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Blog | Alto Pharmacy

Pharmacists are one of the most accessible healthcare professionals, seeing people 5 to 8 times more frequently per year than primary care providers. Their role has become more expansive than ever, and many people don’t realize just how integral they are to a patient’s health and well-being.

National Pharmacists Month Spotlight: Travis Allison

At Alto, fulfillment pharmacists focus on dispensing medication — ensuring that the entire process is carefully monitored every step of the way — while patient advocacy pharmacists tackle anything related to a patient’s wellness, from provider communication and insurance coordination to guidance on managing chronic health conditions.

These specializations have opened up entirely new possibilities for the patient-pharmacist relationship at Alto. And this October, as we celebrate American Pharmacist Month, we’re spotlighting a few of the extraordinary Alto pharmacists who focus on patient advocacy.

To kick things off, we’re pleased to introduce Travis Allison, a patient care pharmacist based in Dallas, Texas.

What motivated you to pursue a career in pharmacy?

I took the long and winding road here. I didn’t always know this was what I wanted to do. In college, I started off as an English major but had a change of heart after a semester or two. I went on to study agriculture, and those experiences made me believe that I could pursue a path grounded in science.

My childhood memories of visiting the pharmacist were always in the back of my mind. As a kid, I hated going to the doctor. When those dreaded appointments were over and done with, I knew I’d get to see a guy in a white coat who would give me something to feel better. I always saw pharmacists as a comfort in the often scary world of healthcare.

What challenges have you encountered as a pharmacist?

At any chain pharmacy — which is where I spent my career before Alto — pharmacists are inundated with so many tasks. It isn’t that these tasks are unrelated to pharmacy, but they interfere with our ability to deliver patient care.

On a typical day, I would give 100 Covid shots and fill 200 prescriptions by myself while also coordinating billing and calling doctors’ offices. We also had to think about corporate metrics. I was frequently asked to get more people signed up for text message alerts and hit specific quotas.

Patient care is always the priority, for any pharmacist, but the reality of my job made it extremely difficult, and often impossible, to give patients the time they deserved.

The first thing I noticed when I started at Alto were the specializations. We have many different team members to handle all the responsibilities that fall on a single person’s shoulders at a traditional retail pharmacy.

I’m a patient care pharmacist, which is distinct from fulfillment pharmacists who process prescriptions.

I review prescriptions for accuracy and initiate medication transfers, and I interact with both patients and doctors. I’m here to answer any questions a patient has about their medication.

In a way, I feel like I’m on the frontlines the same way that I was when I worked in a pharmacy chain. It’s all of what I liked about retail pharmacy without the distractions of retail. We’re able to practice our most important functions as pharmacists without the noise.

We’re utilizing technology in a way no other pharmacy does, which allows us to communicate with patients and providers in so many different ways — without having to rush through interactions.

And the wheels of change turn much more quickly at a tech-oriented company than they do at a massive corporation. At my previous job, you could have conversations all day about what improvements were needed, but nothing ever happened. Alto was founded by software engineers, and we’re able to easily make changes in real time.

As a digital pharmacy, we serve so many patients that only we can serve. I talk to many who are homebound without a caretaker. Because of Alto’s model, I’m able to communicate with them, and we’re often their only point of contact within the healthcare system. I don’t know of another way these patients could get pharmacy care.

The digital model also allows us to get a broader view of healthcare, and to make an impact on a larger scale. Alto was one of few places where New Yorkers could get the Covid medication Paxlovid when it was first introduced.

It was incredible to be sitting here in Texas, getting prescriptions out in New York City just as a new variant came out that was really devastating to the city. At no other pharmacy would I have been able to help out my neighbors in New York.

Those moments where you can tell in a patient’s voice that you seriously helped them. To me, that’s an indication of how well I’m doing my job, much more so than the number of prescriptions processed or dollars generated.

When Jake Moffatt says he loves building things, he means it — sometimes literally. A construction project manager turned engineer, Jake has spent the past decade building software at mission-driven organizations. Today, as Engineering Manager of Alto Assistant, he leads the team that’s bringing wizard-like support to Alto’s customer-facing app.

Get to know Jake Moffatt, Engineering Manager on the Alto Assistant Team

When Jake Moffatt says he loves building things, he means it — sometimes literally. A construction project manager turned engineer, Jake has spent the past decade building software at mission-driven organizations. Today, as Engineering Manager of Alto Assistant, he leads the team that’s bringing wizard-like support to Alto’s customer-facing app.
Below, Jake chats with fellow engineering manager Mike Riess and recruiter Braden Hanlon about the work ahead for Alto Assistant.

I’d love to learn more about your background to get us started. How did you end up in software engineering?

When I was twelve, I started learning to code in QBASIC because I wanted to make video games. Once I wrote my first program, I was hooked! I loved computers and the creative power they gave to anyone willing to invest the time and energy to learn. Since then, I’ve built games, personal tools and projects, desktop software, and a number of apps and services — I love building things. My career has gone back and forth between construction and software engineering, but it’s been firmly in the software industry for the last ten years or so.

