1. Saving Patient Lives
with Data Quality
Medical Error Puts Patients at Risk
A healthcare organization is an ecosystem of living and moving parts. Each piece functions
independently, yet are reliant on one another to achieve a successful patient experience.
From patients to providers, claims to administration, marketing to customer service, data
is generated every day from every corner of the business in order to diagnose and treat
patients.
Each unit has their own processes and systems, accessing and entering data quickly
to keep the ecosystem alive, yet this vital data is prone to a myriad of errors that can be
costly. A national study conducted in 2014 by the Institute of Medicine (IOM) estimated that
approximately 12 million adults (1 in 20 adults) experience a diagnostic error each year1
,
suggesting that about half of these errors could be fatal, putting patients at risk in a world
where patient-centric care is the key to success.

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Beyond the expansive statistics, the IOM study notes specific examples, such as a 51-year old woman with
a family history of heart disease who repeatedly requested her doctor’s office to refer her to a cardiologist
for a stress test. Three months after her initial request, on the day of her first appointment, she died due to
significant coronary artery disease3
. This specific example describes a woman who is personally aware of
her family history and is trying to take preventative measures to address her health issues, despite the delay
in the scheduling of a visit to the specialist. Regardless of whether or not the doctor’s office was unaware of
her family history or did not communicate it to the specialist, the urgency needed to address the woman’s
condition was lost somewhere along the way.
Many factors play a role in misdiagnosing a patient, including poor
identification, incomplete communication and collaboration, limited
resources and improper compliance. Healthcare organizations are
struggling to address these challenges due to their ever-increasing
pool of data and the growing number of regulations setting the
standards for patient experience. Although eliminating every
chance for error may not be possible for your organization, you can
significantly improve the likelihood of the most accurate diagnosis
by pursuing the goal of clean, accurate and streamlined data across
your enterprise.
The Picture of Healthcare Today
It’s no secret that the healthcare industry is becoming more publicly visible and transparent than ever before.
Organizations can no longer measure success with a treat and release strategy, and most now focus on a
present outlook that is focused on the patient experience and quality of care.
Patient-centered Care
It is not a suggestion, but an expectation that healthcare organizations must focus on patient experience
and satisfaction. So much so that there are industry-wide standards and scoring that must be met in order
to qualify for incentive programs and value-based payment arrangements. Organizations must mitigate
risk through reduced costs and improved quality in order to apply for the coveted Meaningful Use and
Accountable Care Organization (ACO) status regulated by the Centers for Medicare and Medicaid Services
(CMS).
Without these highly regarded industry labels associated with your organization, you may be overlooked by
prospective patients and fall behind the competition. The Catalyst for Payment Reform estimated 40 percent
of payments made to healthcare providers in commercial plans are based on value4
. To achieve this kind
of value, high performance in hospital-acquired conditions, readmissions and value-based purchasing are
imperative.
Looking Beyond Meaningful Use
In order to be viewed as patient-centered in the public eye, organizations cannot afford to overlook important
industry standards that are imperative to maintaining or improving their reputation. For Meaningful Use,
organizations had to produce evidence of their certified electronic health record (EHR) technology, housing
data in a structured way to measure performance in quality and quantity. This was required to qualify
for value-based incentive pay, reassuring users that their EHR system offers the technological capability,
functionality and security to help them meet the meaningful
use criteria.
Approximately 12 million
adults (1 in 20 adults)
experience a diagnostic
error each year.

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The effectiveness of Meaningful Use has been long debated. It boasts much progress like an increase in the
standardization of certain data, e-prescribing usage and general electronic medical record (EMR) adoption.
As of March 2016, over 9 in 10 hospitals eligible for the Medicare and Medicaid EHR Incentive Program
achieved Meaningful Use of certified health IT5
. While this progress is impressive, it is widely viewed as
uncertain as to whether or not the program produced the highest level of data quality needed to achieve a
patient-centric experience without error.
There is a broader pursuit of value and quality of care that has
emerged in the introduction of The Medicare Access and CHIP
Reauthorization Act of 2015 (MACRA). Focusing on value over
volume-based rewards, and alternative payment models (APMs),
MACRA aims to make quality care a more realistic achievement for
providers who must comply with Merit-Based Incentive Payments
System (MIPS) 6
. It also emphasizes interoperability, information
exchange and patient access to their health information via APIs.
As MIPS determines providers’ pay grade based on quality,
resource use, clinical improvements and the lackluster standards
of Meaningful Use, it’s more important than ever for healthcare
data to be timely, accurate and consistent. Without a foundation
of consolidated, clean data, the integrity of the entire system is
threatened and interoperability can be difficult to achieve.
Increasing Data Demands
The days of hand written charts and postal service appointment reminders are behind us and vital patient
and provider information is now stored digitally. The ability to see this constantly changing data is necessary
to keep each function of the business running smoothly.
Mergers and Acquisitions Are Booming
The significant rise in mergers and acquisitions in the industry highlights that healthcare organizations
are recognizing their shortcomings and seeking out others to fill the gaps, adapt to technology and find
financial stability. According to a tally by Dealogic, pharmaceutical companies, device makers and health
insurers completed mergers worth $687.5 billion in 2015 alone 7
. However, the problems don’t stop after the
deal is made. Integrating legacy systems and streamlining patient, provider and other data amongst these
companies’ systems provides a challenge all on its own.
Without a foundation of
consolidated, clean data,
the integrity of the entire
system is threatened and
interoperability can be
difficult to achieve.

