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Advanced Healthcare Data Analytics using Data Lake on Cloud – ResolveDatas
Advanced Healthcare Data Analytics using Data Lake on Cloud

Advanced Healthcare Data Analytics using Data Lake on Cloud

Advanced Healthcare Data Analytics using Data Lake
on Cloud

What are Data Lakes?

Data lakes are next-generation data management solutions that can assist organizations in addressing big data concerns and enabling new levels of real-time advanced analytics.Their highly scalable environment can handle massive amounts of data and accept data in its native format from a variety of sources. They offer the basis for machine learning and real-time advanced analytics in a collaborative environment as a complement to your data warehouse.

Data lakes provide information in its raw form, as well as a specific mechanism for accessing data that automatically applies the schema as it is read. The data lake’s users are typically experienced analysts who are familiar with data wrangling techniques that use schema to read or understand the content from unstructured forms.

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Deploying Data Lakes on Cloud

When it comes to implementing a successful healthcare data lake, or even the more recent notion of a data lakehouse, an analytical approach that combines the advantages of cloud data lake adoption is critical. We want a wide range of data consumers with a wide range of workloads to use the data lake instead of using a non-enterprise-class desktop or server-based technologies. Users will benefit from a modern cloud data platform that includes data cataloging, data lineage, data governance, data quality, and other industrial-strength tools and capabilities of a good data platform when they access data lakes.

Because the cloud offers performance, scalability, stability, and availability, as well as a broad range of analytic engines and significant economies of scale, Data Lakes are an ideal workload for deployment on the cloud. Higher security, faster deployment time, better availability, frequent feature and functionality upgrades, more elasticity, better geographic coverage and pricing tied to actual consumption are the major reasons why clients consider the cloud as a benefit for Data Lakes. Moving a data lake to the cloud has a lot of advantages, including cost savings and increased agility. However, in order to reap these benefits, you must first comprehend how to establish data lake architecture on the cloud, which differs differently from standard on-premises architecture. Moving to a cloud-based data lake or a multi-cloud environment is also not possible in one fell swoop. It is a journey is covered over a period of time.

Data Lakes are beneficial to healthcare since they store all of the data in a single location and only map it when needed. Because all of the use cases for the data are unknown when it is put in the data lake, it is hard to know how to arrange the data. To prevent multi-year projects that normally faildata lake methodology of bringing data in and then adding structure as use cases develop is best suited for healthcare.

Clinical data and claims data are the two main types of data used in the healthcare industry. Payers contribute claims data, which is highly standardized and structured contains patients receiving care, their demographics, and the care setting they are in. Because the data is complete and used for reimbursements, it contains all the necessary information to run advanced analytics to improve decision-making.

Clinical data is the second type of data in healthcare that includes patients’ vital and important information, such as diagnoses and medical history, and that is recorded in EHRs and utilized to analyze in real-time.

To obtain relevant insights and perform advanced analytics, the data must first be summarized and examined in an abstract manner. Data is ingested into the data lake, where it is given a unique identifier and a set of metadata tags for each data element. Data lakes are frequently built on the Hadoop Distributed File System, which can store data from a variety of sources, both organized and unstructured, and is a cost-effective repository. This data is then collected and integrated using extract, load, and transform (ETL) methods, which can subsequently be analyzed using Spark, a basic analytical framework.

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Challenges in Building Healthcare Data Lake on Cloud

We’ve all heard the caution that poorly deployed cloud data lakes can turn into data swamps, where untrustworthy volumes of data provide little to no value or insights to the organization. While the majority of healthcare organizations, providers, and payers have recognized the need to provide front-line caregivers with real-time, self-service clinical, patient experience, and operational information, they haven’t always been effective.

We often hear from large and small healthcare organizations that have successfully ingested petabytes of data into their lakes, only to struggle with business adoption because reliable, relevant data is difficult to access, interpret, and of the questionable source.

Need for Data Governance to prevent Data Swamps. Data governance is the solution for preventing a data swamp and delivering a managed data lake. Advanced analytics, and how to systematically and frequently draw business and clinical insights from data, is one of their areas of interest. Data that is dependable and trustworthy is, as we all know, a prerequisite for analytics. ResolveData has established a complete view of how to manage a data lake, assisting healthcare businesses in delivering the essential value.

