MUMBAI, India, Sept. 28 -- Intellectual Property India has published a patent application (202641112580 A) filed by Dr. Prof. C. R. Shamsheer Begum; Prof. Kuntal Mundal; Hetvi Prajapati; Shraddha Pradeep Kumar; Gayatri Shil; Kaveri Dnyaneshwar Patole; Balraj Sharma; Prajakta Pradip Patil; Dr. Uma Rani; and Shweta Manoharrao Fating on September 19, 2026, for A System And Method For Physiologically Adaptive Nursing Workload Control.
Inventors include Dr. Prof. C. R. Shamsheer Begum; Prof. Kuntal Mundal; Hetvi Prajapati; Shraddha Pradeep Kumar; Gayatri Shil; Kaveri Dnyaneshwar Patole; Balraj Sharma; Prajakta Pradip Patil; Dr. Uma Rani; and Shweta Manoharrao Fating.
The application for the patent was published on September 25, 2026, under issue no. 39/2026.
Abstract: ABSTRACT A SYSTEM AND METHOD FOR PHYSIOLOGICALLY ADAPTIVE NURSING WORKLOAD CONTROL The present disclosure is related to a system and method for physiologically adaptive nursing workload management. The system captures physiological and movement data from wearable sensors and synchronises the captured data with clinical-task, patient-acuity, environmental and workflow-interruption data. The processor rejects artefacts, establishes a nurse- specific recovery baseline, determines functional-strain state and accumulated recovery debt, and identifies micro-error precursors from task-execution behaviour. The processor relates the functional-strain state to an anticipated clinical task to produce a care-risk value specific to that task. Candidate interventions are generated and tested against constraints for clinical competency, medication authorisation, continuity-of-care, infection-control, staffing, response-time, and secondary-overload. A selected intervention controls task allocation, task sequencing, assistance requests, or alarm routing. Effectiveness is assessed by comparing the physiological and operational responses after intervention with an estimated response if no intervention had been taken, and subsequently adjusting the choice of intervention. Edge processing extracts privacy-preserving features, while fail-safe state prevents automatic workflow modification when the data quality or prediction confidence is insufficient.
Disclaimer: Curated by HT Syndication.
