Health providers today are surrounded by tons of untapped data from distributed internal and external sources which can translate into important insights for patients, reduced cost of care, predict outcomes, and improve the overall quality of care. Read our whitepaper on Rapid Clinical Information Networks to learn more on 360-degree patient views and track-measure-improve via outcome-based metrics

1. WHITEPAPE
R
Rapid Clinical
Information Networks
Integrating Distributed Clinical Systems for
360 degree Patient View

2. Datashop | Rapid Clinical Information Networks
“ Electronicmedical records are, in a lot of
ways, I think the aspect of technology that is
going to revolutionize the way we deliver care.
And it’s not just that we will be able to collect
information, it’s that everyone involved in the
healthcare enterprise will be able to use that
information more effectively.”
Dr. Risa Lavizzo-Mourey | president and CEO of the Robert Wood Johnson
Foundation, is a national leader in transforming America’s healthsystems so people
live healthier lives and receive the health care they need. A practicing physician with
business credentialsand hands-on experience developingnational health policy, in
2008, Forbes magazine ranked Dr. Lavizzo-Mourey as number 22 on its 100 Most
Powerful Women list. Modern Healthcare also included Dr. Lavizzo-Mourey on its list of
the 100 Most PowerfulPeople in Healthcare.

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Introduction
Healthcare Systems are changing frompractice standards to patient outcome- based
approaches. This calls for rapid data integration across various disparate data silos.
Healthcare systems around the world are struggling with rising costs and uneven quality
of care despite well-intentioned initiatives of enforcing practice guidelines, implementing Electronic
Health Records (EHR), and eliminating fraud. The United States is implementing a fundamental
shift around outcome-based approaches, with patient outcome as the focus .
There are innate challenges with providing the whole picture for patient outcomes among
hospitals with disparate departments and health care delivery organizations with multiple sites.
Admissions in multi-site healthcare delivery organizations have increased rapidly in recent years, as
have mergers and acquisitions in the healthcare provider market. Sixty-nine percent of U.S.
admissions were in multi-site delivery organizations in 2011, compared to 52 percent in 1999.
Even with today’s digital technology, most Health Information Exchanges (HIE), Accountable
Care Organizations (ACO), multi-site healthcare delivery organizations, and providers
struggle with gathering and organizing huge data sets. They fall short of delivering outcomes
and providing meaningful insights from data.
The following are some of the most common outcomes that healthcare organizations want
to deliver:
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Today, instead of focusing on managing these outcomes, organizations spend most of their
energy and effort on handling data from disparate data streams.
Taking the CMS and other governmental initiatives in recent years into account, the shift in
the health care service model is widely apprehensible. ACOs are expanding their networks
to rural and small independent practices for quality reporting and incentivizing value-based
care, while HINs are competing to enroll more providers to establish bigger statewide HIEs.
As the health information networks grow, the scale of shared data and query requests at
the points of care are going to go up at an even higher rate. Despite the concerns on ROI
from new technology setups, more providers are investing in EHR and practice management systems
to be a part of the clinical data sharing networks.
The question remains:
Is there a scalable and cost-effective solution to integrate healthcare data-sets across disparate
systems to achieve 360-degree patient views and track-mea- sure-improve via outcome-
based metrics?
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Challenges faced by ACOs, HIEs and providers
Healthcare leaders face three common data issues when building a clinical information
network:
Disparate Data Silos
Data-setswithin hospitals are at differentfrequencies,export/import
mechanisms, storage systems, and workflows,making it tough for them to talk
to each other. A scalable hyperlocalintegration of data across multiplesites is
very difficult.
Various data-sets in different business divisions exist in hospitals, ranging from Electronic
Medical Record (EMR), Practice Management System (PMS), Laboratory Information
System (LIS), disease registries, case management, patient portals, and many more (see the Inner
Circle). Each one has a different frequency of update, storage systems, and workflows making it
difficult to integrate them together. These data-sets don’t talk to each other, so they don’t provide a
360-degree patient view.
