literature review on laboratory information management system

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Healthcare (Basel)

Digital Management Systems in Academic Health Sciences Laboratories: A Scoping Review

Margareth timóteo.

1 Clinical Research Unit, Antônio Pedro Hospital, Fluminense Federal University, Niteroi 24020-140, Brazil; moc.liamg@oetomitagram (M.T.); rb.ffu.di@zorieuqdl (L.D.); moc.liamg@lraepecioj (J.d.S.); moc.liamg@4revilosnurb (B.O.); rb.ffu.di@jeloineb (B.O.)

2 Post-Graduation Program in Medical Sciences, Fluminense Federal University, Niteroi 24020-140, Brazil

Emanuelle Lourenço

3 Post-Graduation Program in Dentistry, Fluminense Federal University, Niteroi 24020-140, Brazil; rb.moc.oohay@tellets_elleuname

Ana Carolina Brochado

4 Post-Graduation Program in Science and Biotechnology, Fluminense Federal University, Niteroi 24020-140, Brazil; [email protected] (A.C.B.); moc.liamtoh@aacil_ataner (R.B.)

Luciana Domenico

Joice da silva, bruna oliveira, renata barbosa, pietro montemezzi.

5 Independent Researcher, 24128 Bergamo, Italy; [email protected]

Carlos Fernando de Almeida Barros Mourão

Gutemberg alves, associated data.

The data presented in this study are openly available in the Open Science Framework (OSF) database, at doi:10.17605/OSF.IO/KPC3Q.

Good laboratory practices (GLP) increase the quality and traceability of results in health sciences research. However, factors such as high staff turnover, insufficient resources, and a lack of training for managers may limit their implementation in research and academic laboratories. This Scoping Review aimed to identify digital tools for managing academic health sciences and experimental medicine laboratories and their relationship with good practices. Following the PRISMA-ScR 2018 criteria, a search strategy was conducted until April 2021 in the databases PUBMED, Web of Sciences, and Health Virtual Library. A critical appraisal of the selected references was conducted, followed by data charting. The search identified twenty-one eligible articles, mainly originated from high-income countries, describing the development and/or implementation of thirty-two electronic management systems. Most studies described software functionalities, while nine evaluated and discussed impacts on management, reporting both improvements in the workflow and system limitations during implementation. In general, the studies point to a contribution to different management issues related to GLP principles. In conclusion, this review identified evolving evidence that digital laboratory management systems may represent important tools in compliance with the principles of good practices in experimental medicine and health sciences research.

1. Introduction

Laboratory research plays an essential role in providing evidence for translational medicine and sustainable solutions to healthcare [ 1 ]. However, the reliance on experimental medicine demands increased traceability and data integrity, ensuring the quality of transferrable results to the clinical setting. In recent years, the scientific community experienced awareness regarding a reproducibility crisis related to factors such as the pressure for publication, low statistical power, and insufficient supervision [ 2 ]. On the other hand, adequate management, training, and good practices may increase data quality by improving workflow, avoiding errors, and providing traceability [ 2 ].

Good laboratory practices (GLP) may be defined as a quality system encompassing organizational processes and conditions under which studies are planned, executed, monitored, registered, and reported [ 3 ]. The Principles of Good Laboratory Practice were first developed by a group of GLP experts led by the USA, established in 1978 under the Special Program on the Control of Chemicals, based on the FDA’s regulations for non-clinical laboratory studies. The Organization for Economic Cooperation and Development (OECD) published the Principles of Good Laboratory Practice and Compliance Monitoring in January 1998 [ 3 ]. Since then, it represents the primary set of standards available worldwide to ensure quality, reliability, and integrity, providing a solid approach to the management of research laboratories [ 4 ].

However, academic laboratories experience several critical barriers to developing and implementing a GLP-compliant infrastructure [ 5 ]. These limitations include poor training on management, lack of funding for compliance costs, and high staff turnover due to a dependence on students as temporary personnel [ 6 ]. Therefore, laboratory managers at academic centers should explore tools that facilitate supervision and identify critical steps in the laboratory workflow. In this context, digital systems are among the most important tools available for efficient management, ranging from dedicated computer programs to smartphone applications. Laboratory information management systems (LIMS) offer databases and automation [ 7 ] that allow experimental data tracking and storage [ 8 ]. Other software and digital services that fall outside of the original LIMS classification provide a broader offer of solutions to laboratory management [ 6 , 9 ], coping with other aspects of quality assurance related to communication, staff, multiuser equipment schedule and maintenance, standard procedures, and inventory control, which are fundamental in the full spectrum of a laboratory’s workflow [ 10 , 11 ].

Despite the potential effectiveness of these digital tools in meeting specific aspects of laboratory management, it remains unclear how these systems may directly or indirectly contribute to adherence to the GLP principles. In this context, the present review aimed to provide evidence on the theme by scoping the scientific literature for the available digital tools designed to manage health sciences and experimental medicine laboratories and discuss the assessments of effectiveness, acceptance, and their potential for compliance to different aspects of good laboratory practices.

2. Materials and Methods

2.1. protocol and registration.

This review followed the PRISMA recommendations for scoping reviews (PRISMA-ScR) [ 12 ], as shown in the Supplementary Table S1 . The study protocol was registered in the Open Science Framework database under the Digital Object Identifier doi:10.17605/OSF.IO/KPC3Q on 15 July 2020.

2.2. Sources of Information and Research Strategy

The broad question that guided the review was: “Are there available digital tools for the management of academic health sciences laboratories?” Strategies were developed to search for data sources in three different databases: PUBMED ( www.ncbi.nlm.nih.gov/pubmed (accessed on 26 April 2021)), Web of Science (WoS) (clarivate.com/webofsciencegroup (accessed on 26 April 2021)), and the Virtual Health Library (VHL) (bvsalud.org (accessed on 26 April 2021)). The research was carried until April 2021. Grey literature was consulted through the OpenGrey Database (available at http://www.opengrey.eu/ (accessed on 24 May 2021)). The search keys are described in Table 1 , with various combinations of Medical Subject Headings (MeSH) descriptors selected to cover as many articles as possible coping with management software approaches in academic or research settings.

Search keys applied to the three consulted databases.

2.3. Selection of Sources of Evidence

The eligibility criteria were determined on a PIO (Population, Intervention, Outcome) variant of the PICO framework for the selection of studies, more adequate for qualitative reviews [ 13 ].

A structured question was produced, in which, Population (P): academic health sciences laboratories, Intervention (I): the use of digital tools, and Outcomes (O): management for quality. After the references were retrieved from the database search, a group of five trained and calibrated reviewers read all titles and abstracts, applying the eligibility criteria, which included complete works on digital tools that aid in the administration of laboratories in academic or research environments, in health or biomedical sciences, including collections and biorepositories. Studies were excluded if they (i) were entirely out of the subject, (ii) did not address laboratory management, (iii) did not deal with software or digital tools, and (iv) were not proposed or discussed for health sciences or biomedical research. Additionally, articles on software that exclusively assessed experimental data management were considered outside the scope of this review. The inter-examiner reliability was assessed through simultaneous assessment of references by five evaluators, obtaining a Cohen’s Kappa coefficient of 0.93. Doubts and disagreements were resolved in weekly meetings conducted during this stage.

