NURS FPX 6612 Assessment 2 Quality Improvement Proposal


NURS FPX 6612 Assessment 2 Quality Improvement Proposal

NURS FPX 6612 Assessment 2 Quality Improvement Proposal


Capella university

NURS-FPX 6612 Health Care Models Used in Care Coordination

Prof. Name


Quality Improvement Proposal

Accountable Care Organizations (ACOs) acquire this status when they meet the criteria of delivering coordinated care and improved quality of care that enhances patient experience and safety. Healthcare organizations must integrate strategies and processes that promote quality improvement in healthcare services. This assessment entails a quality improvement proposal for Sacred Heart Hospital of Vila Health to acquire accreditation of ACO by improving its technological infrastructure and expanding the hospital’s health information technologies. This will be needed to include quality metrics that organizations can evaluate and assess to comprehend the quality of care delivered to patients. 

Health information technologies (HIT) can improve the quality of care within SHH by enhancing care coordination and facilitating communication among interprofessional team members. When adequate communication technologies are integrated within healthcare organizations, healthcare providers can efficiently and timely share patient health data, preventing treatment delays and medical or treatment errors (Qadri et al., 2020). ACOs also leverage technology within their healthcare systems to enhance care coordination in delivering high-quality care treatments to patients and meet quality metrics to maintain their accreditation.

Ways to Expand SHH’s HIT to Include Quality Metrics

Integrating HIT within SHH and improving currently outdated Electronic Health Records (EHRs) can improve quality. This can be done by integrating specific features within the EHR system to capture and track the quality metrics, such as medication errors, patient falls, patient satisfaction scores, and mortality rates. Enhancing EHR systems will help monitor these quality metrics efficiently and ensure the quality of care delivered (Maziarz et al., 2022). Automated reporting processes can also be developed to extract relevant data for quality metrics. This will allow IT health professionals who can develop the software to automate the data extraction process to draw patient health data for regularly evaluating quality metrics (Hadasik & Kubiczek, 2021). Ultimately, SHH’s quality control department can monitor performance trends and lessen the quality of care. 

The HIT can be further expanded to integrate computerized Clinical Decision Support Systems (CDSS) to prompt medical personnel about recommended diagnostic tests and preventive screenings to promote preventive care at SHH and improve the standard of care. This will result in efficient and coordinated care for patients unable to undergo diagnostic tests, including mammograms or colonoscopies when CDSS can identify risk stratification using data analytics and stratification tools (Kwan et al., 2020). Community health information can be tracked in several ways to improve the quality of care, such as conducting surveys and outreach programs to collect information on community health needs and preferences (Stopa et al., 2020).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Moreover, establishing a community Health Information Exchange (HIE) will enable healthcare professionals to acquire relevant health information across different healthcare entities.  One study showed that the use of HIE by clinicians helped reduce emergency visits by 53% and hospital readmission rates by 61% (Aagard et al., 2023).  Another approach that can allow the aggregation and analysis of health information from diverse sources within the community is population health management tools. These include EHRs that can provide previous health record of patients and give health and disease patterns in community (Martyn et al., 2022).

Lastly, the SHH can incorporate the role of informatics in nursing care coordination by training and educating nursing staff on informatics tools. These tools allow healthcare professionals, mainly nurses, to streamline workflows and measure healthcare outcomes effectively. Quality metrics like improved patient outcomes reduced hospital readmission rates and enhanced patient satisfaction serve as tangible indicators of the positive impact of nurse informaticists in expanding HIT across healthcare organizations (Ye, 2020). The expansion of HIT within the organization will ultimately enable it to acquire ACO accreditation by following the earlier strategies.


Expanding an organization’s health information technologies to incorporate quality metrics within SHH requires careful consideration of several issues, including lack of integration for quality metrics, ineffective data standardization, and limited automation in reporting processes. As SHH’s EHR is outdated, healthcare professionals may encounter a lack of seamless integration for capturing and monitoring quality metrics, leading to inefficiencies in data collection and reporting. Moreover, inconsistent data standards across the organization can hinder accurate and meaningful aggregation of quality metrics. This impedes the ability to derive actionable insights from the gathered data. Furthermore, the manual reporting process may be time-consuming and prone to errors, which limits the organization’s capacity to generate real-time, reliable, quality metric reports (Tayefi et al., 2021).