Multiple care providers in each region add to a new dimension of complexity. A region’s
health ecosystem includes everything from clinics to pharmacies to state agencies (see the Outer
Circle). Data-sets across multiple sites are not integrated, so patient records do not collaborate for
outcome measure reporting.The problemscales up with time as more providers enroll, bringing
in more diverse systems without a universal format.
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Systems Organizations
Electronic Medical Records(EMRs)
Practice Management Systems
Laboratory Information systems
Reporting systems
Disease Registries
Machine Data
Case Management Patient
Portals Communication
Systems
Healthcare Providers
Integrated Delivery Networks
Payors
Pharmacies
Health Organzations
Personal Medical Devices
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Lack of universaldata and interoperability standards
No minimum standards in data structures, export/import mechanisms, and
ETL mechanisms is present in the healthcare domain,making it difficult for different
EHR or PMS systems to talk to each other. Stickiness of legacy systems further adds to
the complexity.
“Minimum Standards” is today’s definition of an agreeable compromise to a set of rules
or standards that everyone in the ecosystem follows, in order to get minimum trackable
information out of the ecosystem without investing too much effort. International Patent
Treaties ensure minimum standard data structure of patents across the world, while
the Securities and Exchange Commission (SEC) ensures minimum standard financial
disclosures via XBRL formats for corporations to report their financials every quarter or year.
Healthcare is far behind in achieving a minimum standard on data.
Hundreds of EMR, PMS, and LIS systems are adopted across hospitals in the United
States today, with everyone having a different reporting structure, data ingestion/export
mechanisms, and data structures. Standards & Interoperability (S&I) framework is a
community of health stakeholders initiated by the Office of Standards and Interoperability
(ONC) in an attempt to reach the minimum standards in data definition, in order for ACOs,
HIEs, Multi-Site Hospitals, and eventually CMS to evaluate outcome-based measures fairly
quickly. But minimum standards aren’t defined yet.
The challenge in bringing data to the same standards lies in the complexity of formats,
variables, and classifications. Legacy EMR or PMS systems in place have their own data formats
and set of challenges to export/import data for data integrationsand normalizations. Moreover, few
data variables are straightforward. For example, provider notes feed into EMR systems regarding
complaints and procedures. Besides provider notes, a minimum standard is to classify every
diagnosis with a code, like ICD 9 or ICD 10. This classification, in order to be scalable, needs a
strong and accurate classification algorithm.
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Delay in information retrieval
There’s an absence of automated means to normalize and integrate data
streams, which adds to a delay in obtaining information on outcome measures.
A lack of real-time processing of incoming data from the source is a big hurdle in making the
best use of patient history alongside new medical records. Any mechanism that is unable to
standardize clinical data streams in an automated way limits the learnings from data to be utilized
into clinical decision-making and personalized care to patients.
Most of the clinical systems (including EMR) were built to solve clinical challenges and are
not very coherent when it comes to sharing data between organizations. Extracting data
from EMR systems is very difficult. The data extracted from clinical systems is extremely
dirty. Mining for a simple clinical variablesuch as haemoglobin a1c will require searching
the entire database, which might still provide inaccurate results, because data may be
masked under various summary variables. Unstructured data, disguised nomenclature,
non-standardized values, and lack of harmonization all contribute to the poor quality of data
– ultimately leading to delays in information retrieval.
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What is Rapid Clinical Information Network
Rapid Clinical Information Network is the most desirableway of gettinga
360- degree patient view of distributed clinical information, using advanced data science.
Rapid Clinical Information Network (RCIN) is the most advanced way of integrating data to
rapidly empower clinical decision making. It allows healthcare organizations to focus on
the most important pieces of the data journey - decisions, rather than the data. Using RCIN,
healthcare leaders can now ask, “How can I make more decisions for better care?” instead of
worrying about, “How can I use more data for making decisions?”
