The ancient symbol of the “Orobouros” — the serpent eating its tail — illustrates the multilayered feedback and uses of PANSURAS to facilitate and accelerate ERAS (Enhanced Recovery After Surgery) programs. Such a feedback loop with machine-learning and on-the-fly statistics facilitates quality systems, and rapid change according to circumstances.

The feedback loop

• The surgeon determines the operation indication.
• Preoperative assessment is performed by the surgeon, with only questionnaire, or at an assessment clinic.
• The surgeon and others in the perioperative team perform a risk assessment based upon known risk factors, those learned by feedback from previous operations, assisted by outcome-driven machine-learning algorithms applied to previous similar operations.
• If there are known correctable risk factors, or the general condition of the patient can be significantly improved, then there is an indication for prehabilitation, or optimization of existing medical conditions. This transforms waiting lists into preparation lists.
• The planned surgical procedure is performed.
• Outcomes of the surgical procedure are registered.
• Regular discussion on and reflection upon the detailed outcomes. PANSURAS provides a detailed list of medical and surgical outcomes, far more detailed than provided by nearly all standard institutes or organizations. This makes it possible to further optimize care by minimizing the chances of each such possible outcome. For example: modify perioperative management protocols to minimize the individual chance of renal, pulmonary, delirium, etc, complications.
• Machine-learning applied to outcomes in individual institutions to learn risk-factors specific to that institution and type of surgery.
• Perioperative management protocols are modified to further reduce the chances of disadvantageous outcomes.
• These data and information used to guide preoperative risk assessment.

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