Architecture Of A Measure

The Architecture of
Performance Measurement
Designing for Efficiency, Diligence and Utility

Where are we now?
As health systems face increasing scrutiny from both
within and without, pressure to develop effective
performance measurement systems is mounting.

How can we gain control?
Health care providers must establish a master
plan with a blueprint that can coordinate disparate
systems, data formats, and languages, among other
measurement challenges.

Where do we begin?
Understanding and application of best practices,
including biomedical informatics, is the first step
in the efforts to resolve the numerous issues
complicating the development of effective
performance measurement systems.

Contents

1
Introduction
Pressure to measure performance and report the results is mounting, and current health informa-
tion technology is struggling to meet the challenge. This article examines the obstacles hampering
efficient generation and delivery of performance measurement data, as well as recommendations
for possible solutions.

2
Section One: Pressure from External Sources
Many factors are driving the mandate for performance measurement, and the list of measurement
criteria is growing. Regardless of which stakeholders are demanding performance measurements,
the three primary focus areas remain improvement, transparency, and value-based purchasing.

5
Section Two: Impact on Operations
Ineffective organization, inadequate technology, and the lack of standardization make executing
an efficient performance measurement system difficult for most health care providers. Time and
resources expended gathering and processing measurement data is often interfering with clinicians’
focus on patient care and very well may exceed the benefits derived from measurement. Finally, trying
to comply with disparate requests from multiple external agencies further frustrates the process.

6
Section Three: Health Information Technology Challenges
Health information technology provides many demonstrable benefits to health care organizations,
including the ability to document information related to cycle time, events, and behaviors involved
in the care delivery process. However, health information technology has not yet delivered the oft
envisioned solution to efficiently capture, organize, and deliver performance measures.

7
Section Four: Performance Measurement in Action
An analysis of AMI.1, a common measurement of the evidence-based practice of delivering aspirin
on arrival to patients with Acute Myocardial Infarction, illustrates the typical state of performance
measurement in many hospitals across the country today.

10
Section Five: Implementing Change
Much work needs to be done to create an industry-wide system that can effectively measure
performance and process the data into results that can be understood by all stakeholders.
Necessary steps comprise engaging clinicians, aligning measurement and HIT activities,
and exploiting emerging health care data management best practices and standards.

16
Conclusion
The demand for performance measurement will only increase, so by addressing the complexities
of the issue and committing to a design process that assesses the current state, defines the future
state and establishes the roadmap towards both a comprehensive, but also an efficient performance
measurement system, health care providers can begin to reap the benefits performance measure-
ment offers for improvement, transparency and value-based purchasing.

AbOuT THE AuTHOrS
Jason Oliveira, MBA is a KSA Principal in the health care information
technology practice specializing in the planning and application of
business intelligence methodologies to health care organization
challenges.

Harvey J. Makadon, MD – a Clinical Professor of Medicine at Harvard
– is a KSA Associate Principal specializing in clinical leadership and
change management in academic medical centers.

Introduction
1

Health care providers face intense scrutiny from external sources such as
insurance companies, regulatory agencies, lawmakers, and a more well-informed
public. As competition increases and the pressure to earn top rankings from
evaluating agencies grows, the focus on performance measurement will become
increasingly important.

Unfortunately, most heath care providers, no matter their size or location, are
hampered by antiquated measurement tools and processes that can’t efficiently
manage the magnitude of data collection and organization now required in today’s
competitive environment. In addition, clinical personnel are often not engaged in
the critical nature of these activities or are charged with only the tactical task of
data collection, which takes time and focus away from patient care.

It’s easy to become cynical about performance measurement because of the
perceived burden most providers face trying to implement and manage a
practical system. However, performance measurement, when effectively
integrated into the day-to-day activities of health care professionals, can offer
tremendous benefits.

This discussion will illustrate the significant challenges facing the implementation
of health information technology (HIT) as it relates to performance measurement,
and focus on the necessary steps that should be taken to ensure that the gains
realized from performance measurement definitely exceed the burden and cost
of the process.

Section One: Section Two: Section Three: Section Four: Section Five: Conclusion
Pressure from Impact on Health Information Performance Implementing
External Sources Operations Technology Measurement Change
Obstacles in Action

Pressure from External Sources
2

Purchasers, insurers, consumers, regulators, and society at large are increasingly demanding
objective measures of health care performance. This demand is expected to converge with the
funding mechanisms of American Health Care and emerge as various value-based purchasing
models, such as pay-for-performance.

In response to these and other drivers – accreditation, peer-pressure to report publicly, links to
payment, the patient safety movement, and internalized drivers for performance management
– health care organizations are essentially mandated to participate in multiple performance
measurement programs.

One study by the Center for Studying Health System Change in Washington D.C. indicated health
care organizations are participating in an average of 3.3 external measurement programs
in addition to CMS and TJC’s ORYX®.

