What are the key elements of a LIMS for medical microbiology? | LabLynx Resources

What are the key elements of a LIMS for medical microbiology?

This brief topical article will examine the informatics needs of the medical microbiology lab, specifically addressing the base set of laboratory information management system (LIMS) or laboratory information system (LIS) functionality (i.e., system requirements) that is critical to fulfilling the information management and workflow requirements of this type of lab. (Going forward, for simplicity, this article will discuss these requirements largely in the scope of a LIMS; however, note that an LIS is equally viable here.) Additional unique requirements will also be briefly discussed.

The medical microbiology laboratory is much like other medical laboratories with its patient- or population-driven focus. However, it has a variety of workflow requirements that manage to separate its needs from most of its biomedical peers. From the necessary creation of derivative specimens to having specific public health reporting requirements, these labs’ workflows have their own unique needs, further affected by a rapidly changing technological and employment landscape.

Base LIMS requirements for medical microbiology labs

A medical microbiology laboratory helps examine and identify microorganisms for both patient treatment and disease prevention and control. Like other biomedical labs, medical microbiology labs increasingly require an informatics solution that meets all or most of its workflow requirements. These requirements are often driven by standardized test methods for the isolation and characterization of microorganisms, which are in turn driven by regulations and accreditation requirements affecting this important societal role. This requires a pre-configured and future-configurable solution like a LIMS that enables medical microbiology personnel to quickly select and use standardized test methods and forms, and make the changes they need to those methods and forms if those changes make sense within the overall data structure of the LIMS.

What follows is a list of fundamental LIMS functionality important to most any medical microbiology laboratory, with a majority of that functionality found in many vendor software solutions.[1][2][3][4][5][6][7][8][9][10][11][12]

Test, specimen, and result management

  • Specimen accessioning and management, with support for unique IDs
  • Specimen batching
  • Barcode, RFID, and label support
  • End-to-end specimen, aliquot, and other inventory tracking
  • Pre-defined and configurable industry-specific test and method management for a variety of tests, including susceptibility, reflex, and molecular testing
  • Pre-defined and configurable industry-specific workflows and worksheets
  • Configurable screens and data fields
  • Specimen collection, testing, instrument, etc. scheduling and assignment
  • Test requesting
  • Data import, export, and archiving
  • Robust query tools
  • Analytical tools, including data visualization, statistical analysis, and data mining tools
  • Document and image management
  • Patient management, including infection history
  • Facility and specimen collection site management
  • Storage management and monitoring

Quality, security, and compliance

  • Quality assurance / quality control mechanisms
  • Mechanisms for compliance with ISO 15189, ASTM, A2LA, CLIA, good clinical practice (GCP), and other standards, requirements, and guidelines
  • Result, method, and batch validation, review, and release
  • Data validation
  • Trend and control charting for statistical analysis and measurement of uncertainty
  • User qualification, performance, and training management
  • Audit trails and chain of custody support
  • Configurable and granular role-based security
  • Configurable system access and use (i.e., authentication requirements, account usage rules, account locking, etc.)
  • Electronic signature support
  • Data encryption and secure communication protocols
  • Archiving and retention of data and information
  • Configurable data backups
  • Status updates and alerts
  • Incident and non-conformance notification, tracking, and management

Operations management and reporting

  • Configurable dashboards for monitoring results and reporting
  • Customizable rich-text reporting, with multiple supported output formats
  • Custom and industry-specific reporting, including for specialized testing
  • Email integration
  • Bi-directional instrument interfacing and data management
  • Third-party software interfacing (e.g., hospital information systems (HIS), other databases)
  • Data import, export, and archiving
  • Instrument calibration and maintenance tracking
  • Inventory and supply management
  • Supplier/vendor/customer management
  • Patient/provider portal

LIMS for Microbiology Lab

Specialty LIMS requirements

Some laboratory informatics software vendors are addressing medical microbiology laboratories’ needs beyond the features of a basic all-purpose LIMS. A standard LIMS tailored for medical microbiology may already contribute to some of these wider organizational functions, as well as more advanced laboratory workflow requirements, but many may not, or may vary in what additional functionality they provide. Some vendor solutions may have been tailored with the direct input of microbiologists who’ve been in the field for decades. In that regard, a medical microbiology LIMS vendor may also include specialized functionality that assists these labs with their unique tasks. This includes the provision of:

