Analyzing Mercy's Big Data Project: Enhancing Healthcare Operations

This report analyzes Mercy's big data analytics project, focusing on improving healthcare operations through enhanced data management infrastructure. It articulates the benefits and values realized, both in the short and long term, such as reduced wait times, fewer claim denials, improved clinical documentation, and enhanced patient safety. The project identifies potential issues in physician behavior and documentation, proposing solutions like scribes and updated policies. Furthermore, it highlights areas for improvement within Mercy's data-management infrastructure, including disaster recovery and public health reporting. The report concludes that big data analytics are crucial for effective healthcare management and administration, contributing to better patient care and financial ROI.

Running Head: HEALTHCARE INFORMATION SYSTEMS 1
Health Information Systems
[Author Name(s), First M. Last, Omit Titles and Degrees]
[Institutional Affiliation(s)]
Author Note
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Table of Content
s
Introduction................................................................................................................................4
Benefits and values realized.......................................................................................................4
Short-term..............................................................................................................................4
Long-term...............................................................................................................................5
Correction of problems & involved parties................................................................................7
Scribes....................................................................................................................................7
Identification of common errors in physician documentation...............................................7
Update policies and procedures.............................................................................................8
Physician dashboards.............................................................................................................8
Areas at Mercy for improvement of data-management infrastructure.......................................9
Improvement of data management infrastructure in Disaster Recovery................................9
Improvement of Data management infrastructure in Public health reporting........................9
Conclusion................................................................................................................................10
References................................................................................................................................11

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Abstract
The aim of this project is to analyze the project objectives of Mercy’s big data
analytics for improving their operations. The articulation of the benefits and values that are
realized from the new data-management infrastructure are explained. The project also focuses
on the identification of the possible behavior and physician documentation issues. The
correction of these problems has been provided as well. Finally, the areas where Mercy can
bring possible improvements in their data-management infrastructure are provided as well.

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Introduction
In today’s world, big data is crucial in almost all the industries, right from business
houses, manufacturing, industrial, services and health care. The implementation of big data
provides prognosis machine learning platforms and analytical techniques for the provision of
solutions that are sustainable like the implementation of personalized medical care and
treatment plans (Al-Jarrah, Yoo, Muhaidat, Karagiannidis & Taha, 2015). Data in healthcare
exists in various forms such as structure, semi-structured and unstructured. It has been
witnessed by the healthcare domain that rational alterations at certain stages from the
viewpoint of the involved stakeholders are present. The following project focuses on the aims
of Mercy’s big data analytics for boosting its operations.
Benefits and values realized
There were several values and benefits that were realized due to the new
infrastructure of the data-management for the board during an evaluation after the
implementation of the infrastructure. These benefits and values were realized both, in short-
term as well as long-term.
Short-term
Access to real-time data reduces wait time for reports - The patients, staff, doctors
and managers have access to the data in real time these days. This helps them in reacting
quickly and knowing what is supposed to be done spontaneously. Real-time information and
data have been helping in meeting the targets for 95% of the patients to be considered and
given attention to by the emergency departments within a short period of time and also the
reports of the patients are also given out quickly.
Reduction in claim denials and missed charge opportunities – When an insurance
carrier or company refuses to honor an individual’s request or that of the provider of the

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individual for paying for his healthcare that is obtained from a professional health care, it is
known as a medical claim denial (Johnson & Nagarur, 2016). There are certain drivers which
help in reducing the recurrence of medical claim denials which are related to medical reasons
(For example, timely reviews, level of care or medical necessity, non-covered services).
These drivers mainly are payer notification, completion of the clinical reviews and action
time for significant status updation. Big data analytics help in preventing the denials of
medical claims. They help in categorizing and quantifying the denials by measuring,
reporting and tracking the trends by department, doctors, payer and procedure (Johnson &
Nagarur, 2016). Data analytics and technology are important for reliable and successful
business intelligence. They help in creating a task force by prioritizing and analyzing denial
trends and determine the resources that are needed for the implementation of solutions as well
as tracking and reporting the progress (Cooke, Xing, Lee & Belletti, 2011).
Improved clinical documentation and physician compliance – Clinical documentation
and physical compliance has an impact on reimbursement and coding. Big data technology
helps in improving the clinical documentation consistency as well as for driving overall
improvement (Raghupathi & Raghupathi, 2014). In order to convey an accurate and
significant image of the patient and derive the needed information by the care team,
improvement in the accuracy and quality is crucial.
Long-term
Improved efficiencies, quality of care and patient safety – Mercy is dedicated towards
giving the best quality care in an environment that is safe and also aims at providing a
guiding principle with continuous improvement. Such a sentiment of Mercy not only inspired
them to serve better, but also guided them to improve their care for the patients and their
families continually.