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Opportunities for Data Lakes on Cloud in Healthcare

The amount of unstructured data in the healthcare industry is enormous, and with this data rising at a rate of approximately fifty percent year on year, we need to transform healthcare into a data-driven industry with improved scalability, performance, and analytic capability.

We have barely scratched the surface of the data lake applications, and in the future, when medical imaging is a necessary part of diagnostics, the problem of unstructured data will be readily managed thanks to the widespread usage of data lakes. In the future of healthcare, data lakes will be a key component, with adoption increasing across the board.

Data lakes can be used in a variety of ways in healthcare, from adhering to the shift to value-based care and giving transparency to rapidly increasing and delivering a holistic view of care services. Here are a few possibilities:

  • By integrating and exchanging data, as well as analyzing clinical and claims data, it empowers a network of PCPs, patients, and specialists to give patients the right care at the right time.
  • The raw data in data lakes is never lost; instead, it is preserved in its original format for subsequent analysis and processing. Because data governorship takes effect on the way out, the user does not need to know how data was ingested beforehand. This improves efficiency while also allowing for more concurrency and faster query processing. It can store enormous volumes of segregated data and can grow and shrink as needed.

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Cloud Analytics in Healthcare

Cloud analytics is a service that analyses the dynamic evolution of models and illustrates cloud service techniques and methodologies. It assists in tracking the value generation of cloud services like ‘IaaS’ and ‘SaaS,’ as well as how cloud providers contribute to the channels.It is a methodology for obtaining a perception of a dataset and creating formal statistical data that can be categorized and modified according to your needs that have been thoroughly and meticulously created. Consider it a business intelligence process that takes place in conjunction with the cloud service you’re using.

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Practical applications of Cloud Analytics in Healthcare

We’ve noticed that while our clients are comfortable obtaining prospective data, they lack the ability to capitalize on relevant benefits for developing clinical practice culture. To address this shortcoming, the healthcare industry needs to use artificial intelligence, machine learning, perspective, and predictive analysis to optimize more data-driven outcomes.

Cloud analytics through electronic records are commonly used to maintain clinical logistics at specialized hospitals and nursing homes. The analytical approach used in these hospitals and nursing homes will help to promote the implementation of real-world evidence-based medicines and treatments, which is the sector’s ultimate goal.

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What is driving Advanced Analytics on Cloud in the Healthcare industry?

The healthcare sector has entered a new data-driven, digital era due tothe widespread adoption of electronic health records (EHR), digitization, and increased investment for innovative delivery methods.

The integration of new data from other connected devices, the Internet of Things, and wearables are opening up new possibilities for displaying improved insights. Patients are now demanding improved treatment and service. Only by personalizing the experience and getting to know patients better, i.e. evaluating patient data, can this be accomplished.

Security is another area where cloud analytics is being employed. Data is being used by organizations to better understand system vulnerabilities and protect their data. Analytics helps to design a better roadmap to reach the patient and find fresh ways to give treatment as patients become more digital and adopt the technology.

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Implementing Advanced Analytics on Cloud – checklist

  • What method will you use to access the cloud? Do you have an on-premise infrastructure or do you rely on cloud providers such as AWS, Azure, GCP, and others? It is different to set up analytics for different systems. While customization is needed on-premise, cloud computing service providers have included a lot of integration as a standard, and automation can be achieved over time to eliminate manual interventions.
  • What specific data requirements do you have, and how will you get it? What data do you need to collect and how are you going to get it? Because the information in the healthcare industry is so sensitive, there are compliances (HIPAA, FHIR, etc.) that must be examined and implemented into the system.
  • What issues would you like to see addressed? The most important question of them all. What kind of business intelligence do you need? Is it necessary for you to have real-time data, or is it not? What knowledge do you want to gain, who are your stakeholders, and how can you make information more accessible to them?
  • How will you handle security and access? Another really important question. What are your cybersecurity plans? There is a different playbook for on-premise than for off-premise. Who are the people who will have access to this information? What can you do to prevent data theft and cyber-attacks?

Cloud analytics has already begun to provide healthcare organizations with measurable ROIs and competitive advantage. There is a huge range of things that can be accomplished with it. However, in order to achieve objectives, the appropriate questions must be asked.As a leader, you must create the ideal environment for development to occur, as well as surround yourself with the right and skilled team and vendors. Organizations that can define their required use cases, in our experience, are almost certain to succeed.

See how we can assist you in making the most of healthcare advanced technology solutions.

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