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Computational Thinking may sound like a practice reserved for programmers and engineers, but in Project Tomorrow’s new series of Computational Thinking at Work we are here to challenge those assumptions. Professionals across every field use computational thinking core practices daily — decomposing complex problems, recognizing patterns, abstracting unnecessary detail to focus on what matters most, and designing step-by-step solutions. Our first story follows a pediatrician through the rhythms of a typical day, revealing how these skills are woven into the fabric of clinical decision-making.
When pediatrician Dr. Sophie Smith walks into her office each morning, she is never quite sure what the day will hold. Her schedule usually includes a familiar outline of well-child visits, routine follow-ups, and sick visits, but what unfolds within those appointments is another matter entirely. A straightforward check-up can pivot into something unexpected. A worried parent’s question can open an unanticipated conversation. “Pediatrics is one of those fields where you walk in every morning knowing the general structure of your day,” she says, “but you can never fully predict the details of what it will bring.”
For Dr. Smith, that unpredictability is at the heart of why she loves her work. No two patients are identical, even when they arrive with the same symptoms. Days rarely end the way she imagined they would at the start. It is a profession built on human complexity: calming a frightened child, easing a parent’s anxiety, making careful decisions with incomplete information, and little margin for error.
At first glance, this world of compassion and clinical instinct seems far removed from the structured logic of computational thinking. But in practice, the same skills that define rigorous problem-solving in computer science—recognizing patterns, filtering what matters from what doesn’t, breaking complexity into manageable steps, and thinking systematically under uncertainty—sit at the very core of what Dr. Smith does every single day.
Pattern Recognition & Algorithm Design: The Foundation of Diagnosis
The moment a child walks through the door, Dr. Smith’s mind is already at work. She has already reviewed the chart including age, medical history, chronic conditions, current medications, previous visits. On the surface, this looks like routine preparation. To Dr. Smith, it is the first layer of pattern recognition: the ability to look at incoming data and immediately ask, Have I seen this before? What does this resemble? What does context tell me about what I’m looking at? “A fever in a two-week-old is a completely different problem than a fever in a ten-year-old,” she explains. “Same input, very different meaning.” Over years of training and practice, she has built an internal library of thousands of such patterns. When new information arrives, her brain searches that library automatically and continuously, looking for the match that fits the case.
This pattern recognition does not happen randomly; it follows a defined order. This is where a second principle of computational thinking informs Dr. Smith’s work: thinking in sequences called algorithms. An algorithm is simply a consistent, repeatable set of steps for working through a problem. Every time Dr. Smith sees a patient, she moves through the same structured process: review the chart, gather the history, assess the child visually, conduct the physical exam, determine additional lab work or tests to further assess the situation, and formulate a plan of next steps. The sequence is deliberate and each step builds on the last, giving her pattern recognition skills new material to work with.
She walks into the exam room and a new layer of observation begins immediately. “How does the child look? Are they sitting up, engaged, playing with a toy in the corner? Or are they glassy-eyed and clinging to their parent?” That visual read happens in seconds and carries significant diagnostic weight because of her training and experience. A child who is alert and irritable is telling a very different story than one who is quiet and withdrawn, even if both arrived with a reported fever of 102. The symptom is the same; the pattern is not.
Next comes the patient history drawn from conversation with the parent and, when age allows, the child themselves. Dr. Smith is quick to point out that this is not a simple fact-gathering exercise. “I’m not just collecting specific pieces of information,” she says. “I’m listening for the relationships between facts. When did the symptoms start? What makes them better or worse? Has anyone in the household been sick?” Each answer is a data point, but what she’s really constructing is a picture of trajectory and context rather than simply a snapshot.
The physical exam follows, and by this point Dr. Smith is rarely starting from zero. She is already working with a short list of hypotheses. “By the time I’m examining the child, I usually already have two or three possible explanations in mind,” she says. Clinicians call this differential diagnosis: a prioritized list of possibilities, ranked by likelihood, but also by risk because missing a rare but serious diagnosis is a fundamentally different kind of error than over-treating a mild one.
This is where pattern recognition becomes something more than matching symptoms to outcomes. Every diagnostic decision carries real weight for the child, for the concerned parent, and for everything that follows. Dr. Smith said that she is “constantly balancing probability against consequence.” This process is very logical, almost mathematical in its structure, even though in the moment it can seem more like intuition. The algorithm provides the structure; pattern recognition identifies the possibilities. And the diagnosis is where clinical judgment takes over.
