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

Introduction 1 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.

Section One: Pressure from External Sources 2 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.

Section Two: Impact on Operations 5 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.

Section Three: Health Information Technology Challenges 6 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.

Section Four: Performance Measurement in Action 7 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.

Section Five: Implementing Change 10 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.

Conclusion 16 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.

1 Introduction

AbOuT THE AuTHOrSJason 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.

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.

2 Pressure from External Sources

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.

ImprovementThe 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.

Section One: Pressure from External Sources

Section Four: Performance Measurement in Action

Section Two: Impact on Operations

Section Five: Implementing Change

ConclusionSection Three: Health Information Technology Obstacles

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

TransparencyThe 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).

4 Pressure from External Sources

Value-based PurchasingCommonly 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.

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.






6 Health Information Technology Challenges

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.






6 Health Information Technology Challenges 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.

8 Performance Measurement in Action

Denominator: Definition of Case Selection to Which Measurement Criteria Applies

All Acute Myocardial Infarction

(AMI) discharges

Case selection queries are run in the patient accounting

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-

istration of a platelet aggregation

inhibitor (aka aspirin)…

… within 24 hours before or after

time of arrival

Information systems will have varying degrees of ability to

capture that specifically a platelet aggregation inhibitor

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.

Capture of aspirin administration prior to arrival, for

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

Comfort care only

Patients with aspirin

contraindications

Active bleeding on arrival

or within 24 hours after arrival

Transfer from another ED

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.

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.

The presence of an adverse reaction or other allergy to

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).

Timing of active bleeding will only be abstracted from phy-

sician and nursing documentation in the medical record.

The case is excluded if the admission source field in the

patient management system accurately depicts another

inter-ED transfer source.

10 Implementing Change

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.






Data Capture

UniversalTranslator

Universal HealthData Model

MeasurementDefinition Model

Data -> Measure

Measure Calculationand Reporting

THE PLAN

THE BLUEPRINT

Patient Care Performance Measurement

PErFOrMANCE ArCHITECTurE

10 Implementing Change 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.


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.


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.

16 Conclusion

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.






16 Conclusion

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.

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