| Course | EDN 818 Innovation and Technology in Health Care |
|---|---|
| Module | Module 4 |
| Paper type | Technology evaluation |
| Length | About 1,102 words, 7 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Doctor of Education |
| Updated | September 2026 |
Free sample paper for EDN 818 Module 4
Robots in the Hallway: Evaluating Autonomous Delivery Robots for a Community Hospital With the NASSS Framework
Student Name
Doctor of Education Program, Aspen University
EDN 818: Innovation and Technology in Health Care
Instructor Name
Month Day, Year
Robots in the Hallway: Evaluating Autonomous Delivery Robots for a Community Hospital With the NASSS Framework
Nurses at Kingsmere Regional Hospital spend time they do not have walking to the pharmacy for missing doses and carrying specimens to the laboratory when the pneumatic tube is down or the item cannot travel by tube. A vendor has offered autonomous mobile robots that navigate hallways and elevators to deliver medications, specimens and supplies, with a claim that each robot saves about 40 nursing and support staff hours a week. Leaders have asked whether to buy six. This paper evaluates the proposal as part of the digital revolution in health care, using a framework designed to explain why so many promising technologies are never adopted or are later abandoned.
The Digital Revolution and Its Record
Digital technologies are changing health care quickly, from artificial intelligence that reads images to sensors, robots and remote care. Topol (2019) described the convergence of human and artificial intelligence as a source of large potential gains in accuracy and efficiency, while cautioning that evidence from real clinical settings was limited and that problems of bias, privacy and safety were real. The record of adoption is mixed. Many technologies that performed well in vendor demonstrations or pilots have failed to scale or have been abandoned, which is why a structured evaluation is worth the time.
The NASSS Framework
Greenhalgh et al. (2017) developed the NASSS framework, for nonadoption, abandonment, scale-up, spread and sustainability, from a review of 28 earlier frameworks and extensive case studies of technology programs. Its seven domains cover the illness or problem being addressed, the technology itself, the case for its value, the staff and patients who must adopt it, the organization, the wider context, and the way all of these interact and change over time. Each domain can be simple, complicated or complex, and the authors found that programs with complexity in several domains were much harder to implement and sustain.
Applying NASSS to Delivery Robots
The evaluation team rated each domain for Kingsmere's proposal.
| NASSS domain | Key question for Kingsmere | Rating |
|---|---|---|
| The problem | How much staff time is spent on transport, and for which items? | Simple: measurable in a two-week time study |
| The technology | Can robots use Kingsmere's elevators, doors and network reliably? | Complicated: needs elevator integration and building changes |
| Value proposition | Do time savings outweigh cost, and who captures them? | Complicated: savings spread across departments |
| Adopter system | Will nurses, pharmacy and laboratory staff trust and use the robots? | Complicated: depends on reliability in the first weeks |
| The organization | Is there capacity to manage the program and respond to failures? | Complicated: needs an owner in facilities and pharmacy |
| Wider context | Do controlled-substance and specimen chain-of-custody rules allow robot transport? | Complicated: requires secure compartments and audit trails |
| Adaptation over time | Will use persist after the novelty fades? | Uncertain: depends on how well the first months go |
Testing the Vendor's Claim
The vendor's claim of 40 hours saved per robot each week comes from other hospitals with different layouts and different transport systems. Kingsmere's own two-week time study found that nurses and support staff spent about 180 hours a week on transport that robots could plausibly handle, which would support four robots at the claimed rate, not six. The team also asked who would capture the savings. Minutes saved by a nurse are real, but they only become value if they return to patient care rather than disappearing into other tasks.
A Caution From Clinical Algorithms
Technology claims deserve independent testing. A widely used proprietary sepsis prediction model had been adopted by hundreds of hospitals before an external validation at an academic health system with 38,455 hospitalizations found poor discrimination: it missed 67% of patients with sepsis while generating alerts for 18% of all hospitalized patients (Wong et al., 2021). Delivery robots carry less clinical risk than a prediction model, but the lesson transfers: a vendor's figures and other hospitals' enthusiasm are not evidence of performance in a new setting.
Workforce and Safety
Two further questions matter. First, the robots would reduce the work of transport aides as well as nurses, and leaders must decide whether to redeploy those aides, whose knowledge of the building and the people in it is valuable, rather than eliminate positions. Second, robots share hallways and elevators with patients, visitors and staff, including patients using walkers and staff pushing stretchers. The pilot must test how robots behave in crowded spaces and emergencies and must keep controlled substances secure with sign-in access and a full audit trail.
A Pilot Design
The team recommends a six-month pilot with two robots serving pharmacy-to-unit delivery on two floors. Measures will include staff hours spent on transport before and after, delivery times, missed or delayed doses, robot failures and time to recover from them, safety incidents in hallways and elevators, and staff and patient impressions. A controlled-substance audit will confirm chain of custody. The pilot's value proposition will be judged on Kingsmere's own data, with a decision at six months on whether to expand, adjust or stop.
Why Not Buy Six Now
Buying six robots at once would commit Kingsmere to a scale its own data do not yet support, and the NASSS ratings show several complicated domains that are better learned about on a small scale. A pilot also gives staff a chance to shape how the robots are used, which the framework associates with sustained adoption. If the pilot shows the claimed value, expansion will be faster and better accepted than a hospital-wide launch would have been.
