emeasure Development: Priorities, Methods, and Opportunities Phyllis Torda, MA and Aldo Tinoco, MD, MPH

Transcription

1 emeasure Development: Priorities, Methods, and Opportunities Phyllis Torda, MA and Aldo Tinoco, MD, MPH DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official policy or position of HIMSS.

2 Conflict of Interest Disclosure Phyllis Torda, MA Aldo Tinoco, MD, MPH Have no real or apparent conflicts of interest to report HIMSS

3 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

4 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

5 Key Questions in Quality Measurement What is important to measure? Where do we go for the data? What does the data tell me about quality?

6 Aiming for Improvement Better Care Healthy People and Communities Affordable Care

7 Quality Measurement Enterprise EVIDENCE DEVELOPMENT GUIDELINE DEVELOPMENT MEASURE DEVELOPMENT MEASURE ENDORSEMENT MEASURE IMPLEMENTATION Create the evidence base for what is effective treatment Use evidence to create guidelines for providers Use guidelines to create performance measures of adherence to guidelines Provide assurance that performance measures are evidencebased, methodologically sound Implement/use measures to understand and improve quality

8 QM Spectrum Comparisons Pay for Performance Local Quality Improvement Projects Public Health Surveillance

9 Quality Measure Evaluation Criteria Importance Feasibility Scientific acceptability Usability Instruments/MMS/MeasuresManagementSystemBlueprint.html

10 Using Measurement to Monitor Improvement Source: NCQA, The State of Health Care Quality 2012

11 Measure Life Cycle Endorsement Implementation Development Evaluation Selection Retirement

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13 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

14 Why emeasures? Measures of specialty care require clinical detail not in claims Measures of efficiency (overuse) require clinical detail Desire for new types of measures, e.g., patientreported outcomes Desire for patient-centered episodic measures Manual review of charts (paper or EHR) is cumbersome and does not support quality improvement

15 Administrative vs. Clinical Data Detail Molecular Molecular and atomic Availability Prevalent but focused on what s billed Increasing prevalence and (mostly) independent of what s billable Timing Post-encounter During encounter

16 From Claims and Paper to EHR Measures Percent of patients with diabetes who had at least One visit with a provider One HbA1c obtained One HbA1c < 8 Sustained control or improvement in HbA1c 80% of blood glucose readings within goal range during the measurement period Claims Paper EHR

17 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

18 Flow of Quality Data Measure Developer Quality Measure Quality Program QDM Patient and Provider emeasure (HQMF) Clinical EHR System CDS QM EBM Quality Report (QRDA) Warehouse

19 Quality Measure Specification Standards Standard terminologies and value sets Quality Data Model (QDM) Health Quality Measures Format (HQMF) Quality Reporting Data Architecture (QRDA) Other conventions to ensure consistency

20 Health Quality Measures Format A quality measure expressed in HQMF format is also referred to as an "emeasure The Header identifies and classifies the document and provides metadata The Body contains the criteria and logic of the measure

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22 Criteria Expressed in an emeasure Denominator AND: "Patient Characteristic: birth date" <= 85 year(s) starts before start of "Measurement Period" AND: "Diagnosis, Active: Hypertension" starts before or during "Occurrence A of Encounter, Performed: Blood Pressure Visit" AND: "Occurrence A of Physical Exam, Finding: Systolic Blood Pressure (result >= 140 mmhg)"

23 Value Set: Hypertension Code Description Code System Version Benign hypertension (disorder) SNOMEDCT Benign essential hypertension (disorder) SNOMEDCT Benign essential hypertension ICD9CM 2012

24 Quality Data Model Information model for quality-related events and observations Building block approach to emeasures Components Criteria for data elements Relationships between data elements, criteria Functions for filtering criteria

25 QDM Data Element Category Diagnosis Category Medication Datatype Diagnosis, Active Datatype Medication, Administered Attributes Start Datetime Attributes Route

