Charting Nurse-led Innovation

ChristianaCare’s Blueprint for Integrating Collaborative Robots into a Hospital Setting

A Message from Ric Cuming & Danielle Weber

As nurse leaders, we are privileged to work with extraordinary caregivers across ChristianaCare and meet nurses nationwide. We see every day that nurses are among the most powerful forces for innovation and excellence in health care.

The COVID-19 pandemic showcased this truth. In the face of uncertainty, nurses brought clarity. In the face of crisis, they brought calm and compassion. They adapted and advocated. They held patients’ hands when loved ones could not be present. They guided each other in dark moments.

The extraordinary pandemic era called not only for strength but also for creativity. ChristianaCare began to examine the systems surrounding our caregivers, asking what more we could do to ease their nonclinical workload and protect their time for patients.

In a prescient moment, we’d seen a demonstration of cobots, or collaborative robots, at another health system. These were not high-tech showpieces. They were functional, consistent and built to assist with routine, time-consuming tasks such as medication runs, supply delivery and equipment transport. As a technology, the robots offered a glimpse of a different kind of workforce support.

These charming delivery engines inspired ChristianaCare’s cobot initiative. With support from generous private donors and the American Nurses Foundation, we launched a pilot with two principal goals: to ease nurses’ workload by reducing non-value-added tasks, giving them more time with patients at the bedside, and to promote nurses’ well-being by alleviating pressures that keep caregivers from being able to care for themselves. Our focus was never on technology for its own sake but on how it might return time and energy to the work that matters most: caring for caregivers and patients.

From the start, this effort was led by nurses. They shaped the use cases. They identified the breakdowns. They guided implementation, informed training and drove the learning forward with clarity and clinical insight.

This blueprint reflects what we have built together. It is a testament to what is possible when nurses are empowered to lead systems-level change.

ChristianaCare is nothing without the letters R and N. Our nurses are creative problem solvers and contributors to the future of health care delivery. We are proud of how they innovate and even prouder to keep learning alongside them.

Warmly,

Ric Cuming

Ric Cuming, EdD, MSN, RN, NEA-BC, FAAN Chief Operating Officer, ChristianaCare

Danielle

Weber

Danielle Weber, DNP, MSM, RN Chief Nurse Executive, ChristianaCare

A Message from Omar Khan

At ChristianaCare, we see innovation not as an endpoint but as a process of inquiry. To innovate is to ask better questions, make better mistakes and learn across disciplines. This mindset guides how ChristianaCare caregivers approach change, particularly when introducing emerging technologies, such as cobots, into clinical care.

When our Nursing team brought cobots to our Newark Campus, it was not to chase a shiny new tool. Rather, we were exploring a deeper question: Can robotics reimagine how care is delivered and alleviate the visible and invisible burdens nurses carry every day?

Most health systems don’t have the opportunity to test technology in its early stages. But thanks to a bold vision and a grant from the American Nurses Foundation’s Reimagining Nursing Initiative, we had the chance to co-create a real-world learning lab—one that brought together robotics, frontline care, informatics and research in a living, breathing hospital environment. And it was nurses who drove it.

From planning to piloting to publishing research, our nurses not only adopted cobot technology but also shaped its use. They asked tough questions, made unexpected discoveries and built new knowledge through the nation’s first Nursing Research Fellowship in Robotics and Innovation, also supported by the ANF grant. Their work has informed operational decisions and is helping to shape a national conversation on robotics in health care.

While the technology is exciting, the greater takeaway is cultural. We proved what is possible when health systems operate as learning systems — when nursing, pharmacy, IT, informatics, facilities, vendors and others collaborate, challenge assumptions and stay grounded in improvement rather than hype.

As you read this blueprint, we invite you to examine both the process and the outcomes. Real innovation does not happen all at once. It unfolds in steps, stumbles and discovery. We are proud to be one of the trailblazers in nursing’s robotics journey, and we are just getting started.

With gratitude,

Omar Khan

Omar Khan, M.D., MHS, FAAFP Enterprise Chief Scientific Officer, ChristianaCare

TABLE of CONTENTS

Executive Summary

PAGE 4

A Blueprint for Collaborative Robotics in Nursing Practice

PAGE 6

Why Cobots and Why Now

PAGE 8

Meet Our Cobots

PAGE 10

Planning, Implementation and Deployment

PAGE 12

Usage, Use Cases and ROI

PAGE 18

Lessons Learned

PAGE 22

Operational Surveillance and Data

PAGE 30

Program Maintenance, Scaling and Sustainability

PAGE 32

Research and Robotics: A Nursing-led Learning Culture

PAGE 35

Conclusion PAGE 40

Appendices

PAGE 42

Executive Summary

ChristianaCare’s three-year cobot pilot, launched in 2022 with funding from the American Nurses Foundation Reimagining Nursing Initiative, set out to explore whether collaborative robots, or cobots, could reduce nonclinical workload for inpatient nurses by taking on repetitive and time-consuming tasks. The goal was to protect nurses’ time for patient care and evaluate the potential of robotics to enhance care delivery in complex health care environments.

From the outset, this initiative was led by nurses and supported by a multidisciplinary team spanning nursing, pharmacy, IT, clinical informatics, operations, facilities, vendor partners and others. Together, they deployed three cobots across more than 80 inpatient units, defined and refined use cases, built realtime surveillance systems and embedded a robust research culture to evaluate impact.

An executive steering committee provided strategic oversight throughout the initiative. The steer included senior leaders from nursing, operations and clinical informatics. The group met regularly to monitor progress, remove barriers and ensure alignment with ChristianaCare’s broader workforce and innovation strategies. Their leadership helped maintain momentum and accountability across all phases of the work.

The pilot surfaced valuable insights, both technical and cultural. Cobots added measurable value in some settings, particularly for pharmacy and ancillary teams. But nurseinitiated use remained limited, revealing gaps between the robots’ capabilities and the real-world complexity of nursing workflows. Task ownership, workflow design and clear communication mattered as much as the machines themselves.

Other key takeaways include:

• Start with a clear purpose. Define use cases early and align them with clinical priorities to drive adoption and impact.

• Know your workflows. Cobots test the systems they enter and require standardized processes to succeed.

• Infrastructure is make-or-break. Elements as simple as door sensors and elevator access can affect reliability and speed.

• Manage expectations. Positioning cobots as a research initiative instead of a finished solution helped build engagement and trust.

• Adoption requires both wonder and utility. Curiosity and emotional connection made staff more likely to engage with the technology.

• Return on investment (ROI) is multidimensional. ROI was seen through a lens of cost savings, consistency, caregiver satisfaction and operational learning.

One of the most significant outcomes was the emergence of a nurse-led research agenda. Within months of launch, ChristianaCare conducted the first known qualitative study of cobot integration in a live inpatient setting. Nurses served as co-investigators, observing 89 deliveries and documenting how cobots were used, perceived and integrated into care.

Findings from that study directly shaped operational improvements and elevated national awareness of nurse-led innovation. The energy generated by this work also inspired ChristianaCare to launch the first Nursing Research Fellowship in Robotics and Innovation, giving frontline caregivers the support and structure to lead future studies and drive the field forward.

By August 2025, ChristianaCare’s cobots had logged more than 48,600 deliveries, traveled 8,300 miles and operated for 32,100 hours. The program achieved several national firsts, including integration with our electronic medical records and elevator systems.

The initiative also clarified where cobots fall short. Tasks that were urgent, sensitive or required informal coordination remained difficult to automate. Autonomy, although improving, remains incomplete. Deliveries often needed some level of human support. And while many departments benefited, nurses were more often recipients than initiators of cobot tasks.

As the grant concluded, ChristianaCare retained two cobots to support high-volume, low-friction workflows such as equipment and pharmacy runs. Nurses remain engaged collaborators, although they are no longer the program’s primary users. Ownership of the program is now transitioning from the original nurse-led research team to operational departments, using a RACI (Responsible, Accountable, Consulted, and Informed) framework to ensure continuity and accountability. As the technology evolves, so perhaps will the opportunities for nurses to use it more.

This blueprint offers real-world evidence, hard-won lessons and practical guidance for others interested in what it takes to bring cobots into complex clinical settings as a meaningful, datainformed strategy for supporting care.

48,600 deliveries

8,300 miles traveled

32,100 hours of operation

April 2022-August 2025

To cite this document: Christiana Care Health System. Smith, S.D, McPherson, S., Mascaro, P., Anderson, B. (Eds). (2025) Charting NurseLed Innovation: ChristianaCare’s Blueprint for Integrating Collaborative Robots into a Hospital Setting. https://christianacare.org/us/ en/for-health-professionals/ nursing/nurse-led-innovation.

A Blueprint for Collaborative Robotics in Nursing Practice

Launched in 2022, the robotics pilot project at ChristianaCare was a research-based initiative to explore how collaborative robots — also known as cobots — can support and enhance nursing practice.

Funded by the American Nurses Foundation Reimagining Nursing Initiative’s groundbreaking $1.5 million grant, the project brought together frontline clinical staff (“caregivers”), technology leaders and researchers to test how these emerging tools could be thoughtfully integrated into hospital workflows.

Robotics in health care is rapidly evolving, reshaping how care is delivered, supported and experienced. While surgical robots have long improved accuracy and recovery, the next frontier is human-machine collaboration to augment the health care workforce. Semiautonomous cobots are designed to work alongside hospital and clinical teams, performing repetitive, time-consuming tasks — such as delivering medications, supplies, lab samples and equipment — so caregivers can focus on patient care.

