Background and Significance
Background and Significance
The convergence of electronic health record (EHR) adoption, increased availability
of electronic health data, development of analytical techniques, and healthcare payment
reform has created an environment ripe for using information technology to improve
delivery of healthcare services.[1]
[2]
[3] The lack of unification has manifested itself in EHR systems used by providers,
quality measures developed by different groups, and use of quality measures in different
ways by different organizations.[4] The organization and coordination of formal approaches for using health information
systems and their data to improve clinical effectiveness is needed.
Quality informatics is defined here as the discipline that “uses healthcare data collected from healthcare
information systems to improve clinical effectiveness.[5]” As part of clinical informatics that broadly studies the organization and use of
information to improve healthcare delivery,[6]
[7] quality informatics is rooted in collecting accurate healthcare quality data, analyzing
those data, and applying findings toward quality improvement programs ([Fig. 1]). Coordination of this work allows clinicians, analysts, and researchers to unify,
align, and share in the work of connecting large-scale data with improvement in healthcare
delivery. These efforts complement and contribute to parallel efforts to using EHR
data for clinical decision support, research, and public health.[8]
[9]
[10]
Fig. 1 The work, tools, challenges, and opportunities of quality informatics.
This letter outlines why quality informatics should be recognized, provides an overview
of the work of quality informatics, and describes the opportunities and challenges
facing the work. Electronic clinical quality measures (eCQMs), part of the Centers
for Medicare and Medicaid Services (CMS) Promoting Interoperability EHR Incentive
Program, will serve as an exemplar of quality informatics in practice.[11]
The Why
To allow for coordinated, efficient, and best use of clinical data, quality informatics
serves as an umbrella to organize formal approaches to utilize healthcare information
systems and data to improve clinical effectiveness. Medicare utilizes quality metrics
that providers must consistently report as a means to tie quality to data through
payments.[12] However, confusion remains about which quality metrics are essential and how they
are used.[4]
Despite the importance of utilizing data from healthcare information systems to improve
clinical effectiveness, their use present challenges. Dixon-Woods et al[13] describe how data from an electronic prescribing decision support system improves
quality and safety indicators. This experience serves as an example, but there is
no path for formally expanding this type of work. Quality informatics allows techniques,
lessons, and approaches from individual experiences to be incorporated with the experiences
of others to create effective approaches for improving healthcare delivery.
eCQMs serve as an example of how unification and coordination is needed to support
the calculation of quality metrics. CMS provides a technical framework; however, providers
are on their own to write code for calculating an eCQM from their systems. Others
describing analytic methodologies involving standards such as the HL7 Quality Data
Model highlight the need for sharing of methodologies to calculate eCQMs.[11]
[14] Quality informatics could guide how methodologies are catalogued, formalized, and
distributed for advancing the ease, reliability, and standardization of eCQM use.
The Work
The goal of quality informatics is to collect effective healthcare quality data, analyze
those data, and apply findings toward quality improvement programs. Quality informatics
tasks are grouped into three categories: (1) project management; (2) technical management;
and (3) reporting. These areas require different skill sets, but each area is inseparable
from the others for quality informatics to be effective and useful.
To calculate an eCQM, providers must have project management infrastructure to staff
and run all levels necessary to calculate the metric. The provider must have an information
system in place to collect, store, and analyze data, as well as write code to calculate
the eCQM. The specifics are beyond the scope of this letter, but it suffices to say
that there are technical components required to manage a healthcare quality information
system (HQIS) that provides the data necessary for quality informatics.[15] Quality informatics establishes and manages the necessary technical architecture
to accurately handle data related to quality and concurrently connect with the project
management and reporting necessary to make use of the system.
Reporting is a very visible component of quality informatics that encompasses quality
metrics determined by institutions, payers, and governments. Quality informaticians
are a critical part in determining the necessary metrics and understanding the capabilities
of healthcare information systems. Metric reporting continues to gain importance with
the increase in payments tied to quality metrics.[4] Areas of emphasis in quality informatics also shift over time as quality metrics
set by state and federal government agencies change their focus. For instance, CMS
now emphasizes EHR interoperability, patient–provider data sharing, and patient access
to records (reflected by the program name change from “Meaningful Use” to “Promoting
Interoperability”[12]). Quality informatics is needed to allow the sharing and establishment of formal
and effective approaches to each step described in the calculation of an eCQM.
The How
To have an effective quality informatics program, strong healthcare quality data and
effective organizational structure are crucial to success. Niland et al described
an effective program that uses a HQIS for effective healthcare quality information
collection, management, analysis, and reporting.[15] Each system component is divided into data, people, and procedural perspectives,
which are key components of a quality informatics program. They especially note that
the socio-technical and knowledge management aspects of a quality information system
present challenges greater than the technical part of the system.
