Unit 4: Laboratory Techniques and their Applications
Unit 4D: Storing and communicating information in a laboratory
Vocational Scenario:
I am a lab technician working in the development department for a pharmaceutical company that
develops and produces new drugs. The lab stores confidential information relating to drug development.
It also stores personal and confidential information relating to volunteers who are used for drug trials.
My manager has asked me to produce a report evaluating the challenges in storing and communicating
the range of information recorded and processed within the laboratory, comparing the systems in the
development laboratory to those in the company’s manufacturing department.
Recording and processing large datasets in a workplace lab:
Booking and sample identification:
In any workplace laboratory, the initial step in handling large datasets is the meticulous booking and
identification of samples. When a sample, such as a chemical substance, is delivered, it is logged into the
system with comprehensive details about its origin, type, and purpose. This booking process is critical
for maintaining an accurate chain of custody and ensuring compliance with regulations such as COSHH
(Control of Substances Hazardous to Health). Each sample is assigned a unique identification number,
which may be generated based on the date, time, and a sequential or random identifier. This unique ID
ensures that every sample can be precisely tracked and traced throughout its lifecycle in the lab.
Data formats and recording systems:
Lab technicians use various recording systems to capture data relevant to their analyses. Depending on
the lab’s specific requirements, these systems can include paper worksheets, laboratory notebooks, and
computer forms. For example, in a healthcare lab, electronic health records (EHRs) store patient
information, test results, and diagnostic images, all linked to the unique sample identification numbers.
Laboratory Information Management Systems (LIMS) are particularly useful for managing large datasets,
integrating sample tracking, data entry, and reporting functionalities in a single platform. This ensures
that all data, from initial readings to final measurements, is accurately recorded and easily retrievable.
Processing and analysing data:
The processing of large datasets involves several key steps, including data entry, validation, analysis, and
reporting. Advanced analytical software and statistical tools are used to process and interpret the data.
For instance, in a medical testing laboratory, raw data from diagnostic equipment is automatically
transferred to LIMS or EHR systems. This data is then validated and analysed to produce test results,
which are reviewed by laboratory personnel for accuracy before being recorded in the final report. The
,analytical process transforms raw data into actionable insights that can inform clinical decisions,
research findings, or industrial improvements.
Reporting and communicating results:
Results generated in a workplace laboratory are specific to the context and purpose of the laboratory's
work. These results, whether from internal research or for external agencies, are communicated to
relevant stakeholders only. Internal day-to-day results might be reported via laboratory notebooks,
equipment printouts, and team meetings, and are eventually put into a comprehensive report. For
urgent cases, results may be communicated directly to individuals, such as a GP, while routine results
often follow a standardised office procedure for documentation and distribution. Effective
communication is essential, requiring the use of standard scientific terminology that all team members
understand. Also, the structure of reports must meet client needs, which might involve simplifying
scientific language or providing verbal presentations.
Types of data collected:
The types of data collected in a workplace laboratory depend on its specific function. Healthcare
laboratories collect data such as patient demographics, medical histories, test results, and imaging data.
Research laboratories gather experimental data, including chemical properties, biological
measurements, and environmental parameters. Industrial laboratories focus on quality control data,
material properties, and compliance testing results. This diversity in data types reflects the varied
objectives and methodologies across different laboratory settings.
Data security and compliance:
Ensuring data security and regulatory compliance is paramount in laboratory operations. Laboratories
adhere to strict data security measures, including encryption, access controls, and audit trails, to protect
data integrity and confidentiality. Compliance with regulations, such as the Data Protection Act 2018
(DPA) and the General Data Protection Regulation (GDPR), is mandatory. These regulations govern the
handling of personal and sensitive information, ensuring that data management practices meet legal
and ethical standards. Also, labs must follow COSHH guidelines to control hazardous substances,
ensuring that all chemicals are properly labeled, stored, and tracked.
Laboratory Information Management Systems (LIMS):
Previously, I mentioned the use of Laboratory Information Management Systems (LIMS) in managing
large datasets within laboratory environments. Given its significance, it is important to delve deeper into
what data is specifically stored on LIMS. LIMS is widely used across various types of labs, including
healthcare, to be specific in the NHS, research, and industrial labs, due to its ability to efficiently organis,
track, and manage vast amounts of information. LIMS are essential tools for managing large volumes of
data efficiently and accurately. The data stored on LIMS encompasses a broad range of information,
including:
Sample information:
, Sample identification numbers: Unique identifiers for each sample to ensure traceability.
Sample source: Information about where the sample originated, such as patient details in a
healthcare setting or the specific batch in a manufacturing process.
Sample type and description: Detailed descriptions of the sample, including type (e.g, blood,
tissue, chemical compound) and any relevant characteristics.
Booking and tracking data:
Sample receipt and booking details: Date and time when the sample was received and booked
into the system.
Storage location: Information on where the sample is stored within the lab.
Chain of custody records: Documentation of each person who handled the sample and the
times of these interactions to maintain traceability.
Test and analysis data:
Test requests and orders: Details of the tests or analysis requested for each sample.
Test methods and protocols: Information on the methods and standard operating procedures
(SOPs) used for testing.
Raw data and observations: Initial data collected from experiments and tests, such as
instrument readings, observations, and measurements.
Intermediate and processed data: Data that has been processed, analysed, and interpreted.
Results and reports:
Test results: Final results from tests and analysis, often including statistical analysis, charts, and
graphs.
Certificates of analysis: Formal documents summarising the test results, often required in
regulated industries like pharmaceuticals.
Quality control data: Information related to the quality control processes, including calibration
data, control sample results, and validation records.
Project management data:
Project planning details: Information about research or development projects, including
objectives, timelines, and milestones.
Task assignments: Records of which personnel are assigned to specific tasks and their progress.
Resource allocation: Information on the allocation and usage of laboratory resources, such as
reagents, instruments, and personnel.
