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Chasm Between Cancer Quality Measures and Electronic Health Record Data Quality.

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Structured electronic health record (EHR) data are insufficient for automated Merit-Based Incentive Payment System (MIPS) clinical quality measure (CQM) reporting in oncology. Inconsistent data elements (DEs) and low fill rates prevent practical MIPS CQM calculations.

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Area of Science:

  • Health Informatics
  • Oncology Quality Measurement
  • Health Information Technology

Background:

  • The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) mandates reporting of clinical quality measures (CQMs) through the Merit-Based Incentive Payment System (MIPS).
  • Eligible clinicians must report MIPS CQMs to optimize reimbursement.
  • Assessing the adequacy of structured data in electronic health records (EHRs) is crucial for MIPS reporting.

Purpose of the Study:

  • To evaluate the adequacy of structured EHR data for reporting MIPS CQMs in oncology.
  • To determine the presence and completeness of data elements (DEs) required for Oncology MIPS (oMIPS) CQMs within the CancerLinQ platform.

Main Methods:

  • Analysis of 19 oMIPS CQMs using the CancerLinQ health technology platform.
  • Evaluation of data element (DE) presence and patient data fill rates across 63 practices and over 1.63 million patients.
  • Assessment of EHR vendor diversity (eight vendors) and practice representativeness.

Main Results:

  • Fill rates for oMIPS-associated DEs varied significantly across practices.
  • On average, only 52% of DEs had at least one populated value per site, and 23% on average across all practices.
  • Only two of 19 oMIPS CQMs (10.5%) were computable for more than 1% of patients due to unpopulated DEs.

Conclusions:

  • Underfilling and inconsistency of DEs within EHR systems make automated oncology MIPS CQM calculations impractical.
  • Current EHR data structures present significant challenges for efficient and accurate MIPS quality reporting in oncology.
  • Further improvements in EHR data standardization and completeness are needed to support automated quality measure reporting.