Proposed Solutions with Rationale 

The proposed solutions for these issues include enhancing HIT integration and improving the efficiency of EHR systems that specifically cater to capturing and tracking quality metrics. The rationale for introducing HIT integration within SHH for quality metrics is to improve overall efficiency by eliminating silos in data collection, It ensures that relevant data is captured seamlessly during routine patient care activities and healthcare burnout due to hectic workload is reduced (Martyn et al., 2022).

Additionally, it is essential to introduce standardized data elements and protocols within the HIT framework to ensure consistency in quality metric reporting. This facilitates accurate data aggregation. Similarly, standardized data elements guarantee consistency in reporting and allow accurate comparisons and analyses. This consistency is essential for benchmarking against industry standards and identifying areas for improvement. Integrate automated reporting tools into the HIT system, which will reduce the manual workload, and the extraction and compilation of the health data process will be streamlined (Hadasik & Kubiczek, 2021).

Information Gathering in Healthcare 

The primary emphasis of information collection in healthcare centers on acquiring, analyzing, and interpreting data to inform decision-making and improve patient care. Moreover, it guides organizations in developing various organizational practices. Integrating robust information-gathering mechanisms empowers healthcare organizations to respond proactively to evolving patient needs and industry trends (Renoux et al., 2020). In SHH, information gathering on patient health data will form the foundation for personalized care plans and clinical decision-making. This will require collecting comprehensive patient health data, past medical histories, lab results, and treatment plans. 

Information collection plays a role in assessing the effectiveness of care delivery and making improvements based on the results after collecting and analyzing health information. For example, adequate information collection within the healthcare system can obtain patient satisfaction scores, clinical health outcomes, and adherence to evidence-based practices (Mubarakali, 2020). Financial information such as revenue, costs, and reimbursement patterns are also crucial for organizational sustainability in SHH. They guide financial planning, budgeting, and resource allocation to optimize operational efficiency.

Additionally, healthcare organizations can collect data on staff performance and training or education. This information will guide SHH in developing strategies that ensure all healthcare workforce remains up-to-date with the latest advancements and the best evidence-based practices (Kumar et al., 2019). All this information helps organizations make informed clinical decisions, optimize resource allocation, and improve patient outcomes. For example, the hospital identifies gaps in post-discharge care by analyzing readmission rates and patient feedback. This led to the development of comprehensive transitional care programs and reduced hospital readmission rates.

Potential Problems with Data Gathering Systems and Outputs

Following are some potential problems that may arise with data gathering systems and output:

  • Healthcare professionals enter inaccurate data due to human errors or outdated information, leading to unreliable data. Moreover, misinterpreting complex data can lead to incorrect conclusions and misguided decision-making. Uncertainties may arise due to the varying skill levels of individuals entering data, differing interpretations of data entry guidelines, or system-related issues. Additionally, inadequate data can result in biased or incomplete analyses, impacting patient care and outcomes. This can be overcome by automated data validation checks and regular audits to enhance data accuracy (Morris et al., 2021). 
  • While the HIT facilitates data sharing, there are risks of data breaches, inadequate security measures, and authorized access, which can compromise sensitive patient information. Uncertainties may stem from evolving cybersecurity threats, the potential for internal data breaches, and compliance with ever-changing data protection regulations.  Data governance policies can lead to better data quality and security. By employing robust cybersecurity protocols and policies on securing health data in technological software, best practices can be obtained for securing patient health information (Kioskli et al., 2021). 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

  • Interoperability challenges cause hindrances in seamless data exchange due to incompatibility among different systems. Uncertainties may arise from incompatible data formats, interoperability issues between systems, and evolving standards in healthcare data exchange. The insightful suggestion to overcome this issue is to adopt standardized data formats and promote investing in technologies to facilitate seamless data exchange (StClair et al., 2020). 
  • Information trapped in isolated databases or departments can impede a comprehensive view of patient care. Different data structures and incompatible systems contribute to data silos, leading to fragmented care. This is prevented by implementing integrated health information systems and encouraging cross-departmental collaboration to promote care coordination and better decision-making (Ranchal et al., 2020).


The HIT expansion is crucial for SHH to acquire ACO accreditation, as HIT promotes the incorporation of quality metrics that manifest the quality of care delivered. Several ways can be employed to enhance the HIT expansion, including maintaining EHR, developing computerized CDSS systems, and automated reported systems. The information collection within SHH can guide the organizations in the decision-making process, financial sustainability, and analysis of the quality of care provided to patients. The potential problems in data gathering systems include data inaccuracy or interpretation, data interoperability issues, and security issues. These issues must be addressed adequately and efficiently to achieve the goal of acquiring ACO accreditation.


