Despite advances in knowledge and technology, approaches to health care discovery and delivery have not broadly kept pace with those advancements. While there have been notable improvements in shaping diagnosis and treatment resulting from knowledge made available through advances in technology, the field generally uses broad population characteristics as the basis for determining the health of, and how to treat, individuals. Today, with the confluence of big data and artificial intelligence (AI), we have an opportunity to tailor diagnoses and treatments precisely as needed for an individual-in other words, to practice precision medicine. The Johns Hopkins University Applied Physics Laboratory (APL) and Johns Hopkins Medicine (JHM), in partnership with the Bloomberg School of Public Health, Johns Hopkins Information Technology, and others across the institution, are working to usher in this new paradigm. These organizations jointly developed the Precision Medicine Analytics Platform (PMAP). This platform pulls data from many sources, aggregates the data, and then provisions needed data to approved researchers in a secure environment where they can apply advanced techniques and other tools to analyze the data. The guiding vision is to create and sustain the ability to accelerate gaining knowledge and value from data and from closing the loop between discovery and delivery, ultimately reducing health care costs and improving patient outcomes.

For nearly 60 years the Johns Hopkins University Applied Physics Laboratory (APL) has collaborated with Johns Hopkins Medicine (JHM) to study pressing health and health care problems and develop innovative solutions. Early accomplishments in ophthalmology, neurophysiology, oncology, and cardiology led to better understanding of and new and improved treatments for various conditions. Today, through its National Heath Mission Area, APL is furthering its partnership with JHM to apply rigorous data analysis and systems engineering practices to the diagnosis and treatment of disease. The collaboration leverages the institutions' systems engineering and medical expertise to create a learning health system that will speed the translation of knowledge to practice while enabling new discoveries through the development and application of advanced analytic tools. This article briefly describes how the partnership has revolutionized health and health care and is poised to continue to do so.

The administration of high-alert medications requires the use of enhanced systems to prevent errors. One commonly used system relies on independent verification of dose changes by a second clinician, a human double check. This system is inefficient, and while it may reduce, it does not eliminate error. We postulate that the ability to integrate interoperable medication infusion pumps and electronic medical records systems with existing dose adjustment algorithms can improve the safety and efficiency in the delivery of high-alert and other medications. We followed step-wise systems engineering practices to develop and build a novel system, the Smart Agent, to semi-autonomously administer intravenous insulin according to our hospital's protocol for nurse-managed insulin infusion in the intensive care units. The prototype system used a commercial medication infusion pump and our existing electronic health record. We believe that this model of interoperable systems integration can be incrementally and broadly developed in the future to improve the safety and workflow of medication infusions.

Reducing the incidence and morbidity of pressure ulcers remains a leading national priority in patient safety. However, the optimal strategy for a hospital or health system to address this safety goal is not straightforward given the number and complexity of available solutions. Leveraging techniques from systems engineering, such as the quality function deployment process, may provide a transparent and objective way to address this challenge. A detailed and practical application of quality function deployment is presented that demonstrates the value of applying engineering practices for prioritizing solutions for pressures ulcers specifically and can easily be adapted to other conditions.

Smart Agent is a web-based solution for establishing bidirectional communication between an infusion pump and an electronic health record (EHR). It eliminates the need for clinician double check of medication administration using an infusion pump. Because the clinician already is using the EHR to review patient health information and update status, the addition of the web service would help eliminate the potential for human error when using a manual system. The Smart Agent process encompasses the reading of pertinent patient data from the EHR, determination of a new medication dosage based on an internal protocol, input of the dosage into an infusion pump, confirmation of the medication dosage acceptance at the infusion pump, and recording the medication change back into the EHR. The widespread use of Smart Agent-type algorithms with bidirectional communication capabilities would result in safer, more efficient provision of care, as well as better value.

