Achieving the Promise of Electronic Health Record-enabled Quality Measurement: a
            Measure Developer’s Perspective

Torda, Phyllis; Tinoco, Aldo

doi:10.13063/2327-9214.1031

Cited by 4 publications

(5 citation statements)

References 1 publication

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“…Although outcome data communicate accountability, they also need to inform decision making and improve population care (Daleiden & Chorpita, 2005; Torda & Tinoco, 2013). As EMR technology, quality measures, and quality indicators become standardized under the ACA, monitoring of outcomes on the healthcare system level can identify potential population needs (e.g., gaps in quality of care, patient-centeredness) to inform future interventions.…”

Section: Quality Indicators: From “I'm Feeling Better” To Outcome Mea...mentioning

confidence: 99%

“…As EMR technology, quality measures, and quality indicators become standardized under the ACA, monitoring of outcomes on the healthcare system level can identify potential population needs (e.g., gaps in quality of care, patient-centeredness) to inform future interventions. For example, EMR-based quality measures can be used to identify trends and patterns in clinical data that may vary by, for example, ACO (Torda & Tinoco, 2013). Similar to quality improvement on the organizational level (Hermann, Chan, Zazzali, & Lerner, 2006), quality improvement on the system level will enable intended audiences such as policymakers to use quality indicators for decision making (Zima et al, 2013).…”

Section: Quality Indicators: From “I'm Feeling Better” To Outcome Mea...mentioning

confidence: 99%

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Training and education in clinical psychology in the context of the Patient Protection and Affordable Care Act.

Chor¹,

Olin²

2014

Clinical Psychology: Science and Practice

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The Patient Protection and Affordable Care Act of 2010 (ACA) is revamping the access, quality, and financing of the health and mental health systems. However, its impact on training and education in clinical psychology is unclear. This article aims to identify specific components of the ACA, in particular the Mental and Behavioral Health Education and Training Grants, that are expected to affect training and education in the field. The article further connects the ACA with four paradigm shifts in clinical psychology that have broad implications for training and education—evidence‐based practices, research methodology, interprofessionalism, and the quality indicator movement. The overarching goal of this article is to begin timely discussions on the future directions of the field under the current healthcare reform.

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Section: Quality Indicators: From “I'm Feeling Better” To Outcome Mea...mentioning

confidence: 99%

Section: Quality Indicators: From “I'm Feeling Better” To Outcome Mea...mentioning

confidence: 99%

Training and education in clinical psychology in the context of the Patient Protection and Affordable Care Act.

Chor¹,

Olin²

2014

Clinical Psychology: Science and Practice

View full text Add to dashboard Cite

show abstract

“…In addition, challenges arise from the use of different EHR systems across hospitals and the heterogeneity of data, resulting in limited interoperability. Limited interoperability and inconsistent data exchange across settings are significant barriers to quality improvement [102]. The interoperability of EHRs with medical data is becoming increasingly valuable because of its potential to increase the availability of data exponentially or directly impact the activation of research.…”

Section: Discussionmentioning

confidence: 99%

“…The interoperability of EHRs with medical data is becoming increasingly valuable because of its potential to increase the availability of data exponentially or directly impact the activation of research. EHR systems can efficiently support data structuring and quality measurement results and exert great impact on patients and their time [102]. Interoperability among EHR systems refers to the linking of data, which improves data usability.…”

Section: Discussionmentioning

confidence: 99%

Challenges of Data Quality in Clinical Data Life Cycle: A Systematic Review (Preprint)

An,

Lim,

Lee

2024

Preprint

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BACKGROUND It is anticipated that electronic health record (EHR) data will inform the development of health policy systems across countries and furnish valuable insights for the advancement of health and medical technology. As the current paradigm of clinical research is shifting toward data-centricity, the utilization of healthcare data is becoming increasingly emphasized. OBJECTIVE We aimed to review the literature on clinical data quality management and define a process for ensuring the quality management of clinical data, especially in the secondary utilization of data. METHODS A systematic review of PubMed articles from 2010 to October 2023 was conducted to assess the quality of electronic health record (EHR) and clinical data. Articles that defined quality management procedures based on the life cycle of clinical data quality management and discussed quality management assessment methods and tools were selected. The articles were categorized into four themes. RESULTS We reviewed 105 papers describing the clinical data quality management process. This process is based on a four-stage life cycle: planning, construction, operation, and utilization. The most frequently used dimensions were completeness, plausibility, concordance, security, currency, and interoperability. CONCLUSIONS Given the importance of the secondary use of EHR data, standardized quality control methods and automation are necessary. This study proposes a process to standardize data quality management and develop a data quality assessment system.

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“…Quality calculations for reporting in this program used information available in EHRs; challenges have been noted in this approach. 10 11 12 Unlike HEDIS, EHRs often calculate measure compliance using only data documented within that EHR, in part due to lack of health information exchange and interoperability challenges. 13 14…”

Section: Background and Significancementioning

confidence: 99%

Using Clinical Data Standards to Measure Quality: A New Approach

D'Amore

Li²,

McCrary

et al. 2018

Appl Clin Inform

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Background Value-based payment for care requires the consistent, objective calculation of care quality. Previous initiatives to calculate ambulatory quality measures have relied on billing data or individual electronic health records (EHRs) to calculate and report performance. New methods for quality measure calculation promoted by federal regulations allow qualified clinical data registries to report quality outcomes based on data aggregated across facilities and EHRs using interoperability standards. Objective This research evaluates the use of clinical document interchange standards as the basis for quality measurement. Methods Using data on 1,100 patients from 11 ambulatory care facilities and 5 different EHRs, challenges to quality measurement are identified and addressed for 17 certified quality measures. Results Iterative solutions were identified for 14 measures that improved patient inclusion and measure calculation accuracy. Findings validate this approach to improving measure accuracy while maintaining measure certification. Conclusion Organizations that report care quality should be aware of how identified issues affect quality measure selection and calculation. Quality measure authors should consider increasing real-world validation and the consistency of measure logic in respect to issues identified in this research.

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Achieving the Promise of Electronic Health Record-enabled Quality Measurement: a Measure Developer’s Perspective

Cited by 4 publications

References 1 publication

Training and education in clinical psychology in the context of the Patient Protection and Affordable Care Act.

Training and education in clinical psychology in the context of the Patient Protection and Affordable Care Act.

Challenges of Data Quality in Clinical Data Life Cycle: A Systematic Review (Preprint)

Using Clinical Data Standards to Measure Quality: A New Approach

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