The role of Big Data in digital healthcare transformation

  • industry Highlights
  • 1 year ago

When we visit our doctor or go to hospital, we have faith in the knowledge that the health care professionals involved are treating us the right way.

However, since the average healthy individual spends no more than ten minutes in a health facility, it is unlikely that a physician will manage to assess the state of your overall health, unless something catastrophic is going on. The so called evidence-based medicine (EBM) approach which physicians usually apply prescribes drugs or selects treatment based on a proven success in clinical research but has its limitations.

Electronic Health Records (EHRs) technologies have been around for a while but, with the data stored across a number of different systems and formats, they are not really designed in an analytical way. Still a staggering 80% of medical and clinical patient information is formed of unstructured data, such as written physician notes, consultant notes, radiology notes, pathology results, hospital discharge notes etc. Let’s pause for a minute and think about it…What will happen in 2020 when healthcare data is expected to reach 25,000 petabytes – a 50-fold increase from year 2012?

Too many patients, very little time

There is an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers to access knowledge for the individual patient, yielding better and faster decisions and outcomes. This is not possible without the development of the “personalized medicine” concept – a framework that leverage patient EHRs and OMICS (primarily genomics) data to facilitate clinical decision-making that is predictive, personalized, preventive and participatory.

With the personalized medicine approach, clinicians are able to:

  1. Validate medical treatments and response to therapies – thus they can predict the possible side effects and detect adverse events to treatments based on genetic make up for each individual patient in comparison to other similar patients
  2. Describe better targeted therapies for individuals – determine easily which drug(s) will work better with each individual patient, instead of adopting an empirically driven approach of trial-and-error.
  3. Make better decisions on risk prediction and focus on prevention, rather than disease management.Collecting genetic information from previous family tree testing can be useful to determine possible diseases in future that can either be avoided, or adequately controlled.

It’s impossible to talk about personalized medicine without recognizing how deeply integrated it is withBig Data. Focusing on the great volume of diagnostic images, genetic test results and biometric information there are several challenges related to data integration, processing and analytics, visualization and interpretation that need to be addressed.

  • Data variety – the collected data need to be integrated – clinical information (e.g. medical diagnosis, medical images, patient histories) and biological data (e.g. gene, protein sequences, functions, biological process and pathways) that have diverse formats are generated from different sources. It is very important to focus on developing tools and techniques to make better sense of data and the use of information for knowledge discovery.
  • Quality of data – to ensure it, data and terminology standardization should be executed. This typically requires extending existing data libraries or development of domain-specific ontologies.
  • Volume of data – having huge amount of data itself does not solve problems. It should be summarized in a meaningful way to deliver properly the information, knowledge and finally wisdom that boost decision-making.
  • Data velocity – healthcare data is and will be continuously changing and evolving. These rapid changes in the data pose a significant challenge to offer fast searching, browsing, and analysis of real-time content. Users should have: (1) the ability to filter, prioritize and rank the data (relevant to the domain, or use case); (2) the ability to process and ingest data quickly; and (3) the ability to cull, evolve, and hone in on relevant background knowledge”

Besides technology and infrastructure challenges for using smart data to enable personalized medicine, there are several other administrative challenges – healthcare organizations and policy makers should promote a fundamental shift in their decision making, and embrace a ‘brave new world’ that promotes data sharing with appropriate security and privacy protection, also new policy guidelines so we can enable faster digital transformation.

Thanks to our previous expertise in healthcare and the renowned capabilities of the SAP HANA Cloud Platform, Sqilline is now working on developing a Breast Cancer Risk Assessment Tool that will evaluate data of people with family history of breast cancer, combining various data sources,  e.g. electronic cancer registries. The overall objective of the tool is to provide opportunities for effective prevention and treatment of breast cancer.


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