The various goals and objectives of these performance measurement programs can be organized
into three categories: improvement, transparency, and value-based purchasing.

Improvement
The foundation of performance measurement is the effort to improve clinical safety, quality,
satisfaction and outcomes. The patient safety and improvement movement continues to be
motivated by seminal events such as the publishing of the Institute of Medicine’s “To Err is
Human” in 2000, and the pursuit of quality and safety by emerging associations such as the
Institute for Healthcare Improvement and National Quality Forum. Numerous studies (e.g.,
RAND , The Dartmouth Atlas ) have highlighted the variation in quality of care and driven the
agenda for performance measurement and improvement.

Developers of Performance Measurement Standards
The Joint Commission (TJC) – TJC administers the National Hospital Quality Measures as part of the ORYX® initiative,
which focuses the accreditation process on key patient care, treatment and service issues. Reporting on core measures
is mandatory for hospital accreditation, and institutions including home health, long-term care and behavioral health
are encouraged to participate voluntarily in non-core measures.

The Agency for Health Research and Quality (AHRQ) – This U.S. Department of Health and Human Services is viewed
by many as a national leader in measurement development and research. AHRQ maintains the National Quality Measures
Clearinghouse (www.qualitymeasures.ahrq.gov), which has the daunting effort to document, define, and manage all known
quality measures across the industry. As of August 2007, the NQMC contained 1,264 individual measure summaries. Since
2001, AHRQ also manages the public use Quality Indicators (www.qualityindicators.ahrq.gov) program for measures of
preventable admissions, inpatient quality, patient safety and inpatient pediatric quality indicators based on publically
available data and ICD-9-CM coding.

Doctor’s Office Quality-Information Technology (DOQ-IT) – This three-year national quality improvement initiative is
designed to assist physicians who wish to purchase and implement EHRs in their practices to improve the quality and safety
of care given to Medicare beneficiaries. The measures focus on areas such as coronary artery disease, diabetes, heart fail-
ure, and hypertension. The program is administered through the 53 quality improvement organizations. Physician practices,
through their certified EHRs, submit data to the QIO Clinical Warehouse via HL7 messages. This is a good example of the
direction the industry needs to take in having clinical documentation automation explicitly support measures data capture.

Pressure from External Sources 3

Transparency
The goal of empowering the health care consumer while driving positive change through transparent
public reporting is relatively new.

In November 2001, the U.S., Department of Health & Human Services announced the Quality
Initiative to ensure quality health care for all Americans through accountability and public
disclosure. With the tag line “Transparency: Better Care, Lower Cost,” the initiative was designed
to empower consumers to make more informed decisions based on their access to data related
to providers’ quality of care. Its assumption was that when health care consumers have better
information about price and quality, they could take greater responsibility for their care through
more rational decision-making. In response, the Quality Initiative purported, providers and
clinicians would improve quality of care as a competitive advantage.

In addition, the health care industry is also seeing a slow but steady rise in Consumer Directed
Health Care, in which lower premiums and higher deductibles merge with decision-making tools,
education, and information. This trend, combined with many private sector transparency initiatives
(e.g., HealthGrades), culminated in President George W. Bush’s signing of Executive Order 13410 in
August, 2006. The order directs the promotion of quality and efficient care in the programs admin-
istered or sponsored by the federal government through the Value-Driven Health Care Initiative,
directed to all federal programs and other purchasers and sponsors of health care.

Consumer resources for Evaluating Health Care Performance
New York State About Health Quality (www.abouthealthquality.org) publishes the Health Care Report Card, which presents
access, service, and quality data for all hospitals and commercial managed care plans in the state of New York. Each quality
measure displays a selection of data that indicates at a glance whether the facilities or organizations are performing above,
at or below average. Many states have similar transparency tools – and more are sure to follow.

HealthGrades (http://www.healthgrades.com) is an independent health care ratings company focused on hospitals,
doctors and nursing homes. Health Grades reports on 32 diagnoses and conditions available from public Medicare
program data sources. According to the company, more than 2.5 million individuals visit its website every month. For
a fee, consumers can also see in-depth hospital reports, as well as data regarding doctors’ board certification, education,
and for 15 states, malpractice events.

Centers for Medicare and Medicaid Services (CMS) Hospital Compare (www.hospitalcompare.hhs.gov) is the consumer
tool provided by the federal government that supplies information about how well hospitals care for their adult patients
with certain medical conditions, including heart attack, heart failure, pneumonia, and surgical care improvement.

The National Commission for Quality Assurance’s Health Plan Employer Data and Information Set (NCQA HEDIS)
provides 60 measures that evaluate health plans, particularly health maintenance organizations. The measures are
organized by effectiveness of care (e.g., use of beta blockers after myocardial infarction), availability of care (e.g., access
to preventive health services), satisfaction of care (e.g., member satisfaction surveys), and utilization (e.g., admissions
per 1,000 members).

