NURS FPX 6416 Assessment 3 Evaluation of an Information System Change
Student Name
Capella University
NURS-FPX 6416 Managing the Nursing Informatics Life Cycle
Prof. Name
Date
Evaluation Report
The primary objective of this project was to enhance operational efficiency and reduce security risks by replacing the outdated paper-based record-keeping system with an electronic health record (EHR) system. Previously, patient care was delayed due to a 5% error rate, lost files, and inaccuracies from manual data entry. On average, retrieving patient information required 20 minutes, which hindered timely clinical decisions.
The EHR implementation occurred in four distinct phases. Phases one and two focused on vendor selection and preliminary staff training, ensuring the foundation for a smooth transition. Phase three emphasized evaluation and continuous improvement, while phase four addressed full deployment and integration into existing workflows. Despite initial resistance from staff and technical challenges, the transition ultimately improved patient safety, data management, and overall care quality.
Quality of Information Framework
The EHR system has significantly enhanced the accuracy and completeness of patient records. Automated data validation processes have reduced error rates from 5% to below 1%, increasing the reliability of patient information. Staff satisfaction has improved dramatically due to the intuitive interface of the EHR and comprehensive training programs that boosted confidence and competency (Mishra et al., 2022).
Stringent encryption protocols and access restrictions protect sensitive information, ensuring compliance with HIPAA regulations (Thapa & Camtepe, 2021). Routine audits are conducted to maintain ongoing adherence to privacy standards, while continuous surveys and feedback mechanisms assess both user experience and security effectiveness (Kabukye et al., 2020). Real-time data updates further enhance the accuracy of patient records and the effectiveness of clinical decisions.
NURS FPX 6416 Assessment 3 Evaluation of an Information System Change
Table 1: Key Features of EHR Quality Improvements
| Feature | Before EHR | After EHR | Impact |
|---|---|---|---|
| Error Rate | 5% | <1% | Increased data reliability |
| Data Retrieval Time | 20 minutes | 2 minutes | Faster clinical decisions |
| Staff Satisfaction | Moderate | High | Improved confidence and engagement |
| Security | Limited | Strong encryption & access control | HIPAA compliance ensured |
| Patient Wait Times | Longer | Reduced | Enhanced patient experience |
Outcomes of Quality Care Framework
The adoption of the EHR system has substantially improved healthcare delivery efficiency. Data retrieval time decreased from twenty minutes to just two minutes, enabling rapid access to patient records and timely clinical decisions. Decision-support tools and real-time data integration have facilitated more precise and personalized care, improving treatment outcomes (Ostropolets et al., 2020).
The system has also strengthened care coordination across departments and multidisciplinary teams, reducing hospital readmission rates and improving overall patient outcomes (Perry et al., 2020). Continuous monitoring of system performance is critical to maintain and enhance the quality of care while addressing emerging challenges proactively.
Structural Quality Framework
Executive leadership played a crucial role in securing funding and organizational support for the EHR rollout. Hardware and infrastructure were assessed for their ability to handle increased data processing and storage demands. The software was evaluated for usability, compatibility, and efficiency, incorporating staff feedback to optimize the interface and functionalities (Watterson et al., 2020).
Regular maintenance and updates have resolved technical issues and enhanced system performance. Network connectivity and data security protocols were strengthened to support the EHR system (Huang et al., 2020). Continuous investment in technology and employee training remains essential to sustain system performance and facilitate ongoing improvements.
Evaluation and Analysis
The EHR implementation was structured across three phases:
Phase 1 (Months 1–2): Vendor selection was completed successfully, although initial resistance from staff accustomed to paper records was observed. Early training sessions helped address these concerns.
Phase 2 (Months 3–4): Focused on system implementation and integration with existing workflows. Minor technical issues required additional training and system adjustments.
Phase 3 (Months 5–6): Emphasized performance evaluation and continuous improvement. Data retrieval times were significantly reduced, and error rates decreased. User feedback collected through surveys informed ongoing system adjustments, highlighting the need for continued support and optimization (Kabukye et al., 2020).
