Transitioning Clinical Quality Measures (CQM) from ... - HIMSS

Transitioning Clinical Quality Measures (CQM) 

from Abstracted to Electronic Measures (eMeasures) 

Guidance Document 

March, 2012 

Developed by the HIMSS NQF Task Force with specific contributions from Zahid Butt MD, Cecilia 

Backman, MBA, RHIA, CPHQ; Melissa Honour, MPH; Bill Bell; and Amy Thorpe, MBA, PMP, FHIMSS. 

The HIMSS National Quality Forum (NQF) Task Force has written this Guidance document to 

provide information about the process of transitioning CQMs from abstracted to eMeasures. 

This document provides the following: 

� Overview 

� eMeasures design/redesign issues 

� Data capture issues in eMeasures 

� Coding and Classification Systems impacting eMeasures 

� eMeasures impact on Clinician Workflow 

� Clinical Decision Support and eMeasures 

� eMeasures Technical Considerations 

Overview 

Quality measurement is at the center of both public reporting and new forms of provider 

payments. Achievement of performance scores and reporting patient level quality data for a large 

number of clinical quality measures are crucial for healthcare providers. To date, NQF has 

endorsed more than 700 measures, with many more in the endorsement pipeline. For almost all 

of these measures collecting and reporting data is a complex, time-consuming, manual process 

for hospitals and physicians. Information collected in electronic health records (EHRs) should 

contain information required for performance measurement, and could be made available to 

automate the entire measurement process. Such a system, if fully developed and properly 

implemented, should eliminate the need for costly manual abstraction and result in greater 

confidence in comparing provider performance. 

Recognizing this promise of eMeasures, the Department of Health and Human Services 

requested that NQF convert, or “retool,” 113 NQF-endorsed quality measures from a paperbased 

format to an electronic "e-Measure" format as part of the Health Information Technology 

for Economic and Clinical Health (HITECH) Act. In July, 2010, 44 of these 113 eMeasures were 

published in PDF format in the Centers for Medicare and Medicaid Services’ (CMS) Electronic 

©2012 Healthcare Information and Management Systems Society (HIMSS).

Health Record Incentive Program Final Rule. NQF provided to CMS updates and general 

implementation guidance for those 44 eMeasures. 

In addition, NQF has supported several initiatives necessary for the EHR Meaningful Use 

Incentive Program and worked in close collaboration with many members of the quality and 

health IT communities, including measure stewards to help create the necessary eMeasures 

reporting infrastructure. The building blocks of this infrastructure include development of the 

Quality Data Model (QDM), Healthcare Quality Measure Format (HQMF) development, e- 

Measure Format Review and the Measure Authoring Tool (MAT). 

In the near future this infrastructure will be leveraged to “re-tool” a significant number of the 

existing endorsed measures or to create “De Novo” eMeasures that will leverage rich clinical 

data within EHRs using more streamlined algorithms. Stage I requires only reporting of 

eMeasures while it is anticipated that actual performance results of an expanded list of measures 

may be required in future Stages of Meaningful Use. 

As this new infrastructure undergoes maturation and quality measurement transitions from 

paper/abstracted measures to eMeasures, several short and medium term challenges will have to 

be overcome. Intelligent design or redesign of clinician workflow to capture key data elements in 

a structured format without overburdening providers will be necessary for short term and crucial 

for long term sustainability. Such documentation within EHRs should primarily support patient 

care with quality reporting as an important “secondary use.” Once these data are captured 

accurately, creating data submission files and generating measures performance rates through 

certified CQM products should not be very difficult. The remainder of this guidance document 

provides more details about key issues relevant to successful eMeasures implementation. 

eMeasures Workflow Design/Redesign Issues 

NQF has invested a great deal of time and effort to “retool” quality measures in support of EHRs 

and Meaningful Use. However, from a provider perspective, this is just the beginning of a new 

journey in quality measurement. Provider organizations will need to focus on both the technical 

and clinical workflow for successful implementations. There are many EHRs on the market and 

they differ in their level of complexity and maturity. In addition, some organizations have 

developed their own internal EHR systems. Thus, from a technical perspective, one size will not 

fit all when embedding the measures into an EHR. Although the measures will be common, the 

systems that will serve as the source for data will be quite different and each system will present 

its own unique set of challenges. 

