clinical microbiology informatics · the clinical microbiology laboratory, allowing the lab to do...

Clinical Microbiology Informatics

Daniel D. Rhoads,a Vitali Sintchenko,b,c Carol A. Rauch,d Liron Pantanowitza

Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USAa; Marie Bashir Institute for Infectious Diseases and Biosecurity andSydney Medical School, The University of Sydney, Sydney, New South Wales, Australiab; Centre for Infectious Diseases and Microbiology-Public Health, Institute of ClinicalPathology and Medical Research, Westmead Hospital, Sydney, New South Wales, Australiac; Department of Pathology, Microbiology and Immunology, VanderbiltUniversity School of Medicine, Nashville, Tennessee, USAd

SUMMARY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026UNIQUE FEATURES OF THE MICROBIOLOGY LABORATORY INFORMATION SYSTEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026

Multiple-Derivative Tracking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1026Laboratory Electronic Notes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027Results Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027

Informatics tools that facilitate standardized results entry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1027Caveats to consider in reporting preliminary and final results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028

Quality Assurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028TOOLS TO FACILITATE APPROPRIATE ANTIMICROBIAL THERAPY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028

The Antibiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1028Advanced Clinical Decision Support Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029Postdischarge Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029Preventing Release of Inappropriate Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029

EXPERT SYSTEMS IN THE LABORATORY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029The Need for Expert Systems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1029The Definition of an Expert System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030Utility of Expert Systems in Antimicrobial Susceptibility Testing and Reporting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030Expert Systems as Alert Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1030Expert Systems as Data Analysis Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031The Future of Expert Systems in the Microbiology Laboratory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031

INSTRUMENT INTERFACES WITH THE LIS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031Interface Commonalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031Interfaces of Common Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031

BD instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1031bioMeriéux instruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032Siemens MicroScan WalkAway . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032

Molecular Testing Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1032Instrument Interfaces of the Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033

TOTAL LABORATORY AUTOMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033Informatics Challenges in TLA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1033

TELEMICROBIOLOGY AND AUTOMATED DIGITAL IMAGE ANALYSIS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034Telemicrobiology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034Automated Digital Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1034The Future of Telemicrobiology and Automated Digital Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035

MICROBIAL IDENTIFICATION AND CHARACTERIZATION USING DATABASES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035Biochemical Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1035MALDI-TOF Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036Nucleic Acid Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036

Database challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1036Considerations in using public databases for microorganism identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037WGS and MGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037

(i) Potential advantages of routine clinical WGS and MGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1037(ii) Informatics challenges. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1038

DNA data in the future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039REPORTING TO PUBLIC HEALTH AGENCIES AND DETECTING OUTBREAKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039

Reporting to Public Health Agencies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039Outbreak Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039

Detection of evolving antimicrobial susceptibility patterns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1039WHONET . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1040

(continued)

Address correspondence to Daniel D. Rhoads, [email protected].

Copyright © 2014, American Society for Microbiology. All Rights Reserved.

doi:10.1128/CMR.00049-14

October 2014 Volume 27 Number 4 Clinical Microbiology Reviews p. 1025–1047 cmr.asm.org 1025

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ownloaded from

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Detection of regional and global outbreaks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1040Detection of local outbreaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041Surveillance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041

Integrative Public Health Informatics Approaches of the Future. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041ACKNOWLEDGMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041REFERENCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1041AUTHOR BIOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1047

SUMMARY

The clinical microbiology laboratory has responsibilities rangingfrom characterizing the causative agent in a patient’s infection tohelping detect global disease outbreaks. All of these processes areincreasingly becoming partnered more intimately with informat-ics. Effective application of informatics tools can increase the ac-curacy, timeliness, and completeness of microbiology testingwhile decreasing the laboratory workload, which can lead to opti-mized laboratory workflow and decreased costs. Informatics ispoised to be increasingly relevant in clinical microbiology, withthe advent of total laboratory automation, complex instrumentinterfaces, electronic health records, clinical decision supporttools, and the clinical implementation of microbial genome se-quencing. This review discusses the diverse informatics aspectsthat are relevant to the clinical microbiology laboratory, includingthe following: the microbiology laboratory information system,decision support tools, expert systems, instrument interfaces, to-tal laboratory automation, telemicrobiology, automated imageanalysis, nucleic acid sequence databases, electronic reporting ofinfectious agents to public health agencies, and disease outbreaksurveillance. The breadth and utility of informatics tools used inclinical microbiology have made them indispensable to contem-porary clinical and laboratory practice. Continued advances intechnology and development of these informatics tools will fur-ther improve patient and public health care in the future.

INTRODUCTION

The local clinical microbiology laboratory’s responsibilitiesrange from characterizing the causative agent of a patient’s

infection to helping detect global disease outbreaks. These pro-cesses are becoming increasingly more complex. Every laboratoryis obliged to continually improve quality while operating moreefficiently. The clinical microbiology laboratory is being chal-lenged to do more work, identify more microorganisms, reportcomplex and changing drug-related information, automate pro-cedures, integrate traditional lab data with molecular findings,and participate in public health reporting and outbreak detection.Informatics provides the tools and processes to satisfy most ofthese demands and also offers unique opportunities to advancethe clinical microbiology laboratory, allowing the lab to do morewith less. Therefore, it is important for microbiologists to be fa-miliar with informatics (Table 1) (1). Although many informaticscomponents are already widely used in clinical microbiology,there are many emerging tools that are not being used routinelybut that could be leveraged by the laboratory. Studies have dem-onstrated that implementation of informatics tools can improvethe efficiency, accuracy, precision, and rapidity of microbiologytesting and reporting (2–7). In this review, we describe the broadimpact of informatics on clinical microbiology and highlight bur-

geoning areas of clinical microbiology informatics, such as its rolein total laboratory automation (TLA), telemicrobiology, and mi-crobial whole-genome sequencing (WGS).

“Clinical microbiology informatics” is the use of information(e.g., data, knowledge, and results) and information tools (e.g.,software, databases, and rules) in the “science and service dealingwith detection, identification, and antimicrobial susceptibilitytesting” of clinically relevant microbes and the communication ofthese results to clinicians (8). A more practical definition includesthe application of information technology (IT) to solve problemsin clinical microbiology by improving workflow, efficiency, reli-ability, and, ultimately, patient care. It is important to stipulatethat the practice of informatics not only involves technology butalso includes the people who use, implement, and maintain infor-mation systems, and it includes the workflow processes that areaffected by this technology. This review discusses those diverseinformatics components that are uniquely relevant to the clinicalmicrobiology laboratory. Specifically, the following topics are ad-dressed: the microbiology laboratory information system (LIS),decision support tools, expert systems, instrument interfaces withthe LIS, total laboratory automation, remote and automated im-age analysis, nucleic acid sequence databases, reporting of infec-tious agents to public health agencies, and outbreak surveillance.Systems, algorithms, and published studies are not exhaustivelystudied in this review. Instead, this article focuses on the breadthof connections that informatics and clinical microbiology shareand the potential improvements that can be realized by increasingthe implementation of informatics tools in clinical microbiology.Some of these informatics solutions have been incorporated intothe workflow of only a few hospitals or laboratories, while otherinformatics components are likely to be more familiar to manymicrobiologists, as they are ingrained into the routine practice ofclinical microbiology. It is important to continually explore newinformatics tools which have the potential to improve the effi-ciency, accuracy, cost, and quality of care related to clinical micro-biology.

UNIQUE FEATURES OF THE MICROBIOLOGY LABORATORYINFORMATION SYSTEM

The microbiology LIS has been in development and use for abouthalf a century (9, 10). Like every LIS, the microbiology LIS needsto be secure, user-friendly, and able to interface with other infor-mation systems. However, there are several unique features of themicrobiology LIS which are not used in other clinical laboratories.

Multiple-Derivative Tracking

Samples sent to the microbiology laboratory often produce morethan a single result, and the final type and number of results aretypically not known until the testing is under way. One sample

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often creates multiple derivatives with unique parent-child rela-tionships (10, 11). For example, consider a singly accessionedcontainer that is sent to the laboratory. This container is thelaboratory’s original asset. Inside the original asset may be a poly-microbial abscess sample, which is the original specimen. Theoriginal specimen will be inoculated into or onto various media.These media immediately become derivative assets that need to belinked to the original accession number. Similarly, each organismthat is cultivated from the original polymicrobial specimen be-comes a derivative specimen. These derivative specimens may beaerobic bacteria, anaerobic bacteria, mycobacteria, and/or fungi,and all of these need to be linked to the original accession number.Additional derivative assets, such as subculture plates or antimi-crobial susceptibility testing (AST) plates, may need to be createdin order to fully characterize the isolated derivative specimens.Properly handling the electronic information associated with asample, such as tracking its derivatives, modifying descriptions ofits derivatives, and linking its derivatives with their accessionnumber, is a unique and essential aspect of the microbiology LIS.The physical derivatives also require accurate tracking and iden-tification on the bench, and the best practice for proper identifi-cation employs the use of bar-coded labels on all assets (12, 13)(Fig. 1).

Laboratory Electronic Notes

Notes need to be made routinely regarding cultured specimens sothat the previously performed work-up is evident to the microbi-ologist who is presently examining the derivatives. Historically,these notes were recorded on physical note cards, but now the LIShas largely replaced these physical notes with electronic notes.Having archived electronic notes on each specimen allows formore permanent and easily searchable record keeping and easierauditing, which can potentially be used in quality improvementefforts.

Results Reporting

The microbiology LIS requires unique features involving how re-sults are conveyed to clinicians. Most clinical laboratory disci-plines report numerical results, such as concentration, titer, orquantity. However, other clinical microbiology laboratory needsinclude reporting nonnumerical results, such as the genus andspecies name of an identified organism (11). Microbiology resultsoften include qualitative, semiquantitative, and/or quantitativedata, which can complicate data structures in the LIS (14). Multi-ple organisms can grow in culture, each with susceptibility results,interpretive comments, and potentially different clinical relevan-cies, so report design is paramount to supporting safe interpreta-tion by health care providers (15). Optimizing the informationdesign of a report, for example, by summarizing and grouping themicrobiology results to improve data visualization, can improveinterpretation of the data (16). Expert systems may also be used inreporting results, and these are discussed in a separate section.

Informatics tools that facilitate standardized results entry.The breadth of potential results that may need to be reportedrequires freedom to enter text as a result. However, there need tobe strict input requirements, more than simply permitting a freetext field for results entry. Standardization in the system is neededto facilitate the use of appropriate nomenclature, to improve con-sistency of reporting, and to simplify auditing so that, for example,the identification of Staphylococcus epidermidis is not reported as“Staph epidermidis,” “S. epidermidis,” “coagulase-negative staph-ylococcus,” or “coag-neg staph” (17).

