NURS FPX 6612 Assessment 2 Quality Improvement Proposal 

Quality Improvement Proposal

The Medicare and Medicaid Services define ACOs as associations that deliver high-quality care treatments freely to Medicare cases through effective care collaboration (Millwee, 2020). The Sacred Heart Hospital (SHH) under Vila Health seeks to acquire the status of an Accountable Care Organization (ACO). Assuming the duty of a case director at SHH, a quality improvement offer will be recommended to further include quality criteria by expanding the sanatorium’s megahit with a broad focus on Electronic Health Records (EHRs). 

Ways to Expand Hospital’s HIT to Include Quality Metrics

The EHR system of SHH is outdated and requires applicable updates to give a better range of quality criteria on mammograms and colonoscopies. There are several ways by which the EHR system of SHH can be bettered by adding spare features analogous to social work tabs, which will integrate patient health data and keep track of visits with cases. Also, the quality criteria applicable to patient care pretensions will be integrated within EHR (Aerts et al., 2021). These quality criteria will be rates of precautionary networks analogous to mammograms and colonoscopies, medicine crimes, patient satisfaction, and quality of care. The SHH will unite with public health departments and other conventions to gather data on cases not entering recommended individual tests, including mammograms and colonoscopies (Dawson et al., 2021). 

By exercising population health data, it will be easier for SHH to identify trends and walls to watch that people encounter during routine individual tests. This data will be analyzed to point to specific case populations, analogous to women seeing gynecologists demanding targeted interventions (Eckelman et al., 2020). These problems can be answered by administering care collaboration strategies analogous to using the EHR to identify at-trouble cases, rehearsing monuments and cautions for providers to engage with at-trouble cases, and promoting care collaboration to achieve advanced rates of cases witnessing precautionary networks. This approach will also help track the health information from the community to make necessary advancements predicated on the gathered data (Watterson et al., 2020). 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Several issues can arise during expanding megahit within the association. 

• The association requires respectable finances to apply practical ways to enhance EHR features and improve interoperability, including quality criteria. Due to a limited budget, the association must meet financial conditions analogous to dealer selection and elevation of EHR systems (Gill et al., 2020). 
• The healthcare association is vulnerable to inconsistent standardized data, which hinders assessing the delicacy of gathered data on quality criteria. 
• The association is also prone to face resistance to changes that are allowed to be executed within EHR and exercised clinically. This resistance to change from staff can impact the effectiveness of including quality criteria without making them adequately used (Cho et al., 2021). 

These issues can be rationally answered by following strategies. 

• Uniting with other healthcare associations for finances can improve SHH’s financial capacity. 
• Introduce the standardized data protocols to maximize delicacy in assessing added-up data on quality criteria. 
• Educate the healthcare staff on the benefits of using the lately upgraded EHR in patient care quality and how it can grease timely care treatments (Cho et al., 2021). 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Expanding the megahit in the terrain of upgrading EHRs at SHH integrates the vital places of informatics in nursing care in the form of nurse informaticists. The nurse informaticist specializes in care collaboration through the effective use of megahit, facilitates care planning, and streamlines communication among healthcare staff. Also, the nurse informaticist conducts training and educational programs on promoting care collaboration by using informatics tools (Gill et al., 2020). The training sessions are adapted to address specific workflows and use cases applicable to nursing care collaboration. Also, informatics enterprises analogous to upgraded EHR use in hospitals foster a culture of continuous improvement as the nurses continue to solicit EHR feedback and apply it in the enterprise (Eckelman et al., 2020). Using megahit and informatics tools within healthcare systems, including SHH, quality criteria can be better incorporated and employed to ameliorate patient care. 

Information Gathering in Healthcare

Healthcare systems use patient health information to assess quality criteria and trends in delivering high-quality care and anatomize the dragging areas. The primary focus of information gathering in healthcare settings like SHH is to gain comprehensive data about cases, processes, issues, and organizational performance. This information is the foundation for validation-predicated decision-making, the development of managerial practices to enhance patient care and functional effectiveness (Hathaliya & Tanwar, 2020). 