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Facing the Realities of Bad Data
As the industry trends towards quality and value-based care, it emphasizes the fact that putting patients
at risk will only create difficulties for the future of your organization. If you are not currently on the road
to pursuing value-based, patient-centered care, then you are falling behind. In 2015, the Health Care
Transformation Task Force, including 20 major health systems, plans and consumer groups, committed to
put 75 percent of their businesses into value-based payment arrangements by 2020, which holds providers
accountable for the cost of care, quality of care and health of the population 8
. Many organizations are now
hurrying to address their quality of care issues, but there are numerous obstacles for them to overcome.
Today, Healthcare enterprises are challenged with:
• Inability to identify patients and providers
• Unknown care teams
• Lack of data governance
• Unnecessary expenditures
Inability to Identify Patients and Providers
Patient and provider data exists in many formats across different
systems and business lines, making it tough for organizations to
promote sharing health information across teams. Organizations
operate in silos, too overwhelmed with the constant influx of patients
to address the data that’s changing at a daily rate. Clinical systems
may have one piece of a patient’s history, but the lab holds another
and billing could have completely conflicting information altogether.
There is no way to access, manage and track a complete view of the
patient since each department only has access to their own patient
information.
Not only is it taxing to keep track of accurate data to ensure that a patient is safely diagnosed and treated,
locating the most up to date information for providers adds another layer of complexity to the operations
of a healthcare organization. Information about providers, such as demographic data, licensing, contact
information and practice associations changes as regularly as patient data.
While the organization may be able to get by today functioning in silos, constant data changes, technological
innovations and public opinion is forcing change in the industry. Patients and providers must be clearly
identified across your enterprise in order to be truly successful at patient-centered care.
Unknown Care Teams
Diagnosis and treatment of a patient requires clear communication and collaboration across care teams. Like
the example of the 51-year-old woman who lost her life to a disease that was a part of her family history, many
patients fall victim to an organization’s failed attempts at addressing health issues in a timely manner. Without
clearly identifying a patient’s care team and recognizing overlaps in care, vital information, like family history,
can get lost in translation.
Patients and providers must
be clearly identified across
your enterprise in order
to be truly successful at
patient-centered care.

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Organizations struggle to create the connections needed to establish a care team which prevents keeping
record of referring providers, tests and procedures previously performed and past visits. If the woman’s
repeated requests to see a specialist had been promptly recorded and communicated amongst her care
team, could she still be alive today? Aside from the inability to connect care teams, many providers aren’t
given access to internal IT systems and applications which makes communicating with other providers nearly
impossible.
Lack of Data Governance
Healthcare data is sensitive and personal, making the regulations imposed to ensure its safety and measure
quality of care arduous and demanding. Meeting the ACO and Meaningful Use criteria is a productive step
toward patient safety, but compliance with The Health Insurance Portability and Accountability Act of 1996
(HIPAA) is a non-negotiable requirement. As if organizations don’t have enough to manage with siloed patient
and provider data, the numerous industry regulations make data governance a must-have. However, without
a central location to manage data across the enterprise, there is a fundamental lack of governance and no
way to implement it universally.
Unnecessary Expenditures
The cost of operational inefficiency due to lack of data connectivity across systems only increases as the
more data is ingested. With varying systems across multiple departments, a number of employees are
performing virtually the same tasks to maintain the same data in different systems. Not only are resources
wasted, duplicative testing and readmission occurs when providers cannot access a patient’s history. Money
is regularly wasted to correct errors and manually resolve data across systems which eats away at the overall
profitability and stability of the organization.
Achieving High Quality Data
Multidomain Master Data Management (MDM) addresses the challenges that your organization is facing
through the creation of a single, integrated solution. MDM for healthcare combines business processes,
technologies and services needed to manage and maintain an accurate, timely and comprehensive view of
your data across multiple channels and business lines within an organization.
Unlike other solutions that attempt to improve your data, but exist independently of your infrastructure, a MDM
solution has the ability to plug in with your existing systems. Sitting amongst your systems, MDM integrates
with each of them to intake their data, cleanse and consolidate duplicate records, enhance the data with
third party resources and publish it out to the original source. Whether it’s patient, provider, payer or other
healthcare data, a MDM solution can work with it to create enhanced data quality across your enterprise.