TABLE 1 Key informatics resources for the clinical microbiologylaboratory

Resource (reference)

Resources for general knowledge and referenceBiomedical Informatics: Computer Applications in Health Care and

Biomedicine, 3rd ed (188)Clinical Diagnostic Technology. The Total Testing Process, vol 1, 2, and 3

(189–191)Infectious Disease Informatics (192)Informatics for the Clinical Laboratory: a Practical Guide for the Pathologist

(193)“The Laboratory Information System: Making the Most of It in the

Clinical Microbiology Laboratory” (194)Pathology Informatics: Theory and Practice (195)Practical Informatics for Cytopathology (196)Practical Pathology Informatics: Demystifying Informatics for the Practicing

Anatomic Pathologist (197)Health Informatics. Practical Guide for Healthcare and Information

Technology Professionals, 6th ed (198)Public Health Informatics and Information Systems, 2nd ed (199)

Standards and guidelines for operations, maintenance, and complianceLaboratory Automation: Bar Codes for Specimen Container Identification

(200)Laboratory Automation: Communications with Automated Clinical

Laboratory Systems, Instruments, Devices, and Information Systems (201)Managing and Validating Laboratory Information Systems (202)“CLIA Program and HIPAA Privacy Rule; Patients’ Access to Test

Reports. Final Rule” (203)Clinical Guidelines for Telepathology (204)Digital Pathology Resource Guide, version 4.0 (205)“Laboratory Computer Services” (20)“Publication of OIG Compliance Program Guidance for Clinical

Laboratories—HHS” (206)“Standards for Secure Data Sharing across Organizations” (207)“Validating Whole Slide Imaging for Diagnostic Purposes in Pathology:

Guideline from the College of American Pathologists Pathology andLaboratory Quality Center” (67)

“Validation of Digital Pathology in a Healthcare Environment” (208)

FIG 1 Image demonstrating the increased information density that can beobtained using 2-dimensional (2-D) bar codes over 1-dimensional (1-D) barcodes and demonstrating that minor damage to a 2-D bar code can be com-pensated 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 interveningspaces to encode data, which can be translated to text via a bar code scannerand its software. The words “Staphylococcus aureus” are depicted in a 1-D barcode using Code 128 symbology (A) and a 2-D bar code using DataMatrixsymbology (B). These symbologies are commonly used to label specimens inclinical laboratories, although numerous bar code formats are available. 2-Dbar codes are becoming the preferred symbology because of their smaller foot-print and robust error correction. For example, even if part of the bar code isslightly damaged (C), the integrity of the information remains intact and canbe read accurately. Bar codes can also be used to enter microbiology results orcomments into an LIS, and the use of bar codes can help to decrease typo-graphical errors and standardize results reporting.


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One way to facilitate standardized input of text into the LIS isby implementing the use of rapid input methods, such as barcodes or keyboard shortcuts that expand into canned text phrases(4, 18, 19). Examples of bar codes that can be used for rapid inputof results are depicted in Fig. 1. LIS software typically has theability to use keyboard shortcuts to enter repetitive texts. For ex-ample, the software can be programmed so that when “;sa” isentered by the scientist on the bench, the entry expands into“Staphylococcus aureus.” These data entry methods allow resultsand comments to be entered quickly and accurately and are mostuseful for entering lengthy, repetitive texts, such as commonlyused footnotes or disclaimers. Using computers to facilitate rapidinput of results and comments can improve standardization ofresults reporting, decrease error rates, and increase productivity(4, 18, 19). Additionally, bar-coded accession numbers can beused on culture plates, which can help to improve workflow (14).

Caveats to consider in reporting preliminary and final results.Because of the time and processes required for microbial cultureand identification, results are often initially reported preliminarilybut subsequently revised or refined. For example, a yeast isolatemay be identified as Candida sp., which is preliminarily reportedto the hospital information system (HIS) via the LIS. After addi-tional testing, the isolate might be classified further as Candidaglabrata. It is important that the most recent and specific findingsare accessible in the HIS and that the preliminary results are sup-pressed but traceable so the clinician viewing the chart can easilyidentify the most accurate and up-to-date results. It is necessarythat the final results replace the preliminary results in the accessi-ble electronic medical record, but it is also necessary that thesepreliminary results be archived (and not overwritten) in case theyneed to be revisited (11).

Quality Assurance

Like all areas of the clinical microbiology laboratory, the quality ofthe LIS and its interface with clinicians needs to be ensured. Sev-eral components of the LIS and HIS need to be operational beforeimplementing a new microbiology test. First, an interface needs tobe present between the LIS and HIS. Second, orders submitted viathe HIS need to be received by the LIS. Third, results in the LISneed to be transmitted downstream to the HIS. Fourth, results inthe HIS, including preliminary reports, final reports, and antimi-crobial susceptibility test results, need to be displayed correctly.The College of American Pathologists requires that these inter-faces be verified every 2 years (GEN.48500) (20).

Software upgrades and network improvements often have un-intended negative consequences that may not be identified with-out proactive investigation, so it is important to check all the in-terfaces that the clinician experiences at regular intervals,including interfaces beyond the results interface. For example, it isnecessary to check and maintain the order entry interface thatclinicians use to be certain that the tests that are visible to theclinicians are truly those tests that are currently being offered bythe microbiology laboratory.

TOOLS TO FACILITATE APPROPRIATE ANTIMICROBIALTHERAPY

Aiding in the appropriate selection of an antimicrobial therapyregimen for a patient is a primary purpose of the clinical microbi-ology laboratory, and two important variables should be consid-ered in working to select appropriate antimicrobial therapy for a

potential infection. The first variable is whether or not an infec-tion is actually present. If an infection is present, the second im-portant variable is whether or not a drug will be an effective ther-apy. Several informatics tools can help the laboratory andclinicians to best determine these variables.

The most rudimentary and universally used informatics toolfor empirical antimicrobial therapy selection is the classic annualantibiogram. However, some have proposed that more advancedinformatics tools should be incorporated into the LIS or HIS andhave demonstrated that such algorithms can improve the qualityand decrease the cost of care (5, 21–23). Expert systems are usefulin helping the laboratory to avoid reporting inappropriate antimi-crobial susceptibility test results that could lead to inappropriatetherapy, and these systems are discussed in another section. Toolscan be used to alert physicians to important microbiology resultsthat may require changing or commencing antimicrobial therapy,but studies suggest that the best way to communicate importantmicrobiology results is still direct communication of those resultsto a treating clinician via a telephone call (24, 25). Rapid notifica-tion of a clinical pharmacist can also be used as a means to expe-dite appropriate therapy alterations, and electronic tools are avail-able to rapidly notify these individuals (26, 27).

The Antibiogram

The antibiogram is helpful in facilitating the selection of an ap-propriate therapy for an infecting organism prior to knowledge ofthe antimicrobial susceptibility test results for the specific isolateinfecting the patient. The antibiogram is a helpful utility that or-ganizes the susceptibility data of the local microbiology labora-tory, but the decision as to which antimicrobials are potentiallyclinically useful depends on more variables than the likelihood ofantimicrobial susceptibility. Other key variables include the site ofinfection, cost of therapy, patient allergies, route of administra-tion, and hospital antimicrobial stewardship policy. However,these variables are often analyzed separately from the antibiograminstead of via an integrated approach.

Although an antibiogram is the simplest means of facilitatingempirical antimicrobial selection, creating the most accurate andhelpful antibiogram still requires refinement of raw data. Al-though expert systems can be used to generate antibiograms (14),software limitations may hinder this refinement (28). In compil-ing data for construction of the antibiogram, care needs to betaken to prevent repeated incorporation of the same organismfrom the same patient from skewing the report (28–30). The Clin-ical Laboratory and Standards Institute (CLSI) guideline recog-nizes that “the methods used to create, record, and analyze thedata” need to be “reliable and consistent” in order to maximize thequality and utility of the antibiogram (31). It has been suggestedthat each species isolated from each patient be incorporated intothe annual antibiogram calculation only once per period, per siteof infection, or per unique phenotype (28, 29, 31). Others suggestaveraging the susceptibility of repeat isolates from a patient. Thatway, each repeat isolate contributes equally to a patient’s suscep-tibility profile, and each patient’s susceptibility profile contributesequally to the antibiogram (28). Guidelines currently recommendincorporating the results for a species only the first time it is iso-lated from a patient in the period being considered (28, 31).

In creating an antibiogram, data stratification may be necessaryfor some bacteria in some patient populations. For example, Pseu-domonas aeruginosa isolates from cystic fibrosis patients may be

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more resistant than isolates from other patients. In instances inwhich stratification yields significantly different findings, it is ap-propriate to list the likelihood of antimicrobial susceptibility foreach subpopulation, whether defined by patient demographics(e.g., patients with cystic fibrosis), organism features (e.g., mucoidstrains of P. aeruginosa), or patient location (e.g., emergency de-partment). Antibiograms should also include 95% confidence in-tervals with the reported susceptibility likelihoods so clinicianscan better interpret the data (28).

Advanced Clinical Decision Support Tools

Advanced clinical decision support tools for clinical microbiologycan be helpful in determining if an infection is present and/ordetermining which therapy is most appropriate. These tools havebeen under development for more than 4 decades (32), but theyare not yet widely used. The decision as to which antimicrobialsare potentially clinically useful depends on multiple variables,such as the patient’s underlying disease (e.g., cystic fibrosis) orlocation (e.g., emergency department), and advanced computer-based decision support tools have been used to aid clinicians inchoosing the most appropriate antimicrobial regimen. There is arecognized need for the development of more software tools tooptimize the clinical selection of appropriate antimicrobial ther-apy (22).

These tools range in complexity and function. Some simplertools work to provide improved antibiograms (33) or work simi-larly to an antibiogram to help in the selection of an appropriateempirical antimicrobial agent (34). Other software can work tosupport the determination of whether or not a true infection ispresent (35) or can alert a physician if a patient is failing therapy(36). Some software tools use rules that are based in Bayesian logicto predict both the presence of an infection and an appropriateempirical antimicrobial (37). Other tools are complex expert sys-tems, which consider patient allergies, laboratory data, physicalfindings, radiological findings, antibiograms, drug interactions,cost, and findings in similar patients to aid in deciding whichtreatment might be most appropriate (21).

Although the traditional antibiogram is a helpful decision sup-port tool for empirical antimicrobial selection, other informaticstools have proven capable of improving upon the antibiogram andoffering clinicians more complete support in assessing the infor-mation that is available and determining the most appropriateantimicrobial therapy regimen. However, their implementationremains sparse, probably because of the large amount of up-frontresources required for implementation (38). Overcoming the ini-tial cost of investment to implement clinical decision supporttools is a common challenge in attempting to institute any newinformatics tool that holds potential, but these tools’ long-termreturn on investment needs to be considered carefully. Further-more, maintaining these tools and keeping them current withnewly published data add significantly to their cost and sustain-ability.

Postdischarge Results

Properly addressing postdischarge microbiology results can helpto ensure that discharged patients with infections are managedappropriately. Upon discharge of an inpatient, microbiology testresults are often pending. Depending on the final test results, achange in therapy for the discharged patient may be warranted;however, appropriate follow-up often does not occur (39). It has

been demonstrated that the use of an automated email system toalert the appropriate physician(s) that a discharged patient hasnew microbiology results and may require a change in antimicro-bial therapy is a helpful tool in improving physician follow-up inthese cases (2). Another option is to use a more hands-on ap-proach in which software flags a chart for review by an infectiousdisease physician when a result of interest is reported after dis-charge. Once the infectious disease specialist has reviewed the re-sult and the patient chart, the specialist can subsequently contactthe treating physician if a change in therapy is potentially indi-cated (40).

