Optimization of patient recruitment for clinical trials

Industry Pharmaceutical and Life Sciences

Specialization Or Business Function Media and Advertising (Clickrate Optimization, Media Mix Analysis), Strategic Business Planning

Technical Function Data Management (Data Modeling), Data Visualization (Dashboards & Scorecards, Statistical Graphics), Analytics (Predictive Modeling, What if/Scenario Analysis, Data Mining, Trend Analysis, Forecasting), Marketing and Web Analytics

Technology & Tools


Project Description

GI Dynamics is currently recruiting patients for a clinical trial for an investigational, first of its kind, medical device for the treatment of Type 2 diabetes in an obese population. More information about the trial can be found here - http://www.endobarriertrial.com/

Over the last year, we have been spending a substantial amount on recruiting patients. The pathway from when a patient inquires about the trial, is screened, has bloodwork done, and is then enrolled in the trial is a complex one. We would like to develop a sophisticated approach to determine what is and what isn't working on a city by city basis. This will then be used to optimize spend across different advertising mediums.

Currently, all patient inquiries are tracked in a portal on www.galenrecruitment.com. We receive weekly updates in excel on patient progress through the recruitment pathway. For example, this will provide information as to whether the patient has filled out the questionnaire, whether they have been called by a screener for additional screening, whether they have been referred to the site, etc. In addition, there is another spreadsheet which tracks media campaign spends. 

There are two stages to this project:

  • firstly, we need to better understand the substantial historical data which we have. This will help drive future decision making. 
  • secondly, we need to run analytics on a bi-weekly basis (at least) to determine which campaigns are and aren't working. This will support our continued learning and optimization. 

Below are some key metrics and questions we would like answered: 

  • On a site by site basis, time spent to go from inquiry to randomization 
  • ROI on different advertising mediums by site 
  • If we have $X to spend at a site in NY, what is the best medium to advertise on and how many patients will we enroll in the trial? (predictive analytics)
  • Correlation between recruitment rate by site to prevalence of obesity and diabetes in the region 
  • How do we increase conversion of visitors on the site? 
  • Why are patients not qualifying and ways to improve this? 
  • Establishment of patient tracking 
  • Time distribution from first contact by site following website request 
  • Is there a correlation between the amount of time to contact a patient and their qualifying for the trial? 
  • What are realistic goals to set for each site. 

What we learn at this stage will also facilitate our improved understanding of commercialization of this product. If this project goes well, there is an opportunity for an expanded role. 

Project Overview

  • Posted
    September 29, 2014
  • Planned Start
    October 06, 2014
  • Delivery Date
    June 30, 2015
  • Preferred Location
    Cambridge, Boston, Lexington, surrounding areas , Massachusetts, United States

Client Overview


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