eMeasures’ Impact on Data Capture 

Organizations will need to make accommodations for more structured and standardized data 

within their EHR. Most systems today accommodate ICD-9-CM and CPT codes, the systems 

will in future need to support additional data standards such as LOINC, Rx Norm, and 

SNOMED. eMeasures rely heavily on standardized taxonomies and vocabularies to capture data 


for quality measurement. This will create the need to develop a more robust, highly 

sophisticated vocabulary within the EHR. The starter set of data generally offered by a vendor 

will no longer meet the needs of a healthcare organization. Vendors will need to devote 

development time to retool their systems to accommodate new types of data sets and providers 

will need to devote implementation resources so that these data are captured in a structured 

format at the point of care. Decisions will need to be reached on how these data will be 

represented and captured within an EHR. Design considerations will include whether to use 

electronic forms for highly structured data capture or utilize other technologies such as natural 

language processing to take less structured documentation and encode it as structured data for 

quality reporting. Standardized vocabulary usage and the technical infrastructure that will 

support it will be two important areas of consideration when implementing eMeasures. 

Data Governance and Data Quality 

As noted above, EHRs will continue to evolve to incorporate new types of data sets and will be 

captured in a structured format at the point of care. Decisions will need to be reached on how 

this data will be represented and captured within an EHR. These decisions and the resulting 

design of data capture methodologies constitute the metadata, literally the “data about the data.” 

This metadata describes the overall design of the EHR and documents the design decisions that 

guided the specific system design to capture data necessary to satisfy the reporting requirements. 

As requirements for reporting grow, this design will have to evolve. These changes must be 

managed through a formal data governance process so that proposed changes can be evaluated 

and managed, and the organization can be assured that the overall system continues to meet all 

the reporting requirements. Key stakeholders from clinical and operational areas and quality 

reporting should be involved as well as information technology to ensure proposed changes do 

not adversely impact the ability to meet quality reporting specifications. For example, there may 

be a request to streamline a charting screen to improve usability and speed of documentation for 

an end user. By evaluating this change request through a data governance process, all 

stakeholders collaborate to ensure that the changes to the system do not result in the loss of the 

ability to capture key quality data points as was the intent of the original design. Finally, an 

integrated and rigorous change control process will ensure that approved changes are 

implemented and that appropriate communication about the change will be provided to all the 

users of the system. The metadata itself is also updated so that it continues to function as a 

repository of the cumulative design decisions that are reflected in the current system build 

version. 

Data Validation 

Data validation is the process by which collected data is tested and shown to be accurate and 

reliable. In Stage 1 for Meaningful Use, there is no specific requirement for meaningful users to 

demonstrate how they are validating the quality data they are submitting. The requirement is to 

report the calculated numerators, denominators, and exclusions though “attestation”. This is 

likely to change quickly over the next few years. As quality reporting moves from pay for 

reporting to pay for performance in general, it will be critical to ensure that data is documented 

correctly in the source system and that quality measures are calculated correctly by the reporting 

system. A formal process for proactively reviewing quality data and measure calculation should 

be a collaborative effort amongst the key stakeholders from clinical and operational areas and 


quality reporting. This process should be designed to ensure that data is being captured and 

reported correctly. First, compliance in documentation in the source systems must be examined 

to ensure that data is being captured as expected. Secondly, the quality of that data should be 

evaluated to ensure that what is recorded electronically agrees with other information in the 

patient’s record. Finally, technical issues affecting data transmission should be evaluated to 

determine if the source system agrees with the output as contained in the quality reporting 

module. By working to establish a collaborative data governance process now, organizations 

will be well positioned to meet the growing requirement of electronic reporting of quality data in 

future. 

Coding and Classification Systems impacting eMeasures 

As noted, eMeasures will use many data standards including ICD-9-CM/ICD-10-CM, CPT, 

LOINC, Rx Norm and SNOMED as a means to capture and report data. A brief description of 

each of these coding and classification systems that have been selected as standards in the 

Meaningful Use Incentive Program and have a direct impact on eMeasures reporting follows: 

• ICD-9-CM/ICD-10-CM – ICD is the International Classification of Diseases and Related 

Health Problems that is used to classify disease as well as signs, symptoms, abnormal 

findings, complaints, social circumstances, and the external causes of disease or injury. The 

classification system is published by the World Health Organization and it is used to track 

morbidity and mortality worldwide. ICD has a long history of use in the U.S. It was first 

used for reporting morbidity and mortality in 1900. The first two volumes of ICD-9-CM 

address diagnoses. The third volume of ICD-9-CM addresses procedures and was developed 

for use in the U.S. mostly for inpatient care. 

