Datica Blog

4 Challenges & Solutions for Big Data Capturing

Kris Gösser

Kris Gösser

VP of Marketing

June 1, 2016

Data capturing is becoming an increasingly automated process across all modern interactions. Specifically in healthcare, electronic health record systems are digitizing existing patient records to be used in patient care, surgical procedures, and clinical trials. For instance, data streams captured by medical instruments during surgical procedures are fed into an integrated health record system in real-time; also, wearables worn can live stream data directly into clinical and hospital servers while patients recover at home.

With so many channels, data in healthcare is multidimensional and commonly segmented. From a pharmaceutical perspective, in order for Big Data analytics to be successful, attention must be given to vast scale data collection from various entities over extended periods. Big Data support must also be incorporated into interoperable health record systems and this data must be presented intuitively through visual analytics. Despite the buzzing potential of Big Data analytics, there are still challenges impeding complete integration in today’s medical field.

Unabridged Data Collection

The eventual goal behind Big Data, and just about every healthcare initiative, is to reach a state of improved care with a reduced cost. To do so, the key is to leverage complete data, including EHR data. The issue is that in most healthcare organizations, critical clinical data such as physical symptoms, medical history, orders, and diagnostic notes are entered manually and nothing is foreseeably going to change that. Also, clinical data stored in contrasting provider systems can prove to be incompatible with one another. The solution lies in the adoption of data capturing automation to minimize manual data entry, which is also essential to data quality improvement.

Challenges in Data Quality

Ensuring data quality in healthcare is extremely consequential, so don’t let anyone tell you different. Effective solutions may include the adoption of automatic data capturing and/or utilization of artificial intelligence to verify the data. Additionally, healthcare organizations should have a data management process enacted to ensure pristine and accurate data.

Data Timeliness, Distribution, & Access

An ongoing Big Data issue is efficiently distributing data. The solution is present in the healthcare cloud’s computing technology, allowing organizations to scale technology infrastructure in an agile manner by completely outsourcing and abstracting away hardware layers.

Personal Data Collection

Today we are experiencing an increase in mobile device dependency and interconnection, a phenomena healthcare organizations are taking full advantage of by collecting patient data through wearables and fixed monitoring devices. Present in both healthcare facilities and patient homes, this exercise has evoked concerns for patient confidentiality, an ongoing conflict between providing personalized healthcare and patient privacy protection that must always be addressed.

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