Pressure from External Sources
4

Value-based Purchasing
Commonly referred to as pay-for-performance or P4P, public and private payers are developing
purchasing initiatives as part of a broader national movement to improve the quality and cost-
effectiveness of funded health care services. These initiatives augment or reduce payments to
hospitals or physicians based on measured and demonstrable performance.

As evidence of this trend, the 2005 Deficit Reduction Act specified that hospitals must report
quality process measures or receive two percentage points less than the market basket in
their reimbursement rates. As of October 2008, hospitals are now required to report 42
measures. Needless to say, the compliance rate for voluntary reporting of measures has
increased dramatically

Pay for Performance Incentives
Bridges to Excellence – This is a consortium of stakeholders and purchasers providing bonuses to clinicians for adherence to
safety practices as demonstrated by quality measures. Core programs include: Physician Office Link, which qualifies bonuses
based on implementation of specific processes to reduce errors and improve quality (up to $50 per patient/year); Diabetes
Care Link, which qualifies bonuses up to $80 per patient/year, while purporting to save employers up to $350 per patient/
year; and Cardiac Care Link, for bonuses up to $160 per patient/year with employer savings of up to $390 per patient/year.

Physician-Hospital Collaboration Demonstration (PHCD) – Started in 2007, this initiative considers quality and costs through
the immediate post-discharge period and beyond to examine the impact of gain sharing activities on longer-term outcomes
(e.g., mortality, readmissions) and utilization of services. Incentive payments are only allowed for documented significant
improvements in quality of care and savings in the overall costs of care. The demonstration tracks patients for an entire
episode of care, which generally extends beyond a hospitalization, to determine the impact of hospital-physician collaborations.

Premier Hospital Quality Incentive (HQI) Demonstration – This three-year program, which received a three-year extension in
2007, is designed to determine whether economic incentives are effective in improving the quality of inpatient care. The dem-
onstration involves the Centers for Medicare and Medicaid Services (CMS) partnership with Premier Inc., a group purchasing
organization of not-for-profit hospitals. Specifically, the Premier HQI Demonstration recognizes and provides financial rewards
to hospitals that demonstrate high-quality performance in 34 quality measures associated with five clinical conditions. The
measures are aligned with ORYX®, National Quality Foundation, and other CMS performance reporting initiatives (see above).
Hospitals in the top 20% are recognized and given a financial bonus. By year three of the program, hospitals will receive lower
payments if they score below clinical baselines. Second-year results published January 2007 showed overall quality increased
by 11.8%, which translates to better care for more than 800,000 patients. During the program’s second year, the CMS awarded
incentive payments of $8.7 million to the 115 top-performing hospitals.

Provider Payment Reform for Outcomes, Margins, Evidence, Transparency, Hassle Reduction, Excellence, Understandability
and Sustainability (PROMETHEUS) – The premise of this group is to guide health plans and providers to voluntarily collabo-
rate through negotiations reflecting specific payment principles that support value-based purchasing. Performance measures
are required to enable such collaborative goals.

Physician Quality Reporting Initiative (PQRI) – President Bush signed the Tax Relief and Health Care Act of 2006, which
authorized the establishment of a physician quality reporting system by CMS. PQRI established a financial incentive for
eligible professionals to participate in a voluntary quality reporting program. Eligible professionals who successfully reported
a designated set of quality measures on claims for dates of service from July 1 to December 31, 2007, could earn a bonus
payment, subject to a cap, of 1.5% of total allowed charges for covered Medicare physician fee schedule services. This initiative
is separate from the DOQ-IT initiative. In July 2008, CMS announced that it would pay out more than $36 million in incentives.

Obstacles in Action

Impact on Operations 5

A team of researchers from the Center for Studying Health System Change and Mathematica
Policy Research assessed (through a survey of hospitals) the impact of performance reporting
on hospital operations. The assessment included data collection, review processes, feedback,
quality improvement and resource allocation. Key study findings include the following:

> Organizations are participating in multiple external reporting programs:
an average of 3.3 programs in addition to CMS and TJC.

> Twenty percent of those surveyed stated that reporting programs interfered
with one another due to differing criteria or data collection procedures.

> Reporting programs are deemed poorly coordinated both externally and internally.

> Human resources devoted to quality measurement and improvement have
increased, but remain inadequate.

> Inadequacy of current information technology solutions to support data abstraction
and measurement calculations is driving the need for staffing.

Another analysis by Van Dusen, a quality measurement specialist at Premier, found that the
time required to extract the 43 data elements necessary for a patient with acute myocardial
infarction (AMI), a prevalent measurement area, is 20 to 25 minutes per patient.

The burden of performance reporting is likely to grow before it improves. The drivers behind
transparency, the roll-out of value-based reimbursement, and clinical improvement initiatives
continue to intensify. Once-voluntary programs and demonstration projects will likely become
mandatory over the next several years. CMS has also identified new types of measures it could
potentially collect including efficiency, emergency care, patient experience, and pediatrics.