26 QDM Data Expression Category Datatype Diagnosis Attributes Diagnosis, Active Start Datetime Physical exam, finding: diastolic blood pressure (result >= 90 mmhg) starts concurrent with FIRST Diagnosis, Active: hypertension

27 Quality Reporting Data Architecture QRDA Category I Single Patient Report QRDA Category II Summary Reports QRDA Category III Calculated Reports

28 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

29 Reasons for Quality Measure Testing To create measures that are scientifically rigorous, feasible and usable for their intended purpose To identify and resolve barriers in measure implementation To distinguish a good measure from a good EHR measure

30 Objectives of Field Testing Feasibility testing: Evaluate the availability of data from specified data source Scientific soundness: Identify meaningful differences in performance across accountable entities Usability: actionability of measurement results for performance improvement

31 Feasibility of an EHR-calculated Measure Multiple sites attest to the ability to capture data elements and perform the logic of the emeasure Identify gaps and how to overcome them Determine the scope, generalizability of findings

32 Data Element-level Feasibility Data Element Clinical Workflow EHR Capability Collection Errors Heart Failure Furosemide Ejection fraction NYHA Class (I-IV)

33 What is the threshold for feasibility? ONC Certification Rule or other emeasures An evidence-based best practice guideline A prevalent clinical practice or a local policy One provider who demonstrated improvement One EHR system that can calculate the measure

34 Reliability and Validity

35 Reliability Testing Parallel-forms reliability, inter-method reliability Compare manually-calculated results with EHRcalculated results Inter-rater reliability Compare results between different manual reviewers or between implementations Signal-to-noise ratio Variability between different measured entities vs. variability within a single measured entity

36 Reliability Testing - Steps Multiple sites implement the emeasure EHRs generate an automated report Use test patients to check accuracy of programming Results are compared against the results obtained from manual chart review

37 Validity of a Quality Measure Independent of data source, unlike reliability Face validity is tested by asking test sites how well the definitions and logic capture the intent of the quality measure Convergent validity is tested by comparing the results of the quality measure with the results from other measures of similar intent

38 Usability of a Quality Measure Extent to which performance results can be used for accountability and performance improvement to achieve the intended goals of the quality measure Tested by soliciting input from subject matter experts, public comment, test sites and focus groups with stakeholders

39 Challenges in Field Testing Validating the implementation of the emeasure Delineating the facets of feasibility Workflow vs. technology vs. implementation Selecting the gold standard Distinguishing abstraction method from data source Testing measures for future EHR capabilities using simulated patient data

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41 Learning Objectives Describe the development of clinical quality measures that use data from EHR systems and how they differ from quality measures that rely on administrative/medical claims data Describe the best practices in selecting clinical quality measures that are appropriate for calculation by EHR systems Recognize the role of specification and terminology standards used in the development of emeasures Distinguish between the different types of testing involved in emeasure development Identify opportunities for involvement by clinicians, vendors, and other stakeholders in future emeasure development

42 Exploring new approaches in measure development Unlocking unstructured data Non-EHR health IT Building upon local success in quality improvement Early, iterative multi-stakeholder involvement Enhancing the standards to support innovation

43 New Measure Opportunities Coordination of care Measures of change over time linked to patientclinician choices/discussions Overuse and appropriateness Treatment intensification Data derived from workflow, not new data collection

44 New Challenges, Opportunities Some data from claims not in EHR Structured fields may not exist Innovative workflows for new types of measures Systems for calculating measures need to have access to both EHR certification requires structured fields for specific quality measures Identify early adopters, learn from them, promote best practices

45 New Challenges, Opportunities cont d Need for interoperability across care settings Need for standardization in reporting requirements Providers feel overwhelmed, need parsimony Identify early adopters, learn from them, promote best practices Improve precision of certification requirements Develop specialtyspecific sets of measures and composites

46 Final Words Quality measurement is needed to track progress toward the triple aim Quality measurement strategies are the driving force behind changes in provider perception and accountability Take the opportunity for research to define measures, assess the impact on practices, understand patient perspectives

47 Thank You! Phyllis Torda, MA Aldo Tinoco, MD, MPH