In recent years, healthcare institutions have been looking for ways to improve levels of patient care and make better informed clinical decisions. They have also been endlessly searching for ways to cut costs and make better business decisions.
This is now possible through digitization and big data development and analytics. Today, the digital transformation of healthcare institutions has also been enhanced by the increased integration of the Internet of Things (IoT) that also generates data.
These innovations have helped the industry enhance efficiencies by streamlining processes while enabling access to the vital data they need to make these important decisions. Smart devices like wearables along with cloud technology and increased connectivity have brought about the seamless access to personalized medicine driven by data integration and analytics.
However, according to Verizon, the level of IoT adoption in the industry is still comparatively low with only an 11% increase. When compared to manufacturing that saw an increase of 84% over the same period of time, you can say that healthcare still has a long way to go.
The reason behind such a low adoption rate can be attributed to concerns surrounding security, costs, and data integration. When it comes to IoT in healthcare, security is certainly getting a lot better, but its something that has to be addressed continuously regardless of whether you incorporate IoT into your infrastructure or not.
The costs of incorporating smart devices still have to be justified based on the potential benefits like increased revenue. To do this, business leaders in healthcare organizations can explore related use cases to make an informed decision.
Unintegrated Data Offers Little to No Value
The integration of data is a lot more complicated, but it’s vital to deriving any real value that can benefit both patients and clinicians. It’s a significant challenge as different proprietary IoT devices and the large quantity of data they generate don’t always align well with healthcare applications and electronic medical records (EMR).
In the early days of big data, this wasn’t a problem as data was just collected and stored in warehouses to be accessed later by big data talent in healthcare IT teams. But this approach falls short as it takes too long and doesn’t respond well to the introduction of new data sources.
For IoT in healthcare to be successful, the rapid integration of data is vital. So when adopting IoT solutions, it’s important to define the data as an environment made up of velocity and volume with a variety of data inputs.
For example, some patients are now monitoring their own health with wearables and mobile applications. They’re doing this to have better access to cost-effective healthcare solutions. For it to work, the data that’s generated needs to be rapidly integrated.
Patients are also now taking home remote and virtual monitoring devices for a period of time and the data these devices generate needs to be analyzed now, not nine months down the road. When data analytics is taking place in real-time, healthcare institutions will be better equipped to provide more efficient patient care.
The medical device market is also expected to go through a period of acceleration and is expected to reach $343 billion by 2021. The medical device connectivity market will also mature and grow at a CAGR of 38% until 2020. All this will enhance the level of remote and virtual healthcare when the data generated is rapidly integrated and analyzed.
To achieve this, the healthcare industry has to embrace the concept of data lakes because it’s highly suited to collecting data from multiple sources in a variety of data formats (like proprietary or SQL). This data can then be resolved at runtime by leveraging technologies like Apache Spark or Hadoop.
As this approach is highly flexible and responsive, adding new devices and sources will be a seamless experience. By incorporating the data lake model, you can also collect all the data from a variety of data streams and develop a single view with the help of predictive analytics.
For all this to come together and provide the highest value for both clinicians and patients, healthcare organizations need to develop highly interoperable strategies and solutions. It’s important as critical patient data needs to be connected and made available across all touch points to provide a detailed picture that can result in enhanced patient care and treatment plans.
Key Benefits of Data Integration in Healthcare
While adoption levels of IoT technology might be slower than other industries, there’s no denying that it’s going to play an important role in healthcare going forward.
With the integration of data in healthcare, clinicians can now benefit from the ability seamlessly search across healthcare systems to get the whole picture of an individual patient’s EHR. This could include the following information:
- End-of-life decisions
- Medical history
This information can also play a critical role and save lives when paramedics have to make quick decisions. At the same time, this data can be shared immediately with the ER and the patient’s healthcare provider. This can ensure that the receiving hospital is adequately prepared to deliver time-dependent care.
IoT can also ensure that patients living in rural areas receive much-needed care with the help of telemedicine and data analytics. Furthermore, all this data can also be analyzed at a macro level to help healthcare professionals identify trends (like the spread of diseases).
By embracing big data and IoT, healthcare organizations also have a real opportunity to access quality, cut costs, and ensure that every decision made, has a good chance of being the right one.
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