In addition to the focus on external performance reporting is the growing list of internal
monitoring, measurement, and benchmarking activities across the organization and its many
clinical departments. Chronic-disease-specific and population-specific data registries for
diabetes, pediatrics, ICU, and cardiovascular are exacerbating the data capture and reporting
responsibilities of participating organizations.

Obstacles in Action

Health Information Technology Challenges
6

HIT in general, and electronic health records (EHRs) specifically, promised a bright future of
online health information available to multiple stakeholders for multiple objectives.

Evaluating the quality, safety, appropriateness, and satisfaction of services can deliver essential
information that allows providers to demonstrate value and differentiate from competitors.
Organizations that use this information proactively and strategically not only benefit from
internally driven performance management plans, but provide support to Six Sigma, LEAN,
and related methodologies.

At a minimum, even within existing measure sets, HIT could potentially allow for whole popula-
tion-based measures vs. the sampling driven by the paper medical record reality. The unique
capabilities of HIT, such as deep process insight tied to computerized provider order entry and
bedside medication administration systems, offers a host of new possible measures related to
cycle time, events, and behaviors of interest in the care delivery process, all previously kept locked
up on paper. The potential of HIT is that health care organizations will – for the first time – be
able to measure their value equation in a meaningful way, ideally at a lower cost and burden.

However, HIT is not yet making performance measurement easier. Virtually all health care
organizations, including providers, payers, pharmaceutical companies and research organizations
suffer from a glut of unstructured and unformatted content, including dictated/transcribed
physician notes and scanned documents. Health information is dispersed among multiple
operational information systems, and many provider organizations still rely on paper-based legal
medical records.

HIT solutions such as physician order entry, clinical documentation and e-prescribing have
improved health care operations and quality, but still require time-consuming performance
data capture, measurement, and reporting. Often performance measurement data collection
occurs as an additional task for doctors and nurses to conduct, rather than being a by-product
of patient care delivery.

The premise that HIT adoption will drive new efficiencies requires that the industry increase its
adoption. Current EHR adoption rates are estimated to range from only 10% to 25%, depending
on which surveys are referenced and how an EHR is defined. The good news is that stakeholders
across public and private interest groups are driving adoption and numerous initiatives are
underway. This HIT adoption will lay the foundation for capturing meaningful details of health
care clinical operations. It then becomes the objective of performance measurement to define
and create actionable insight through data.

Obstacles in Action

Performance Measurement in Action 7

To illustrate an example of the current challenges of capturing and exploiting performance
measurement data, consider the analysis of one of the most common performance measures
in hospitals around the country: AMI.1, or aspirin on arrival for AMI patients.

AMI.1 is an “aligned” measure in that it has been adopted by CMS, Hospital Quality Alliance, TJC,
Agency for Healthcare Research and Quality, and NQF. It is based on the substantial peer review
finding that early use of aspirin in patients with Acute Myocardial Infarction significantly reduces
adverse events and subsequent mortality.

Examining the typical architecture of this measure by defining the denominator and numerator
case selection criteria reveals how this seemingly straightforward measure, even with prevalent
HIT solutions in place, requires significant staff intervention for accurate sampling, calculation
and reporting.

A typical HIT environment is presented to highlight the sources and challenges of measurement
data collection. In this illustration, personnel are using a 10-year-old patient management and
patient accounting system; a brand new clinical information system with document imaging,
orders communication and results viewing; and the typical array of ancillary systems for laboratory
sciences, medical records abstraction and encoding, emergency department documentation, and
pharmacy management. How have these solutions helped or hindered data capture and reporting
for the AMI. 1 performance measure?

As the example on the following page demonstrates, the industry’s typical solutions for automat-
ing care processes still have a long way to go to serve the dual purposes of care administration and
performance measurement. Observing and recording the subtle nuances of the health care process
is complicated and time-consuming. Current technology still requires significant abstraction of
performance data from paper-based medical records and electronic free-form text documentation,
often requiring professional interpretation of the data before transferring to a new format specific
to the measure. Thus the ability to automate performance measurement calculation and reporting
remains limited. Lastly, the AMI measure as shown can be supported to a significant degree with
prevalent HIT but there are many measures such as Hand Hygiene that are completely reliant on
observation and hash-mark capture that HIT has no conceivable solution to address.

Case Exclusion Definition Data

2007 Focus Group
In 2007, KSA facilitated a College of Health Care Information Management Executives Spring Forum Focus Group.
Participants included CIOs from diverse health care organizations, and the focus of discussion was informatics-enabled
performance measurement. Several common themes emerged:
1. the need to enable multiple internal and external reporting programs
2. the challenge of scanned documents as the source of clinical insight
3. the frustrations of trying to define, capture, and manage information in one synchronized effort
4. the lack of meaningful alignment of IT efforts with performance and value management activities
The CHIME Focus Group discussed ways to implement interventions to ease the burden of performance reporting by
expanding information management technologies such as coordinated technology and measurement planning, natural
language processing, and measurement management platforms.

