Transdisciplinary Learning Lab to eliminate patient harm and reduce waste

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DESCRIPTION (provided by applicant): Health care has little to show for its $800 billion investment in health information technology (HIT): productivity is flat and preventable patient harm remains the third leading cause of death in the U.S. The usability of many technologies is pathetic; devices do not talk to each other; it is needlessly burdensome to get data out of HIT systems and write decision support tools; TREATMENTS protocols are underspecified, working on one harm at a time rather than all patient harms; and outcomes are largely assumed rather than measured. In short, health care is grossly under-engineered. The Johns Hopkins Armstrong Institute Learning Lab is uniquely positioned to apply robust systems engineering methods by leveraging our transdisciplinary team of clinicians, researchers and engineers from the Johns Hopkins University's Applied Physics Lab (APL), and Schools of Medicine, Nursing, Public Health, Engineering, and Arts and Sciences. By using these methods, we will realize our goal of partnering with patients, their loved ones and others to eliminate preventable harm, optimize patient outcomes and experience, and reduce waste in healthcare. Our comprehensive program plan incorporates design thinking and systems engineering, using a model our APL team developed for the US Navy's submarine force. This can provide healthcare with a model for systematically envisioning and iterating a broad system objective and the necessary component activities required to realize that objective. In this proposal, our Learning Lab will work on three significant and interdependent patient safety areas that integrate systems engineering and health delivery research: 1) develop high-level design requirements for an Ideal ICU, utilizing design thinking and system engineering methods; 2) leverage open application programming interfaces to engineer interoperability between EHRs and infusion pumps; and 3) develop and implement an indicator of unit-level stress in an engineered care system to predict and mitigate risk