My favorite place to be in a company is working on something new that has a big impact or addresses an important problem. In a previous role, I built an app that helped people find roommates in a city where housing is prohibitively expensive. At that same company, I led the team responsible for creating a new vertical that eventually split off into its own company.

The goal of Alto Assistant is to help customers help themselves before reaching out to support. Our first project is introducing a wizard-like support “assistant” (hence the team name!) that guides the customer through a series of questions to narrow down the area of support. We’ve already identified a few tasks the assistant can help customers complete on their own, including making changes to upcoming deliveries or scheduling new orders.

In cases where we can’t help them just yet, our new tool generates data to help us to learn exactly what customers need help with. It’s a top priority to bake analytics into every new feature we build; even though we have years of experience supporting our customers — and we know intuitively which problems they frequently experience — we don’t have conclusive data to draw upon just yet.

We plan to start with a couple of frequent use cases and measure how often customers reach out to us for support. Then, we’ll chip away at the problem until we’ve seen a substantial improvement in the user experience. From there, we’ll move on to the next biggest problem and repeat the cycle. There’s a reason “Chip away it” is an Alto value: It’s the playbook that we’ve used to automate other critical parts of the pharmacy value stream like intake, billing, and fulfillment.

What opportunities are you most excited about?

Support drives much of the cost of every shipment today, so this is a high-impact area for our business. A year from now, it would be a stellar outcome if the Assistant team could reduce the number of issues that customers need support on by half.

We’re also thinking about the user experience beyond the app. We plan to improve the tooling that our care specialists use to provide support as well. We’ve already discussed some promising ideas like creating the concept of a conversation in support communications rather than an infinite stream of messages and building out enriched communications. We also intend to develop a custom tool that can expedite a customer’s prescription when they need their medication ASAP.

The possibilities are many, and it will take a team of determined, passionate, empathetic people to solve these challenging problems. I’m super excited to see what the Assistant team will do together.

Any lessons learned from previous experience that you’re applying to your role at Alto?

There’s always the temptation to tackle everything at once with new business initiatives. My experience leading new verticals at Apartment List and Snapdocs taught me the importance of being laser-focused on one or two key metrics instead of trying to build out a fully-realized product from the beginning. That’s exactly what we’re doing on the Alto Assistant Team with our clear focus on reducing communications per shipment — how often customers reach out for help because they could not find what they needed in our app.

It’s also important to recognize that we won’t have all the answers right out of the gate! I like to think of product development as a three-step sequence: learning, growth, and maintenance. Alto Assistant is so young that we’re very much still in the learning phase. We maintain a tight iteration loop with everything we ship: launch, measure the impact, and use the results to iterate and refine our hypotheses about what our users want. Our final product could be anything from a customer support wizard to an AI chatbot to a ticketing system — we have to rely on our key metric of communications per shipment, experimentation, and user feedback to shape the direction.

What got you excited to join Alto? What would you pass on to an engineer thinking about applying to the company?

I was impressed by the Altoids I met during the interview process. I knew I would be working with people from whom I could learn a lot, like Elnaz, who started as an individual contributor and went on to become Director of Engineering and, now, Alto’s first Head of Strategy & Operations. I was also impressed by the quality of the interview process itself — a good sign that the company was well-organized internally. Finally, the pitch for the role was extremely clear, with already-established key metrics that would directly contribute to the company’s bottom line and help Alto become more profitable.

For engineers, I think Alto is a great place for both technical challenges and career growth. We’re still small enough that every individual gets exposure to a broad portfolio of high-impact projects. It’s also a great environment for learning about how a business works — the Product & Technology organization does a great job of sharing information and presenting problems in a way that gets to the root business problem we’re trying to solve. There’s a clear line between any project a team is working on and a foundational business metric. Most importantly, we’re a company that makes a truly positive impact on our users’ lives.

Interested in learning more about engineering at Alto? Follow us on LinkedIn.

Redux is great for managing client state: it allows data to be synchronized across the whole app, reduces prop drilling, and makes it easier to understand when state changes occur.

Server state is a different story. Sure, it’s nice to have data available everywhere in our app, which is why we tend to fetch data from the server and dump it into our Redux store. But because our client application doesn't own that server data, it’s hard to know when it has become stale. And what if multiple components trigger calls to fetch the same data? How do you dedupe requests? How do you decide when to refetch?

Why We Switched to React Query for Managing Server State

Redux is great for managing client state: it allows data to be synchronized across the whole app, reduces prop drilling, and makes it easier to understand when state changes occur.

Server state is a different story. Sure, it’s nice to have data available everywhere in our app, which is why we tend to fetch data from the server and dump it into our Redux store. But because our client application doesn't own that server data, it’s hard to know when it has become stale. And what if multiple components trigger calls to fetch the same data? How do you dedupe requests? How do you decide when to refetch?

Below, we’ll dive into how we’re using React Query at Alto to solve these problems and achieve separation of client and server state.