• EHRs can be practical tools to gather information about cases’ clinical health data and estimate treatment performed and achieved issues. For illustration, the information displayed on EHR, including patient demographics, medical history, lab results, medicine lists, and treatment plans, can be. Used by clinicians to make well-informed and apprehensive opinions about case care (Eckelman et al., 2020). 
• The association can also gather data on quality criteria and performance pointers to assess the effectiveness of healthcare installations. For illustration, medicine crimes display the need to include interventions that promote safe medicine administration (Lv & Qiao, 2020). 
• Information gathering in the association also encompasses functional data analogous to staffing situations and resource operation. These data help healthcare associations identify openings to improve the quality of care and overall organizational performance (Lv & Qiao, 2020). 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

At SHH, healthcare associations can gather information on these aspects and interrogate about patient health data through EHR and particular interviews. One analogous illustration from SHH includes communicating with a case named Caroline McGlade, combating bone cancer, and describing her lack of knowledge in conducting mammograms and precautionary care. The information handed out shows a dragging factor behind precautionary care at SHH due to financial constraints and the need for further education about preventative care (Ye, 2021). Using this information, adapted strategies can be developed and executed to promote precautionary care, essential for SHH to qualify as ACO. 

Potential Problems with Data Gathering Systems and Outputs

Healthcare information plays a vital part in perfecting organizational performance. This is possible by gathering ample data from healthcare systems and determining logical labors. The data collection can be from patient doors, EHRs, functional data, financial data, dashboard metric evaluation, and case-reported feedback. Data-gathering procedures can stem various problems that lead to poor data affairs (Aerts et al., 2021). These problems include 

Privacy and Security Concerns

Healthcare data-gathering systems must stick to strict insulation and security regulations to cover patient confidentiality and prevent unauthorized access or breaches. Failure to adequately guard sensitive case health information can affect legal and ethical consequences analogous to action, suits, eroded patient trust, and damaged organizational character. These enterprises can be addressed by administering robust data encryption and user authentication mechanisms to help prevent data breaches and cyberattacks (Hathaliya & Tanwar, 2020). 

Data Overload and Information Overload

Devilish data collection and reporting can overwhelm healthcare providers, leading to information overload and cognitive fatigue. This results in impaired clinical decision-making and workflow effectiveness as clinicians must sift through large data volumes to prize pivotal findings. This can be overcome by prioritizing data collection efforts to capture practicable information that directly informs the decision-making process and quality improvement action. Also, dashboards can be erected to present data in a concise or user-friendly format that facilitates quick interpretation and decision-making (Ye, 2021). 

The misgivings in dealing with these challenges also persist. There is a query in anticipating all safety risks and cyberattacks due to evolving technologies, which may question the effectiveness of security measures integrated within the system. The point of the query lies in the delicacy of large volumes of data pulled by healthcare professionals. This requires effective tools to show the delicacy and wholeness of data gathered (Ihnaini et al., 2021). The healthcare professionals at SHH must be aware of these problems when performing data collection processes. Therefore, the suggestions handed out must be considered to help the issues from arising during the data collection process. 

Conclusion

To conclude, SHH under Vila Health can be good as an ACO by prioritizing technology expansion by upgrading EHR. The system excrescences must be linked and addressed to ensure care collaboration with minimum use of informatics tools. Information gathering is essential to anatomize the dragging areas that bear advancements. Still, the problems that can potentially arise during the data collection process must be addressed beforehand to avoid posterior complications. 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Hathaliya, J. J., & Tanwar, S. (2020). An exhaustive survey on security and privacy issues in healthcare 4.0. Computer Communications, 153(1), 311–335. https://doi.org/10.1016/j.comcom.2020.02.018

Ihnaini, B., Khan, M. A., Khan, T. A., Abbas, S., Daoud, M. Sh., Ahmad, M., & Khan, M. A. (2021). A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Computational Intelligence and Neuroscience, 2021, 1–11. https://doi.org/10.1155/2021/4243700  

Lv, Z., & Qiao, L. (2020). Analysis of healthcare big data. Future Generation Computer Systems, 109, 103–110. https://doi.org/10.1016/j.future.2020.03.039 

Millwee, B. (2020). Accountable care organizations in Medicaid. Journal of Ambulatory Care Management, 43(1), 11–14. https://doi.org/10.1097/jac.0000000000000318 

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. https://doi.org/10.1097/hmr.0000000000000222 

Ye, J. (2021). The impact of electronic health record–integrated patient-generated health data on clinician burnout. Journal of the American Medical Informatics Association, 28(5). https://doi.org/10.1093/jamia/ocab017