An organizational structure that supports quality improvement needs to be in place
for effectively utilizing healthcare data for improvement. An informatician alone
cannot complete the range of tasks required for an impactful quality informatics program.
They must work with a team of analysts, data scientists, and information technology
professionals. A culture of quality improvement must exist in the larger organization
to achieve effective quality improvement results. In key informant interviews, Millery
and Kukafka found a theme that healthcare information technology cannot be implemented
effectively for quality without an underlying culture of quality improvement across
the whole organization.[16]
The connection between healthcare quality metrics and healthcare service processes
must be supported. Approaches like eCQMs alone will not improve quality; they must
be incorporated into quality improvement processes to effect change. De Lusignan described
how informatics forms a crucial component of the quality improvement process within
England's National Health Service.[17] By establishing the necessary HQIS, having the proper analytical tools, and building
the organizational structure and culture, the foundation can be laid to build an effective
quality informatics program that supports an organization's efforts to improve clinical
effectiveness.
The Opportunities
Multiple opportunities present themselves for harnessing the capabilities and importance
of quality informatics. The large amounts of healthcare data have given rise to the
notion of “big data” in health care, characterized by the “five V's” (volume, velocity,
variety, veracity, and value).[18] The variety of data includes the many different data elements that come from many
different patients served by many different providers, which in turn provide large
quantities of data in realtime with varying quality that can be used to promote clinical
excellence. Big data promises to improve the quality of healthcare delivery through
the use of data analytics.[19] As more data are collected, tools to support analysis and reporting continue to
be developed, which will allow quality informaticians to gain more insight. For instance,
commercial products enable healthcare systems to understand their quality of healthcare
delivery. Using big data, predictive analytics also offers an opportunity to improve
the quality of care if the right conditions are met for quality input data.[20]
Expanding the results of real-time analysis that are reported to clinicians as data
are collected is a growing area of interest. For example, real-time quality data dashboards
have been shown to impact the quality of care delivered in emergency departments and
pediatric intensive care units.[21]
[22] The continual development of EHRs, clinical big datasets, and sophisticated analytics
coupled with reporting mechanisms holds promise for quality informatics to grow in
its capabilities and opportunities to use information for the betterment of healthcare.
As methodologies and techniques are developed that allow for the better use of healthcare
data toward improving clinical effectiveness, quality informatics will serve as a
home to incorporate these technologies into existing methodologies and distribute
this knowledge to those who will benefit.
The Challenges
Quality informatics holds strong potential as a field to align with work done with
different departments and fields to improve the quality of healthcare delivery. Designing
an effective HQIS is a key component of a quality informatics program, but can be
difficult. Challenges remain that can hinder the potential of achieving effective
results, such as the quality, volume, and use of data from HQISs.
A great challenge quality informaticians are faced with is how to gather quality data.
Sukumar et al have noted problems in the quality of large datasets in health care.[23] Furthermore, data-quality issues are only addressed as they come at the moment.
Defining, recognizing, and ensuring the quality of data are crucial. Without quality
data, all further products of the system will potentially be compromised. A new approach
is thus needed to systematically approach the quality of data. Organizations and informaticians
must ensure the proper system is in place to supply the organization with qualified
data. Data-quality assessment frameworks are being formalized for characterizing healthcare
data quality for secondary uses.[24]
[25]
[26]
[27]
[28]
[29]
[30] Notable challenges include development of metrics to assess data quality,[24] definition of completeness users choose to use,[25] and potential biases in datasets.[26]
Challenges facing systems and their attempts to use data for quality improvement include
usability, interoperability, full integration, and data mining.[31] In April 2018, CMS launched a request for information from providers to gather information
that will inform quality measures based on the exchange of information between providers
and patients.[32] CMS also created its first Data Elements Library to place standards and data elements
in one location to support the development of tools for interoperability.[33]
New methodologies are required to optimize data management, extract meaningful information,
and conduct a proper workflow with the data because big data present new challenges
due to the volume of data.[19] As part of data governance, healthcare organizations need to ensure that the correct
technology and staffing are in place for proper curation and maintenance of vast amounts
of data to address their analytic needs. Each of big data's five V's poses a unique
set of challenges that must be addressed to make effective use of available data.[34] Unstructured data in the EHR (e.g., patient notes) present challenges and necessitate
use of natural language processing (NLP) to transform freetext into structured data
for subsequent analyses. For example, electronic quality (eQuality) assessment tools
have been developed that incorporate NLP for automated quality measurement (e.g.,
for disability exams,[35]
[36] postoperative complications,[37] and asthma care[38]
[39]).
