1 Contact us today for expert step-by-step instructions to complete NURS FPX 6424 Assessment 1 with confidence. References for NURS FPX 6424 Assessment 1 References coming soon. Best Capella professors to choose from for NURS-FPX6424 Class Buddy Wiltcher, EdD, MSN, APRN, FNP-C JacQualine Abbe, MSN, DNP (FAQs) related to NURS FPX 6424 Assessment 1 Question 1: What is NURS FPX 6424 Assessment 1 about? Answer 1: NURS FPX 6424 Assessment 1: a brief, table-format summary documenting the practicum conference call discussion.