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For two years I have spent my Saturdays at the intake desk of Eastside Community Clinic, registering about sixty patients a shift and keeping the day’s schedule. Most of the work is quiet and exact: confirming a date of birth, finding an insurance number, moving an appointment when a doctor runs late, explaining to a parent why the next opening is on Thursday. Since 2024 I have coordinated the Saturday shift, which means that when the schedule breaks, I am the person standing between the problem and the people waiting for it to be solved.

It breaks more often than anyone outside the desk would guess. A patient arrives for an appointment the system says does not exist. Two patients arrive for the same slot. A follow-up visit that a doctor ordered never reaches the calendar at all. Each time, the cost lands on the person in front of me: a longer wait, a second trip across the city, a missed visit that someone has to rebook by phone on Monday. For my first months at the desk I treated these as bad luck. I apologized, found a gap in the afternoon, and moved on.

One Saturday last winter, a mother arrived with two children for vaccinations the system had no record of. She had booked by phone three weeks earlier and had the confirmation number written on the back of her hand. The booking existed, but it had been entered under a visit type that the scheduler did not show on the vaccination calendar. We found it after forty minutes. By then the morning’s vaccine slots were full, and she had to bring both children back the following week. Nobody had made what anyone would call a mistake. The record had simply been entered one way and searched for another.

I stopped treating these moments as bad luck because of my other job. Since 2022 I have worked as an operations data analyst at Northline Logistics in Seattle, where I build the weekly demand and staffing reports for a dispatch floor of forty people. Before that, as an operations intern at Cascade Freight, I cleaned a year of dispatch logs and built the company’s first weekly on-time report. Both roles taught me the same lesson early: a report is only as trustworthy as the way its data was entered, and most of the errors that matter are made long before anyone opens a spreadsheet.

At Northline that lesson became concrete. For several months our dispatch schedule kept failing in the same way. Jobs that should have been assigned on Monday surfaced on Wednesday, already late, and every week the operations team spent hours repairing the schedule by hand. The accepted explanation was that dispatchers were rushed. I was asked to build an exception report that would at least show where the late jobs came from.

The report showed something nobody had expected. The late jobs were not random. Almost all of them had been entered with intake codes that meant slightly different things at different sites, so the scheduling system read them as lower priority than they were. The people were not careless; the codes were inconsistent, and the system had no way to notice. I traced the failure back through the intake records in SQL, documented each conflicting code, and rebuilt the validation step so that an ambiguous code was caught at the moment it was entered rather than days later in a report.

The technical fix was the easy half. The new validation step held because I worked it out with the people who entered the codes every day, not around them. I sat with dispatchers during their busiest hours, learned which shortcuts they relied on and why, and changed the step until it asked for the right information without slowing them down. When the late jobs stopped appearing in my exception report, it was because the people at the start of the process trusted the new step enough to use it.

I also learned how to show that a fix had worked. Before the change, I measured how many jobs each week were entered with an ambiguous code and how many of those ran late. After the change, I tracked the same numbers every week for three months and shared them with the dispatch team, not only with the managers. Watching the line fall was more persuasive to the people on the floor than anything I could have said, and it taught me that evaluation is part of the design, not a step that comes after it.

The experience changed how I approach my own work. I now begin every new report by asking where its data is first entered and by whom, and I spend time with those people before I write any code. It is slower at the start and much faster at the end, because the problems I find on the floor are the ones that would otherwise surface weeks later as unexplained numbers. More than any single technique, that habit is what I want to carry into healthcare.

Standing at the clinic desk the following Saturday, I recognized the same failure from the other side. The appointment that does not exist, the double booking and the lost follow-up are not three problems. They are one problem: a record entered in a way the system cannot read correctly, discovered only when a person arrives and the record fails them. At Northline the cost of that failure was a late delivery. At Eastside it is a patient who waited an hour, or a child who missed a vaccination, or someone who simply did not come back.

That difference is why I am applying to study health informatics. I want to design and improve the systems clinics use to schedule and track patients, and I want to do it with the same attention to the people at the start of the process that made the Northline fix last.