By reducing time spent on these support functions, cobots allow nurses and ancillary staff to focus on work that requires their full clinical expertise, attention and empathy. As the field of collaborative robotics continues to evolve, we anticipate even greater potential to strengthen care delivery through thoughtful task delegation.

Cobots: Questions asked and answered

As a health system celebrated for nursing innovation and achievement, ChristianaCare entered this space to investigate several questions: Can robotics meaningfully strengthen care delivery by eliminating the burden of nonessential tasks on nurses? How might this technology support nursing in dynamic environments so that they can practice at the top of their licenses?

Cobots are still in their early phase. This moment is akin to the first generation of mobile phones — limited but full of promise. The technology is not fully autonomous, nor is it seamless. But that’s exactly why our work matters now. By

engaging early, ChristianaCare nurses are helping shape how robotics can transform care delivery, ensuring that cobot technology advances in ways that support nursing care, uphold clinical values and improve patient outcomes.

At ChristianaCare, we approached this work as part of our commitment to innovation and caregiver well-being. From the start, this was a people-centered approach. Nurses, pharmacy technicians, operations leaders and IT teams codeveloped our use cases. Every cobot task was designed to fit within real workflows and support actual pain points, from delivering discharge medications to transporting patient belongings.

We deployed our first cobots in three medical-surgical units at our Newark Campus, a physically complex environment of 1.3 million square feet with nine floors. From there, the program expanded to more than 80 inpatient units and ancillary departments. The lessons ChristianaCare learned along the way are helping to drive the sustainability of our cobot program, as well as the evolution of cobots as a tool for the future of health care.

Helping to chart the course for cobots

This blueprint provides detailed insights into these lessons, covering a variety of areas. We examine everything from the planning process and use case selection to infrastructure upgrades, education strategies and how to measure the return on investment (ROI).

This is an evolving space, and there’s more to learn. But what’s clear is that cobots can be a valuable part of a broader workforce strategy. When implemented thoughtfully, they can unburden staff, improve workflow efficiency and even spark curiosity and delight in the care environment.

This blueprint reflects ChristianaCare’s unique experience with cobots, as we explored the technology as a solution to advance the nursing profession in ways that enable nurses to operate at the highest level of their training.

Did we ultimately succeed? The answer is multifaceted and, as with any early innovation, surprising: Cobots have value, for sure. But whether they are boons to clinical nurses depends on several factors apart from hardware and software, both of which are also critical considerations. This publication attempts to highlight some of these factors – technological and otherwise — for other health systems to consider when embarking on a cobot journey.

By sharing our experience, we aim to help other organizations navigate their cobot journey. This publication is a resource for health systems, caregivers and technology partners working to understand and expand the use of cobots in patient care.

At ChristianaCare, we’re proud to be part of this exploratory moment — and even more excited about where it will lead.

Why Cobots and Why Now

Across the country, hospitals and health systems face mounting pressure. Caregivers do more, with fewer resources, in increasingly complex care environments. ChristianaCare is no exception. Our internal challenges mirror national trends: high rates of turnover and vacancy among nurses, a growing mismatch between workforce capacity and patient needs and a load of nonclinical work that pulls nurses away from the bedside. This section provides a grounding in how these realities inspired us to bring cobots into the workplace.

A profession under strain

Research published in the Journal of Nursing Management1 concluded that clinical nurses spend as much as 33% of their shifts “hunting and gathering” supplies to support patient care.

The COVID-19 pandemic only compounded this challenge. At ChristianaCare and across the nation, nurse caregivers faced cognitive overload, emotional exhaustion and constant workflow disruptions. Many were in crisis mode for months, working in systems not built for long-term strain.

Our leadership saw the urgency. We aimed to alleviate the nonclinical burden on nurses and provide them with more time for what matters most: caring for patients. This meant thinking differently. We asked: How can we protect nurses’ time, energy and focus so they can care for others and themselves?

The answer led us to the innovative idea of using cobots to offload repetitive, timeconsuming tasks. Our vision was that the collaborative robots would support nurses in practical, meaningful ways, enabling them to reclaim time at the bedside.

1Grosso, S., Longhini, J., Tonet, S., Bernard, I., Corso, J., de Marchi, D., Dorigo, L., Funes, G., Lussu, M., Oppio, N., Grassetti, L., Pais Dei Mori, L., & Palese, A. (2021). Prevalence and reasons for non�nursing tasks as perceived by nurses: Findings from a large cross�sectional study. Journal of Nursing Management, 29(8), 2658–2673. https://doi.org/10.1111/jonm.13451

Reimagining nursing through innovation

We introduced cobots as a direct response to a system-level challenge and as part of a larger commitment to nursing innovation.

From the outset, the project was nurse-led. Clinical nurses helped co-design use cases, test the technology, provide real-time feedback and participate as research collaborators.

The purpose of the initiative was multifaceted. First and foremost, we looked to reduce the burden of nonclinical tasks on nurses so they could stay focused on critical thinking, direct care, patient education and team collaboration. And we aimed to help pioneer the use of cobots in nursing, helping to shape a sustainable model for how technology can support the clinical workforce.

We were exploring what worked, what didn’t and what needs to evolve. We also considered the ROI not just in financial terms, but in time saved, nurse satisfaction and improved patient care.

Importantly, ChristianaCare did not position cobots as a standalone solution. From the beginning, leadership was clear that cobots are one tool among many in the effort to support the health care workforce. They were and are not and never will be a substitute for human connection or clinical expertise. Rather, they should always be part of a broader ecosystem of innovation in nursing.

This project also positions ChristianaCare as a national leader in nursing-driven technology. In the face of workforce strain, ChristianaCare chose to innovate with nurses — not just for them.

Meet Our Cobots

At first glance, ChristianaCare’s cobots of choice look friendly and futuristic. At about five feet tall, they are among the more humanoid cobots on the market. But behind the charm is thoughtful design. Every feature — from meeping sounds to badge-access drawers — helps our cobots navigate hospital spaces, interact with people and carry out delivery tasks safely. This section provides an overview of key characteristics for the cobots employed by ChristianaCare.

Cobot features and abilities

Appearance

ChristianaCare’s cobots are service robots built for routine hospital deliveries. Standing about five feet tall, they feature a rounded head with expressive digital eyes, a touchscreen chest, storage drawers and a robotic arm.

Mobility and autonomy

At ChristianaCare, cobots operate as a semiautonomous delivery system in the inpatient setting. They use LiDAR, onboard cameras and internal mapping software to navigate hallways, detect obstacles and travel between designated pickup and drop-off points. A preloaded hospital map guides their movement, with routing data for hallways, unit entrances and delivery zones.

While cobots can travel independently on single-level routes and avoid minor obstructions, they often require a clinical robot associate (CRA). These CRAs assist with difficult navigation as needed to ensure successful deliveries.

Storage and access

Built into our cobots’ torsos are three locking drawers of different sizes. These drawers are badge-accessible, allowing authorized staff to load or retrieve items.

Functionality

Users interact with our cobots through a touchscreen or a badge access system. The cobot emits soft auditory signals, such as meeping sounds and chimes, to indicate arrival or task updates. It can also wave using its robotic arm and display heart-shaped eyes as part of its interactive features. The arm is also designed to press wall plates that open doors.

Human-to-cobot interface

Caregivers submit requests through freestanding kiosks. The cobot then navigates to the pickup location, typically near the nurses’ station, and chimes once to prompt caregivers to load the item. After its drawer is filled, the cobot travels to the assigned drop-off point. Upon arrival, it chimes again to signal retrieval.

Operational capabilities

Our cobot program was designed for almost continuous coverage. Each cobot can work up to 22 hours, when not charging, although our vendor advised maintaining a 16-hour-a-day schedule for each. When not on a delivery task, cobots return to a charging station to maintain operational readiness. ChristianaCare maintained 24-hour cobot coverage by having three cobots rotating charging and active time during the span of the initiative.

Environmental integration

Cobot deployment required specific hardware to support their functionality, such as standalone tablet kiosks positioned on nurses’ stations, specialized QR codes for cobots to scan that would open automated doors, and charging stations. The cobots completed deliveries across a range of clinical and nonclinical areas, including inpatient units, radiology, pharmacy, equipment rooms and administrative locations.

Planning, Implementation and Deployment

ChristianaCare’s cobot deployment was rooted in thoughtful planning, cross-disciplinary collaboration and real-world testing. This section outlines how the team planned, implemented and deployed cobots into the setting we selected for piloting. From infrastructure upgrades to workflow redesign to caregiver education, every decision aimed to make cobots a trusted, functional partner in care. This section provides an overview of the project timeline and the key milestones throughout the pilot’s three-year life cycle. (See Appendix A for planning to deployment checklist.)

ChristianaCare setting

From April 2022 to August 2025, ChristianaCare deployed three cobots at our Newark Campus, a Magnet® designated hospital with more than 1,000 licensed inpatient beds. The complex, multilevel acute care hospital setting spans 1.3 million square feet with nine floors, including 31 inpatient units that comprise 900 beds, more than 2,000 doors and 43 elevator bays. The campus also offers three pharmacies located on the basement, first and second levels. Its expansive emergency department has approximately 100 beds.