Data collection, analysis, and reporting cannot be separated from clinical practice
and quality improvement programs on the ground. Informatics must be effectively tied
to the larger organizational systems to truly achieve the goal of quality improvement.
For quality metrics to be an effective part of quality improvement, they must be integrated
into the whole system. As Burstin et al note, “To make significant improvements in
U.S. health care, a closer connection between measurement and both evolving national
data systems and evidence-based improvement strategies is needed.”[40]
Alongside the technical and organizational challenges of quality informatics, ethical
questions require consideration. The collection of patient data raises questions of
who owns those data, who has access, how the data are secured, and who regulates these
data. Genomics faces similar ethical challenges surrounding the collection, use, and
sharing of patient data.[41] When patient data are incorporated into metrics, ethical questions emerge about
patient selection for metrics, how the mission of healthcare providers changes when
guided by metrics, and how the connection of monetary elements tied to metrics influences
organizational decisions. Cohen et al discuss these ethical and legal challenges in
the development and use of predictive analytics in health care.[42]
Each healthcare organization faces challenges to accomplish these tasks relative to
their unique situations and organizational structures. How these organizations choose
to develop an infrastructure to overcome these issues and maximize efficiency will
depend on myriad factors, but effective leadership will be a key piece of any plan
to effect change. For example, Tang et al showed that coding data contained in the
EHR compared with manual chart review identified more diabetic patients correctly
and led to significantly different quality measures without adding to the administrative
burden.[43] However, there are burdens associated with reporting quality measures, and research
is needed to ensure that the amount of work that goes into reporting quality is then
rewarded with a meaningful piece of information for that healthcare system to use.[44] Informed and effective leadership is necessary to take these situations and translate
examples from the literature into a meaningful reality for that organization. Leadership
needs to develop a plan that guides data from collection to reporting in a smooth
pipeline and connect that with the systems for improvement. Without key leadership,
these areas have the potential to function as independent silos, wasting resources
and potentially reducing impact.
Conclusion
Healthcare systems are accountable for monitoring, reporting, and improving the quality
of clinical care. As the need for reporting and healthcare data grows, quality informatics
is needed to coordinate and manage quality improvement projects, HQISs, technical
components of information systems, and the reporting of data for promoting clinical
excellence. Although institutions face challenges to develop effective quality informatics
programs, quality informatics presents growing opportunities. Quality informatics
enables working groups and guidelines to be developed for supporting coordination
and sharing of work to improve healthcare delivery.
Clinical Relevance Statement
Clinical Relevance Statement
Quality informatics aims to utilize the information contained in healthcare information
systems to help healthcare practitioners achieve improvements in clinical effectiveness.
As healthcare information systems continue to grow, reporting requirements continue,
and payment models move toward an emphasis on quality metrics, quality informatics
is needed to coordinate and support the work done to achieve these results.
Multiple Choice Questions
Multiple Choice Questions
-
What are the components of the work of quality informatics?
-
Quality improvement programs, electronic health record management, and data analysis
-
Healthcare quality metric calculations and quality improvement program management
-
Project management, technical management, and reporting
-
Technical infrastructure management, healthcare quality metric calculations, and reporting
Correct Answer: The correct answer is option c, project management, technical management, and reporting.
Quality informatics encompasses the gathering of information, processing of information,
and reporting of information toward improving clinical effectiveness. Project management
is necessary to ensure that the proper organizational infrastructure exists to gather,
analyze, and then utilize the information from a healthcare quality information system.
Technical management ensures that the healthcare quality information system can adequately
gather and analyze the data needed, and reporting of quality metrics and outcomes
is a growing requirement to payers and government agencies and these metrics can also
be used for internal quality improvement programs.
-
What are some of the greatest challenges facing quality informatics?
-
Construction of an effective healthcare quality information system, data analysis,
and quantity of metrics to be reported
-
Gathering quality data, analyzing large volumes of data, and connecting data to quality
improvement programs
-
Technical difficulty of data analysis, employee investment in quality improvement
programs, and effective project management
-
Effective input of data into a healthcare quality information system and ease of data
extraction from the healthcare quality information system
Correct Answer: The correct answer is option b, gathering quality data, analyzing large volumes of
data, and connecting data to quality improvement programs. To have data to analyze,
data must first be put into and gathered from an effective and efficient healthcare
quality information system. Developing this system requires multiple technical and
human resources that must be intentionally designed and trained. The quantity of healthcare
data now stored in electronic health records presents technical challenges to analyze
such large quantities of data. Once collected and analyzed, for quality informatics
to actually improve clinical effectiveness, the information gathered cannot be separated
from the operational processes in the healthcare setting. The data need to be connected
to quality improvement programs to allow the data to help inform the improvement programs
and for the programs to help improve the quality metrics.
