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Related Concept Videos

Quality Assurance01:19

Quality Assurance

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Data Validation01:03

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Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Testing a Claim about Standard Deviation01:19

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A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
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Health Information Technology (HIT)
Health Information Technology, commonly called HIT, integrates advanced information systems and technology in healthcare settings. Its primary functions include:
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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Rules Based Data Quality Assessment on Claims Database.

Mary A Gadde1, Zhan Wang2, Meredith Zozus2

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Studies in Health Technology and Informatics
|July 2, 2020
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Summary

Data quality issues in healthcare claims data can lead to patient harm. A new rules-based system, tested on Medicaid claims, identified data defects, highlighting the need for improved data integrity in electronic health records (EHR).

Keywords:
Data qualityHealthcare Rules

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Area of Science:

  • Health Informatics
  • Data Quality Assessment
  • Clinical Data Management

Background:

  • Persistent data quality problems in coded clinical and administrative data, used for healthcare billing and decision-making, pose risks of iatrogenesis.
  • Increasing data sharing via Health Information Exchanges (HIEs) and integration into electronic health records (EHRs) may amplify potential harm from data errors.
  • Despite Centers for Medicare & Medicaid Services (CMS) guidelines and penalties for erroneous Medicaid claims, reports of low diagnostic data quality metrics are common.

Purpose of the Study:

  • To evaluate the effectiveness of a rules-based data quality assessment system, previously validated on EHR data, when applied to aggregated claims data.
  • To identify and quantify data quality defects within a state's All Payer Claims Dataset (APCD), specifically using Medicaid claims data.
  • To determine the feasibility of applying EHR-based data quality rules to claims data for improved data integrity.

Main Methods:

  • Applied a recently developed rules-based data quality assessment and monitoring system to a limited set of aggregated Medicaid claims data.
  • Utilized rules previously tested on Electronic Health Records (EHR) data.
  • Focused analysis on a state's All Payer Claims Dataset (APCD).

Main Results:

  • The rules-based system successfully identified data quality issues within the Medicaid claims dataset.
  • The study demonstrated the applicability of EHR data quality rules to claims data.
  • Results indicate the presence of data quality defects in claims data, despite regulatory oversight.

Conclusions:

  • A rules-based data quality assessment system can be effectively applied to claims data, including Medicaid and All Payer Claims Datasets (APCD).
  • The findings underscore the ongoing challenge of data quality in healthcare claims and the need for robust monitoring systems.
  • Implementing and adapting EHR data quality checks for claims data is crucial for enhancing the reliability of clinical decision-making and reducing patient risk.