Table 2: EHR Implementation Timeline and Key Activities
| Phase | Duration | Focus | Key Outcomes |
|---|---|---|---|
| Phase 1 | Months 1–2 | Vendor selection & early training | Staff resistance identified; initial training completed |
| Phase 2 | Months 3–4 | Implementation & integration | Minor technical issues; workflow adjustments made |
| Phase 3 | Months 5–6 | Evaluation & continuous improvement | Reduced error rates; improved retrieval times; user feedback collected |
| Phase 4 | Deployment | Full system integration | Ongoing monitoring; system performance optimization |
Recommendations for Further Improvement
To maximize EHR effectiveness, ongoing training programs are essential for addressing staff skill gaps and promoting professional development. A dedicated technical support team can facilitate rapid resolution of system issues. Regular updates to decision-support tools and system functionalities enhance clinical decision-making and patient care quality (Kawamoto & McDonald, 2020).
Implementing a structured user feedback mechanism helps identify challenges and implement solutions promptly. Investments in infrastructure and technology will enhance system performance and scalability. Routine audits ensure operational efficiency and regulatory compliance, while stakeholder engagement reduces resistance and supports continuous improvement (Yigzaw et al., 2020).
Conclusion
The EHR system has significantly improved data accuracy, operational efficiency, and patient satisfaction. By reducing error rates and data retrieval times, the system has optimized workflows and informed clinical decision-making. Despite initial obstacles, the EHR system has proven its ability to enhance patient care through better integration and management of health data. Sustained investment in training, technology, and stakeholder involvement is vital to fully realize its potential and ensure ongoing improvements in healthcare delivery.
References
Huang, C., Koppel, R., McGreevey, J. D., Craven, C. K., & Schreiber, R. (2020). Transitions from one electronic health record to another: Challenges, pitfalls, and recommendations. Applied Clinical Informatics, 11(05), 742–754. https://doi.org/10.1055/s-0040-1718535
Kabukye, J. K., Keizer, N., & Cornet, R. (2020). Assessment of organizational readiness to implement an electronic health record system in a low-resource settings cancer hospital: A cross-sectional survey. PLoS ONE, 15(6), e0234711. https://doi.org/10.1371/journal.pone.0234711
Kawamoto, K., & McDonald, C. J. (2020). Designing, conducting, and reporting clinical decision support studies: Recommendations and call to action. Annals of Internal Medicine, 172(11_Supplement), S101–S109. https://doi.org/10.7326/m19-0875
NURS FPX 6416 Assessment 3 Evaluation of an Information System Change
Mishra, V., Liebovitz, D., Quinn, M., Kang, L., Yackel, T., & Hoyt, R. (2022). Factors that influence clinician experience with electronic health records. Perspectives in Health Information Management, 19(1), 1f. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013220/
Ostropolets, A., Zhang, L., & Hripcsak, G. (2020). A scoping review of clinical decision support tools that generate new knowledge to support decision-making in real-time. Journal of the American Medical Informatics Association, 27(12), 1968–1976. https://doi.org/10.1093/jamia/ocaa200
Perry, M. F., Macias, C., Chaparro, J. D., Heacock, A. C., Jackson, K., & Bode, R. S. (2020). Improving early discharges with an electronic health record discharge optimization tool. Pediatric Quality & Safety, 5(3), e301. https://doi.org/10.1097/pq9.0000000000000301
NURS FPX 6416 Assessment 3 Evaluation of an Information System Change
Thapa, C., & Camtepe, S. (2021). Precision health data: Requirements, challenges and existing techniques for data security and privacy. Computers in Biology and Medicine, 129(1), 104130. https://doi.org/10.1016/j.compbiomed.2020.104130
Watterson, J. L., Rodriguez, H. P., Aguilera, A., & Shortell, S. M. (2020). Ease of use of electronic health records and relational coordination among primary care team members. Health Care Management Review, 45(3), 1–10. https://doi.org/10.1097/hmr.0000000000000222
NURS FPX 6416 Assessment 3 Evaluation of an Information System Change
Yigzaw, B., Budrionis, A., Ruiz, M., Henriksen, E., Halvorsen, K., & Bellika, J. G. (2020). Privacy-preserving architecture for providing feedback to clinicians on their clinical performance. BMC Medical Informatics and Decision Making, 20(1). https://doi.org/10.1186/s12911-020-01147-5