The objective is to use Artificial Intelligence (AI) for identifying which surgical patients have a likelihood ratio of developing an infection. We included in the study all the patients who underwent surgeries with wound class considered clean at a regional public hospital in Brazil. The first step was a retrospective analysis of risk factors and a correlation test for identifying which clinical variables are best related to post-discharge infections. Then, we developed an Artificial Neural Network (ANN) for pattern recognition to detect incidence of infections. The ANN can make accurate predictions in 77.3% of the cases in which an infection will occur, and the AUROC of the model is 0.9050. Thus, it is possible to take actions before the patients develop it, improving the quality of life and mental health as well as avoid increasing costs.
Infection control teams collect and produce information on epidemiologic surveillance for prevention of Healthcare Associated Infections (HAIs). Value Stream Mapping (VSM) is a Lean method for developing process through flow efficiency. The aim of the study was to use VSM to identify opportunities for improvement in the infection control department. Flow of information and infection control activities were reviewed using VSM and a questionnaire, where time required for each task was measured. The actual VSM went through multidisciplinary analysis and an ideal VSM was created without considering resource limits. The ideal VSM was reviewed to identify the improvements easily implemented and the ones that would require more time or resources. The actual VSM analysis addressed work overload for Key Performance Indicators (KPI) production, data management (fragmentation, access and redundant work, storage, time between tasks, time typing) and tasks performed retrospectively, when less information is available and with no opportunity to correct protocol deviation. The implementation of the ideal VSM provided a faster and more efficient HAIs analysis, London protocol for HAI cases and surgical prophylaxis evaluation became real time tasks, and all surgical surveillance was improved. A mobile app was proposed as an intervention and became a long-term project. If completely implemented, the ideal VSM would result in 15.7 less work hours/month, having the working time optimized for patient care. VSM is an important tool for epidemiologic surveillance in infection control allowing better data management, continuous workflow, and new information production with potentially fewer work hours.
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