An invisible change has taken place in our hospitals, doctors’ surgeries and clinics as each patient is now surrounded by a sea of data.

Thanks to the breadth and validity of modern diagnostic results, there’s more data available for interpretation in a patient’s electronic health record (EHR) than ever before.

These EHRs can potentially hold everything of importance to our health, even down to changes in the very strands that make up our physical being, such as DNA and RNA.

For example, companies like molecular diagnostics specialist QIAGEN help labs analyze samples of DNA and RNA for healthcare and life sciences research, making the results easy to interpret.

As ever more complex data is collected at an increasing rate, the focus is shifting to extracting real value from it, which QIAGEN believes is crucial if we are to benefit from the insights it provides.

The point of this research is not only to sequence people’s genomes and amass findings on genetic variations, but to enable physicians to interpret patients’ genetic profiles and make informed judgments on conditions or treatments.

Success will depend on the curation and interpretation of data, and here the abundance of digitized information is creating new opportunities and challenges.

Opportunities with big data in healthcare

When big data analysis pays off, the benefits are immense. Early gains that illustrate the opportunities include:

Prevention and early diagnosis Analyzing millions of case histories to identify warnings that precede visible symptoms can enable physicians to spot disease early and offer preventive measures or early treatment, which can reduce the cost burdens of chronic disease and end-of-life care.

Personalized medicine It is still early days for using genomic information to guide medical care, but already cancer therapy has been revolutionized by matching individuals’ gene variations with suitable drugs. Other diseases will follow as the knowledge base grows.

Population health Healthcare systems are applying analytics to data about large groups of patients. These provide insights about how outcomes can be improved, quality care rewarded, and costs reduced.

Discovery Scientists can now mine vast pools of data and explore the causes and connections of illnesses, reducing the time spent on observations, diagnoses and trial-and-error tests.

Potential benefits multiply as different sources and types of data are blended to provide deeper, richer insights into complex biological and medical problems.

Healthcare systems are applying analytics to data about large groups of patients.

Challenges of complexity and usability

The sheer quantity and complexity of data can be difficult to handle. Healthcare information encompasses many different types of data – such as clinical, genomic, lifestyle and economic. These often cover large populations over long periods, allowing researchers to trace the different influences on health over generations.

However, at a large scale, healthcare technology suffers from a lack of shared systems. Physicians’ offices and hospitals may have different software systems, while clinical IT platforms holding data or images may not mix well with external providers’ databases.

Organizing data into compatible formats for analysis is a formidable hurdle and the quality of information can vary substantially between sources.

Providers differ in the accuracy and level of detail on reported diagnoses and treatments, and even seemingly precise information, such as genomic results from DNA and RNA sequencing, can hide biases introduced by outdated methods or technologies.

Maximizing the value of big data

However, there are opponents to the computerized oversight of patient-provider interactions. They worry that caregivers may devote too much time to admin chores such as data entry, and human clinical decisions could be ceded to algorithms.

In addition to developing talent for traditional medical careers, investment is also needed in recruiting and training professionals.

Many also fear a potential threat to security of patient information, whether from hacking or intervention by governments, insurers or employers, who might seek to use such details for their own benefit.

But to improve health, as well as boost efficiency and contain costs, we need to focus on enabling providers to sort out the tidal wave of data and extract the insights that can improve care. We also need to understand what role governments and society can play in maximizing these benefits, by for example:

Supporting research – We need to press for public and private funding of basic research that can be used a range of health and research organizations. We must include research about data science in healthcare IT, since outcomes depend on the quality of systems and algorithms for interpreting data.

Investing in infrastructure – Many pools of data are fragmented and disorganized. If they are tucked into systems that are not mutually compatible they have limited value. We need to be able to track patients through healthcare systems, over time, including any input on genomic variations, to effectively improve outcomes.

Enhancing electronic health records – While EHRs are proliferating, they vary greatly in the type and quality of information they hold and many caregivers complain about using them. We need to focus on producing agreed platforms and standards that make EHRs available, and useful, across fragmented IT systems.

Enabling data sharing – Policymakers should balance privacy concerns and lifesaving research by encouraging ‘open’ sharing of aggregated, but secure and anonymous, information on diagnoses, treatments, outcomes, genomic variations and related data.

Developing talent – Managing and curating huge pools of data demands new skills, especially as data increasingly influences clinical practice. In addition to developing talent for traditional medical careers, we need to invest in recruiting and training professionals such as data scientists, bioinformaticians and genetic counselors to work with doctors and patients.

QIAGEN believes that as healthcare data streams continue to grow exponentially in size and complexity, so will their potential to yield insights that improve healthcare for everyone.

Societies will gain unique insights from this invaluable information, which promises to save lives, reduce costs and transform medicine itself, so it is important policy makers and corporate leaders support and guide that process.

Bettina Haedrich is Vice President of Corporate Business Development at QIAGEN.