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Alto Pharmacy Technician

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Blog | Alto Pharmacy

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. 

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

Each type of user has a different product or set of products we’ve built for them. From within each product, we collect, transmit, process, and store a tremendous amount of sensitive information. However, our biggest area of security, privacy, and compliance risk is our internal pharmacy management tool called Wunderbar that is used by our operations (ops) users every day to process prescriptions. Securing access to Wunderbar is mission-critical to protect our data and our business, and, of course, we need our super-secure implementation to be super-smooth to avoid slowing down our hard-working ops users.

Developing Robust Authentication Using Rails, Omniauth, and Okta

Alto Pharmacy aims to deliver a better pharmacy experience for everyone who needs medication. To achieve that, we need to enable three different types of users:

Individual customers who need prescriptions

Healthcare providers: the nurses, doctors, physicians assistants, and other medical professionals who prescribe medications

Operations users: the Altoids who intake prescriptions, bill insurance, fill prescriptions, and more

We wanted to be able to change our Wunderbar session lifetimes without hampering either our security efforts or the usability of our product. Moreover, we wanted session lifetimes of other apps to be able to change appropriately with business risk. Before starting this project, we’d implemented a Google OAuth 2.0 authorization flow that allowed ops users to log into Wunderbar via Google and take advantage of transparent session refresh for a limited amount of time.

However, while Google Workspaces was (and is) great for our email, documents, and calendars, it isn’t as configurable or extensible as a proper SSO solution. One painful side effect of this limitation was that, in order to ensure our refresh sessions were limited to twelve hours in Wunderbar, any user of the Alto Gmail drive also had to log back in every twelve hours just to see email — even though there’s not nearly the same level of sensitive data in email.

Plus, our IT team had already picked the tool of the future: Okta. And the goal was to enable SSO, or Single Sign On: if we still used Google for SSO, it wouldn’t really be SSO!

It was clear we needed to move to Okta for authenticating in Wunderbar, too.

Okta is a big company with many different authentication mechanisms. For us, the big question was really: do we want to authenticate via SAML or Open ID Connect (OIDC)? There are a lot of great resources around comparing these two standards and what they’re good at; here’s a good chart comparing and contrasting parts of these two authentication flows:

From this Medium post, which is a great primer on the differences

For us, the most attractive thing about SAML is that it can be used to provision user accounts from your upstream IDP; for example, Okta could be configured to push username, email, group, role, etc., information to our platforms with which we could automatically provision user accounts.

In the end, we decided the OIDC implementation would provide for a very similar user experience to that which our operations users were already accustomed to, with the same capability to perform transparent token refresh.

This transition was pretty easy because we were moving from Google OAuth 2.0 flow to Okta’s OIDC flow. OAuth 2.0 and OIDC are very similar; just know that OAuth 2.0 is technically an authorization framework (once you have a user, what can they access) vs an authentication framework (actually identifying and validating the user). However, there were some odd things about our previous implementation, and I only had one person to blame for those choices: myself. It’s great to get the opportunity to re-implement our authentication system — in a fast moving startup, you rarely get the chance to revisit problems so soon!

The biggest issue at hand with our previous Google flow was the way in which I’d chosen to re-authenticate users whose Wunderbar sessions had expired. In this case, OAuth provides a handy mechanism: a refresh token. Here’s how access tokens and refresh tokens are exchanged from the client and the authentication server in OAuth 2.0 flows:

At the time, we had session lifetimes of 60 minutes when accessing Wunderbar. These cookies also lasted 60 minutes. We faced a problem: if somebody walked away from their laptop for 61 minutes after authenticating, when they came back, how could we make it so they didn’t have to go back through the Google OAuth 2.0-initiated login flow from the beginning?

My solution at the time persisted a secure cookie with the refresh token value client-side with a longer expiration length. Then, if the user didn’t have a valid access token, we’d simply present the refresh token back to the server and get a new access token all over again. This worked well, but exposing secrets to the client always presents a risk of compromise. Refresh tokens are longer-lived, so they're of even higher value to protect!

Okta’s documentation made it very easy to stand up a simple authentication server and set up a local development server. Okta has a great blog post guide with tools we were already using (Rails and Omniauth, specifically) to get up and running. Beyond that, our biggest challenge was ensuring that we had a smooth rollout.

