The role of Big Data in healthcare

Big data - analytics and graphs shown on a laptop

Big Data has become an increasingly important topic in many industries, including healthcare. It refers to extremely large and complex data sets that require advanced computational and analytical tools to process, analyze, and interpret. These data sets may include medical records, patient information, clinical trial results, and research findings.

Increasingly, big data is playing a more important role in the healthcare industry, and its use is expected to continue to grow in the coming years. As more data becomes available, healthcare professionals will have access to new tools and techniques that can improve patient outcomes and reduce healthcare costs.

As the use of Big Data in healthcare continues to grow, several trends have emerged. Here’s how Big Data is impacting healthcare.

Trend: machine learning and artificial intelligence

Machine learning (ML) and artificial intelligence (AI) are rapidly transforming the healthcare industry, with their applications ranging from medical imaging and diagnosis to drug discovery and personalized treatment plans. 

These technologies can process large amounts of patient data and provide insights that can help healthcare professionals make more accurate diagnoses, develop more effective treatments, and improve patient outcomes.

One significant application of ML and AI in healthcare is medical imaging. Radiologists and other healthcare professionals can use ML algorithms to analyze medical images and detect patterns and anomalies that may be difficult to spot with the naked eye. For example, Google’s DeepMind has developed an AI system that can detect signs of diabetic retinopathy in retinal images with high accuracy, potentially allowing for earlier diagnosis and treatment.

Another area where ML and AI are being used is drug discovery. ML algorithms can analyze large databases of chemical compounds and identify promising candidates for new drugs. For example, Atomwise has developed an AI platform that can screen millions of compounds and predict which ones are most likely to be effective against a particular disease.

ML and AI can also be used to develop personalized treatment plans for patients. By analyzing patient data such as medical history, genetics, and lifestyle factors, algorithms can identify the most effective treatments for individual patients. IBM’s Watson for Oncology analyzes patient data and recommends personalized treatment plans for cancer patients based on the latest medical research and guidelines.

Overall, ML and AI are having a significant impact on the healthcare industry by improving diagnostic accuracy, accelerating drug discovery, and enabling personalized treatment plans. As these technologies continue to advance, they have the potential to transform healthcare by improving patient outcomes, reducing costs, and increasing access to care.

Trend: social determinants of health

In recent years, there has been a growing focus on the social determinants of health (SDOH), which are the social and economic factors that influence health outcomes. These factors can include income, education, housing, access to healthy food, and social support, among others. SDOH are increasingly recognized as key drivers of health disparities and are being incorporated into healthcare delivery and policy decisions.

Big data can play an important role in understanding and addressing SDOH. By analyzing large amounts of data on demographics, health outcomes, and social and economic factors, researchers can identify patterns and correlations that can help healthcare professionals develop targeted interventions and policies. 

For example, a study published in The Journal of American Medicine found that using big data to analyze zip codes in California can help identify areas with high rates of hospitalizations due to preventable conditions, which can then be targeted for interventions to address SDOH.

Another example of the use of big data to address SDOH is the All In: Data for Community Health initiative, which brings together health systems, public health departments, and community-based organizations to use data to address SDOH in local communities. The initiative has developed a platform that allows partners to share data and resources to develop and implement interventions that address SDOH.

Overall, the growing focus on SDOH and the use of big data to address them is an important step in improving health equity and reducing health disparities. By understanding the social and economic factors that influence health outcomes, healthcare professionals and policymakers can develop targeted interventions and policies that address the root causes of health disparities and improve health outcomes for all populations.

Trend: Data sharing and privacy

Data storage and sharing are critical components of healthcare delivery and research, particularly in the context of big data. With the explosion of data in healthcare, including electronic health records, genomics data, and medical imaging, the ability to store and share large amounts of data is essential for improving patient outcomes and advancing medical research. However, data storage and sharing also present significant challenges and potential risks.

One of the major positives of data storage and sharing is the potential to accelerate medical research and drug development. By sharing large amounts of data across institutions and researchers, it is possible to identify patterns and correlations that may not be apparent when data is siloed. 

The Million Veteran Program in the United States is a national research program with over 900,000 participants looking at how genes, lifestyle, military experiences, and exposures affect health and wellness in Veterans

One concern, however,  is the potential for privacy violations, particularly as large amounts of patient data are shared across multiple institutions and researchers. In addition, the complexity of big data can make it difficult to ensure that data is accurate and reliable, leading to potential errors and biases.

To address these challenges, best practices for data storage and sharing include secure data storage and transmission, data de-identification, and transparency around data use and access. For example, the Global Alliance for Genomics and Health has developed a framework for responsible data sharing in genomics research that emphasizes data privacy, transparency, and patient consent.

Similarly, the Cancer Genome Atlas project, which has collected genomic data from over 11,000 cancer patients. The project has made this data publicly available, resulting in over 33 tumour types and 10 cancers being identified, allowing researchers to study the genetic basis of cancer and develop new treatments and therapies.

While there are potential risks and challenges, cloud-based data allows for easy access to patient data from any location, which can improve collaboration between healthcare providers. Shared data is also more scalable and cost-effective when compared to on-premises solutions.

Trend: Predictive analytics

Predictive analytics is a form of advanced analytics that uses historical data to make predictions about future events or outcomes. In healthcare, it can be used to identify patients who are at high risk of developing certain conditions, predict hospital readmissions, and develop personalized treatment plans.

Another benefit is the potential to reduce healthcare costs. By identifying patients who are at high risk of hospital readmissions, healthcare professionals can develop targeted interventions to prevent readmissions, reducing the costs associated with hospitalization. 

The impact of predictive analytics in healthcare has already been significant. For example, a study published in the Journal of the American Medical Association found that using predictive analytics to identify patients who are at high risk of developing sepsis can reduce mortality rates by 53%. Another study found that using predictive analytics to develop personalized treatment plans for patients with heart failure can reduce hospitalizations by 30%.

By identifying patients who are at high risk of developing certain conditions and developing targeted interventions, healthcare professionals can provide more effective and efficient care, leading to better outcomes for patients and healthcare systems.

The final word

Big data is the future of healthcare. With the vast amounts of patient data being generated by electronic health records, wearable devices, genomics research, and medical imaging, big data has become a critical tool for improving patient outcomes, reducing healthcare costs, and advancing medical research.

This is a two step proccess: 1) fill out this form, 2) select a convenient time.

Not ready to book a demo but have a question? No problem! Please call or send us your question.