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Data Quality Assurance via Perioperative EMR Reconciliation
Abstract
This research examines systematic quality control checks (QCCs) developed to safeguard the data quality assurance of perioperative electronic medical records (EMRs). The resulting perioperative EMR reconciled data supports patient care documentation, patient billing, perioperative data analysis, and regulatory agency audits. This case study identifies specific perioperative nursing care documentation as EMRs and demonstrates how data QCC rules, an embedded QCC review process, and QCC rule violation reconciliation applied to perioperative EMRs are applicable to ensure data quality within integrated hospital information systems. Identification of existing limitations, potential capabilities, and the subsequent contextual understanding yield an a priori framework for data quality assurance of perioperative process EMR data. Based on a 174-month longitudinal study of a large 1,157 registered-bed academic medical center, the case results are discussed as well as theoretical and practical implications with study limitations.
... Multiple definitions of DQ were discussed in the literature (Multimedia Appendix 5 [17,18,[20][21][22]31,54,[67][68][69][70][71][72][73][74][75][76][77]). There was no consensus on a single definition of DQ; however, an analysis of the definitions revealed two perspectives, which we labeled as the (1) context-agnostic perspective and (2) context-aware perspective. ...
Background:
The promise of digital health is principally dependent on the ability to electronically capture data that can be analyzed to improve decision-making. However, the ability to effectively harness data has proven elusive, largely because of the quality of the data captured. Despite the importance of data quality (DQ), an agreed-upon DQ taxonomy evades literature. When consolidated frameworks are developed, the dimensions are often fragmented, without consideration of the interrelationships among the dimensions or their resultant impact.
Objective:
The aim of this study was to develop a consolidated digital health DQ dimension and outcome (DQ-DO) framework to provide insights into 3 research questions: What are the dimensions of digital health DQ? How are the dimensions of digital health DQ related? and What are the impacts of digital health DQ?
Methods:
Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a developmental systematic literature review was conducted of peer-reviewed literature focusing on digital health DQ in predominately hospital settings. A total of 227 relevant articles were retrieved and inductively analyzed to identify digital health DQ dimensions and outcomes. The inductive analysis was performed through open coding, constant comparison, and card sorting with subject matter experts to identify digital health DQ dimensions and digital health DQ outcomes. Subsequently, a computer-assisted analysis was performed and verified by DQ experts to identify the interrelationships among the DQ dimensions and relationships between DQ dimensions and outcomes. The analysis resulted in the development of the DQ-DO framework.
Results:
The digital health DQ-DO framework consists of 6 dimensions of DQ, namely accessibility, accuracy, completeness, consistency, contextual validity, and currency; interrelationships among the dimensions of digital health DQ, with consistency being the most influential dimension impacting all other digital health DQ dimensions; 5 digital health DQ outcomes, namely clinical, clinician, research-related, business process, and organizational outcomes; and relationships between the digital health DQ dimensions and DQ outcomes, with the consistency and accessibility dimensions impacting all DQ outcomes.
Conclusions:
The DQ-DO framework developed in this study demonstrates the complexity of digital health DQ and the necessity for reducing digital health DQ issues. The framework further provides health care executives with holistic insights into DQ issues and resultant outcomes, which can help them prioritize which DQ-related problems to tackle first.
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