2.4. Critical Appraisal

A critical appraisal was conducted with the selected references, applying an instrument described by Whittemore and Knafl [ 14 ], considering two relevant criteria: (i) methodological and theoretical soundness and (ii) relevance of the data to the proposed question of the review. The methodological assessment considered whether studies presented adequate identification and traceability of the software, evaluating effectiveness, applicability, or acceptance. The adherence to the review’s question was considered according to the description of management functions, target users and environment, and software limitations. Each present parameter was scored with 1 point, to a maximum of 4 points. No study was excluded based on this assessment classification, even though the score was included as a variable in the data analysis stage. In general, studies of lower scores contributed less to the analytical process.

2.5. Synthesis of Results and Data Charting

The main characteristics of the selected studies were collected and tabulated, including year and country of conduction, name and type of digital tool, topics of laboratory management issued by the software, target public and environment of application, accessibility, and whether the software was free or paid. The data extraction was performed in conjunction with five authors in regular meetings. A specific table was produced solely with the studies that performed evaluations of effectiveness or acceptance, with the respective outcomes. A chart was produced connecting the management topics issued by the different tools and the respective sections/chapters from the Organization for Economic Cooperation and Development (OECD) GLP Principles [ 3 ].

Figure 1 shows the results for the search strategy and screening of databases. The PUBMED database provided 855 entries, while 183 entries were identified in WoS and 550 in the VHL. After combining the 3 results, 352 duplicate articles were identified and excluded. After applying the exclusion criteria, 523 articles were considered off-topic, appearing in searches because of common words and often dealing with clinical/hospital-related issues, but not with experimental medicine. Of the total articles identified, 160 were excluded because they did not deal with software or digital systems, and 534 did not speak about management. From the screening result, 19 articles were selected to compose this Scoping Review, and 2 additional articles were identified manually upon reading the selected references. The twenty-one elected references included studies proposing new software or revisiting already available tools for novel management applications. Some authors also evaluated the impact of changes during and after implementing the systems, either qualitatively or quantitatively.

PRISMA flowchart of study screening and selection.

Table 2 shows a critical appraisal performed for the selected articles at the methodological level and relevance to our broad question. Of the 21 articles selected, 9 evaluated the effectiveness and pointed out the limitations. Another eight did not evaluate but described limitations, and four studies did not evaluate or point out the limitations of the systems used, only describing the implementation or development of the systems in an expository manner. Nevertheless, all articles adequately identified the investigated software, their management purposes, and the environment/professionals served by its functionalities were considered relevant and contributed to some extent to the qualitative discussion on the theme.

Critical appraisal of the sources of evidence.

To quantify the criteria, “1” means present, and “0” means absent.

Table 3 describes the main characteristics of the twenty-one selected studies related to the present research question. It can be observed that the selection ranged from studies of the earlier days of the use of personal computers in laboratories [ 15 , 16 ] to current cloud computing and mobile applications [ 25 , 29 ]. In addition, some references studied the complexities of the concomitant use of several integrated tools [ 11 , 30 ]. In accordance with the search criteria, the studied environments consisted of academic, health-related laboratories, as well as biorepositories and biobanks. Consistently, the target users were managers and staff common to these laboratories, including technicians, researchers, doctors, and students.

Main characteristics of the selected studies.

Thirty-three programs/systems were identified in the twenty-one studies, with eight exclusively available for installation on desktop computers and the rest available online, including cloud-based systems, that is, with storage on online servers and availability on demand. Twenty-one of the studied systems were commercially available, charged programs/services, while twelve were free-of-charge for some of their functionalities. Among the non-charged software, two were custom systems designed exclusively for the studied laboratory (Biobank Portal and CCLMS).

Table 4 summarizes the results of the nine studies that assessed the impact of implementing computerized management systems. All of them reported positive results with the use of digital-assisted management. However, problems were identified related to technical constraints (either hardware or software) and limited acceptance of users who resist changing already established procedures, thus impairing the use of some systems to their full potential. Furthermore, the need for staff training and participative management was also recognized to achieve engagement of users to digital-assisted laboratory administration.

Results of the studies that assessed the impact of implementing computerized management systems.

N.D.: non-determined number of participants.

Regarding the management subjects issued according to each laboratory, digital systems were employed for several different uses, from purchases and administrative tasks to control of cell collections, inventories in general, as well as data storage and management of animal colonies.

All the thirty-two described software issued one or more topics of management recommended by documents of good laboratory practices [ 3 ], including experimental workflow, data storage, integration with laboratory equipment, statistical analysis, comparison of experimental data, animal colonies, biorepositories, inventory, and risks. The integration of work demands of academic health sciences laboratories and items of compliance with the GLP guidelines are identified in the chart presented in Figure 2 .

The main applications of the identified software on the different sections and chapters of the OECD GLP Principles [ 3 ].

4. Discussion

4.1. contributions to adherence to glp principles.

While the search strategy from the present review identified several different laboratory management systems, few of the eligible studies provided a focused discussion on this topic. The lack of direct scientific evidence limits the present review to quantitatively assess to what extent digital systems can collectively contribute to accreditation achievement. On the other hand, all the identified software accounted for management issues related to at least one of the GLP principles, and, in some studies, more than one software was used to meet the different demands related to quality systems.

In this sense, the approach proposed by Timoteo et al. [ 6 ] could be applied to the present sources of data to chart the main topics of management affected by these programs and systems related to good practice guidelines. The chart presented in Figure 2 shows how the types of management supported by the software in academic laboratories are related to several items from Section II of the OECD GLP Principles [ 3 ]. Such relationship is revealed by an emphasis on the responsibilities of staff and facilities management, work planning, availability of standard operational procedures (SOPs) that cover all study activities, procedure analysis, use and maintenance of equipment, as well as the application of standards for receiving test samples, its chain of custody and logistics, control of inventory, and the traceability of reagents and validation of methods.

For a better understanding of the functions of these systems, a brief presentation of them will be made, with an emphasis on meeting the computerized systems to the GLP principles listed in Figure 2 .

4.1.1. Workflow

The GLP principles require precise definitions of the different steps during the performance of the study, as described in item #8 of the OECD document [ 3 ], including the responsibilities of the personnel involved, the facilities and status of equipment employed (item #3), among other factors. Furthermore, quality assurance (item #2) requires identifying and monitoring critical steps, checkpoints, and possible sources of errors. Among the different systems identified in the present review, some described digital tools dedicated to managing such workflow of study performance in a systematized fashion.

In the late 1990s, Goodman and colleagues [ 16 ] presented Labflow, a software dedicated to genetics and mapping studies. Workflow management was not recognized as a study topic at that time and, while LIMS already existed, there was no commercial LIMS product that supported workflow management in a specific sense. In this scenario, LabFlow appeared among the first digital solutions, with a workflow model in which objects flow different laboratory tasks (such as DNA extraction, selection of clones, sequence analysis) under programmatic control. An essential point of this software was already allowing the programmer to customize their workflows to different laboratory needs.

Anderson et al. [ 19 ] described, in 2007, the implementation of the Microarray Gene Expression Analysis (MGEA), a software package developed by Rosetta Biosoftware (a subsidiary of Merck Inc.), that helped to integrate workflow information related to experimental design, data collection, and bioinformatic analysis of genomic results. Despite the high costs of the license and its renewals, the authors expected that implementing a commercially available service would bring advantages such as security in terms of support for operation and uniformity between different research centers, thus facilitating communication between employees. However, their qualitative analysis observed that the system was not used to its full potential, and its acceptance by staff would demand ongoing training and even an evolution of academic curricula towards the use of bioinformatics tools.