Project life cycle

ChristianaCare’s cobot initiative progressed through three phases: planning, implementation and deployment. While presented sequentially, these phases often overlapped. Planning extended into early rollout. Deployment required ongoing governance. Flexibility, data-driven decisions and cross-functional coordination were critical throughout.

A structured, multichannel communication plan supported all phases. Messaging was tailored for each phase and stakeholder, including executive leaders, frontline caregivers, ancillary users, other patient and non-patient-facing caregivers and vendor teams, and designed to evolve with the initiative.

Planning (2020–early 2022)

The planning phase began in 2020, when ChristianaCare Nursing leadership began exploring the potential of robotics to support Nursing. Activities during this phase included:

• Securing funding and partners: We identified our cobot vendor partner, and a longtime ChristianaCare donor provided the initial funding for two cobots. After a rigorous and competitive submission process with hundreds of applicants from across the country, ChristianaCare was selected as one of 10 recipients of the American Nurses Foundation’s Reimagining Nursing Initiative, which supported our three-year pilot.

• Building our leadership and stewardship approach. We formed a nurse-led, interdisciplinary planning and implementation team that included nursing, pharmacy, IT, clinical informatics, operations, facilities, vendor partners and others. Our electronic medical record (EMR) and elevator vendors were also brought in as critical collaborators. And two full-time CRAs served as liaisons between the cobots, our planning and implementation team and our vendor.

ChristianaCare’s chief nursing informatics officer provided strategic oversight. A dedicated project manager coordinated daily operations and communication. Clinical nurse informaticists led staff education and feedback collection. A nurse scientist oversaw the research effort, supported by bedside nurse participants. A senior-level executive steering committee met monthly to align progress with organizational goals and clear roadblocks.

Timeline Snapshot

2020 Identifying the Opportunity

• Began exploring robotics as a way to reduce nurse burden from nonclinical tasks and allow more time for direct patient care.

Early 2022 Pre-Implementation Readiness

• Selected as one of 10 American Nurses Foundation’s Reimagining Nursing Initiative grantees.

• Partnered with our cobot vendor.

• Developed initial implementation road map.

• Began aligning use cases with clinical and operational goals.

• Completed two months of onsite machine learning (cobots).

• Installed 60 kiosks and charging stations across our Newark Campus.

• Conducted Wi-Fi validation, badge drawer access integration and elevator mapping.

• Engaged caregivers through learning system modules, hands-on demos and social runs.

• Developing an implementation road map: The team organized efforts across three core domains:

• Technical integration: Our IT and engineering teams began by conducting site surveys to assess Wi-Fi signal strength throughout the hospital, especially in high-traffic zones and basement areas such as the pharmacy.

They connected cobots to the hospital’s secure wireless network and configured kiosk software to integrate with ChristianaCare’s existing systems.

Sixty kiosks were installed across inpatient units, radiology, pharmacy and equipment rooms. The kiosks allowed caregivers to initiate delivery requests, requiring stable network access, power and clear signage to support ease of use.

Engineers also worked closely with our elevator partner to test compatibility between the cobots and the hospital’s elevator control system. Fully autonomous elevator navigation wasn’t possible at launch of the project. CRAs helped guide the robots to enter and exit on the correct floors.

Before go-live, cobots completed weeks of supervised “learning runs,” mapping hospital hallways and workflows. These supervised runs helped the cobots learn to navigate elevators, avoid physical barriers and adapt to hallway traffic and environmental variables.

• Workflow readiness: The first three cobot kiosks were deployed to units identified by the project lead as super users. There wasn’t a formal use case list or structured plan for the super users. Pharmacy stepped forward as an early adopter, requesting that cobots be available to deliver to all units. From there, the expansion followed a progression, and the team prioritized use cases based on two factors: a department’s readiness to use cobots and whether delivery items could physically fit in our cobots’ drawers.

• Staff engagement: Recognizing that technology adoption depends on staff trust and comfort, the cobot team included education and communication in the project plan from the start. Cobots were introduced as a tool to support caregivers, never to replace them. Internal messaging also emphasized that this was a nurse-led workforce and research initiative.

The cobot team appointed on-unit champions and provided training through ChristianaCare’s learning management system, with tailored modules based on role. In-person coaching, practice sessions, and live demonstrations helped staff build confidence on the job. To further normalize the presence of the cobots, our team sent informal “social deliveries,” like chocolate and giveaways, to units before clinical deployment.

Implementation (April 2022–late 2023)

In April 2022, we deployed two cobots across three medicalsurgical units and our inpatient pharmacy. Full-time CRAs assisted cobots in guiding movement, troubleshooting errors and supporting unit engagement.

Initial cobot tasks included delivering non-tubeable medications and equipment to nursing units.

As real-world use began, the team continuously monitored performance data — task volume, delivery times, failure rates — and adjusted accordingly. Early findings showed high uptake by pharmacy and ancillary staff. Nurse-initiated requests were limited, prompting refinements in delivery drop-off points, ownership clarity and task prioritization.

By mid-2022, the program had expanded considerably. Kiosk and charging station access increased to 80 inpatient units, making cobots more broadly available throughout the hospital. New use cases were introduced, including the delivery of discharge medications and patient belongings.

At the same time, we launched a nurse-led research study to examine cobot-human interaction, deepening understanding of how the technology integrates into clinical workflows.

In 2023, the team added a third cobot to serve the Women’s and Children’s building and introduced the Meds-to-Beds workflow, delivering discharge medications from the outpatient pharmacy to patient rooms. CRA coverage adjusted to two CRAs for three cobots.

Implementation messaging to caregivers emphasized transparency, training and feedback. Unit huddles, job aids, QR code surveys and real-time coaching created a supportive, iterative environment. Vendor sessions ensured coordination across cobot software and internal hospital systems. (See the Lessons Learned section for takeaways about messaging.)

As we rolled out the cobots, our External Affairs team launched a robust media campaign to spotlight the initiative, securing widespread attention across different types of media. The proactive outreach positioned the deployment as a nurse-led innovation story, generating earned media and visibility for ChristianaCare as a leader in health care innovation.

April 2022 Initial Go-Live

• Deployed two cobots to three medical-surgical units and inpatient pharmacy, focused on delivery of non-tubeable medications and equipment.

• Supported by two full-time CRAs, one per cobot.

Mid–Late 2022

Expansion & Research Launch

• Scaled to 80 inpatient units with expanded kiosk access.

• Introduced new use cases, including transporting radiology discs, patient belongings and discharge materials.

• Launched first nurse-led study on cobot-human interaction.

2023

Added Capacity & Workflow Integration

• Added a third cobot to serve the Women’s and Children’s building.

• Added Meds-to-Beds use case, delivering medication from outpatient pharmacy to patients before discharge.

• Continued nurse-led research and performance monitoring.

• Shifted CRA support to two CRAs for three cobots.

2024 Optimization & Nursing-led Innovation

• Rebalanced use cases toward nursing support away from pharmacy.

• Became first cobot site to launch EMR integration with the equipment room.

• Added nurse call integration.

• Became first cobot site to launch elevator API.

• Expanded EMR-triggered equipment deliveries to 80 beds.

• Introduced Nursing Research Fellowship in Robotics and Innovation.

• Adjusted CRA coverage to one CRA supporting three cobots from 9 a.m. to 4 p.m.

2025 Evaluation, Replication & Strategic Planning

• Surpassed 48,600 deliveries, 8,300 miles traveled and 32,100 hours of operation as of August 2025.

• Continued investment in nurse-led research.

• Decided to continue with two cobots for support of ancillary staff to indirectly support nursing.

Deployment (2024–August 2025)

Deployment activities from 2024 onward focused on automation, optimization and long-term sustainability. Two key integrations marked system maturity:

• Elevator autonomy (go live April 2024). Integration with our elevator control system allowed cobots to complete independent elevator trips during off-peak hours, 5 p.m. to 7 a.m.

• EMR and nurse call system integration (go live April 2024). From the start, ChristianaCare wanted cobots to integrate into our EMR system, automatically retrieving and delivering items based on EMR orders. And we became the first of our vendor’s sites to launch EMR integration.

Using a custom application programming interface, we connected cobots to our EMR platform. When a provider placed an order for equipment — like a PCA pump — the system triggered a task, notified the equipment room and dispatched a cobot, often before a nurse opened the chart.

The EMR integration launched on a 40-bed unit and expanded to 120 beds within three months.

A second integration linked cobots to the nurse call system on 12 beds in our medical-surgical unit. A bedside button summoned a preloaded robot, enabling nurses to access supplies without leaving their patient’s side. (While the workflow functioned in a technical capacity, the request volume was low. The limited number of button presses didn’t justify the cost of maintaining and scaling the integration. Nurses didn’t, ultimately, find it useful, so we scaled back usage.)

As the cobots’ technical capabilities matured, CRA support scaled to one CRA for three cobots from 9 a.m. to 4 p.m. Task volume, delivery speed and endpoint diversity all increased. However, not all workflows scaled equally. The team regularly reassessed use cases based on usage, user experience, and operational value and used agile project management standards throughout deployment.

Deployment communications focused on performance, ownership and celebration. Teams received monthly updates, dashboard snapshots, milestone recognitions and next-phase planning insights. Communications reinforced the program’s evolution and invited feedback on future applications.