What Patients Would Notice
Patients rarely see transport work, but they feel its failures: a first dose of an antibiotic delayed because it is waiting in the pharmacy, or a nurse who leaves the room to fetch a missing medication and does not return for twenty minutes. The pilot will therefore track time from order to first dose for new intravenous antibiotics on the pilot floors, a measure patients experience directly. If robots shorten that interval, the case for expansion will rest on patient benefit as well as staff time.
Conclusion
Autonomous delivery robots could return staff time to patient care, and the problem they address is real and measurable. The NASSS evaluation shows that the technology, value proposition, adopter system, organization and regulatory context are all complicated, though none is intractable, and that the vendor's claims overstate what Kingsmere's own data support. A focused pilot, measured on local results and designed to protect safety, chain of custody and the transport staff, is the right way to test whether this piece of the digital revolution belongs in Kingsmere's hallways.
References
Greenhalgh, T., Wherton, J., Papoutsi, C., Lynch, J., Hughes, G., A'Court, C., Hinder, S., Fahy, N., Procter, R., & Shaw, S. (2017). Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. Journal of Medical Internet Research, 19(11), Article e367. https://doi.org/10.2196/jmir.8775
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://doi.org/10.1038/s41591-018-0300-7
Wong, A., Otles, E., Donnelly, J. P., Krumm, A., McCullough, J., DeTroyer-Cooley, O., Pestrue, J., Phillips, M., Konye, J., Penoza, C., Ghous, M., & Singh, K. (2021). External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients. JAMA Internal Medicine, 181(8), 1065-1070. https://doi.org/10.1001/jamainternmed.2021.2626
What the EDN 818 Module 4 instructions ask for
Aspen describes EDN 818 as preparing leaders for technological advances and the digital revolution, and since the Module 4 instructions are kept within the classroom, this example evaluates one digital technology for adoption. Such assignments generally ask you to describe a technology, assess its fit and value for an organization and recommend whether and how to adopt it. Work through a structured framework instead of an advantages-and-drawbacks list, so the evaluation reaches people, organization and context as well as the device. Test the vendor's claims against your own data. Consider who gains and who bears costs, including staff whose work changes. Address safety and regulation. Recommend a path that fits the evidence, which is often a measured pilot rather than a full purchase.
Inside the EDN 818 Module 4 example
The evaluation begins with nurses' lost time in transport and the vendor's proposal. It sets the technology against both the promise and the failure rate of digital health, then explains the NASSS framework and its seven domains. A three-column table rates each domain for the hospital, finding most complicated rather than simple, from elevator integration to chain-of-custody rules for controlled substances. A two-week time study shows about 180 staff hours a week of transport that robots could handle, enough for four robots rather than six. A section draws on an external validation of a sepsis prediction model to argue for local testing. Workforce, hallway safety, patient-facing measures such as time to first antibiotic dose, a six-month pilot and the reasons not to buy six now close the paper.
EDN 818 Module 4 rubric: what earns full marks
Technology evaluations are graded on a systematic framework, use of evidence, attention to people and context, and a recommendation proportionate to what is known. This paper applies all seven NASSS domains and rates each, which covers the ground graders expect. The support comes from three APA sources: the Journal of Medical Internet Research article that introduced NASSS, Topol's Nature Medicine review of artificial intelligence in medicine and a JAMA Internal Medicine external validation of a proprietary sepsis model. Testing the vendor's claim with a local time study is the step that turns the paper from description into evaluation. The pilot design, with measures of staff time, delivery performance, safety and patient experience, shows a leader planning to learn rather than simply to buy.
Common EDN 818 Module 4 mistakes, and how to avoid them
Evaluations often repeat vendor marketing, listing features and benefits without testing them. Find one claim you can check against your own organization's data and check it. Another weakness is focusing on the device and ignoring the people and processes it touches; a framework like NASSS helps you cover those. Name who will own the technology after launch. Consider effects on jobs honestly. Plan how you will stop or adjust if results disappoint. Choose a technology your organization is actually considering, if possible, so your evaluation has real stakes. When a framework's domains seem to overlap, a tutor can help you sort your evidence into them and decide which domains carry the most risk.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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EDN 818 Module 4 questions, answered
What does EDN 818 Module 4 usually ask for?
Aspen's EDN 818 explores technological advances and prepares leaders for the next phases of the digital revolution, so an evaluation of a specific digital technology is a typical fourth assignment. Follow your classroom prompt.
What is the NASSS framework?
A framework for judging why health technologies are adopted, abandoned or fail to spread, asking questions in seven domains from the condition and technology to the organization and wider context.
Why test a vendor's claims locally?
Performance depends on local conditions, and widely adopted tools have underperformed when tested independently, so a hospital's own data are the best guide to value.
Where can I find a free EDN 818 Module 4 sample paper?
Scroll up for the full evaluation of autonomous delivery robots, with a table rating the seven NASSS domains and a pilot plan with measures.
What should a leader ask before buying a new health technology?
Whether it solves a measurable local problem, whether vendor claims hold with local data, who will use and own it, what could go wrong and how a pilot would test it.