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The Future of Data Analytics in Oncology and Rare Diseases

Innovation will aid research and marketing, but not without some ethical concerns. Here’s what to know and how to make sure it’s most effective. 

Key Takeaways:

  • AI will be used to mine ever deeper insights, while ML will provide the means for predictive analytics.
  • Deprecation of third-party cookies will require a forward-looking data strategy.
  • Ethical concerns mount with tech innovations in data analysis.

Data analytics is vital for every stage of the product life cycle in oncology and rare disease, from research and development to market access, marketing, and commercialization. And analyzing real-world data can offer researchers key insights that can potentially improve the quality and precision of care.

Data in this space is not currently standardized, is rarely shared, and is siloed and fragmented. These issues are not unique to rare diseases and oncology, but are pain points for all medical researchers. Solutions have been proposed, but none have been adopted, and tools such as AI are providing deeper insights into the data that is available in the meantime.

In this article, we’ll explore the importance of data analytics, the current tools and techniques, and its future in oncology and rare diseases. We’ll also look at the challenges and concerns around data and the different use cases surrounding its usage.

The importance of data analytics in oncology and rare diseases

On the clinical side, data analytics can improve care by identifying patterns and predicting outcomes. Here’s how it can have a profound impact:

Real-world data

Real-world data that captures the continuum of care – appointments, labs, diagnostics, assessments, diagnosis, treatment and post-surgical – gives researchers an end-to-end view for insights into:

  • Risk factors for disease development
  • Appropriate treatment and therapy
  • Short and long-term outcomes, including progression, response, remission, relapse, recurrence, and death 
  • Any complications or adverse events that require additional care.

Put together, real-world evidence not only provides better patient care but also generates ideas for new approaches as well as label expansions. 

Big data

Big data analytics uncovers everything from customer preferences to hidden patterns so you can gain actionable insights that innovate treatment options and patient care. This also helps you predict outcomes, can be used to personalize experiences, and is instrumental in identifying markets that have a high potential for growth.

Data analysis and predictive analysis are aided by a number of technological tools. These include, but are not limited to:

Machine learning

Machine learning (ML) helps detect patterns in large datasets to improve diagnostics and decision-making. It also helps pharma companies reduce time-to-market and decrease research costs. Using predictive analytics allows for early detection as well as identifying high-risk patients, and can decrease the cost and time involved in drug discovery by predicting how well potential drugs will perform. It also helps identify existing drug combinations that can create a new treatment.

Predictive modeling

Predictive analytics help pharma companies more accurately forecast sales by analyzing historical sales data, market trends, and HCP behavior. It also provides a pathway for accelerated drug development, reduced operating costs, clinical trial efficiency, and risk mitigation.

Social media

At the very heart of healthcare lies the communication between patients and providers. Social media has changed the way patients and doctors interact and becomes a great source of patient behavioral information, sentiments, and correlations. 

Data analytics and marketing campaigns

Since the dawn of the modern internet, third-party cookies have been fundamental building blocks for digital advertising targeting and metrics. The deprecation of third-party cookies by web browsing platforms can affect your planning, execution, and measurement of your marketing strategies. Third-party cookies perform cross-site tracking, retargeting, and ad-serving. 

Circumventing the need for third-party cookies means establishing a forward-looking data strategy that emphasizes first-party data collection, privacy-compliant activation and personalization, and optimization of existing technology to enhance data collection. 

It’s important to develop relationships with data partners – such as publications, health information sites, and any platform that collects data from your potential audience, such as the AMA Physician Professional database, peer-reviewed literature, claims data, and even vital records – without third-party data. Here’s why these relationships matter.

The future of data analytics in oncology and rare diseases

The future of oncology and rare diseases rests within the future of data analytics. The emergence of new devices and technological innovation is accelerating healthcare data of all kinds. 

Data analytics will continue to use ML in the future, and other artificial intelligence (AI) tools will also play a significant role. AI is emerging in AI-enabled clinical support, for example, comparing patients with similar profiles and alerting providers to trends they may have overlooked.

Big data will be used to test for drug interactions not caught by small studies, too, and will improve precision and personalized medicine. The biggest game-changer will be improved predictive analytics as ML continues to “train” and improve pattern identification to generate meaningful insights for predictive diagnosis, drug discovery, and inform strategies for effective marketing, also. 

Data analysis: ethical challenges and concerns

Big data is a boon to all industries, but it has raised some new ethical concerns in healthcare. Data has to contain some personal information to be effective. The challenges lie in respecting the autonomy of participants, equity, and privacy protection. Let’s address each of these in turn.

  • Respecting patient autonomy
    Informed consent is usually obtained when conducting research. The Revised Common Rule, designed to strengthen protections for research volunteers, doesn’t extend to big data research, only requiring broad consent. A remedy is needed, but there is no current consensus.
  • Achieving equity
    Big data analysis results can perpetuate disparities, favoring overrepresented populations over underrepresented groups which leads to potential benefits for one and harm for the other. Equity in data analysis is achieved through careful construction of ML algorithms and study of analysis, comparing it with other research findings. 
  • Privacy protection
    Protecting patient privacy is always a concern, and AI tools make privacy more precarious with their ability to scan and extract information across the web – including from social networks. Then there is de-identified genetic data, which can be easily reattached, and new guidelines are needed for de-identified data.

Big data provides and will continue to provide substantial benefits, but ethical risks should be anticipated, and robust cybersecurity measures put into place.

Despite the challenges of obtaining and protecting data, continued investment in data analytics is vital to benefit patients with cancer and rare diseases. Data analytics give companies greater insight into potential demand and efficacy, accelerate discovery and development, create targeted medications, and offer business intelligence for more effective sales and marketing. 

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