Field Notes Newsletter #6 - October 2026
The Phone Is Not the Project: Why Clinical Mobility Succeeds or Fails at the Point of Care
By Myron Wallace, President, ProMobix
Every year, health systems spend millions putting smartphones in the hands of nurses, techs, and physicians. And every year, a surprising number of those deployments quietly underdeliver. The devices and networks work, and the apps load, yet care teams still juggle pagers, hunt for colleagues, and toggle between systems that don't talk to each other.
The problem is rarely the technology. The problem is that we treat clinical mobility as an IT project when it is really a care delivery project.
When a device refresh is scoped around hardware counts, Wi-Fi coverage, and mobile device management (MDM) enrollment, the success criteria become “phones deployed” rather than “care improved.” Those are very different finish lines. A nurse doesn't measure success by whether her device enrolled correctly. She measures it by whether she got to the right patient faster, whether alarms reached her instead of the whole unit, and whether she spent less time documenting and more time at the bedside.
Getting there requires three shifts in how organizations approach mobility.
Start with workflow, not with the device
Before anyone picks a handset, someone should walk the floors and understand how communication happens today. Who calls whom? Where do alarms go? What gets lost at shift change? The answers shape everything from nurse call integration to role-based directories, and they are almost never the same across two units, let alone two hospitals.
Choose purpose-built over prestige
Many organizations feel pressure to buy the most expensive consumer smartphone available. But a clinical device lives a harder life. Hot-swappable batteries that keep the phone running during a battery change, durability through constant disinfection, and shared-device workflows matter far more at 3 a.m. on a med-surg floor than a premium camera does.
Plan for the last mile
The most overlooked phase of any deployment is the moment a device meets a clinician. Training at the elbow, go-live support on every shift, and a feedback loop that captures what's not working are what turn a rollout into adoption. Without them, even well-designed solutions drift back to workarounds within months.
Why it matters now
Rural and community hospitals are receiving new transformation funding, and many vendors are approaching them with promises of complete solutions. Smaller organizations often lack the internal staff to evaluate those promises. Spending those dollars on tools that add burden rather than remove it would be a missed opportunity these communities can't afford.
The good news is that the path forward isn't complicated. It starts by shifting the focus from technology deployment to what the technology is supposed to accomplish for clinicians and patients. Define success in clinical terms. Involve the care team from day one. Measure outcomes such as response times, alarm fatigue, and time at the bedside, not just devices shipped.
When we do that, the phone stops being the project. It becomes what it should have been all along: a tool that gives clinicians time back for their patients.
When Training Feels Easy, It May Not Be Working
By Matt Hays, Ph.D., SVP, Research & Analytics for Amplifire
Healthcare organizations invest enormous amounts of time and money in training. At the end of most training experiences, learners are asked how much they liked it or whether they believe it will be useful in their day-to-day work. Learners tend to base their responses on how dynamic the trainer was and how rapidly it felt like they were learning the material. When smile scores look good, learners and educators alike assume the training worked.
Unfortunately, you can’t feel when you’re learning. You can only feel how you’re performing during learning. And the two are not always the same; learning can feel good without being good. In fact, a learning experience that feels particularly easy is a lot like a workout where you don’t break a sweat; it probably isn't doing much for your long-term goals.
Learning Should Require Some Work
On the other hand, some of the most effective learning strategies make the experience harder, not easier.
Consider the difference between re-reading information and retrieving it. Re-reading a clinical guideline is easy. When you get to the end, you’ll probably feel like you didn’t need to read it at all because you know it so well. If, instead, you tried to recall the guideline from memory while applying it to a patient scenario, the task would be harder.
That difficulty has value; it is desirable. UCLA Professor Robert Bjork coined the phrase “desirable difficulties” to describe learning strategies that challenge the learner but, as the learner overcomes those challenges, create more durable, transferable learning. Retrieving instead of re-reading, waiting for corrective feedback, and spreading your learning out over time: all of these reinforce knowledge, skills, and abilities.
Not all difficulties are desirable; you wouldn’t want to work out with a two-ton dumbbell. And you wouldn’t want to lift weights in order to get better at making a differential diagnosis. The value of difficult training emerges when the challenge can be overcome and when overcoming it aligns with the goal of enabling clinicians to retrieve and correctly apply the appropriate knowledge in a particular situation.
The Greater Risk May Be What We Think We Know
For patient outcomes, physicians’ confidence may be more important than their knowledge. An uncertain clinician may consult a colleague or check a reference. But when a clinician is sure they’re right, they have little reason to stop and verify their beliefs. They simply act. If they’re wrong, that action is a mistake; their Confidently Held Misinformation™ can cause errors, near misses, and unnecessary pressure on the safety systems designed to protect patients.
As a result, effective training needs to be more than just one-size-fits-none information delivery. It needs to uncover learners’ misconceptions, especially when they’re confidently held.