Preventing Release of Inappropriate Results

Reporting of positive results for patients who have a low pretestprobability of infection but may produce positive laboratory find-ings, for example, urine cultures for hospitalized patients withasymptomatic bacteriuria, may lead to suboptimal patient man-agement. Some laboratories have gone as far as withholding theroutine release of such results in an attempt to decrease inappro-priate antimicrobial therapy (41). Others have attempted to guidethe interpretation of positive urine cultures by adding instructivecomments which encourage clinicians to verify that signs orsymptoms of infection are present before treating the patient(C. A. Rauch, presented at Preventing Healthcare-Associated In-fections: Whose Problem Is It?, Lenox, MA, 3 November 2010).However, in many clinical situations, positive microbiology re-sults help to verify and characterize an infection, and preventingthe release of inappropriate susceptibility test results is paramountin facilitating selection of an effective antimicrobial therapeuticregimen. The laboratory is expected to identify “unusual or incon-sistent antimicrobial testing results,” such as vancomycin-resis-tant staphylococci, and subsequently to investigate such findingsmore thoroughly (42). It is important that laboratory scientists bewell trained and capable of identifying unusual results. However,humans should not be the first-line gatekeepers to recognize theseunusual results. The identification of these unusual results canand should be performed by laboratory instrumentation software,middleware, or the LIS. Similarly, the laboratory is responsible forsuppressing antimicrobial susceptibility test results that are notappropriate for clinical consideration (e.g., clindamycin for en-terococci), and this suppression can be performed more reliablyby software created by skilled laboratory professionals. Softwaretools can also be incorporated into the workflow to identify andcharacterize potential contamination in molecular testing (43).The use of these types of software is a practical means by whichinformatics tools can offload some of the work from humans andthereby increase efficiency and improve the consistent quality ofresults reporting, which supports appropriate antimicrobial us-age. The implementation of an expert system is a way by whichantimicrobial susceptibility interpretations can be determinedand reported appropriately, and expert systems are described inthe next section.

EXPERT SYSTEMS IN THE LABORATORY

The Need for Expert Systems

Traditionally, simple rules have been used by microbiologists forlinking the detected phenotype of an organism to a clinically ac-tionable finding. For example, the detection of cefoxitin resistancein Staphylococcus aureus is used to infer resistance of the organism




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to penicillins and cephalosporins. However, identifying resistancemechanisms can be less straightforward in other situations, suchas identifying and distinguishing extended-spectrum-beta-lacta-mase (ESBL)-producing, AmpC-hyperproducing, and wild-typeEnterobacteriaceae bacteria (38, 44). Because of the variability ofmicroorganism phenotypes and in vitro test results, as well as thepossibility of multiple resistance mechanisms, a simple flow chartor table can quickly evolve into a complex algorithm.

Rules for reporting results can be dependent upon patient de-mographics, the specimen’s source, or antimicrobial resistance.Instead of relying on humans to investigate these criteria, recallthe associated rules, and accurately implement the rules, the rulescan be built into the system to selectively release results. It is nosurprise that the microbiology laboratory has turned to expertsystems (aka knowledge-based systems) to attempt to organizealgorithms into more usable and useful systems.

The Definition of an Expert System

An expert system is a form of artificial intelligence. It containsthree main parts: a knowledge base (i.e., known facts), an infer-ence engine (i.e., the rules), and a user interface (38). An expertsystem is software that combines a database of information with aset of rules to help make a conclusion about an input (Fig. 2). Thesame conclusion can potentially be achieved by a human. How-ever, the advantage of using an expert system is that the systemalways “remembers” all of the rules involved in the decision-mak-ing process, so it is able to quickly and consistently produce thesame objective output for a given input.

Utility of Expert Systems in Antimicrobial SusceptibilityTesting and Reporting

In the clinical microbiology laboratory, expert systems may beemployed most commonly in automated antimicrobial suscepti-bility testing (AST). These expert systems can alert the user to anunusual AST pattern for an identified organism, alter the ASTinterpretation of one antimicrobial based upon the interpretationof test results for a second antimicrobial, suppress the report forthe AST of an antimicrobial if appropriate, add a footnote to aninterpretation, and allow laboratories to customize the rules thatare used by the expert system. Winstanley and Courvalin recentlywrote a thorough review of clinical microbiology expert systems(38).

In practice, a user (or instrument) inputs MIC values into an

expert system. The software then compares the input to theknowledge database by using the inference engine, and the systemthen outputs the appropriate susceptible, intermediate, or resis-tant (SIR) interpretation. Expert systems can also be used to inferthe mechanism of antimicrobial resistance by comparing a clinicalisolate’s MIC values to a curated database of strains which havehad their MIC values and mechanism(s) of resistance character-ized (45). In addition to having the antimicrobial breakpoints inits database, the expert system also has rules which check to see ifsome drug results need to be reported as resistant even though theorganism might test as sensitive. For example, if S. aureus is resis-tant to cefoxitin, then the cephalosporin results need to be re-ported as resistant, even if these results appear individually to besensitive in vitro, as the cefoxitin result is a more robust indicatorof resistance.

Becton, Dickinson, and Company (BD) uses an expert system(BDXpert) to analyze the identification and AST of microorgan-isms from its BD Phoenix system to recognize if modifications oradditions should be made to the AST results (i.e., MIC) or inter-pretation (i.e., SIR) before they are reported to clinicians (46).Similar expert systems are employed by the Siemens MicroScanand bioMeriéux Vitek systems (38). BDXpert examines the ASTinterpretations initially identified by the instrument and maymake changes to the interpretations before communicating theresults to the LIS. Some examples of modifications or additions toresults include the following (46):

1. An organism with intrinsic resistance to an antimicrobialmay produce a result that suggests that the organism is sen-sitive to the antimicrobial, but the expert system will con-vert the result to “resistant.”

2. Inferred susceptibility of an organism to an antimicrobialthat has not been tested but which is based on the suscepti-bility test result of a different antimicrobial may be re-ported.

3. A comment may be added to provide clinically relevant in-terpretation of a result. For example, an S. aureus isolatewith cefoxitin resistance will receive an additional educa-tional comment explaining that penicillins and cephalospo-rins should not be used for therapy. Additional commentsmay be added to support infection control- and prevention-related efforts according to the needs of the institution.

In addition to altering AST results when necessary, expert sys-tems can withhold inappropriate results or release appropriateresults that are typically not reported for a number of reasons. Forexample, antimicrobial test results can be withheld when the pa-tient’s age or the specimen source suggests that the drug’s usewould be inappropriate (14). In other cases, AST results that aretypically suppressed can be released if the patient is critically ill(e.g., residing in an intensive care unit) or if the patient’s isolate isresistant to first-line therapy (14). This general approach has beenreferred to as “cascade” reporting and has been used in efforts tooptimize the use of antimicrobial drugs.

Expert Systems as Alert Tools

Expert systems can also be used to inform the appropriate person-nel of new or important information. An expert system can alert alaboratory scientist on the bench when additional work-up needsto be performed on an isolate. A system can also alert a laboratory

FIG 2 Schematic representation of an expert system. An expert system is aform of artificial intelligence that allows a user to use software rules (inferenceengine) together with a knowledge database to make a conclusion (output)about an input.

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scientist if a similar isolate was recently worked up by the labora-tory, which would suggest that the current isolate may not need arepeat AST performed. An expert system can send email notifica-tions to hospital staff when a patient needs to be placed in contactprecautions, or it can email the microbiology director when thehealth department needs to be contacted (14).

Expert Systems as Data Analysis Tools

Expert systems can be used to aggregate and report metrics ofinterest. For example, MIC trends of an organism can be visual-ized, infection rates in a given hospital unit can be identified, orblood culture contamination rates associated with a particularclinical area or phlebotomist of interest can be examined (14).

The Future of Expert Systems in the MicrobiologyLaboratory

There is unrealized potential for full integration of the microbiol-ogy laboratory’s expert systems with clinician alerts and advancedclinical decision support tools to optimize the flow of microbiol-ogy data and to translate those data into improved patient care. Bydecreasing suboptimal or unnecessary antimicrobial therapy, de-velopment of antimicrobial resistance and therapy-related com-plications can be minimized. Potential also exists to incorporateexpert systems into total laboratory automation systems, in whichexpert systems could have the ability to “read” plate cultures andreport autoverified results in some instances.

INSTRUMENT INTERFACES WITH THE LIS

An interface comprises a combination of software and hardwarethat facilitates the electronic exchange of data over a network. Thiscommunication between devices or computers is accomplished byusing a protocol, i.e., a set of digital rules and functions. Today’sclinical microbiology laboratory routinely uses instrumentationfor three components of analysis: taxonomic identification of anisolated colony, determination of the AST of an isolated colony,and detection of microbial growth in blood cultures. Phenotypicor biochemical identification and susceptibility testing instru-mentation is typically combined into a single instrument (e.g.,MicroScan [Siemens], Phoenix [BD], or Vitek [bioMérieux]). Aseparate instrument is often used for the detection of microbialgrowth in continuously monitored blood cultures. Electronicallytransmitting information (e.g., computerized physician test or-ders) from the LIS to these devices and bidirectional exchange ofinformation determined by such instruments with the LIS areimportant means for streamlining laboratory workflow, decreas-ing human errors, and expediting results reporting (14). An in-creasing number of stand-alone niche instruments are used inmicrobiology for nucleic acid testing or antigen testing, and someof the tests for which they are used are laboratory developed. Un-fortunately, interfacing each of these peripheral instruments withthe LIS is not always pursued or is deemed too resource intensive,because these interfaces may need to be built and maintained byin-house support teams, but the lack of an instrument-LIS inter-face can result in inefficient and error-prone daily workflow in thelaboratory. It has been demonstrated that manual entry of micro-biology results is a source of laboratory errors (47, 48), and otherclinical laboratories have demonstrated that the development ofin-house interfaces between instruments and the LIS can be usefulin reducing manual entry errors and hands-on time (49, 50). Inorder to optimize the efficiency and accuracy of all instrument

testing, interfaces between laboratory instruments and the LISshould be employed for data transfer whenever possible.

Interface Commonalities

Most instruments and databases used in clinical microbiology lab-oratories within the United States are cleared by the U.S. Food andDrug Administration (FDA) for in vitro diagnostic testing, andthey have software designed and maintained to interface with themicrobiology LIS, which enables bidirectional communicationbetween the instrument and the LIS. This communication typi-cally occurs via a serial port (RS-232) and follows the CLSI speci-fication guidelines LIS01-A2 and LIS02-A2 (formerly maintainedas guidelines ASTM E1381 and E1394). These guidelines specifycommunication protocols for structuring content and the dataelements contained within those structures, as well as data transferrequirements. The LIS02-A2 standard is applicable to all text-ori-ented instruments. Electronic microbiology testing can be com-plex, as messages may have different test priorities (e.g., stat, rou-tine, or callback), contain multiple requests (i.e., a battery of testorders) and/or results (e.g., MICs) for one patient, include differ-ent report types (e.g., preliminary and final results), incorporatecomments of various lengths, or be flagged to trigger alerts ordisplay in a desired manner in a downstream electronic healthrecord. Communicating this information between instrumentsand information systems requires robust interfaces designed tohandle these complex messages.