Performance Measurement in Action
8

Denominator: Definition of Case Selection to Which Measurement Criteria Applies

All Acute Myocardial Infarction Case selection queries are run in the patient accounting
(AMI) discharges system based on discharges where the principal diagno-
sis code includes an ICD-9-CM code indicating AMI (e.g.,
410.00 - AMI ANTEROLATERAL, UNSPEC)

Numerator: Examples of Measurable best Practice Criteria
Many numerators in the prevailing measure sets are heavily reliant on capturing the presence or absence
of a specific clinical intervention and the timeline around that intervention.

Subset of discharges with admin- Information systems will have varying degrees of ability to
istration of a platelet aggregation capture that specifically a platelet aggregation inhibitor
inhibitor (aka aspirin)… was ordered, dispensed, and actually administered to an
emergent patient, but our example environment does not.
Aspirin is a floor stock and administration is documented
in the paper medical record. The potential for the future
is the use of the FDA National Drug Code and accurate
administration event capture (e.g., 12843010106 – Aspirin
325 mg oral tablet Bayer) through pharmacy cabinets and
drug administration bar coding.

… within 24 hours before or after Capture of aspirin administration prior to arrival, for
time of arrival example, at the patient’s home or in the ambulance is
in the ambulance paper report. Accurately capturing
and recording the time of arrival is also a challenge
and often solely recorded in the ED medical record.

Performance Measurement in Action 9

Exclusions: Cases That are Excluded From Selection
Not every case fits the clinical definition and needs to be excluded from the calculations for fair and balanced
results. In order to discover whether each case is appropriate to the measurement criteria, staff must access
various electronic data sources or those that are not electronically organized at all.

<= 18 years of age Pediatrics is naturally excluded. The algorithm to calculate
age must use the month and day portion of admission date
and birth date as recorded to yield the most accurate age.

Comfort care only If there is any indication that the provision of aspirin was to
provide only comfort, such as for terminally ill patients, then
the case is excluded. This is not readily available information
without reviewing the free-form physician, nurse practitio-
ner, or physician assistant notes as typed into the ED docu-
mentation system. If comprehensive coding is being applied
to ED cases, then ICD-9-CM code V66.7– Palliative Care
could be recorded in the medical records coding system.

Patients with aspirin The presence of an adverse reaction or other allergy to
contraindications aspirin excludes the case. Allergies are not readily available
in an encoded manner in most hospital information systems
to date. The abstractor commonly reviews physician notes
for such indications or allergy lists if such are maintained
by the organization in the clinical information system.
ICD-9-CM does not capture this clinical observation, but
SNOMED, rarely implemented as part of clinical documenta-
tion systems, does (e.g., 292044008 – ASPIRIN ADVERSE
REACTION).

Active bleeding on arrival Timing of active bleeding will only be abstracted from phy-
or within 24 hours after arrival sician and nursing documentation in the medical record.

The case is excluded if the admission source field in the
Transfer from another ED
patient management system accurately depicts another
inter-ED transfer source.

Obstacles in Action

Implementing Change
10

While HIT has not yet resolved many of the issues, the lessons of today will lead to more
cost effective and efficient applications and improved results tomorrow. Necessary steps
to implement change include:

1. continued rationalization of performance measurement activities which must involve a
clear definition of standard measures and unified reporting processes throughout health
care organizations

2. coordination of operational clinical information technology with performance measurement
activities to plan a big-picture perspective for all stakeholders

3. infusion of biomedical informatics capabilities, such as natural language processing, controlled
medical vocabularies, and standardized health care data models into information systems

4. application of data warehousing and business intelligence methodologies to organize and
utilize the data that is captured

Figure 1 outlines an overview of promising approaches and health care informatics solutions
that when combined with clinician engagement, performance management program design,
vendor business partnerships, data architecture planning, and technology deployment could
revolutionize the performance measurement process.

PErFOrMANCE ArCHITECTurE

THE PLAN

THE BLUEPRINT

Patient Care Performance Measurement

Measurement
Definition Model
Data Capture

Universal
Data -> Measure
Translator

Measure Calculation
Universal Health
and Reporting
Data Model

Implementing Change 11

Coordinated Measurement Activities
More deliberate standardization and coordination of abstraction, coding, and reporting activities
would facilitate more consistent or complementary information technology solutions. One effort
to reach this goal was initiated by an agreement negotiated between TJC and CMS to completely
align current and future common Hospital Quality Measures as they relate to their condition-specific
measure sets. Their goal was to implement complete alignment in time for January 2008 patient
discharges to be reported.

Organizations’ internal measurement activities must also be better coordinated. Each discipline
(e.g., oncology, cardiology, neonatology, pediatrics, ICU) collects data and independently reports
to national medical societies and associations. The nursing department also collects data for core
measures and Magnet status benchmark data reporting. In addition, efforts to expand measurement
activities have infused nearly every department, leaving public reporting of activities to TJC and
CMS to any of several departments, from External Affairs to the Department of Quality.

