Precision of EMR Data: The Case for a Drug and Alcohol Service

Error-laden data can negatively affect clinical and operational decision making, research findings and funding allocation. This study examined the number and types of data errors in an electronic medical record (EMR) system in a Drug and Alcohol service. Specifically, errors in service data were examined. Three months after the implementation of the EMR system, 9,379 errors were identified from ten error reports generated between March 2015 and May 2016. The errors were grouped into four types: mismatched data fields (60.5%), duplicate medical record error (3.2%), date/time error (8.8%) and blank field error (27.4%). The errors can be prevented by adding functions, such as alert messages in the EMR system. How and why the errors occur need to be investigated in future studies.

[1]  Kerin Robinson,et al.  The Risk and Consequences of Clinical Miscoding Due to Inadequate Medical Documentation: A Case Study of the Impact on Health Services Funding , 2009, Health information management : journal of the Health Information Management Association of Australia.

[2]  P. Torkki,et al.  Time Accuracy of a Radio Frequency Identification Patient Tracking System for Recording Operating Room Timestamps , 2006, Anesthesia and analgesia.

[3]  H. Bauchner,et al.  Medication Errors Related to Computerized Order Entry for Children , 2006, Pediatrics.

[4]  Michael J Ward,et al.  Effects of Common Data Errors in Electronic Health Records on Emergency Department Operational Performance Metrics: A Monte Carlo Simulation. , 2015, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[5]  He Zhang,et al.  Inferring Clinical Workflow Efficiency via Electronic Medical Record Utilization , 2015, AMIA.

[6]  Usman Qamar,et al.  Identification and Correction of Misspelled Drugs Names in Electronic Medical Records (EMR) , 2016, ICEIS.

[7]  Mark Hoogendoorn,et al.  On the Advantage of Using Dedicated Data Mining Techniques to Predict Colorectal Cancer , 2015, AIME.

[8]  R. Helmreich.  On error management: lessons from aviation , 2000, BMJ : British Medical Journal.

[9]  Dean F Sittig,et al.  Matching identifiers in electronic health records: implications for duplicate records and patient safety , 2013, BMJ quality & safety.

[10]  Ya Zhang,et al.  A machine learning-based framework to identify type 2 diabetes through electronic health records , 2017, Int. J. Medical Informatics.

[11]  Alexander Turchin,et al.  Analysis of Data Errors in Clinical Research Databases , 2008, AMIA.

[12]  James J. Cimino,et al.  Use of Clinical Alerting to Improve the Collection of Clinical Research Data , 2009, AMIA.

[13]  Andy Podgurski,et al.  Big Bad Data: Law, Public Health, and Biomedical Databases , 2013, The Journal of law, medicine & ethics : a journal of the American Society of Law, Medicine & Ethics.

[14]  Christina Pagel,et al.  Dead reckoning: can we trust estimates of mortality rates in clinical databases? , 2008, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[15]  Hardeep Singh,et al.  Prescription errors and outcomes related to inconsistent information transmitted through computerized order entry: a prospective study. , 2009, Archives of internal medicine.

[16]  R Khajouei,et al.  The Impact of CPOE Medication Systems’ Design Aspects on Usability, Workflow and Medication Orders , 2010, Methods of Information in Medicine.

[17]  Ping Yu,et al.  Fitting clinical workflow: The case for wound care in a residential aged care home , 2014, HIC.

[18]  Dean F Sittig,et al.  A red-flag-based approach to risk management of EHR-related safety concerns. , 2013, Journal of healthcare risk management : the journal of the American Society for Healthcare Risk Management.

[19]  Michael J Ward,et al.  Operational data integrity during electronic health record implementation in the ED. , 2013, The American journal of emergency medicine.

[20]  O. Dalesio,et al.  Can an online clinical data management service help in improving data collection and data quality in a developing country setting? , 2011, Trials.