  • Derivative asset management, linking, and tracking: Unlike many other labs in the biomedical sciences, a medical microbiology lab will end up creating (e.g., via cell culture) multiple derivative assets from a single accessioned specimen. For example, a specimen suspected of polymicrobial infection may require derivative specimens representing “aerobic bacteria, anaerobic bacteria, mycobacteria, and/or fungi, and all of these need to be linked to the original accession number.”[1] Having sufficient LIMS tools to improve management of cultures and other derivatives is thus quite useful. As Rhoads et al. note, “properly handling the electronic information associated with a sample, such as tracking its derivatives, modifying descriptions of its derivatives, and linking its derivatives with their accession number, is a unique and essential aspect of the microbiology LIS.”[1]
  • Support for notations on primary and derivative assets, as well as other entities: Given the above about primary specimens and the culturing of derivatives, it’s vital that careful note-taking is performed at the various stages of analysis and interpretation by microbiologists. This electronic note-taking—in the past performed on physical note cards[1]—in turn can improve quality and patient outcomes. As such, many informatics systems will provide note-taking functions at granular levels for a variety of entities in the system.[1]
  • Robust support for interfacing with a vast variety of systems and instruments: While system and instrument interfacing is largely de facto required out of most any LIMS, this interfacing is essential to and more complex for a majority of medical microbiology labs. Communication between the LIMS and any HIS, for example, must be unhindered and clear such that analytical and interpretive orders placed in the HIS make their way to the LIMS, and results from the lab are readily transferred back to the HIS in a standardized and readable format. This interfacing must be verified periodically to ensure high levels of quality and patient outcomes. As such, the microbiology LIMS must have robust interfacing support using standardized protocols that ensure clear and rapid bidirectional communication between other informatics systems and laboratory instruments.[1] This includes total laboratory automation (TLA) instruments and systems, which have gradually become more viable for the microbiology lab.[1][5][8][13] Additionally, as the early promise of automated microbiology image analysis[1] has progressed to full-fledged systems with artificial intelligence (AI) components[5][14][15], the modern microbiology LIMS need to effectively interface with these systems too. Finally, connections to a wide variety of third-party databases used for identification and surveillance of microorganisms and their resistances, including WHONET[4][10], are important and must be addressed by any microbiology LIMS.[1]
  • More robust, standardized, optimized, and automated result entry and reporting: Microbiology labs in particular have multiple requirements and methods for reporting analytical and interpretive results, compared to other clinical laboratory disciplines, which usually report in a largely quantitative way. The microbiology lab will need to report and interpret complex qualitative, semi-quantitative, and quantitative data and information, using long and repetitive text strings (e.g., Staphylococcus epidermidis) in both preliminary and final results. Ensuring ease-of-use with keyboard shortcuts for long, repetitive text strings, while also ensuring succinct, standardized terminology (e.g., using a common internationally accepted data model[16]) and clear and accurate test results or interpretations is imperative. A LIMS can apply a more “synoptic” approach to reporting typically found with surgical pathology, supporting highly configurable layouts, the enforcement of standardized nomenclature, and appropriate result highlighting mechanisms to ensure more confident report interpretation by both microbiologists and treating physicians.[1]
  • Analytical and reporting support for susceptibility testing and antibiograms: An antibiogram is a cumulative summary or “overall profile of [in vitro] susceptibility testing results for a specific microorganism to an array of antimicrobial drugs,” often given in a tabular form.[17] There are multiple approaches to antibiograms for a wide variety of susceptibility testing, common to microbiology labs.[18] While it’s not common for a LIMS or LIS to have extensive data analysis capabilities, some may support the sometimes complex work of generating antibiograms.[1][6][7] At a minimum, the LIMS should have robust reporting capabilities able to handle the nuances of susceptibility testing and antibiograms, particularly to the standard CLSI M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data.[1][19] Ideally, the LIMS can even run the analyses themselves, pulling data from the LIMS and HIS, but again this may not be common.
  • Support for flexible public health reporting: In addition to supporting susceptibility testing and antibiogram analysis and reporting, the medical microbiology LIMS will also need to provide flexible reporting capabilities to allow such labs to meet public health reporting requirements. As Rhoads et al. note, “the reportable findings are somewhat dynamic, often with annual modifications, so informatics support requires ongoing vigilance to keep up with expectations of public health authorities,” including “efficiently communicating with public health agencies and rapidly identifying outbreaks.”[1] In the U.S., this means microbiology information systems like LIMS need to be National Notifiable Diseases Surveillance System (NNDSS)-compatible.[20] The COVID-19 pandemic, however, highlighted that standards-based international collaboration from and interoperability with these and other systems is vital to public health scenarios beyond a country’s borders. This means the LIMS must support internationally-recognized terminologies such as LOINC, SNOMED CT, and UCUM for reporting and sharing data within and beyond national borders.[16]
  • Decision support systems for medical microbiology: Just as there are decision support systems (DSS) built into some LIMS that can guide pharmacogenomics (PGx) decisions made with the aid of molecular diagnostics testing[21], a handful of LIMS or HIS options may incorporate DSS to better guide microbiology-based decisions on antibiotic prescription.[1][22] (DSS has already been used for other purposes in LIMS.[23][24]) Those DSS may be guided with the help of supervised machine learning (ML) algorithms or other AI components.[22] However, such systems have their own acquisition and implementation costs, and maintaining the DSS with relevant data at regular intervals to ensure the DSS’ usefulness adds additional costs the microbiology lab must weigh.[1]