The Old Way: Fetch Server Data and Store It in State

Historically, we would fetch data and set the response in our React component state.

fetchUser(params) {
 this.setState({ isLoading: true });
 
 return getUser(params)
   .then(({ data }) => {
     this.setState({
       isLoading: false,
       user: data.user,
     });
   })
   .catch(() => {
     toaster.toast({ title: 'Something went wrong...' });
     this.setState({ isLoading: false });
   });
}

To make that data accessible from other components, we’d set that data in the global Redux store. This works fine, except that you have to manage your own loading state, the logic for when and when not to make the request, updating the store when mutating data on the server, and knowing when data is stale.

The React Query Way: Fetch and Cache Server Data

With React Query, fetching data and using that data is more declarative.

const { data, error, isLoading } = useQuery(
 // This is our Query Key, which is unique per query so that React
 // Query can manage the cache for us.
 ['user', { userId }],
 
 // This is our Query Function that actually does the data fetching.
 // We can use anything we want: built-in fetch, axios, etc.
 fetchUser({ userId }),
 
 // This query will not execute until the userId exists.
 { enabled: !!userId },
);

Here, we define our query for a user with a Query Key and a Query Function. The Query Key is unique per query, so React Query can manage our cache for us, and if any part of the Query Key changes, the query will automatically fetch new data. In this example, if the user ID changes, the query will fetch new data for that specific user and store that data in the cache.

Naming is hard, but caching is harder. React Query gives us caching of server data out of the box with cache invalidation and request deduping. If we use this same query with the same Query Key in another component, it'll check the cache: if the data is already there, React Query will simply return it, avoiding extra network requests!

Additionally, useQuery runs wherever you use it — no more fetching data when a component mounts. You just use the query and display the data when it’s ready. The cherry on top? The query returns status updates by default, so we don’t need to store isLoading in component state all the time.

Three Optimizations We Get With React Query

When we rebuilt Alto Connect, our web app for healthcare providers, we found impactful benefits in using React Query to help us manage server state instead of using Redux. Alto Connect provides instant access to patient and prescription information and enables messaging with the pharmacy. Because healthcare providers need to quickly switch between patients and clinics, caching data is important to provide a delightfully fast experience. Apart from the cache, what other optimizations do we get by using React Query? Below are a few that helped us.

Once we’ve queried for some data, it gets entered into our cache, so we can utilize that cached data when using the same query in a different component or when fetching new data for a stale query in the background.

React Query is configured with aggressive but sane defaults and considers cached data as stale. Stale queries will always query for new data when the following criteria are met:

The query is optionally configured with a refetch interval

The fetching for new data when the browser window or tab is refocused is especially useful because the healthcare providers using Alto Connect often switch between multiple browser tabs. If there’s an update to a task, status, or message while they’re checking a patient’s medical chart, it will be immediately reflected in the UI when they come back to Alto Connect.

We also set a refetch interval for our queries involving messages. In the old app, we used WebSockets to enable real-time messaging. Although it’s possible to integrate WebSockets with React Query, we opted for a polling solution instead. Because our app isn’t a real-time messaging application, and we don’t need messages to show up immediately, we set a refetch interval on our messages query to poll for new messages every 30 seconds.

const { data, error, isLoading } = useQuery(
 ['user', { userId }, 'messages'],
 fetchMessages({ userId }),
 {
   // This query is disabled from automatically running if we don’t have a userId
   enabled: !!userId,
 
   // Refetch every 30 seconds
   refetchInterval: 30000,
 
   // Continue to refetch while the tab/window is in the background
   refetchIntervalInBackground: true,
 },
);

This is where the enabled option, which disables a query when it evaluates to false, is particularly powerful. We can pause polling for messages if we route to another page and we know that we won’t make any unnecessary requests if there isn’t a valid userId. We don’t have to worry about closing any socket connections or manually clearing an interval.

Most web apps have some sort of navigation menu, and if you already measure or understand your user behavior, you can prefetch data before users even navigate to a different page. Custom hooks makes it easy to expose a function to prefetch data:

export const usePrefetchUsers = () => {
 const queryClient = useQueryClient();
 const prefetchUsers = async () => {
   await queryClient.prefetchQuery(
     ['users'],
     fetchUsers,
   );
 };
 
 return { prefetchUsers };
};

We prefetch queries when users mouseover or hover over a navigation item so that when they navigate to the next page, the data is either actively being fetched or is already in the cache waiting.

We’ve been talking a lot about queries, but what about mutating data? Mutations, in contrast to queries, are imperative. While queries run automatically when we use them, we need to fire a mutation method in order to run mutations. And in order for our user experience to feel snappy, we can optimistically update the cache and then invalidate it after successfully executing the mutation. useMutation gives us some nice side-effect handlers to deal with every stage of the mutation lifecycle. Now when the user sends a message, that new message is immediately reflected in the UI and we keep the data in sync by invalidating the cache and refetching the messages.

With Redux, we needed to build out solutions to each challenge of managing server state. React Query, on the other hand, gives us those tools right out of the box. We no longer need to worry about when to invalidate or fetch new data, making the user experience more predictable — and the developer experience more enjoyable.