There was an issue in how we’d test and use this flow in our staging environments, however. At the time we were working on this (early 2020), there were two issues:

Our sandbox environment URLs were non-deterministic and could be varied depending upon how an engineer user named their cluster.

Okta apps did not support wildcard redirects in their oauth redirect URLs. This controls where, upon hitting Okta’s servers, they should redirect your payload to handle the meat of authentication.

For a time, our clever infrastructure engineering team managed around these issues with the use of an intermediary keycloak server. However, Okta now supports wildcard redirects that we’re successfully using today:

The biggest functional change to our authentication flow was in our refresh flow. The biggest challenge in landing this change was in how we deployed it to end users in production.

We didn’t want to expose the refresh token because it presented too much risk. What do we do instead? Something a little bit risky, but that we preferred after threat modeling our scenarios. Security engineering is often about making tradeoffs given a number of constraints. Today, our session lifetimes are down to fifteen minutes, and our refresh lifetime for an active session (e.g. somebody making at least one request within this window) is 60 minutes. If somebody comes back after their access token expires, that’s fine; we’ve persisted the encrypted refresh token to the database, and we can use attributes from the expired but still cryptographically valid token in order to look that refresh token up.

This was crucial for Okta especially because Okta refresh tokens are valid for 100 days by default!

Disruptive change is hard. We didn’t want to disrupt the workday of our hard-working ops users who are caring for our many customers and provider partners. The biggest intent here was to keep a safety blanket fallback to the old authentication flow until we were quite confident in its functionality. This broke down into a multi-phase approach:

1. Dark-launch the new Okta flow hidden behind a URL and with a small allow-list of users (mostly just the Security team) assigned to the new application in Okta.

2. Ask for volunteers! These volunteers would go about their normal work day in a variety of functional areas (customer care, pharmacy, fulfillment, engineering) using Okta instead of Google.

3. Change the default login flow to use Okta, but keep Google working just fine as a secondary action hidden behind a URL.

This allowed us to always ensure that we didn’t disrupt peoples’ days too much!

We achieved our goals and now, nearly two years later, we’re successfully using Okta’s OIDC flow within our core pharmacy management app. What did we learn in the process?

Not every API is necessarily fully implemented in every client. At the time of implementation, Okta’s Ruby client wasn’t fully supported. In order to support our refresh flow, we needed to upstream a small change into the client library, which required a little investigation.

Exposing secrets involves risk! In our case there wasn’t a glaring need to expose refresh tokens client-side at all.

Slow rollouts are great, especially with disruptive changes! A big thank-you to our team members who were willing to be part of the experiment group last year when this rolled out.

Interested in learning more about engineering at Alto? Follow us on LinkedIn and check out our open positions.

Last year, we set out to tackle this technical debt by splitting the User model into separate concerns and refactoring the entire system into this new paradigm.

Tackling Technical Debt and Improving Security: How Alto Untangled an Overloaded User Model

At Alto, we have three types of users: customers, healthcare providers (nurses, doctors, etc), and operations users who run the pharmacy. For each type of user, we maintain a separate application (though they share a monolithic backend for now): our customer app; our Alto Connect app for providers; and our internal pharmacy management tool called Wunderbar, which powers the pharmacy experience from prescription intake to fulfillment.

Early on, we made the pragmatic choice to represent both customers and operations users with a single User model, distinguishing them with a “roles” attribute. This required repetitive checks that, over time, slowed down our engineering teams, presented substantial privacy risk, and posed difficult scaling challenges as we evolved our monolith into different microservices. 

The challenges of relying on a single User model for different types of users

The notion of a User is core to our concept of identity from which authentication and authorization proceeds. From a security perspective, it’s difficult to build an extensible and clean authentication and authorization system when you’re overloading one model for two very different populations of users. From a privacy perspective, this muddied customer records (which are subject to more restrictions because they’re healthcare records) with workforce records, hamstringing many analytics efforts.

Authentication: Alto employees who were also Alto customers were required to use their work emails for their customer accounts. Not only was this restriction frustrating for Alto employees, but it also posed a security risk: employees should only be able to access Wunderbar via corporate SSO, not their personal account passwords. 

Authorization: Engineering new authorization capabilities required intricate knowledge of the idiosyncrasies of our user structure. This both slowed down development and created tremendous risk that any mistake could easily escalate user permissions.

Privacy: Because they existed in the same table, both customer records and Alto operations user records were classified as Protected Health Information, or PHI, which severely limited what we were able to do with them.