In 2019, Gaffney et al. [ 11 ] described the design and implementation of GEM-NET, a software that allowed members of the C-GEM (Center for Genetically Encoded Materials, USA) to integrate research efforts connecting six laboratories spread across three university campuses. GEM-NET was designed to support science and communication by integrating task management, scheduling, data sharing, and internal communications. A set of more than 20 tools was organized, including two applications customized for the Institution’s specific needs of workflow management. The tools are highly interconnected, but the set can be divided into access control, data storage, data navigation, project monitoring, teamwork, internal communication, and public engagement. The authors conclude that GEM-NET provides a high level of security and reliability in workflow management.

4.1.2. Data Management

In different items of the GLP principles, a need is described for the secure storage, filing, and retrieval of research data (item #7.4), including study plans, raw data, final reports, test system samples, and specimens (item #8.3), and their related archiving facilities (item #3.4). Furthermore, item #7 (standard operating procedures) requires the preparation and observance of documents that guarantee the quality and integrity of the data generated by the studies. Sub-item #7.4, for example, describes that in the case of computerized systems, validation, operation, maintenance, security, change control, and the backup system must be observed.

Within the selected studies, we found the report of computerized systems to manage data from various laboratory environments and how they were made available to the research groups. In the early 1980s, Delorme and Cournoyer [ 15 ], in a microbiology laboratory of a University Hospital, tested the CCIS/VS (Customer Information Control System/Virtual Storage), consisting of customer data repository, using a central computer shared with medical records databases, admission offices, patient accounting, and other medical-administrative services. The system also served as a virtual storage system, including data from microbiological samples. It performed activities such as report printing, data quality control, epidemiological assistance, germ identification, teaching, and research in the different subspecialties of microbiology. The authors carried out a qualitative and quantitative assessment identifying an improvement of workflow without increasing personnel, together with a reduction in the time for the production of reports, system downtime, and other parameters.

Viksna et al. [ 20 ] focused on collecting, storing, and retrieving data on research participants and biomedical samples through electronic management. For this, they proposed the PASSIM (Patient and Sample System for Information Management), a web-based customizable system that could be used for sending, managing, and retrieving samples and data from the research subject, ensuring the confidentiality of the records. This tool was instrumental in managing information in clinical research studies involving human beings and replaced the more expensive LIMS, which requires investments of time, effort, and resources that were not always available.

Electronic laboratory notebooks (ELN) are programs designed to replace traditional research notebooks. These electronic tools may register protocols, field/lab observations, notes, and other data inserted through a computer or mobile device, offering several advantages over paper notebooks [ 19 ]. Machina and Wild [ 22 ] investigated the importance of ELNs when integrated with other computer tools, such as laboratory information management systems, analytical instrumentation, data management systems, and scientific data. They observed that the type of laboratory (analytical, synthesis, clinical, research) was a primary source of differences when trying to integrate ELN with the available tools. Therefore, based on the observation that there was no well-established path for the effective integration of these tools, the authors decided to review and evaluate some of the adopted approaches.

Calabria et al. [ 24 ], in 2015, introduced adLIMS, a software for managing biological samples (primarily DNA) and metadata for patient samples and experimental procedures. The authors described how it was possible to produce this system by customizing a previous open-source software, ADempiere ERP. First, they collected the requirements of the end-users, verifying the desired functionalities of the system and Graphical User Interface (GUI), and then evaluated the available tools that met the desired requirements, ranging from pure LIMS to content management and corporate information systems. The authors report that the system supported critical issues of sample tracking, data standardization, and automation related to NGS (next-generation sequencing).

By 2021, Cooper et al. [ 30 ] reported using integrated systems that ensure the sharing of essential data for current research. The authors followed the 15 years of development and implementation of the LabDB system, initially projected to manage structural biology experiments, which could be improved into a sophisticated system that integrates a range of experimental biochemical, biophysical, and crystallographic data. The LabDB central software module handles data from the management of laboratory personnel, chemical stocks, and storage locations. It is currently used by the American/Canadian consortium CSGID (Center for Structural Genomics of Infectious Diseases) and several prominent research centers. The authors identified the difficulties and resistance of some researchers in adopting these systems as the main limitation, often due to the necessary effort to import data from electronic notebooks or laboratory spreadsheets, with which most researchers are already familiar. Nevertheless, the authors consider that this effort is worth it since these older approaches do not remove or even track inconsistencies and do not adapt well to the requirements of modern research.

It is essential to notice that, for accreditation purposes, hosted services (cloud archiving, backup, or processes) require written agreements describing the responsibilities of the informatics services. Test facility management must be aware of potential risks on data integrity resulting from third-party storage.

4.1.3. Equipment

Adherence to the GLP principles speaks to the adequate management of research equipment (OECD item #4), including their adequate calibration, maintenance, scheduling, and responsible staff in the test facility. Several commercially available systems, such as QRESERVE, cited by Perkel [ 9 ], are entirely dedicated to these functions, with integrated reservation calendars, administration of equipment status and availability, a repository of maintenance documentation, and a registry of use time. Other all-purpose management systems such as Labguru have most of these functions on a specific equipment module. That was also the case of the freely available (for individual researchers) Quartzy until 2016, as reported by Timóteo et al. [ 6 ]. This study described how the implementation of the software optimized the shared use of equipment on a multiuser clinical research unit and the advantages of allowing equipment scheduling, check-in, and check-out remotely, even using mobile phones.

4.1.4. Animal Facilities

Several procedures related to pre-clinical studies conducted with animals are issued in the GLP principles, mostly in item #5 of the OECD document (test system) and subsection #5.2 (Biologicals). These include a proper registry of housing, handling, and care of animal test systems to ensure the quality of the data. Additionally, records of source, date of arrival, and arrival condition of test systems should be maintained. Two selected studies described the use of vivarium monitoring software to ensure the remote control of stocking, accommodation, handling and care of animals, identification of colonies, and inventory of supplies.

Milisavljevic et al. [ 8 ] described, in 2010, the Laboratory Animal Management Assistant (LAMA), a software modified from the LIMS proposal to optimize small animal research management. It was initially developed to manage hundreds of new mouse strains generated by an extensive functional genomics program in Canada. The authors realized that they needed greater availability of suitable, easy-to-use systems and software interfaces. LAMA was implemented for a broad community of users, allowing individual research labs to track their colonies in a larger facility, independently. This open-access software is still available to the research community.

Allwood et al. [ 23 ] described, in 2015, how smartphones could help researchers in the remote management of animal colonies. The authors proposed Lennie, an app that introduced a new method for managing small to medium-sized animal colonies, allowing users to remotely access the facilities, and create and edit several functions virtually from anywhere. Its use contributes to the optimization of workflow and planning of experiments, offering a user-friendly experience. Possible updates to the functionalities were also suggested, such as camera integration with the calendar, permission for data sharing, and permanent storage.

4.1.5. Biobank/Repository

In order to comply with the GLP standards, samples that arrive at a laboratory must have records that include the characterization and reference, date of receipt, expiration date, quantities, and storage data, following item #6.1 (receiving, handling, sampling, and storage). This issue is of utmost importance for managing biobanks and biorepositories, creating a need for specific software for successful management.