Aagard, C. S., Glenn, J., Nañez, J., Crawford, S. B., Currey, J. C., & Hartmann, E. (2023). The impact of community health information exchange usage on time to reutilization of hospital services. The Annals of Family Medicine21(1), 19–26. 

Hadasik, B., & Kubiczek, J. (2021). On enhancing and automating the COVID-19 case reporting system in Poland. Humanities & Social Sciences Reviews9(4), 209–213. 

Kioskli, K., Fotis, T., & Mouratidis, H. (2021). The landscape of cybersecurity vulnerabilities and challenges in healthcare: Security standards and paradigm shift recommendations. The 16th International Conference on Availability, Reliability and Security 

Kumar, Y., Sood, K., Kaul, S., & Vasuja, R. (2019). Big data analytics and its benefits in healthcare. Studies in Big Data, 3–21. 

Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., Grimshaw, J. M., & Shojania, K. G. (2020). Computerised clinical decision support systems and absolute improvements in care: Meta-analysis of controlled clinical trials. BMJ370, m3216. 

Martyn, T., Montgomery, R. A., & Estep, J. D. (2022). The use of multidisciplinary teams, electronic health records tools, and technology to optimize heart failure population health. Current Opinion in Cardiology37(3), 302–306. 

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Maziarz, M. P., Chastain, A. M., Perera, U. G. E., Cohen, C. C., Stone, P. W., Woo, K., & Shang, J. (2022). Health information technology adoption at U.S. home health care agencies: Results from a multi-methods study. Home Health Care Management & Practice35(2), 97–107. 

Morris, A. H., Stagg, B., Lanspa, M., Orme, J., Clemmer, T. P., Weaver, L. K., Thomas, F., Grissom, C. K., Hirshberg, E., East, T. D., Wallace, C. J., Young, M. P., Sittig, D. F., Pesenti, A., Bombino, M., Beck, E., Sward, K. A., Weir, C., Phansalkar, S. S., & Bernard, G. R. (2021). Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions. Journal of the American Medical Informatics Association28(6), 1330–1344. 

Mubarakali, A. (2020). Healthcare services monitoring in cloud using Secure and Robust Healthcare-Based Blockchain(SRHB)approach. Mobile Networks and Applications 

Qadri, Y. A., Nauman, A., Zikria, Y. B., Vasilakos, A. V., & Kim, S. W. (2020). The future of healthcare internet of things: A survey of emerging technologies. IEEE Communications Surveys Tutorials22(2), 1121–1167. 

Ranchal, R., Bastide, P., Wang, X., Gkoulalas-Divanis, A., Mehra, M., Bakthavachalam, S., Lei, H., & Mohindra, A. (2020). Disrupting healthcare silos: Addressing data volume, velocity and variety with a cloud-native healthcare data ingestion service. IEEE Journal of Biomedical and Health Informatics, 1–1.

Renoux, J., Veiga, T. S., Lima, P. U., & Spaan, J. (2020). A unified decision-theoretic model for information gathering and communication planning. Örebro University Library (Örebro University)  

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

StClair, J., Ingraham, A., King, D., Marchant, M. B., McCraw, F. C., Metcalf, D., & Squeo, J. (2020). Blockchain, interoperability, and self-sovereign identity: Trust me, it’s my data. Blockchain in Healthcare Today 

Stopa, S. R., Szwarcwald, C. L., Oliveira, M. M. de, Gouvea, E. de C. D. P., Vieira, M. L. F. P., Freitas, M. P. S. de, Sardinha, L. M. V., & Macário, E. M. (2020). National health survey 2019: History, methods and perspectives. Epidemiologia E Serviços de Saúde29, e2020315. 

Tayefi, M., Ngo, P., Chomutare, T., Dalianis, H., Salvi, E., Budrionis, A., & Godtliebsen, F. (2021). Challenges and opportunities beyond structured data in analysis of electronic health records. WIREs Computational Statistics13(6). 

Ye, J. (2020). The role of health technology and informatics in a global public health emergency: Practices and implications from the COVID-19 pandemic. JMIR Medical Informatics8(7), e19866. 

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