Big Data holds the promise to upend healthcare operations in ways we have never seen before. The Insights leveraged from health data can help doctors in giving precision medicine and delivering personalized care for better outcomes. However, getting access to these benefits is not easy and there are challenges on every turn. Many obstacles prevent healthcare enterprises from realizing the possible business benefits of Big Data. Right outcomes and ROI requires layered integration between high throughput technologies.
Challenges Facing the Healthcare Intelligence
1. High Throughput Technologies: The life sciences sector is in interesting times where high throughput technologies for genome sequencing, imaging, etc., are generating enormous quantities of data. These data sets are demarcated by large, semi-structured, or heterogeneous models which cannot be comprehended by other systems. Also, it is hard to breakdown these data sets, visualize them in a proper manner. Moreover, this data cannot be stored for new statistics and computational methods. It can be a big operational challenge to feed this data in an excel sheet and summarize or collect it for different purposes. On the contrary, an integrated ecosystem strategically addresses synchronization needs between new and old technologies.
2. Scalability Issues: Healthcare technologies have made huge advancements, however, many enterprises still prefer manual black boxes and traditional warehouses for collecting the data. Silos have captivated their information systems for a long period of time, a cultural intransigence. It becomes a tedious task to connect these systems with external ecosystems for smoother data exchange. It becomes really hard to scale the data for different purposes. Data workloads flowing through sensors, mobile applications, and imaging sensors, etc., overwhelms their data transport network and bends IT under its weight.
3. Problems of Size: In Big Data, the precision comes with the size of data. Bigger the data, better are the chances of outcomes. The data of 5000 people will yield better results than the data collected by 50000 people. The available systems need to study things on a large scale. Without huge data, teams can draw wrong conclusions or interpret data in the wrong way. To do this effectively, teams should have the right technology to bring data from multiple sources.
4. Computational Power: Another relevant challenge in the life sciences or healthcare sector is the computational power which is doubling after every year. Many enterprises don’t have the firepower to process data generated by these systems in a smooth and efficient way. Processing & mapping data generated by sensors, trackers, ECGs, and monitors is an uphill task for healthcare enterprises.
Researchers don’t get access to the data at the right time. The IT teams deliver the data in a report factory. These reports are highly rigid and it takes a lot of hard work to track down the data and putting it in the repository. Refining the data and meeting their expectations can be hard work.
5. Complex Data Models: Life sciences and healthcare companies use complex data models. Building reports as per this data models are like ICD9 code and 453 can be really challenging. This leaves a huge potential for drawing the wrong conclusions.
Data Integration Strategy to Drive the Big Data Strategy
A fair share of problems come from the integration side. Many of the problems can be climbed with a start-to-end integration layer. The integration layer can help teams in inter-wiring all technologies, i.e.., EEG monitors, cerebral oxygenation monitors, etc., for seamless data exchange between them. Close connections between researchers and experts help teams in studying things at scale & breadth. Caregivers can compare data of different patients to suggest evidence-based medication to patients.
Data integration can help research teams in realizing many business benefits like faster disease discovery, reduced emergency visits, prevention of adverse drug effects, etc., An Intelligent mesh of networks enable teams to capture data in a right way and bring it faster to the production.
Uberization of Healthcare
Uber a San Francisco based company has leveraged integration in the right manner for changing the face of travel and transport. Uber has redefined the ways we book a cab or travel without the need for inventing a single thing. The organization has just brought together all maps, GPS, tracking systems, etc., and layered them as one application. This use case has relevance for the healthcare industry as well.
Integrating all diagnostics, thereupatics, and doctors can deliver similar benefits. In this way, our smartphones can become our new hospitals. Digitization can help teams in transforming a reactive system into a proactive system.
Enterprises and international organizations can make sick care cost-effective and hassle-free. Consumers can search for medical professionals and get a consultation in a few simple clicks. They don’t need to miss their office or travel miles to get the consultation. Similarly, doctors can use digital diagnostics to check patients and craft disease management plans for them.
This advantage will drive a lot of innovations in the healthcare sector. For instance, patients can make use of the health-related applications on iOS and Android and track sleep patterns, diet, lifestyle, etc. Patient data can be shared with the doctors for rectifying disease in the early stages before they become bigger problems. Doctors can suggest evidence-based care to the patients provide them result based care. Patients can become CIOs by maintaining a lineage of reports and become CIOs of their own health. Thereupatics can be restructured or re-engineered for real effectiveness. On top of that, we all can move into an era of quantified health where ‘quality of life’ is guaranteed to everyone.
Summary: The technology advantage with Big Data and they mostly come from the integration side. Know how data integration helps Big Data in moving from theory to practice.