Key performance indicators during full-scale deployment included task completion time, delivery volumes, endpoint utilization, failure rates and caregiver satisfaction. The cobot team regularly reviewed these metrics and shared them with the cobot steer to guide operational decisions.

Also in 2024, we published our first research focused on integrating cobots into a complex hospital setting and introduced our Nursing Research Fellowship in Robotics and Innovation. (See the Research and Robotics section for more about the fellowship.)

Messaging during deployment shifted toward ownership, sustainability and visibility of success. Communications celebrated milestones, recognized contributors and invited feedback on next-phase planning. Dashboards, leadership briefings and reports helped maintain engagement and transparency across stakeholder groups.

The ANF grant period for operations ended in August 2025. (See Program Maintenance, Scaling and Sustainability for more about program sustainability.)

Usage, Use Cases and ROI

The ChristianaCare team identified standard, repeatable cobot tasks to support nurses and staff, such as delivering medications, equipment and patient belongings. Chosen for predictability and frequency, these early use cases laid the groundwork for safe cobot operations while reducing workflow disruption. They also enabled testing deeper integration with hospital systems. The project team developed a preliminary ROI model to evaluate labor cost avoidance, efficiency and system-wide benefits. Although still evolving, this model provides a foundation for assessing value. The chapter outlines these foundational use cases and introduces various ways of understanding ROI.

Usage

As of August 2025, ChristianaCare’s cobots logged more than 48,600 deliveries, 8,300 miles traveled and 32,100 hours of operation.

Average pickup and drop-off times for cobot deliveries held steady at around 38 minutes, with a median of 32 minutes. However, tasks involving the pharmacy had significantly more delays, contributing to a higher proportion of pickups and drop-offs exceeding 30 and 45 minutes. After pharmacy was removed from cobot workflows, average pickup and drop-off times remained steady at about 38 minutes, but the distribution of delays improved. The proportion of pickups exceeding 30 minutes dropped from 54% to 18%, and those more than 45 minutes fell from 26% to just 9%, signaling smoother, more predictable operations.

When CRAs supported cobots, delivery tasks averaged about five minutes faster compared to those completed without human support. Although the number of tasks stayed about the same, CRAs helped prevent extreme delays, making the system more consistent and dependable for frontline caregivers.

48,600 deliveries

8,300 miles traveled

32,100 hours of operation

April 2022-August 2025

Use cases

Despite the program’s original aim to reduce nursing workload, early data showed that inpatient pharmacy, the equipment room and radiology teams were the most frequent senders. Nursing units and medical records were the primary receivers. Nurses were often the end users but less frequently the ones initiating tasks.

This imbalance prompted a strategic shift midway through the initiative. Inpatient pharmacy usage was intentionally reduced to accommodate new use cases that better supported nursing workflows, such as EMR-triggered deliveries. A few early-morning inpatient pharmacy jobs remained active to serve procedural areas. These tasks had previously required nurses to walk to the pharmacy and removing them would have reinstated that process.

Several months later, ChristianaCare’s outpatient pharmacy began using cobots for its Meds-to-Beds program. As part of this workflow, cobots delivered discharge medications directly to inpatient units, which reduced last-minute pharmacy runs and helped improve patient throughput. While the sender had changed, the goal remained the same: to reduce nonclinical workload for nurses.

The adjustments aimed to bring the project back in line with its nursing-centered goals. But, even after our inpatient pharmacy was no longer a primary sender, usage patterns still showed that nursing units received more deliveries than they initiated.

Our most active users throughout the engagement have been:

Senders

• Pharmacy (inpatient, outpatient and satellite), even after pharmacy was scaled back

• Equipment room

• Radiology

Receivers

• Nursing units

• Medical records

Common use cases included:

Pharmacy Deliveries

• Meds-to-Beds, delivery of discharge medications from outpatient pharmacy to patient units

• Non-tubeable medications

Equipment Room Deliveries

• Equipment and supplies to all hospital units

Nursing Deliveries

• Patient belongings

• Equipment to and from Clinical Engineering

• Patient slings to units

Radiology Deliveries

• CDs to the unit for patients

• Contrast to the unit to prep patients

Health Information Management Deliveries

• Patient records

Return on investment

ChristianaCare’s cobot team discovered that ROI is multidimensional. ROI can include hard financial savings, but also softer, equally important outcomes related to time, trust and workplace culture.

For us, this included tangible cost savings and operational improvements over time. Throughout the pilot, we evaluated a dynamic set of factors that explored several ROI streams, among them:

• Reduced FTE (full-time employee) costs for deliveries

• Reduced overtime

• Lower nonproductive time

• Decreased agency staffing

• Value to caregivers

Each of these measurements requires different types of data. Financial metrics are modeled against hourly wages, while value to caregivers is reflected in feedback, adoption trends and operational efficiency, among others.

Additional considerations in calculating ROI include:

• Task ownership and workflow shifts. One of the most important lessons was understanding where value truly accrued. Although cobots were intended to support nurses, most tasks were initiated by ancillary teams such as pharmacy and equipment room. Cobots frequently delivered items to nurses but were rarely requested by them. This meant that while cobots indirectly supported nursing workflows, they did not always produce time savings that were easily visible or measurable to nursing staff. (See Lessons Learned for takeaways about workflow value.)

• The importance of baseline data. A key insight from implementation was the need to define success early on and with precision. In many cases, baseline data, such as time to medication administration or time nurses spent retrieving equipment, were not available in a format that allowed for direct comparison after cobot deployment.

For organizations planning to evaluate ROI based on outcomes like timely treatment or reduced off-unit activity, it is essential to measure current state performance first. How long do these tasks take now? Is timing a pain point? What delays are caused by delivery logistics? How much improvement would justify the cost of robotic delivery? Without answers to these questions upfront, it becomes difficult to evaluate impact or define what success truly looks like. (See Lessons Learned for more about ROI.)

• Infrastructure and environmental fit. ROI cannot be separated from the space in which the robot operates. Much of our ROI was shaped by our hospital’s physical environment. Autonomy depended on elevators, badge reader timing and automated doors. Inconsistent or outdated infrastructure slowed the cobots down and introduced friction into workflows.

• Scale and use case prioritization. ChristianaCare originally planned for six cobots but ultimately maintained three. This decision reflected a shift toward deploying robots in areas where they could provide consistent, high-volume and meaningful contributions. While cobots were slower than humans in highpriority tasks like PCA pump delivery or patient belongings drop off, they added real value in less time-sensitive or previously under-supported areas, such as heating pad delivery or medical record transfers at night — neither of which are nurse tasks, however.

• Expectation management and framing. ROI was influenced not just by what cobots did, but by how we introduced them. Our early communications created expectations that cobots would immediately transform clinical workflows. In reality, the project was really an early-stage research effort exploring the potential of automation. Framing the project as applied innovation rather than an immediate solution would have helped align expectations with reality and preserved trust among staff. Managing that narrative is a critical component of future ROI strategies.

Lessons Learned

As a pioneering site for cobot integration, ChristianaCare has built, tested and evaluated one of the nation’s most comprehensive robotic delivery programs in health care. Our deployment of cobots generated operational value, sparked curiosity and established a foundation for future innovation. It has also surfaced important lessons learned — in purpose, people, technology and infrastructure, measurement and impact and ROI — which we share in this section. Although, it’s important to note that knowledge gained is woven throughout this blueprint. Several of these lessons overlap in nuance.

Purpose, use cases and workflows

Lesson: Define purpose early and clearly Takeaway: Establish and communicate a clear use case strategy from the outset. Prioritize tasks that align with clinical impact, operational goals and caregiver workflows — and balance them with low-friction tasks that build familiarity and trust.

The first challenge in any implementation is deciding what the technology should do and for whom. At ChristianaCare, we explored use cases through three lenses:

• Patient-facing tasks that directly impact care, such as timely medication delivery.

• System-level objectives, like streamlining discharge or optimizing equipment use.

• Caregiver relief, including offloading errands like transporting belongings or returning equipment.

Balancing these aims required trade-offs. We found that high-value clinical tasks drive long-term adoption, but low-barrier, simple requests — like delivering documents or snacks — helped build early trust and momentum.

We also found that shifting workflow priorities midstream, such as reducing inpatient pharmacy use to focus on nursing needs, caused confusion and operational misalignment. We learned that establishing a clear purpose early on, revisiting it intentionally and communicating it consistently across stakeholders are essential for maintaining engagement and progress.

Lesson: Understand who benefits

Takeaway: Today’s cobots can support several tasks, although not necessarily the tasks nurses most want help with.

During the three-year pilot, ChristianaCare’s cobots logged thousands of miles, making tens of thousands of deliveries and saving caregivers millions of steps. Yet only a fraction of their tasks were initiated by nurses, the group the program was originally designed to support.

Most requests came from pharmacy and ancillary teams. Nevertheless, many of these tasks indirectly supported nursing by reducing interruptions and streamlining care, thereby fostering stronger interdepartmental collaboration and reinforcing a team-based approach to patient support.

Through surveys, focus groups, external discussions and hallway conversations, we heard loud and clear what nurses wanted most from a cobot. Their top requests were practical and time-sensitive, for example:

• Bring non-tubeable medications quickly and directly to the right room.