Interfaces of Common Instruments

Instrument interfaces are the nuts and bolts of informatics forclinical laboratories. This component of informatics typically re-quires more technical expertise than clinical expertise, but theproper functioning of these interfaces is essential for smooth op-erations. It is important that data be arranged in a standard fash-ion according to specific protocols when being relayed betweensystems. Health level 7 (HL7) is the health care electronic messag-ing standard used by most laboratories (Fig. 3). Increasingly,middleware components and expert systems are being incorpo-rated into the IT infrastructure and analysis pipelines, so impor-tant reporting decisions and autoverification are often made be-fore results data ever reach the LIS. The interfaces of somecommon instruments are briefly discussed below.

BD instruments. BD instruments include blood culture(BacTec FX or 9000 series), mycobacterial detection and AST(BacTec MGIT 960), and identification and AST (BD Phoenix)instruments. Workstations that directly interface with the instru-ment by using a private network are supplied by BD. BD uses adata management system (EpiCenter) hosted by a server that isdesigned to integrate patient demographics it receives from theLIS, results it receives from BD instruments, and results frommanually performed microbiology tests. EpiCenter can be net-worked to multiple clients to allow many workstations in the lab-oratory to access EpiCenter. EpiCenter and the LIS send messagesto each other containing patient information, order information,and test results. EpiCenter is designed to allow results to be pushedto the LIS or pulled by the LIS, depending on user preferences. Thesystem also allows the LIS to inform EpiCenter of an ordered test,and EpiCenter can associate this ordered test with results in itsdatabase as the results become available. EpiCenter can requestpatient information from the LIS if it encounters an accessionnumber for which it does not have demographics. Newer BD




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instruments have been designed be able to interact directlywith an LIS.

bioMeriéux instruments. bioMeriéux instruments includeblood culture (e.g., BacT/ALERT3D) and identification and AST(e.g., Vitek 2) instruments. These instruments can interface withthe LIS, or results from these instruments can be centralized in anObserva workstation, which is then interfaced with the LIS (51).Some instrument interfaces follow the ASTM E1381-specified guide-lines, but other interfaces rely on a nonstandard “bioMeriéux com-munications protocol” (52). bioMeriéux’s blood culture instru-ments allow for varied levels of LIS integration, depending uponthe instrument capabilities and user preferences. There are op-tions for no integration with an LIS, direct LIS interface, or LISinterface by means of bioMeriéux data management systems(BacT/VIEW or Observa). Interfacing with the LIS through anymeans requires the use of BacT/LINK (middleware). BacT/LINKenables bidirectional communication between BacT/Alert and theLIS. Test orders can be pushed to the BacT/Alert by the LIS, orthe BacT/Alert can pull test orders from the LIS. Subsequently, theinstrument can push results to the LIS as changes in the detectedresults are identified. bioMeriéux’s Vitek instruments allow forsimilar bidirectional interface with the LIS via data managementsoftware (bioLIAISON or Observa).

Siemens MicroScan WalkAway. The MicroScan system per-forms identification and AST, and it uses Siemens’ LabPro soft-ware for interfacing with the LIS. LabPro allows for bidirectionalsharing of information between the MicroScan system and theLIS. LabPro can push and pull information to and from an LIS.LabPro “[t]ransmits and receives patient, specimen, and isolateorder information to/from the laboratory system manually or au-tomatically” (53). This allows LabPro to collect patient informa-

tion and results from manually performed test results recorded inthe LIS and to collect results from automated tests performedby the MicroScan system. The expert system can identify resultsthat need further review (38), and LabPro can communicate re-sults to the LIS.

Molecular Testing Interfaces

A growing number of FDA-approved molecular tests and labora-tory-developed tests are used in clinical microbiology, rangingfrom PCR assays designed to detect single pathogens to high-throughput parallel sequencing of DNA designed to detect multi-ple species simultaneously (54–56). Many of these systems arelaboratory developed, but FDA-approved instruments are not un-common. One FDA-approved example is Gen-Probe’s (Holog-ic’s) Tigris DTS system, which can be used for the detection ofNeisseria gonorrhoeae and Chlamydia trachomatis. Although theTigris DTS receives orders sent by the LIS, a Tigris DTS user canstill manually request the LIS to send orders to the instrument.Subsequently, when a work list is complete, the Tigris DTS pushesresults to the LIS (57).

Instruments employed for laboratory-developed moleculartests vary in their interoperability with the LIS. On one hand aresystems that have no direct communication link with the LIS andrequire results from the instrument to be entered manually intothe LIS, and on the other hand are instruments that are fully inte-grated with the LIS system by means of bidirectional interfaces,whereby data flow automatically, avoiding the need for manualdata entry. Factors that play a role in determining the level ofinstrument-LIS integration include the capabilities of the instru-ment and LIS being used, the technical support (vendor and lab-oratory IT staff) available for establishing and maintaining cus-

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 clinicalmicrobiologists are seldom exposed to the HL7 messaging format. It is helpful for clinical microbiologists to know what an HL7 message might look like, so theycan communicate more effectively with information technology staff. The depicted example relays the positive result of a Chlamydia trachomatis test that wasdetermined using a Tigris DTS instrument (Hologic Gen-Probe, Inc., San Diego, CA). The test that was ordered and performed is represented by an LOINC codewithin 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 foremphasis), 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”), andinformation about results and the reporting of the results (OBX or “observation of results”).

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tom interfaces, and the level of priority placed on integration bythe laboratory director and administration. Preferably, resultsfrom instruments, whether performing FDA-approved tests orlaboratory-developed tests, should be integrated seamlessly intothe LIS, which can decrease the chance for random human errorsand decrease the time spent performing and checking manual dataentry.

Instrument Interfaces of the Future

As the demand for sophisticated laboratory instrumentation, au-tomation, and electronic reporting increases, so too does the needfor better software to clearly collect, display, and integrate ongoingspecimen processes and reported results. Middleware (e.g.,bioMeriéux’s Observa and Myla) has been developed to addressthis need. Middleware not only connects legacy to newer systemsbut also permits data exchange and management of complex datathat the LIS cannot handle. Middleware solutions can also help tounify microbiology results into a more manageable and user-friendly centralized system. This approach will continue to pro-mote lab automation, increase productivity, expedite workflow,and promote standardization while minimizing the opportunityfor human errors. Unifying interfaces with middleware will even-tually blur the line between the LIS and instrument software.

TOTAL LABORATORY AUTOMATION

The clinical microbiology laboratory faces “an ever-increasingload of work not matched by an increase in the number of peopleavailable to do it,” as Williams and Trotman described in the1960s, and which still seems to hold true today (58). The labora-tory has been looking to automation to overcome this shortcom-ing for the last half century. Williams and Trotman envisioned amore totally automated laboratory where “[i]t should be possibleto mechanize the inoculation of specimens on to culture platesand their transfer to the incubator, and similarly, when the bacte-riologist has picked the colony of interest and selected its identifi-cation program, it should be possible to transfer it to the variousidentification media, or set up cultures for antibiotic sensitivitydetermination mechanically without undue difficulty” (58). Au-tomated instruments have become the workhorses of many clin-ical laboratories, but automation has traditionally been used onlyfor discrete portions of the analysis pipeline, which typically hasbeen limited to identifying the presence of growth in blood cul-tures and determining the identity and susceptibility of isolatedorganisms. Only recently is Williams and Trotman’s dream ofmore complete laboratory automation beginning to be realized.This approach is commonly termed “total laboratory automa-tion” (TLA) (59, 60).

Several commercial entities are now manufacturing productsand systems (e.g., WASP [Copan], Previ-Isola [bioMeriéux], In-nova [BD], and InoqulA [Kiestra]) that they hope will expandautomation in the microbiology laboratory (59–61). These sys-tems have been implemented in some laboratories to automateprocesses including inoculation, identification of growth onplated specimens, the subculture of colonies of interest, and inoc-ulation for organism identification and susceptibility testing. Eu-rope has some laboratories that have already moved to TLA (62),and some laboratories in the United States are preparing to imple-ment TLA (63). The potential advantages of TLA are plentiful andinclude decreased operational costs, improved standardization ofprocessing and testing, increased throughput capability, increased

numbers of isolated colonies per plate, management of variousspecimen types simultaneously, decreased turnaround times,greater specimen traceability, and decreased human workloads(59, 64). However, these systems are only beginning to be evalu-ated in formal studies. The systems may not be as efficient asadvertised, and their real impact on routine testing needs to beevaluated (65). Initial studies have demonstrated that TLA canhelp to double the sample throughput per technologist per day,which has enabled laboratories to handle increased specimennumbers with decreased staff (N. Bentley, M. Farrington, R.Doughton, and D. Pearce, poster 1792 presented at the 21stEECMID, Milan, Italy, 7 to 10 May 2011 [http://www.poster-submission.com/search/download/13592]; G. Humphrey, C.Malone, H. Gough, and F. M. Awadel-Kariem, poster 1793 pre-sented at the 21st EECMID, Milan, Italy, 7 to 10 May 2011 [http://www.poster-submission.com/search/download/13593]) (59).However, fully realizing TLA requires “intelligent instruments”with flexible solutions and open architecture that are not yet avail-able. These intelligent instruments would be able to act on theirown interpretation of cultured samples and then make decisionsindependently of human analysis by using software rules. No in-strument is yet capable of handling diverse specimens and inter-preting all of the data that a culture plate provides, but once aninstrument can interpret the growth on plates, then true TLA inthe microbiology laboratory can occur. Until that time, automa-tion will work to aid humans on the bench with the processing andanalysis of specimens.

Informatics Challenges in TLA

Advances in informatics have allowed for TLA to become a real-istic option for the clinical microbiology laboratory (66). The flowof information from instrument to instrument in the automatedmicrobiology laboratory is a new challenge for clinical microbiol-ogy informatics. Traditionally, information has been transmittedbetween a single instrument and the LIS, sometimes only unidi-rectionally (61). Now, however, informatics pipelines are neededto facilitate the automated flow of data through and between mul-tiple instrument components (or workbenches), the LIS, and theHIS, so that the test ordered in the HIS is translated to the appro-priate inoculation of culture media by the automated instrumen-tation. Additionally, findings from one instrument in the systemmay need to be able to direct the actions of subsequent instru-ments in the work-up process, and this may include tasks such asbar code-driven automated specimen handling, which is commonin other areas of the clinical laboratory. As the options for instru-ment automation increase and the implementation of these sys-tems increases, the necessity for interfaces between different com-ponents from different vendors will also increase (59).

Automated acquiring of digital images or topography (x, y, andz coordinates) of culture plates is an emerging area of informaticsthat is often linked to TLA. Using analysis software to comparegrowth between scans can help to detect and characterize growthof microorganisms. The human component of digital cultureplate analysis can be undertaken remotely, which may result inmoving a microbiologist’s workspace from the benchtop to theoffice. It is possible that these new and increasingly automatedinstruments, which are able to capture images and monitorgrowth, may also be able to quantify microbial growth in ways thatwere previously interpreted qualitatively by humans (e.g., colonycolor, shape, texture, size, odor, time to growth, and pattern of




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hemolysis on sheep blood agar media). Incorporating the use ofpattern recognition software that can interpret these images couldenable the automated system to make decisions about how towork up, report results, and interpret the findings of a specimenwithout direct human input. Creating this type of intelligent in-strument would allow for even more complete TLA.

