New Paradigms for Measuring Clinical Performance Using Electronic Health Records

Jonathan P. Weiner; Jinnet B. Fowles; Kitty S. Chan

Int J Qual Health Care. 2012;24(3):200-205.

Abstract and Introduction

Abstract

Measures of provider success are the centerpiece of quality improvement and pay-for-performance programs around the globe. In most nations, these measures are derived from administrative records, paper charts and consumer surveys; increasingly, electronic patient record systems are also being used. We use the term 'e-QMs' to describe quality measures that are based on data found within electronic health records and other related health information technology (HIT). We offer a framework or typology for e-QMs and describe opportunities and impediments associated with the transition from old to new data sources. If public and private systems of care are to effectively use HIT to support and evaluate health-care system quality and safety, the quality measurement field must embrace new paradigms and strategically address a series of technical, conceptual and practical challenges.

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Abstract and Introduction
Measuring Quality in the Era of Electronic Health Records
A Framework for Electronic Quality Measures
Digital Opportunities: Digital Challenges
References

Table 1. HIT/EHR-based e-quality measures (e-QMs) of provider performance
1. Translated (Level-1 e-QM): Measures based on traditional data collection approaches such as administrative (e.g. insurance claims) records which have been translated for use with EHR-based platforms (example: process of care measure such as percentage in scope patients receiving a lab test or mammography screening)
2. HIT assisted (Level-2 e-QM): Measures that while possible with non-EHR data sources would not be operationally feasible without the assistance of HIT (examples: blood pressure or BMI information on 100% of target population)
3. HIT enabled (Level-3 e-QM): Innovative measures that are not possible without comprehensive HIT (examples: percentage of abnormal test results read and acted upon by a clinician within 24 h, percentage of in-scope patient encounters where decision support modules were appropriately applied)
4. HIT system management: Measures used primarily to manage and evaluate HIT systems. (Examples: percentage of all prescriptions ordered via e-prescribing, percentage of EHR 'front sections' that are updated periodically.) These can be considered a measure of care structure
5. e-iatrogenesis: Measures of patient harm caused at least in part by the HIT system. (Example: the percentage of patients for whom the wrong drug was ordered due to an error intrinsic to an e-prescribing system; percentage of critical lab findings that did not result in patient notification)

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