datashop care

WHITEPAPER

Rapid ClinicalInformation Networks

Integrating Distributed Clinical Systems for

360 degree Patient View

Datashop | Rapid Clinical Information Networks

“ Electronic medical records are, in a lot ofways, 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 health systems so people

live healthier lives and receive the health care they need. A practicing physician with

business credentials and hands-on experience developing national 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 Powerful People in Healthcare.


Introduction

Healthcare Systems are changing from practice 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-measure-

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-sets within hospitals are at different frequencies, export/import

mechanisms, storage systems, and workflows, making it tough for them to

talk to each other. A scalable hyperlocal integration of data across multiple

sites 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 problem scales 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 universal data 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 integrations and 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 variable such 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 desirable way of getting a

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 troubling data 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 continuous basis 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 subscribers and

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 contribute data 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, modular connectors that – like the data

sources they integrate – can be standards-based, product-based, or completely customized.

Hosting these connectors within this architecture allows Datashop Care to provide unified

administration, configuration, and monitoring tools for an organization’s various systems 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 project management 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 Reporting Mechanism

• Monitoring Dashboards

• Pipeline Management

• Discovery Surveys

• Analytics Engine (Predictive, Descriptive, Prescriptive)

• Reporting Engine

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Architecture Diagram ofDatashop Care

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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

innovation through 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 – please contact us at

[email protected]

Innovaccer, Inc.

Stanford Financial Square,

2600 El Camino Real, Suite 415

Palo Alto, CA 94306

United States

+1 714 729 4038

mailto:[email protected]