Boutin et al. [ 25 ] carried out a study on a complex system of various software that contributed to the management of a Biobank. The core object of management was an extensive repository of samples and data available to researchers. The platform requires robust software and hardware, as they work with large amounts of data stored and transferred to research groups. In the study, the authors described each of the five custom and commercially available information systems integrated into the existing clinical and research systems, and discuss safety, efficiency, and challenges inherent in the construction and maintenance of this infrastructure. Constrack was used to manage patient data. The Enterprise Master Specimen Index (EMSI) is a sample indexing system, STARLIMS manages inventory, GIGPAD manages data and integrates equipment, and the Biobank Portal is the customized application that connects all the systems.

Manca et al. [ 28 ] assessed the structure of a central laboratory of the Antibacterial Resistance Leadership Group (ARLG) in the USA. This group leads the evaluation, development, and implementation of laboratory-based research and supports standard or specialized laboratory services. The laboratory included both a physical and a virtual biorepository. They developed digital procedures for reviewing and approving strain requests, providing guidance during the selection process, and monitoring the transfer of strains from the distribution laboratories to the requesting investigators.

Paul et al. [ 29 ] also describe a Biobank management system, with great emphasis on data storage in clouds. The authors evaluated that biobanks have become an essential resource for health research and drug discovery. However, collecting and managing large volumes of data (bio-specimens and associated clinical data) requires biobanks to use more advanced data management solutions. Paul and Chatterjee [ 27 ] point out that in the current COVID-19 pandemic scenario, that requires global and quick actions, virtual biobanks present a crucial role in several different fronts, from diagnosis to research. Without the need to physically use biological samples, these banks may allow sharing medical data and networks for better cooperation between biobanks at the national and international levels.

Recently, Dennert, Friedrich, and Kumar [ 1 ] explained the various implications of the inventory management of biological samples from various research areas, employing different cryopreservation methods. Such management must ensure the availability of items, easy tracking, and the optimization of shared space among the various research groups. For this, the authors presented the various stages of developing an inventory data model using the Microsoft Access database, after several phases that included training, planning, implementation, and maintenance, as well as the establishment of manuals and protocols for standardized data entry. Using the software development lifecycle (SDLC), the authors attained a database construction model. This model requires frequent communication with users to provide transparency and quality improvement.

4.1.6. Risk Management

Identifying incidents and risk assessment is an essential part of the GLP standards that requires an adequate work plan and a quality assurance program (OECD document item #2). Item #8.3 of the GLP states that all data changes during the conduction of a study must always be registered and responsible for the change to ensure traceability, enabling a complete audit trail to show all changes without masking the original data.

The work of Dirnagl et al. [ 27 ] discusses how error management is fundamental to comply with international standards while studying the implementation of the LabCIRS (Laboratory Critical Incident Reporting System), a simple, accessible, and open-source critical incident reporting system for pre-clinical and basic academic research groups. The software was implemented by establishing an electronic quality management system, which allowed accessibility through any laboratory computer, enabling incident reports that included photo uploads and automatic alerts for new reports and archiving.

4.1.7. Inventory

Item #6.2 of the GLP principles clearly states that all material from a study must be adequately identified, including the batch number, purity, composition, concentrations, or other characteristics, to define each item or reference item properly. It also indicates the need to keep the receipt and expiration dates, quantities received/used in the studies, and storage instructions for the stock of materials. In this review, several articles emphasized this need to monitor inventories with the help of computerized systems.

Nayler and Stamm [ 17 ], in 1999, described a laboratory management software, ScienceLab Database (SLD), which offered a management platform for molecular biology research laboratories. The program primarily manages the stock of biological samples, including plasmids, antibodies, cell lines, and protocols, and included an ordering and grants management system. The authors considered that this system met the specific needs of a small to medium-sized research laboratory, helping to organize inventories of valuable reagents, storing, and maintaining information about these items, and simplifying orders and processes.

By 2016, Catena et al. [ 26 ] developed the AirLab, a cloud-based tool with web and mobile interfaces, to organize antibody repositories and their multiple conjugates. Due to the large number of data generated by these collections, the authors recognized the need for dedicated software. The work demonstrated that Airlab simplifies the purchase, organization, and storage of antibodies, creating a panel to record results and share antibody validation data.

Yousef et al. [ 21 ] described the LINA (Laboratory Inventory Network Application) as a set of Windows-based inventory management software configured to work on a computer network with multiple users. Designed for small molecular biology laboratories, it uses Access databases to assign a new identifier to each new reagent, providing a library that helps with research and comparing DNA sequences. It later faced several features, such as expanding the types of tables available, compatibility with other operating systems, barcoding, and improvement of security issues. According to the authors, the resources provided by LINA are comparable to those available in commercial databases, with the advantage of providing a free database maintenance application for academic laboratories.

In an opinion article published in Nature’s section “Toolbox”, Perkel [ 9 ] describes several low-cost computerized electronic inventory systems as a means to overcome tortuous searches, old notebooks, out-of-date spreadsheets, and “frost-encrusted freezer boxes” to identify laboratory samples and resources. Besides programs discussed by other authors in this review, such as LINA and Quartzy, the article cites other systems such as OpenFreezer, a free web-based system to register sample data such as location, origin, and biological properties, the cloud-based StrainControl (DNA Globe, Sweden), a software free for individual researchers that provides support for managing different lab-organism strains, molecules, and chemicals, the mLIMS, developed by BioInfoRx (Madison, WI, USA), designed to track rodent colonies, LabGuru (BioData, Cambridge, MA, USA), a widely known paid cloud-based all-in-one Electronic Notebook, and CISPro (BioVia, Waltham, MA, USA), described as a functional Institute-wide tracking system for shared resources. Despite differences in accessibility and several resources, all of these systems share similar search engines linked to customizable databases.

Timoteo et al. [ 6 ] evaluated, by 2020, the impact of implementing a multi-module, free-of-charge online management system (Quartzy, Quartzy Inc., Santa Clara, CA, USA) in the workflow of a Brazilian academic clinical research laboratory on the perception of users. Until 2016, the software modules could assist in various aspects and demands of the laboratory, including user communications, multiuser equipment management, material inventory, research documents, and tracking of supply orders. Unfortunately, Quartzy was recently updated to a simpler version, consisting only of an inventory and purchase tracking system that connects researchers to hundreds of life sciences brands and suppliers.

4.2. Evaluating Impacts and Limitations

Effectiveness is a fundamental point to be considered in the potential role of software for laboratory management. However, most of the eligible studies identified in our search did not investigate the reported systems’ impact either through qualitative or quantitative assessments. Moreover, despite the performance of evaluations, few studies identified or discussed the limitations and drawbacks of the studied information systems. The studies with evaluations reported, among several aspects, improvement of the organization, workflow, traceability, reliability, acceptability, and good use of the software. Decreased process errors were reported that were made manually, thereby gaining productivity and reducing work. In some specific cases, they positively evaluated the control of frozen cells, generating efficiency and better results in partner laboratories. On the other hand, regarding limitations, older articles (before 2000) identified problems that were more related to system performance, which was sometimes slow and needed adjustments at a time when information technology was still incipient. The limitations from the most current systems are more related to a selective satisfaction and acceptance of software tools, specific according to the function and objective of each group and, in some cases, the resistance by researchers and staff to abandon old ways and migrate to digital tools, which were not used to their full potential within the laboratory.