• Retrieve supplies or equipment from other units, such as IV bags, IV fluids, Foley catheters, IV tubing, PureWicks, blood tubing, wound care supplies, linen, patient slings, pantry items and straight catheter kits, and transport patient belongings

• Help locate providers or assist with unit tasks, like turning off lights or delivering food.

Of these, at the time of this publication, our cobots can do a few reliably without human support, e.g., deliver medications, run paperwork and bring items from centralized equipment rooms. Even then, the value depends on context. For example, cobots deliver insulin from the pharmacy to a centralized drop-off location near or at the central nurse’s station, rather than to an individual nurse or patient’s room. Nurses reported having to search for the delivery or call the pharmacy, potentially erasing any time saved.

The technology also doesn’t support on-the-fly requests between units, like when a nurse walks to another unit to grab contrast dye, IV fluid or a feeding bag. Using cobots for this requires formal coordination: Nurse A has to call Nurse B, who has to stop what they were doing, call the cobot, wait for it to arrive, load the item and send the cobot. What used to be a three-minute workaround might now take fifteen.

Lesson: Know your formal and informal workflows

Takeaway: Cobots reflect and test existing workflows. They add the most value when supporting clearly defined, well-prioritized and consistent workflows — not when dropped into broken or informal systems. To ensure success, map, refine and redesign processes before introducing automation.

Cobots can expose current formal or informal ways of working within a unit, team or system. As we deployed cobots, we found standing inefficiencies and gaps that had long gone unnoticed among ChristianaCare teams.

For instance, nurses frequently borrowed supplies from other units because their own storage was insufficient. This informal “cup of sugar” practice only became visible when nurses resisted using cobots. The issue wasn’t the technology; it was the absence of a standardized workflow into which cobots could integrate.

In response, we began designing new workflows around the cobots, including centralized “stat rooms” that they could reliably access. This gave the cobot a real process to plug into on units like the “go to,” which had been unofficially subsidizing others.

In contrast, when teams mapped out under-supported workflows like low-priority equipment deliveries or nighttime chart collection, cobots added real value.

Lesson:

Scale for real-world versus theoretical value

Takeaway: Scale only where cobots outperform. Focus on tasks where cobots outperform human workflows in effort, cost or consistency.

As the team expanded from two robots to three, new use cases emerged organically, some from staff suggestions and others from observed gaps in workflows. For example, cobots supported overnight medical record transfers, specimen drop-offs and supply runs that weren’t time-sensitive but still important.

In another instance, our EMR integration offered similar insights. We developed a use case in which cobots automatically delivered patient-controlled analgesia, tube feeding pumps and heating or cooling pads based on provider orders. The team piloted this on a 40-bed unit. Technically, the integration was successful. Cobots delivered equipment as ordered. But nurses felt the time cobots took to deliver items was risky for time-sensitive supplies and preferred manual retrieval.

People

Lesson: Cross-functional stewardship drives success

Takeaway: Engage all relevant departments from the start. Early collaboration leads to stronger planning, smoother implementation and more effective change management.

Introducing cobots in a complex hospital setting involves nearly every department and assembling a cross-functional planning team early in the process will help ensure a smooth rollout.

Cobot deliveries intersect with nursing, pharmacy, IT, clinical informatics, operations, facilities, vendor partners and others. Each group contributed critical insights during the planning, implementation and deployment phases. For example, pharmacy helped define safe medication workflows, facilities guided elevator and door integration and IT ensured EMR and network compatibility.

This broad collaboration prevented overlooked barriers — such as access to secure storage areas, transport timing or endpoint clarity — that can hinder adoption. Equally important, it fostered shared ownership. Teams didn’t feel cobots were imposed on them; instead, they helped shape the program.

Just as important was our executive steering committee, which met monthly to oversee strategic alignment, monitor progress and resolve roadblocks. This governance structure helped maintain momentum and ensured the work aligned with ChristianaCare’s mission and strategic vision.

Lesson: Train broadly

Takeaway: Don’t assume limited users. Train broadly and reinforce often, especially around task ownership and receiving protocols.

Our initial plans centered on a few units serving as “super users.” But it became clear quickly that more than our super users needed to know how to receive from the cobots, especially since most tasks flowed to nursing units from departments like pharmacy and equipment.

Early on, we expanded our training to include multiple learning formats (kiosk, live demo, job aids) that helped staff — senders and receivers — feel confident with the cobots.

Lesson: Consider rollout to build buy-in

Takeaway: Even simple tech needs thoughtful rollout. Confidence beats competence.

Early on, many nurse caregivers didn’t use cobots, not because they were hard to operate, but because they weren’t intuitive in a high-stress environment. Nurses were unsure who was responsible for loading or unloading drawers. Some users were unsure about using the kiosk or badging in. Others didn’t feel the task was important enough to justify learning a new tool.

These hesitations weren’t rooted in resistance but were more about time, trust and workflow friction. Caregivers weren’t willing to spend five minutes figuring out the robot if they could do the job in four.

To build adoption, we shifted our focus from training to experience. We staged snack deliveries, ran contests, and dressed cobots in team gear. These playful engagements turned the robot from a tool into a teammate; something staff wanted to use, not something they had to.

Lesson: Message realistically

Takeaway: Don’t promise transformation; do invite participation.

Be mindful of messaging from the start. At the start, we overly promised that cobots would transform how nurses practice and smooth their workflows. The reality was more nuanced. More often than not, the cobots helped ancillary staff more than nurses.

When expectations exceeded the technology’s capabilities, disappointment followed. Reframing the initiative as a pilot — an opportunity to test and learn about robotics — shifted the tone. Nurses felt invited into an innovation and research partnership, which opened the door to curiosity, collaboration and constructive feedback.

Additionally, it was critical to manage how our vendor engaged with staff. While our robotics partner played a critical role, direct frontline interactions sometimes blurred lines. To maintain trust, ChristianaCare took the lead on all education and expectation-setting. This clarity reinforced our commitment to supporting caregivers first.

Technology and infrastructure

Lesson: Understand infrastructure

Takeaway: Assume your building isn’t ready or will need upgrades and budget time and resources for infrastructure fixes that accommodate cobot technology.

Today’s hospitals were built for people, not robots. Even with advanced programming, our cobots struggled with elevator systems, tight corridors and inconsistent door timing. In several routine cases, deliveries required a CRA.

As use cases expanded, so did complexity. Routing had to account for nontraditional pathways, including cutting through active lab spaces, navigating basement corridors and using elevators configured to comply with infant and pediatric security protocols. The cobots also had to adapt to intermittently locked units established for elopement precautions, as well as manage evolving naming conventions for units and endpoint identification across departments.

ChristianaCare’s team tackled these challenges head-on, developing elevator software integrations, rerouting delivery paths and working door-by-door to improve access.

Lesson: Interface matters

Takeaway: A kiosk isn’t enough. To drive real adoption, cobots need to meet staff where they are, with app-based, mobile-friendly and real-time access.

A major barrier to everyday use wasn’t cobots but how caregivers were expected to interact with them. Today, calling cobots requires using an iPad kiosk, typically mounted at a central location at the nurse’s station on each unit. The setup creates multiple challenges.

First, it assumed caregivers would go out of their way to engage. In reality, nurses rarely have time to leave a patient’s bedside, walk to a kiosk and navigate a multistep request. The process was also unfamiliar and didn’t mirror how nurses typically request help, which is often by phone, text or verbal callouts to nearby colleagues.

Second, the interface lacks personalization. It doesn’t remember common or repeat requests, can’t tie into the EMR to pull patient-specific information and doesn’t support quick status updates, such as estimated time of arrival.

Lesson: Full autonomy remains aspirational

Takeaway: Cobots demonstrated partial autonomy but required frequent human intervention to function reliably in our complex hospital environment.

Although our cobots navigated basic routes independently, real-world conditions often exceeded their current capabilities. They avoided spills and minor obstacles successfully, but struggled with elevators, automated door timing and unpredictable hallway traffic.

CRAs routinely intervened using remote controls to assist with elevators, manual doors or rerouting when cobots stalled or erred. While their ability to sense and act autonomously shows promise, our cobots’ performance often depended on human oversight and a controlled environment. We found true autonomy to be a futurestate goal, not a present-day reality.

(See Program Maintenance, Scaling and Sustainability for more on program maintenance.)

Measurement and impact

Lesson: Define impact

Takeaway: Decide what success looks like before launch and collect baseline data in addition to post-launch data.

Perhaps our most important takeaway is that you can’t measure impact if you don’t define it first. Initially, we didn’t establish a clear baseline for how long tasks take, how often nurses left their units or what workflows cost in time and resources. Without those benchmarks, measuring improvement was difficult and anecdotal.

After going live, we adjusted our approach, developed dashboards, conducted time studies and launched the nurse-led research component of the initiative. These now serve as the foundation for our ongoing analysis, staff engagement strategies and technology evaluation.

Return on investment

Lesson: ROI is multidimensional

Takeaway: Assessing ROI calls for a layered approach that accounts not only for measurable cost savings but also for intangible yet vital gains in time, trust and workplace culture.

We initially defined ROI as reducing nurses’ delivery duties. However, data showed that nurses were more often recipients rather than initiators, challenging our early assumptions about who would benefit most.

As a result, we expanded our definition of value to encompass caregiver time reallocation, system efficiency, interdepartmental collaboration and caregiver satisfaction. Our measurement strategy evolved accordingly, integrating cost metrics (such as reduced full-time hours, overtime and turnover risk) with softer but essential indicators, including engagement and cultural acceptance.