Attribution: Our robust audit logs were significantly less useful when a user ID in the transaction log might refer to either an ops user or a customer.

State management: Some updates pertaining to admin identity lived on the customer model, some lived on the employee model, and others lived in other various models.

Architecture: Tight coupling would have prevented us from decoupling our monolithic system into smaller services.

Our backend is primarily a monorepo Rails application that shares a tremendous amount of functionality between our customer, provider, and ops user-specific domains. Generally we use JWTs to secure customer and ops user sessions. These JWTs are issued only once valid credentials have been present.

Ops users are required to log in via Okta SSO, enforcing both valid username/password as well as a two-factor authentication (2FA) prompt. We use Devise and Omniauth to wire up the OIDC flow with our upstream identity provider, and we use CanCan to expand a set of roles per-user into a set of capabilities that restrict what resources they can access/modify.

Ops users primarily work in our internal pharmacy management app. However, the state of these users was shared between several different tables: Users, which otherwise refer to customers, and Employees, which share some ops user-specific state tied to Users.

Large change sets of code being deployed at once are harder to code review, harder to unwind if something goes wrong, and are more likely to be more disruptive. For this project, we knew that such a large architectural change had to be broken up into many more-digestible pieces if we were to be successful. The plan:

1. Create a new dedicated model for our ops users

# == Schema Information
#
# Table name: wunderbar_users
#
#  id                        :bigint(8)        not null, primary key
#  user_id                   :bigint(8)        indexed
#  email                     :string           not null, indexed
#  first_name                :string           not null
#  last_name                 :string           not null
#  token_last_invalidated_at :datetime
#  roles_mask                :integer          default(0), not null
#  title                     :string           not null
#  team                      :string           not null, indexed
#  is_active                 :boolean          default(TRUE), not null
#  slack_user_id             :string
#  pod                       :string

This was the easy part: create the database structure for a properly isolated admin user type, which we called a WunderbarUser.

We now had our admin user state split between Employees and Users, with a new table that engineers and analysts wanted to use that was not yet populated, and we had to make progress while allowing new hires and new customers alike to onboard to our platform.

2. Synchronize state between the old tables and new table

Next, we created a WunderbarUser for any User who was, or ever had been, a Wunderbar user. This required some historical digging —we had a lot of people who were onboarded, and even some offboarded, before we even had the intermediate Employee record!

Two major choices at this stage really helped the project succeed. First, we ensured that the WunderbarUser records all had IDs matching the corresponding User records. This is because a lot of our legacy audit records (that we’re required to preserve and keep useful as a Covered Entity under HIPAA) referenced an `admin_user_id` column populated with the ID of the User. If we just created all new entries for WunderbarUsers, these old logs would require at least one level of indirection (e.g. querying back to User) to make useful.

     # Create with the same ID as the user that we're creating this from.
     wunderbar_user_attributes = {
       id: user.id,
       user_id: user.id,
       email: user.email,
       roles_mask: user.roles_mask,
       date_of_birth: user.date_of_birth,
       token_last_invalidated_at: user.token_last_invalidated_at,
       first_name: user.first_name,
       last_name: user.last_name,
       preferred_first_name: user.preferred_first_name,
       is_active: false,
     }

Second, we created a new WunderbarUser any time we created a new Employee record (e.g. when we onboarded a new employee) and synchronized any changes between Users, Employees, and WunderbarUsers tables. As a side note: we knew we’d have to protect this syncing logic to ensure that we only copied each change once — otherwise, our callbacks would fire forever!

3. Migrate functionality slowly to use the new ops user model

We migrated logic to reference WunderbarUsers where previously we’d referenced Users and/or Employees. Authentication, authorization, audit logs, and pharmacist licenses were all updated here — albeit painstakingly!

We then migrated all of the foreign keys our various Postgres database tables held to either the Users or Employees tables to instead reference the WunderbarUsers. This ended up being around a hundred foreign keys, but a constraint-only migration that fortunately didn’t involve any data changes.

In short: our goal was to do this change slowly and steadily enough that nobody would notice until, one day, we could announce to the team, “Hey, we did a big security-pertinent project, and now you can change your customer email to be anything.”

Authentication: While we use the same JWT format for each, our customer and operations user authentication systems are now totally separated.

Authorization: With separate user types, we have separate capabilities and domain-specific authorization checks for each area, enforced in code.

