Critical assessment of challenges of LIS and data quality

The focus of this section is to understand the dynamics of the NHLS LIS, key operational challenges and its role in monitoring antimicrobial resistance to nosocomial pathogens in the country. The DISLab was designed to be flexible, changing and revolving all the time, hence it is subject to constant improvement.

4.1.7.1 Different version of DISALab

The NHLS LIS was running on different versions of DISALab due to differences in the roll out time and user preferences, such that changes that are suggested globally might not all be affected by different laboratory managers in different laboratories. Some laboratories might prefer to make few modifications based on their needs while others might not have changed anything at all. This made it difficult for all laboratories to work on standardised data protocols. Such a situation could have propagated differences in blood culture data quality, including differential AMR rates observed from various laboratories and which aggregates at the CDW. It might also be due to differential laboratory practices leading to selective testing of certain antibiotics. (section4.2, Table 4.1)

4.1.7.2 Replication and data errors

The CDW cannot replicate data in the database since data found at the repository were electronically transferred from the different sources. However, the CDW programmer does interrogate the data that gets extracted and performs data extraction error identification exercise as standard practice. The errors assessed are mostly those on codes that were used by the laboratories, e.g., the identification code in the CDW database for example Universitas hospital is 53. Before any changes could be effected on the data, ample verification of data is

done. Therefore, it is recommended to incorporate an error rate into the system that is universally acceptable.

4.1.7.3 Lack of access to original data source

The CDW does not have access to the original data source, such as the laboratory request forms. However, DISALab maintains master data (standard reference data) for all NHLS laboratories, which means that laboratories use the same table of codes. The master data is administered at the NHLS central corporate office and facilitates easier merging of data from different laboratories without any major problems.

4.1.7.4 Duplicate data

Duplicate entries can pose a challenge. The blood culture data entry must have the same ID number, name and surname, date collected, area/place, hospital and results. Unless the blood culture data entries have all similar records, that particular entry could only be assumed as a duplicate entry.

4.1.7.5 Variation in LIS

There are major challenges relating to the NHLS LIS, which might originate from wide variation in the operations of the LIS between different laboratories. These appear to lead to wide variations in procedures that are used for gathering and reporting of blood culture data.

Some of the underlying causes of such wide variability might be:

 Different reporting styles between different laboratories, including different names used by different instruments.

 Instruments used vary between different laboratories (other laboratories use more advanced instruments than others) as documented in section 4.2

 Lack of standardisation across different laboratories which might affect scope of generated data.

4.1.7.6 Structure of LIS

Often unforeseeable errors might have been difficult to deal with despite the fact that the program has been regularly monitored to enhance accuracy of the data that was entered onto the LIS database. The system generates a turnaround time of blood culture results i.e. the system has the ability to demonstrate time in and time out in terms of the sample processing and results outcome. However, this approach might have been problematic in conditions when the date structures were different i.e. dd-mm-yy or yy-dd-mm, but also where time was not entered into the database. The system might then have registered‘00’ for such data points indicating missing data on time. For this reason, laboratory data has to be treated with caution due to such omissions.

4.1.7.7 Capacity of the LIS

How large is the interconnectivity? Due to the large capacity of data handling, the LIS can be challenging in terms of its effectiveness. Should the LIS be too large, it might not function effectively. Hence utilisation of individual or separate servers by each laboratory as a way of making the system effective, becomes a major limitation in terms of national antimicrobial resistance surveillance programs, as the individual local servers are not interconnected to each other hence aggregating data becomes problematic. This means that each server transmits data separately into one central repository. Due to the efficiency of the network

services, or phone line, not all data might end being transmitted to the central repository.

Some data get lost en-route the electronic transmission.

4.1.7.8 Database structure

The CDW operates on a relational database model. For a researcher to access data from the central repository there is need to design a query to extract the data of interest, and then assess what each query is giving back in terms of the data parameters that a researcher is interested in. To improve validity and reliability of data extracted from the CDW, there is need to create a micro strategy for the LIS, which would enhance the data that an individual wants to retrieve.

4.1.7.9 Data Security System

The LIS data is password protected and each of the local laboratories has an electronic gate keeper, to monitor and minimise data errors that could happen, but also to access the data for research use (https://labresults.nhls.ac.za). Since data comes from varying sources, it’s usually unclean with numerous errors hence strict measures need to be taken before making appropriate use of the data

4.1.7.10 Turn around time

In a situation where time is not recorded, it is advisable to use laboratory time as a starting point so as to be able to calculate the turn-around time for the laboratory results. However, if time entered into the system is ‘o’ instead of leaving the cell blank, the system will read ‘0’

as real zero time instead of missing data. In this situation data would obviously end up being skewed as it would cause ‘o’ inflated data.

4.1.7.11 LIS Performance

To achieve sustained data quality, the following procedures need to be followed:

 The laboratory clerks that register patients’ details into the LIS need to undergo regular

intensive refresher training as they do not have basic knowledge of laboratory sciences as such they might not understand all processes involved with blood culture data.

 There is need to settle on a standard data collection tool, modify the tool as necessary as

is possible and when required, so that the laboratories settles on the real required data elements and in so doing, enhance aggregation of quality data into the database.

 There is need to filter out data errors so that data suits the needs of the user. To

thoroughly ascertain data quality, it is advisable to exclude irrelevant data elements in the database.

 For the researcher to be able to access valuable information there is need to invest a lot of

input into the laboratory information system because correct input data is essential to ensure accuracy and reliability of blood culture data.

 There is need for standardisation of laboratory procedures and skills building i.e.

providing similar training to laboratory technicians/technologists, registry clerks etc., across sites, with the aim of generating same competencies which will ultimately improve overall data output.

 Incorporation of an automatic review program into the LIS to detect data errors and ensure accuracy of the data at the point of data entry into the database system.