Conclusion

The medical microbiology laboratory is similar to other clinical labs in its patient- and population-driven focus. However, the work of analyzing and identifying microorganisms in specimens is anything but straightforward. With its unique workflows and public health duties, we find this lab has a sometimes significantly different workflow than an ordinary clinical diagnostics laboratory. In a rapidly changing technological and microbiological environment, compounded by economic and employee realities, an informatics system like a LIMS or LIS can help do a lot of the heavy lifting and streamline medical microbiology operations. This article sought to identify the key elements of these software solutions as they relate to the medical microbiology lab, addressing both fundamental functionality and specialty functionality uniquely developed for such labs. This includes the important workflow management aspects of preparing and tracking derivative specimens like cell cultures, as well a variety of interfacing and reporting tools in high demand for microbiology operations.

References

  1. Rhoads, Daniel D.; Sintchenko, Vitali; Rauch, Carol A.; Pantanowitz, Liron (1 October 2014). “Clinical Microbiology Informatics” (in en). Clinical Microbiology Reviews 27 (4): 1025–1047. doi:10.1128/CMR.00049-14. ISSN 0893-8512. PMC PMC4187636. PMID 25278581. https://journals.asm.org/doi/10.1128/CMR.00049-14
  2. Aller, Raymond D.; Salazar, Vincent (30 May 2016), Truant, Allan L.; Tang, Yi‐Wei; Waites, Ken B. et al.., eds., “Microbiology Laboratory Information Systems” (in en), Manual of Commercial Methods in Clinical Microbiology (Wiley): 377–385, doi:10.1002/9781119021872.ch20, ISBN 978-1-118-13112-1. https://onlinelibrary.wiley.com/doi/10.1002/9781119021872.ch20
  3. Carey, Roberta B.; Bhattacharyya, Sanjib; Kehl, Sue C.; Matukas, Larissa M.; Pentella, Michael A.; Salfinger, Max; Schuetz, Audrey N. (1 July 2018). “Practical Guidance for Clinical Microbiology Laboratories: Implementing a Quality Management System in the Medical Microbiology Laboratory” (in en). Clinical Microbiology Reviews 31 (3): e00062–17. doi:10.1128/CMR.00062-17. ISSN 0893-8512. PMC PMC6056841. PMID 29720490. https://journals.asm.org/doi/10.1128/CMR.00062-17
  4. Turner, P.; Rupali, P.; Opintan, J.A. et al. (12 September 2020). “Laboratory informatics capacity is a neglected component of effective antimicrobial resistance surveillance in resource-limited settings”. Oxford University Research Archive. Oxford University. Retrieved 17 April 2024. https://ora.ox.ac.uk/objects/uuid:fd143935-d2c1-459c-bdd7-623001113037/files/rnv9353211
  5. Vandenberg, Olivier; Durand, Géraldine; Hallin, Marie; Diefenbach, Andreas; Gant, Vanya; Murray, Patrick; Kozlakidis, Zisis; van Belkum, Alex (18 March 2020). “Consolidation of Clinical Microbiology Laboratories and Introduction of Transformative Technologies” (in en). Clinical Microbiology Reviews 33 (2): e00057–19. doi:10.1128/CMR.00057-19. ISSN 0893-8512. PMC PMC7048017. PMID 32102900. https://journals.asm.org/doi/10.1128/CMR.00057-19
  6. “Video tutorials – Microbiology Results (Antibiogram)”. SLCLAB Informática SL. 2023. Retrieved 17 April 2024. https://slclab.com/en/videos-en.aspx
  7. “Molecular ID LIS Solution”. BGASoft, Inc. 2023. Retrieved 17 April 2024. https://www.limsabc.com/molecular-id/
  8. Futrell, K. (5 December 2023). “Address Lab Staffing Shortages with Integrated Automation Solutions – White Paper”. Orchard Software. Retrieved 17 April 2024. https://www.orchardsoft.com/white_paper/automation-contributes-to-laboratory-efficiency/
  9. Charles River Microbial Solutions (3 September 2021). “Data Integrity in the Microbiology Lab”. Rapid Microbiology. Rapid Test Methods Ltd. Retrieved 17 April 2024. https://www.rapidmicrobiology.com/news/data-integrity-in-the-microbiology-lab