Last summer, Moringa Fellows Irma Figueroa and Adrian Mendez joined Alto as the company’s second batch of engineering interns. Here, they share their learnings, accomplishments, and advice for future Alto interns.

Two Engineering Interns Weigh In: What to Expect From Your Alto Internship

The Moringa tree flourishes almost anywhere it’s planted. It’s hardy, fast-growing, and resistant to drought — an apt metaphor for the two interns who joined us from the Moringa Fellowship program last summer.

“Before Alto, I had never actually contributed to a large codebase,” admits Irma Figueroa, an intern on the Supply Chain Team. By the end of their three-month internships, however, Irma and fellow intern Adrian Mendez had shipped impactful features for Alto’s internal pharmacy management system and customer-facing app that are still in production today.

Below, we chat with Irma and Adrian to learn what it was like interning at Alto, which projects they worked on, and what advice they’d give to future interns. 

Adrian, who interned on the Patients Team before returning to join as a full-time software engineer last fall, was surprised by the freedom he had to choose what he worked on during his internship: “Early on, my manager gave me a rundown of the projects the team was juggling and the option to choose which one interested me the most,” he says. “I was able to decide the kind of experience I was going to have.” 

Adrian adds that meetings with his mentor evolved over the course of his internship, as he became more familiar with the codebase: “From the start, we had weekly meetings to talk about whatever was on my mind. Those meetings started off as general advice and questions and turned into more time to pair or run things by each other.”

Because both Adrian and his mentor were working on the same, long-term project, some of those meetings were also dedicated to project work. This included “sitting with [my mentor] while creating the project outline, pairing on larger tasks, and syncing up on progress.” 

Which projects did you work on during your internship? 

On the Supply Chain Team, Irma was tasked with a unique inventory challenge: Technicians at our production pharmacy locations use handheld scanners to scan the barcodes on newly-received inventory. Our technicians often need to scan hundreds of inventory items at a time — making it all too easy to lose track of which items have been successfully scanned onto which shelves. 

“I created a reusable counter component, displayed in the header of the scanner app, that would represent the number of items scanned into a shelf.” Irma says. The best part? Getting to test the handheld scanner herself. “I really enjoyed using the scanner for testing purposes. It was rewarding to see my code in action and definitely super fun!” 

On the Patients Team, Adrian worked on a project to add a homescreen to the customer-facing native app. Previously, the app dropped users into a list of all their active medications, which made it difficult to know where to look or what to do next. The new homescreen allows users to schedule the prescriptions most relevant to them and to view or edit any upcoming deliveries. 

“The first step was to create new additions to the design system — essentially taking the designs and extracting system-wide components. After that, the fun part was taking all those components and putting them together like building blocks!” Adrian says.

It was especially rewarding, he adds, to have been part of the project from start to finish. “I joined right as the design phase started, and I was there mostly every step of the way. For how short my time here was, having helped create this landmark project is really cool to think about.” 

What advice would you give to future Alto interns?

Irma and Adrian agree: Don’t be afraid to ask for help. “You'll often feel challenged and possibly intimidated, but be patient with yourself and remember you have an entire team of experienced engineers ready to help,” Irma says. 

“Everyone at Alto is more than willing to help you out, so don't be afraid to ask questions! You're on the same ship as everyone else, so your success equals everyone’s success,” Adrian adds. “Just remember that your work got you here, and they wouldn't have invited you if they thought you couldn't handle it. They made an investment in your ability to grow, so stay curious.” 

Interested in interning at Alto? Check out our careers page here.

With more engineers contributing to Alto’s codebase than ever before, we needed to establish better boundaries: which team owns what? Here’s how we developed a tagging-based solution to code ownership and drove and measured adoption.

How We Established Better Boundaries and Clearer Code Ownership at Alto

Some problems are nice to have. Over the course of 2020 the engineering team at Alto saw explosive growth, expanding from about fifteen to more than seventy individual contributors. Growth at this pace required us to rethink our team structures and priorities more than once. This growth felt amazing: the new capacity enabled us to make progress on our backlogs like never before.

But still, there were challenges. We saw a steady rise in merge conflicts as more people contributed to the same code base. Build times increased as more and more code was contributed. A big influx of new perspectives and talent also meant new gaps in historical context and responsibility. New teams took big bites out of their new priorities — great! But what about the old ones? Who was going to fix that bug? Who needs to review this PR? Who is accountable for this performance problem?

Who, in short, owned what? By the end of 2020, it was clear to us that a good, practical, working answer to this question would be the critical first step to continuing smooth, rapid, distributed iteration on our product.

Up to this point, the large majority of product development at Alto had happened in the context of a Ruby on Rails monolith, which — over a half decade of intense development — had grown pretty large. We’d stuck more or less to the default Rails convention of organizing code by function rather than feature, which meant that our code was distributed across a few mostly flat and very broad subdirectories such as '/models' and '/controllers', making it difficult to learn much about the structure and ownership of our business domains by merely looking at how the code was laid out. We knew that this was not going to work for us over the long term.

