934
Table 2. Screening Criteria Performance in Detecting Probable Cases of SSI Variable
No. of Flagged Cases
Overall algorithms only Antibiotics prescribed
Microbial cultures ordered ID consultation requested
13,511 10,910 6,985 2,048
NOTE. SSI, surgical site infection; PPV, positive predictive value; ID, infectious disease.
Sun Young Cho et al
No. of Identified SSI Cases
556 516 512 309
Sensitivity, % 96.7 89.7 89.0 53.7
PPV, % 4.1 4.7 7.3
15.1
Specificity, % 67.6 74.0 83.8 95.6
Table 3. Number of Flagged Cases and Sensitivity of SSI Detection Using Semiautomated Surveillance, According to the NHSN Risk Index Conventional Surveillance
Semiautomated Surveillance
NHSN Risk Index
0 1 2 3
No. of Surgical Procedures
29,129 10,262 1,108 17
No. of Identified SSI Cases
285 247 41 2
Rate, % (95% CI)
0.98 (0.87–1.09) 2.41 (2.11–2.70) 3.71 (2.74–4.98) 11.76 (3.29–34.34)
NOTE. NHSN, National Healthcare Safety Network; SSI, surgical site infection; CI, confidence interval.
with results of medical record survey.24 Therefore, billing codes were not considered in this study. An analysis of SSI detection by the electronic screening algo-
rithms showed that 9.4% of the superficial SSI cases could not be detected by the algorithms. This finding can be explained in 2 ways. First, some patients with mild superficial SSI are managed without antibiotics or wound culture, and these cases can be missed by the current screening algorithms. Second, the diagnosis of superficial SSI is often delayed or missed when its signs and symptoms occur after hospital discharge, and patients are given antibiotics at clinics close to their homes. Clearly, the current algorithms must be complemented to avoid missing such SSI cases. A surgeon’s voluntary reporting can complement such limitations. In fact, 9 of the 19 SSI events that could not be flagged by the screening algorithms had been voluntarily reported by surgeons during the conventional SSI surveillance period. A major challenge in developing fully automated SSI surveil-
lance methods involves the conflict between increasing sensitivity and increasing the PPV. It may be possible to develop screening algorithms specific for each type of surgery. Ultimately, the use of artificial intelligence technologies (eg, natural language processing and machine learning) could be a promising solution in devel- oping fully automated SSI surveillance systems. This study had several limitations. First, the dataset used as a
reference standard originated from conventional SSI surveillance techniques, which are largely dependent on chart reviews by IPs. As such, it is possible that some cases of SSI went undetected. In particular, identification of superficial SSIs developing after dis- charge may have affected the validation of our study. Second, our results are from a single center and may not be generalizable to all hospitals. Third, our semiautomated SSI surveillance system is limited in that the PPV generated by the algorithms was low, and all flagged cases must still be reviewed by IPs. In particular, 75% of
surgeries with a risk index of 2 or 3 still needed chart reviews. Nevertheless, it improved the efficiency of the IPs by significantly reducing workload while providing a high SSI detection sensitivity. In conclusion, our semiautomated surveillance system with elec-
tronic screening algorithms, together with chart reviews of selected cases, provided high-validity surveillance results. The semiautomated surveillance method significantly reduced the workload of the IPs compared to the conventional surveillance method.
Supplementary material. To view supplementary material for this article, please visit
https://doi.org/10.1017/ice.2018.116
Financial support. No financial support was provided relevant to this article.
Conflicts of interest. All authors report no conflicts of interest relevant to this article.
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2. Magill SS, Edwards JR, Bamberg W, et
al.Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.
3. Zimlichman E, Henderson D, Tamir O, et al. Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 2013;173:2039–2046.
4. Klevens RM, Edwards JR, Richards CL Jr, et al. Estimating health care- associated infections and deaths in US hospitals, 2002. Public Health Rep 2007;122:160–166.
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No. of Flagged Cases
7,036 5,626 833 16
No. of Identified SSI Cases
269 244 41 2
Rate, % (95% CI)
0.92 (0.82–1.04) 2.38 (2.10–2.69)
Sensitivity, %
94.4 98.8
3.71 (2.74–4.98) 100 11.76 (3.29–34.34) 100
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