ISDS Annual Conference Proceedings 2017. This is an Open Access article distributed under the terms of the Creative Commons Attribution-
Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution,  
and reproduction in any medium, provided the original work is properly cited.

ISDS 2016 Conference Abstracts

Collecting Infectious Disease Data from LARS and 
Improving Data Quality in Taiwan
Chih-Ting Yeh*, Chia-Lin Li, Chih-Jung Ke and Chi-Ming Chang
Epidemic Intelligence Center, Centers for Disease Control, Taipei City, Taiwan

Objective
To improve data quality and sustain a good quality data collected 

by Laboratory Automated Reporting System (LARS), we use a Three-
stage Data Quality Correction (3DQC) strategy to ensure data accuracy.

Introduction
To immediately monitor disease outbreaks, the application of 

laboratory-based surveillance is more popular in recent years. 
Taiwan Centers for Disease Control (TCDC) has developed LARS to 
collect the laboratory-confirmed cases caused by any of 20 pathogens 
daily via automated submitting of reports from hospital laboratory 
information system (LIS) to LARS since 2014 [1]. LOINC is used 
as standardized format for messaging inspection data [1, 2]. There 
are 37 hospitals have joined LARS, coverage rate about 59% of all 
hospitals in Taiwan. Recently, more than 10,000 of data are collected 
weekly and used in monitoring pathogen activity [3]. Therefore, it 
is important to ensure data quality that the data will lead to valuable 
information for public health surveillance.

Methods
A 3DQC strategy was designed to improve data accuracy and 

carried out by teamwork among TCDC, Taiwan Association for 
Medical Informatics (TAMI) and IT Company (Figure 1). In the first 
stage of 3DQC, IT Company checked data format. In the second stage, 
TCDC verified information between hospital inspection reports and 
data receiving in LARS. In the third stage, TAMI evaluated LOINC 
mapping and TCDC monitored stability of data transmission. After 
correcting the data, hospitals were approved to join LARS.

Results
During the first stage of 3DQC, we observed that some problems 

with syntax error in data (e.g. incorrect patient identification number, 
or lack of residence codes). Because some data were stored in Hospital 
information system (HIS) but not in LIS, an error may occur while 
hospital accessed records from HIS. In the second stage, 50-70% 
of inspection reports provided by each hospital had problems with 
semantic information error. For example, a positive result of influenza 
A on a screening flu test recorded in LIS but hospital transferred the 
wrong result with influenza B positive into LARS. In the third stage, 
we found that 20-30% of terms mismatched to LOINC code. This 
study categorized these terms into two groups (1) the Exception 
codes, which were considered reasonable and (2) the Error codes, and 
also reviewed Error codes and made a modified advice for hospitals 
to improve LOINC mapping. Through 3DQC strategy, the LOINC 
mapping rate raised from 40 to 80%, Exception codes mapping was 
20%, and the total mapping rate was near 95-99% (Figure 2). So 
far, most hospitals have maintained a good quality data even they 
formally participate in LARS.

Conclusions
This study suggested that 3DQC can effectively detect problems 

and reduce errors of data collected from LARS, and indicated that 
effect of 3DQC can be maintained even hospital formally participates 
in LARS. Future research will focus on development of automatic 
programming of 3DQC to ensure high-quality data.

Figure 1. A Three-stage Data Quality Correction strategy

Figure 2. Rate of LOINC Mapping, 2015-2016

Keywords
laboratory automated reporting system; three-stage data quality 
correction; surveillance

Acknowledgments
We thank all the collaborating hospitals for providing data and cooperating 
with data correction, and also thank Mr. Chih-Yang Yeh from TAMI for 
providing expert advice for LOINC mapping.

References
1. Chang CM, Kuo HW, Ye YW, Liu YL, Chuang JH. Implementation of 

a Nationwide Automated Laboratory Reporting System for Infectious 
Disease Surveillance Based On the Epidemic Information Exchange 
Platform and the Use of LOINC Standard. JTAMI. 2015 Mar;  
24(1):1-10.

2. McDonald CJ, Huff SM, Suico JG, Hill G, Leavelle D, Aller R,
Forrey A, Mercer K, DeMoor G, Hook J, Williams W, Case J,
Maloney P. LOINC, a universal standard for identifying laboratory
observations: a 5-year update. Clin Chem. 2003 Apr;49(4):624-33.

3. Kuo HW, Chang CM, Ke CJ, Liu YL, Liu DP. Apply Laboratory
Automated Reporting System on Infectious Disease Surveullance.
JTAMI. 2016 Mar; 25(1):13-22.

*Chih-Ting Yeh
E-mail: emilyyeh@cdc.gov.tw

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * 9(1):e32, 2017