TELEMICROBIOLOGY AND AUTOMATED DIGITAL IMAGEANALYSIS

The capture, transmission, and remote or automated analysis ofphotomicrographs is becoming a reality in pathology (67–70).Remote analysis of microbiology (telemicrobiology) has been im-plemented only sparsely, but there is growing interest in the trans-mission of microbiology images for remote analysis and consul-tation (71). Remote analysis might be performed by an off-siteexpert or could be performed by a technologist in an office downthe hall. Studies that have used telemicrobiology have demon-strated its usefulness, and they have identified shortcomings thatneed to be considered in moving forward. Automated analysis ofmicrobiology images has the potential to transfer the burden ofrepetitive, time-consuming visual examinations from humans tocomputers. Automated image analysis is being explored for theroutine diagnosis of malaria and may also be employed for readingculture plates. With the advancement of telemicrobiology and au-tomated image analysis comes the need for implementation andvalidation guidelines, which are not yet available. The future oftelemicrobiology and automated digital image analysis is uncer-tain. However, interest in these technologies is growing, and asindicated above, there is a vision to integrate telemicrobiologyinto TLA systems.

Telemicrobiology

Microbiology testing often requires the visual analysis of an expertin order to properly interpret a sample or culture. However, ex-pertise is not always available at the location of need. For example,expert parasitologists are scarce, and the likelihood of a laboratoryhaving on-site expertise is low. The Centers for Disease Controland Prevention’s (CDC’s) DPDx service from the Division of Par-asitic Diseases fills this gap in expertise by freely offering aid inremotely diagnosing parasitic infections by telepathology. Tele-microbiology requires a team approach where the local group cansupply the clinical history and electronic images and the remoteconsultant can provide the diagnostic expertise after interpretingthe image(s). An image without relevant metadata is of limitedvalue. For this reason, it is important that DPDx requires that allimage submissions be accompanied by CDC Form 50.34 (version1.2; www.cdc.gov/laboratory/specimen-submission/pdf/form-50-34.pdf). This form requires patient information, includingsource site, stage of illness (e.g., asymptomatic, acute, or chronic),type of infection (e.g., lower respiratory tract, soft tissue, or sep-sis), therapeutic agents used during the disease course, epidemio-logical extent of disease (e.g., carrier, contact, or outbreak), travelhistory, vector exposure, and immunization history. The impor-tance of a patient’s clinical history is often essential in providingthe best diagnostic evaluation in medicine, and the clinical historyremains an important component in the remote analysis of mi-crobiology.

McLaughlin et al. performed a study to evaluate the use of tele-microbiology in analyzing captured images of Gram stains fromfresh specimens (72). While their study suggests that the remote

interpretation of Gram stains is feasible and accurate, it also re-vealed four potential shortcomings which need to be consideredcarefully before implementing telemicrobiology as a part of rou-tine clinical analysis: suboptimal field selection, suboptimal num-ber of fields analyzed, insufficient image quality, and inappropri-ate case identification due to technical error. In this study,discrepant results were attributed to shortcomings in all four ofthese areas.

Scheid et al. authored the first report describing the routine useof telemicrobiology in clinical practice. Their telemicrobiologysystem was first implemented in a German military field hospitalin 2003 (3). The system employed in this study used teleconfer-ence image software (i.e., DISKUS), which allowed the person inthe field (the photographer) to collect the images and to discussthe images with an off-site expert (the consultant). The study em-ployed the use of both high-power microscopic images for cellularmorphology analysis and low-power microscopic images for bac-terial colony morphology analysis. In the study, multiple micro-biology samples were analyzed, including thick and thin bloodsmears for malaria, stool samples for parasites, diluted bacterialsuspensions, Gram stains of patient specimens, and bacterial col-onies on plates. The authors reported that the telemicrobiologysystem was used as part of everyday microbiology testing, and itwas instrumental in identifying important diagnoses, such as ma-laria and dermal leishmaniasis. This study is important because itconfirmed the utility of routine telemicrobiology in a real-worldsetting in which the remotely stationed photographer was reliantupon the off-site consultant for analysis. The study also demon-strated one of the main shortcomings of static (“store-and-for-ward”) telemicrobiology, which is that the testing sensitivity islimited by the acuity and expertise of the photographer. As theauthors state, “when transmitting static images in bacteriologyand parasitology, the expertise of transmitting users is of decisiveimportance, for they cannot transmit what they do not notice.”Overcoming this shortcoming could potentially be achieved byallowing the off-site consultant to analyze the entire glass slide viarobotic telemicroscopy and/or whole-slide imaging (WSI) insteadof only analyzing a few selected static microscopic fields of inter-est. To date, studies validating the use of WSI have been limitedlargely to anatomical pathology (i.e., surgical pathology, frozensection use, and cytopathology). Moreover, hardly any of the cur-rent WSI scanners are capable of digitizing glass slides while using100� (oil magnification) objectives, which are often needed tobest visualize microorganisms. This may explain why prior studiesusing WSI scanners to routinely digitize glass slides at a magnifi-cation of �20 reported that pathologists were unable to clearlyvisualize microscopic organisms (73).

Automated Digital Image Analysis

Automated or semiautomated digital image analysis has been im-plemented successfully in cytology, cytogenetics, and hematologylaboratories, in which repetitive, high-volume image analysis oc-curs (69, 70). However, automated image interpretation in theclinical microbiology laboratory has not been widely imple-mented. The majority of the work published in the area of micro-biology digital image analysis has been focused on tests requiring alarge amount of human time for analysis, such as the analysis ofsputum smears for acid-fast bacilli or the analysis of blood smearsfor Plasmodium spp.

Although automated microscopic digital image analysis tools

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are not being used routinely in clinical microbiology, one canimagine their potential utility for screening slides for acid-fastbacilli (74), interpretation of colony Gram stains (75), or simplebacterial culture interpretations (e.g., colony counts) (76–79).The use of automated digital image analysis can also increase theamount of information obtained from samples (80), standardizethe interpretation of samples (77–79), and/or decrease the humantime required to analyze samples (81–83).

Outside the United States, the potential role of automated mi-croscopic image analysis in detecting blood parasites, such as Plas-modium, has been recognized because of the repetitive and high-volume nature of such testing. Although blood parasites aretypically identified by the hematology laboratory, not the micro-biology laboratory, the role of detection of parasites by automatedimage analysis is relevant to the discussion of microbiology infor-matics. Numerous studies have been published which describe theuse of automated digital image capture and analysis to determineparasite presence, load, and/or species (84–90). Work in this areais ongoing to facilitate rapid and convenient remote analysis. Forexample, one study describes the use of automated image collec-tion, analysis, and subsequent remote consultation by short mes-sage service (SMS) messaging a composite image of infected cellsto an off-site expert for confirmation (85). Studies have identifiedthat automated digital image capture and analysis have the poten-tial to be used as a low-cost screening tool to detect malarial in-fections in resource-poor settings (86), and results are encourag-ing, with sensitivities approaching 100% in early studies (89).

The Future of Telemicrobiology and Automated DigitalImage Analysis

Regulations, guidelines, and administrative matters (e.g., accred-itation and malpractice matters) related to the practice of telepa-thology are beginning to emerge (67). While many of the docu-ments related to these topics do not specifically addresstelemicrobiology, they are applicable to the practice of digital mi-crobiology. Like the CDC’s DPDx consultation program, remotetelemicrobiology allows microbiology laboratories to have rapidaccess to off-site expert opinions. Currently, formal microbiologyexpert consults are typically performed by sending an isolate orsample to another laboratory for repeated or additional work-up.This type of consult can drastically lengthen the specimen’s turn-around time and often results in some amount of redundant test-ing. In contrast, informal expert consults occur daily between col-leagues (e.g., ASM’s ClinMicroNet Listserv), but this type ofconsultation may in some instances increase the laboratory’s lia-bility by relying on an unofficial opinion. Building a virtual infra-structure for remote telemicrobiology could help to prevent suchliability, delays, and redundant testing that accompany traditionalconsultations.

Some authors believe that telemicrobiology and automated dig-ital image analysis should be incorporated within the local labo-ratory as part of a larger system of TLA where cultures are exam-ined on a computer monitor in an office or by automated patternrecognition software instead of at the bench (64, 91). Initially, thesimplest of culture interpretations (i.e., no growth) could be in-terpreted by automated image analysis software and reported ac-cordingly, without the need for human verification, which is sim-ilar to how negative blood cultures are currently reported. It hasbeen suggested that this type of system may be helpful in increas-ing productivity and accuracy and decreasing exposure to patho-

gens (64, 91). A notable potential advantage to this approach is theability to electronically preserve and recall images of a sample’scolony morphology at earlier time points, recall images from apatient’s previous testing, and quickly cross-reference images ofconcurrent culture specimens from the patient that were sampledfrom different sources (59, 64). Providing image records of cul-ture plates is one way in which telemicrobiology and TLA couldprovide information beyond what is currently available in theclinical microbiology laboratory. These images could also be usedin quality assurance, proficiency testing, and training programs.

MICROBIAL IDENTIFICATION AND CHARACTERIZATIONUSING DATABASES

Databases are commonly used for the identification of microor-ganisms. Common databases include biochemical reaction data-bases, matrix-assisted laser desorption ionization–time of flight(MALDI-TOF) mass spectrum databases, and nucleic acid se-quence databases, and less frequently, high-performance liquidchromatography databases are used for the identification of my-cobacteria (92–94). In the clinical microbiology laboratory, one ofthe more familiar and tangible databases utilized may be that ofthe analytical profile index (API; bioMérieux), which identifiesisolates by their pattern of biochemical reactions. Currently, manymicroorganisms are identified using biochemical testing on auto-mated instruments that use databases and software for profilematching. In some laboratories and situations, this may turn theinstrument database and software into somewhat of a black box,but humans need to maintain their knowledge and understandingof biochemical reaction interpretations in order to recognize po-tential errors, to troubleshoot, and to ensure that accurate resultsare obtained. New to the scene of clinical microbiology areMALDI-TOF instruments such as the bioMérieux Vitek MS andBruker MALDI Biotyper instruments, which use spectral data-bases for the identification of microorganisms. These instrumentsidentify an organism by comparing its mass spectrum to a spectraldatabase of organism “molecular fingerprints.” These instru-ments have spectral databases curated by their manufacturers, buta user may be able to add spectra to the database. The third com-monly used type of database for microbial identification is thenucleic acid sequence database (e.g., NCBI’s GenBank), which isused to identify microbes by their unique nucleic acid sequences.Although identification of an organism by its genetic material isnow a gold standard, using these databases to interpret the identityof a microbe is not always as straightforward as one might hope.

Biochemical Databases

Manufacturers have developed and maintain databases that areused for the identification of microorganisms via their biochemi-cal metabolism. The identification system compares the qualita-tive findings of multiple biochemical tests to its database of knownreactions that are specific for a group of taxa, and by this means thesystem is able to determine the most probable identity of an un-known isolate. Often, different test panels are required to be usedfor different groups of microorganisms (e.g., Gram-positive bac-teria, anaerobic bacteria, yeast, etc.), and the selection of an inap-propriate test panel can affect the sensitivity and specificity of thetest.