Future implementers should anticipate this complexity. Rigid assumptions about user roles can limit insight. Designing for shared benefit and measuring success across clinical and operational domains yields a more complete picture of value.

Lesson: Scale volume for ROI

Takeaway: Low task volume limits ROI.

Because cobots come with high fixed costs, including licensing, infrastructure upgrades and human support, the value of the program depends heavily on utilization. When cobots average only a handful of deliveries per day, the per-task cost is disproportionately high.

To justify the investment, we needed to scale the number of tasks more than the number of robots. Consistent, high-volume usage is critical to making the program sustainable. Even with the benefits of 24/7 availability and reliable performance, our cobot program doesn’t deliver value unless cobots are in regular use. Successful scaling means growing task volume alongside infrastructure and training, so that each robot is actively contributing to operational efficiency.

Lesson: Delight has value

Takeaway: Wonderment, curiosity and delight can be an ROI, boosting morale and cultivating goodwill.

Although cobots were introduced to reduce delivery burdens and optimize workflows, they also sparked unplanned but meaningful moments of engagement. Caregivers and patients often expressed wonderment at the human-like robots.

Caregivers dressed them up for holidays, patients took selfies, and staff greeted them like colleagues. This wonderment and joy cultivated familiarity, encouraged use and helped normalize the presence of automation — it also sent the message that ChristianaCare is a forward-thinking, innovative health care system.

The emotional connection supported adoption and helped departments indirectly impacted by nursing workflows feel included. It also created goodwill that strengthened cultural integration. We found that small moments with a cobot matter to our caregivers and visitors.

Teams can consider designing for these moments from the start, through social deliveries, seasonal fun or spontaneous interactions, because joy boosts morale and supports sustained change.

Operational Surveillance and Data

To ensure safe, effective cobot operations throughout the life of our pilot — and beyond — ChristianaCare built a real-time surveillance system grounded in a risks, assumptions, issues and dependencies (RAID) methodology. The team tracked issues, dependencies and task performance across multiple data sources, including vendor logs, RAID entries, manual observation and EMR integration. This approach created a responsive, cross-functional process to monitor cobots in action, surface disruptions and guide operational improvements. This section provides an overview of our surveillance approach and the data we collected along the way from

Operational surveillance

For program surveillance, ChristianaCare’s cobot team developed a data-driven strategy rooted in a RAID methodology. The RAID framework supported real-time visibility into how cobots perform and are affected by environmental and system factors. It also enabled us to adapt operations in response to our RAID findings.

The complexity of our deployment setting required coordination across clinical workflows, physical infrastructure and vendor technologies. Our RAID log, housed in SharePoint, became the single source of truth for tracking all cobot-related performance issues. Each entry documented:

• Location and description of the issue.

• Responsible individuals or teams.

• Proposed and actual solutions.

• Dependencies (e.g., door sensors, elevator software, endpoint mapping).

• Resolution status and remediation date.

Issues were reviewed weekly during standing operations meetings, and our log created shared situational awareness among all stakeholders. By using a common format to document incidents and dependencies, the project team could quickly escalate, triage and track actions across the system.

Data

Five primary data sources also supported operational surveillance. Each provided a different perspective on system performance and was used to validate RAID entries, inform prioritization and guide implementation adjustments.

• Vendor-provided performance data: Our cobot vendor supplied detailed logs for each delivery task. These included task ID, queue start and end time, travel time and path taken, loading and unloading timestamps and completion status (success, fail, stall). These logs formed the technical baseline for measuring robot throughput, autonomy and failure patterns.

• RAID issue logs: The RAID log itself functioned as both a data repository and an action register. Entries captured technical failures (e.g., elevator integration stalls), workflow inconsistencies (e.g., no one unloading items) or environmental friction (e.g., door timing mismatches).

• Live shadowing and manual observation: Informatics and nurse caregivers conducted direct observation of cobots during active service. These field reviews recorded human-cobot interactions, navigation disruptions and loading handoffs. Shadowing helped validate whether vendor-logged delivery times reflected what occurred in real time.

• Comparison evaluations: Staff retraced cobot delivery routes on foot to compare task durations. These comparisons identified delays not visible in digital logs, such as long pauses at doorways or repeated attempts to trigger a badge reader. These insights informed door timing adjustments and unit-level routing changes.

• Caregiver feedback via QR code surveys: Kiosks and robots were equipped with QR codes linking to a short feedback form. These surveys captured frontline user experience, including missed deliveries, endpoint confusion or inconsistent handoffs. Responses were aggregated weekly and reviewed alongside RAID and vendor data to capture discrepancies or recurring patterns.

• EMR-linked data: For select workflows such as equipment and feeding pump deliveries, robotic tasks were mapped to provider orders within the EMR. This allowed measurement of time from order placement to delivery, and informed the design of automation rules. EMR data was also used to identify whether items delivered by a cobot were received and documented appropriately.

The surveillance system and data revealed consistent, actionable information, allowing the team to optimize cobot operations without relying on trial and error. For example:

• Door timing adjustments were made in high-failure locations, where cobots regularly failed to pass through automated doors before they closed.

• Nighttime and early morning routing was prioritized after task logs showed faster delivery and higher autonomy during low-traffic periods.

• EMR linkage identified mismatches between delivery and documentation, leading to the creation of new charting fields to capture time-of-intervention for items delivered by a cobot.

Additionally, RAID allowed the project team to quickly identify system-wide dependencies that required collaboration with external vendors. Door access controls, elevator systems and naming conventions across robotics software and the EMR all emerged as critical integration points that needed to be constantly maintained and aligned.

Program Maintenance, Scaling and Sustainability

ChristianaCare’s experience integrating collaborative robots into nursing workflows shows that innovation does not end at deployment. Maintenance, scaling and sustainability each demand diligence, crossteam partnership and accountability. During our pilot, nurses were key to identifying friction points, guiding process adjustments and shaping what cobots could — and could not — do in a busy, real-world hospital setting. This section details how we’ve approached maintaining, scaling and sustaining cobots. It also expands on some of the lessons learned in the previous section.

Maintenance

Maintaining a hospital-based cobot program is a daily operational effort extending beyond software updates or technical uptime. It involves ongoing adaptation, particularly when it comes to navigating the physical layout of our hospital.

One of the most consistent challenges with our cobots has been navigating the facility. Door sensors, elevator access and hallway congestion often limit cobot movement. Simple infrastructure issues — a door closing too quickly or a hallway crowded with stretchers — can bring a cobot to a standstill.

Such situations are not often simple or even one-time fixes. Our team must regularly assess and revisit pain points to ensure reliable performance. In one case, staff walked the entire campus to identify doors that caused delays. ChristianaCare ended up retrofitting badge readers and adjusting door timers to allow cobots more time to pass through. Solutions called for collaboration among facilities, security, IT and nursing leaders.

Hardware updates also introduced challenges. Subtle changes like reduced drawer size or new battery routines disrupted previously stable workflows. Each change required local testing and coordination with vendor engineers to maintain performance. ChristianaCare’s maintenance approach included regular touchpoints with the vendor team to flag and address hardware quirks in real time.

On the software side, the cobot’s behavior required frequent refinement: Unit naming conventions, drop-off locations and communication gaps caused repeated confusion. Nurses often didn’t know who was supposed to unload a delivery or where the item belonged. In some cases, deliveries were delayed or misrouted because the system’s destination names didn’t match the language nurses used on the floor — units like “MICU” or “3E” were inconsistently labeled in long cobot interface dropdown menus with more than 80 options. Items sometimes sat idle until someone claimed them. The robot’s arrival chime wasn’t always heard, and without a clear protocol for ownership, even routine deliveries created disruption. These usability gaps revealed the need for clearer communication, better endpoint naming and workflow alignment at every step.

Our project team worked to bridge these gaps by simplifying software interfaces, refining endpoints and aligning technical terms with everyday nursing practice. RAID tracking became a foundation for operational visibility. Weekly log reviews helped the team identify persistent issues, assign ownership and escalate fixes. Over time, RAID surveillance helped guide strategic improvements in both the physical environment and the delivery workflows.

Scale

As ChristianaCare expanded our cobot program, it became clear that scaling was as much about managing expectations, culture and workflow disruption as it was about adding more cobots.

Because most deliveries originated from departments like pharmacy or the equipment room, every receiving unit needed to be ready. If the pharmacy sent a delivery, all downstream units had to know how to receive it. This triggered a scaling up of receivers and system-wide shift that included hardware installations, staff training and workflow redesign across dozens of inpatient units.

Even with training, nurses sometimes struggled to integrate cobots into their routines. For example, teams accustomed to informally borrowing supplies from neighboring units found that using cobots introduced time and complexity. What used to be a quick hallway exchange now required someone to load a robot, dispatch it and notify the receiving team. The added steps turned a simple task into a more formal and time-consuming process. (See Lessons Learned: Know your formal and informal workflows.)

In response, the team focused on scaling use cases that matched cobots’ strengths. These included predictable, asynchronous tasks that did not require frequent coordination or immediate response. Centralized supply runs, document transfers and nonurgent deliveries were especially successful. High-priority needs like urgent medications or equipment were tested but scaled back after proving less compatible with clinical workflows.

