Success Stories

Using Machine Learning to Build a Patient-Centered Pricing Model

We used data science to help our client qualify more prospective patients without sacrificing their standard of care.

A male dentist smiles as he consults with a patient.

Project Overview

Our client, a national healthcare network, wanted to increase the utilization of each Center by moving prospective patients through the qualification process more quickly while also increasing conversion — and still prioritizing patient care. They brought in RevGen Partners to help them build a data-driven sales process that would optimize product pricing and balance the desire for better profitability with Center utilization and the ability to move more patients forward. Their aim was to take the guesswork and variability out of the sales process for patients and doctors.

Client Challenge

Our client needed this new process up and running as soon as possible, however there were several challenges to work through before anything could be implemented.

Limited access to doctors

Our proven UX/UI design approach relies heavily on the Voice of the Customer input to ensure the design will meet the actual end-user needs; however, to minimize business disruption during their busiest time, we had extremely limited access to the end-users.

In-flux data strategy

The client was still finalizing the details of their cloud strategy, as well as the data architecture, platforms, and sources needed for any data science modeling.  

Culture of independence

Even though they recognized the need for help on this project, the client’s culture of independence meant that they desired to own backend development, as well as training, despite their own limited bandwidth.

Needed to integrate with existing sales practices

The technological solution needed to be built into the client’s legacy sales support systems (Salesforce), complement the well-established sales approach, and be easily adoptable for all personnel.

Approach

Our approach centered on an iterative process that would create a core project team including client team members to gather UX/UI feedback, engage in usability testing, develop training, and roll out the new application during the Alpha and Beta phases. This would allow us to leverage the client’s subject matter expertise into refining and improving the Salesforce UI and the machine learning model, as well as developing and perfecting the training in advance of the full-scale launch.

Solution

Data Science, UX/UI Design, Application Development and Change Enablement worked in parallel to build a dynamic pricing application that improved revenue and profitability, kept patient and provider needs top of mind, and maximized the business value and adoption of the solution.

Gathered UI/UX and data source requirements

We pulled together a collaborative project team that included doctors and sales personnel to understand current needs, goals, and “as-is” process barriers. This informed the user-centric design requirements of the UX/UI for the application to drive maximum adoption and utilization of the app.

Built a Machine Learning model for price optimization

Through our data discovery process we were able to build a machine learning model that considered several variables including demographics, case complexity, and center capacity to predict probability that a patient would undergo treatment at a specific price point. It was key that the model only ever suggested applying a discount – the pricing never flexed upward.

Architected a flexible technology solution

While the UI was required to be in Salesforce, we hosted our APIs in Google Cloud, developed the models in Python with PyGAD, LightGBM, Flask, and other libraries, while using SQL and Google Big Query to collect the Salesforce data for model training.

Coached and trained pilot hubs

We infused change enablement best practices throughout the process, working with users to assess the impact of change and developing an interactive and unique training approach that included coaching aids and training content. Initially, we delivered to three Alpha Pilot centers, applied learning from those centers, and then broadened the roll out to our Beta Pilot centers to prepare for the full-scale deployment.

Results

The new model was a considerable success, helping franchises drive more revenue and growth while maintaining their high standards of patient care.

More confidence in data, enabling informed decisions

Our model improved the data integrity, which reduced calculation errors and pricing subjectivity. It also provided greater transparency into the critical information driving pricing decisions, promoting a shared understanding.

Adapts to the needs of the client and market

With our machine learning model and flexible cloud solution, this application can change and adapt to shifts in the market or client’s strategy. This enables them to scale and grow without losing personalization.

Drives business growth

The data-driven insights are fueling the client’s next level of growth, not just in revenue, but in employee experience and patient service. 

Preserves the human connection

The new application was simple and patient-driven, which enabled the doctors to continue focusing on what mattered most: patient care.

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