NURS FPX 6612 Assessment 2 Quality Improvement Proposal

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

Data gathering helps to analyze the efficacy of medical services and make modifications based on health information acquisition and evaluation outcomes. For example, appropriate data collection in the medical system can improve patient satisfaction, medical outcomes, and compliance with treatment procedures based on evidence (Mubarakali, 2020). Economic information, including income, expenditures, and insurance patterns, is also critical for the viability of SHH. This information assists in financial strategy and distribution of resources to improve efficiency in health organizations’ operations.

Furthermore, medical organizations can gather information about employee performance and the impact of training on efficient patient care. This data can aid SHH in implementing plans that ensure that all medical professionals are updated and have expertise for novel evidence-based clinical practices (Robert, 2019). The information collection enables health organizations to make more educated medical choices, streamline utilization of resources, and boost patient experiences. For instance, a medical facility like SHH can evaluate readmission rates and patient feedback to identify follow-up gaps. This resulted in the development of extensive coordinated medical services and lower hospitalization rates (Dash et al., 2019).

Potential Problems with Data Gathering Systems and Outputs

Information collection is complicated, and its management is critical to improve patient outcomes. Medical professionals contribute incorrect data caused by negligence, human error, or outdated information, resulting in appropriate data (Rahman et al., 2024). Furthermore, misunderstanding complicated information can result in inaccurate and poor decision-making. Uncertainties can develop because of individuals’ variable levels of proficiency in recording data, distinct understandings of information entry requirements, or system-associated errors. Furthermore, limited data can lead to an inaccurate and insufficient evaluation, affecting healthcare outcomes.

Automation of data validation examinations and periodic inspections can help improve the accuracy of information (Rahman et al., 2024). Although HIT promotes data exchange, information breaches, insufficient safety measures, and illicit access may be possible, which can jeopardize critical medical data (Yeo & Banfield, 2022).

NURS FPX 6612 Assessment 2 Quality Improvement Proposal

 Uncertainties arise from growing security risks, the possibility of personal information violations, and adherence to continually shifting safeguarding standards.  Administration standards can improve data integrity and safety. Best practices for protecting patient health information can be attained by implementing effective cyber-security rules and regulations for safeguarding medical records in technical tools (Yeo & Banfield, 2022).

Furthermore, interoperability and information-sharing issues impede efficient data exchange because of inconsistency in the different healthcare systems. Uncertainties can arise due to conflicting data formats, compatibility concerns, and emerging medical information exchange standards. The most effective approach for addressing this challenge is establishing uniform data formats and promoting technology funding that enables efficient information exchange (Costin & Eastman, 2019). 

Conclusion

HIT expansion is vital for SHH to achieve ACO recognition because it encourages the adoption of quality measures that demonstrate the standard of care provided. Managing EHR, establishing CDSS systems, and automating reporting processes contribute to HIT expansion. The data collected in the SHH can help health organizations make decisions and ensure economic viability and patient care quality.

References

Costin, A., & Eastman, C. (2019). Need for interoperability to enable seamless information exchanges in smart and sustainable urban systems. Journal of Computing in Civil Engineering, 33(3), 04019008. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000824

Dash, S., Sushil Kumar Shakyawar, Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0217-0

Forman, T. M., Armor, D. A., & Miller, A. S. (2020). A review of clinical informatics competencie