Advancing precision and personalization in healthcare with data science

The Indian healthcare analytics market is estimated to grow to approximately USD 1.05 Bn by 2026, driven by increasing adoption of analytical solutions both in India and globally. By harnessing the transformative power of data science in combination with advances in genomics, microbiomics and molecular biology, the healthcare industry is moving from a 'one-size-fits-all' approach to a model where treatment strategies are meticulously tailored to the individual needs of each patient. 

Analyzing patient data for insights 

Patient data analysis empowers healthcare professionals to make informed decisions based on patterns and correlations between lifestyle choices, genetics and health outcomes. Data sources include Electronic Medical Records (EMR), Electronic Health Records (EHR), Electronic Patient Records (EPR), Web Services (WS) data, sensor data, transactional data and biometric data. Machine learning and analytics tools can diagnose patients, discover biomarkers and subtype diseases by analyzing data from these sources. 

Driving precision and personalized medicine through data science

Precision medicine and personalization, both pivotal in the contemporary healthcare landscape, are chiefly governed by few critical dimensions. Firstly, individual-centric drug discovery helps prioritize personalization and gears research efforts towards understanding genetic, epigenomic and proteomic variations among individuals. Secondly, drug impact assessment through advanced data analytics tools can be extended to pharmacogenomics to analyze how a patient’s genetic makeup influences their response to drugs. This leads to tailored drug prescriptions, minimizing the risk of adverse drug reactions and enhancing treatment efficacy. Another significant aspect is customized interventions for drug adherence. Data and analytics enhance patient care by ensuring they consistently follow the medication regimes tailored to their lifestyle. This includes reminder mechanics, health check track and personalized timely support from healthcare providers.

Leveraging predictive analytics for precision healthcare

Predictive analytics utilizes past data to predict future outcomes. It can help identify individuals at high risk of hospital readmissions, enable interventions to reduce repeated stays and analyze genomic data for better treatment responses. 

Exploring genetic variability 

One of the groundbreaking contributions of data science in healthcare is the exploration of genetic variability. Through genomics, we have been able to realize that diseases such as cancer, diabetes and Alzheimer's Disease (AD) are not solely distinct entities but a complex interplay of multiple genes and environmental factors. Taking the example of Cardio-Vascular Diseases (CVD)–traditionally, risk prediction models have relied on a few key factors, such as age, cholesterol levels and blood pressure. However, they do not account for genetic predisposition, which is where data science comes into play.

Examining the human microbiome 

Emerging studies suggest that the human microbiome, significantly influences our health and disease states. Its role in diseases such as obesity, Inflammatory Bowel Disease (IBD) and even neurological disorders is being unraveled, providing a fertile ground for precision medicine. Data science tools can analyze large and complex microbiome datasets, identifying microbial signatures that could predict disease course and response to therapy, or even serve as therapeutic targets.

Molecular biomarkers and disease progression

Data science integrates high-dimensional biomarker data to unravel the intricate biology underlying diseases. AD provides an apt illustration of this concept. Traditional diagnostic methods, such as clinical assessment of cognitive function, often detect the disease at advanced stages. However, by leveraging data science, researchers have identified that changes in the levels of Aβ and tau proteins in the cerebrospinal fluid or blood can detect AD years before clinical symptoms appear. 

India's G20 presidency prioritizes the need for an inclusive, responsive, and adaptive framework to manage health emergencies like COVID-19. In support, India is shifting from a 'data-driven' to a 'data-first' approach, recognizing the crucial role of 'data for development.' For instance, the National Digital Health Mission aims to create a connected ecosystem of healthcare divisions, benefiting rural areas in monitoring and providing effective healthcare services.

We can therefore conclude that integration of data science with genomics, microbiomics and molecular biology, can lead to accurate diagnoses, prognoses and personalized therapeutic strategies. This approach has the potential to revolutionize healthcare outcomes for all. 

(The author is office managing principal at ZS)

Manish Menon

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Manish Menon is office managing principal at ZS.