The IT department in turn scrambles to support the varied demands for data management, often
resulting in duplicative, conflicting and complicated measurement data capture and reporting
processes. Unfortunately, many health care providers have no IT support for data capture and
management, so each department creates its own system for capturing and reporting data using an
Access database or Excel spreadsheet, without the benefit of expert data management design or any
understanding of best practices.

Therefore, efforts must be directed to developing and implementing a more coordinated and rational
designs for performance measurement processes and solutions. Some organizations are even consid-
ering dedicating teams of specialists to coordinate and monitor diverse performance measurement
processes so that clinicians can focus on patient care and not a time consuming activity such as filling
out measurement data collection forms. At the very least, transparency is necessary throughout the
organization so that performance measurement processes can be evaluated for redundancy, scale,
and possible gaps.

Architecture of a Master Plan
Before implementing, HIT solutions must be carefully coordinated for both operational patient care
requirements and downstream information needs such as performance measurement. These tasks
are being managed by a new cadre of specialists, including chief information medical officers, as well
as medical, clinical and nursing informaticists who can enable health care providers to utilize
biomedical informatics to facilitate automation of clinical data generation and capture.

However, coordination must go beyond collection to translation into formats that are relevant for
downstream stakeholders, such as external reporting agencies, chief quality officers, research admin-
istrators, revenue cycle directors and service line leaders. The organization’s HIT vendors must also
be engaged.

Therefore, a logical development would be to incorporate a master architect who can incorporate
multiple requirements and varied users into a cohesive application architecture design and produce a
blueprint. This professional, or team, will need to incorporate data modeling, biomedical informatics,
standardized medical vocabularies, information interchange and service-oriented architectures. The
ability to design mature data governance structures will also be critical to future success.

Implementing Change
12

When these master architects begin to build this future system, they will have to resolve the
challenges associated with each of the issues discussed within this section.

universal Health Data Model
A critical factor that directly affects the ability of any HIT system to consistently transfer
information among users is the development and implementation of a universal health data
model delivered through a shared standardized language. This model would determine how
medical terms are described and communicated, thus making the information relevant to all
users. Development of such a model would alleviate the need for proprietary point-to-point
data interchanges, the task of translating among disparate data models, and the time-consuming
inconvenience of manual human interpretative interventions.

In addition to synchronizing the syntax of the language of medical data, a common universal
vocabulary must be implemented to allow users from around the world to share key medical
concepts and information seamlessly without need for translation. Today, the majority of
medical records remain largely contained within free-form natural language text and images.
In order to see significant progress in the success of HIT solutions, data must be universally
understood, controlled, structured, and electronically legible in order to clearly describe
organisms, substances, observations, diagnoses, procedures and diseases.

Leading the charge is the Health Level 7 (HL7) Version 3 Reference Information Model. RIM
offers a standards-based communication tool for key health care subjects, and is designed
to provide a unified framework for all information used by any of the HL7 specifications. RIM
infuses HL7’s widely accepted communication features, including query and message control
and structured documents such as Clinical Document Architecture.

Another significant advance came when the U.S. Department of Health and Human Services
negotiated an agreement with the College of American Pathologists to offer its Systematized
Nomenclature of Medicine – Clinical Terms (SNOMED-CT) freely available to U.S. users and
developers. The SNOMED vocabulary contains more than 150,000 terms for a controlled
vocabulary covering the entire medical record.

Another positive sign is the availability of the Logical Observations Identifiers, Names and
Codes vocabulary maintained by the Regenstrief Institute, developed to facilitate the exchange
of results and observations.

The World Health Organization has also provided a commonly used set of vocabularies for
detailing performance measurement for diagnoses and procedures. Its system, called
ICD-9-CM, is currently widely used, but will be replaced by CMS mandate in 2011 by a much
more granular and detailed ICD-10-CM version.

These are just a few examples of many specialized data models and vocabularies that span
every practice area from nursing to behavioral health. Until additional guidance is provided,
the disparity of terminology will severely limit the effectiveness of any HIT solution.


universal Health Translator
Even if a universal conceptual data model is adopted with a standardized vocabulary, incompatible
data models and terminology will still linger. Therefore, a translation system that integrates
domain-specific models could bridge the gap, providing a thesaurus to translate terms among
disparate systems.

The most popular and comprehensive translator on the market today is the Unified Medical
Language System® of the National Library of Medicine. It is a very large, multi-purpose, and
multi-lingual vocabulary database that contains information from more than 100 knowledge
sources about biomedical and health- related concepts, their various names, and the relationships
among them.

The UMLS® is built from the electronic versions of many different thesauri, classifications, code
sets, and lists of controlled terms, and also distributes associated lexical programs for system
developers. UMLS® is available at no charge to anyone who agrees to the license terms. However,
vendors and provider organizations must still incorporate this architecture into all applications
and health care data interchanges in order to reap its benefits. Unfortunately, there is little incentive
for either to do so today.

