Big data is beginning to mature across many industries. However, in healthcare the work is just getting started. That’s understandable since healthcare has unique privacy rules and regulations that complicate their big data efforts beyond the issues that other industries face. Even so, progress was made in 2014 and next year will see even more. Here are four of my predictions on what we can expect to happen in healthcare big data this year.
1. Intelligent wearables will begin to have a role in healthcare and provide even more data.
No, I’m not talking about wearables of the Fitbit ilk which are largely designed for consumer use. Rather I’m referring to wearables used for patient care. We’ll see wearable connectivity in smart fabrics extended to patient gowns, bed sheets, and bed/table covers, X-ray protective shields, prosthetics, and exoskeletons, to name but a few.
What data can these types of wearables provide beyond that which current monitoring devices provide?
Smart gowns, bed sheets and bed/table covers can conceivably report how much the patient is sweating, variances in sweating by time of day and/or before or after medications. They can even detect chemicals in the sweat to help with disease diagnosis. Some will be designed to collect data on bacteria and viruses the patient has been exposed to or has. Smart fabrics such as these can also report sleep patterns and patient distress patterns, as well as other patient movements. This can be particularly helpful in caring for patients who cannot verbally communicate or who are unable to articulate the information.
Smart x-ray shields can produce and report data on actual x-ray exposure per test and also possibly detect a patient’s radiation levels. In addition, lab coats and scrubs can self-adjust to warm or cool a surgeon while he or she is operating and accumulate data on the physician—such as stress level, alcohol or drug levels, and overall health condition.
Prosthetics will improve in response to the patient and in overall comfort but they will also transmit data on the condition of the device, including wear and tear and usage patterns, as well as monitor the patient’s overall health. They will also detect and report data on early signs of ulcerations or abrasions from wearing the device.
Exoskeltons designed to assist the elderly and the disabled will generate data ranging from their location to activity levels and quality of life scoring. They too may also monitor vitals. For those recuperating from an illness or surgery, exoskeletons will be designed to adjust the degree of support to wean patients back to their own strength and to automatically offer more support if the patient becomes fatigued.
2. Issues with data fluidity will begin to be addressed in earnest.
EMRs and EHRs are great in concept but far from perfect in practice. Vendors will be more focused next year on improving the flow of data between health practitioners treating a single patient. But healthcare operations are unlikely to be satisfied with the early attempts at this. Many will skirt these problems with work-arounds—such as turning to automation that will manage complex application dependencies over multiple applications and infrastructure to unlock data silos and redirect data flow.
3. Healthcare data standards and interoperability will continue to evolve and improve.
While big data tools are great in handling disparate data sets, it’s still easier for researchers to integrate and use data if it is standardized. Several groups and organizations have been working towards this end. Indeed, some standards have been formed and you can see a list of them on the American Health Information Management Association (AHIMA) webpage. The American National Standards Institute (ANSI) coordinates the development and use of standards within the
Widely accepted standards will go far in ensuring data interoperability and sharing. Examples of organizations and groups working on interoperability and sharing issues include the National Institute of Health’s Big Data to Knowledge Initiative (BD2K), the collaboration known as National Patient-Centered Research Network (PCORnet), and Optum Labs, a consortium. 2015 will see continued cooperation between such efforts worldwide and a better, more reliable fusion of data as a result.
For a glimpse into some of the thoughts behind efforts aimed at improving and enabling healthcare and research data utilization, watch this NIH video.
4. More healthcare data will flow to the patient.
Data from patient records will begin to flow to patients in earnest next year. For the first time in the healthcare industry’s history, a vast number of patients will be able to see their own healthcare data. For the healthcare industry, this poses both opportunities and problems next year.
On the opportunity side, patients can help clean up data by reporting errors and updates which will aid healthcare providers in achieving and maintaining data integrity. Further, as more healthcare providers deploy automation, patients will enter some data themselves such as making their own appointments online and reporting diets and activities as requested by their providers. Additionally, patients can respond to lab test orders and reminders if they recently had those tests elsewhere. This will lead to the elimination of multiple testing which reduces healthcare costs, frees healthcare resources, and points providers to test results that can be quickly obtained from another source—thereby speeding patient care and provider workflows.
On the negative side, expect a deluge of patient calls and emails requesting explanations of their healthcare data. To prevent being overwhelmed with such, consider using or forming a specialized call center to field patient phone calls, emails and chats. You may also want to include a self-service online feature that enables patients to look up terms and information on their own.
2015 will bring more changes than these, of course. But these four will arguably be the most impactful. What do you see happening next year? Please share your thoughts in the comment section below.
Pam Baker is a regular nuviun contributor, the editor of FierceBigData and author of Data Divination: Big Data Strategies. For more expert insights from Pam, follow her on Twitter @bakercom1 and at FierceBigData.
The nuviun blog is intended to contribute to discussion and stimulate debate on important issues in global digital health. The views are solely those of the author.