Building Data Lakes for
Healthcare providers- 2021
Guide and Roadmap

 

Digital transformation improvements have arrived at a time when healthcare organizations are scrambling to increase the efficiency of their electronic health records (EHR). As a result, healthcare providers must develop new analytical models to identify at-risk patients, prevent adverse events, and practice evidence-based medicine.

What is a Data Lake?

Data lakes are next-generation hybrid data management solutions that can help organizations address big data challenges and enable new levels of real-time analytics. Their highly scalable environment can handle massive amounts of data and accepts data in its native format from a variety of sources thereby providing the framework for machine learning and real-time advanced analytics in a collaborative environment.

Why are Data Lakes crucial for modern healthcare organizations?

To fully customize and integrate the massive volume of data to be used for their specific requirements, healthcare systems must employ data storage facilities. Healthcare and life sciences organizations must use Data Lakes to holistically discover, integrate, cleanse, and analyze unstructured and structured data, as well as to regulate and manage all data assets at the same time. Unstructured, semi-structured, and big data are easily supported by Data Lake, which is designed on an intelligent data analytics platform that also enables doctors to apply advanced analytics tools and methods.

Data Lakes also aid in gaining faster insights from medical device sensors, EMR/EHR applications, social media, doctor’s notes, and claims data using machine learning assisted automation, as well as self-service data preparation, optimal data processing, and rapid intelligent data discovery.

Data Lakes are increasingly appealing among healthcare organizations because it allows professionals to conduct research and development, in-depth analysis of patient outcomes, clinical trials, fraud detection, and waste management. Predictive and prescriptive analytics in Data Lakes can help support healthcare use cases and initiatives. Caregivers may make informed, data-driven decisions at the point of care with unprecedented volumes of patient data numbers at their fingertips, thanks to Data Lakes.

Application of Data Lakes in the healthcare industry

Due to the difficulties in accessing unstructured data, a large amount of significant unstructured data stays unexplored. This makes it difficult to adapt to rapidly changing expectations for preventive care and rapid diagnosis and treatment. With the vast amounts of medical data available in most healthcare enterprises, such as payers, providers, pharmaceutical companies, and third-party vendors, there are unique opportunities to harness various sources of data for analytical insights.

Organizations can use these insights to improve care quality, cut expenses, and avoid resource waste. This is where the Data Lake turns into a powerful asset. Rather than manually pulling data from several sources and combining it, data is placed directly into a single Data Lake capable of passing across numerous systems. Because the data is well organized, any type of external analytic technique may be utilized to better integrate it and extract relevant insights for clinicians and patients.

As the healthcare industry transitions from volume-based to value-based, it’s predictable that the demand for more meaningful data is growing rapidly across the field. Currently, data is stored in traditional EDWs. In addition, the requirement to exploit relevant patterns and trends in data has prompted an increase in healthcare organizations employing a Data Lake method to integrate that data into their operations.

Some of the initial challenges healthcare providers face employing Data Lakes

While the healthcare industry discovers a variety of benefits from big data, developing and managing a data lake might present new challenges. The following are the three most significant challenges in creating data lakes:

• Pulling in multidimensional data from a variety of sources
  Collecting and integrating varied health data from disparate data sources is one of the most pressing and common difficulties. Data swamps can occur when a huge amount of data is ingested from various sources.
• Keeping data quality and consistency
  Issues with data quality and reliability might easily go unnoticed. Another challenge is to keep data quality high and make data lakes dependable.
• Security and access control
  Breach of patient data, health records, and personal identities has increased dramatically in the digital era. Another challenge is securing a data lake using a different access control model, such as role-based or view-based access control.

How are Data Lakes transforming the healthcare industry?

A comprehensive view of patient care:
Using the data lake strategy, healthcare organizations can collect and standardize a wide range of data, including claims, clinical information, health surveys, administrative data, patient registries, and data from EHRs and EMRs. All of this information may be merged to generate a complete view of a patient, which can help with a range of use cases such as better outcomes, cost-cutting measures, and medical decision-making and quality-improvement activities.