Data Capture
Not only do different data models use disparate vocabularies, so do clinicians. When medical personnel
are working directly with patients, they can’t be expected to communicate in the controlled medical
vocabularies understood by the data capture systems. Therefore, an additional translation layer is
required to take what medical staff members observe and report and transform it into language the
HIT system understands. Furthermore, this translation step should occur in conjunction with the
clinical process rather than as a separate endeavor.

One significant challenge is capturing structured clinical information from clinicians who prefer free
text natural language, and who aren’t likely to alter their methods anytime soon. Efforts to promote
interoperability attempted by the DOQ-IT certification of EMR solutions, designed to generate
quality measures, has not provided a seamless link between human thought process and measure
data capture. Data capture in certified EMRs still relies on a series of yes/no questions and ICD-9
coding that is not inherent in a clinician’s normal documentation. A goal would be to have the
measure’s yes/no questions answered through translation of a clinician’s desired documentation style.

Informatics research is helping to bridge the divide between clinician’s natural language documenta-
tion and the data capture system’s controlled vocabulary for decision support and measurement
processes. These interface terminologies can also reverse the display of computer-stored patient
information into simple text readable by clinicians. Efforts to correlate these terminologies with
clinical documentation and formal knowledge representation are ongoing, and require active
participation from software vendors in the research and development process.

Implementing Change
14

Measurement Definition Model
In addition to standardizing terminology, vocabulary, and language, performance measurement
standards themselves must also be clearly defined so that they align consistently with the data
capture and reporting process.

One significant effort underway is the Healthcare Information Technology Standards Panel’s
Quality Use Case Requirements, Design and Standards Selection project. The Quality Workgroup
of the American Health Information Community has been given the broad charge of recommending
how HIT can meet the following challenges:

1. provide the data needed for quality measures

2. automate the measurement, feedback, and reporting of
comprehensive current and future quality measures

3. accelerate the use of clinical decision support to improve
performance on these measures

4. align performance measures with HIT’s capabilities and limitations

The summary of its efforts offers a broad framework for measurement definition, from its
history and evidence to the designation of numerators, inclusions, and exclusions.

Another less preferable reaction to these challenges has been to relax the definition of
measures, and therefore, the sources of data. For example, the Doctor’s Office Quality
Information Technology and California Integrated Healthcare Association measures require
electronic sources of data based on ICD-9-CM and CPT coding as a proxy to clinical insight.
So while this eases the burden of performance measure calculation, it also diminishes the
clinical relevancy, specificity, and sensitivity of the measure. A study by Dr. Paul Tang of the
Palo Alto Medical Foundation demonstrated that the sensitivity of claims-based generation
of quality measures is greatly reduced when compared to EHR data sources, including
medication lists, problem lists, progress notes, and lab results. In yet another example, 25%
of the gold standard diabetic cases identified by professional medical abstractors using the
full medical record were missed when only an administrative data set was used, while 97%
were identified using EHR data sources.

Finally, these standardized measure definitions should ultimately be summarized into standard
Structured Query Language or Arden Syntax that can be shared across the industry. Once
condensed into a standard language, and based on standard data models and vocabularies,
the definitions become shareable and easily referenced.

Translation of Data into Performance Measures
In the 1970s, innovation allowed the separation of the physical storage of data in databases from
the data definition language (DDL) that described it. Today, a similar revolution is necessary to
help the measures definition model that defines the components of measures evolve to a point
where it can translate the physical data stores of the various operational clinical information
systems (i.e., CIS, LIS, EHR, EMR) into performance measures.


The translation of data into measurements will encompass systems that are able to identify, for
example, that the AMI.1 measure requires a data point to designate that the patient showing
signs of Acute Myocardial Infarction has aspirin contraindications as defined by its measurement
definition model.

In this example, applying the translation layer for a particular health care organization, the required
data point would be found in Table Y, Data Element Z, and should have the value of 292044008 for a
true response, and otherwise provide a false response. The fact that the value 292044008 even exists
is the beneficial result of adopting a universal health data model and controlled medical vocabulary
within the organization’s clinical automation portfolio.

Commercial vendors, including Siemens, Eclipsys, and Cerner, are beginning to introduce products
that incorporate some of the necessary architectural components. For example, Siemens has
implemented beta sites of its Soarian Quality Measures. At one of its beta sites, Reading Hospital
and Medical Center, the time required to review and extract core measures was reduced. The
average time required to review heart failure patients improved from more than 22 minutes per
chart to just under 7 minutes, and AMI record reviews improved from more than 330 minutes
per chart to just over 10 minutes a chart. Eclipsys and Cerner have offered similar solutions.

