Downstream applications may include additional

Downstream applications may include additional algorithms for causality assessments following successful case ascertainment. This may help the clinician for example, to discriminate whether a specific adverse event was triggered by ‘natural’ infection, orexin agonist or drug administration, autoimmune disease, or other causes. The timely paper-free transmission of unbiased safety data to health authorities represents one potential application of the VACC-Tool (Linder et al., 2010; Al-Tawfiq et al., 2014). To date, vaccine safety monitoring relies on passive surveillance systems such as VAERS (Vaccine Adverse Event Reporting System), large linked databases, and ICD-codes, with significant limitations with respect to data standardization (Lankinen et al., 2004; Baker et al., 2015). Automated case classification will contribute significantly to the timely diagnosis of important infectious diseases, which may be under-recognized in routine care and therefore under-represented in surveillance datasets (Kelly et al., 2013; Dale, 2003). The recent outbreak of enterovirus 71-associated CNS infections provides a practical example, where automated case classification in real-time might have been useful to surveillance clinics and reference laboratories (Zander et al., 2014). Further development and improvements of the VACC-Tool could include the ability to ‘flag’ any clinically suspect cases prompting automated reporting of anonymized data signals. If integrated into the routine EHR workflow, the VACC-algorithms could be programed to prompt consultations by infectious disease or infection control specialists, thereby improving patient outcomes and quality of care (Zwaan et al., 2010; Jaffe, 2015). In a auricle modified version adjusted to self-reported outcomes, the VACC-Tool could also be used to strengthen the patient voice in disease surveillance (Duffy, 2015). The language would need to be adjusted to accommodate the direct reporting by laypersons willing to participate in surveillance programs, thereby providing immediate feedback to healthcare providers. For the long-term monitoring of infectious disease outcomes, patients could be encouraged to report any improvements over time. Finally, automated case classification should be made available to promote standardized, user-centered patient care in both, high- and low-resource settings (Rath et al., 2010). The case criteria do accommodate their application diverse settings, as any specific case criteria may be met with different levels of diagnostic certainty. If used widely, the VACC-Tool will enable precision medicine based on unbiased assessments in real-time, regardless of the setting.
Conclusion
Author Contributions
Conflict of Interest Statements
Acknowledgments