Decomposition: Breaking a Complex Problem into Manageable Parts
Pattern recognition and algorithm design give Dr. Smith a framework for approaching each patient, but the information itself does not arrive neatly organized. A single patient encounter can generate an overwhelming amount of data: chart notes, lab values, growth curves, a parent’s verbal history, her own real-time observations, and sometimes records from outside specialists. To make sense of it all, Dr. Smith breaks the larger, complex problem into smaller, more digestible pieces—a skill known as decomposition. Each piece may have its own algorithm, but the goal is not to oversimplify; it’s to make the whole tractable by understanding the relevance and importance of each component. Rather than solving everything at once, she isolates the parts, works through each one, and reassembles her understanding.
Dr. Smith does this instinctively throughout every appointment. The chart review, the visual assessment, the patient history, the physical exam—each is a deliberate breaking-down of a complex diagnostic question into components that can be examined on their own terms. Each piece of the encounter is, in a sense, its own smaller problem: What does the history tell me? What does the exam confirm or contradict? What do the numbers indicate? By working through the complexity in layers rather than all at once, the answer emerges piece by piece with each smaller question resolved and examined until the larger picture becomes clear enough to act on.
Abstraction: Identifying a Patient’s Real Needs
Dr. Smith shared that a hallmark of her work is that what brings a patient in and what they actually need are not always the same thing. She explained that “A mom might come in with her four-year-old for what she thinks is just an ear infection and I begin picking up on something that suggests a broader developmental concern. Or I have a teenager scheduled for a routine sports physical, and within the first few minutes of conversation, I realize there’s an anxiety issue we need to address.” The appointment impetus is a starting point; what it becomes depends on what she observes.
In computational thinking, this skill is called abstraction—the ability to look past the surface details of a problem and discern what is actually significant. A computer scientist practicing abstraction makes a disciplined judgment about which information matters and which can be set aside without losing the essence of the problem. Dr. Smith does exactly this, continuously, throughout every patient encounter. Beneath the rhythm of the events in the appointment, she is always asking: What is really going on here? What else do I need to know to come to a diagnosis? Are there other issues we need to address?
Part of what makes that question so challenging is the sheer volume of information. Over years of practice, she has internalized the pattern recognition and algorithmic approach into an instinct for relevance, constantly assessing what is actually moving her thinking and what is not. If a piece of information shifts her assessment or changes her plan, it matters. If it does not move the needle, she sets it aside for now, not discarding it permanently, but deprioritizing it.
What drives that instinct, more than anything, is a sensitivity to surprise. Information that confirms what she already suspects is useful strengthens the pattern. But the unexpected detail, the finding that doesn’t quite fit, is often the one that deserves the most careful examination. A slightly elevated white blood cell count in a child who looks unwell, with a fever she cannot yet explain, does not just add to the picture, it disrupts it. And that disruption is itself a signal. “It’s either a clue I hadn’t considered, or it’s something that’s leading me astray,” she says. “Either way, I want to examine it carefully.” This, she suggests, is one of the more underappreciated demands of clinical medicine: not just knowing what to look for, but staying genuinely open to what you did not expect to find.
The Foundation of High-Quality Care
When Dr. Smith walks into the exam room, each caregiver is looking for someone who will listen, examine, explain, and provide high-quality care. What they do not see is all that is happening as she does this: the active pattern recognition before she even says hello, the algorithm quietly structuring each step of the visit, the decomposition keeping complexity from becoming chaos, and the abstraction cutting through noise to find what actually matters. These are not separate tools Dr. Smith reaches for consciously or deliberately. Over years of training and practice, they have become the blueprint for how she thinks.
Computational thinking in medicine, as in programming or engineering, is not a technique applied to a problem from the outside; rather, it is the thinking itself. While every morning begins with the uncertainty of what she will encounter, it is these skills that allow her to know how to tackle each problem or question so she can provide the best care for her patients and families.
Since 2020 Project Tomorrow’s Computational Thinking Project has been supporting the adoption and integration of Computational Thinking in partnered schools in NY, MI, and PA, by providing professional development to elementary school teachers. The goal of this professional development is to increase teachers’ competency, confidence and comfort with integrating CT concepts and practices within their everyday curriculum. To learn how Project Tomorrow can support CT integration in your schools, contact CT@tomorrow.org. You can also download our latest case study HERE for more information.
