20: Microbiology Laboratory Information Systems

Microbiology Laboratory Information Systems

Raymond D. Aller and Vincent Salazar

Department of Pathology, University of Southern California Keck School of Medicine, Los Angeles, CA, USA

A microbiology information system is focused on the particular information processing needs of the clinical microbiology laboratory, and should be distinguished from a general laboratory information system, which has been configured to attempt to provide functionality to support the operation of a microbiology section. However, in some cases the designers of a general clinical laboratory information system (LIS) have undertaken a separate design effort to create a comprehensive microbiology system. Our understanding of microbiology laboratory information systems has continued to evolve since the previous edition of this book [13].

20.1 In general, microbiology laboratory information systems fit one of three categories

  1. Complete microbiology LISs provide the entire range of functionality needed in a clinical microbiology laboratory from accessioning, receiving, and processing to reporting of specimens for direct examinations and cultures. It includes clinical workup and reporting as well as management analysis such as quality control and quality assurance monitoring.
  2. Instrument management and middleware modules focus on connectivity between analytical instruments and a general laboratory information system. Typically, these provide additional capabilities in the form of decision rules, report formulation, and many other features that a general laboratory information system may lack.
  3. Specialized modules for a broad spectrum of particular tasks. Examples include infection control management and more advanced “business intelligence” management reports. These are in addition to reporting, which is standard in a comprehensive microbiology system, such as antibiogram and trend analysis reports.

In any consideration of microbiology information systems, it is important to recognize how the data generated and used in microbiology differ from other laboratory disciplines such as chemistry and hematology. We list here a few contributing factors. There are doubtless others:

  1. A very high level of technical skill is required to perform bacteriology, mycology, mycobacteriology, parasitology, and virology. A huge knowledge base is required to transform raw observations in the microscope and on the culture plate into putative organism identifications.
  2. Microbes evolve, while chemistries do not. Once rules are established to accept and validate glucose or creatinine results, those remain very stable over years, but knowledge around microorganisms changes. For example, the FDA (Food and Drug Administration) updates recommended antimicrobial susceptibility testing breakpoints for the commercial assays it approves and reviews. In addition, the CLSI (Clinical and Laboratory Standards Institute) annually publishes new criteria for what levels of antibiotic response constitute sensitive, intermediate, and resistant organisms. An issue arises when CLSI differs from FDA breakpoints. Information system decision tables must be updated for each of these, each time new criteria are published.
  3. Timing: most chemistries produce results in a couple of hours. A microbiology culture may “hang around” for days, or even weeks. While a culture is being monitored, there is often more than one stage of reporting (preliminary, final). Therefore, the information system must have multiple “slots” into which to insert results, and the significance of those “slots” (preliminary, final) must be clearly distinguished.
  4. Textual versus numeric: chemistry and hematology values are rather standard from one laboratory to the next, and follow well defined rules. Microbiology organisms, on the other hand, are represented with idiosyncratic abbreviations, and these textual codes will often differ between laboratories even within the same health system. Microbiology results take a variable number of characters. Often, a block of text commentary may be included.
  5. Clusters of data are linked together in structured ways from specimen type, to culture focus (e.g., aerobic vs. viral), to colony count, to microorganisms, and to sets of susceptibilities.
  6. There is a hierarchical arrangement of results rather than a simple linear listing of values (as you would have in a chemistry panel).

    1. The overall specimen is at the top level.
    2. For each specimen/source, there may be a series of specific types of cultures (aerobic, anaerobic, fungal, acid-fast bacteria (AFB), viral).
    3. For each culture there are a series of observations, which can include direct examinations or smear results (i.e. Gram stain, Ziehl Nielsen, or other stain pertinent to the culture type), plate observations (colony count and colony morphology), and isolated organisms.
    4. Identification tests for those organisms.
    5. Susceptibility testing of an organism to various antibiotics.

  7. Need to aggregate data across microorganisms, and across clusters over time.

    1. Tracking the same organism across multiple specimens from the same patient helps assess the clinical significance of those isolates.
    2. If multiple bacteria are of concern, a collation of the susceptibility pattern for all of them is needed to select an antibiotic that will be active against all.
    3. Tracking the same organism across multiple patients is essential for infection control systems.