One way to do that is to ask questions at the start of a training session and have learners rate their confidence in their responses. Trainers can identify what someone doesn’t know and the subset they’re sure they do.
Measure What Matters After the Training Ends
Asking questions is usually reserved for the end of training and is a common way to evaluate training efficacy. But an immediate post-test only asks a learner to repeat what the trainer just said and gives no clue about how long the training will last.
It’s also common to ask training-satisfaction questions. That has its place, but whether someone enjoyed a course tells us very little about whether the learning will change behavior.
It’s even more common to mark someone as “trained” if they completed the training, which can require as little as sitting in the same room as a PowerPoint presentation.
What we really want to know is whether the learner can retrieve and apply what they need when they need it. And so the measure of training success needs to tie back to the reason the training was done in the first place. For example, if training was designed to reduce patient falls, we should look at whether the number of falls decreased after the training rolled out sitewide. We could also look at indicators of process improvements (e.g., a post-training decrease in the count of glossy magazines found on the floor in patients’ rooms).
That shifts the conversation from “Did employees complete the training?” to “Did the training change what happens in practice?”
Healthcare does not need clinicians who are simply good at completing training. It needs clinicians who can access and apply the right knowledge when it matters. It’s harder to measure, but that difficulty is desirable.
From the Field
Notable conversations you need to hear
Healthcare organizations invest billions in workforce training every year, but completing a course doesn’t necessarily mean the learning will stick or change behavior. In this episode of Beyond the Blueprint, cognitive psychologist Matt Hays, SVP of Research and Analytics at Amplifire, joins Tim Jones and Kris Baird to explore what cognitive science tells us about how people actually learn. They discuss why one-size-fits-all training often falls short, how techniques such as repeated retrieval and spaced learning can improve retention, the risks of confidently held misinformation, and why healthcare leaders should look beyond completion rates and satisfaction surveys to measure what really matters: changes in behavior, quality, safety, and patient outcomes. Listen to the full episode to discover why improving healthcare training may start with rethinking what learning really looks like—and how we know when it works.
Sound Bites
The moments that made us hit rewind
As healthcare moves beyond the hospital walls, reliable connectivity is becoming increasingly important to keeping clinicians, devices, and patient information connected. In this episode of Beyond the Blueprint, Gregg Malkary talks with Myron Wallace, President of ProMobix, and Dr. Allen Moore, Connected Healthcare Leader at T-Mobile, about where 5G can complement traditional Wi-Fi and support the next generation of connected care. They explore how connectivity affects mobile clinical workflows, cybersecurity and resiliency, care continuity, remote monitoring, and the ability to extend care into the home. Most importantly, the conversation challenges healthcare leaders to start with the clinical outcome—not the technology—and consider what infrastructure is needed to reliably support clinicians and patients wherever care happens. Listen to the full episode to learn how 5G could help health systems build a more connected, resilient environment for care in motion.
Sound Bites
The moments that made us hit rewind
Footnotes
Further reading for curious minds
Every great conversation leaves you with something to explore. Footnotes is where we collect the books, articles, research, podcasts, and other resources mentioned or recommended by our guests, along with a few additional references that add context to the ideas discussed. Think of it as a curated reading list for anyone who wants to dig a little deeper. You're welcome!
Further reading:
1.) “Repeated Retrieval During Learning Is the Key to Long-Term Retention” — Jeffrey D. Karpicke & Henry L. Roediger III.
Research showing that repeatedly retrieving information—not simply restudying it—can significantly strengthen long-term retention. This directly supports Matt Hayes’ discussion of why testing yourself and spacing learning over time can be more effective than traditional study methods.
2.) “Desirable Difficulties in Vocabulary Learning” — Robert A. Bjork & Judith F. Kroll.
Explores the counterintuitive idea of “desirable difficulties”: learning conditions that may feel more challenging in the moment but can ultimately improve retention and the ability to apply knowledge later. It reinforces the episode’s point that how well someone performs during training isn’t necessarily a good measure of how much they’ve actually learned.Read the article
3.) “Boston Children’s Hospital Rolls Out Hybrid 5G Network as It Plans to Unify on Epic” — Healthcare IT News.
This is the strongest fit. The episode discusses Boston Children’s as an example of using 5G to support clinicians and mobile devices, and the article explains the hospital’s hybrid 5G strategy, including extending connectivity across campuses, remote practitioner locations, and eventually into patients’ homes.Read the Healthcare IT News article
4.) “T-Mobile’s Hybrid 5G Network for Boston Children’s Hospital: Combining Robust Secure Connectivity with the Mobility to Serve Patients Wherever, Whenever the Need Arises” — IDC.
This Market Note goes deeper into the hybrid-network concept discussed in the episode—particularly why healthcare needs connectivity that can move securely between the hospital, remote clinics, and home-based care. It also discusses potential use cases including hospital-at-home, connected EMS, remote monitoring, and AI-enabled workflows.Read the IDC executive snapshot
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