Our long term vision started to coalesce around a notion of “better boundaries.” First, we would modularize the monolith along the lines suggested by Shopify’s packwerk utility. Then, we would carve some of those modules out into separate engines that could be built and tested separately. Finally, we’d deploy and operate some of those engines independently as their own services. Clear ownership was fundamental to this plan: on either side of an effective boundary, you need to know not just what it is but also who owns it. We lacked that clarity.

The RFC (Request for Comments) we ultimately adopted opened with four questions:

How might we produce a domain model and do something useful at the same time?

How will we avoid imposing (the wrong) prescriptive model from the top down?

How will we keep it up to date and accurate?

We considered jumping straight into moving files around into a more modular structure. Ultimately we decided against this approach as a first step for a number of reasons. The benefit of moving files around is mostly abstract — it’s not immediately clear what to do with a better module structure. It’s relatively expensive to move files around in a Rails app — doing so typically requires you to rename the contents (and update all its callers), which amounts to a lot of busy work, and it’s extra expensive to be wrong. And the in-between state is rough: which example should I follow in deciding where to put my new file? First you are obligated to create a map of the future state, and then to keep that map in sync with reality as you go.

Instead, we opted for a lightweight solution that would allow us to map our domains and their owners in the code itself — helping to ensure it’s up to date and accurate — without having to restructure it at the same time. We’d do this by tagging code in code comments, and then building tools to leverage these tags in various ways to increase visibility and enforce ownership. This has the nice effect of producing a new domain map incrementally from the bottom up, rather than prescriptively from the top down.

We called this the '@owners' tag, and it looks like this:

# @owners { team: platform, domain: orders }
class Wunderbar::OrdersController < Wunderbar::WunderbarController
  def show
		...
  end

  # @owners { team: inventory }
  def out_of_stock
    ...
  end
end

We based our tooling around extending the YARD documentation tool to recognize this as a custom tag. You can teach YARD to recognize a custom tag by passing a command line option like '--tag owners:"Owners"', which by default will cause the tag value to appear in generated documentation under the header “Owners”. The Ruby API for declaring a tag programmatically is shown in the snippet below as well.

We wanted the tag to be interpreted hierarchically to handle cases where ownership has diverged within the same class or module, such that we might want to tag one or two methods differently from the rest, as well as to ease the burden when all of the classes / modules / methods under a namespace belong to the same team and domain, since you can just tag the shared parent. We set an initial goal around getting 100% of our methods covered by at least a “team” value, at which point we would require all code to be covered in our build process.

To get a basic feel for how to work with these tags, make sure the 'yard' gem is available, tag a couple of your files, and then try running the code below in your project. It will print out the tag associated with each tagged code block.

require 'yard'
# Define the custom tag.
YARD::Tags::Library.define_tag('Owners', :owners)

# Parse your codebase. You might want to pass a more specific path here.
YARD.parse('.')

# Load all the method, class and module code blocks into memory
code_blocks = YARD::Registry.all(:method, :class, :module)

# Print the tag associated with each tagged block.
code_blocks.each do |code_block|
  tag = code_block.tag('owners')
  # automatically tag nested classes, modules and methods.
  while tag.nil? && code_block.namespace != nil && [:class, :module].include?(code_block.namespace.type)
    code_block = code_block.namespace
    tag = code_block.tag('owners')
  end
  print "Code Block: #{code_block}, tag: #{tag.text}, line: #{tag.line}" if tag
end

We ultimately developed a handful of CLI tools based on this technique that allow us to produce reports measuring the percentage of code covered by team and domain at different levels, among other things:

bin/yardowners --help
Usage: bin/yardowners [options]
        --methods                    view by methods
        --files                      view by files
        --teams                      teams report
        --domains                    domains repot
        --team=TEAM                  filter by team
        --domain=DOMAIN              filter by domain
        --codeowners                 update codeowners
        --lint                       lint owners tags
        --require-domains            lint for presence of domain in  
owners tags
    -h, --help                       display this help and exit

We use this CLI to add checks to our CI process around coverage / correctness, as well as to report progress toward our goals of 100% coverage.

To get the tagging process started across the organization, we dumped all of our class and module names (thanks again, YARD!) into a big spreadsheet, and then asked each team to get together and spend a bit of time putting their names next to the ones they owned. We adopted the attitude that it was better to have the wrong value than no value, since it would be easy to adjust later.

We covered a good 80% of the code using this spreadsheet by the time each team had taken an initial pass. From there, we filled the blanks in with “TODO”, and then wrote a little script to automatically insert the tags into the code (again based on the YARD metadata, which conveniently gives you the line numbers for the top of each class / module declaration).

At that point, we enabled a linter requiring 100% team owner coverage on all new PRs. We set a new goal to eliminate TODOs, and chipped away at it over the course of a month or so. We then repeated this process for the “domain” values, driving that to 100% as well. We found that the spreadsheet-based divide-and-conquer approach allowed us to cover a lot of ground very quickly.