• Enhanced query processing:
  The implementation of data governance in the data lake decreases clinical researchers’ effort in determining the amount of data ingested. This results in increased patient information efficiency, improved concurrency, improved query processing, and faster outcomes. This enables researchers to make quick judgments and aids in the administration of care.
• Faster time to insight:
  Previously, clinicians used manual forms to keep track of patients and various diagnostic tests. As a result, data accessibility and visibility were reduced, and a gap in the care delivery network was common. Using efficient data processing, machine learning, assisted automation, self- service data preparation, and intelligent data discovery, a culture can now be developed in which data is appreciated, insights are derived from the acquired data, and relevant care interventions are deployed.
• Efficient healthcare management:
  When healthcare professionals have real-time access to comprehensive clinical and claims data, the delivery of healthcare will be more efficient and timely. Query processing is one of the functions that could benefit the most from Data Lakes. Data Lakes improve query processing because the available insights from previously-stored and analyzed data can speed up the process of responding to inquiries.
• Easy analysis of medical data:
  Clinicians and data science teams can use the Data Lake to thoroughly cross-verify data and include reliable external and internal data sources for mining and analysis. Providers may enhance the accountability of clinical services with such future-health-care insights, which opens up a new arena of data science for spotting patterns, trends, correlations, and discoveries that can have a big influence on integrated patient care.
• In-time patient monitoring:
  By regularly monitoring a patient’s vital signs, healthcare organizations can deliver more integrated care to their patients. The data collected by multiple monitors may be analyzed in real- time, and notifications can be issued to caregivers so they are aware of any changes in a patient’s condition. Physicians can use machine learning algorithms to evaluate real-time events to gain insights that would aid in making lifesaving decisions and allowing for appropriate interventions.
• Evidence-based practice (EBP):
  Clinical experience, patient values, and the best research data are all factored into the decision-making process for patient care in evidence-based practice (EBP). Data Lakes provide a cost-effective platform for EBP. The phrase evidence-based implies that some types of evidence are sufficient to guide general analysis but lack the clinical precision required for a larger approach to multivariate analysis that includes data from other sources outside of the EHR.
  
  EHR/EMR systems reflecting internal-based records published clinical or trial research in form of datasets, publications, and applications, government assets such as surveys, publications, collective libraries, genomic research, patient-reported data, family medical history, exercise or diet regime, data from smart devices are all examples of resource evidence. The body of evidence is extensive, broad, and substantial and therefore Data Lake provide an ideal platform to assimilate such huge volumes of data with such widely varying content.

What is the roadmap for building a healthcare data lake?

Collecting, storing, and analyzing data generated by patients is a significant barrier, and many organizations have data they can’t process through or use effectively. Healthcare data lakes contain a wealth of information that can be used to improve patient care, but they require considerable IT infrastructure design to organize and make the data available. Organizations prepared to turn collected health data into action should develop a roadmap to help them use data to improve workflow and patient care.

Planning for structured and unstructured data

Clinicians, patients, and connected devices collect organized and unstructured data. Structured data is information that is stored in a specific format, such as a file. Because it has clear boundaries and is prepared and stored in a defined format, structured data is easier to evaluate and store. Patient demographic data, diagnosis and procedure codes, medication codes, and other EHR data are usually created in a uniform, structured manner.

Data can be sorted using several technologies to make it more accessible and actionable. Data lakes may be transformed from storage dumps to active tools by utilizing solutions like Hadoop. Hadoop is a distributed data storage and analytics platform that is open-source. Hadoop distributes vast volumes of data over multiple processing nodes before combining the findings. Because the system works with smaller batches of localized data rather than the entire warehouse’s contents, data can be processed faster.

Assessing cloud service models for Data Lakes

Gone are the days when healthcare organizations relied solely on on-premise infrastructure to construct their data architecture. Given the benefits of the cloud, healthcare businesses must develop a solid cloud strategy that takes advantage of the cloud’s advantages while remaining secure and compliant.

Individuals’ medical records and personal health information are protected under the Health Insurance Portability and Accountability Act (HIPAA). To ensure the confidentiality, integrity, and availability of electronically protected health information (ePHI), adequate administrative, physical, and technical measures are required. Amazon Web Services(AWS) utility-based cloud services are being used by an increasing number of healthcare providers, payers, and IT professionals to process, store, and transfer ePHI subject to HIPAA.

Consider object storage for Data Lakes

Object storage is a low-cost approach to store large amounts of data, from petabytes to exabytes, in a single location. Unlike tape, where you must know the serial number, trace the tape, and physically retrieve it, data stored on an object is constantly available. Object storage employs unique IDs that enable data to be saved anywhere within the storage pool. Object storage allows healthcare organizations to expand their data analytics capabilities while also providing a scalable infrastructure.

Organizations must categorize their data and identify what it will be used for, regardless of how the Data Lake is approached. Building a roadmap to employ the correct storage options for the data produced becomes easy after the data use has been identified. Making data usable for analytics begins with organizing it and making it available when needed.

LakeSuperior-
a Data Lake solution by ResolveData

LakeSuperior is a cloud-based healthcare Data Lake from ResolveData. Complex, disparate, structured and unstructured data can be integrated, aggregated, and normalized using this solution. The data can then be augmented to provide real-time analytics and insights to assist in resolving both clinical and business problems.

LakeSuperior which is a centralized repository allows healthcare organizations to store, discover, govern, and share data at any scale and organizations need not have a pre-defined schema to process raw data. Therefore, one can accelerate machine learning processes and make mission- critical decisions fast.

To learn how ResolveData’s LakeSuperior is transforming modern healthcare organizations, talk to our experts today.