Humans can never be replaced, however their work can always be augmented via smart
machines. That is the underlying philosophy behind RCIN. It creates an environment where data
and technology can reduce manual intervention and setup automated data streams and pipelines,
which can then be rapidly utilized for decision making. RCIN is a structured approach to solve
some of the most troublingdata problems mentioned above for ACOs, HINs, and , providers
(Hospitals and IDNs).
Real-time Automated Data Integration
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RCINs help organizations to achieve improved collaboration and process efficiency. Access
to integrated data can benefit both health network administrators and members. ACOs can derive
insights on the cost and quality metrics on a continuousbasis from their central database to engage
with providers in a faster and better way. HIEs can expand their network to providers with legacy
EMR systems and stream data in real-time, adding more value to their data subscribersand
contributors. Providers’ access to the patient history will not be limited to their own organization,
and more well-informed and personalized care decisions can be made at the point of care.
Providers can have a real-time flow of EHR, lab, diagnoses, procedures, devices and physician’s
notes, and the patient can also contributedata through targeted surveys and information sharing.
Automated Structuring and Cleaning of Data
Using RCIN,scalability is easily achieved in terms of expanding participants to remote
and small clinics. Once a connector has been written or configured for a particular clinical data
system, it can be reused at other health organizations that also use that clinical data system.
Adding or changing a connector is a matter of reconfiguration, not reinstallation. Clinical Data
Sources can be configured to adjust to the requirements of participating organizations while
minimizing impact on the existing installation.
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Datashop Care
Building a Rapid Clinical Information Network
The problem of integrating heterogeneous clinical data sources across multiple
organizations has been one of the primary barriers to building a wide health information network.
Clinical data sources can vary from widely-used, standards-compliant products to custom data
formats particular to an organization.
Datashop Care is an advanced clinical data science platform to address this problem with
a highly interoperable architecture that hosts reusable, modularconnectors that – like the data
sources they integrate – can be standards-based, product-based, or completely customized.
Hostingthese connectors within this architecture allows Datashop Care to provide unified
administration, configuration, and monitoring tools for an organization’s varioussystems and
interfaces. With these capabilities,all within a unified architecture, Datashop Care can effectively
address the range of integration challenges with maximum reusability and manageability.
Using Machine learning, Natural Language Processing and proprietary automation
algorithms, Datashop Care offers a suite of data management and integration engines to build a
Rapid Clinical Information Network. It is also integrated with projectmanagement features to
monitor the data pipeline and triage issues with role-based access. Some of the major features that
Datashop Care offers include:
• Intra-organizational Rapid Clinical Information Network
• Cross-organizational Rapid Clinical Information Network
• Data Extraction Connectors for most of the Clinical Systems (EMRs, PMS etc)
• 360 degree patient view
• CMS ReportingMechanism
• Monitoring Dashboards
• Pipeline Management
• Discovery Surveys
• Analytics Engine (Predictive, Descriptive, Prescriptive)
• Reporting Engine
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Architecture Diagram of
Datashop Care
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13. Datashop | Rapid Clinical Information Networks
About Innovaccer
At Innovaccer, we create products that transform the way organizations use data. Our products
and services are deployed at Hospitals, Accountable Care Organizations (ACO), Health
Information Exchange (HIE), critical government, commercial, and non-profit institutions around
the world to solve sophisticated and world changing problems. Simply put, we accelerate
innovationthrough the power of data.
© Innovaccer Inc 2015
Innovaccer, Innovaccer Inc, and Innovaccer Datashop are trademarks of Innovaccer Inc. All other
company and product names may be trademarks with which they are associated with. Datashop
Care is a proprietary technology and Intellectual Property of Innovaccer.
To know more about how Datashop Care can help you build a Rapid Clinical Information
Network, advantages, timelines and other features– pleasecontact us at
health@innovaccer.com
Innovaccer, Inc.
Stanford Financial Square,
2600 El Camino Real, Suite 415
Palo Alto, CA 94306
United States
+1 714 729 4038