Powerful Consulting, Fueled by Data Science.

Value Across the Healthcare Ecosystem

By gathering and processing data across the healthcare ecosystem, Alira Health Data Science can bring value to every stakeholder’s objectives.

  • Patients: Ensuring access to the best providers and therapies available, while focusing available time and energy on fighting the disease.
  • Providers: Providing optimal care to their patients, while keeping budget and time under control.
  • Payers: Lowering coverage cost, while keeping patients and providers satisfied.
  • Suppliers: Creating new approaches to improve the health of a well-defined patient population, while allowing profitable returns to fuel further innovation.
  • Governments: Implementing policies that control healthcare expenditures, while allowing patients access to innovative medical solutions.

For Pharmaceutical, MedTech, and BioTech companies, Alira Health Data Science can activate your data and optimize resources.

Our Expertise
For companies looking to activate the vast amounts of data they create, Alira Health helps make sense of this data, providing added value to the industry.

Patient Identification
We use data science to find the broadest eligible patient population and precisely identify patients that will benefit from specific treatments, all while optimizing your economic resources.

The Role of Data Science
Through the optimization of data science, we support clinical and pre-clinical research, as well as helping companies allocate their commercial resources in an optimal manner.

Complex tools, proven process, clear insights

  1. Define the Objective

  2. Collect and Select Relevant Data

  3. Select and Apply the Right Algorithms

  4. Identify Trends and Generate Insights

  5. Make Decisions that Generate Value

  6. Prove & Monitor Value Creation

Limitless Applications

Data science can illuminate opportunities and pathways throughout your business, including:

Reimbursement Optimization
Goal: To maximize reimbursement chances and decrease claim rejection.
By performing qualitative research and combining it with reimbursement claim data, successful and unsuccessful reimbursement claims can be compared through data science in order to identify key determinants for either outcome.

Inventory Optimization
Goal: To improve communication, lead times, and effectiveness in the value chain.
By creating a simulated environment based on identified supply chain nodes and their related information flow hindrances combined with the application of machine learning, supply and demand are able to be closely aligned.

Optimization of Physician Targeting at Launch
Goal: To identify optimal physician targets for a successful product launch.

By collecting relevant data and analyzing it with the right algorithms, physician profiles can be developed and used to select a beneficial physician target pool to maximize the chance of a successful product launch.

Commercial Response Models
To determine the sales potential as a result of multiple interacting commercial factors.

By simulating a large amount of possible factors influencing sales potential, the optimal combination of determinants can be identified, helping to achieve higher sales potential.

Treatment Guidelines/Practice Misalignment Analysis
Goal: To understand the mismatch between guidelines and actual treatment pathways.

By analyzing current physician medical practices and comparing them to prescribed guidelines, a treatment model more closely representing reality can be developed, aiding in understanding the mismatch between real and prescribed practices.

For more information, please contact Piergiulio.