ICD-10 is a newer version of ICD and it became available for use worldwide in 1994. Many 

countries have adopted it since that time. However, because the U.S. reimbursement systems 

are so heavily tied to the use of ICD 9, ICD-10-CM will not be implemented in the U.S. until 

October 1, 2013. The next version, ICD-11 is now under development and will be available 

for use in 2015. The National Committee for Health Statistics and CMS oversee all changes 

and modifications to ICD in the U.S. 

• CPT – The Current Procedural Terminology (CPT) coding system was developed for use by 

the American Medical Association (AMA). It is used exclusively for professional billing and 

hospitals use it for billing outpatient procedures and services. This system is also intricately 

tied to reimbursement in the U.S. It is overseen by a CPT Editorial Panel at the AMA that 

determines updates and enhancements. CPT focuses only on the services rendered as 

opposed to ICD-9-CM which focuses on both the diagnoses assigned to a patient as well as 

services provided. CMS classifies CPT as a Level 1 Healthcare Procedure Coding System 

(HCPCS) for billing and reimbursement purposes. 

LOINC – LOINC was originally developed to facilitate the movement of clinical results data 

between a laboratory system and the other systems used to support patient care. LOINC 

stands for Logical Observation Identifiers Names and Codes. Its purpose is to facilitate the 

exchange and pooling of results for patient care, research, and outcomes management. The 

laboratory portion of this coding system addresses all aspects of the laboratory. The clinical 


observation portion of the coding system deals with clinical data such as vital signs, 

intake/output, pulmonary ventilator management, and many other clinical observations. The 

LOINC effort is housed in the Regenstrief Institute, an internationally respected non-profit 

medical research organization that is affiliated with Indiana University. 

• RxNorm – RxNorm is a standardized nomenclature that provides normalized names for 

clinical drugs and drug delivery services. This normalization allows for the linking of the 

RxNorm code to many drug vocabularies commonly used in pharmacy management systems 

as well as drug interaction software. For example, RxNorm can link to First Databank, 

Micromedex, MediSpan, Gold Standard Alchemy, and Multum. This linking allows 

RxNorm to standardize and mediate messages between systems that do not use the same 

software or the same vocabulary. RxNorm is produced by the National Library of Medicine. 

• SNOMED CT – Like ICD-9-CM, SNOMED CT is an international terminology system. 

SNOMED CT is used to define clinical medicine and disease. It was initially developed by 

the College of American Pathologists. SNOMED stands for the Systematized Nomenclature 

of Medicine and CT stands for the computable form of this classification system. It was 

designed to support both human and veterinary medicine. It is a multiaxial, hierarchical 

system that is based on 11 axes that serve as a basis for defining medical concepts. These 

axes address topography; morphology; living organisms; chemicals; function; occupation; 

diagnosis; procedure; physical agents, forces, and activities; social context; and general. 

Through mapping, many other systems that code and classify diseases or conditions can be 

mapped to SNOMED CT in order to normalize data within an electronic system. An 

example of this is the mapping of a standardized nursing language such as NANDA to 

SNOMED CT. ICD-9-CM has also been mapped to SNOMED CT. Since it supports such a 

large number of medical concepts it is often considered by organizations when building a 

common vocabulary within an electronic record system and is included in many eMeasures 

specifications. 

eMeasures’ Impact on Clinician Workflow 

Workflow design (or redesign) is crucial for a successful implementation and to derive value 

from eMeasures. For example, if the eMeasure speaks to an appropriate action that should be 

taken in response to an event, the action needs to be documented at the time it is taken rather 

than at end of shift if the data is to be accurately reflective of what occurred. These measures 

will not only be dependent upon what is documented by clinicians, but they will also be 

dependent upon variables such as time, care setting, patient demographics, and other variables 

that are not generally the primary focus of a caregiver. Thus, it will necessitate that all 

documentation become more accurate and timely. 

Changes in the way information is captured will have a direct impact on all disciplines 

documenting within a medical record. It is anticipated that a portion of the documentation will 

become more structured and there will be more data standardization between organizations and 

vendor products. It will be imperative that documentation be complete so that the results 

published accurately identify the activity of the organization and its performance rates on quality 

measures. 