  8. Many specialized rules must be applied. Certain combinations of organism identification and susceptibility dictate a changed interpretation. For example, if an organism is resistant to certain antibiotics, then certain antibiotics that appear effective may be contraindicated.
  9. Limits on frequency of repeating certain assays: some laboratory assays are quite expensive, and are needed clinically only at a certain frequency. For example, it would be a waste of resources to perform an HIV viral load daily. A set of maximum ordering frequencies should be built into the order entry subsystem.
  10. What constitutes an “abnormal” result as we feed information to the electronic health record (EHR)? This has been an issue, also, as we send results to the public health surveillance system. Typically, microbiology systems have not marked results as “normal” or “abnormal”. As long as only humans were reviewing and acting on the results, this worked fine. But in the age of automated case triage, we need to build in algorithms that will classify the typical textual results into “review not needed” and “need to review” buckets (if one prefers to avoid the “abnormal” terminology). Further, any textual result that the algorithm does not recognize should be routed to the “need to review” category. Furthermore, the microbiology section is often responsible for performing and reporting immunology and serology assays. Since the characteristics of these test results are more akin to chemistry and hematology, it may be advisable to use the “general laboratory” section of the LIS. However, this requires that microbiology staff be comfortable in rather different segments of the LIS.
  11. Other laboratory sections have a finite list of possible outcomes versus the almost infinite scope of Bergey’s Manual for microbiology results. This implies that the automated rules will handle the common cases, and the cases not recognized would be flagged. Not only is there a wide scope of names, but also the taxonomists frequently change the name of common (and rare) bacteria and other microorganisms. While this may make good sense for molecular genetic purists, it can wreak havoc in a system that is not configured to deal with three or four differently named organisms as equivalent entities. Of course, this also adds complications to reporting of cultures, as we try to explain to our clinician colleagues that this is not a new, unfamiliar organism, but rather one that they know well, but under a different name.

20.2 What are the key features of software to support management of microbiology?

Systems must produce and handle the clustered data described above. Even if a comprehensive system has been designed, it will only evolve to usability by implementing a prototype in a real laboratory, and then incrementally improve the ability of that prototype to effectively handle microbiology data. The concept of developing theoretical specifications, writing a final system to those specifications, then implementing is ludicrous.

20.2.1 Reflexive action based on results

A variety of regulatory and accrediting bodies have promulgated regulations and guidelines for microbiology practice to include reflexive action based on results (Logical Progression, Susceptibility Rules). These expectations should be built into the logic of the microbiology information system. Principal among these are the CLSI, the FDA, and the College of American Pathologists (CAP). The software should immediately alert technologists, management, and/or treating clinicians to sentinel circumstances (e.g., methicillin resistant Staphylococcus aureus, or MRSA). These may require patient isolation, special antibiotic cocktails, or other uniquely designed interventions.

20.2.2 Quality control management

There is an extensive array of quality control (QC) checks inherent in running a microbiology laboratory, from culture media sterility and growth promotion to incubator temperatures and its atmospheric conditions. While it is possible to monitor and follow these with paper records, it is far more efficient and complete to rely on your microbiology laboratory information system to monitor and track these results, and remind the manager/supervisor if a required QC check has been missed.

20.2.3 Inventory management

Given the myriad of diverse supplies and reagents required in a complex microbiology laboratory, it is helpful for your primary LIS to assist in tracking usage and stocks of these supplies, and to ensure that they are re-ordered in plenty of time.

20.2.4 Report Module

The Report Module may include ad hoc or user-specified database queries, antibiograms, contamination rates, among others. After patient reports have been issued, there are many other important applications of the data.

  1. What is the pattern of antimicrobial resistance as a guide to empiric antibiotic treatment and also to detect changes (such as emergence of resistant strains)?
  2. How often are cultures contaminated? For blood cultures, is there a pattern of contamination that might be improved by retraining certain groups of staff collecting these cultures?

