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Data Lake for Healthcare - Does It Deliver? - ResolveDatas
Data Lake for Healthcare – Does It Deliver?

Data Lake for Healthcare – Does It Deliver?

With time, healthcare data only continues to grow. The variety of delivery methods and data formats also keeps expanding. To manage all this, we need a strong data management strategy. By creating a cloud data lake for healthcare, it is easy to empower an organization to handle these hurdles in an organized way.

The Current Status of Data in Healthcare – Volume and Variety

The volume and variety of data stored during clinical trials is expanding rapidly. Effective diagnostics technologies and clinical data lake enterprise collect a huge variety and volume of data to use it as and when needed.

A large volume of healthcare data gets generated from electronic health records, biosignal data sets, imaging files, proteomics, health insurance companies, and patient wearables. A whopping 90GB of data is present in a single genome binary ailment map record.

Not to forget, data sources generate a variety of data that needs to be compared in clinical trials. This data has both structured as well as unstructured inputs. With a mixed data variety coming from clinical diagnostics, literature, pathology results, and patient wearables, it makes it extremely difficult to use old technology to review this data. Do you know that more than 1 million articles on biomedical science are published every year? This is nearly 2 research papers every minute. Therefore, technology-driven systems can only ingest and digest such a huge volume and variety of healthcare data

Challenges of Handling Data in Current System

The healthcare industry has to face many challenges that act as a roadblock to their progress. But if it is successful in overcoming these challenges, many opportunities are going to open up. This, in turn, will be beneficial to the patients and healthcare workers alike. One of the greatest challenges is managing volumes of ever-increasing data.

Let’s take a look at the challenges facing healthcare data management.

One of the primary challenges is to reduce cost while enhancing care. Without proper accumulation and management of clinical data, this isn’t possible. The right data assessment will help providers to identify high-risk individuals who have chronic health conditions.

Operation efficiency is measured by the total input against the corresponding result. The challenge, in this case, is to truncate the input and increase the number and quality of the product.

What is Being Done to Tackle The Challenges of Handling Data in Healthcare?

Big data helps healthcare to switch from a treatment-based reactive approach to a more preventive model. Intelligent use of data will speed up the progress of customized approaches for better patient engagement. This will lead to better compliance. Data helps in shedding lighting on health drivers for various segments of the population.

Data analytics can take the healthcare industry to new heights. The partnership between analytical tools and data management will bring more improved and innovative practices. This, in turn, will be beneficial to the stakeholders of the industry. Using the ACO(Accountable Care ) model helps the healthcare groups to provide better-improved healthcare at low cost.

It will help with current patient analysis. So, it becomes easier to treat patients. If healthcare providers use the data to learn about the demands of the patients, they can match their equipment and workers to cater to their patient’s requirements. Real-time data offered by data management will help healthcare operations and finance managers to accurately assess the assets versus the projected profits of the company.

What is Data Lake?

Data Lake by James Dixon is a repository holding a large amount of data in its native format until it is required. When data is stored in the native format, it helps in accommodating any design changes or schema requirements in the future.

If crucial data sources are being dispersed among on-premises software providers, data centers, public datasets, or third-party data providers, a Big Data Lake is the ideal solution. It provides the foundation to store third-party, public, and on-premise datasets at high performance and low price.

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Benefits of Data Lake in Healthcare

Data Lake is a reliable and flexible platform offering endless potentials that can be beneficial for the healthcare industry. Even though Data Lake is a fairly new term, some companies have already made this into reality, such as Google and Facebook.

When it comes to the healthcare industry, data is pulled into Data Lake, where every data element is assigned an exclusive identifier with some metadata tags. A majority of the time, Data Lake is structured on a Hadoop platform that accumulates data from disparate sources.

Here are some of the key benefits of Data Lake.

  1. Scalable: Hadoop platform offers are flexible enough to support huge clusters while sticking to a fixed cost-performance curve even as it scales. So, Data Lake storage scales horizontally for catering to a requirement at a decent cost. Hadoop lets you run codes closer to storage, enabling quicker processing of large data sets.
  2. Plugin Disparate Data Source: Data Lake can ingest multi-structured datasets from different sources. So, it can store almost all types of data, such as sensor data, multimedia, binary, and XML.
  3. Acquire High-Velocity Data: For efficient high-speed data streaming, Data Lake uses tools that can collect data and queue it. This ability helps the healthcare industry acquire a massive amount of data, and integrate it with a massive amount of historical data.
  4. Adds Structure: For making sense of a large amount of data stored in this structure, it is important that you create some structure around data and then pipe it into the analysis applications. Adding a structure on unstructured data can easily be done after it is stored in Data Lake. It offers a unique platform where you can apply a structure on different datasets in the same repository with improved details. Thus, it helps you process the combined data in advanced analytic scenarios.
  5. Stores in Native Format: With Data Lake, there is no need for the data to be pre-modeled. It offers immediate access to real-time data. This improves the delivery of analytical insights and provides unmatched flexibility to ask questions and seek deeper answers.
  6. Use Any SQL: After the data is ingested, cleansed, and stored, in Data Lake’s structured storage, you can use the existing PL-SQL scripts. IMPALA and HAWQ, are tools that offer the flexibility to run large parallel SQL queries while integrating advanced algorithm libraries. Performing the SQL processing inside Data Lake can decrease the time of achieving results. It will consume much fewer resources than performing SQL processing outside of it.

As the healthcare industry is transitioning to a world of electronic records, the amount of data being collected has increased. This data comes in different forms. A scalable Data Lake can accommodate large amounts of data in raw form.

How can Healthcare Facilities Adopt Data Lake Rapidly?

So, how can healthcare facilities start to build out on Data Lake? Well, the first step is to adopt the required Big Data architectures.

Phoenix Children’s Hospital started its journey by using an on-premises Microsoft SQL server for the ETL process (extracting, transforming, and loading packages). It adopted the reporting services application of Microsoft, too. However, while several providers, such as Oracle provide both cloud and on-premises data lake solutions, a majority of the organizations are adopting cloud for Data Lake technology and architecture.

There has been a significant increase in cloud-based options. Many vendors are also offering Big Data infrastructure as part of the cloud offering. They can offer advanced security for enterprise Data Lake that the healthcare industry requires.

Another crucial aspect to start a Data Lake journey is to understand the investment required to adopt it. Data Lake is a large undertaking but for several organizations, it is worth it.

With Data Lake analytics and applications from data scientists and actuaries, data can be leveraged and consumed by payers and providers in many ways using Data Lake. As the amount of data keeps growing in the healthcare field, a cost-effective and flexible solution, such as Data Lake can bring about a great difference. ResolveData can help healthcare providers to implement Data Lake in their system rapidly. With our intelligent data management solutions, you can outperform your competitors by offering improved healthcare systems.

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