This first blog post on the topic of ‘the use of data-science in oncology’ considers how rapidly data-science in oncology is evolving throughout the lifecycle of oncology drugs. The aim of this project / publication is to identify companies, particularly focused on SME’s, that are developing solutions in data-science for oncology.
The key activity from this project is to categorize and understand what the different companies are doing and what data-sources they are incorporating.
This project is being carried out by Laila Mehkri who is currently doing an internship at Synergus RWE. She is an MD by training and has recently completed her Master’s in Health Economics.
The pace of data generation has increased over the years, as larger amounts of data is stored and made available for research. However, analyzing collected information to support new solutions, provides new opportunities and challenges. The ability to gain insight from within these vast depots of data has become a significant opportunity in oncology in terms of supporting the development and approval of new drugs as well as to support the oncologist in the daily practice.
Due to the heterogeneity in oncology disease manifestations, various treatment options are available. This introduces complexities in patients’ preferences and when deciding on the most optimal treatment for individual patients. To facilitate reliable decisions, large amounts of data is required and more so, the use of data-science is of tremendous importance to make these solutions possible.
Below is a list of companies we have identified, which are using data-science in oncology:
- Aetion
- Berg
- Clinerion
- Cognizant
- Concerto Health
- Cota Healthcare
- Curemetrix
- Deep cube
- Deep Lens
- Exploris
- Ezra
- F1 Oncology
- Fitango health Oncology
- Flatiron
- Healthmyne
- IBM Watson Healthcare
- Lancor Scientific
- Mendel.ai
- Noona
- Oncology analytics
- Oncology Information Service
- Oncology Ventures
- Oncora Medical
- Oncospace
- Pubgene
- Regeneron
- RTI Health Solutions
- saleSEER
- Sharp analytics
- Sophia Genetics
- Syapse
- Tempus
We have also identified different data sources used by these companies for their solutions. These include the following:
- Electronic Medical Records (EMR)
- Claims data
- Genomic data bases
- Drug databases
- Social media/web databases
- Mobile applications data
- Wearables and sensors data
- Cancer registries
- Clinical trial data
- Chemotherapy regimen data
- Epidemiological data
Through this research, we further identified different high-level solutions that these companies are working on, these are listed below:
- Drug development
- Precision medicine
- Clinical trial recruitment
- RWE research to optimize different solutions
- Systems to help physicians in decision making
Going forward, we will continue discussing how each of these companies use data-science according to their description and summary of evidence. Overall, the purpose of this blogpost is to promote further discussions on the topic of data-science in oncology. Therefore, we would appreciate feedback about other companies and solutions that could enrich our findings and help advance this project. Please feel free to engage on the LinkedIn group where you can share additional comments or questions.