Once we were able to parse our '@owners' tags, the first and most important thing we did with them was to automatically generate our CODEOWNERS file. We enforce that each tagged team maps to a team name in github and check that CODEOWNERS is up to date during the build process. This new process introduced some friction, as teams started getting tagged every time a file they were tagged on changed. This turned out to be great, because it sparked a number of very productive negotiations around ownership and scope that got hashed out in code review. We keep our CODEOWNERS file synced with Sentry, too, so that exceptions raised by files you tagged as yours get assigned to your team automatically. We also track and report on the performance of individual endpoints by team according to how they are tagged.

The new owners tag hasn’t fixed all our problems by any means, but it has proved fundamental to our ability to operate as a growing set of increasingly independent teams with well specified responsibilities. In subsequent posts, we’ll talk more about how we’re using our new domain map and a suite of other cool tools to build better boundaries within and ultimately escape from our monolith.

To automate a manual, error-prone pharmacy workflow, we turned to event streaming. Here’s what we learned from one of the first Kafka use cases at Alto.

How We Used Kafka to Build an Event-Driven Automation Pipeline and Scale Our Pharmacy

Imagine this: You're at the doctor’s office, and your doctor just wrote you a new prescription. Minutes later, before you even check out with the receptionist, you already have a notification from Alto Pharmacy on your phone that your prescription is ready. All you have to do is pick the date and time that works best for your free courier delivery.

This is the future we’re building at Alto: a future where a prescription can be transcribed, billed, and verified by a pharmacist in five minutes, flat. To get there without dramatically increasing Alto’s head count, we need to automate much of the process, starting with the first step: transcribing, or intaking, a prescription.

As a pharmacy, we receive prescriptions in many different forms. Some are sent electronically, in a structured format, but others are handwritten, phoned in, or even faxed. To represent all of these forms in a single table, we previously required every incoming prescription to be transcribed by an Alto team member, who would answer questions like:

Whom is this prescription for? Is this customer already in our system?

Where did this prescription come from? Who wrote it?

What is the relevant prescription information (medication to dispense, quantity, days supply, directions, etc.)?

This manual process is both time-consuming and prone to human error, which, if uncaught, could impact our ability to process the prescription downstream. Furthermore, at our busiest times, it could take up to two hours for a team member to get through the pending intake queue — unacceptable if we hope to have prescriptions ready to schedule before people even leave their doctor’s office. 

Automating intake required close collaboration between three teams:

Data Science, who built the decision-making algorithms 

Data Engineering, who served up the results of those algorithms

Product Engineering, who used those results to automatically intake prescriptions 

To allow the teams to operate in parallel without toe-stepping — and in the language most suitable for each (the Data Science and Data teams prefer Python, while product engineers work mainly in Ruby) — we separated the code into two environments: 

1. Data Prod Interface (DPI), where the code for generating automation results lives

2. Wunderbar (our proprietary pharmacy operations platform), where the code for requesting and using those automation results lives

The key question: to establish robust communication between these two environments, should we use an API-based approach, or an event-driven one? Each has its pros and cons. 

Legacy approach everyone at Alto is familiar with

Existing enforcement of strong types and data

Difficult to scale with increased data & requests

Requires ongoing authorization / authentication

Infrastructure relevant to other business areas

Automatic retries, reprocessing, and queueing

Our existing infrastructure currently utilizes a hefty API-based structure, so there was a natural tendency to go down this path, but we knew that beginning to migrate services towards an event-based architecture with Kafka at its heart would help us in the long term.

Kafka is a framework implementation developed by LinkedIn, now open-source with the Apache Software Foundation. It aims to provide a unified, high-throughput, and low-latency platform for handling real-time data feeds. 

Alto uses Amazon Web Services as our cloud provider, and luckily, they offer an out-of-the-box managed solution for Kafka, MSK. To configure Kafka with our system, our infrastructure team worked with AWS to set up managed instances to comply with our business requirements. Our Infrastructure team will share more details about this work in an upcoming blog post. 

Kafka offers many benefits. The asynchronous nature of the message system allows for retries, reprocessing, and queueing as well as easy recovery from outages. In addition, the infrastructure work done for setting up the managed cluster would be a one-off workstream. When complete, we’d be able to re-use the same setup and configurations across various business needs to streamline other projects that leverage Kafka. 

Setting up for success: defining a strict contract

One element we had to get right from the start was the data contract. As we send messages between DPI and Wunderbar, we need to ensure messages are produced with the correct schema, so that downstream consumers know what to expect.

Protocol Buffers, or Protos, is an open-source, cross-platform data format used to serialize structured data. Developed by Google, Protos allow you to define how you structure your data once and then use generated code to easily write and read the structured data in a variety of different languages. 

At Alto, we’ve been using Protos for the past few years to speed up development with auto-generated code and ensure data continuity between our Ruby on Rails backend and JavaScript / React / React Native frontend. Protos may seem antithetical to JavaScript and Ruby since they’re both dynamically typed languages, but in practice, they have saved us countless hours of debugging and back-and-forth Slack messages. 