The team also found that effective scaling depended on task volume, not just the number of cobots in use. Cobots come with fixed infrastructure and support costs. When they were underutilized, the cost per delivery increased. To improve value, the program needed to grow the number of tasks assigned to each robot. The more consistently cobots were used throughout the day, the more efficient and costeffective the program became. Long-term sustainability required not just more cobots, but more work for them to do.

Sustainability

Sustaining the cobot program requires a deeper understanding of value beyond cost per task. Cobots were never intended to replace nurses or ancillary staff. They were meant to offload routine work and reinforce workflow reliability. But when task volume fluctuated, it surfaced critical questions about efficiency, ROI and operational fit. (See Lessons Learned: Scale for real-world versus theoretical value.)

These insights prompted the team to reassess performance thresholds and refine the delivery model. High-value, repeatable tasks were prioritized. Use cases with low volume or unclear ownership were phased out. At the same time, leaders weighed the reliability cobots offer — 24/7 availability, no time off, no benefits — against the limits of human staffing. While not always faster, cobots offered consistency. This consistency became a strategic asset in environments shaped by turnover, variability and demand.

Sustainability also means keeping staff engaged. Some nurses were eager to innovate. Others saw the cobot as a disruption. The team learned that adoption increased when the program was framed as a learning initiative, not a finished solution. Lowering expectations and inviting feedback has empowered nurses to shape the future of the technology rather than resist it.

When the grant period supporting operations concluded, we began transitioning from an exploratory initiative to a permanent program. As of publication, we will retain two cobots, employing them primarily for our Meds-to-Beds program and transport of supplies from our equipment room to patient floors. Nurses won’t be priority users, reflecting a shift from our pilot vision to a long-term strategy that’s grounded in frontline experience and operational data.

As the project transitions to permanence, ChristianaCare is working to shift ownership of cobot workflows from the original Nursing-led team to other departments. This includes defining roles and responsibilities. A RACI framework will help support this handoff and ensure continuity. As part of this transition, new operational owners will assume responsibility for task ownership, maintenance, support and performance monitoring. Agreement on these roles is underway to support long-term sustainability.

Research and Robotics: A Nursing-led Learning Culture

From the early stages of our cobot pilot, ChristianaCare treated robotics as a catalyst for inquiry instead of a fixed solution. This spirit of curiosity and reflection is part of a larger research culture embedded throughout nursing that led to us becoming one of 10 health systems nationally selected for the American Nurses Federation Reimagining Nursing Initiative. With support from the ANF, research became one of our cobot project’s central arms. Within months of deployment, our first nurse-led study was underway. It would go on to shape internal decisions and contribute to national conversations around robotics in nursing. This section outlines the research arm of the pilot, including the spin-off of a first-of-its-kind nursing research fellowship in robotics.

First robotics study: A real-time look at robot integration

ChristianaCare’s first formal research study — “Integrating Collaborative Robots into a Complex Hospital Setting: A Qualitative Descriptive Study”— sought to understand how cobots function in a live inpatient environment. (Birkhoff, S.D., Merring, P., Spence, A., Bassett, W., & Roth, S. (2024). Integrating collaborative robots into a complex hospital setting: A qualitative descriptive study. DJPH, DOI:10.32481/ djph.2024.12.05)

After deploying cobots, our nurse scientist-led research team observed the robots functioning in real acute care hospital units across all shifts, days of the week and routes. Nurses were co-investigators in the research study, actively engaged in all aspects of the research process

The study was co-authored by Susan Smith, Ph.D., RN, ChristianaCare’s program director of technology research and education. An experienced researcher and mentor, Smith guided bedside nurses to serve as co-investigators. Over the course of seven weeks in November and December 2022, the team documented 89 cobot deliveries totaling 33 hours of observation. They recorded deliveries on various units at different times of day and observed interactions with nurses, visitors, patients, pharmacy staff, clerks and environmental services.

The research team used an inductive coding approach to reveal four primary findings similar to our lessons learned overall:

• Humanization of the cobots: Caregivers and patients alike anthropomorphized cobots. Many waved, smiled or spoke to the cobots in passing. This emotional response shaped how staff accepted or rejected their presence. Cobots that meeped or changed their display to hearts were perceived more favorably than those that didn’t. Some units introduced cobots to new hires as a team member.

• Usability in practice: The interface and functionality were intuitive for some caregivers but created confusion for others. Tasks like drawer opening, kiosk usage and handling delivery notifications required brief, targeted training. Inconsistency in understanding who was responsible for loading or unloading the robot led to miscommunications in several units.

• Autonomy limitations: The cobots navigated hospital hallways well but struggled with elevators, closed doors and dynamic obstructions. They relied on CRAs to complete many of these transitions. These limitations highlighted the importance of defining clear workflows and support roles.

• Operational fit and function: In certain scenarios, cobots reliably offloaded routine delivery tasks. However, in others — especially during high-traffic or high-acuity periods — their use introduced delays or confusion. Cobots worked best when integrated into predictable, nonurgent workflows, such as equipment transport or routine pharmacy deliveries.

The pilot revealed that ChristianaCare’s dynamic and unpredictable hospital environment presented unique challenges for early-stage cobots with limited communication capabilities. Full workflow integration required a degree of human support, particularly to navigate elevators, manual doors and congested hallways. Still, as the technology evolves, cobots show real potential to augment and enhance hospital operations.

Throughout the pilot, the study’s findings have been widely disseminated at the local, regional, national and international levels. These efforts have raised awareness that nurses are leading this pioneering research. To date, more than 25 internal and external dissemination activities have increased visibility and shared insights that are helping other teams both within and beyond ChristianaCare better understand what it takes to implement cobots in complex care environments.

Second study: Emerging themes

A second study is currently underway, with preliminary findings available to share internally and externally. Although the manuscript won’t be submitted for peerreviewed publication until late 2025, early analysis has surfaced several promising themes in how cobots are used and perceived at ChristianaCare, as well as their benefits. Among the key takeaways:

• Decisions. Staff evaluate each task through both operational and clinical lenses. From a logistics standpoint, they consider whether cobot use is practical, factoring in travel distance, urgency and unit-specific constraints.

From a clinical perspective, urgency and medication type often drive decisionmaking. For example, nurses are more likely to use a cobot for nonurgent or routine items, and less likely when medication requires immediate or sensitive handling. Ultimately, choices reflect a balance between efficiency, staffing levels and patient care priorities.

• Prioritization. Cobots have the potential to facilitate the management of competing demands. Cobots allow staff to reallocate time to more pressing, patient-centered responsibilities by taking over routine errands, such as supply runs or equipment transport. In a high-acuity environment, this shift helps reduce task saturation and promotes smarter prioritization.

• Workflows

• Nursing: Although cobots are integrated into hospital logistics, their direct impact on nurse workflows remains modest. Most cobot requests originate from ancillary departments, and nurses often serve as the recipients rather than the initiators. As a result, their day-to-day workflow remains largely unchanged, although it has been slightly streamlined by reduced interruptions.

• Pharmacy. Pharmacy technicians and pharmacists see the most notable benefit. Because certain medications cannot be sent via the tube system, a pharmacy technician would have to leave their department to deliver the medications, shifting coverage to pharmacists and/or other technicians in their absence. Using the cobots instead for these routine, non-tubeable medication deliveries, pharmacy technicians could stay in their practice area preparing medications.

• Ancillary caregivers. Cobot deliveries also helped clinical engineering, radiology and transport teams. By handling low-complexity tasks, such as delivering equipment and paperwork, cobots enable these teams to focus on higher-value functions. This cross-departmental relief supports a more coordinated and effective care environment.

Nursing Research Fellowship in Robotics and Innovation

The success of the first study and the energy it generated among frontline nurses inspired the internal ChristianaCare team leading the ANF grant to think even bigger. They imagined what might be possible if more nurses had the opportunity and support to participate in similar research efforts. The idea launched the creation of a forward-looking initiative with even more potential to shape the use of robotics in nursing — the Nursing Research Fellowship in Robotics and Innovation.

Funded with our initial grant from the ANF’s Reimagining Nursing Initiative and launched in August 2024, the first-of-its-kind fellowship builds on ChristianaCare’s history of nursing research and innovation that responds to a clear need in the field.

As health systems adopt new technologies, from artificial intelligence to digital monitoring to automation, we believe nurses must always be at the table, asking questions and driving evidence. The fellowship creates space for this role.

Fellows are supported through structured didactic sessions, informatics training, writing workshops and mentorship. The curriculum spans essential topics from human-centered design and implementation science to health equity and abstract writing. Throughout the program, fellows served as co-investigators, contributing to study design, data analysis and manuscript preparation.

Fellows are not passive learners; they’re active participants shaping the future of nursing and robotics. They collaborate on institutional review board-approved studies, conduct literature reviews, participate in national conferences and map out future funding strategies. Importantly, they learn how to evaluate technology not just by its novelty, but by how it can improve care and care delivery.

This program also offers frontline nurses a platform to explore innovation from their perspective. Too often, nurses are brought in late to technology adoption efforts. The fellowship reverses that trend, placing nurses at the center of discovery. The goal is that these fellows will go on to lead future projects, inform system-wide strategies and build a growing community of nurse researchers who understand both the science and the practice of robotic innovation and integration.

