The document discusses an automated data collection system implemented at MedImmune to address inefficiencies and errors from manual data transcription. Key points: 1) A custom application was created to monitor instruments on the MedImmune network in real-time, collect generated data, and store it directly in a database. 2) This eliminated manual transcription errors and reduced data handling time for scientists by up to 97%. 3) The system currently collects data from 65 analytical instruments of 8 types transmitting results directly to the database.

1. For Internal Use Only – Not for Distribution
Benefits
 Implementation of 1st
Generation Data Listener
reduced data handling time by scientists by
97%
 Elimination of manual data uploading errors
 2nd
Generation upgrade reduced instrument set
up times in the system by an average of 85%
 This Data Listener automated solution is not
available on the market, providing a
competitive advantage to MedImmune
Automation System
Capabilities Delivered
 Immediate data availability after samples are run
on networked analyzers
 65 analytical instruments (8 different types)
transmit data to the database in real time
 Easy extension to other functional groups
Automation of Analytical Instruments
Rohan Jain
BPD Informatics | MedImmune | One MedImmune Way 20878 Abstract No: 180
Abstract
The data generated by the analytical
instruments is a key part of an experiment
executed by the scientists in the laboratory.
Historically, this data was accessible to
scientists either in the form of a printout or
by manually annotating the data from the
instrument panel. The data would then be
entered manually into a computer to perform
various calculations and analysis. This manual
process led to transcription errors and
resulted in effort-time inefficiencies. To
address these issues, the BPD Informatics
team created a custom Windows Forms
Application to monitor and process data in
real-time as it is generated by instruments
connected to the MedImmune network. This
application runs 24 hours a day, seven days a
week to collect all of the data from the
instruments and store it to a database. Along
with the data collection system, multiple
excel templates, specific to the analytical
instruments, were created to retrieve data
from the database in order to perform
calculations, data analysis, and graphically
represent the data. This not only reduced the
time spent for manually entering the data,
but also eliminates the manual transcription
errors. These tools were built in-house by the
BPD Informatics group.
Initial State
The sample data that the scientist obtain
from different analyzers is critical to the
experiment and to the project. This data
reported by the analyzer is used for purposes
such as creating trends and entries into lab
notebooks. Initially this data was manually
transcribed from paper printouts produced by
analyzers into a database due to the
unavailability of commercial software on the
market. This method of manual transcription
allowed for shortcomings to present
themselves in the form of:
Process inefficiencies, such as wasted time
due to manual data entry
Poor integrity of the data due to
transcription errors
Data printouts being lost or thrown away
Analyzers being out of paper and unable to
produce a printout
Loss of data due to a hard drive crashing on
a PC connected to an analyzer
Project Objective
The objective of the project was to develop
an automated system to monitor, process,
and store in a database, the data in real-time
as it is generated by instruments connected
to the MedImmune network. This strategy
reduces shortcomings that are otherwise
present to the process and the integrity of
the data.
Approach
The requirements of the automated system
were that it should be able to collect, process,
and store the data in real time, use a non-
proprietary interface and database, and allow
for various types of analyzers to be connected
to the network to be interfaced into the
system.
1st
Generation (Initial System):
‒ The system collected data from
various instruments, processed the
data, and then stored it to the
database (Figure 2).
2nd Generation (Current System):
‒ The improved system is driven by
tables in a database that allows for
the dynamic addition of instruments
(Figure 3).
3rd Generation (Future System):
‒ The advanced system will be server-
based, which will offer a
maintenance-free data collection
solution, thus further reducing BPDI
response time.
Figure 4: Current System
Acknowledgements
 Core team: Robert Heckathorn and Deniz
Koteen
 Originating team: CCFS Gaithersburg
References
Heckathorn, R. & Koteen, D. (2008, August).
Automating Data Acquisition from PCC&F Lab
Instruments. Gaithersburg, MD: Author.
Figure 2: 1st
Generation Data Listener
Figure 1: Work Flow of Sample data to database
Samples
Sample Run
on
Instruments
Print Outs Check For
Mistakes
Manually
enter data
from
printouts
Upload Data
to Database
Figure 5: Pre-Data Listener Osmometer Work Flow Diagram
Figure 6: Post-Data Listener Osmometer Work Flow Diagram
Analyzers
Databases
MedImmune
Network
Figure 3: 2nd
Generation Data Listener
Charts created
Using BPD Console
Data Listener
application