NUR 621 Business Plan for Change/Process/Item

Sample Answer

Proposal

The Intensive Care Unit (ICU) is a busy place where real-time tracking of patients is required to obtain help at the right time. However, the current monitoring system in the ICU lacks advanced features like predictive alerts and continuous data analysis that are important for better patient outcomes.

This business plan proposes adding a new advanced patient tracking system in the ICU. This system will use artificial intelligence (AI) to continuously analyze data and send predictive alerts. The approach is meant to drive better patient outcomes and shorter response times, further increasing ICU healthcare productivity.

Rationale

Need for the New Monitoring System

The ICU’s current patient monitoring system is archaic and cannot perform continuous data processing, which makes it unlikely to recognize the first signs of patient deterioration. This limitation delays the interventions and places patients at unfavorable outcomes.

Keim-Malpass and Moorman (2021) have argued that highly advanced patient monitoring systems with artificial intelligence-based prediction alerts can significantly reduce the response time of a healthcare provider to the patient’s bedside and improve their overall outcomes.

This process would be a move regarding technological advances to bring the ICU up to date based on new standards prescribed in the complied guidelines for patient care.

Best Option Evidence and Advantages

The monitored surveillance system proposes applying artificial intelligence algorithms to analyze data, noting trends a human observer might miss. Identifying slight shifts in an individual’s vitals allows doctors to respond early to a critical condition, such as a heart attack.

Research has shown that using these technologies makes it possible to reduce the death rates in the intensive care unit by up to 20% (Saqib et al., 2023). Another advantage of the system is its integration with electronic health records, which assists with comprehensive patient care through better documentation.

Potential Disadvantages

While the new system has various advantages, there are also some indisputable disadvantages. The main concern is the absorber production cost and the cost of installing the absorber on the building’s thermal system. Also, several disturbances might be anticipated in the ICU functionality when implementing the new system because personnel will require training to operate the new system effectively.

Another possible problem is AI utilization, which is generally reliable but not 100% accurate and sometimes triggers false alarms (Malcolm et al., 2022). However, these drawbacks are offset by the benefits the system brings to the patient and increased organizational performance.

Implementation Plan

Steps for Implementation

Approval and Funding As procedures and policies in different hospitals may vary, Alsayaydeh et al. (2023) note that the researcher should get permission from the management of the intended hospital to conduct the study and obtain financial support. This objective will entail pitching the business strategy to change agents in the organization, such as the hospital board and the finance department.

Vendor Selection Many suppliers of AI-based surveillance solutions will be considered (Malcolm et al., 2022). Another criterion to be used is the capability of the system, cost of systems, vendor support, and compatibility of the identified system with the current EHR systems.

Staff Training Develop a comprehensive training scheme for personnel dealing with patients in the ICU, such as nurses, doctors, and technicians (Malcolm et al., 2022). Training will entail operating procedures, deciphering the alarm generated by the system, and problem-solving.

System Installation The chosen vendor must be consulted to install the monitoring system in the ICU (Saqib et al., 2023). This process will entail installing the hardware gadgets, installing the software, and integrating the system with the hospital environment, among other things.

Pilot Testing Use the system on a sample number of patients and introduce it step by step so that they can gradually get acquainted with it (Keim-Malpass & Moorman, 2021). Supervise the accomplishment, gather the personnel’s opinions, and apply any modifications if required.

Full Deployment The above system will be implemented in the ICU (Alsayaydeh et al., 2023). Continued monitoring and support will ensure smooth operation at the initial stage.

Ongoing Evaluation and Support Effectively communicate with the vendor to constantly update the system (Holmes et al., 2024). Numerically self-assess the system’s effectiveness and the improvement it brings to patient conduits.

Implementation Timeline

The implementation process is expected to take six months, divided as follows:

• Month 1 Approval and funding
  
• Month 2 Vendor selection
  
• Month 3 Staff training
  
• Month 4 System installation
  
• Month 5 Pilot testing
  
• Month 6 Full deployment and ongoing evaluation

Costs/Benefits

Costs

This cost estimate for the new patient monitoring system is $500000, comprising costs like software and physical equipment, installation costs, and integration with the electronic health record system. The total allocation for staff training will be $50,000, whereas the testing and assessment of pilot projects will cost an extra $30,000 (Holmes et al., 2024; Malcolm et al., 2022). The expected annual cost for maintenance and assistance for the subjects is $20,000.

