Big Data Analytics in Life Sciences and Healthcare: An Overview

Introduction

The life sciences and healthcare industries historically have generated large amounts of data driven by patient profiling, compliance and regulatory requirements, and scientific research. These massive quantities of data hold the promise of supporting a wide range of medical and healthcare functions, including clinical decision support, disease surveillance, and clinical analytics.

Data in life sciences and healthcare is expected to grow exponentially in the coming years and will be beyond the capability of the traditional methods of data management and data analytics. In addition, healthcare reimbursement models are changing; meaningful use and pay for performance are emerging as critical new factors in today’s healthcare environment. It is vitally important for life sciences and healthcare organizations to acquire the available tools, infrastructures, and techniques to leverage this vast amount of data effectively, or risk losing potentially millions of dollars in revenue and profits.

This white paper provides an overview of big data analytics as an emerging discipline in life sciences and healthcare. It explores the characteristics of this trend and the benefits of leveraging big data analytics within these sectors. It also touches on the challenges and future directions of big data analytics in the life science and healthcare industries.

Big Data in Life Sciences and Healthcare

Big data in life sciences and healthcare refers to electronic healthcare datasets so large and complex that they are difficult (or impossible) to manage with traditional software, hardware, data management tools, and/or methods. Big data in life sciences and healthcare is overwhelming not only because of its volume but also because of the diversity of data types (variety) and the speed at which it must be managed.

The totality of data related to patient healthcare and wellbeing makes up big data in life sciences and healthcare. This includes:

  • Clinical data from CPOE (computerized physician order entry) and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data).
  • Data in electronic patient records (EPRs).
  • Machine generated/sensor data (such as data from monitoring vital signs).
  • Social posts (including Twitter feeds, blogs, status updates on Facebook and other platforms, and web pages).
  • Less patient-specific information (emergency care data, news feeds, and more substantially, scientific research articles in biomedical journals).

Leveraging Big Data in Life Sciences and Healthcare

By discovering associations and understanding patterns and trends within the data, big data analytics has the potential to improve care, lower costs, and save lives. Thus, the application of big data analytics in life sciences and healthcare takes advantage of the explosion of data and removes previous restrictions on analytic capabilities. By extracting insights from their data, organizations can make better, informed decisions and leverage new opportunities. When big data is synthesized and analyzed, healthcare providers, drug manufacturers, and other stakeholders in the life sciences and healthcare industries can develop more thorough and insightful diagnoses and treatments, delivering higher quality care at lower costs and achieve better outcomes overall.

The potential for big data analytics in life sciences and healthcare to lead to better outcomes exists across many scenarios.

Healthcare and Payers

  • Analyzing patient characteristics and the cost and outcomes of care to identify the most clinically effective and cost-efficient diagnoses and treatments.
  • Identifying, predicting, and minimizing fraud by implementing advanced analytic systems for fraud detection and checking the accuracy and consistency of claims.
  • Analyzing large numbers of claim requests rapidly in the pre-adjudication phase to reduce fraud, waste and abuse.

Evidence-Based Medicine

  • Combining and analyzing a variety of structured and unstructured data – EMRs, financial and operational data, clinical data, and genomic data – to match treatments with outcomes, predict patients at risk for disease or readmission, and provide more efficient care at reduced cost.
  • Applying advanced analytics to patient profiles (e.g., segmentation and predictive modeling) to identify individuals who would benefit from proactive care or lifestyle changes (for example, those patients at risk of developing a specific disease who would benefit from preventive care lifestyle changes).
  • Using historical data to personalize medical care by predicting and/or estimating developments or outcomes, such as which patients will choose elective surgery, will not benefit from surgery, are at risk for medical complications or hospital-acquired illness, or will have possible co-morbid conditions.
  • Executing gene sequencing more efficiently and cost effectively to make genomic analysis a part of the regular medical care decision process and the growing patient medical record.

Real-Time Healthcare and Clinical Analytics

  • Collecting and publishing data on innovative medical treatment procedures, assisting patients in determining the care protocols or regimens that offer the best value.
  • Aggregating and synthesizing patient clinical records and claims datasets in real time to provide data and services to third parties (e.g., licensing data to assist pharmaceutical companies in identifying patients for inclusion in clinical trials).
  • Detecting individual and population trends more rapidly and accurately by developing and deploying mobile applications that help patients manage their care, locate providers, and improve their health.
  • Monitoring medical devices, including wearables, to capture and analyze in real-time large volumes of fast-moving data, for safety monitoring and adverse event prediction, enabling payers to monitor adherence to drug and treatment regimens and detect trends that lead to individual and population wellness benefits.