Experience: Onboarding as a new Altoid (somebody who works here; come join us!) is totally separate from the customer experience, and you no longer need to link your accounts together.

Privacy: Our ops users who choose to be customers enjoy the full privacy afforded to our customers without tying their account to their work identity in any way.

Attribution: Legacy audit logs are still index-able and query-able with the same IDs, just in a slightly different table.

State management: Customer state lives on the User table, and ops user state lives on the new WunderbarUsers table. Our callbacks on each have been dramatically pared down, and our intermediary Employee table is entirely gone, along with a number of other now-ancillary tables.

Architecture: We have a clear path to federate RBAC checks without shipping our entire customer database between services.

Bonus: Performance and code readability generally improved by removing complex joins and join tables and isolating concerns between the customer and ops user domains.

Another Bonus: Significant refactorings and increased test coverage along the way while we cleaned up some very old, very stale code.

Large architectural changes of legacy systems with heavy use are rarely 100% smooth. Here are a few learnings to keep in mind the next time you tackle a hefty piece of technical debt. 

1. Always validate your assumptions (e.g. the data your constraints are against) when foreign keys are concerned. Multiple times we assumed, “Hey, we’ve migrated a bunch of really big tables that are really old (insurance claims, prescriptions, and more), so there’s no way we could have another admin user that we haven’t created a commensurate WunderbarUser for, right?” Wrong! Your data can always have inconsistencies, but a simple SQL query (e.g. validate that all values of the column you’re migrating the foreign key to have a matching WunderbarUser) can save a lot of time and headaches.

2. When migrating foreign key constraints on tables with active reads in Postgres, ensure you break it into two steps. Migrate your foreign key with `{ validate: false }`

 def up
   remove_foreign_key :inventory_orders, to_table: :users, column: :created_by_id
   add_foreign_key :inventory_orders, :wunderbar_users, column: :created_by_id, validate: false
 
   validate_constraint
 end

Validate the named constraint of your foreign key separately

 def validate_constraint
   validate_constraint_statement = 'ALTER TABLE "inventory_orders" VALIDATE CONSTRAINT "fk_rails_abcd1234";'
   ActiveRecord::Base.connection.execute(validate_constraint_statement)
 end

3. When working with legacy tables, be mindful of legacy systems. Our Billing service used a very outdated golang ORM that panicked if a column specified by the `struct` in source no longer existed; this caused a late-night outage that made auto billing requests back up the next day.

4. Scoping projects is hard! This was a larger project than our team had ever scoped and delivered before. Once all the dust settled, this project took several months longer than our initial scoping estimates.This project was worth doing, but better estimates allow us to prioritize better. If we’d had a great scope of this immense project upfront, would we have chosen to do it first? Also, this was a tremendously difficult project to separate into material stages; all the benefit was really backloaded.

In the end, this was a very satisfying and important project to complete. We wouldn’t have been able to accomplish it without a lot of help from our colleagues on the Platform team, the Security team, and across many other functions atAlto. Very large projects that involve a lot of moving pieces and impact other teams are always scary — but we can’t let that fear prevent us from making the changes we know we need to do in order to be successful long-term.

The Engineering Team is hiring! Learn more about open positions here.

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.

Enhance the security of our app using more frequent authentication checks. As a healthcare app that displays Personal Health Information (PHI), conducting more frequent authorization checks decreases the likelihood of PHI becoming exposed to an unauthorized third party.

Have the ability to revoke biometric access from a particular user. Similarly, we want to be extra careful to ensure that if a device is lost or stolen, we have a fallback plan to invalidate a user's session and protect the user’s PHI. For email and password initiated sessions, this happens when a user changes their password, as any change in login credentials will invalidate all active sessions for that user. We needed a similar contingency plan for biometric login.

There are several ways to use a mobile device's biometrics to authenticate a user, with varying degrees of security.

No server-side authentication. Simply integrate with the device's biometrics as an extra layer of authentication. If the biometric scan is successful, let the user proceed into the app.

Provides an extra check and gives your app a sense of modern polish

No server-side validation means that nothing is being validated against your system, so this is not a good substitute for account authentication. Therefore, even if you add this check, the user should still be prompted to re-enter their email and password at the same frequency as before.

Given this, this approach wouldn't help us reach our goal of reducing the number of times a user must enter their email and password.

This approach is a good fit if your application already has an existing login and session management system, and you want extra authentication checks throughout the user's session without modifying any other part of the system.