Item Cost

• System Implementation $500,000
  
• Staff Training $50,000
  
• Testing & Pilot Projects $30,000
  
• Annual Maintenance $20,000
  
• Total $600,000

Monetary Benefits

• Reduced ICU Mortality Rates Jones and Williams (2019) published a paper stating that better surveillance reduces mortality by as much as 20%. It can also lead to substantially lower costs by decreasing the number of days in the ICU and the need for additional medical interventions.
  
• Operational Efficiency The new system, which uses ICU resources better, is expected to reduce response times by 15% (Malcolm et al., 2022). This efficiency gain can hasten the availability of ICU beds, quicken the treatment of more patients, and raise hospital revenues.

Non-Monetary Benefits

• Enhanced Patient Care Based on insights from Holmes et al. (2024), the system’s ability to predict the signs of deterioration will enhance the hospital’s status as a healthcare innovation hub, leading to better patient care and productivity.
  
• Staff Satisfaction Keim-Malpass and Moorman (2021) maintain that decreasing manual monitoring and using AI-based alerts and notifications will positively impact employee satisfaction. Lower effort and stress will also positively affect the workplace environment.

Evaluation Methods

To evaluate the success of the proposed system, several methods will be employed

1. Clinical Outcomes Monitoring Compare the overall frequencies of critical events, response times, and death rates of the intensive care unit patients before and after the system’s implementation (Malcolm et al., 2022). Lower death rates and reaction times would be hallmarked as success indicators.
   
2. Staff Feedback Surveys Conduct surveys to get feedback from ICU personnel about the system’s usability, effectiveness, and impact on their workflow (Saqib et al., 2023). Positive feedback will indicate a successful adoption.
   
3. Patient Satisfaction Surveys Solicit feedback from patients or their families to establish their level of satisfaction with the care offered in the ICU (Alsayaydeh et al., 2023). Once the system has been implemented, new satisfaction figures will be recorded.
   
4. Financial Analysis After six months of using the system, an assessment of the cost that the system has incurred in the hospital is conducted (Malcolm et al., 2022). Among the most critical measures of success, it will be necessary to indicate a high return on investment (ROI).
   
5. Ongoing Monitoring Establish an administrative committee to monitor the system’s performance and ensure frequent modifications (Keim-Malpass & Moorman, 2021). This committee will ensure the delivery of the best patient care and confirm whether the system is fit for use within the ICU’s changing needs.
   

Conclusion

Implementing a high-end patient monitoring system in the ICU is a wise business decision because it complements the hospital’s commitment to high-quality and excellent care. However, it is crucial to note that although the implementation of the given system requires certain investment and can potentially encounter some obstacles, its ability to improve the patient’s condition, simplify the work of the healthcare center’s employees, and support the staff makes it an invaluable addition to the facility. With a practical and well-coordinated approach to planning, executing, and evaluating, this business plan aims to dramatically improve the quality of ICU care to improve the results for patients, staff, and the hospital.

References

• Alsayaydeh, J. A. J., Yusof, M. F. bin, Halim, M. Z. B. A., Zainudin, M. N. S., & Herawan, S. G. (2023). Patient health monitoring system development using ESP8266 and Arduino with IoT platform. International Journal of Advanced Computer Science and Applications, 14(4). https //doi.org/10.14569/ijacsa.2023.0140467
  
• Holmes, E., Holmes, E., Holmes, E., Holmes, E., Holmes, E., & Holmes, E. (2024). A model-based cost-utility analysis of an automated notification system for deteriorating patients on general wards. PloS One, 19(5), e0301643–e0301643. https //doi.org/10.1371/journal.pone.0301643
  
• Keim-Malpass, J., & Moorman, L. P. (2021). Nursing and precision predictive analytics monitoring in the acute and intensive care setting An emerging role for responding to COVID-19 and beyond. International Journal of Nursing Studies Advances, 3, 100019. https //doi.org/10.1016/j.ijnsa.2021.100019
  
• Malcolm, R., Shore, J., Stainthorpe, A., Ndebele, F., & Wright, K. (2022). Economic evaluation of a vision-based patient monitoring and management system in an acute adult and an older adult mental health hospital in England. Journal of Medical Economics, 25(1), 1207–1217. https //doi.org/10.1080/13696998.2022.2147753
  
• Saqib, M., Iftikhar, M., Neha, F., Karishma, F., & Mumtaz, H. (2023). Artificial intelligence in critical illness and its impact on patient care A comprehensive review. Frontiers in Medicine, p. 10. https //doi.org/10.3389/fmed.2023.1176192