Revolutionizing healthcare: How data science elevates precision and personalization

In the realm of healthcare, a profound transformation is underway as data science takes center stage, reshaping the landscape with precision and personalization. Through the power of advanced analytics and intricate data analysis, data science empowers medical experts to tailor treatments with unprecedented accuracy. This evolution marks a monumental shift towards patient-centric care, where each individual's unique medical profile guides the path to improved health outcomes. As data science revolutionizes healthcare, the synergy between technology and medicine ushers in a new era of personalized and effective treatments. Recently, Manish Menon, office managing principal, ZS spoke to Dataquest along the same lines.

DQ: How can data science contribute to improving precision and personalization in healthcare treatments?

Manish Menon: Data science is pivotal in enhancing healthcare precision and personalization by utilizing advanced analytics to analyze complex datasets. This enables healthcare experts to make informed decisions tailored to individual patients. Leveraging genomic insights, particularly in drug response and disease dynamics, healthcare professionals can prevent illnesses, enhance diagnoses, prescribe safer medications and administer more effective treatments.

Data sources encompass Electronic Medical Records (EMR), Electronic Health Records (EHR), Electronic Patient Records (EPR), Web Services (WS) data, sensor data, transactional data and biometric data. The data can then be analyzed for diagnoses, biomarker discovery and disease subtyping to make more efficient decisions. 

The key areas where AI contributes within healthcare treatments are:

1. Probably the most known case is how AI helps in drug discovery. Here AI-powered algorithms help identify the right candidates and also aid the whole clinical development process
2. Identifying the optimum diagnosis paths to ensure the diagnosis is done on time. For instance, we have seen that diagnosis used to take approximately 7 years within the area of rare diseases and there is much focus now within the industry to reduce this at the diagnosis phase
3. Identifying factors that impact patient journeys especially components like genomics, gut microbiomes and other personal factors to predict key patient events (e.g. disease progression)
4. Ensuring therapy/medication adherence can be improved by engaging the patients on time with the right message and the right channel. Adherence has been a key challenge for the healthcare industry and AI technologies bring in aspects of personalization that help elevate the adherence

DQ: What are some challenges that arise when trying to implement precision medicine strategies, and how can data science address those challenges?

Manish Menon: Understanding the Challenges within precision medicine is key to shaping the growth of such capabilities within your organizations.

One of the key challenges is to ensure that the data is compliant and adheres to local and federal regulatory laws. The dilemma of using personal data but at the same time ethically using it is what creates the challenge. There are now technologies and frameworks to work past this in terms of platforms that are GDPR/ HIPAA/ HITRUST compliant, tokenization technologies and sophisticated capabilities like clean rooms from AWS to share data compliantly. Digital tools are being used quite often now with patients defining how their data can be / should be shared which is now also being covered in legal provisions.

The second challenge we see is creating the right technology ecosystem that can manage diversity of data and analytics requirements. This means managing structured / unstructured data sets, data quality management, complex data integration requirements and making the data analytics ready. The technology ecosystem should also be able to bi-directionally interact with genomics systems, EMR systems and other various data sources.

A softer aspect is to define the right team to work on precision medicine programs. The right team requires the right mix of science, technology, data stewardship, legal and analytics capabilities. This is not an easy task and has to be done strategically.

DQ: Can you explain how predictive analytics models are being used to improve healthcare outcomes for patients?

Manish Menon: Technology and pharma companies are collaborating to develop prediction capabilities to map oncology patient journeys. By leveraging advanced analytics and patient data, these models can anticipate the trajectory of a patient's cancer diagnosis, treatment, and recovery. Such models analyze historical data and consider factors like patient demographics, medical history, and treatment protocols to provide healthcare professionals with valuable insights. This enables them to foresee patient needs, optimize resource allocation, and ultimately enhance patient care and outcomes within oncology settings and beyond. 

Within ZS we are at the forefront of such questions and helping our clients to:

1. Leverage genomics and disease ontologies to identify various pathways building in complex predictive insights in the disease journey.
2. Elevate patient engagement with the objective of enriching patient experience. This includes strategies to understand and ensure medication / therapy adherence is elevated, patients get the right financial assistance to get access to the right therapy and ensuring patient communication is also empathetic.

DQ: What are some key factors that influence the accuracy and reliability of predictive models in healthcare settings?

Manish Menon: First is the quality and quantity of the input data which plays a critical role. Healthcare predictive models require diverse, comprehensive, and high-quality datasets that accurately represent the patient population and their medical history. 

Model selection, algorithm choice, model training and validation are essential steps. Robust training with sufficient data, rigorous cross-validation techniques and careful hyperparameter tuning are necessary to ensure that the predictive model generalizes new, unseen data and doesn't overfit the training data. Lastly, ethical considerations are paramount to avoid inequitable outcomes for different patient groups. Regular updates and continuous monitoring of the model's performance in real-world settings are essential to maintaining accuracy and reliability over time, as healthcare systems and patient data evolve.