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Mercy Data Management Infrastructure

Data management infrastructure is crucial in medical documentation as it is the center focus determined in billing. Under billing, documentation will accrue losses to the facility, whereas the over-billing will oppress unprivileged patients. This paper analyses the improvement of administration and clinical processes, development of a centralized stuffing to improve security and efficiency, maintenance of proper billing, the prediction of epidemics, and reduction of medical bills brought forth by Mercy’s adoption of the new data management infrastructure for medical documentation.

The new data management infrastructure has led to the Improvement of administration and clinical processes. Mercy insists on documenting every medical detail on a patient, either a diagnosis or a medical complication discovered during the hospital stay, calculating all the resources utilized for treatment or care, hence proper billing. Under documentation of medical data had proved to result in losses incurred by the facility. According to (Jain, Shastri, & Sharma, 2022), fluctuation in billing results in billing and incompatibility with financial administration, giving an instance of the Emergency Department where physicians regularly administer critical care although they document inconsistently. Due to this frequent mistake of underbilling, an accumulation of $1.8 million was made by the inconsistent documentation per year. However, the new data documentation that asserts detailed documentation has increased billing by 263 percent. Critical Medical documentation has reduced billing errors, therefore, maintaining proper finances.

In addition, Mercy’s new data management infrastructure dictation on real-time medical documentation has opportunely impacted screening practices, community outreach, and prescription custom. Following a publication by (Ash, Tuten, Bohenek, & Latham, 2021), the burden of opioid prescription was reduced by approximately 46 percent of morphine milligram similar per distinctive patient from 2016 to 2019. More so, these authors complement Mercy to develop a database with prescribing data, arguing that this new implementation has posed a challenge to hospitals to partner with local schools and create electronic health record reinforcements to change opioid prescribing customs. The new infrastructure’s impact on the medical practices has improved performance effectiveness.

They are implementing new data management infrastructure rules to ensure accurate hospital billing. Mercy has established rules that govern medical documentation to warrant a hospital stay. Implementation of preventive measures will drive physicians always to be keen and medical documentation specialists to always double-check to confirm the information for billing to avoid losses. According to Muzumbdar (2020), errors by physicians and clinicians result in weight confirmation disparities. Muzumbdar affirms that the new measures led to a decrease in errors of weight documentation per month by 47.8 percent. Preventative measures on medical documentation are crucial in avoiding incorrect documentation leading to financial losses.

The Development of a centralized staffing system enhances the maintenance of the security of medical documents. Bowie (2019) states that centralized staffing is required for an organization to create business performance competence on policy development, pay practice, technology, and resource governance. Bowie argues that in sequence to certify compliance to the process, unit-level permits to certain pay codes in the scheduling and recruitment, technology is limited to the central staffing office. An organization’s structure plays a big role in effectiveness and efficiency.

Data management infrastructure use in outbreak prediction of epidemics and preventable illnesses. Analysis of collected charts on medical documentation gives the frequency of medical conditions and therefore should predict future outcomes, hence developing preventive measures against curable diseases. According to (Ghafil, Matsushima, Ding, Henry, & Inaba, 2021), mercy health records from electronic health records used the statistics gathered to determine ratios of the spread of COVID 19, which led to the virus being declared a public health emergency, which resulted in lockdown. Data collected by the new data management infrastructure proved useful in predicting the spread of COVID 19. Statistics of medical documentation are convenient in forecasting epidemic outcomes.

Moreover, the data management infrastructure by Mercy is utilized to reduce medical costs. Data analysis shows the frequency of treatment on different ailments. Considering which diseases lead patients mostly to the facilities, reducing treatment costs can come in handy. Also, more chronic illnesses would do better with a discount, which will promote hope to the patient and the community. Additionally, Sims, Melton, and Ji (2018) analyzed a study on under-privileged patients with hepatitis C virus receiving treatment without any cost in the multidisciplinary hepatitis C virus. Patients underwent full treatment and healing. Lower costs in medical expenditure lead to more treatment completion, therefore, higher rates of recovered patients.

In conclusion, the new data management infrastructure adopted by Mercy filled gaps in medical documentation. Improvement of administration and clinical processes has resulted in more efficiency and effective performance in health facilities. Additionally, the newly adopted infrastructure has made it easy to access real-time data. It allows better analysis for accurate billing, saving hospitals losses, and successful impact upon screening practices, prescriptions, and community outreach. Moreover, initiating centralized stuffing would prove paramount in ensuring efficient data documentation and privacy adherence. Also, the frequency in the medical documentation demonstrated its essentiality in predicting the spread of COVID 19.


Ash, N., Tuten, J., Bohenek, W., & Latham, B. (2021). A comprehensive approach to addressing the opioid epidemic in a large health system. American Journal of Health-System Pharmacy78(4), 320-326.

Bowie, D. (2019). Centralized vs. decentralized staffing: Two case studies: Strive for balance to improve outcomes. American Nurse Today14(6), 41-45.

Ghafil, C., Matsushima, K., Ding, L., Henry, R., & Inaba, K. (2021). Trends in trauma admissions during the COVID-19 pandemic in Los Angeles County, California. JAMA network open4(2), e211320-e211320.

Jain, S., Shastri, N. J., Sharma, N., & Conners, G. P. (2022). Optimizing Critical Care Documentation in a Pediatric Emergency Department. Pediatric Emergency Care38(2), e997-e1002.

Muzumdar, S. (2020). Accurate Weight Documentation: How To Adhere To Best Practices. Patient Safety2(1), 15-21.

Sims, O. T., Melton, P. A., & Ji, S. (2018). A descriptive analysis of a community clinic providing hepatitis C treatment to poor and uninsured patients. Journal of community health43(4), 725-730.


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