Figure 1 illustrates an example of workflows and clinician structured data capture for a Stroke 

eMeasure: Antithrombotic Therapy on Discharge. 

Figure 1. Used with permission of Encore Health Resources. 

This example shows how data collection as specified in a measure impact several different 

workflows. Admission Order Sets, Discharge Order Sets, Problem List and Admission 

Assessments are used in this example to capture the necessary data. These data are used by the 

eMeasures algorithm to determine whether this case is selected in the denominator for the 

measure. Denominator exclusions will likely be challenging for providers. Though variables 

such as comfort measures or involvement in a clinical trial are relatively rare for a particular 

measure set, new workflows designed to capture these data discreetly will require every patient 

to be assessed for both of these. Providers will likely view this as additional work without 

significant added value. 

This example also illustrates how eMeasures could have a direct impact on existing coding and 

abstracting practices within medical records. As providers start entering final diagnosis codes 

and EHRs become more capable of automatically encoding and abstracting data, both coders and 

abstractors will move away from these functions and their efforts will be redirected towards data 

analysis. Organizations can then focus more on the information presented by the measures which 

will lead to continuous quality improvement for the betterment of patients and provider 

organizations. 


eMeasures and Clinical Decision Support (CDS) 

Clinical Decision Support (CDS) at the point of care has great potential for improving quality of 

care. CDS rules could be made available or preferably embedded in the workflow to assist 

providers in improved compliance with clinical practice guidelines and documentation. One of 

the challenges to utilizing CDS for inpatient quality measures currently is that patients qualifying 

for a particular measure set are often identified from billing data which is completed after the 

patient has been discharged. At this point, it is too late to impact the patient’s care. As shown in 

the above diagram, new workflows will be required to capture discreet data during care delivery 

for eMeasures and will have clinicians completing the problem list early in the patient’s stay. 

Identifying patients at this early point will allow for decision support to be embedded throughout 

the stay, and give the opportunity to layer decision support to capture measures that may be 

missed by a particular clinician. This should dramatically improve quality measure rates and get 

us closer to every patient receiving the right care every time. As shown in the example in Figure 

1, the eMeasures framework lends itself to incorporate CDS rules in key workflows positively 

impacting performance and outcomes. 

eMeasures Technical Framework 

Successful implementation of eMeasures requires several technical considerations for EHR 

vendors and provider organizations implementing eMeasures. 

Vendor Considerations 

Coding Management Systems: eMeasures require clinical data to be mapped to the most up-to 

date version of ICD-9-CM/ICD-10-CM, CPT, LOINC, Rx Norm and SNOMED coding. 

Historically, vendors did not always provide coding management systems or tools to facilitate 

this process. To accommodate these requirement vendors will need to provide implementation 

tools to manage and update coding systems. The coding management system should be designed 

similar to other configuration tools and allow for automated mapping of standard clinical 

elements such as orders, results, observations, and problems to the appropriate coding schemas. 

The vendor should provide tools that allow for the flexibility of mapping to custom database 

fields since many providers and hospitals have customized their EHR implementations. 

Additional consideration should be provided for the implementation of new codes or required 

deletion of expired codes. 

Provider Considerations 

Key stakeholders within an organization should be involved from the beginning of the process. 

Evaluation and feedback should be sought from all stakeholders (programmers, analysts, 

clinicians) across all levels of involvement (mild, moderate, and heavy). Information from all 

locations and all formats should be accepted to collect comprehensive feedback. 

Completing a readiness assessment prior to initiating eMeasures implementation and integration 

efforts will be very helpful to both providers and vendors. Sufficient time and resources should 

be allocated to managing inter-organizational issues at the planning stage of the project. 


In summary, this guidance document provides a roadmap for successful transition from 

abstraction-based performance measurement to a more direct electronic platform, pointing out 

key differences between the two. Informaticians, providers and quality improvement personnel 

should work closely with each other to leverage the opportunities and to deal with the challenges 

of this new paradigm designed for improved patient care. 

Contact Us 

For more information, contact Jonathan French, Director, Healthcare Information Systems, at 

jfrench@himss.org.

Transitioning Clinical Quality Measures (CQM) from ... - HIMSS

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