For this project, we turned again to Protos to define the schema for messages produced by DPI and Wunderbar. We defined IntakeAutomationRequest and IntakeAutomationResult messages in Go and generated the types (Sorbet, Flow, etc.), which both DPI and Wunderbar now refer to when producing or consuming messages.

Because Wunderbar is already configured to run on AWS and houses our existing intake logic, it was easy enough to send a prescription to DPI for automation. Where we would normally create a task for an operations team member to manually transcribe the prescription, we instead produce a message to our “request automation” Kafka topic using the ruby-kafka library.

Our consumer, instantiated in an executable that runs on a dedicated worker, consumes messages from a “receive automation” Kafka topic. First, the consumer verifies that a given message corresponds to a prescription we previously sent to DPI. Then, it persists any automation results included in that message. If the automation results are sufficient, we skip intake altogether. Otherwise, we create an intake task to be handled by an operations team member.

Because this project was among our early use cases for Kafka, we wanted to be prepared for the worst-case scenario: we produce a message requesting automation results, and for whatever reason—maybe DPI is down—we don’t hear back. For us, we defined a reasonable time frame as fifteen minutes, in order to avoid building up a large backlog of work that would have to be offloaded back onto our operations team.
To this end, we store metadata about the automation lifecycle in an intake_automation_results table, which includes timestamps representing when we sent a message to DPI and when we received a response back. A cron job runs every minute to check that table for rows where received_at is blank, and sent_at was more than fifteen minutes ago. If this occurs, we flag the prescription to be manually intaken by an operations team member.

The diagram below illustrates our intake automation architecture.

Intake automation is now live at Alto. We’re seeing prescriptions fly through in just a few minutes — during which time our automation creates a user profile if one doesn’t already exist, links the new prescription to that profile, and finds and bills the user’s insurance; a pharmacist reviews the prescription for clinical accuracy; and the user receives a text message to schedule their delivery. This single flow, prior to intake automation, once took up to four hours to complete!

Going from zero to one is quite a feat, and it wouldn’t have been possible without an event-based approach. Now that intake automation has laid the groundwork, we know that Kafka will only continue to provide exponential value to Alto as we grow and migrate more of our services to this event-based architecture. 

In our previous post, we discussed how we designed our biometric authentication system with both privacy and a better user experience in mind. In this post, we'll show you exactly how we did it.

Considerations and Approaches for Implementing Biometric Login: Part 2

In Part 1, we discussed how we designed our biometric authentication system with both privacy and a better user experience in mind. In this post, we'll show you exactly how we did it.

To build a new authentication mechanism, we opted to use the react-native-biometrics library. This library interfaces with the device's biometrics, generates a keypair, and stores the private key within the device's keystore. When logging in with biometrics, the library creates a signature using the stored private key, and the signature is then sent to the server for validation. (Note: this library is now unmaintained. At the time of writing, this implementation can instead be accomplished with the following maintained libraries: expo-local-authentication to access device biometrics, keypair to generate a keypair, react-native-keychain to access the device keystore.)

Check if biometrics are supported and configured by the user for this device.

const { available } = await ReactNativeBiometrics.isSensorAvailable();

If yes, prompt the user for a biometric scan.

const { success } = await ReactNativeBiometrics.simplePrompt({ promptMessage: 'Confirm ID' });

If the scan succeeds, generate a keypair. The react-native-biometrics library's createKeys method will automatically save the private key to the device's keystore, and return the public key counterpart. Send the public key to your server.

const { publicKey } = ReactNativeBiometrics.createKeys('Confirm biometrics');

// Save the public key to your database
post('/save_biometric_key', { key: publicKey, device_id: 'xyz' })

When the user tries to login with biometrics, scan their biometrics. This time, if the scan succeeds, create a signature using the stored private key. The react-native-biometrics library's createSignature method handles all of this.

// Prompts for scan
// On scan success, accesses private key and uses it to generate a signature, which is then returned
const signatureResponse = ReactNativeBiometrics.createSignature({
  promptMessage: 'Face or fingerprint ID for Alto Pharmacy',
  payload: BIOMETRIC_SIGNATURE_PAYLOAD,
});

Send the signature to the server to validate against the public key. If validation succeeds, the user has successfully authenticated with biometrics and can proceed into the app.

// Call your server API to validate the signature against the public key that you've stored in the database
const validationSuccess = await post(
  `/validate_biometric_signature`,
  {
    signature: signatureResponse.signature,
    payload: BIOMETRIC_SIGNATURE_PAYLOAD,
		device_id: 'xyz',
  },
);

if (validationSuccess) {
  // Login Success
} else {
  // Login failure
}

import ReactNativeBiometrics from 'react-native-biometrics';

const setUpBiometricLogin = async () => {
  // Check if biometrics are supported and configured on this device
  const { available } = await ReactNativeBiometrics.isSensorAvailable();

  if (available) {
    // Prompt user to scan biometrics
    const { success } = await ReactNativeBiometrics.simplePrompt({ promptMessage: 'Confirm ID' });
      
    if (success) {
      // If scan is successful, generate a keypair, then save the public key to the server
      const { publicKey } = await ReactNativeBiometrics.createKeys('Confirm biometrics');

      post('/save_biometric_key', { key: publicKey, device_id: 'xyz' })
    }
  }
}

const loginWithBiometrics = async () => {
  // Scan user's biometrics. On scan success, generate a signature
  const signatureResponse = await ReactNativeBiometrics.createSignature({
    promptMessage: 'Face or fingerprint ID for Alto Pharmacy',
    payload: BIOMETRIC_SIGNATURE_PAYLOAD,
  });

