Start Date
16-8-2018 12:00 AM
Description
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.
Recommended Citation
Ryan, James (Jim); Doster, Barbara; Daily, Sandra; and Lewis, Carmen, "Data Quality Assurance via Perioperative EMR Reconciliation" (2018). AMCIS 2018 Proceedings. 30.
https://aisel.aisnet.org/amcis2018/Health/Presentations/30
Data Quality Assurance via Perioperative EMR Reconciliation
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.