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If our 51-year-old woman’s family history of heart disease had been managed by a MDM solution, how might
it have helped to save her life? By improving the quality of the data through MDM, the providers may have
been able to recognize the urgency of the woman’s condition. By managing the woman’s information with a
MDM solution, the following steps would occur:
Resolve any duplicates of her medical data by combining them into an accurate, high confidence entity
called a Golden Record by associating matching algorithms with business rules. Then, deliver a single view of
her data across all departments and touch points for providers, administrators and others to view.
Relate her Golden Record to her entire care team, including her primary physician and referred specialist,
creating relationships between resolved patients and providers, locations and encounters. This increases the
chance that the woman’s critical information regarding her family history is delivered quickly and accurately to
the specialist.
Enhance her Golden Record by incorporating critical data elements from data sets within the enterprise and
from third party sources to create a more accurate and informed view of her medical history.
Extend the resolved and enhanced master data Gold Record to front-line business processes and analytical
applications to improve patient experience at point of engagement, produce better insights and make
better business decisions. This extension prevents the necessity of the woman’s repeated requests to see a
specialist.
By achieving data quality through a MDM solution, healthcare organizations can:
• Accurately match and link records across disparate systems to create a trusted, single view of patients
resulting in consistent treatment
• Ensure that the enterprise has up-to-date and accurate information about a provider’s licenses and
privileges through the monitoring of and auditing processes for specific locations and assets
• Identify a patient’s care team and improve care coordination between multiple providers for better
outcomes
• Manage and track patient referrals from a specific provider organization or from a team in multiple
locations
• Reduce billing errors and claims processing failures by ensuring that all provider addresses, IDs and
clinical codes are up-to-date and accurate
• Prove compliance with Meaningful Use and MACRA standards and safeguard all patient data according
to HIPAA regulations

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Conclusion
On any given day, healthcare organizations juggle a number of systems and processes in order to
pursue successful patient experiences. To meet daily demands, vital data is accessed and managed
quickly, exposing it to critical errors. Master Data Management for healthcare integrates into your existing
infrastructure, combining business processes and technologies to manage, augment and promote your data
across multiple channels and business lines.
The goal of simply treating and releasing patients is a thing of the
past, replaced by the concept that patients themselves are the goal
for a successful healthcare system. Without a solid foundation of data
quality, organizations not only risk patient lives, they risk the future of a
better, more personalized healthcare industry.
1, 2, 3
National Academies of Sciences, Engineering, and Medicine. “Improving Diagnosis in Health Care”. Washington, DC: The National Academies Press, 2015.
doi:10.17226/21794. Retrieved July 7, 2016. http://www.nap.edu/download/21794
4
Bobbi Brown. (2015). Health Catalyst. “Top 7 Healthcare Trends and Challenges from Our Financial Expert”. Retrieved July 6, 2016. https://www.healthcatalyst.
com/top-healthcare-trends-challenges
5
Office of the National Coordinator for Health IT analysis of data. (2016). HealthIT.gov. “Hospital Progress to Meaningful Use”. Retrieved July 8, 2016. http://
dashboard.healthit.gov/quickstats/pages/FIG-Hospital-Progress-to-Meaningful-Use-by-size-practice-setting-area-type.php
6
Centers for Medicare and Medicaid Services. (2015). “The Medicare Access & Chip Reauthorization Act of 2015: Path to Value”. Retrieved July 19, 2016.
https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/Value-Based-Programs/MACRA-MIPS-and-APMs/MACRA-LAN-PPT.pdf
7
John Russell. (2015). Chicago Tribune. “2015: A banner year for health care mergers and acquisitions”. Retrieved July 6, 2016. http://www.chicagotribune.com/
business/ct-healthcare-mergers-acquisitions-1229-biz-20151228-story.html
8
Emily Rappleye. (2015). Becker’s Hospital Review. “20 major health systems, payers pledge to convert 75% of business to value-based arrangements by 2020”
Retrieved July 8, 2016. http://www.beckershospitalreview.com/finance/20-major-health-systems-payers-pledge-to-convert-75-of-business-to-value-based-
arrangements-by-2020.html
Without a solid foundation
of data quality, organizations
not only risk patient lives,
they risk the future of a
better, more personalized
healthcare industry.

8. For more information,
visit www.stibosystemshealthcare.com
About Stibo Systems Healthcare
Stibo Systems Healthcare is a subsidiary of Stibo Systems, the global leader in multidomain Master
Data Management (MDM) solutions. During the last 30 years, Stibo Systems has helped hundreds of
companies to develop a trusted source of operational information. A privately held subsidiary of the
Stibo A/S group, which was originally founded in 1794, Stibo Systems’ corporate headquarters is
located in Aarhus, Denmark.
The healthcare subsidiary was created to address the critical security and safety requirements of
enterprise healthcare data while building solutions to handle its unique workflows and disparate
sources. Our team of experts works in concert with Stibo Systems at large to ensure consistency
and quality across industries and customers, but was specifically chosen for their healthcare tenure
to address today’s dire need of data unification and sharing.
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