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Financial ROI – Return on Investment is very important in healthcare industry.
However, much focus is not provided to the same field. As a result of which the cost of the
industry rises and the revenues are not able to recover the former. In today’s settings of
healthcare, most of the primary are not responsible for the dimensions of improvement in
patient’s care experience, populations’ health, and the reduction of the health care’s per
capita cost. The focus of the clinicians is on the improvement of the experience of patients
and quality. This in turn improves the population’s health. The financial managers aim at
reducing the per-capita costs. The implementation of the new data-management infrastructure
helps in maintaining the Return on investment which is a valuable measure for the
illumination and to leverage the connection between operational, clinical and financial
information.
Improved decision-making and clinical outcomes - The scenario of decision-making
process of businesses has been changed due to big data analytics. Businesses are capable of
storing, analyzing and transforming huge amounts of data into relevant and meaningful data
easily. The collection of information does not help the decisions of the company, it becomes
incapable without the analytics which in turn make the data meaningful and significant. In
today’s business environment, big data has helped in improving the decision-making and
results of clinical activities.
There are also several costs which might arise if the new system is not implemented.
These costs would include the costs of software, hardware, required personnel for the project
(such as data architects, clinicians, knowledge managers, result analysts) maintenance,
training, consulting fees, travel, materials, etc.

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Correction of problems & involved parties
Scribes
The possible issues of behavior and physician documentation can be very well
identified with the help of data-management infrastructure. In the complex and difficult case
of patients, scribes are considered to be gifts for the physicians who need task balancing and
efficiency maximization as well as optimizing quality care at the same time. Electronic health
records (EHR) aim at making things easier for the healthcare providers by giving accurate
and centralized documentation of details of the examined patients (Yan, Rose, Rothberg,
Mercer, Goodman & Misra-Hebert, 2016). However, the efficiency has been hampered due to
the technicalities of such a system, as there are interferences due to doctor-patient
relationships, which has led to a highly data-driven pressure on the physicians, consequently
leading to near burnout. It is very crucial for coming up with techniques for alleviating
pressure and workload while maintaining the quality care that is demanded by the health care
model that are value-based. In such cases, medical scribes which are well-certified, act as
important support system for buffering the additional load of patient cases which passes
through the electronic records or for helping healthcare providers for maximizing profit as
well as quality (Johnson, 2017). Medical scribes can help in optimizing the usage of EHR and
provides accuracy, reliability and relevance in the realized patient information. For proper
and quality patient care for which incentive and physician focus is necessary, medical scribe
documentation helps in restoring the same and preventing physical or near burnout. Scribes
also help in reducing the medical errors and connecting the financial and medical workflow
(Yan, et. al., 2016).
Identification of common errors in physician documentation
Physician documentation is not only important for the reimbursement, but is also
crucial for managing the risks and care continuity. For proving accurate and significant

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records in an efficient way, Electronic Health Records (EHR) is an important tool (Romano
& Stafford, 2011). However, its mis-use, could lead to an increased risk of fines, recoupment
or even jail time. There are certain common errors related to the physician documentation.
These are, no proper history or complain of present illness; no systems review; the carry
forward of information from visit to visit; only positive findings being recorded; only
diagnosis being listed at every assessment visit; utilization of the same diagnosis list for all
the patients; preparation of documentation lacking a complete assessment; documentations
that do not support manipulation level; lack of internal monitoring and audit policies; lack of
training and education in E.H.R. requirements. The identification of these common errors of
the physician documentation subsequently provides opportunities for the training and
education in peer reviews and Grand rounds for the physicians.
Update policies and procedures
The policies and procedures of physician documentation in data-management infrastructure
should be updated. The data should be collected only when it is known and documented. The
data collection should be done completely and accurately. The adequate integrity, relevance,
security and accuracy of the data should be ensured. Duplication of data should be
discouraged and the timely data destruction should be considered for quality management of
data infrastructure.
Physician dashboards
Digital dashboards help in providing an in-depth view of various predefined metrics
by the means of charts, graphs, speedometers, gauges, or other mechanism that can easily be
understood. The basic concept of dashboard interface is that it helps in masking the
complexity of the latent and concealed business analytic infrastructure by the means of
displaying the report or ad hoc query results.