However, definitive solutions are still years away, so interim approaches for translation must be
found. One approach is Natural Language Processing (NLP), which is making the leap from pure
academic research to commercially available products such as health care vocabulary management
programs and natural language translation engines. Commercial vendors including Dictaphone,
A-Life Medical, Language and Computing, and the HIT vendor, Siemens, are all bringing NLP
solutions to the health care market.

Measurement Calculation and reporting
A rule of thumb regarding data warehousing and business intelligence across industries is that 70%
of corporate effort is spent collecting, capturing, massaging, and/or otherwise preparing data to be
measured. That leaves only 30% of corporate effort to analyze the data and take action. A health care-
specific study titled “Envisioning the Roadmap for a National Hospital Quality Reporting,” published
in June 2006, reached the same conclusion: Corporate effort dedicated to data analysis and clinical
performance improvement, after data collection and data reporting activities, was on the tail end of
a distribution curve.

A mature collection of TJC-certified core measure reporting systems, internal quality dashboards,
and point solutions that support measure reporting and analysis has existed for years. The challenge
has always been capturing and translating the data into formats that were in sync with those systems,
and that required medical abstraction and human intervention. If the prescriptive actions discussed
here are taken, and the architectural elements of measure definitions, data capture, universal data
models, and translation layers are put into place, the generation and reporting of performance
measurements will become easily integrated into standard practice.

Obstacles in Action

Conclusion
16

An essential component of improving health care quality is the measurement of services provided
and the outcome of those services. The cost and burden of measurement activities, however, should
not exceed the derived benefits. Emerging informatics solutions can be applied to improve the
process, though they have not yet evolved into tools that can resolve all issues. To support this
evolution, organizations need to commit to explicit planning and implementation efforts to bring
about the effective solutions they need.
Where are we now?
Evaluate current data management capabilities, processes and technologies to identify their
strengths and weaknesses. Consider how the organization measures up against recognized best
practices regarding enterprise information architecture, data governance, coordinated measure-
ment processes, data warehousing, support services, skill sets, and informatics capabilities.
Where do we want to be?
Determine short- and long-term requirements and identify gaps with the organization’s current
methods. Weigh the organization’s many unique drivers and realities when developing the vision
for the future.
How do we get there?
Create a roadmap for optimal movement from the current to the future state. This map will likely
include such initiatives as these:

> evaluating technical data integration and informatics platforms
> redesigning data capture processes
> determining what to do with aging decision support systems,
> implementing information quality programs
> establishing data governance structures
> working with vendors to inform product development plans
> deploying translational solutions such as a medical natural language platform

In addition, support must be established to empower the accomplishment of these initiatives,
primarily through these steps:
> determine and prioritize investment requirements,
> recruit and organize support resources
> manage a complex architecture

Application of biomedical informatics can improve data capture, and data warehousing and
business intelligence strategies can harvest and apply the captured data for performance
measurement. These interventions collectively will enable effective performance measurement
and ease the burden, cost and inefficiencies for participating organizations.

Footnotes
l Hoangmai H. Pham, Jennifer Coughlan, and Ann S. O’Malley, The Impact Of Quality-Reporting Programs On Hospital Operations,
HEALTH A F FA I R S ~ Vo l u m e 2 5 , Nu m b e r 5, September/October 2006, pp1412-1422

ll Linda T. Kohn, Janet M. Corrigan, Molla S. Donaldson, Editors, “To Err Is Human: Building a Safer Health System”, Institute of
Medicine (IOM), National Academy Press, 2000, Washington D.C.

lll E. McGlynn, S. Asch, J. Adams, et al., The Quality of Health care Delivered to Adults in the United States, N Engl J Med, June 26, 2003,
Massachusetts Medical Society

lV J. Wennberg, “Dartmouth Atlas of Health Care 2008: Tracking the Care of Patients with Severe Chronic Illness”, The Dartmouth
Institute for Health Policy & Clinical Practice and Robert Wood Johnson Foundation

V Scalise, Dagmar, “Quality paperwork is never done”, Hospitals & Health Networks, Storyboard, 81(1):26, 2007

V1 HITSP Quality Use Case: Requirements, Design, and Standards Selection v1.0, Health Information Technology Standards Panel,
Population Health Technical Committee, July 20, 2007

V11 Tang, PC MD, et al. “Impact of Using Administrative Data for Clinical Quality Reporting: Comparing Claims-Based Methods with
EHR-Based Methods”, J Am Med Inform Assoc. 2007;14:10–15., funded by CMS.

Jason Oliveira, Principal
T 212 508 8311
C 917 742 2784
E joliveira@kurtsalmon.com

Harvey J. Makadon, Associate Principal
T 404 253 0105
C 617 510 3464
E hmakad@kurtsalmon.com

About Kurt Salmon Associates
Kurt Salmon Associates Health Care Group is the
premier management consulting firm for today’s
leading hospitals and health systems. We work
closely with our clients to create tailored solutions
for their strategic, facility development, operational
and information technology planning needs. Our
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