NURS FPX 6416 Assessment 3 Evaluation of an Information System Change

Evaluation Report

We wanted to switch from our old paper-based record-keeping system to an EHR system to make things run more smoothly and lower the risk of security breaches. Because of lost lines and homemade data entry crimes, a 5% error rate caused patient care to be delayed and more safety measures to be put in place. It usually took 20 minutes to get back information about a patient. There were three steps to committing the crime. The first two were mostly about picking the right seller and training staff early on. The third phase was all about judging and always making things better. The fourth step was to set up the system and plant it. At first, there was some pushback and technical problems, but in the end, the change has made data operation, patient safety, and the quality of the watch better.

Quality of Information Framework

The EHR system has made case records much more accurate and full. Thanks to automatic data confirmation systems that have cut the error rate from 5 to less than 1, case records are now safer than ever. Stoners are very happy with the system because it has a stoner-friendly interface, and staff have been trained well enough to be more confident and skilled (Mishra et al., 2022). To protect private information and break the rules of the Health Insurance Portability and Responsibility Act (HIPAA), there are strict access controls and strong encryption styles (Thapa & Camtepe, 2021).

Regular checkups are done to make sure that these conditions for sequestration are always being met. Improvements in patient satisfaction have led to shorter wait times and better care delivery. The stoner experience and sequestration measures are evaluated and enhanced through continuous monitoring and feedback (Kabukye et al., 2020). The improvement of data reliability and case management significantly depends on the system’s ability to incorporate real-time updates.

Outcomes of Quality Care Framework

The electronic health record (EHR) system has made healthcare delivery much more efficient. The average time it takes to get data back has been cut in half, from twenty twinkles to just two. This makes it much easier to get case records and get opinions faster. The use of real-time data and decision-support systems has improved treatment quality by providing more informed clinical opinions and personalized case care (Ostropolets et al., 2020).

The EHR system also improves care collaboration by making it easier for different departments and brigades to talk to each other about treatment. The method has clearly had a big effect on patient care, as shown by lower rates of readmission to the hospital and fewer treatment problems (Perry et al., 2020). To keep care effective and improve its quality, constant supervision is necessary. It also helps to find and fix any new problems that may come up.

Structural Quality Framework

Senior directors have been important in getting support for the EHR deployment and providing strong support for it. This has led to a lot of support from all parts of the organization. It’s fully tested for effectiveness to make sure the tackle can handle the EHR system’s data processing and storage needs. Watterson et al. (2020) say that the program has been tested to see if it works, if it is useful, and if it works well with other systems. Staff feedback was helpful in figuring out what changes needed to be made to the software’s user interface and features.

By fixing specific problems as they come up, streamlining, and regularly maintaining the system, it has made it work better. To make the EHR system work better, the IT infrastructure was improved, including better network connectivity and data security protocols (Huang et al., 2020). To keep the system running well and to support its ongoing development, both technology and hands-on training must be constantly invested in.

Evaluation and Analysis

In Phase 1 (Months 1–2), we were able to name the EHR seller even though some staff members who were used to the paper-based system were initially against it. The first training sessions went over these problems, but it was clear that more help was needed. The main goals of Phase 2, which lasted from months 3 to 4, were to enforce the EHR system and make it work with current workflows. Some problems that didn’t last long happened during this time, making it hard to do extra training and change the system settings.

In Phase 3, which covered months 5–6, the focus shifted to measuring and improving the system’s performance based on feedback from stoners and other performance indicators. Some small businesses needed constant specialized help, but overall, the times for data recovery and the number of mistakes were much better. To ensure the system’s success, it was essential to gather stakeholder feedback through evaluations and assess its performance (Kabukye et al., 2020). The transfer went well, but the results show that the last problems need to be fixed and the system needs to work better.

Recommendations for Further Improvement

Setting up ongoing training programs can help your staff learn new skills and grow, which will make the EHR system work better in the long run. A group of trained support staff can quickly fix any problems with the system. To make clinical decision-making and patient care better, decision-support tools and system features should be streamlined on a regular basis (Kawamoto & McDonald, 2020). A good feedback system can help you find problem areas and deal with new ones. Putting more structure and technology into the system can make it work better and handle more users.

Regular reviews and checkups can help make sure that everything is working well and that the rules for sequestration are being followed. Getting stakeholders involved in the process of making things better all the time can help keep people interested and make them less resistant to change (Yigzaw et al., 2020). We can be sure that the EHR system will work well for our business and keep giving our cases the best care possible by following these steps.

Conclusion

Since the EHR system was put in place, there have been big improvements in the accuracy of data, the effectiveness of care, and patient happiness. The technology has improved workflows and clinical decision-making by speeding up the process of getting data and lowering crime rates. The EHR has shown that it can improve patient care by bringing together and managing more data, even though it faced some challenges at first. To get the most out of the system’s future, it’s important to stay committed to ongoing training, spend money on new technologies, and work hard to get stakeholders involved.

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 affect clinicians’ use of 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 create new knowledge to aid real-time decision-making. American Medical Informatics Association Journal, 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). Enhancing early discharges through an electronic health record discharge optimization tool. 5(3), e301 of Pediatric Quality & Safety. https://doi.org/10.1097/pq9.0000000000000301

Thapa, C., & Camtepe, S. (2021). Precision health data: prerequisites, obstacles, and current methodologies for safeguarding data security and privacy. Computers in Biology and Medicine, 129(1), 104130.  https://doi.org/10.1016/j.compbiomed.2020.104130

NURS FPX 6416 Assessment 3 Evaluation of an Information System Change

Watterson, J. L., Rodriguez, H. P., Aguilera, A., & Shortell, S. M. (2020). The ease of using electronic health records and how well primary care team members work together. Health Care Management Review, 45(3), 1. https://doi.org/10.1097/hmr.0000000000000222

Yigzaw, Budrionis, Ruiz, L., Henriksen, Halvorsen, and Bellika. (2020). Architecture that protects privacy while giving clinicians feedback on how well they do their jobs. BioMed Central Medical Informatics and Decision Making, 20(1).

https://doi.org/10.1186/s12911-020-01147-5