Still early in its first year, the fellowship has already created new pathways for nurses to lead, question and experiment. And at a time when the profession is facing unprecedented strain, it offers a rare opportunity to envision a future where nurses help shape the tools and systems designed to support them. (See Appendix B. for the inaugural cohort publications and presentations.)

Sustaining a culture of learning and innovation

ChristianaCare is establishing a new standard for nurse-led innovation in robotics. Through its dedicated fellowship, active research initiatives and national dissemination efforts, the health system is cultivating a strong foundation for robotics discovery grounded in clinical insight.

The research team includes 12 contributors, eight of whom are nurses. Their work has informed internal technology decisions and contributed to the broader science of nursing and automation. By connecting lived experience with structured inquiry, ChristianaCare is equipping nurses with the tools to shape the future of care delivery through evidence, leadership and multidisciplinary collaboration.

Conclusion

The cobot initiative at ChristianaCare marked one of the most comprehensive early deployments of collaborative robotics in a live, inpatient hospital setting. It was not simply a technology rollout but a structured, nurse-led inquiry into how cobots can support care delivery and relieve workforce strain for nurses.

The three-year, grant-funded pilot yielded measurable operational results, initiated original nurse-led research and deepened our understanding of what it takes to incorporate robotics into complex clinical environments. As of August 2025, our cobots completed more than 48,600 deliveries, integrated into our EMR and elevator systems — both firsts — and contributed to national discussions on nursedriven innovation.

The work was not without challenges. Nurse adoption was limited, and several intended use cases proved incompatible with the pace and variability of clinical care. Full autonomy is still future-state. Yet, these limitations were part of our learning and helped us to better define where cobots add value and what conditions must be in place to support this value over time.

We also learned that ROI requires a multidimensional approach. The team initially focused on reducing delivery-related workload for nurses, but real-world use patterns revealed that ancillary teams initiated most cobot tasks. As a result, the definition of ROI expanded to include operational consistency, interdepartmental collaboration, caregiver satisfaction and cultural acceptance.

Financial measures such as reduced overtime and full-time employee costs were considered alongside more qualitative indicators like trust, engagement, and morale. The team also learned that low task volume can drive up per-delivery costs, underscoring the need to match cobots with high-frequency, low-friction workflows.

Governance was a critical consideration. A multidisciplinary project team guided every step of the pilot, and an executive steering committee provided strategic alignment and operational accountability throughout. A RACI framework now supports the transition from an exploratory pilot to an operational program managed by ancillary departments. ChristianaCare continues to operate two cobots focused on high-volume, low-friction delivery workflows.

The broader impact may be cultural as much as operational. Nurses did not just participate in this effort; they led it. From co-authoring research to informing implementation strategy, our nursing caregivers shaped how the technology was used and how it will evolve. This spirit continues through ChristianaCare’s pioneering Nursing Research Fellowship in Robotics and Innovation, which ensures that caregivers remain central to future inquiry and decision-making.

For other health systems exploring robotics, this blueprint offers practical insights, tested strategies and honest reflections on ChristianaCare’s unique cobot experience. It emphasizes that success depends not on the sophistication of the machines but on the strength of the teams implementing them, the clarity of the systems they operate within and the vision to view innovation as a continuous learning process rather than a final destination.

Appendices

Appendix A: Blueprint checklist for cobot implementation

This checklist offers a practical tool for health systems considering cobots. It reflects ChristianaCare’s lived experience, drawing from real-world use, nursing feedback, and research insights. Use it to assess readiness, design your rollout, and plan for long-term success.

Planning and strategy

Define your purpose early.

 Clarify goals (e.g., reduce nursing burden, improve throughput, test automation).

 Frame the effort as a pilot or research initiative, not a ready-made solution.

Choose tasks strategically.

 Start with repeatable, low-risk jobs to build familiarity.

 Prioritize high-frequency, low-complexity use cases.

 Be flexible. Your task list will evolve.

Know your workflows.

 Redesign inefficient processes before automating.

 Document current-state workflows so you can measure change.

Set metrics upfront.

 Decide how success will be measured.

 Gather baseline data early (e.g., nurse time off unit, equipment retrieval time).

People and engagement

Build a cross-functional team.

 Team might include nursing, pharmacy, IT, clinical informatics, operations, facilities, vendor partners and others.

Identify senders and receivers.

 Train broadly — every unit may receive cobot deliveries.

 Use kiosks, job aids, coaching, and live demos.

Create early wins.

 Start with “social” runs (e.g., snacks, giveaways) to reduce hesitation.

 Position cobots as team helpers, not replacements.

Manage messaging.

 Set realistic expectations: this is early-stage tech, not a fix-all.

 Keep vendor messaging aligned with project goals.

Technology and infrastructure

Walk your space.

 Assess door width, elevator access, tight turns, and high-traffic zones.

 Identify doors that may need new sensors, badge readers, or timing adjustments.

Know your integration points.

 EMR and nurse call integration requires technical planning and support.

 Map naming conventions and standardize endpoints.

Make the interface intuitive.

 Kiosk-only systems often fail to meet nursing needs.

 Consider mobile access, saved favorites, and real-time tracking features.

Plan for variation.

 Drawer size, battery life, and behavior can differ by robot model.

Measurement and impact

Track what matters.

 Define both quantitative (e.g., reduced FTE hours) and qualitative (e.g., staff satisfaction) metrics.

 Use a RAID log to track risks, assumptions, issues and dependencies.

Use multiple data sources.

 Combine vendor logs, manual observation, EMR data and staff feedback.

 Create new EMR fields if needed to measure use (e.g., heating pad application).

Monitor and adapt.

 Review surveillance data regularly to guide operational adjustments.

 Use pilot data to refine ROI models over time.

Maintenance, Scaling and Sustainability

Keep cobots running.

 Regularly assess door performance, hallway access and software updates.

 Schedule ongoing vendor check-ins.

Scale intentionally.

 Expansion requires hardware, training and workflow redesign.

 Stick to cobot-friendly tasks: asynchronous, low-touch and nonurgent.

Sustain with purpose.

 Don’t chase volume. Focus on meaningful, repeatable value.

 Position cobots as part of a broader workforce support strategy.

 Reassess performance thresholds and adjust as needed.

Research and Learning Culture

Empower nurses to lead.

 Treat cobot deployment as a research opportunity.

 Involve nurses as co-investigators.

Invest in education.

 Offer fellowships or training on informatics, design and technology evaluation.

Share what you learn.

 Disseminate findings across internal councils and national venues.

 Document failures as well as successes — both inform progress.

This checklist offers a framework that evolves with your staff, setting and technology. Start small. Learn fast. Stay grounded in what staff need.

Appendix B: Overview of inaugural fellowships cohort presentations and publications

With close mentorship, the nurse fellows in our Nursing Research Fellowship in Robotics and Innovation learned how to write and submit several compelling abstract submissions and author their first publications.

Accepted publications with nurse fellows as lead authors and mentored by ChristianaCare Ph.D. nurses:

Abernathy, B. & Shady, K. Innovation Collaboration: Harnessing the power of nurse scientist and bedside nurse partnerships. Delaware Journal of Public Health. September 2025.

Mitchell, E. & McCulloh Nair, J. Stop the bleed: How a standardized policy and reporting may assist healthcare organizations with tackling the workplace violence crisis. Delaware Journal of Public Health. September 2025.

Rackie, H. & Pawlow, P.C. Reimagining nursing through innovation and design thinking. Delaware Journal of Public Health. September 2025.

Tallo, M. & Smith Birkhoff, S. Beside brain breaks: How stepping back into education can step healthcare forward. Delaware Journal of Public Health. September 2025.

Accepted Abstracts with nurse fellows as lead presenters and mentored by the fellowship team:

Tallo, M.; Abernathy, B., Rackie, H., Shady, K., Mitchell, L., & Smith Birkhoff, S.D. (Poster). Combating bedside burnout with research for retention: An innovative fellowship model. Sigma Theta Tau International Conference. Seattle, WA, July 17-20, 2025.

Tallo, M., Abernathy, B., Merring, P., Mitchell, E, Rackie, H., Rogers, S., Roth, S., Spence, A., Taherzadeh, S., & Smith, S.D. (Poster). Implementing advanced robotic technology: Perspectives from nursing and hospital staff. 20th Annual Nursing Research Conference, (virtual), November 7, 2025.

Abernathy, B., Rackie, H., Shady, K., Tallo, M., Mitchell, L., & Smith Birkhoff, S.D. (Poster). Class in session: Leveraging hospital-based fellowships as a bedside to PhD Pipeline. Navigating the future: Healthcare professionals’ perspectives on cobot integration into hospitals. 48th Biennial Sigma Convention 2025, Indianapolis, IN, November 8-12, 2025.

Mitchell, E., Rackie, H, & Tallo, M. (presentation). What is the nursing research fellowship in robotics and innovation. Nursing Grand Rounds, ChristianaCare, Newark, DE, April 2025.

Mitchell, E., Rackie, H., Abernathy, B., Tallo, M., Shady, K., Smith, S.D. (Presentation). A new frontier: Creating a hospital-based nursing research robotics and innovation fellowship to enhance nursing practice. 20th Annual Nursing Research Conference, (virtual), November 7, 2025.

Abernathy, B, & Spence, A. (Presentation). Embracing the future: Hospital staff’s perspectives on robotic integration. Nursing Grand Rounds, ChristianaCare, Newark, DE. April 2025.

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