NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Name

Capella university

NURS-FPX 6612 Health Care Models Used in Care Coordination

Prof. Name

Date

Quality Improvement Proposal

The Medicare and Medicaid Services define ACOs as organizations that deliver high-quality care treatments voluntarily to Medicare patients through effective care coordination (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 manager at SHH, a quality improvement proposal will be recommended to better include quality metrics by expanding the hospital’s HIT 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The EHR system of SHH is outdated and requires appropriate updates to provide a better range of quality metrics on mammograms and colonoscopies. There are several ways by which the EHR system of SHH can be improved by adding extra features such as social work tabs, which will integrate patient health data and keep track of visits with patients. Additionally, the quality metrics relevant to patient care goals will be integrated within EHR (Aerts et al., 2021). These quality metrics will be rates of preventive screenings such as mammograms and colonoscopies, medication errors, patient satisfaction, and quality of care. The SHH will collaborate with public health departments and other clinics to gather data on patients not receiving recommended diagnostic tests, including mammograms and colonoscopies (Dawson et al., 2021).

 By utilizing population health data, it will be easier for SHH to identify trends and barriers to care that people encounter during routine diagnostic tests. This data will be analyzed to pinpoint specific patient populations, such as women seeing gynecologists needing targeted interventions (Eckelman et al., 2020). These problems can be solved by implementing care coordination strategies such as leveraging the EHR to identify at-risk patients, practicing reminders and alerts for providers to engage with at-risk patients, and promoting care coordination to achieve higher rates of patients undergoing preventive screenings. This approach will also help track the health information from the community to make necessary improvements based on the gathered data (Watterson et al., 2020).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Several issues can arise during expanding HIT within the organization:

• The organization requires adequate finances to implement practical ways to enhance EHR features and improve interoperability, including quality metrics. Due to a limited budget, the organization must meet financial requirements such as vendor selection and upgrading EHR systems (Gill et al., 2020).
• The healthcare organization is vulnerable to inconsistent standardized data, which hinders evaluating the accuracy of gathered data on quality metrics.
• The organization is also prone to face resistance to changes that are thought to be implemented within EHR and practice clinically. This resistance to change from staff can impact the effectiveness of including quality metrics without making them adequately used (Cho et al., 2021).

These issues can be rationally solved by following strategies:

• Collaborating with other healthcare organizations for funds can improve SHH’s financial capacity.
• Introduce the standardized data protocols to maximize accuracy in evaluating aggregated data on quality metrics.
• Educate the healthcare staff on the benefits of using newly upgraded EHR in patient care quality and how it can facilitate timely care treatments (Cho et al., 2021).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Expanding the HIT in the context of upgrading EHRs at SHH integrates the vital roles of informatics in nursing care in the form of nurse informaticists. The nurse informaticist specializes in care coordination through the effective use of HIT, facilitates care planning, and streamlines communication among healthcare staff. Moreover, the nurse informaticist conducts training and educational programs on promoting care coordination by using informatics tools (Gill et al., 2020). The training sessions are tailored to address specific workflows and use cases relevant to nursing care coordination. Similarly, informatics initiatives such as upgraded EHR use in hospitals foster a culture of continuous improvement as the nurses continue to solicit EHR feedback and apply it in initiatives (Eckelman et al., 2020). Using HIT and informatics tools within healthcare systems, including SHH, quality metrics can be better incorporated and utilized to improve patient care.

Information Gathering in Healthcare

Healthcare systems use patient health information to assess quality metrics and trends in delivering high-quality care and analyze the lagging areas. The primary focus of information gathering in healthcare settings like SHH is to obtain comprehensive data about patients, processes, outcomes, and organizational performance. This information is the foundation for evidence-based decision-making and the development of managerial practices to enhance patient care and operational efficiency (Hathaliya & Tanwar, 2020). 

