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===Spreadsheets=== | ===Spreadsheets=== | ||
As computing technology evolved and became more affordable, software makers had even more incentive to develop relevant and approachable software solutions to solve businesses' workflow challenges. Among these software solutions was the spreadsheet. Derived in concept from the paper-based ledgers accountants and traders would use, the electronic spreadsheet suddenly allowed businesses to perform calculations automatically, saving users time. Laboratories picked up on this electronic, ledger-based approach to documenting experimental results and making routine analytical calculations. However, as labs of all types fell under greater scrutiny from regulators, the spreadsheet method of documentation and calculation of analytical results began to show inefficiencies and inadequacies, including difficulty in preventing changes to fields and maintaining an accurate representation of the who, what, when, and where of recorded values. | As computing technology evolved and became more affordable, software makers had even more incentive to develop relevant and approachable software solutions to solve businesses' workflow challenges. Among these software solutions was the spreadsheet. Derived in concept from the paper-based ledgers accountants and traders would use, the electronic spreadsheet suddenly allowed businesses to perform calculations automatically, saving users time.<ref name="MeikleTheHist">{{cite web |url=https://blog.sheetgo.com/spreadsheets-tips/history-of-spreadsheets/ |title=The history of spreadsheets |author=Meikle, H. |work=Sheetgo Blog |accessdate=15 December 2023}}</ref> Laboratories picked up on this electronic, ledger-based approach to documenting experimental results and making routine analytical calculations. However, as labs of all types fell under greater scrutiny from regulators, the spreadsheet method of documentation and calculation of analytical results began to show inefficiencies and inadequacies, including difficulty in preventing changes to fields and maintaining an accurate representation of the who, what, when, and where of recorded values. | ||
===Databases=== | ===Databases=== | ||
Databases also came into popularity with the advent of computing technology. Tabular and relational representation of data points, with the ability to assign labels to those data points, became useful for the electronic storage and retrieval of all sorts of data.<ref name="FortuneABrief20">{{cite web |url=https://learn.saylor.org/mod/page/view.php?id=21059 |title=A Brief History of Databases |work=CS403: Introduction to Modern Database Systems |author=Fortune, S. |publisher=Saylor Academy |date=17 December 2020 |accessdate=15 December 2023}}</ref> Like the spreadsheet, it's not surprising that some laboratories latched on to the idea of keeping track of experimental, analytical, and quality control data in a database. However, these are best used for structured data, and as electronic type of data and information evolved into more sophisticated forms such as images, audio, and other unstructured formats, the database begins to show its weaknesses. | |||
===ERPs=== | ===ERPs=== |
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[[File:|right|300px]] Title: What are the alternatives to a laboratory information management system (LIMS)?
Author for citation: Shawn E. Douglas
License for content: Creative Commons Attribution-ShareAlike 4.0 International
Publication date: December 2023
Introduction
Examples of LIMS alternatives
A laboratory information management system (LIMS) is a modern solution to the increasingly demanding workflow needs of most laboratories, particularly those performing activities in regulated industries. However, laboratory work wasn't always conducted with the help of such a software system, and a LIMS isn't always the answer for a lab looking to manage its workflow and operations. Whether it's the simplicity of the lab's operations or the perceived costs of acquiring, maintaining, and updating a LIMS (or even heavy stakeholder resistance to updating old familiar processes to more modern ones), other alternatives still exist for laboratories, including paper-based systems, spreadsheet software, database software, or enterprise resource planning (ERP) software. This section will briefly discuss those options, while the following section will address their potential deficiencies.
Paper-based systems
Before software-based systems, labs used laboratory notebooks, notepads, Xeroxed report templates, and instrument printouts (granted, a step up from recording from pure observation) to document analytical and research processes. Even in 2023, we still find labs that stick to these older methods, and in some cases, this may still work. These methods are viewed as being low-cost, easily transportable, easy to copy (i.e., back up), relatively flexible to use (e.g., write, draw, chart, etc.), and easy to sign and date.[1] However, paper notes aren't always easy to read, can be readily damaged or destroyed, can be difficult to search, require more manual time-consuming methods, and can be difficult to integrate with other paper-based data and information.[1]
Spreadsheets
As computing technology evolved and became more affordable, software makers had even more incentive to develop relevant and approachable software solutions to solve businesses' workflow challenges. Among these software solutions was the spreadsheet. Derived in concept from the paper-based ledgers accountants and traders would use, the electronic spreadsheet suddenly allowed businesses to perform calculations automatically, saving users time.[2] Laboratories picked up on this electronic, ledger-based approach to documenting experimental results and making routine analytical calculations. However, as labs of all types fell under greater scrutiny from regulators, the spreadsheet method of documentation and calculation of analytical results began to show inefficiencies and inadequacies, including difficulty in preventing changes to fields and maintaining an accurate representation of the who, what, when, and where of recorded values.
Databases
Databases also came into popularity with the advent of computing technology. Tabular and relational representation of data points, with the ability to assign labels to those data points, became useful for the electronic storage and retrieval of all sorts of data.[3] Like the spreadsheet, it's not surprising that some laboratories latched on to the idea of keeping track of experimental, analytical, and quality control data in a database. However, these are best used for structured data, and as electronic type of data and information evolved into more sophisticated forms such as images, audio, and other unstructured formats, the database begins to show its weaknesses.
ERPs
Deficiencies in most LIMS alternatives
To be sure, a LIMS is an investment for any sized laboratory, whether it's almost exclusively monetary (with some other organization doing a majority of the heavy lifting, as with a cloud-based solution) or some combination of monetary and in-house resource expenditure (as with a self-hosted solution located on-premises, whether that solution is a commercial proprietary offering or an open-source offering). Even an open-source LIMS still requires the lab to lean on an employee or third-party consultant to set up, configure, and maintain the software (or even modify the source code), as well as maintain the local IT infrastructure to support it. The open-source route may make sense for small, single labs with a couple of instruments, but the lack of regulatory-driven functionality like an audit trail in all but a few open-source LIMS (e.g., SENAITE[4]) may significantly restrict the available options to such labs.
This brings up the point of what a lab typically sacrifices with LIMS alternatives such as paper notebooks, spreadsheets, and ERP solutions. These alternatives rarely address[5][1][6][7][8][9][10]:
- regulatory need for ensuring analytical results haven't been maliciously or accidentally modified (such as with audit trails that clearly and properly maintain the metadata surrounding an inputted analytical or some other value, as well as any changes made to it);
- regulatory need for clearly and accurately indicating a wide variety of metadata about a given sample or analysis;
- regulatory need for software tools like spreadsheets to be validated;
- regulatory need for ensuring data is contemporaneous (i.e., "current") and not fabricated post-analysis;
- regulatory and internal need for data integrity beyond what audit trails provide;
- regulatory and internal need for maintaining, archiving, and disposing of data and information for a designated period of time, whether it's paper or electronic;
- regulatory and internal need for recorded values to be treated uniformly for all lab operations, using the same units, rounding rules, formulas, limits, etc.;
- regulatory and internal need for security of proprietary lab data and information, including methods, analytical values, and associated reports;
- internal need for accurate and timely analytical results that have been officially validated/approved by one or more individuals (with that validation/approval getting properly documented);
- internal need for timely retrievable of data and information to more readily support decision-making and audits;
- internal needs for more than one user to access, add, and modify lab data and information;
- internal needs for more timely recording of analytical values from instruments; and
- internal needs later on to convert paper-based data and information into structured, readable, and importable electronic formats.
This is not to say that paper-based laboratory notebooks, spreadsheets, and ERPs can't work for small, lightly-regulated laboratories. However, as more laboratory activity across all industries gains more regulatory oversight, and as clientele of said labs increasingly demand more timely, accurate, and defensible analytical results, todays laboratories are under pressure to move beyond little print-out slips from instruments, paper notebooks, controlled worksheets, and non-validated software tools that aren't purpose-built for labs.
Conclusion
References
- ↑ 1.0 1.1 1.2 Liscouski, J. (November 2023). "LII:A Science Student's Guide to Laboratory Informatics". In Douglas, S.E.. LIMSwiki.org. https://www.limswiki.org/index.php/LII:A_Science_Student%27s_Guide_to_Laboratory_Informatics. Retrieved 13 December 2023.
- ↑ Meikle, H.. "The history of spreadsheets". Sheetgo Blog. https://blog.sheetgo.com/spreadsheets-tips/history-of-spreadsheets/. Retrieved 15 December 2023.
- ↑ Fortune, S. (17 December 2020). "A Brief History of Databases". CS403: Introduction to Modern Database Systems. Saylor Academy. https://learn.saylor.org/mod/page/view.php?id=21059. Retrieved 15 December 2023.
- ↑ "SENAITE - Features". SENAITE Foundation. https://www.senaite.com/features. Retrieved 13 December 2023.
- ↑ Liscouski, J. (April 2022). "Improving Lab Systems: From Paper to Spreadsheets to LIMS" (PDF). LabLynx, Inc.. https://www.lablynx.com/wp-content/uploads/2023/03/Improving-Lab-Systems-From-Paper-to-Spreadsheets-to-LIMS.pdf. Retrieved 13 December 2023.
- ↑ Ferrero, M.S. (2007). "Chapter 8: GLP Documentation". In Weinberg, Sandy. Good laboratory practice regulations. Drugs and the pharmaceutical sciences (4th ed ed.). New York: Informa Healthcare. pp. 223–240. ISBN 9780849375842. https://books.google.com/books?id=JR5i0Nz5UWEC&pg=PA233.
- ↑ AlTarawneh, Ghada; Thorne, Simon (2017). "A Pilot Study Exploring Spreadsheet Risk in Scientific Research". arXiv. doi:10.48550/ARXIV.1703.09785. https://arxiv.org/abs/1703.09785.
- ↑ McDowall, R.D. (2020). "Are Spreadsheets a Fast Track to Regulatory Non-Compliance?". LCGC Europe 33 (9): 468–76. https://www.chromatographyonline.com/view/are-spreadsheets-a-fast-track-to-regulatory-non-compliance. Retrieved 13 December 2023.
- ↑ Ma, Ming-Wei; Gao, Xian-Shu; Zhang, Ze-Yu; Shang, Shi-Yu; Jin, Ling; Liu, Pei-Lin; Lv, Feng; Ni, Wei et al. (6 November 2023). "Extracting laboratory test information from paper-based reports" (in en). BMC Medical Informatics and Decision Making 23 (1): 251. doi:10.1186/s12911-023-02346-6. ISSN 1472-6947. PMC PMC10629084. PMID 37932733. https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-023-02346-6.
- ↑ McDowall. R.D., "Chapter 13. Get Rid of Paper: Why Electronic Processes are Better for Data Integrity" (in en), Data Integrity and Data Governance (Cambridge: Royal Society of Chemistry): 281–304, doi:10.1039/9781788013277-00281, ISBN 978-1-78801-281-2, http://ebook.rsc.org/?DOI=10.1039/9781788013277-00281