Research & Development

  • Improving predictive modeling to lower attrition and produce a leaner, faster, more targeted R&D pipeline in drugs and devices.
  • Leveraging statistical tools and algorithms to improve clinical trial design and patient recruitment to better tailor treatments to individual patients, thus reducing trial failures and speeding new treatments to market.
  • Analyzing clinical trials and patient records to identify follow-on indications and discover adverse effects before products reach the market.

Public Health

  • Analyzing disease patterns and tracking disease outbreaks and transmission to improve public health surveillance and speed response.
  • Improving data models to better predict virus evolution, leading to more accurately targeted seasonal vaccines (e.g., choosing the annual influenza strains).
  • Turning large amounts of data into actionable information that can be used to identify needs, provide services, and predict and prevent crises, especially for the benefit of populations.

Challenges in Big Data Analytics in Life Sciences and Healthcare

The potential represented by big data analytics also comes with significant challenges, particularly around strategy, governance, and timeliness.

Big Data Analytics Strategy

Big data analytics solutions in life sciences and healthcare, and in fact in most industries, must support the key functions necessary for processing the data. The criteria for evaluation may include availability, ease of use, scalability, ability to manipulate data at various levels of granularity, ability to analyze data without IT intervention and with the users’ preferred tools of choice, privacy and security enablement, quality assurance, and transparency.

To address these challenges, organizations need an actionable roadmap comprising people, process, and technology improvements that result from a comprehensive assessment of their existing data management capabilities, prioritized data-related goals, and business value drivers.  The data management strategy should identify an organization’s pain points and address them through disciplined yet agile phased execution. The result should be a timely and cost-effective strategic approach that provides incremental business benefits at the conclusion of each phase.

Big Data Governance

The important managerial issues of data stewardship and data quality have to be considered and woven through an organization’s continuous data acquisition and data cleansing. Life sciences and healthcare data is rarely standardized and is often fragmented or generated in legacy IT systems with incompatible formats.

To address this issue, organizations need good data governance. Governance, the human aspect of managing data, encompasses the people, processes, and technology required to ensure the accuracy, timeliness, and effective use of data across the enterprise.  Strong operations, the processes required to effectively manage information environments and platforms, support good data governance. Without a well-developed governance program and robust operations, organizations struggle with inaccurate and poor-quality data, leading to untrustworthy results and decisions. Organizations need to develop the tools necessary to effectively and confidently manage their data assets in specific information environments.

Real-Time Analytics

Finally, to ensure the most current and applicable insights, real-time big data analytics is a key requirement in life sciences and healthcare. The lag between data collection and processing has to be addressed. Also, the dynamic availability of numerous analytics algorithms, models, and methods is necessary for large-scale adoption.

Organizations need to implement delivery tools and technologies that not only seamlessly interface with big data platforms, but also drive real-time data analytics.

Conclusion

Big data analytics has the potential to transform the way life sciences and healthcare organizations use sophisticated technologies to gain insights from their clinical and other data repositories to make informed decisions. Analytics allow organizations to investigate and explore data to identify relationships, trends, and patterns to reveal insights that, when combined with business context, create knowledge. In the future, the implementation and use of big data analytics will spread rapidly.

To that end, several challenges must be addressed. As big data analytics becomes more mainstream, concerns such as guaranteeing privacy, safeguarding security, establishing standards and governance, and continually improving the tools and technologies need to be resolved in a compliant and cost-effective manner. Only then will organizations garner the true benefits from big analytics in life sciences and healthcare.

About Knowledgent

Knowledgent is an industry information consultancy that helps organizations transform their information into business results through data and analytics innovation. Our expertise seamlessly integrates industry experience, data analyst and scientist capabilities, and data architecture and engineering skills to uncover actionable insights. We not only have the technical knowledge to deliver game-changing solutions at all phases of development, but also the business acumen to evolve data initiatives from ideation to operationalization, ensuring that organizations realize the full value of their information.

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