Tie biometrics to our existing email and password authentication. Use the user's biometrics to unlock an encrypted version of their email and password credentials. When a user sets up biometric login, store the user's login credentials within the device's keystore — the keystore will automatically encrypt the credentials. After a successful biometric scan, retrieve those credentials from the keystore and send them to your existing login API.

Still easy to implement, no backend changes needed

Biometric scan can be used in place of entering an email and password

Changing email and password credentials will revoke biometric access: if a user changes their credentials, their biometrics will access stale credentials and fail to login. (This is somewhat of a con as well — it provides a poor user experience, but it's an edge case that should happen infrequently.)

There is still only one way for a user to authenticate server-side, but now two ways to pass server-side authentication — essentially weakening your app's existing account security.

This approach effectively hands out internal login credentials to third party storage. While not a huge risk considering a device's secure storage is encrypted and trusted by most users, it’s not necessarily a best practice either.

This approach is a good fit if your application doesn't contain sensitive data, and your goal is to make it more convenient for a user to login by replacing email and password with a biometric scan.

Create a separate authentication mechanism for biometrics. Introduce a new authentication mechanism specifically for biometrics, resulting in two independent forms of server-side session authentication: email with password and biometrics. The implementation could look like generating a keypair or a shared secret to save and validate against the server whenever a user attempts to login with biometrics.

Biometrics is now tied to its own secure server-side authentication mechanism, which means it can be used independently of email/password (or even replace it, if you choose to).

Two layers of authentication adds security to the app. Not only does a user need a valid email/password session, they also need a valid biometric session.

It provides fine-grain control over a user's biometric session. You can invalidate a user's biometric session by invalidating the public key and can even invalidate a biometric session without impacting an email-and-password session if needed.

Requires building and maintaining a separate authentication mechanism

This approach is a good fit if your application contains sensitive data (e.g. PHI or financial information), and your goals are to make it both more convenient for a user to login by replacing email and password with a biometric scan while also strengthening the security of your account management system.

Because we are a healthcare company dealing with sensitive data, we chose this last path. It required greater engineering investment, but we agreed it was imperative that we maintain a secure system while introducing biometric login for users.

In part 2, we talk implementation details, including the library we used, code snippets, and questions to consider before you get started. Interested in learning more about engineering at Alto? Follow us on LinkedIn.

When engineering incidents happen, typical deployment pipelines are just too slow—especially if customers’ medications are on the line. Here’s the speedier rollback process we use at Alto Pharmacy to ship fixes in 3 minutes, flat.

How Alto Reduced Time to Resolution for Production Incidents

Production incidents caused by newly-merged code happen to us all. When they do, most engineering organizations discuss the root cause and how to prevent similar incidents in the future. At Alto, we evaluate two additional metrics:

Time to detection: How quickly did we know something was wrong?

Time to resolution: How quickly did we restore service?

Time to resolution is especially critical for us as a pharmacy, where a long-running incident could block a user from getting their medication on time.

Over 30 times a day, an engineer at Alto hits the merge button, deploying a new build to production. Our well-automated CI/CD pipeline, built by our Infrastructure team, handles the 3-step deployment process:

1. Build: install Ruby & JavaScript dependencies

The average runtime for our standard deployment pipeline is 30 minutes—reasonable for a CI/CD pipeline, but too long when shipping a fix for a production incident.

We introduced a “hotfix” workflow as a first pass at an expedited deployment pipeline for incident response. A hotfix is a commit that introduces new code to address the incident by either removing or reverting the buggy code that introduced the issue in the first place or by shipping new code that fixes the initial issue. 

The hotfix deployment pipeline reduces incident time to resolution by triggering a specific CI job that removes the test step from our standard pipeline, reducing the workflow to just the Build and Deploy steps.

Blog

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Developing Robust Authentication Using Rails, Omniauth, and Okta

Tackling Technical Debt and Improving Security: How Alto Untangled an Overloaded User Model

Considerations and Approaches for Implementing Biometric Login: Part 2

Considerations and Approaches for Implementing Biometric Login: Part 1

How Alto Reduced Time to Resolution for Production Incidents

Celebrating American Pharmacist Month At Alto

Celebrating National Coming Out Day at Alto

Introducing Christopher Tidball, Director of Product Management - Supply Chain & Fulfillment

The Next Evolution of Our Company Values

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.