The Benefits and Challenges of Data-Driven Personalized Medicine

March 13, 2024

A few decades ago, the idea of doctors prescribing medicine tailored to an individual’s genetic makeup with the help of technology was something straight out of science fiction. After all, for much of human history, healthcare has followed a standard approach to prevention, diagnoses, and treatment. Relying on population and statistical averages, physicians made recommendations and treatment plans that balanced medical standards with what was best for each individual patient.

Today, however, what was once a fantasy is now reality. Since the sequencing of the human genome in 1990, healthcare has become increasingly personalized. Widespread genetic testing that is more affordable and more common allows healthcare professionals to prevent and treat disease based on a patient’s genetic profile alongside other environmental and lifestyle factors.

Commonly known as personalized or precision medicine, doctors can now determine if a patient is predisposed to disease, what therapies will have the best chance of success, and what is the ideal dosage for a medication to ensure it’s effective. In short, precision medicine empowers possibilities that would otherwise have been unrealized.

As technology continues to advance, tools like AI and machine learning, blockchain, and next-generation sequencing are pushing the boundaries of personalized healthcare. Let’s explore how these technologies are being applied in innovative ways and the benefits and challenges of using technology to advance precision medicine.

AI and Machine Learning Applications

Over the last few years, AI has seen increasing growth and acceptance within the healthcare industry. While AI supports health professionals in many ways, from fully virtual deep-learning health information systems to surgery assisting robots, one of its most useful applications is in diagnostics. By recognizing detailed patterns and hidden structures, AI and machine learning technologies have matched or outperformed clinicians in image-based detection and diagnostics. AI tools can also help to reduce diagnostic errors and support decision making by healthcare professionals.

Augmenting human capabilities with AI can have impacts for specialties like oncology, primary care, and imaging. For example, recent research found that a breast cancer algorithm trained on mammogram images using machine and deep learning models, was able to predict biopsy malignancy and distinguish between normal and abnormal screening results at a level similar to radiologists. Given this level of accuracy, the machine learning model could assist radiologists in breast cancer detection as a second reader while reducing the likelihood of missed diagnoses.

When it comes to personalized medicine and improving care, AI can help with both risk prediction and diagnosis as well as therapy planning. Genome-informed prescribing, one of the first areas to show how personalized medicine can operate at scale, could be expanded with machine learning algorithms that predict what patients need based on genomic information. Deep learning has also been used to combine scientific knowledge with findings from sequencing to identify links among genome variations and disease. As AI continues to grow and develop within the healthcare field, so will its applications and uses, pushing the boundaries of what is possible in personalized medicine.

Blockchain and Data Security

Originally developed for supporting cryptocurrencies, blockchain technology has found applications in many other sectors including healthcare. A decentralized and distributed ledger technology that ensures transparency, security, and data immutability, blockchain offers useful data management and collaboration possibilities when applied to personalized medicine. Since most healthcare records are now digital, information security and privacy are more important than ever. Blockchain’s decentralized structure offers a safe way to store personalized healthcare information.

Blockchain also offers a unified and secure platform for sharing patient information among providers. The exchange of patient data across systems is critical for effective personalized medicine and blockchain facilitates this exchange, ensuring collaboration among providers while keeping data secure. Additionally, thanks to blockchain’s inherent traceability and transparency healthcare institutions and researchers can train machine learning on data from electronic medical records without the need for data centralization and without risking data privacy in the process.

Next-Generation Sequencing and Effective Treatment

Precision medicine relies heavily on genomic data in order to provide patients with the best treatment possible at the right time. However, sequencing an individual genome used to be a long and costly process, limiting the ability of personalized medicine to apply genomic insights. Now, however, next-generation sequencing technology allows for the fast and accurate sequencing of multiple genes for a fraction of the cost, offering new information about the genetics of human disease.

Next-generation sequencing technology can be especially helpful in understanding diseases like cancer. By sequencing a patients’ tumor, physicians can match them to therapies that will target the genetic alterations that drive the tumor’s growth. Referred to as sequenced-matched therapies, these treatments are informed by genomic and clinical data along with patient preferences to ensure each patient is treated in ways best suited to their cancer.

While barriers to accessing genomic therapies remain and patient outcomes like treatment response are controversial, next-generation sequencing technology is an exciting development healthcare leaders should keep an eye on.

The Challenges of Personalized Medicine

While there are many benefits to using new technologies to encourage data-driven personalized medicine, these advances are not without their ethical and societal implications.

First, new treatments are often considered experimental by health insurance providers, meaning that many cutting-edge genomic-based precision medicine therapies won’t be covered by traditional insurance. This has the potential to exacerbate healthcare disparities, creating a gap in coverage between those who can afford to pay out of pocket for personalized treatments and those who can’t.

In addition to increasing healthcare disparities, personalized medicine relies on data that often resides in multiple platforms across various organizations. While blockchain offers a solution for managing and sharing siloed data in a comprehensive way, in the long-term healthcare systems will need a reliable framework for managing all this data that ensures quality, minimizes risk, and satisfies regulatory compliance requirements like HIPAA.

Despite these challenges, personalized data-driven medicine is here to stay. New technologies will continue to encourage innovation that improves patient outcomes; CRB will be here to provide the skilled healthcare technology professionals needed to design, implement, and manage these advanced solutions.

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