Digital health solutions continue to grow in both number and capabilities. Despite these advances, the confidence of the various stakeholders — from patients and clinicians to payers, industry and regulators — in medicine remains quite low. As a result, there is a need for objective, transparent, and standards-based evaluation of digital health products that can bring greater clarity to the digital health marketplace. We believe an approach that is guided by end-user requirements and formal assessment across technical, clinical, usability, and cost domains is one possible solution. For digital health solutions to have greater impact, quality and value must be easier to distinguish. To that end, we review the existing landscape and gaps, highlight the evolving responses and approaches, and detail one pragmatic framework that addresses the current limitations in the marketplace with a path toward implementation.

Digital health solutions continue to grow in both number and capabilities. Despite these advances, the confidence of the various stakeholders - from patients and clinicians to payers, industry and regulators - in medicine remains quite low. As a result, there is a need for objective, transparent, and standards-based evaluation of digital health products that can bring greater clarity to the digital health marketplace. We believe an approach that is guided by end-user requirements and formal assessment across technical, clinical, usability, and cost domains is one possible solution. For digital health solutions to have greater impact, quality and value must be easier to distinguish. To that end, we review the existing landscape and gaps, highlight the evolving responses and approaches, and detail one pragmatic framework that addresses the current limitations in the marketplace with a path toward implementation.

Objectives This study aimed to use a systems engineering approach to improve performance and stakeholder engagement in the intensive care unit to reduce several different patient harms. Methods We developed a conceptual framework or concept of operations (ConOps) to analyze different types of harm that included 4 steps as follows: risk assessment, appropriate therapies, monitoring and feedback, as well as patient and family communications. This framework used a transdisciplinary approach to inventory the tasks and work flows required to eliminate 7 common types of harm experienced by patients in the intensive care unit. The inventory gathered both implicit and explicit information about how the system works or should work and converted the information into a detailed specification that clinicians could understand and use. Prototype ConOps to Eliminate Harm Using the ConOps document, we created highly detailed work flow models to reduce harm and offer an example of its application to deep venous thrombosis. In the deep venous thrombosis model, we identified tasks that were synergistic across different types of harm. We will use a system of systems approach to integrate the variety of subsystems and coordinate processes across multiple types of harm to reduce the duplication of tasks. Through this process, we expect to improve efficiency and demonstrate synergistic interactions that ultimately can be applied across the spectrum of potential patient harms and patient locations. Conclusions Engineering health care to be highly reliable will first require an understanding of the processes and work flows that comprise patient care. The ConOps strategy provided a framework for building complex systems to reduce patient harm.

Project Emerge took a systems engineering approach to reduce avoidable harm in the intensive care unit. We developed a socio-technology solution to aggregate and display information relevant to preventable patient harm. We compared providers' efficiency and ability to assess and assimilate data associated with patient-safety practice compliance using the existing electronic health record to Emerge, and evaluated for speed, accuracy, and the number of mouse clicks required. When compared to the standard electronic health record, clinicians were faster (529 ± 210 s vs 1132 ± 344 s), required fewer mouse clicks (42.3 ± 15.3 vs 101.3 ± 33.9), and were more accurate (24.8 ± 2.7 of 28 correct vs 21.2 ± 2.9 of 28 correct) when using Emerge. All results were statistically significant at a p-value < 0.05 using Wilcoxon signed-rank test (n = 18). Emerge has the potential to make clinicians more productive and patients safer by reducing the time and errors when obtaining information to reduce preventable harm.

This paper offers a perspective on how a Systems Approach framework, that involves Systems Thinking and the System Development Lifecycle, can be applied in collaborative innovation to ensure that healthcare achieves value, safety, accessibility, and positive patient outcomes. Approaching problem solving in healthcare from a Systems Thinking perspective reinforces the need to maintain a holistic, rather than reductionist, view when designing systems. Coupled with the System Development Lifecycle that provides a requirements-driven process that progresses from conception through realization, one can create a feedback loop to ensure a continually improving system. Utilizing this approach through transdisciplinary teams, involving both engineers and healthcare professionals, that consider challenges from an integrated conceptual framework, allows the team to formulate and evaluate solutions based on the insights from across the disciplines. The result is the creation and deployment of robust solutions that improve value and advance learning.

To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocortico-graphic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p <; 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.