Utilising Data-Science to Boost US Market Access in Volatile Times

Contributed by

face
face
main_img

Dr Philipp Diesinger and Dr Gabriell Máté outline why a data-driven approach can help optimise patient copay assistance programs in the US across a wide range of patient groups, increasing access and driving down costs in the process.

 

2020 has been an exceptionally turbulent year – especially for the healthcare sector and the pharmaceutical industry. Maintaining production facilities and avoiding supply chain disruptions have rendered significant challenges during a global pandemic.

In addition, the strategically important US market has been displaying high degrees of political volatility and uncertainty with the rapidly approaching November elections.

During such periods, means to mitigate risk and stabilise business operating income are of crucial importance. In the pharmaceutical domain, data science solutions have demonstrated high impact on financial performance by boosting formerly manual processes with data-driven decision making. US market access can significantly be improved with a data-driven approach utilising:

  1. large amounts of claims data together with
  2. coded implementations of the market’s business rules, regulations and payer/patient group networks as well as
  3. the help of computer science methods like complex decision trees.

The impact of patient copay assistance programs can significantly be increased utilising hundreds of millions of claims data records in combination with AI-based optimisation methods. Utilizing such approaches have achieved sustainable and recurrent increase of business operating income above USD 118 million annually

Pharma manufacturers implement copay assistance programs that provide eligible patients copay cards and e-voucher that reduce patient copay, promote patient adherence and retention. For a given patient group, high copay typically correlates with low patient retention. In such a case, profit per patient is high, however, the total number of prescriptions is low. On the other hand, low copay creates high patient retention but low profit per patient.
Finding the right balance between copay and prescription volume turns every patient group into a mathematical optimisation problem.

Pharmaceutical organisations still relying on traditional manual processes to set up patient copay assistance programs can only offer tailored solutions to a handful of patient groups.
In contrast, semi-automated data and insight driven methods can optimise copay assistance programs across hundreds of different patient groups with widely varying coverage thereby realising otherwise neglected market opportunities, increasing revenue and reducing cost.

 

In the US increasing portions of the population are suffering from chronic diseases. Currently around 40 percent of US Americans suffer from such a condition. Half of these patients even have multiple conditions. One quarter of them benefit from non-generic prescription medication.

A typical payer might insure ten million lives. On average one million of them will be chronic disease patients treated with non-generic prescription drugs potentially benefitting from copay assistance.

Patient groups with high copay are much more likely to switch medication during treatment to a competitor product. It is estimated that more than five percent, or 50,000 patients, switch away from a payer’s product every three months.

Of these switching patients, around 80 percent are typically just slightly above a copay threshold which would have allowed them to stay on the treatment. The remaining 20 percent, however, are often paying very high copay and require significantly higher copay assistance.

Moving copay assistance away from the 20 percent (who would switch away anyways) to the 80 percent will prevent those patients from switching. This avoids disruption of patient access and reduces potential interruption of treatment.

For the pharmaceutical company, keeping four percent of the otherwise switching patients every quarter will equal an additional 150,000 patients per year.

Chronic disease prescriptions required on a monthly basis can cost more than USD 100, leading to a revenue increase of USD 70 million per year.

 

Market Access experts can use data-driven and semi-automated patient copay assistance program optimisation to set up copay-card and e-voucher programs that provide better access for patients while maximising program impact and cost-efficiency.

Implementations of such systems understand hundreds of business rules and constraints such as:

  • regulatory effects
  • variation of programs across states
  • behavioural analysis of patient claims patterns
  • prioritisation of e-vouchers over copay cards and
  • complex cost-structures of copay assistance programs
  • business constraints including lower or upper limit of copay or maximum copay discount.

Copay assistance programs can then be optimised for each brand choosing a specific business strategy such as maximisation of profit, return on investment or prescriptions. Such data-driven solutions provide simulated business case overviews in an early stage to show users the expected impact of the copay optimisation on profit, revenue and cost as well as expected changes in prescriptions. Users can furthermore gain insights by exploring the parameters of the optimal program such as eligibility criteria, minimal patient copay or maximum discount.

These new data-science methods allow market access experts to:

  • quickly and efficiently analyse the business impact of currently running programs
  • evaluate and compare arbitrary programs
  • automatically generate thousands of different assistance program scenarios with desired program characteristics.

These functionalities not only ensure an increase of access for patients and higher revenues for the companies, but they also improve the efficiency of program selection and the related implementation process.

 

References

[1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4874730/

[2] https://pubmed.ncbi.nlm.nih.gov/22080794/

[3] https://pubmed.ncbi.nlm.nih.gov/22080794/

[4] https://www.bloomberg.com/quicktake/drug-prices

Add Your Comment


You must be logged in to post a comment.

Related Content

Latest Report