• EHRs can be practical tools to gather information about patients’ clinical health data and evaluate treatment performed and achieved outcomes. For example, the information displayed on EHR, including patient demographics, medical history, lab results, medication lists, and treatment plans, can be. Use by clinicians to make well-informed and mindful decisions about patient care (Eckelman et al., 2020). 
• The organization can also gather data on quality metrics and performance indicators to assess the effectiveness of healthcare facilities. For example, medication errors display the need to include interventions that promote safe medication administration (Lv & Qiao, 2020).
• Information gathering in the organization also encompasses operational data such as staffing levels and resource utilization. These data help healthcare organizations identify opportunities to improve the quality of care and overall organizational performance (Lv & Qiao, 2020).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

  At SHH, healthcare organizations can gather information on these aspects and inquire about patient health data through EHR and personal interviews. One such example from SHH includes communicating with a patient named Caroline McGlade, combatting breast cancer, and describing her lack of knowledge in conducting mammograms and preventive care. The information provided shows a lagging factor behind preventive care at SHH due to financial constraints and need for more education about preventative care (Ye, 2021). Using this information, tailored strategies can be developed and implemented to promote preventive care, essential for SHH to qualify as ACO.

Potential Problems with Data Gathering Systems and Outputs

Healthcare information plays a vital role in improving organizational performance. This is possible by gathering ample data from healthcare systems and determining logical outputs. The data collection can be from patient portals, EHRs, operational data, financial data, dashboard metric evaluation, and patient-reported feedback. Data-gathering procedures can stem various problems that lead to poor data output (Aerts et al., 2021). These problems include:

Privacy and Security Concerns

Healthcare data-gathering systems must adhere to strict privacy and security regulations to protect patient confidentiality and prevent unauthorized access or breaches. Failure to adequately safeguard sensitive patient health information can result in legal and ethical consequences such as litigation, lawsuits, eroded patient trust and damaged organizational reputation. These concerns can be addressed by implementing robust data encryption and user authentication mechanisms to prevent data breaches and cyberattacks (Hathaliya & Tanwar, 2020).

Data Overload and Information Overload

Excessive data collection and reporting can overwhelm healthcare providers, leading to information overload and cognitive fatigue. This results in impaired clinical decision-making and workflow efficiency as clinicians must sift through large data volumes to extract key findings. This can be overcome by prioritizing data collection efforts to capture actionable information that directly informs the decision-making process and quality improvement initiative. Moreover, dashboards can be built to present data in a concise or user-friendly format that facilitates quick interpretation and decision-making (Ye, 2021). 

The uncertainties in dealing with these challenges also persist. There is uncertainty in anticipating all safety threats and cyberattacks due to evolving technologies, which may question the efficiency of security measures integrated within the system. The point of uncertainty lies in the accuracy of large volumes of data extracted by healthcare professionals. This requires effective tools to show the accuracy and completeness 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 provided must be considered to prevent the issues from arising during the data collection process.

Conclusion

To conclude, SHH under Vila Health can be qualified as ACO by prioritizing technology expansion by upgrading EHR. The system flaws must be identified and addressed to ensure care coordination with maximal use of informatics tools. Information gathering is essential to analyze the lagging areas that require improvements. However, the problems that can potentially arise during the data collection process must be addressed beforehand to avoid subsequent complications.

References

Aerts, H., Kalra, D., Sáez, C., Ramírez-Anguita, J. M., Mayer, M.-A., Garcia-Gomez, J. M., Durà-Hernández, M., Thienpont, G., & Coorevits, P. (2021). Quality of hospital Electronic Health Record (EHR) data based on the International Consortium for Health Outcomes Measurement (ICHOM) in heart failure: Pilot data quality assessment study. JMIR Medical Informatics, 9(8), e27842. https://doi.org/10.2196/27842 

Cho, Y., Kim, M., & Choi, M. (2021). Factors associated with nurses’ user resistance to change of electronic health record systems. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01581-z 

Dawson, W. D., Boucher Oucher, N. A., Stone, R., & Van Houtven, C. H. (2021). COVID‐19: The time for collaboration between long‐term services and supports, health care systems, and public health is now. The Milbank Quarterly, 99(2). https://doi.org/10.1111/1468-0009.12500 

Eckelman, M. J., Huang, K., Lagasse, R., Senay, E., Dubrow, R., & Sherman, J. D. (2020). Health care pollution and public health damage in the United States: An update. Health Affairs, 39(12), 2071–2079. https://doi.org/10.1377/hlthaff.2020.01247 

Gill, E., Dykes, P. C., Rudin, R. S., Storm, M., McGrath, K., & Bates, D. W. (2020). Technology-facilitated care coordination in rural areas: What is needed? International Journal of Medical Informatics, 137, 104102. https://doi.org/10.1016/j.ijmedinf.2020.104102 

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 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal