Information can impact the healthcare environment in many ways. Medical errors can disable and even kill patients. To prevent errors, many healthcare providers currently use or are implementing bar-code point-of-care (BPOC) medication administration systems. However, studies show that clinicians often override valid warnings, become habituated to warnings, and even administer doses that differ from the written order. Evidence also suggests that numerous inappropriate warnings may result in “warning fatigue” and that the large volume of warnings decreases productivity and provides incentives for dangerous workarounds.
UC Berkeley IEOR Professors George Shanthikumar and Zuo-Jun Shen have proposed to develop an IT optimization model that will increase efficiency, effectiveness, and incentives of BPOC systems. Their dynamic method matches resources (e.g. nurses, beds, availability of equipment) to demand (patient load). This work aims to use models, populated with local facility data, to help create “clinician friendly” care environments and to engineer safe, resilient processes.
Patients will benefit through safer, more effective care, and clinicians and staff will benefit through streamlined, safe and resilient processes. Further, this work will result in minimal disruption of care delivery since the impact of process changes can be studied before implementation. Other benefits include patient throughput increases and overtime reductions, and increasing profitability and capacity of safety-net providers.
Hospitals are being considered for research partners in this work, and provide real-world facility data from their extensive network of facilities, and provide clinical and operational expertise to inform modeling efforts.