Personalized Intervention Mechanism for a Medical Device Manufacturer

  • Published: March 28, 2024

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Read how Reveal collaborated with a leading medical device manufacturer, by training a machine learning model to increase treatment adherence in patients and improve the effectiveness of frontline providers.

 

Poor Adherence, Dwindling Patient Outcomes

For our client, patient non-adherence posed a significant challenge. Despite correct diagnosis, appropriate treatment plans, and timely interventions, patients’ lack of adherence was often leading to unfavorable prognoses. This noncompliance not only impacted patient outcomes but also escalated costs for providers. Our client approached us with a specific requirement – to devise a personalized intervention mechanism for sleep apnea patients to improve treatment adherence.

 

Game-changing Approach by Reveal

With a firm grasp of our client’s requirement, an ML model was trained to predict patients who were most likely to deviate from the prescribed treatment plan. The model took the prediction one step further and even suggested which patients were more likely to respond with interventions like coaching, reminders, etc. It used over 300 features on the patient to make these predictions. To improve patient adherence and compliance, a provider-facing application was designed which made suggestions on when or how to reach the patient. The provider was able to trigger emails and calls to notify the patient.

 

Big Wins

With the aid of the predictive ML model, our client observed the following results –

  1. Better prognosis was achieved due to significant improvement in patient treatment adherence
  2. Improved individual patient adherence favored the reimbursement issues faced by the providers
  3. Effectiveness of frontline providers increased as the model helped them identify patients most likely to respond to interventions
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