Clinical microbiology informatics

Abstract

The clinical microbiology laboratory has responsibilities ranging from characterizing the causative agent in a patient's infection to helping detect global disease outbreaks. All of these processes are increasingly becoming partnered more intimately with informatics. Effective application of informatics tools can increase the accuracy, timeliness, and completeness of microbiology testing while decreasing the laboratory workload, which can lead to optimized laboratory workflow and decreased costs. Informatics is poised to be increasingly relevant in clinical microbiology, with the advent of total laboratory automation, complex instrument interfaces, electronic health records, clinical decision support tools, and the clinical implementation of microbial genome sequencing. This review discusses the diverse informatics aspects that are relevant to the clinical microbiology laboratory, including the following: the microbiology laboratory information system, decision support tools, expert systems, instrument interfaces, total laboratory automation, telemicrobiology, automated image analysis, nucleic acid sequence databases, electronic reporting of infectious agents to public health agencies, and disease outbreak surveillance. The breadth and utility of informatics tools used in clinical microbiology have made them indispensable to contemporary clinical and laboratory practice. Continued advances in technology and development of these informatics tools will further improve patient and public health care in the future.

FIG 1

Image demonstrating the increased information density that can be obtained using 2-dimensional (2-D) bar codes over 1-dimensional (1-D) bar codes and demonstrating that minor damage to a 2-D bar code can be compensated for by the remaining portion of the bar code. Bar codes use solid lines (1-D bar codes) or blocks (2-D bar codes) in combination with intervening spaces to encode data, which can be translated to text via a bar code scanner and its software. The words “Staphylococcus aureus” are depicted in a 1-D bar code using Code 128 symbology (A) and a 2-D bar code using DataMatrix symbology (B). These symbologies are commonly used to label specimens in clinical laboratories, although numerous bar code formats are available. 2-D bar codes are becoming the preferred symbology because of their smaller footprint and robust error correction. For example, even if part of the bar code is slightly damaged (C), the integrity of the information remains intact and can be read accurately. Bar codes can also be used to enter microbiology results or comments into an LIS, and the use of bar codes can help to decrease typographical errors and standardize results reporting.

FIG 2

Schematic representation of an expert system. An expert system is a form of artificial intelligence that allows a user to use software rules (inference engine) together with a knowledge database to make a conclusion (output) about an input.

FIG 3

Anonymized HL7 message, which would be sent from an LIS to an HIS. Information technology staff often work with this type of message, but clinical microbiologists are seldom exposed to the HL7 messaging format. It is helpful for clinical microbiologists to know what an HL7 message might look like, so they can communicate more effectively with information technology staff. The depicted example relays the positive result of a Chlamydia trachomatis test that was determined using a Tigris DTS instrument (Hologic Gen-Probe, Inc., San Diego, CA). The test that was ordered and performed is represented by an LOINC code within the message, i.e., 21190-4 (bolded and underlined for emphasis). A SNOMED code within the message, i.e., G-A200 (bolded and double underlined for emphasis), indicates that the result is positive. Information in the message is divided into sections which include patient identifying information (PID), information about where the order originated (ORC or “common order”), information about the test that was ordered (OBR or “observation request”), and information about results and the reporting of the results (OBX or “observation of results”).

FIG 4

Proposed informatics pipeline for microbial genomic analysis in the clinical microbiology laboratory. The clinical purposes of genomic sequencing can be varied, and multiple parallel pipelines and their associated expert systems can be used to analyze data depending on which endpoints are desired. One goal of the microbiology laboratory is to unite the information obtained from these various pipelines into a clinically relevant and concise report that can guide patient care. A second goal is to characterize a potential agent of an emerging outbreak so that future samples can be monitored for this agent of interest. The genomic characterization of this outbreak agent can be shared with public health agencies, which can also alert other laboratories to monitor their samples for this emerging agent. Solid arrows show the flow of analysis. Dotted arrows show the transfer of information about outbreak agents into expert system knowledge databases. Wet laboratory analysis is shown in red, and dry laboratory analysis is shown in blue. Information available to clinicians is shown in yellow. Asterisks mark data that may be appropriate for long-term storage.

FIG 5

Agents of notifiable infectious diseases and their associated data often travel through layers of agencies, including clinics, laboratories, and public health registries. This generic figure depicts the flow of information associated with a patient diagnosed with gastroenteritis who is tested for Salmonella. Although human input or interpretation may be required at various nodes, information is often generated, transmitted, and received digitally (solid arrows). Various terminology and messaging standards, such as SNOMED, LOINC, and HL7, are employed to ensure the standardization, efficiency, and security of the process. However, many clinical laboratories still use paper to transmit information. The more often information is exchanged between two nodes, the more incentive exists to create a robust electronic interface between these nodes. Therefore, transmitting information by paper (via fax or post) is most likely to be used between nodes that rarely communicate with each other. Electronic reporting can expedite information communication and therefore expedite the detection of outbreak clusters. Information between the patient and clinician is often exchanged verbally (dotted arrows), although electronic communication with patients is likely to become more common. (Courtesy of The Royal College of Pathologists of Australasia, adapted with permission.)