NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Student Name

Capella University

NURS-FPX 6612 Health Care Models Used in Care Coordination

Prof. Name

Date

Quality Improvement Proposal

The Centers for Medicare & Medicaid Services define Accountable Care Organizations (ACOs) as healthcare entities that voluntarily deliver high-quality care to Medicare beneficiaries through coordinated and patient-centered approaches (Millwee, 2020). Sacred Heart Hospital (SHH), operating under Vila Health, aims to achieve ACO status. As a case manager at SHH, this quality improvement proposal outlines strategies to enhance quality metrics by expanding the hospital’s Health Information Technology (HIT), with a particular emphasis on upgrading Electronic Health Records (EHRs).

Ways to Expand Hospital’s HIT to Include Quality Metrics

The current EHR system at SHH is outdated and limits the hospital’s capacity to monitor quality metrics effectively, especially for preventive screenings like mammograms and colonoscopies. Upgrading the EHR system will improve its functionality by adding modules such as social work tabs, which integrate patient health data and track follow-ups. Furthermore, incorporating quality metrics directly into the EHR will allow real-time monitoring of key performance indicators, such as preventive screening rates, medication errors, patient satisfaction, and overall quality of care (Aerts et al., 2021).

SHH plans to collaborate with local public health departments and affiliated clinics to collect information on patients who are missing recommended preventive screenings (Dawson et al., 2021). Using population health data, SHH can identify patterns and barriers in care delivery, such as women who frequently visit gynecologists but do not complete recommended screenings (Eckelman et al., 2020). By leveraging EHR alerts, reminders, and care coordination strategies, the hospital can target these at-risk populations and improve preventive care adherence. This approach also allows the integration of community health data to inform improvements in hospital care.

Table 1: Proposed HIT Enhancements and Targeted Quality Metrics

HIT Enhancement	Purpose	Target Metrics
Social Work Tabs	Track patient visits and health history	Preventive screening completion, follow-up adherence
Integrated Alerts & Reminders	Notify providers of at-risk patients	Mammogram & colonoscopy rates
EHR Analytics Module	Population health trend analysis	Medication errors, patient satisfaction, preventive care metrics
Data Sharing with Public Health	Identify patients missing screenings	Outreach completion rates, preventive care coverage

Challenges in Expanding HIT

Several challenges may arise while expanding HIT and upgrading EHR systems:

1. Financial Constraints: Implementing advanced EHR features and ensuring interoperability requires significant financial investment. Budget limitations may impact vendor selection and system upgrades (Gill et al., 2020).
2. Data Standardization Issues: Inconsistent data standards can compromise the accuracy of quality metrics, making evaluation difficult.
3. Resistance to Change: Staff may resist adopting new EHR features or clinical workflows, limiting the effectiveness of integrated quality metrics (Cho et al., 2021).

Strategies to Address Challenges:

Challenge	Proposed Solution
Financial constraints	Collaborate with other healthcare organizations to secure funding; prioritize cost-effective EHR vendors
Data standardization	Implement standardized protocols for data entry and reporting
Staff resistance	Conduct targeted training on EHR benefits, care coordination, and quality improvement outcomes (Cho et al., 2021)

Role of Nurse Informaticists in HIT Expansion

Upgrading EHRs at SHH emphasizes the essential role of nurse informaticists in coordinating care through HIT. Nurse informaticists facilitate care planning, streamline communication among staff, and implement training programs tailored to nursing workflows. These initiatives foster a culture of continuous improvement, enabling nurses to provide feedback on EHR usability and inform future system enhancements (Gill et al., 2020; Eckelman et al., 2020). Through informatics tools, quality metrics can be effectively tracked and leveraged to enhance patient care outcomes.

Information Gathering in Healthcare

Comprehensive information gathering is critical for assessing quality metrics and identifying gaps in patient care. At SHH, data collection includes patient demographics, clinical histories, lab results, medication lists, and treatment plans. This information supports evidence-based decision-making and operational efficiency (Hathaliya & Tanwar, 2020; Eckelman et al., 2020).

Table 2: Key Sources of Information for Quality Improvement

Information Source	Purpose	Example Use
EHR Clinical Data	Evaluate patient care outcomes	Track preventive screening rates, medication errors
Patient Interviews	Identify knowledge gaps	Tailor patient education programs (e.g., Caroline McGlade case for mammogram awareness)
Operational Data	Optimize resource utilization	Adjust staffing levels, improve workflow efficiency
Patient Feedback	Measure satisfaction & quality of care	Implement targeted quality improvement initiatives

Case example: Caroline McGlade, a breast cancer patient, lacked knowledge about preventive screenings. Collecting her information through EHR and interviews highlights gaps in patient education and financial barriers to care (Ye, 2021). Addressing these gaps ensures better preventive care adherence and supports SHH’s ACO objectives.

Potential Problems with Data Gathering Systems

While HIT expansion provides significant opportunities, challenges may arise during data collection and analysis:

1. Privacy and Security: Strict regulations must protect patient confidentiality. Failure to safeguard data can result in legal consequences and erode patient trust (Hathaliya & Tanwar, 2020).
2. Data Overload: Excessive information can overwhelm clinicians, reducing workflow efficiency and decision-making quality (Ye, 2021).
3. Data Accuracy and Completeness: Ensuring reliable, complete data remains a challenge, particularly with large datasets and evolving technologies (Ihnaini et al., 2021).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Mitigation Strategies:

Problem	Mitigation Strategy
Privacy & security concerns	Use encryption, multi-factor authentication, and access controls
Information overload	Prioritize actionable data and utilize dashboards for concise visualization
Data accuracy uncertainties	Employ validation protocols and continuous monitoring tools to ensure data integrity

Conclusion

Sacred Heart Hospital can achieve ACO status by prioritizing EHR upgrades and HIT expansion. Addressing current system limitations, integrating nurse informaticists, and leveraging comprehensive information gathering will improve care coordination, preventive care, and quality metrics. Proactively addressing potential challenges in data collection ensures that SHH can implement sustainable, evidence-based quality improvement initiatives.

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

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

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

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

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

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