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Areas at Mercy for improvement of data-management infrastructure
At Mercy, there are various areas where the data-management infrastructure could
possibly improve. These can be the areas of Disaster recovery, C&BI, Research, Public
health reporting, Increased data sharing via Health information exchange (HIE)/ Continuum
of Care (CoC), Population health management, Waste Reduction, Performance Metrics – Key
Performance Indicators (KPIs), Evidence-based medicine, Patient profiles, Quality Indicators
(QI)/Quality Measure (QM), Predictive analytics, Incorporation of other sources of data.
Improvement of data management infrastructure in Disaster Recovery
The most important function of a disaster recovery planning is rebuilding the IT
infrastructure before the encountering any manmade or natural disaster. The first step should
be to develop a Health Insurance Probability and Accountability Act (HIPAA) security
compliance for quick data back-up. Mercy hospital can opt for a HIPAA compliant backup
from reliable vendors who are willing to sign a business agreement with them. Mercy can
migrate its hospital database to a cloud back-up. Although a cloud-based back-up seems the
best option, it is necessary that a Mercy has a data recovery plan. Hence, Mercy may either
train its IT staff to accomplish the task of data recovery or outsource to a service provider.
Improvement of Data management infrastructure in Public health reporting
The wasteful spending could be reduced by the healthcare providers by leveraging
techniques of big data analytics and population health management. The industry leaks huge
amounts of money every year because of preventable or wasteful spending across the
continuum of care. However, this number could potentially be reduced by using an approach
of population healthcare management which is preventive and proactive. Implementation of
the tools and techniques of big data analytics could also help in reducing such wastage. Data
infrastructure management assists to draw a differentiation between low and high value
services of healthcare in order to eliminate or reduce the wastage. Another benefit of the

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same is that the inefficiencies in the financing and delivery is also reduced, which in turn
helps in the production of greater value. According to studies, reduction of wasteful
expenditures help in decreasing the costs and also leads to quality care.
Conclusion
It can be concluded that big data analytics help in the better management and
administration of the healthcare sector. There are several benefits and values of implementing
data-management infrastructure for the steering committee of the healthcare provider. These
benefits are the improved efficiencies, patient’s safety and quality of care. Big data helps in
have access to real-time data which in turn helps in reducing the wait time for reports. They
also help in the reduction of claim denials and missed charge opportunities and in improving
physician compliance as well as clinical documentation. Data management also helps in
improving clinical results and improving decision making.

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References
Al-Jarrah, P.D. Yoo, S. Muhaidat, G.K. Karagiannidis, K. Taha, (2015). Efficient machine
learning for big data: a review. Big Data Res., 26(4), 87-93.
Cooke, C. E., Xing, S., Lee, H. Y., & Belletti, D. A. (2011). You wrote the prescription, but
will it get filled? Nearly 16% of antihypertensive prescriptions in this study went
unfilled. Managed care denials played a big part, but a third of the time patients didn't
pick up medications. E-prescribing feedback could help reverse these rates. Journal
of Family Practice, 60(6), 321-328.
Johnson, E. (2017). Impact of Medical Scribes in Healthcare: A Systematic Review. London,
UK: Sage.
Johnson, M. E., & Nagarur, N. (2016). Multi-stage methodology to detect health insurance
claim fraud. Health care management science, 19(3), 249-260.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and
potential. Health information science and systems, 2(1), 3.
Romano, M. J., & Stafford, R. S. (2011). Electronic health records and clinical decision
support systems: impact on national ambulatory care quality. Archives of internal
medicine, 171(10), 897-903.
Yan, C., Rose, S., Rothberg, M. B., Mercer, M. B., Goodman, K., & Misra-Hebert, A. D.
(2016). Physician, scribe, and patient perspectives on clinical scribes in primary
care. Journal of general internal medicine, 31(9), 990-995.