To adequately assess the impact of these electronic management systems, different methodological approaches are available, such as pre/post-tests evaluating quantitative indicators of performance and provision of services. However, as Timoteo et al. [ 6 ] discussed, the complex nature of the provided services of multiuser, academic research facilities may impair the obtention of feedback through quantitative indicators. In this sense, the perception and attitudes of staff towards the management system may contribute to understanding its impact on the workflow and the search for quality at academic clinical research laboratories, as well as provide data for the development or improvement of actions and strategies toward quality and compliance [ 31 , 32 , 33 ]. In this sense, validated tools may provide a means to standardize the evaluation of laboratory management software, allowing comparisons on the effectiveness and adequacy of these systems in different applications. Two studies [ 18 , 21 ] proposed the use of an important tool to investigate the effectiveness and efficiency of the software, the system usability scale (SUS). This tool, developed by John Brooke at Redhatch Consulting (UK), consists of a simple, ten-item attitude questionnaire using a Likert scale to provide a global view of subjective assessments of usability, which was validated as providing reliable results even with small samples/study groups, which was the case of most identified studies in this review. Therefore, it may represent a potential tool (although underestimated until the present moment) for further studies on implementing laboratory management systems.

Different studies point out that staff training is one of the most important factors of success of the implementation of these systems and a key part in acceptance and adapting to a new management model. Dirnagl et al. [ 27 ] evaluated the impact on staff attitudes toward incident reporting after one year of implementation, observing that training led to greater adherence to the goal of complying with international quality standards and mature culture of error management. Timóteo et al. [ 6 ] performed a qualitative evaluation of the staff perception on software implementation, where most users stated that constant training and leadership were pivotal for the successful use of the software. On the other hand, Anderson et al. [ 19 ] reported that limited access to training was a barrier to software use during the implementation of MGEA, and that the lack of ongoing training might have contributed to a progressive de-emphasizing of the system use among the laboratory staff. These data point to the need of careful planning by the PIs to ensure continuous and inclusive training on the implementation program of management systems.

4.3. Software Availability

Regarding availability and accessibility, until 2010, most of the identified programs had to be downloaded/installed to specific laboratory computers [ 19 , 30 ], but were sometimes able to integrate local area networks (LANs), as described by Delorme and Cournoyer in 1980 [ 15 ]. In the past decade, technology has advanced to online software, expanding even to applications (apps) on mobile phones, reflecting the current expectations of users and consumers. With app technology permeating all fields of our daily lives, it would be natural for this technological paradigm to reach laboratory and research technologies. Indeed, a big leap was identified towards the proper integration between lab management systems and the new mobile universe. Real-time communication makes it possible, for example, that inventory checks, equipment scheduling, and data verification of an animal colony be performed while in transit. Multicenter studies can share data in real-time, as recently observed in the fast development studies of vaccines against SARS-CoV-2 since 2020, relying heavily on technological development and efficient data management [ 34 ].

Begg et al. [ 35 ] discussed how computer systems are of particular importance in the process of GLP certification in low- and middle-income countries, even though their role is not always emphasized on accreditation systems around the world. This review identified that the knowledge on laboratory management software is mainly originated, as expected, from developed, high-income countries, with advanced information technology industries and significant investment in technology and support for universities and study centers (USA, Germany, Canada, United Kingdom, Switzerland). In a critical view, it may indicate an economic bias in the technological development on the theme, as developing countries maintain a role as consumers of technology and not as producers and developers, reflecting little investment in this (and other) technological areas.

The costs of implementing computerized systems may represent one of the main challenges for public Academic Health Centers since these Institutions, in general, face tight budgets to support several laboratories, researchers, and research lines. Such limitations are expected to be potentialized when considering low- to middle-income countries, which could benefit from low-cost or cost-free initiatives.

In general, the development and maintenance of information systems are made possible by providing subscription services to ensure the tool’s sustainability. The present review identified some systems that addressed a full spectrum of fundamental issues in the management of academic laboratories, such as inventory control and organization and equipment scheduling, on a free-of-charge basis, as it incorporated catalogs from various sponsors (reagent suppliers) and suggests these products when orders are placed [ 9 ]. However, such a business model probably did not match the maintenance costs of the platform, as Quartzy has shut down all functions not related to inventory/purchases by 2016, and recently included a fee for Institutional users. It is also possible that users from outside the USA and Europe could not use the vendor-related functionalities, as customer services and representatives in regions such as South America would not connect directly to the system [ 6 ]. On the other hand, LINA is an example of a system that could remain free-of-charge, even though limited to the needs of small molecular biology laboratories [ 21 ], with much simpler functionalities compared to well-known commercial applications such as Labguru. Other services, such as QReserve, have both free and paid versions with increased functionalities, allowing low-budget academic laboratories to use some free resources, such as equipment reservation and management, through a more straightforward interface.

A usual profile among entirely free software originates from in-house academic software, such as Biobank Portal and CCLMS, customized for the personal use of the developer group, usually without widespread use in other institutions. Even though they may present advantages on issuing specific demands of developers, the lack of a profound, systematic evaluation of performance on most selected studies does not allow to infer whether these are more or less effective than commercial software. In this sense, Boutin et al. [ 25 ] report that the laboratory IT framework may face challenges common to industry settings, where cost-overrun is prevented by planning the cost-effectiveness of purchasing commercially available vs. designing in-house custom applications. An interesting way to achieve broader applicability for such software is to use open-source codes, such as Boutin et al. [ 25 ], paving the way for other programmers to adapt the tool to different laboratory specificities. It is important to notice that investments from government bodies worldwide could also contribute to the development of freely available tools as part of public policies focused on increasing overall quality and adherence to good practices in health sciences research. In this sense, the encouragement of startups involving interdisciplinary initiatives can turn universities and academic centers into important stakeholders in covering technological gaps in low- or middle-income countries [ 36 ].

4.4. Review Limitations

The present Scoping Review has limitations mainly related to the impossibility of exhausting the literature on laboratory software, reflected in the choice of not including programs that dealt only with the transmission and handling of analysis results and laboratory data, such as pure LIMS or analytical bioinformatics software. Despite their fundamental role, these types of software have already been widely discussed [ 37 , 38 , 39 , 40 ], and most of these systems were not designed to support the management of staff and shared resources, for example. Additionally, the scientific literature probably does not reflect the abundance of available software since developers and the scientific community usually treat them as a commercial tool rather than a research topic. Nevertheless, regardless of such limitations, the present review was able to map a framework that points to the great applicability of these systems in the search for quality and good practices in academic experimental medicine laboratories, where restrictions regarding the availability of resources and staff and limited management experience are common restrictions. Therefore, the gaps identified here can serve as an indication for new studies that seek to assess, quantitatively or qualitatively, the impact of implementing these tools on the best practices at academic health Institutions.

5. Conclusions

The present literature review mapped several studies in the last four decades, proposing and evaluating the impact of digital tools in the management of health sciences research laboratories to several different applications, ranging from administrative workflow management and data traceability to virtual biobanking. These functions have the potential to contribute to the adherence to different GLP principles. However, the evidence for their effectiveness is still limited and requires further investigative efforts.

Acknowledgments

The authors acknowledge the financial support in scholarships from the Brazilian agencies CNPq, CAPES, and FAPERJ. The authors acknowledge the technical support by Jean Carlos Nascimento.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/healthcare9060739/s1 , Table S1: Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist.