  // Validate the signature against the public key saved on the server
  const validationSuccess = await post(
    `/validate_biometric_signature`, 
    {
      signature: signatureResponse.signature,
      payload: BIOMETRIC_SIGNATURE_PAYLOAD,
      device_id: 'xyz',
    },
  );

  if (validationSuccess) {
    // Login Success
  } else {
    // Login failure
  }
}

1. build a way to store a public key associated with a device

2. write a method to validate a signature against a public key

3. expose endpoints to save a public key, and validate a signature

Once biometric authentication is built, there are additional decisions to make. The right decisions for your app will depend on your goals; the approach we took here at Alto may not align with what is best for your app.

How should biometric sessions interact with our existing email and password sessions?

We opted to treat email and password sessions as longer-lived primary sessions, with biometrics as a shorter-lived secondary session on top of that primary session. This means that a user must first login with email and password in order to use biometrics on subsequent app opens. If the user logs out for any reason, they will need to log in again with email and password before they can use biometrics again.

How often should we prompt the user for biometrics vs. email and password?

We opted to prompt the user for biometrics far more frequently than email and password, but made this configurable in case we want to adjust.

We use tokens and cookies for session management.

There are plenty of edge cases to consider as you're developing biometric capabilities for your app, including: what should happen when the biometric scan fails, or when the signature validation fails? What should happen if a user disables biometric permissions from their device? What should happen when two users share a device? What should happen if 'risky' activity is flagged for this account? How can we disable biometric login if needed?

Face and fingerprint ID login has clear benefits for both the end-user and the application itself, but it comes with a host of implementation decisions. If you're considering building biometric login for your app, I hope this article gives you clarity on what biometrics is, the different ways you can integrate it into your app, and the tradeoffs and edge cases to consider.

At Alto, the Patients Engineering team worked closely with our Security Engineering and Product teams to align on our goals and build the best solution for our needs. Our Security team offered deep expertise on the authentication strategies to adopt and avoid, and our Product team kept us tethered to building the best experience for our users.

Working as a team is one of the best parts about developing at Alto. If you want to meet the awesome team I'm talking about, come join us - we're hiring!

Face and fingerprint ID login is a convenient, seamless way to gain app access on a mobile device. Instead of typing in a lengthy, hard-to-remember username and password, many apps allow you login with just a quick scan of your face or fingerprint (also known as biometrics).

Considerations and Approaches for Implementing Biometric Login: Part 1

It's a desirable feature for both our end user and the Patients Team here at Alto — we want to make it as easy as possible for users to log in while also performing more frequent authentication checks. Improve the user experience and the security of our app? Sounds like a win-win.

As an engineer, implementing biometric login might seem easy, as mobile devices already capture, manage, and encrypt biometric data and expose APIs for app developers like myself to initiate a biometric scan. All I need to do is call that API and wait to hear if the scan is successful or not. What isn't as simple, though, is figuring out what to actually do with a successful biometric scan response. What does a successful scan mean, and how does that relate to the username and password credentials that users currently use to access our app?

This post walks through the decision points we encountered while implementing biometric login and how our healthcare privacy concerns influenced the final implementation approach.

Prior to implementing biometric login, the only way for users to access the Alto app was with email (username) and password. A valid email and password generates a session, which expires at some frequency. Once the session expires, the user must re-enter their email and password to regain access to the app.

From the beginning, we knew that biometrics would need to be built with our existing email and password session management in mind. This is made clear in the Apple developer docs: "Use biometrics as a supplement to something you’re already doing. Don’t depend on biometrics as your only authentication option."

Make it easier for users to login. Nobody likes typing in an email and password — especially on a mobile app. Our goal was to reduce the number of times a user must enter their email and password.

Blog

National Pharmacists Month Spotlight: Travis Allison

Get to know Jake Moffatt, Engineering Manager on the Alto Assistant Team

Why We Switched to React Query for Managing Server State

Two Engineering Interns Weigh In: What to Expect From Your Alto Internship

How We Established Better Boundaries and Clearer Code Ownership at Alto

How We Used Kafka to Build an Event-Driven Automation Pipeline and Scale Our Pharmacy

Considerations and Approaches for Implementing Biometric Login: Part 2

Considerations and Approaches for Implementing Biometric Login: Part 1

Celebrating American Pharmacist Month At Alto

Celebrating National Coming Out Day at Alto

Get to Know the Team That Has Doctors Calling Alto “The Pharmacy of The Future”

Get to Know the Team That Builds Alto's Customer-Facing Apps

Switch to a better pharmacy.