A proprietary database is supplied as part of a manufacturer’smethod for identification, or the database is incorporated into themanufacturer’s expert system. Except for verification or valida-




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tion testing, the end user (e.g., the clinical microbiology labora-tory) has little responsibility for maintaining these databases. Anadvantage of these databases is that they are curated by the man-ufacturer and are part of an FDA-approved in vitro diagnosticmethod, but this can also be a disadvantage. Because only themanufacturer can manipulate the database after receiving FDAapproval, it can be cumbersome to make database changes. It canbe a decade or more before a newly described and/or rarely iso-lated species makes its way into the database (e.g., Serratia ure-ilytica was described in 2005 [95] and is not yet in some of themajor manufacturers’ databases [as of 2014]).

MALDI-TOF Databases

A MALDI-TOF identification system compares an averaged massspectrum from an unidentified microorganism to its database ofknown spectra. Pattern-matching software identifies the taxon(s)to which the unidentified microorganism is most similar. Gener-ally, any bacterium or yeast that grows routinely in the microbi-ology laboratory can be identified using the same preparationmethod, and unlike the case with biochemical identification data-bases, prescreening of the isolate to select an appropriate test paneland database is not routinely necessary.

Currently, Bruker or Vitek systems are used for microbial iden-tification by MALDI-TOF. The software in these systems com-pares a clinical sample to the system’s respective database, butthese two systems use different algorithms for organism identifi-cation. Bruker’s system converts the raw spectra from a test sam-ple into a numerical list of peaks (MALDI Biotyper software, ver-sion 3.1 [Bruker Daltonik GmbH]; Help menu ¡ MALDIBiotyper OC workflows ¡ classifying unknown samples bymatching their spectra or MSPs). These peaks are then comparedto the reference database. First, if all of the most prominent peaksin a reference spectrum (typically 70 peaks) are present in the testsample, then a score of 1 is assigned. If none of the peaks in areference spectrum are present in the test sample, then a score of 0is assigned. Second, and similarly, if all of the most prominentpeaks in the test sample are present in a reference spectrum, thena score of 1 is assigned, and if none of the peaks in a test sample arepresent in a reference spectrum, then a score of 0 is assigned.Third, the symmetry of the peak intensity (peak height) of thereference spectrum and the test sample is determined. Perfectsymmetry is assigned a score of 1, and complete asymmetry isassigned a score of 0. These three scores, ranging from 0 to 1, aremultiplied together and multiplied by 1,000. The product is con-verted to a logarithmic value to base 10, and this gives the finalscore. Bruker suggests that a final score of �2.0 is typically suffi-cient to classify a test sample to the species level.

Vitek’s MALDI-TOF system approaches the identification ofmicrobes slightly differently from the Bruker method. Vitek’s al-gorithm is well described in the supplemental material preparedby Rychert and colleagues (96). The Vitek algorithm classifies eachidentified mass peak as belonging within a “mass bin,” where theaverage bin contains peaks that fall within a range of about 10 Da.The software uses supervised machine learning to identify binsthat correlate with a taxon. For example, a bin that is always filled(sensitive) and only filled (specific) when a given taxon is analyzedwill be assigned a large positive weight. In contrast, a bin that isnever filled when a given taxon is analyzed but is often filled whenother taxa are analyzed will be assigned a large negative weight.Bruker’s software is also capable of positively weighting peak im-

portance, but machine learning is not used to facilitate appropri-ate weighting of peaks. In the Vitek system, in comparing a testsample to a reference spectrum, each peak-containing bin in thetest sample is multiplied by the weighted coefficient assigned tothe bin as determined by the reference spectrum, and these prod-ucts are summed. A confidence value as to whether or not the testsample matches the reference spectrum is determined based uponthe sum from the test sample. A confidence value of �60% withsufficient difference from the confidence values for other taxa istypically sufficient to classify a test sample to the species level.

If a user chooses to be proactive in developing a laboratory’sMALDI-TOF identification system, the user can add to the sys-tem’s spectral database as he or she encounters microorganismsthat are not well represented in the database. In recent reviews ofMALDI-TOF analysis, authors repeatedly emphasized the impor-tance of actively adapting the MALDI-TOF spectral database tomeet the local needs of the laboratory (97, 98). Although MALDI-TOF analysis is very accurate overall, errors or failures in identifi-cation that do occur can be traced to inadequate databases orclerical errors in building these databases, so development of anin-house database should be performed carefully, emphasizingthe need to maintain and improve quality (6, 97).

In identifying an organism, it may be most appropriate to com-pare the spectrum of an unknown isolate to the most rigorouslycurated database first (e.g., a database that is part of an FDA-approved system) and subsequently to compare the spectrum topotentially less rigorously curated databases (e.g., a research useonly [RUO] database, an in-house database, or a database sharedby a colleague) if the well-curated database poorly identifies theorganism. MALDI-TOF analysis also has the ability to attempt toidentify multiple organisms from a single inoculum, but it is notyet clear in which circumstances this type of database query ismost appropriate.

Although MALDI-TOF databases currently being used for clin-ical applications are either proprietary from the manufacturer,developed in-house, or a mixture of the two, efforts are beingmade to create public spectral databases that are freely available(99). However, many variables are involved in generating spectra,so the quality and utility of a public database for clinical use re-main to be proven.

Nucleic Acid Databases

The use of genotyping to identify microorganisms is becomingincreasingly common in the clinical microbiology laboratory (55,100–102). Nucleic acid databases are commonly used for the iden-tification of microbes. Typically, genotyping assays target DNAregions with high interspecies variation and low intraspecies vari-ation that are flanked by conserved regions of DNA for whichprimers can be designed (e.g., the 16S rRNA gene, hsp65, and theinternal transcribed spacer [ITS]). These regions are employed toidentify a microorganism by comparing the isolate’s nucleic acidsequence with a reference database. However, using these data-bases is not as straightforward as using the databases of biochem-ical reactions or MALDI-TOF spectra. Additionally, WGS of mi-croorganisms is moving toward becoming a routine test in clinicalmicrobiology, with informatics challenges of its own (101, 103–106).

Database challenges. Nucleic acid sequence analysis is usually alaboratory-developed test, with the local microbiology laboratorybeing responsible for developing, validating, and maintaining the

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test. There is no single nucleic acid database upon which users rely,and it is common that large laboratories use a hybrid databasecomprising reference sequences created in-house and referencesequences derived from external databases. Possibly the biggestchallenge in using DNA sequence data to identify microorganismsand their genomes is that the largest and most comprehensivenucleic acid databases (e.g., GenBank) are also the most poorlycurated and error-prone databases because they are created by acommunity of users with no peer review process. It has been wellestablished that errors are present in open public databases, andthese can lead to incorrect microbial identification (107–109).Conversely, the most trustworthy databases that are more closelycontrolled (e.g., MicroSeq) contain fewer sequences and are there-fore less exhaustive in their coverage, and potentially less fre-quently useful (100, 110, 111).

Considerations in using public databases for microorganismidentification. The current lack of freely available, robust, andreliable methods for identifying clinical bacteria by genotyping isrecognized as a barrier (108). Tools are available that can facilitateand streamline the traditional means of analyzing and comparingpublic database sequences with microbial DNA sequences of in-terest, but these approaches require significant hands-on time andare not designed for high-throughput clinical work (112). Othersare making efforts to create more user-friendly and clinically ori-ented tools that translate raw sequence data into an identifiedmicroorganism result (113–115), but these tools are not widelyused or accepted. Multiple papers have proposed algorithms orguidelines suggesting how best to approach nucleic acid databasequeries and to identify an appropriate result (100, 107, 108, 111,113, 116). These varied proposals highlight the lack of consensusregarding how best to use DNA sequence data together with ref-erence databases to identify microorganisms and to further ensureclinical reliability of results.

Tortoli cautions that even a perfect match of a queried nucleicacid sequence with a reference database sequence should not beassumed to correctly identify the microorganism. Rather, he sug-gests that users of public databases perform additional investiga-tions before deciding that the sequence being queried is truly pos-itively identified (111). It is appropriate to consider the followinginformation in the reference database regarding the reference se-quence being considered a potential match (111):

1. Specimen source. Reference strains can provide more reli-able identification than clinical or environmental strains,and it may be most appropriate to identify an organism as amore poorly matched reference strain than a more closelymatched nonreference strain.

2. Date of submission. Microbial taxonomy and nucleic acidmethods are frequently changing, and findings from morecurrent submissions may be preferred over older submis-sions.

3. Submitter. The reliability (or lack thereof) of the personwho submitted the sequence to the database should be con-sidered.

4. Maximum identity. Basic Local Alignment Search Tool(BLAST) searches can rank the alignment of the queriedsequence to that of sequences in the database by using dif-ferent parameters, and the search results may be ranked bysimilarity score by default. However, it is best to rank the

alignments by “maximum identity” instead of “similarityscore.”

One consistent difficulty in identifying microorganisms bytheir DNA is determining when an imperfect database match is“close enough” to allow the microbiologist to identify the queriedsequence as belonging to the same taxon as the imperfect match inthe database. Commonly, arbitrary percentages of sequence ho-mology are employed as cutoff values (100, 116), but the amountof homology that should be required to deem a sequence a matchshould ideally be based on both the specific region of DNA beingsequenced and also the taxon being considered the microorgan-ism’s potential identity. For example, the 16S rRNA gene is com-monly useful in delineating bacterial species, but the 16S rRNAgenes of mycobacteria are notoriously similar between species. Soa 99% match of a 16S rRNA gene to a staphylococcal speciesshould be interpreted differently than a 99% match to a mycobac-terial species (100). Additionally, it is recommended to considerthe phenotypic and genotypic findings together (not indepen-dently) in attempting to identify a microorganism, as this mayprevent misidentifications (100, 108). Identifying organisms bytheir DNA sequences has become a gold standard in microbialidentification. However, interpretation of a sequence by compar-ing it to a reference database has caveats, and it is important toconsider these challenges in attempting to interpret a DNA se-quence.

WGS and MGS. As the cost of nucleic acid sequencing declinesand the analysis pipelines improve in speed and ease of use, moreand more voices are heralding the advent of clinical microbialWGS and metagenomic sequencing (MGS) (101, 105, 106, 117–122). WGS is typically performed by using high-throughput se-quencing (HTS) to analyze nucleic acid fragments from an iso-lated pathogen; however, WGS can also be attempted on apathogen that has not been isolated and cultivated. The sequencednucleic acid fragments are aligned with each other and assembledinto contiguous DNA sequences by use of genome assembly andalignment software. WGS is typically done in an attempt to char-acterize an infectious agent. In contrast, MGS is performed usingHTS to analyze nucleic acid fragments directly from a patientsample or after enriching the sample for nucleic acids of interest.MGS is typically done in an attempt to characterize an infection ormicrobial community. The advent of WGS and MGS in the clin-ical microbiology laboratory will hinge on informatics capabilitiesand resources that will be greater than those of any other clinicalmicrobiology informatics endeavor to date. Clinical WGS andMGS provide potential advantages for better characterizingpathogens and the microbiome, but they also present new infor-matics challenges.