Author Contributions

Conceptualization, G.A., M.T. and B.O. (Bruna Oliveira); methodology, G.A. and C.F.d.A.B.M.; software, P.M.; formal analysis, M.T., R.B., J.d.S. and L.D.; investigation, M.T., E.L., J.d.S. and G.A.; resources, P.M. and C.F.d.A.B.M.; data curation, C.F.d.A.B.M. and G.A.; writing—original draft preparation, M.T., E.L., A.C.B. and L.D.; writing—review and editing, G.A. and C.F.d.A.B.M.; project administration, B.O. (Beni Olej). All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

The Role of Laboratory Information System in Improving the Delivery of Laboratory Services: A Recent Systematic Review

Affiliations.

• 1 Department of Public Health, College of Health Sciences, Saudi Electronic University, Riyadh, Saudi Arabia.
• 2 Pathology and Laboratory Department, King Abdulaziz Medical City, Ministry of National Guard, Riyadh, Saudi Arabia.
• 3 Department of Public Health, College of Health Sciences, Saudi Electronic University, Dammam, Saudi Arabia.
• PMID: 36111772
• DOI: 10.2174/1386207325666220914112713

Background: Recently, laboratory information systems (LIS) have become necessary for every laboratory to improve the decision-making process and achieve better treatment and diagnostic results. By standardizing laboratory's tests, procedures, and workflows, the software enables laboratories to improve patient care, reduce human error, and constructively lower operating costs. Implementing LIS has a multidimensional impact on improving the delivery of laboratory services.

Objectives: This paper aims to investigate how patient services can be improved by laboratory information system.

Methods: This paper is based on a review conducted by searching PubMed, Google Scholar, Saudi Digital Library and Research Gate for English language articles published from 2015 to 2021 and focused primarily on laboratory information systems.

Results: The literature searches yielded a total of 30 articles that were then initially screened based on the titles and abstracts. Seven articles were excluded because they did not primarily address LIMS for biosafety, automated verification of test results in the core clinical laboratory, clinical biochemistry, or the impact of health information technology on patient safety, or were not written in English. The remaining 23 articles were then screened in full text.

Conclusion: Advanced laboratory information systems may eliminate diagnostic errors in the preanalytical, analytical, and postanalytical phases. In addition, they can incorporate genomic data at the analytical stage to generate useful reports for providers and patients.

Keywords: Laboratory; health services; information system; multidimensional impact; postanalytical phases; service delivery.

Copyright© Bentham Science Publishers; For any queries, please email at [email protected].

Publication types

• Systematic Review
• Clinical Laboratory Information Systems*

Laboratory Information Management Systems (LIMS)

• First Online: 10 March 2021

Cite this chapter

• Kyle Boyar 2 ,
• Andrew Pham 3 ,
• Shannon Swantek 4 ,
• Gary Ward 5 &
• Gary Herman 2  

675 Accesses

3 Citations

Even in the year 2020, the majority of well-established analytical laboratories are utilizing paper documents and excel spreadsheets to generate and compile data. While it is possible to run a lab with these basic tools, it makes it subsequently very difficult to scale up operations when operational efficiency is tied to pen and paper. Additionally, with the nationwide rise in legal and regulated cannabis markets, a significant amount of effort and attention to detail must be placed on laboratory personnel in order to ensure regulatory compliance. In this chapter, the functionalities of basic LIMS are discussed and how these functionalities can improve productivity, enhance traceability and testing quality, as well as reduce errors. Improvements to these systems are discussed and how the implementation of these enhancements can better inform laboratory personnel, encourage good laboratory practices, and ultimately, increase efficiency and reduce regulatory paperwork.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Quality Glossary—Q. Accessed 25 May 2020

Google Scholar  

Fuels registration, reporting, and compliance help. Accessed 25 May 2020

Nelson S (2017) A seed-to-sale shakeup. Cannabis Business Times, 30 May 2017. Accessed 25 May 2020

Wood S (2018) MJ Freeway raises $10M to improve marijuana tracking software, expand operations. The Inquirer. Philadelphia Media Network (Digital), LLC, 20 Sept 2018. Accessed 25 May 2020

McVey E (2018) Chart: dominant player emerging for state cannabis seed-to-sale tracking contracts. Marijuana Business Daily, 10 Sept 2018. Accessed 25 May 2020

Download references

Author information

Authors and affiliations.

Tagleaf, Inc., Santa Cruz, CA, USA

Kyle Boyar & Gary Herman

ILP Scientific LLC, Westminster, CA, USA

Andrew Pham

Enlightened Quality Analytics LLC, Boulder Creek, CA, USA

Shannon Swantek

GK Ward Associates LLC, Vancouver, WA, USA

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Gary Herman .

Editor information

Editors and affiliations.

E4 Bioscience, Charlevoix, MI, USA

Shaun R. Opie

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Boyar, K., Pham, A., Swantek, S., Ward, G., Herman, G. (2021). Laboratory Information Management Systems (LIMS). In: Opie, S.R. (eds) Cannabis Laboratory Fundamentals. Springer, Cham. https://doi.org/10.1007/978-3-030-62716-4_7

Download citation

DOI : https://doi.org/10.1007/978-3-030-62716-4_7

Published : 10 March 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-62715-7

Online ISBN : 978-3-030-62716-4

eBook Packages : Chemistry and Materials Science Chemistry and Material Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• E-ISSN : 2277-7873
• IMPACT FACTOR

Sed mattis varius tellus, ut varius quam faucibus nec. Nam accumsan urna eu magna porttitor malesuada. Suspendisse lacinia vel urna.

• 123 6th St. Melbourne, FL 32904
• Phone: (888) 123-4567
• Email: [email protected]

Review Article

Laboratory Information Management System (LIMS): A Review

Published On - September 17, 2018

Received Date: 19/04/2013, Accepted Date: 23/04/2013

Manuscript number: IJPRS/V2/I2/00069

2,015 Views

HTML Full Text

Google Citation

Patel, N. K., Sharda, A. M., Patel, R. C., Dixit, P. B., Vyas, H. A.

Author's Affiliation

Laboratory Information System (LIS) streamlines workflow in the laboratory and eliminates time-consuming paperwork. The business today is getting increasingly pressured to be more productive and efficient with less traditional resources. If we look at a typical production process, for instance, the level of automation and optimization that has permeated into the production floor is quite astounding. Many industries have had a tremendous impact in bringing to the market the automation and optimization solutions to streamline -the production floor. The other ends of the spectrum are the back-office transaction oriented processes like Inventory Management, Materials Management, Financials and so on, Therefore a lab’s ability to process data quickly and its ability to disseminate that information efficiently throughout the plant is vital for the success of the production process. Laboratory Information Management System (LIMS) can help in managing the information flow within the lab and can be the modus operandi of connecting the lab with the rest of the organization. For instance, imagine all the information that is generated in a lab.

Automation, Optimization, Laboratory management

Cite This Article

Patel, N. K., Sharda, A. M., Patel, R. C., Dixit, P. B., Vyas, H. A. (2013). Laboratory Information Management System (LIMS): A Review. International Journal for Pharmaceutical Research Scholars (IJPRS), 2(2), 16-19.

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Recent Articles

Synthesis of Various Cynuric Chloride Based Pyrimidones And Their Antimicrobial Activity Read More

Study of Synthesis And Antimicrobial Activity of Novel Pyrimidones From Chalcones and Urea Read More

Protective Effect of Phytic Acid in Gentamycin Induced Nephrotoxicity in Rats Read More

Blogs Agaram Technologies | LIMS | SDMS | ELN :

• Life Sciences & Biotech Industry
• Certifications

What is Laboratory Information Management System — A Comprehensive Guide

• Amrit Manthan
• April 23, 2024

Understanding Laboratory Information Management Systems (LIMS) should not be a barrier for professionals who want to enhance their laboratory operations.