  10. Arcta Solutions (13 July 2023). “SEDRI LIMS – A Microbiology Focused Open-Source LIMS” (PDF). Arcta Solutions. Retrieved 17 April 2024. https://www.paho.org/sites/default/files/2023-10/p15-20223-cde-relavra-d2-sesion-3-sedri-lims-experiencia-desarrollador-matthew-king.pdf
  11. “Leeuwenbook – Portable Microbiology Laboratory Solution”. Corpex. Retrieved 17 April 2024. https://www.corpex.info/leeuwenbook.html
  12. “TD NexLabs Microbiology” (PDF). Technidata SAS. February 2018. Retrieved 17 April 2024. https://www.lig-systems.ch/wp-content/uploads/2018/02/GB-MKT555_flyer_TDNexLabs_Microbiology_EN.pdf
  13. Antonios, Kritikos; Croxatto, Antony; Culbreath, Karissa (30 December 2021). “Current State of Laboratory Automation in Clinical Microbiology Laboratory” (in en). Clinical Chemistry 68 (1): 99–114. doi:10.1093/clinchem/hvab242. ISSN 0009-9147. https://academic.oup.com/clinchem/article/68/1/99/6490228
  14. Sandle, T. (22 December 2021). “Enhancing rapid microbiology methods: how AI is shaping microbiology”. European Pharmaceutical Review. Retrieved 17 April 2024. https://www.europeanpharmaceuticalreview.com/article/166302/enhancing-rapid-microbiology-methods-how-ai-is-shaping-microbiology/
  15. “BIOMIC V3”. Giles Scientific, Inc. 2024. Retrieved 17 April 2024. https://www.biomic.com/biomic-v3.html
  16. Rinaldi, Eugenia; Drenkhahn, Cora; Gebel, Benjamin; Saleh, Kutaiba; Tönnies, Hauke; von Loewenich, Friederike D.; Thoma, Norbert; Baier, Claas et al. (23 September 2023). “Towards interoperability in infection control: a standard data model for microbiology” (in en). Scientific Data 10 (1): 654. doi:10.1038/s41597-023-02560-x. ISSN 2052-4463. PMC PMC10517923. PMID 37741862. https://www.nature.com/articles/s41597-023-02560-x
  17. Antimicrobial Resistance and Stewardship Initiative, University of Minnesota (February 2020). “How to Use a Clinical Antibiogram” (PDF). Retrieved 17 April 2024. https://arsi.umn.edu/sites/arsi.umn.edu/files/2020-02/How_to_Use_a_Clinical_Antibiogram_26Feb2020_Final.pdf
  18. Gajic, Ina; Kabic, Jovana; Kekic, Dusan; Jovicevic, Milos; Milenkovic, Marina; Mitic Culafic, Dragana; Trudic, Anika; Ranin, Lazar et al. (23 March 2022). “Antimicrobial Susceptibility Testing: A Comprehensive Review of Currently Used Methods” (in en). Antibiotics 11 (4): 427. doi:10.3390/antibiotics11040427. ISSN 2079-6382. PMC PMC9024665. PMID 35453179. https://www.mdpi.com/2079-6382/11/4/427
  19. Simner, Patricia J.; Hindler, Janet A.; Bhowmick, Tanaya; Das, Sanchita; Johnson, J. Kristie; Lubers, Brian V.; Redell, Mark A.; Stelling, John et al. (19 October 2022). Humphries, Romney M.. ed. “What’s New in Antibiograms? Updating CLSI M39 Guidance with Current Trends” (in en). Journal of Clinical Microbiology 60 (10): e02210–21. doi:10.1128/jcm.02210-21. ISSN 0095-1137. PMC PMC9580356. PMID 35916520. https://journals.asm.org/doi/10.1128/jcm.02210-21
  20. “Integrated Surveillance Information Systems/NEDSS”. Centers for Disease Control and Prevention. Retrieved 17 April 2024. https://www.cdc.gov/nndss/about/nedss.html
  21. “Redefining LIS”. Emgenex, Inc. 2024. Retrieved 17 April 2024. https://www.emgenex.com/lis
  22. Egli, A.; Schrenzel, J.; Greub, G. (1 October 2020). “Digital microbiology” (in en). Clinical Microbiology and Infection 26 (10): 1324–1331. doi:10.1016/j.cmi.2020.06.023. PMC PMC7320868. PMID 32603804. https://linkinghub.elsevier.com/retrieve/pii/S1198743X20303670
  23. Díaz-Gimenez, Macarena; Carratala Calvo, Arturo; Vinyals-Bellido, Inmaculada; Corchon-Peyrallo, Africa; Hervas-Romero, Ausias; Pozo-Giraldez, Adela; Rodriguez-Borja, Enrique (15 June 2021). “Decision support system through automatic algorithms and electronic request in diagnosis of anaemia for primary care patients”. Biochemia medica 31 (2): 250–257. doi:10.11613/BM.2021.020702. PMC PMC8047786. PMID 33927552. https://www.biochemia-medica.com/en/journal/31/2/10.11613/BM.2021.020702
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