My preparation for this work is uneven in useful ways. My degree is in statistics, and my coursework in regression, experimental design and relational databases gave me the tools I use every week. In a course project I built a logistic regression model on a public hospital readmission dataset in R. It was my first contact with clinical data, and it showed me how much a model depends on choices made at the point of care: which diagnosis was recorded, when, and by whom. At Northline I work daily in Python, SQL and Tableau, and I built the weekly staffing forecast for the dispatch floor that the operations managers now plan around.

Building that forecast taught me a second lesson that I expect to need in health informatics. A model that is accurate on paper can still fail in practice if the people using it do not understand what it is telling them. My first version predicted demand well but presented it in a way the managers did not trust, and they kept planning from their own estimates. The second version was less sophisticated and far more useful, because I built it around the decisions the managers actually had to make each Monday morning. In a clinic, where the decisions are about patients rather than deliveries, I expect that lesson to matter even more.

What I lack is the clinical side of the record. I know how a dispatch job moves through a system from entry to completion; I do not yet know, in any rigorous way, how a patient record is structured, how it moves between a clinic, a laboratory and a hospital, or which standards govern how those systems exchange it. I have seen the edge of this problem from the intake desk, where a referral from another provider sometimes arrives as a fax that a volunteer has to type in by hand. I want to understand why that is still true and what it would take to change it.

A graduate program in health informatics is the most direct way to fill that gap. I want to study clinical data standards and interoperability, so that I understand how records are built and how they travel. I want to study the design and evaluation of clinical workflows, so that a change to an intake process can be tested before it reaches a patient rather than after it fails one. And I want to work on applied projects with clinics and health systems, because the most important thing I learned at Northline is that a system is only as good as its fit with the people who use it.

I am particularly interested in coursework and research that treat data quality as a design problem rather than a cleanup task. Most of my career so far has been spent finding errors after they happened. I want to learn how to design intake systems in which the most common errors cannot happen at all, and how to measure honestly whether a change has reduced them.

At Eastside I have already tried to apply what I learned at Northline on a small scale. As shift coordinator, I keep a simple log of every scheduling problem we meet on Saturdays: what happened, how the record had been entered, and how long it took to resolve. After a few months the log showed that most of our problems came from three kinds of entry. I shared it with the clinic manager, and together we changed the way one kind of phone booking is recorded. It is a modest change, made with a notebook rather than a database, but it has already reduced the number of families we have to turn away.

I also bring something that I hope will be useful to my classmates. I have spent two years on both sides of the same failure: as the analyst who finds it in the data and as the volunteer who meets it at the desk. I am comfortable translating between the people who build systems and the people who use them, and I speak both English and Mandarin, which at Eastside has often meant helping families complete registration forms that were written for someone else. That experience has made me careful about who a system is designed for and who it quietly leaves out.

I am looking, too, for a community of classmates and faculty who come to healthcare from different directions. Some of the most useful conversations I have had at Eastside were with nurses and physicians who explained why a single field on a form mattered to them. I expect to learn as much from classmates with clinical backgrounds as from my coursework, and I hope my experience in operations and data will be useful to them in return.

I have thought carefully about the timing of this decision. Three years in operations analytics have given me a practical grounding that I would not have had if I had applied straight from my undergraduate degree, and two years at the clinic have made it clear to me that this is the field I want to commit to, not a passing interest. I am ready to take a step that changes the direction of my work rather than its title.

After graduating, I want to work with community clinics and the health systems that support them, building and improving the intake and scheduling systems that decide how long a patient waits and whether a follow-up visit actually happens. Clinics like Eastside rarely have an analyst on staff, and the tools they use were usually designed for much larger hospitals. I would like to help close that gap, first as an analyst or informatics specialist inside a health system, and later by leading projects that bring better intake design to smaller clinics.

In the longer term, I hope to contribute to the standards and practices that shape how patient information is entered in the first place. The failures I have seen are not dramatic. No single one of them makes the news. But they repeat thousands of times a day, and each one costs a patient time, trust or care. I would like to spend my career making them rarer.

The intake desk taught me who pays when a record is wrong. Northline taught me how to find out why. I am applying to this program to learn how to prevent it, and to bring that knowledge back to the desk where I started.