Yet often, it is.

You may have a clear objective to enhance accuracy and speed in your laboratory, but the technical aspects of a Laboratory Information Management System can deter even the most enthusiastic professionals.

Breakthroughs in lab technology are sometimes sidelined due to these hurdles.

But it doesn’t have to be this way.

Whether you are a lab technician, a scientist, or a project manager, understand this: You do not need a background in software engineering to benefit from a Laboratory Information Management System.

You can either work with a consultant or adopt user-friendly LIMS platforms that simplify rather than complicate your processes.

Achieving a high-tech, efficient lab operation should not be an elusive goal.

Embracing LIMS is easier than you might think, and our guide is designed to demystify this crucial technology for you.

What is a Laboratory Information Management System?

A Laboratory Information Management System (LIMS) is a software system designed to streamline the management of laboratory data and operations. You can utilise it in various scientific and industrial settings, tackling the large volumes of data that have to be dealt with in pharmaceuticals, healthcare, environmental science, manufacturing and research institutions.

Core Functionalities of LIMS – What LIMS is Capable Of

A Laboratory Information Management System (LIMS) encompasses a range of features designed to streamline laboratory operations, manage data and ensure regulatory compliance.

Let’s take a deeper look.

By centralising and automating these processes, the software will help you with the improvement of efficiency, accuracy and productivity during your lab operations.

Overall, LIMS plays a crucial role in modern laboratories by optimising workflows, managing data effectively, ensuring quality and compliance and facilitating informed decision-making processes.

Role of a Laboratory Information Management System as Opposed to Other Software in a Lab

Commonly used software in a laboratory are LIMS (Laboratory Information Management System), LIS (Laboratory Information System) and ELNs (Electronic Laboratory Notebook). But to reap maximum benefits out of them, it is necessary for you to look at the comparison of their purpose.

   – LIMS (Laboratory Information Management System): Primarily focuses on managing laboratory samples, data, and workflows. It streamlines processes related to sample tracking, data management, and quality control.

   – LIS (Laboratory Information System): Specialized for clinical and healthcare laboratories, focusing on managing patient information, test orders, and results. It ensures compliance with regulatory standards and integrates with electronic health records (EHR) systems.

   – ELN (Electronic Lab Notebook): Aimed at documenting experimental procedures, observations, and results in research and development laboratories. It facilitates collaboration among researchers and enables data analysis and sharing.

• Sample Management:

   – LIMS (Laboratory Information Management System): Provides extensive sample tracking capabilities, manages sample storage, and records sample metadata throughout its lifecycle.

   – LIS (Laboratory Information System): Manages patient sample information, including sample accessioning, test orders, and result reporting in clinical settings.

   – ELN (Electronic Lab Notebook): May include basic sample tracking features, but its primary focus is on documenting experimental procedures and results rather than managing physical samples.

• Data Management:

   – LIMS (Laboratory Information Management System): Organizes and manages large volumes of structured laboratory data, including test results, instrument data, and sample information.

   – LIS (Laboratory Information System): Manages patient data, test results, and diagnostic information, ensuring accuracy and compliance with regulatory standards in clinical laboratories.

   – ELN (Electronic Lab Notebook): Captures experimental data, observations, and research findings in a structured format, facilitating data analysis, and interpretation.

• Workflow Automation:

   – LIMS (Laboratory Information Management System): Automates laboratory workflows, including sample processing, testing, and result reporting (with electronic signature), to improve efficiency and reduce manual errors.

   – LIS (Laboratory Information System): Streamlines clinical laboratory workflows, automating processes such as test ordering, specimen processing, and result verification to enhance productivity and patient care.

   – ELN (Electronic Lab Notebook): Provides workflow templates and automation tools for documenting experimental procedures, enabling researchers to follow standardized protocols and workflows.

• Integration with Instruments:

   – LIMS (Laboratory Information Management System): Integrates with laboratory instruments and equipment to automate data capture, analysis, and result reporting, ensuring data accuracy and consistency.

   – LIS (Laboratory Information System): Interfaces with diagnostic instruments and laboratory analyzers to receive and process test results, facilitating seamless integration with laboratory workflows.

   – ELN (Electronic Lab Notebook): May offer limited integration with laboratory instruments for data capture and analysis but is primarily focused on documenting experimental procedures and results.

• Regulatory Compliance:

   – LIMS (Laboratory Information Management System): Ensures compliance with regulatory standards and industry regulations, such as GLP, GMP, and FDA requirements, through audit trails, electronic signature, version control, and documentation.

   – LIS (Laboratory Information System): Adheres to regulatory standards specific to clinical laboratories, such as CLIA, HIPAA (Health Insurance Portability and Accountability Act), and CAP (College of American Pathologists) accreditation requirements.

   – ELN (Electronic Lab Notebook): Supports compliance with research standards and guidelines, such as FDA regulations for laboratory data integrity and documentation practices.

 This section has illustrated the critical role of a Laboratory Information Management System and how it distinctly positions itself against other laboratory software like LIS and ELN. Each system has its unique strengths tailored to specific laboratory needs — LIMS with its comprehensive sample and data management designed for a variety of lab environments, LIS focusing on clinical specifics and patient-centric data, and ELN enhancing the documentation and collaboration in research settings.

More than mere technology, digital transformation in the lab setting is about equipping the workforce with the right tools to enhance operational efficiencies and decision-making. Success in modern labs is increasingly dependent on the ability to generate and utilize data effectively, allowing for dynamic adaptation to market needs.

In this context, LIMS emerges not just as a tool, but as a pivotal component of digital transformation, unlocking significant value for laboratories. It boosts agility, ensures compliance, and enhances data integrity, positioning businesses to innovate and scale swiftly, meeting the rapidly evolving demands of the scientific and medical landscapes.

Stakeholders that will benefit from a Laboratory Information Management System 

Implementing the LIMS software in a lab will benefit various individuals, organisations and industries. Take a look at the following stakeholders of the system to see how you will benefit depending on your role in accordance with the system:

Overall, LIMS benefits a wide range of stakeholders across industries by improving laboratory efficiency, data management, compliance and decision-making processes. It enables organisations to enhance productivity, ensure data integrity and achieve your goals in laboratory operations, research and quality assurance.

