Stealth Mode Automated Data Collection

A stealth mode of data collection is needed to introduce new technology without disrupting current manual processes. By stealth mode, we mean automated collection of data that is normally observed, yet irregularly captured because of lack of time and tedious manual data entry procedures. It is essential data for managing operations that is irregularly registered in a high-stress environment, leading to erroneous perceptions that generate to suboptimal organizational response.

RFID technology can automatically monitor flow of patients, staff, supplies, and equipment. Baseline data can be captured for critical process points, bottlenecks identified, and process improvement plans developed. Monitoring critical events and evoking selective orchestration of behavior is required across multiple health care information systems and care providers that move a patient through the perioperative system with dozens of points of clinical and administrative interaction.

Passively monitoring operations with RFID technology provides real-time data useful in constraint theory analyses [16], an approach that can identify bottlenecks and target selected initiatives that cause radical improvement in throughput in an enterprise in a short period. In addition, sensing systems combined with workflow engines and inferencing applications can anticipate future events, and trigger interventions that alter the course of action, potentially saving patients' lives, and certainly improving efficiency. RFID capabilities are now being integrated into 802.11 access points such that the RFID data can be seamlessly delivered to a central network or database, with transaction specific accuracy for the location of patients, staff, and hospital assets [14]. Future refinements will allow real-time determination of procedures performed by proximity of a clinicians, patient, and instruments for an appropriate period. Intelligent video can identify the nature of processes underway in an operating room.

Baseline data gathered can be used for targeting high-yield process interventions. In the initial phases, this category of process improvements targets should be implemented manually. When the manual solution demonstrates success and the return on investment (ROI) is positive, real-time process monitoring can be implemented to sustain initial gains, widely deploy the implementation within the organization, and support an ongoing process improvement methodology.

Data mining of historical information can generate new insights for process intervention. Real-time adaptation, combined with postprocess automated reflection generating new strategies for future adaptation is a powerful feedback process that can progress a system from strength to strength through continuous process improvement.

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