(i) Potential advantages of routine clinical WGS and MGS.HTS methods provide large amounts of high-quality informationin a relatively short period. WGS and MGS can offer informationabout microorganisms, including antimicrobial resistance genesor pertinent mutations, virulence and toxin genes, organism iden-tification, and epidemiological typing by single nucleotide poly-morphism (SNP) or multilocus sequence typing (MLST) analysis(118, 121). WGS and MGS may provide clinically relevant infor-mation regarding exactly which unique genes, pathways, and or-ganisms are present in an infection (123–127). Emerging evidencesuggests that WGS improves the sensitivity and resolution of lab-oratory-based surveillance (106, 128). Specifically, WGS enhances




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the identification and tracking of outbreaks in community andhospital settings through the recognition of covert clusters as wellas reconstruction of transmission events. Recent proof-of-con-cept studies have demonstrated the superiority of WGS to currentsubtyping methods (119, 129, 130). Work is actively being done tocreate a Global Microbial Identifier (GMI) system that will enableusers to submit a genome sequence as a query to compare to theGMI database (http://www.globalmicrobialidentifier.org/About-GMI). The GMI system would then report information about thestrain, such as its identity, treatment options, and the global loca-tion(s) where the strain or similar strains have been identified, tothe user. MGS also has the power to investigate the microbiomeand complex infections (55, 56, 131, 132), and it can detect fastid-ious and difficult-to-culture organisms, such as that reported in acase of neuroleptospirosis (133).

(ii) Informatics challenges. Bioinformatics approaches are var-ied across the global community. Currently, there is no platformoffering complete data processing, database management, anddata warehousing capabilities; therefore, institutions are currentlyrequired to establish their own data analysis pipelines or to linktogether a variety of commercial, open-source, and in-house soft-ware packages and data sets that contain information about mi-crobial genomes of interest. Establishing an efficient informaticspipeline for data generation, analysis, and storage for WGS orMGS is absolutely necessary if clinical laboratories are going toaccurately and cost-effectively interpret the data within a reason-able time frame. These pipelines should work to improve the qual-ity of data and to minimize the amount of human time requiredfor analysis by maximizing the use of software to eliminate tech-nically poor data (e.g., chimeras and misreads) (134–136), mean-ingfully compare the sequences to an appropriate database or ref-erence strain, and annotate and/or interpret the comparison (Fig.4) (137–140).

(a) Data analysis challenges. Analysis of WGS and MGS datarequires multidisciplinary teams of microbiologists, informati-cians, clinicians, and epidemiologists, with institutional supportfor resources and personnel. Microbiologists and epidemiologiststypically need to be upskilled in genomic sequencing and its ap-plications.

The goals of using WGS and MGS are not always the same, butthe goals should guide the analyses. In using WGS to identify anewly identified microorganism, aligning and closing the genomeare useful steps to ensure full characterization and optimal primerdesign for future rapid nucleic acid tests. However, in using WGSor MGS to elucidate virulence genes within a strain, aligning thegenome is not as important as annotating virulence genes’ func-tions. In using genomic sequencing for outbreak surveillance pur-poses, the most important step in analysis is the identification ofdiscriminatory features (e.g., SNPs, mutations, or a unique gene)(141). The motivations for clinical WGS and MGS can be varied,and it is important to consider the goal(s) of the assay in deter-mining how the data will be analyzed. If multiple analysis goals arepursued, then it may be most effective to use multiple parallelpipelines for the analyses, as reported for the genomic analysis ofcancer (142).

(b) Data storage and sharing challenges. Routine and frequentuse of WGS and MGS has the potential to rapidly produce largevolumes of digital information that requires storage. However, itis not yet clear which information needs to be stored and whichcan be discarded. For example, does the entire data set from a

sequencing run need to be stored, or only the aligned sequences?Or only the identified virulence genes and drug resistance mark-ers? In using next-generation sequencing, many laboratories haveopted for in-house solutions instead of cloud computing for stor-ing data. At present, Health Insurance Portability and Account-ability Act (HIPAA)-compliant cloud storage solutions to handleclinical work are limited. Currently, significant variability in thestoring of sequence and secondary data files exists among labora-tories. The most common approach to data storage is to rely onexternal hard drives, but this is not a sustainable plan going for-ward. A scalable capacity for warehousing, data compression, andsystematic backup is needed. As noted, external cloud services arecurrently not considered a viable option because they typically donot comply with medical confidentiality regulations.

FIG 4 Proposed informatics pipeline for microbial genomic analysis in theclinical microbiology laboratory. The clinical purposes of genomic sequencingcan be varied, and multiple parallel pipelines and their associated expert sys-tems can be used to analyze data depending on which endpoints are desired.One goal of the microbiology laboratory is to unite the information obtainedfrom these various pipelines into a clinically relevant and concise report thatcan guide patient care. A second goal is to characterize a potential agent of anemerging outbreak so that future samples can be monitored for this agent ofinterest. The genomic characterization of this outbreak agent can be sharedwith public health agencies, which can also alert other laboratories to monitortheir samples for this emerging agent. Solid arrows show the flow of analysis.Dotted arrows show the transfer of information about outbreak agents intoexpert system knowledge databases. Wet laboratory analysis is shown in red,and dry laboratory analysis is shown in blue. Information available to clini-cians is shown in yellow. Asterisks mark data that may be appropriate forlong-term storage.

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The complications of sharing microbial organisms and theirsequences recently made headline news as a disagreement betweenreferring and reference laboratories occurred (143). Ownership ofnucleic acid sequences and its relevance to clinical laboratories areareas with ongoing legal debate and ramifications (144). The in-tellectual property rights related to the sequencing of Middle Eastrespiratory syndrome coronavirus (MERS-CoV) and the subse-quent patenting of a diagnostic assay have reinforced the legal andethical challenges of sharing microbial genetic material and datatransfer (143).

(c) The need for guidelines. The standardization of quality met-rics, such as calibration standards, validation methods, acceptabledata reliability, test robustness, result reproducibility, and datastorage, are critically needed for microbial WGS. Additionally,proficiency testing programs that cover both wet (in vitro testing)and dry (in silico analysis) portions of genomic assays are urgentlyrequired. Appropriate data storage guidelines regarding the typeof data that should be stored, the means of storage, and the dura-tion of storage are necessary, as the cost of storage will soon exceedthe cost of data generation. Another question that is relevant inthis era, which is placing a growing emphasis on patients’ geneticrights, is the unresolved question of who owns the data and whoseresponsibility it is to procure it. Are there certain sequences thatare reportable to public health agencies? Should an individual’smicrobiomic data be protected as carefully as an individual’s hu-man genomic data (145, 146)?

DNA data in the future. Although current efforts to improvemicrobial identification are still focused on creating better ways toculture organisms (121), the use of culture-free identification bymeans of massively parallel nucleic acid sequencing has shownpotential in clinical studies (55, 56, 131). The use of nucleic acidanalysis directly from samples can overcome culture bias (55, 147,148). In the future, genomic or metagenomic microbial analysesmight replace or augment the current approach in clinical micro-biology of culturing and identifying isolated microbes. Also, ourperspective of what constitutes a species or a taxon might funda-mentally change as DNA sequencing continues to become morecommonplace and as the computational tools associated withthese analyses continue to improve. Microbiology systematicsmight fundamentally shift to a taxonomic system based upongenotyping (149).

The identification and classification of infection might alsochange dramatically. Computer algorithms can analyze and clas-sify complex infections by using more variables than can currentlybe considered by a clinician or microbiologist. Analyses may un-ravel the variables that enable a pathogenic microbial ecosystem totake hold in a susceptible host. In the future, the microbiologylaboratory might be expected to understand, evaluate, and recom-mend therapies based upon the analysis of pathobiomes (150),functionally equivalent pathogroups (126), supragenomes (124,151), epigenomes (152, 153), or impaired or pathogenic micro-biomes (154–157). The use of WGS and MGS may provide morecomplete quantitative and qualitative identification of all of themicrobes and relevant resistance and virulence genes that are pres-ent in a sample, and these types of analyses have the potential tobetter direct patient care (55, 56, 101, 122, 124). Therapies such asprobiotics or fecal microbiota transplantation may be indicateddepending on the results of the laboratory evaluation of a patient’smicrobiome (154, 155).

REPORTING TO PUBLIC HEALTH AGENCIES AND DETECTINGOUTBREAKS

Local clinical microbiology laboratories are responsible for re-porting the identification of certain infectious agents to variouspublic health agencies (city, county, and state). These reports areused by public agencies to track incidences and attempt to identifyoutbreaks. The reportable findings are somewhat dynamic, oftenwith annual modifications, so informatics support requires ongo-ing vigilance to keep up with expectations of public health author-ities. Additionally, the local clinical microbiology laboratory isinvolved in recognizing local institutional outbreaks and workingto prevent them (158). Informatics tools and electronic commu-nication are key in efficiently communicating with public healthagencies and rapidly identifying outbreaks (Fig. 5). Once identi-fied, there are needs for data and reports throughout the incident,often with evolving parameters of interest. Having informaticsexperts participate in incident management from the beginning,to assist those in the microbiology laboratory as well as those man-aging the outbreak outside the laboratory, is optimal.

Reporting to Public Health Agencies

Laboratory reporting of reportable infectious agents to publichealth agencies or departments is not currently a seamless elec-tronic process. A first step in streamlining the reporting process isto have all the stakeholders share a nomenclature. The frontrun-ner for this standard is a combination of using Laboratory LogicalObservation Identifiers Names and Codes (LOINC) to identifythe microbiology test that was ordered, using the SystematizedNomenclature of Medicine (SNOMED) to identify the results as-sociated with the test that was ordered, and arranging the trans-mission syntax in accordance with health level 7 (HL7) standards(Fig. 3) (159–161). Recently, work began to formally link LOINCand SNOMED (162). The CDC has been working to facilitate theelectronic transmission of information from the local microbiol-ogy laboratory to public health agencies through the use of theNational Electronic Disease Surveillance System (NEDSS) (163,164). The goal of NEDSS is to automate the reporting and analysisof public health surveillance data (163). As of 2012, 46 of 50 statesin the United States use information systems that are NEDSS com-patible for reporting notifiable microbial findings to the CDC(161). The goal of electronic reporting to public health agencies isto capture more reportable events, with increased timeliness andcompleteness, than those obtained with paper reporting, and thispossibility has been demonstrated (7, 159).

Outbreak Detection

Timely detection of infectious disease outbreaks is needed glob-ally, regionally, and locally. The responsibility of identifying wide-spread outbreaks is largely shouldered by state public health agen-cies, the CDC, and the World Health Organization (164).Detecting, tracking, and modeling outbreaks are areas of contin-ued interest in academia and public health (165, 166). Rapid de-tection is of key importance, because decreasing the time to detec-tion can significantly decrease the adverse impact of an outbreak(167).

Detection of evolving antimicrobial susceptibility patterns.Detection of the emergence or increasing prevalence of antimicro-bial-resistant organisms is a concern for both local and globalclinical microbiology. Software exists that can link the local labo-ratory with other regional and global laboratories in the bidirec-




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tional exchange of antimicrobial susceptibility data. Two of theseprograms which have been used since the 1990s are WHONETand The Surveillance Network (TSN) (30). Until recently, bothWHONET and TSN were used to analyze antimicrobial suscepti-bility data from multiple laboratories to determine patterns ofmicrobial susceptibility, and these networks continue to be usedto share information (164, 166, 168). However, as of 2014, theTSN is not supported and is no longer available.