Importance of a Laboratory Information Management System  

The importance of Laboratory Information Management Systems (LIMS) in lab management cannot be overstated. Here are several key reasons why LIMS is crucial for effective lab management:

• Streamlined Workflows : LIMS helps streamline laboratory workflows by automating routine tasks such as sample tracking, data entry, and result reporting using electronic signatures and approvals. This automation reduces manual errors, improves operational efficiency, and allows laboratory staff to focus on more critical tasks.
• Improved Data Management: A Laboratory Information Management System centralizes and organizes laboratory data, including sample information, test results, and instrument data. This centralized data management system ensures data integrity, accessibility, and traceability, facilitating efficient data analysis, interpretation, and decision-making.
• Enhanced Sample Tracking: With LIMS, laboratories can track samples throughout their lifecycle, from sample accessioning to storage, testing, and disposal. This capability ensures sample traceability, reduces the risk of sample mix-ups or loss, and supports compliance with regulatory requirements.
• Quality Control and Assurance: A Laboratory Information Management System incorporates quality control measures to ensure the accuracy, reliability, and consistency of laboratory results. It enables laboratories to implement quality control protocols, monitor instrument performance, and track deviations to maintain quality standards and compliance with regulatory requirements.
• Compliance Management : A Laboratory Information Management System helps laboratories comply with regulatory standards and industry regulations, such as GLP, GMP, ISO, and FDA requirements. It maintains audit trails, version control, and documentation to demonstrate compliance during regulatory inspections and audits, reducing the risk of non-compliance penalties and sanctions.
• Integration with Instruments and Systems: A Laboratory Information Management System integrates with laboratory instruments and systems to automate data capture, analysis, and reporting processes. This integration eliminates manual data entry errors, ensures data consistency, and improves interoperability between different laboratory systems and devices.
• Resource Optimization: By optimizing workflows, automating tasks, and improving data management, LIMS enables laboratories to optimize their resources, including personnel, equipment, and consumables. This optimization results in cost savings, increased productivity, and improved resource utilization in laboratory operations.
• Facilitated Collaboration: Laboratory Information Management System facilitates collaboration among laboratory staff, researchers, and external stakeholders by providing a centralized platform for data sharing, communication, and collaboration. It enables real-time access to laboratory data and results, fostering collaboration across interdisciplinary teams and external partners.
• Data Security and Confidentiality: Laboratory Information Management System employs robust security measures to protect sensitive laboratory data from unauthorized access, modification, or loss. It ensures data security and confidentiality, safeguarding intellectual property, patient information, and regulatory compliance in laboratory operations.
• Continuous Improvement and Decision Support: LIMS provides valuable insights into laboratory performance, trends, and metrics through reporting and analytics capabilities. This data-driven approach enables laboratories to identify areas for improvement, make informed decisions, and drive continuous quality improvement initiatives.

Overall, LIMS plays a vital role in modern lab management by improving efficiency, data management, quality control, compliance, and decision support. It enables laboratories to meet the challenges of today’s dynamic and highly regulated environment, ensuring the delivery of high-quality and reliable laboratory services.

Going Paperless with a Laboratory Information Management System 

Achieving a paperless lab environment using Laboratory Information Management Systems involves leveraging the capabilities of LIMS to digitise and automate your laboratory processes, data management & documentation. As technology advances, LIMS will become an essential and modern tool for your laboratory. The system will further assist you with:

Overall, LIMS plays a critical role in achieving a paperless lab environment by digitising laboratory processes, data management & documentation. It improves efficiency, data integrity and compliance while contributing to environmental sustainability efforts.

Adopting Laboratory Information Management System to replace Legacy Applications 

Adopting a Laboratory Information Management System (LIMS) to replace legacy applications is a pivotal step for modernizing lab operations.

But often, it is met with resistance.

Legacy systems might be familiar, but their limitations in scalability, efficiency, and integration are increasingly apparent. The transition to LIMS can seem daunting, plagued by the fear of complex data migrations and workflow disruptions.

Yet, the need for change is undeniable.

For laboratory managers, researchers, and IT professionals, understand this: transitioning to LIMS doesn’t require you to be an IT guru.

You can work alongside migration experts or choose LIMS solutions designed for ease of transition and user-friendliness.

Moving to a more advanced, integrated, and compliant system should not be seen as a hurdle, but as an opportunity for significant enhancement.

Embracing LIMS to replace outdated applications is less about technical challenge and more about strategic advancement. Let’s see how.

• Assessment of Current System :

It is important to evaluate the existing legacy system to identify its strengths, weaknesses and limitations. Determine the reasons for transitioning to a Laboratory Information Management System , such as outdated technology, scalability issues, regulatory compliance requirements, or workflow inefficiencies.

• Define Requirements and Objectives :

While defining your requirements and objectives for the new Laboratory Information Management System implementation, consider factors such as laboratory workflows, sample types, data management needs, regulatory compliance requirements, integration with instruments and systems and user preferences.

• Selecting the Right Laboratory Information Management System Solution :

Research and evaluate different Laboratory Information Management System solutions available in the market. Prioritise factors such as functionality, scalability, flexibility, ease of use, vendor support and cost.

• Customization and Configuration :

While the ability to customize and configure a Laboratory Information Management System differs from vendor to vendor, most minor changes that could help the LIMS align better and work with your existing systems are generally accommodated. Make sure to define data fields, user roles, access permissions and system configurations to align with your laboratory’s operations.

• Data Migration :

To ensure a seamless and less stressful retention of data that was documented in a relatively traditional manner, develop a data migration strategy, including data mapping, cleansing and validation, to ensure accurate and complete data transfer. Test the data migration process thoroughly to identify and address any issues or discrepancies.

• Training and User Adoption :

Getting used to a new system can be a little overwhelming in the get-go. To tackle this while adopting and effectively using LIMS, laboratory staff will require comprehensive training sessions, workshops and tutorials. Familiarise users with the system’s features, functionalities and best practices and encouraging user engagement & feedback will address any concerns or challenges in the beginning stages. Once that is accomplished, there will be seamless and skillful usage of the system in the long run.

• Pilot Testing and Validation :

Conduct pilot testing of the new LIMS in a controlled environment to evaluate its performance, functionality and usability. Involving key stakeholders and end-users in the pilot testing phase will aid in gathering feedback and identifying areas for improvement. Also, validate the system against regulatory requirements and quality standards to ensure compliance.

• Deployment and Go-Live :

It is finally launch day, where you deploy the new LIMS in the production environment. It is vital to coordinate with IT teams, vendors and stakeholders to ensure a smooth transition from the legacy system to the new LIMS. Monitor system performance and user feedback during the initial deployment phase and address any issues or concerns promptly.

• Continuous Improvement and Optimization:

Continuously monitoring and optimising the LIMS implementation will ensure its effectiveness, efficiency and alignment with laboratory goals and objectives. Gather user feedback, track key performance metrics and implement enhancements & updates as needed, for it will improve system functionality and your satisfaction.

• Post-Implementation Support :

Providing ongoing support and maintenance is essential for LIMS. This will ensure smooth operations and address any technical issues, user queries, or system updates. Establish a helpdesk or support system for assistance with troubleshooting, training and system enhancements. It is crucial to regularly review and update system documentation, SOPs & training materials to ensure relevancy.

By following these steps and engaging stakeholders throughout the process, laboratories can successfully adopt LIMS from a legacy system. The LIMS system will thus undoubtedly improve efficiency, data management & compliance, and to achieve your laboratory goals and objectives.

Laboratory Information Management Systems (LIMS) have revolutionised the way laboratories operate, transforming traditional paper-based processes into efficient, digitised workflows. With LIMS, you can seamlessly enforce sample management, automate your workflows, improve data integrity and ensure compliance with regulatory standards in your lab. When you finally get to adopt LIMS, it will enable your laboratory to enhance productivity, reduce any errors during manual data management and assist you in making informed decisions based on reliable data.

Moreover, as technology continues to evolve, LIMS will play an increasingly vital role in driving innovation, collaboration and efficiency in your laboratory operations. For a robust and user-friendly LIMS solution, you can consider Agaram’s Qualis LIMS . 

Qualis LIMS offers a comprehensive suite of features designed to streamline workflows, enhance data integrity, and empower your lab functions to achieve its full potential. With its emphasis on configurability and regulatory compliance, Qualis LIMS can be your powerful partner and boost innovation and scientific progress. 

Learn more here .

Recent Posts

What is Laboratory Information Management System — A..

Agaram Technologies at Analytica 2024