Using No-Code AI to Predict Medical Appointment No-shows

Use Cases

Artificial Intelligence is making it possible for hospitals to predict which patients will skip their next appointment, and then contact them with a reminder.

Predicting medical appointment no-shows can bring huge financial and operational benefits for health care providers. No-shows are highly expensive, as each no-show is a direct loss of revenue. Missed appointments also throw a wrench in the scheduling of a doctor’s office.

However, gaining real value from AI in healthcare is about more than just model building. Healthcare providers need to implement those models and predictions in the real-world. What good is a prediction if it’s not acted upon? In this guide, we’ll explore how to deploy our no-show prediction model via Zapier, a no-code automation tool.

1. Building a medical appointment no-shows prediction model

To start, simply make a Pro account on Obviously.AI — you get a 14-day free trial, with no credit card needed to sign up.

Once you log-in, you’ll see a dashboard with an “Add Dataset” icon on the left. For this example, upload a simple tabular dataset from Kaggle, which features 10,527 medical appointments and 14 associated variables.

Our goal is to predict a KPI called no-show, which is either "Yes" or "No".

After clicking to upload a CSV file, you’ll need to verify the dataset, and simply check off that the first row is the column names and that all requirements are met.

Now, we select the column we want to predict, which is called “no-show.” In the background, a series of machine learning models compete to create the most accurate predictions for no-show.

And we’re done! Now, we can also see how various attributes impact the medical appointment no-shows KPI. The main drivers discovered by the automated machine learning models were scholarship (or social welfare recipience) and alcoholism.

Let’s move on to building a Zapier sequence, so you can deploy predictions in the real world, and predict medical appointment no-shows on new data.

2. Building a Zapier Flow

Now, make a free Zapier account, if you haven’t already. We’ll start by connecting Zapier to a dataset in Google Sheets. We’ll select “New Spreadsheet Row in Google Sheets” as the trigger to activate a Zapier sequence.

After selecting that trigger, I simply located my demo Google Sheet, which I titled “New Data.”

This sheet should have the exact same column names and data types as the file I used to make the predictive model (minus the actual target column of “no-show”).

For instance, since my training data had columns like Gender and Age, and so on, I use those exact columns in this Google Sheet.

Next, we’ll select “Webhook,” and then “Custom Request.” This is how we’ll integrate with Obviously AI.

To set up the Custom Request, add in these details:

The data field is the most important, which requires matching up the column names in your data with the column names used in your model. Check the documentation if you’re unsure!

To set it all up, you’ll also need your Report ID and API key. Your API key can be found in your account, which gets added as a “Header” in Zapier. To get the Report ID, head to the “Export Predictions” tab in Obviously AI, and click on the shareable report link.

The Report ID is the ID you see in that URL. In the example above, it’s “cff97990–7385–11eb-9a76-af3704ed1fd7.” As you can see in the documentation, this gets added in the bottom of the “Data” field in Zapier.

Now, we can test our Zap, which sends data from our new data sheet to our medical appointment no-shows prediction model, and we’ll get the medical appointment no-shows probability as an output. With these predictions in Zapier, we can now do anything we want with them: Send them to a Slack channel, send an email to our team, add the prediction to our sheet, or anything else.

For now, let’s just update our sheet with the estimated medical appointment no-shows. This can be done with the action titled “Update Spreadsheet Row in Google Sheets.”

Then, simply add the probability created in the aforementioned webhook.

Now, let’s take it a step further. We can create a “filter” in Zapier to send an email notification when the predicted medical appointment no-shows is above a threshold that we set. I set up a filter that gets activated when the medical appointment no-shows probability is higher than .7.

If the medical appointment no-shows probability is higher than .7, we can automatically send a notification email so that we’re aware of the situation.


Zapier is a powerful way to automate practically anything in your workflow. With Obviously AI, you can integrate the power of machine learning to create AI-driven workflows, including to predict medical appointment no-shows and bring huge financial and operational benefits for health care providers.

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