Wearable technology has an intimacy with the user that offers huge possibilities for the medical profession. The ultimate mobile technology can be worn around the wrist or clipped onto the ear (there’s even a contact lens device in development) and it can be with the patient all day or at least the active part of the day.
Wearables don’t impede the user, they can still get on with their life, allowing medics to use wearable monitoring devices to collect vital bio-data on patients at risk of strokes, heart attacks and other potentially fatal conditions.
The data collected could be things like blood pressure or glucose levels. And because they can be streamed back from each user throughout the day, the data volumes are huge.
“The challenge lies not in collecting this data, but in harnessing, parsing, and analyzing it to create competitive advantage. Insights and intelligence derived from fast-moving data sets can help inform strategy decisions, spur innovation, inspire new products, enhance customer relationships, and bolster operations.”
PWC:Big data can give Life Sciences firms a big edge
This remote monitoring of at risk patients is just one source in the explosion of data created by new medical technology.
The established information systems used by clinicians are Electronic Health Records (EHR), Provider Order Entry (POE) Systems, Medical Accounting Systems and Picture Archiving and Communication Systems (PACS).
Previously, they aggregated data over the weeks and months of the quarter. Now, with the volumes of inflowing data, they have to be stored, organised, and mined in real time. The end point of much of this data is fuelling predictive models that allow doctors to find out who’s most at risk from serious diseases.
PinnacleHealth used IBM’s analytics technology to build a algorithmic model that allowed hospital staff to predict who among their chronic obstructive pulmonary disease (COPD) patients, was most at risk of readmission to hospital.
Pinnacle reported 85% accuracy for the model and this cut readmission rates by 30%, improving the health of long-term suffers and relieving pressure on scarce medical resources in client hospitals.
The imperative in creating new technology that manages these data in hospitals and clinics has, according to Professor Tomohiro Sawa, writing in a white paper published by Intel, successfully driven down the costs of big data in the health sector even though data volumes have gone up.
The example he gives is the cost of analysing Genome data: clustering algorithms have become critical to applying the deepening knowledge of the genome to healthcare. This genome research, has allowed medical scientists to analyse tens of thousands of genes in hundreds of thousands of patients, with one practical benefit being the identification of a type of Leukemia having a better prognosis than a closely related variation of the disease.
Big data tech can also be applied to the patients’ living environment. Social Network Service (SNS) is a sub-type of big data. It’s proved itself very useful in monitoring sufferers of very rare conditions.
Probability dictates that there’ll only be a few sufferers of a rare condition in any one hospital’s locale, but if it could pull in data from a wide enough area, ideally the whole world, researchers could then get enough subjects to make their results statistically valid.
This type of research can only work with the integration of different types of big data and Professor Sawa says that this is the next level of the new science, integrating these huge data streams to give a more accurate picture of patients and disease.
IBM addressed this very issue when they created their Infosphere Master Data Management platform. It allows healthcare CIOs to connect information systems that individually might, with great success, be able to handle the huge data volumes being pulled in but these systems aren’t talking to each other and important opportunities to add value to the patient’s experience are being missed, or the sources of poor performance in healthcare are being overlooked. IBM have set out the problems and solutions they’re offering in the infographic below.
Hitherto, the attention most people got from doctors and clinicians was heavily rationed due to the extreme shortage of doctors and nurses, but the nexus of medicine and big data is creating new technology that can monitor our health and predict events that previously were sudden and even fatal. This could mean a new healthcare model where contact between the medics and patient is reduced with shorter treatment queues; reducing the workload for GPs and A&E staff alike.
