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      Evaluating Detection and Diagnostic Decision Support Systems for Bioterrorism Response

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          Abstract

          We evaluated the usefulness of detection systems and diagnostic decision support systems for bioterrorism response. We performed a systematic review by searching relevant databases (e.g., MEDLINE) and Web sites for reports of detection systems and diagnostic decision support systems that could be used during bioterrorism responses. We reviewed over 24,000 citations and identified 55 detection systems and 23 diagnostic decision support systems. Only 35 systems have been evaluated: 4 reported both sensitivity and specificity, 13 were compared to a reference standard, and 31 were evaluated for their timeliness. Most evaluations of detection systems and some evaluations of diagnostic systems for bioterrorism responses are critically deficient. Because false-positive and false-negative rates are unknown for most systems, decision making on the basis of these systems is seriously compromised. We describe a framework for the design of future evaluations of such systems.

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          Performance of four computer-based diagnostic systems.

          V Dennis, Brian Jackson, Felicity A. Baker … (1994)
          Computer-based diagnostic systems are available commercially, but there has been limited evaluation of their performance. We assessed the diagnostic capabilities of four internal medicine diagnostic systems: Dxplain, Iliad, Meditel, and QMR. Ten expert clinicians created a set of 105 diagnostically challenging clinical case summaries involving actual patients. Clinical data were entered into each program with the vocabulary provided by the program's developer. Each of the systems produced a ranked list of possible diagnoses for each patient, as did the group of experts. We calculated scores on several performance measures for each computer program. No single computer program scored better than the others on all performance measures. Among all cases and all programs, the proportion of correct diagnoses ranged from 0.52 to 0.71, and the mean proportion of relevant diagnoses ranged from 0.19 to 0.37. On average, less than half the diagnoses on the experts' original list of reasonable diagnoses were suggested by any of the programs. However, each program suggested an average of approximately two additional diagnoses per case that the experts found relevant but had not originally considered. The results provide a profile of the strengths and limitations of these computer programs. The programs should be used by physicians who can identify and use the relevant information and ignore the irrelevant information that can be produced.
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            Unlocking clinical data from narrative reports: a study of natural language processing.

            Nicholas P Clayton, Jerald B. Johnson, P Alderson … (1995)
            To evaluate the automated detection of clinical conditions described in narrative reports. Automated methods and human experts detected the presence or absence of six clinical conditions in 200 admission chest radiograph reports. A computerized, general-purpose natural language processor; 6 internists; 6 radiologists; 6 lay persons; and 3 other computer methods. Intersubject disagreement was quantified by "distance" (the average number of clinical conditions per report on which two subjects disagreed) and by sensitivity and specificity with respect to the physicians. Using a majority vote, physicians detected 101 conditions in the 200 reports (0.51 per report); the most common condition was acute bacterial pneumonia (prevalence, 0.14), and the least common was chronic obstructive pulmonary disease (prevalence, 0.03). Pairs of physicians disagreed on the presence of at least 1 condition for an average of 20% of reports. The average intersubject distance among physicians was 0.24 (95% Cl, 0.19 to 0.29) out of a maximum possible distance of 6. No physician had a significantly greater distance than the average. The average distance of the natural language processor from the physicians was 0.26 (Cl, 0.21 to 0.32; not significantly greater than the average among physicians). Lay persons and alternative computer methods had significantly greater distance from the physicians (all > 0.5). The natural language processor had a sensitivity of 81% (Cl, 73% to 87%) and a specificity of 98% (Cl, 97% to 99%); physicians had an average sensitivity of 85% and an average specificity of 98%. Physicians disagreed on the interpretation of narrative reports, but this was not caused by outlier physicians or a consistent difference in the way internists and radiologists read reports. The natural language processor was not distinguishable from the physicians and was superior to all other comparison subjects. Although the domain of this study was restricted (six clinical conditions in chest radiographs), natural language processing seems to have the potential to extract clinical information from narrative reports in a manner that will support automated decision-support and clinical research.
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              An array immunosensor for simultaneous detection of clinical analytes.

              M L Feldstein, Jennifer Scruggs, Frances Ligler … (1999)
              A fluorescence-based immunosensor has been developed for simultaneous analysis of multiple samples. A patterned array of recognition elements immobilized on the surface of a planar waveguide is used to "capture" analyte present in samples; bound analyte is then quantified by means of fluorescent detector molecules. Upon excitation of the fluorescent label by a small diode laser, a CCD camera detects the pattern of fluorescent antigen:antibody complexes on the sensor surface. Image analysis software correlates the position of fluorescent signals with the identity of the analyte. This immunosensor was used to detect physiologically relevant concentrations of staphylococcal enterotoxin B (SEB), F1 antigen from Yersinia pestis, and D-dimer, a marker of sepsis and thrombotic disorders, in spiked clinical samples.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Emerg Infect Dis
                Journal ID (iso-abbrev): Emerging Infect. Dis
                Journal ID (publisher-id): EID
                Title: Emerging Infectious Diseases
                Publisher: Centers for Disease Control and Prevention
                ISSN (Print): 1080-6040
                ISSN (Electronic): 1080-6059
                Publication date (Print): January 2004
                Volume: 10
                Issue: 1
                Pages: 100-108
                Affiliations
                [* ]University of California San Francisco-Stanford Evidence-based Practice Center, Stanford, California, USA
                [† ]Stanford University School of Medicine, Stanford, California, USA
                [‡ ]VA Palo Alto Healthcare System, Palo Alto, California, USA
                [§ ]Kaiser Permanente, Redwood City, California, USA
                []Rhode Island Hospital, Providence, Rhode Island, USA
                [# ]Brown University School of Medicine, Providence, Rhode Island, USA
                Author notes
                Address for correspondence: Dena M. Bravata, Center for Primary Care and Outcomes Research, 117 Encina Commons, Stanford, CA 94305-6019, USA; fax: 650-723-1919; email: bravata@ 123456healthpolicy.stanford.edu
                Article
                Publisher ID: 03-0243
                DOI: 10.3201/eid1001.030243
                PMC ID: 3322751
                PubMed ID: 15078604
                SO-VID: 5dceb71f-64b9-411a-b6e7-f197c0372d45
                History
                Categories
                Subject: Research

                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: detection,expert systems,population surveillance,diagnosis,bioterrorism,public health
                Data availability:
                ScienceOpen disciplines: Infectious disease & Microbiology
                Keywords: detection, expert systems, population surveillance, diagnosis, bioterrorism, public health

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