Care coordination, defined as
the deliberate organization of patient care activities between two or more
participants (including the patient) involved in a patient's care to facilitate
the appropriate delivery of health care services,1 has
been recognized as a priority area for improving health care delivery in the
U.S.2 Robust measures of care coordination processes will be essential tools to
evaluate, guide, and support efforts to understand and improve deficits in care
This report presents an
assessment of the potential for measuring care coordination processes using
data from electronic data sources, in particular from existing and emerging
health information technology (IT) systems such as electronic health records
(EHR), health information exchanges (HIE), and all-payer claims databases
(APCD). This assessment relies on background research and input from individual
and group discussions with a panel of informants with expertise in health IT
systems development and use, HIEs, EHRs, APCDs, insurance plans, health data
standards, and quality measurement. This effort did not aim to develop new
measures of care coordination, but to synthesize the background relevant to
such future work.
Advantages of Measurement Using
Interest in using electronic
data, in particular data from health IT systems, for care coordination
measurement has promising advantages over data most commonly used today to
measure coordination processes (e.g., surveys, chart review). Electronic data
- Minimal data collection burden.
Structured data may be automatically extracted for quality measurement.
- Rich clinical context. Health IT
systems populated with clinical data (e.g., information on physician orders,
laboratory results, etc) offer a view of processes of care and clinical
outcomes not possible from data sets based only on claims data.
- Longitudinal patient data aggregated
from multiple sources over time. EHRs, HIEs and APCDs aim to aggregate
information for individual patients temporally from multiple providers,
settings, and payers into a single location.
Current Challenges of Measurement
Using Electronic Data
Panelists identified a number
of challenges in using electronic data for care coordination measurement:
- Underutilization of health IT
system capabilities, such as use of structured data fields.
- Clinical workflow barriers, which lead
to limited attention to and documentation of coordination processes.
- Lack of data standardization, in
particular coding of lab results and medication information.
- Limited health IT system
- Unknown clinical data quality in
various electronic data sources.
- Limitations in linking data.
- Technical hurdles to accessing
- Business models related to Health
IT that facilitate competition rather than cooperation, especially in ways that
prevent a full picture of the steps taken to care for a patient across settings
Key Expert Panel Recommendations
to Advance the Potential for Care Coordination Measurement Using Electronic
also recommended a number of ways to address these key challenges.
address underutilization of health IT system capabilities and clinical workflow
- Align structured data fields with decision support
- Create protocols for non-physician clinical or support
staff to enter selected information in structured fields.
- Explain and, ideally, demonstrate how and under what circumstances structured data improves care coordination, and
ultimately care delivery.
- Design health IT functionality to capture coordination
activities more explicitly, both to support team practice and to measure the
extent of these activities.
address lack of data standardization and limited health IT system
- Continue to support development of
standards, both in areas where standards are undeveloped and by motivating
adoption of existing standards through incentive programs.
- Align measurement and payment
incentive initiatives with key standards gaps, such as coding of lab results
and medication information.
- Align measure specifications with
existing guidelines or elements of other quality measures.
- Develop well-defined measure
concepts that will give vendors, EHR users, and HIE administrators clearly
defined data elements to build into systems.
address unknown clinical data quality in various electronic data sources:
- As part of measure development efforts, include an
evaluation of the reliability and accuracy of any electronic data used for
- Disseminate and, preferably, publish evaluations
of data quality.
address limitations in linking data:
- Communicate the value of linked data to policymakers
and the public.
- Develop strategies for overcoming privacy
address technical hurdles to accessing data:
- Consider the accessibility of data to end-users when
designing health IT systems.
- Consider the resources required to extract data from
health IT systems when choosing a product.
- Consider whether any additional EHR certification
requirements could help improve the ease of extracting data from within EHRs.
To address business
models that facilitate competition rather than cooperation:
- Support and widely disseminate projects that
demonstrate the value of information sharing.
- Seek out evidence that can demonstrate any cost
savings for institutions that result from information sharing or other care
- Bring leaders of competing health care organizations
together to facilitate dialog and encourage information sharing.
Opportunities Using Electronic Data
opportunities for measuring care coordination using electronic data that are
likely to be feasible within the next 2-to-3 years.
Near-term measurement opportunities
Use Meaningful Use measure
data elements in new measures of care coordination
Certified EHRs from
providers and hospitals participating in the Meaningful Use incentive program
Use CCD/CCR messages to
confirm transmission of key pieces of information during care transitions
EHR or HIE* and claims data
(APCD, payer files)
Use EHR data to confirm
inclusion of key information from other health care settings within primary
EHR and claims data (APCD,
Use EHR audit files to
evaluate whether information transferred from other settings is viewed by providers.
EHR audit files
Use claims data to confirm
follow-up care occurred within expected time frame
APCDs, HIE* (if includes
claims data) or payer files
Use claims data to examine
instances of redundant testing
APCDs, HIE* (if includes
claims data) or payer files
Would be enhanced by
addition of clinical data from EHR or HIE*
database; CCD—continuity of care document; CCR—continuity of care record;
EHR—electronic health record; HIE—health information exchange.
*HIEs are only a data source
if they include a data repository that stores data rather than just
Next steps required to
implement these care coordination measure concepts include:
- Develop methods to link clinical
and claims data and examine the reliability and accuracy of the linkage.
- Investigate the validity and
quality of the specific data elements used within measures.
- Assess the accuracy of data
automatically extracted from EHRs, for example by comparison with manual chart
- Carefully specify measures, with
clear definitions of numerator, denominator, and exclusions. Develop risk
adjustment models where necessary.
- Elicit clinical input and
synthesize evidence from published literature and evidence-based guidelines,
when available, to inform measure development.
Long-term Measurement Opportunities
Panelists also discussed some
measurement opportunities that are promising in the long term, but likely not
feasible within the next 3 years. These opportunities and their challenges included:
- Evidence that data are being
linked across sites or across providers. Using aggregation of clinical
information from multiple settings as evidence of care coordination will likely
require further development of interoperability infrastructure, the evolution
of EHRs and how data are recorded within them, and further conceptual
development around what constitutes coordinated care.
- Lack of documentation in a
coordinating practice's EHR (for example, of a primary care provider or medical
home) regarding health care utilization in other settings (indicator of potentially
poor care coordination). A limited application of this kind of measure is
likely to be feasible in some cases within the next few years, but broader
application is likely more distant. To be applicable to a broad patient
population, such measures would require use of APCD data for the denominator
linked with documentation from an EHR for the numerator. Currently, the ability
to link APCD data with outside data sources, such as EHRs, is possible in only
a limited number of States that collect identified data within their APCDs and
permit such linkage. In addition, confirming the absence of information about
outside health care utilization will pose a significant challenge given the
variability in where and how such information is documented in health IT
- Linking EHRs to patient registries
would offer a potentially rich data source for quality measurement. However,
panelists emphasized that near-term measurement using patient registries, with
or without linking to EHRs, is not likely. Although particular registries
contain some data elements that would be useful for care coordination
measurement, the lack of standard design or data elements included in various
registries makes it impractical to design care coordination quality measures
around registry data at this time.
The advantages of reduced
measurement burden, rich clinical context, and longitudinal data have made
electronic data, in particular data from health IT systems, the target of a
growing interest in measuring care coordination processes in new ways. Feedback
from experts who participated in this project suggests reason for optimism
about this possibility, even while recognizing many challenges that must be overcome
to make such measurement feasible. The rapidity with which the health IT
landscape is changing will almost assuredly help resolve many of these
challenges. Indeed, our discussions with panelists and review of background
materials suggest that changes likely to resolve many challenges are already
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