WHONET. The WHONET system (http://www.who.int/drugresistance/whonetsoftware/en/) was developed a quartercentury ago by the WHO Collaborating Centre for Surveillance ofAntimicrobial Resistance. At the center of the system is theWHONET software, which is freely downloadable (http://www.whonet.org/dnn/Software/tabid/68/language/en-US/Default.aspx). TheWHONET system works by translating laboratory data that arecurrently available in an LIS to a WHONET universal file format(169). A software utility, BacLink, can be used to facilitate thistranslation. Because the data are translated into a universal fileformat, the local data can be shared and compiled so that regional,

national, or international groups can analyze the data (164). Thesystem is designed to enable individual laboratories (or groups oflaboratories) to manage their AST results, identify the emergenceof resistant microbes, identify the spread of resistant strains, andidentify trends in AST quality control testing (170, 171). Analyseswhich WHONET can facilitate include examples such as the iden-tification of changes in Escherichia coli isolate resistance within acountry over time (172, 173), prospective surveillance of Shigellaoutbreaks in a country (166, 174), and, potentially, a means ofglobal strain tracking (175).

Detection of regional and global outbreaks. Although locallaboratories are not usually considered directly responsible foridentifying regional and global outbreaks, local laboratories areessential in providing information to public health agencies sothat identification of these outbreaks is possible. The role that thelocal laboratory plays in the detection of global outbreaks is typi-cally confined to its responsibility to report the detection of noti-fiable infectious agents to the public health agencies which overseethe laboratory’s region (176). However, the detection of some

FIG 5 Agents of notifiable infectious diseases and their associated data often travel through layers of agencies, including clinics, laboratories, and public healthregistries. This generic figure depicts the flow of information associated with a patient diagnosed with gastroenteritis who is tested for Salmonella. Althoughhuman input or interpretation may be required at various nodes, information is often generated, transmitted, and received digitally (solid arrows). Variousterminology 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 incentiveexists to create a robust electronic interface between these nodes. Therefore, transmitting information by paper (via fax or post) is most likely to be used betweennodes that rarely communicate with each other. Electronic reporting can expedite information communication and therefore expedite the detection of outbreakclusters. Information between the patient and clinician is often exchanged verbally (dotted arrows), although electronic communication with patients is likely tobecome more common. (Courtesy of The Royal College of Pathologists of Australasia, adapted with permission.)

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local outbreaks carries global public health significance, and allidentification of global outbreaks begins locally. One way to facil-itate timely identification of regional outbreaks is to provide theregional public health agencies with clinical and microbiologicalinformation from the local laboratory in as timely a way as possi-ble. The rapidity of regional outbreak identification can be en-hanced by using electronic reporting by local laboratories (7, 159).

Detection of local outbreaks. Local clinical microbiology lab-oratories actively play a role in the detection of local disease out-breaks. Local outbreak identification is best achieved by activesurveillance by infection preventionists in partnership with theclinical microbiology laboratory and in combination with “virtualsurveillance,” in which mathematical algorithms are used to iden-tify possible outbreaks independently of hypothesis-based orquery-based investigations (29). These local outbreaks could becommunity-acquired infections (e.g., annual seasonal influenzaoutbreaks) or hospital-associated infections (e.g., nosocomial in-fections). Software has been used successfully to mine the data ofclinical microbiology laboratories in search of aberrancies thatmay be an indication of a community outbreak (177). Softwarehas also been used to identify nosocomial infections by autono-mously reviewing laboratory data and medical records, and thissoftware has demonstrated increased sensitivity over the manualanalysis of records for the identification of nosocomial infections(17, 51, 178).

Surveillance. Outbreak detection can be accomplished by atleast these three ways: prospective organism surveillance, labora-tory surveillance (including virtual surveillance), and syndromicsurveillance. Prospective surveillance using environmental bio-sensors is the most rapid means of detection (167), but the localmicrobiology laboratory does not typically play a role in this typeof screening. The local laboratory is integral to the traditionalsurveillance systems that are in place, which typically identify out-breaks after they have occurred. A goal of laboratory outbreaksurveillance is to transform this surveillance from a retrospectivemeans of surveillance into a real-time means of proactive surveil-lance by using informatics tools to continually analyze laboratorydata, with the goal of identifying outbreaks as they are occurring(179). Syndromic surveillance is increasingly being used to at-tempt early identification of outbreaks or bioterrorism (180, 181).One of the most highly publicized systems using syndromic sur-veillance is Google Flu Trends (182, 183).

Unlike prospective organism surveillance and laboratory sur-veillance, syndromic surveillance does not rely on definitive clin-ical diagnoses or the identification of microbes. Instead, syn-dromic surveillance infers the presence of an outbreak byanalyzing patient signs and symptoms or analyzing public behav-ior (184, 185). Syndromic surveillance is performed completely insilico and has the potential to identify an outbreak before the lab-oratory can identify a causative agent. This surveillance does notrequire the implementation of new physical tests but relies on theanalysis of already available information. Obtaining informationfrom Internet users is often a part of syndromic surveillance (185).For example, Yelp.com has been used successfully by public healthofficials to identify food-borne illness outbreaks in New York City(186). Syndromic surveillance has a lower specificity of detectionthan laboratory surveillance, so it has been suggested that syn-dromic surveillance can be used as a screening tool for detectingan outbreak, which can then be confirmed using laboratory sur-veillance. In this situation, detection of a potential outbreak by

syndromic surveillance would alert the local laboratory to increaseits vigilance or adjust its testing protocols so as to increase thelaboratory’s sensitivity and rapidity of identifying an outbreak(167). Although this type of integrated system is feasible andwould improve patient care, it has not yet been employed.

Integrative Public Health Informatics Approaches of theFuture

Increasingly, standardized electronic reporting, such as NEDSS, isbeing used by local laboratories to report to their regional healthagencies, and standardized electronic reporting correlates withimproved completeness and rapidity of reporting, which allow forquicker detection of regional outbreaks. Additionally, data analy-sis surveillance software has demonstrated improved detection oflocal disease outbreaks beyond that which humans have been ableto identify without these tools. With the growing clinical use ofWGS and MGS, the need for globally integrated informatics toolsthat can identify and characterize whole genomes, such as GMI,are needed for outbreak detection (187). Integration of prospec-tive, laboratory, and syndromic surveillance systems in combina-tion with rapid standardized reporting of events has the potentialto improve outbreak detection beyond what any one of these sys-tems can do independently.

CONCLUSIONS

The clinical microbiology laboratory is required to generate, ana-lyze, and interpret an ever-increasing amount of information. In-corporating the use of informatics tools to improve the quality oflaboratory workflow and processes is paramount for data to effec-tively and efficiently be evaluated and communicated. Informaticstools can help microbiologists to more capably keep track of spec-imen work-ups in the laboratory, automate their workloads, iden-tify clinically relevant characteristics of microorganisms, remotelyshare digital images for teleconsultation, quickly distribute accu-rate and appropriate results, perform more thorough and rapiddisease surveillance, and (most importantly) provide patients andthe public with better health care. The continued developmentand implementation of informatics tools are needed in order tocontinue to help the laboratory to produce, interpret, and com-municate the most useful information. Guidance is required inorder to best develop and implement these informatics tools, spe-cifically in areas of telemicrobiology and microbial MGS andWGS. When used properly, informatics tools can help the clinicalmicrobiology laboratory to do more with less while improving thequality of patient and public health care.

ACKNOWLEDGMENTS

V.S. was supported by the National Health and Medical Research Coun-cil’s Career Development Award.

None of the authors have any financial interests or support from in-stitutions or companies mentioned in the article.

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Daniel D. Rhoads is a Chief Resident who istraining in clinical pathology at the Universityof Pittsburgh Medical Center. In 2004, he re-ceived a B.S. in biology from Millersville Uni-versity of Pennsylvania and completed medicaltechnology training at Lancaster General Col-lege of Nursing and Health Sciences. In 2006,Dan began working as a research scientist at theSouthwest Regional Wound Care Center andthe Research and Testing Laboratory in Lub-bock, TX. While studying chronic woundsthere, he acquired his interest in biofilm infections and his desire to unravelthe complexity of microbial communities by using informatics tools. In2012, Dan received his M.D. from Texas Tech University Health SciencesCenter, and he has recently taken coursework in the Department of Biomed-ical Informatics at the University of Pittsburgh. Dan has accepted a MedicalMicrobiology Fellowship at the Cleveland Clinic for the 2015–2016 aca-demic year.

Vitali Sintchenko is a tenured Associate Profes-sor of the Sydney Medical School at the Univer-sity of Sydney and Director of the Centre forInfectious Diseases and Microbiology-PublicHealth at Westmead Hospital in Sydney, Aus-tralia. He is a Fellow of the Royal College ofPathologists of Australasia and earned his Ph.D.in medical informatics from the University ofNew South Wales. He is a member of the MarieBashir Institute for Infectious Diseases and Bio-security. His research focuses on infectious dis-ease informatics, bacterial genomics-guided public health laboratory sur-veillance, and disease control. He has authored two books and more than 150scientific papers. He has been actively involved in disease outbreak investi-gations and the design of biosurveillance systems. He currently chairs thePublic Health Laboratory Network of Australia.

Carol A. Rauch received her M.D. and Ph.D. atJohns Hopkins University and continued hereducation with residency training in pathologyand laboratory medicine and fellowship train-ing in medical microbiology at Yale New HavenHospital. At Baystate Health, the Western Cam-pus of Tufts University School of Medicine, herroles included Medical Director of Clinical Mi-crobiology, Chief of Clinical Pathology, andMedical Director of Laboratory InformationSystems. She is currently Medical Director ofClinical Pathology and Associate Professor of Pathology, Microbiology &Immunology at Vanderbilt University School of Medicine. She recentlychaired Division C for ASM. Through 20 years in clinical laboratories andclinical microbiology, her interests have included patient safety, quality inlaboratory testing, and bioterrorism preparedness. Her professional experi-ence has led to an appreciation of the critical role of pathology informatics inhealthcare, as well as the special needs of microbiology in information sys-tems as drivers of quality medical and public health information.

Liron Pantanowitz is an Associate Professor inthe Departments of Pathology and BiomedicalInformatics at the University of Pittsburgh. Dr.Pantanowitz obtained his M.D. in South Africaand specialized in pathology at Harvard Uni-versity in Boston, MA. He subsequently com-pleted cytopathology and hematopathology fel-lowships. Dr. Pantanowitz is currently theDirector of Cytopathology at the University ofPittsburgh Medical Center (UPMC) Shadyside.He is also the Director of the Pathology Infor-matics Fellowship and Associate Director of the Pathology Informatics Di-vision at UPMC. He is the immediate Past President of the Association forPathology Informatics (API), and he has also served on several key commit-tees for other societies, such as the CAP, ASCP, USCAP, and ATA. He haspublished many peer-reviewed articles and book chapters, written severaltextbooks, and given talks around the world. Dr. Pantanowitz is currentEditor-in-Chief of the Journal of Pathology Informatics.




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