A typical system will incorporate dozens of such special reports. In addition, management and medical staff will ask other questions about microorganism patterns, and these can be addressed by “ad hoc” or “report writer” functions asking arbitrary questions of the database.

20.2.5 Instruments

What types of analytical instruments are key? We will not attempt to duplicate the extensive coverage elsewhere in this book of the variety of new instruments and measurement technologies. For each instrument, a method must be devised to convey the results produced by that instrument (identified by a specimen number) into the microbiology information system. Often, the results will be more than a short text field. Automated plate readers will send images. Susceptibility testing devices will transmit arrays of antimicrobial results. Genetic-based assays may send tens of thousands of base pairs per organism. With the growing array of instruments being developed for the clinical microbiology laboratory, which may have capabilities from specimen plating to organism identification and antibiotic testing, it will become even more important to optimally interface these instruments to the LIS.

The types of instrumentation to be considered include (but are not limited to):

  1. microbial identification

    1. biochemical based
    2. newer technologies, such as mass spectrometry, MALDI-ToF, and others
    3. genetic markers and sequencing;

  2. susceptibility testing

    1. traditional
    2. genetic;

  3. plate inoculators/streakers;
  4. automated plate readers;
  5. automated growth and detection systems (Bactec, BacT/ALERT, MGIT, TREK, and others);
  6. total laboratory automation systems – incorporating automated plate transporters, automated plate streakers, and automated incubators.

20.2.6 Interfaces and additional features

The laboratorian must learn from their colleagues in other organizations to beware of the caveats and lessons learned in connecting a microbiology information system with the “typical” hospital information system (HIS). Often the HIS or the EHR does not have internal structure to properly store microbiology results. They only know how to store short phrases. Therefore, it is common for microbiology laboratory information systems (MLIS) to formulate a page image report, and transmit that to the HIS, which then displays it as such. This is known as a “text blob”. The advantage of a page image is that HIS/EHR does not garble display. On the other hand, the downside of a page image is that it obscures granular data on the MLIS report (e.g., susceptibility results) that cannot be trended, or individually analyzed (“sliced and diced”) by applications on the HIS/EHR.

In site visits to health systems that implemented the “text blob” microbiology interface, this inability to trigger on individual microbiology data was a major source of dissatisfaction. Instead, use of the standard HL7 2.5.1 OBR/OBX structure more effectively represents individual microbiology results. The HL7 is the industry standard tool for communication between medical information systems, and has been implemented, worldwide, by essentially every developer of clinical systems. Implementation guides developed by the CDC make use of HL7 2.5.1 for transmission of communicable disease data. The Meaningful Use regulations, as published by the US Federal Office of the National Coordinator for Health Information Technology, specify use of HL7 2.5.1 in order to qualify for Meaningful Use incentive payments. When the implementers have followed the rules, HL7 standards have been highly successful in conveying discrete results to the HIS. However, this does imply that the HIS/EHR have internal programming to display the discrete results to the clinician in a meaningful way. For more complex report types, and if the EHR has shown its inability to display properly formatted reports, we advocate sending both page image reports (for clinicians review) and fielded detail data (for trending and granular data analysis). For transmission of page image reports, the most widely used mechanism has been to use PDF (portable document format), although some systems can now support HL7 CDA (clinical document architecture).

Whenever fielded data are transmitted, it is important to represent test names, and organism identifications, using standard LOINC (logical observation identifier names and codes) and SNOMED (systematized nomenclature of medicine) codes. In general, LOINC is used to identify what test was run (e.g., a viral culture, or imipenem susceptibility), while SNOMED is used to provide organism identification (e.g., S. aureus) and in some cases specimen identification (sputum, urine). Use of LOINC and SNOMED is not only responsible laboratory practice, but it is now required in US Federal Meaningful Use regulations.

20.3 Microbiology information systems have evolved over several decades

Stay updated, free articles. Join our Telegram channel

Dec 10, 2017 | Posted by in MICROBIOLOGY | Comments Off on 20: Microbiology Laboratory Information Systems

Full access? Get Clinical Tree

Get Clinical Tree app for offline access