Building Secure Data Pipelines for the Healthcare Industry—Challenges and Benefits

Anwesha Banerjee • Last Modified: June 14th, 2023

ETL in Healthcare FI

The healthcare industry has seen an exponential growth in the use of data management and integration tools in recent years to leverage the data at their disposal. Unlocking the potential of “Big Data” is imperative in enhancing patient care quality, streamlining operations, and allocating resources optimally. One of the best ways of putting this huge volume of data to proper use is by building secure data pipelines that facilitate ETL in healthcare.

Data pipelines extract data from various sources like Electronic Health Records (EHRs), claims databases, and devices that are a part of the Internet of Things (IoT) ecosystem, load it to the desired destination, and process the same to make informed decisions. 

Now, without further ado, let’s dive right into the blog to understand the need for data pipelines in orchestrating data movement and analysis.

Why Is Data Processing Important in the Healthcare Industry?

ETL in Healthcare: Healthcare Data Sources
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Integrating medical data into one single source of truth is crucial for understanding a patient’s health. This involves extracting structured, unstructured and semi structured data from various disparate sources and breaking data silos. Using advanced solutions, like ETL and ELT tools, to analyze this data can provide a holistic view of important information, and process medical data in real-time. 

On that note, let’s take a look at more reasons why data processing in the healthcare industry is crucial:

  • To enhance operational efficiency- Extracting and processing data from multiple sources helps the healthcare sector to streamline operations and cut costs. Analyzing data on patient demographics, medical history, equipment utilization, etc., help optimize staffing allocation and other resources, thus, minimizing wastage.
  • To improve the quality of patient care- From pediatrics to geriatrics, processing “Big Data” helps healthcare professionals tailor personalized healthcare plans for patients and enhance the quality of care. Relevant and digitized information about a patient can provide insight into the pattern of their hospital visits and treatment, and enable healthcare professionals to come up with corrective measures. 
  • To seamlessly access key information- Accessing key information about a patient has become easier, with the healthcare industry gravitating more and more towards cloud-based storage services and adopting advanced data integration and processing tools.
  • To reduce human error- Automated data processing in the healthcare industry can be used to analyze a patient’s reports and assist doctors in prescribing medication more accurately as opposed to manual data processing, which leaves a broader margin of human error. 
  • To aid in research on chronic diseases- One of the more important roles of data processing in the healthcare industry is that “Big Data” can be used for intensive research on chronic diseases by analyzing data on symptoms, treatment, medication, transmission methods, cause, prevention, etc.

Role of ETL in Healthcare

The ETL process includes three stages— Extract, Transform, and Load. In the context of the healthcare industry, data is first extracted from multiple sources like EMRs, lab reports, patient billing information, public health records, etc., before being enriched and transformed into a more usable format. Transformation includes data cleaning, standardization, normalization, and removing redundancies to name a few. Finally, this transformed data is loaded to the destination, like a data warehouse, for data visualization that helps in drawing valuable insights. 

There are several benefits of ETL in healthcare. For one, it helps in understanding a patient’s health better and improving the course of treatment. It also allows healthcare providers to make informed operational decisions. An excerpt from this research paper by Toan et al., could throw more light on the process of ETL:

The ETL (Extraction-Transformation-Load) process is a series of operations that allows source data to be syntactically and semantically harmonized to the structure and terminology of the target CDM. The ETL process to support data harmonization typically comprises two sequential phases, each of which is performed by skilled personnel with different expertise. In phase 1, subject matter experts in the source data (e.g. EHR, claims data) identify the appropriate data elements required to populate the target database for extraction and specify the mappings between the source data and target data elements. This step requires knowledge about the structure and semantics of both the source and target data, such as expertise in the local EHR implementation and use, and local terminologies. In phase 2, database programmers implement methods of data transformation and the schema mappings for loading data into the harmonized schema.

Data Pipelines in the Healthcare Industry

Data pipelines facilitate the ETL process in healthcare and help to create reports in a timely and efficient manner. This further allows healthcare providers to enhance the quality of patient care among other things. However, building scalable and secure medical data pipelines isn’t always an easy task. There are several challenges that data engineers might face while setting up a superior quality data pipeline for the healthcare industry.

In this section, we’re going to look at the various facets of building a healthcare data pipeline, including its benefits and challenges. 

Uses of Data Pipelines and Analytics in the Healthcare Industry

The uses of healthcare data pipelines and analytics are many. From improving the turnaround time for delivering reports to helping in resource optimization, there are a myriad benefits of medical data pipelines. 

Let’s explore the uses of data pipelines in the healthcare industry in more detail:

  • Improving the TAT (turn around time) for delivering laboratory reports- Data pipelines can be used to extract data from various healthcare sources, and process and analyze the same to make crucial decisions in real-time. For instance, diagnostic labs can deliver reports with remarkable efficiency, and help healthcare professionals make quick decisions regarding a patient’s condition. Furthermore, data pipelines and analytics can help healthcare providers identify patients with high risk of developing specific health issues and intervene before the conditions exacerbate. This allows healthcare providers to develop personalized treatment plans and enhance patient care.
  • Aiding advanced clinical research- The growth of any industry boils down to research and planning. And healthcare data pipelines can aid researchers by enabling data collection and integration, data standardization, cohort identification, data processing, analysis, follow-ups and collaboration. This further provides a robust infrastructure for managing research data, and advancement of evidence-based medicines and treatment plans. 
  • Streamlining operations– Data pipelines can be used to automate many of the routine tasks that are involved in healthcare data. They can help professionals identify the areas where efficiencies can be enhanced, such as freeing up staff time and reducing wait time for clinical reports.
  • Optimizing monetary resources- One of the more crucial uses of data pipelines in the healthcare sector is that of optimal resource allocation. Data pipelines can be used to track revenues, reimbursements, expenses, etc., and also identify fraudulent transactions such as overbilling or unnecessary procedures. 

Let’s look at a real-world example of data pipelines helping the healthcare industry leverage data efficiently and improve patient care.

Redcliffe Labs, a leading healthcare diagnostic lab in India, has revolutionized its operations by adopting a modern data stack powered by Hevo Data. With Hevo’s secure and user-friendly cloud-based data integration platform, Redcliffe has been able to gain near-real-time access to data from multiple sources, thus, enabling them to make informed decisions and draw remarkably quick insights. 

Using Hevo’s automated no-code solution, Redcliffe was able to build data pipelines in under 15 minutes and orchestrate the movement of data seamlessly.

We performed a time comparison for building pipelines in-house versus with Hevo; it took 8 hours in-house but only 15 minutes with Hevo.

– Prabhat Kumar, CTO, Redcliffe

They were also able to improve their TAT significantly, delivering reports in just 12 hours after receiving the booking, with a 96% success rate. 

Best Data Pipelines for the Healthcare Industry

Simply put, ETL or ELT tools, like data pipelines, are solutions that facilitate the process of data ingestion, movement and transformation. There are several such tools available on the market that are used in the healthcare industry. Let’s glance through a few of those in this section:

Hevo Data

ETL in Healthcare: Hevo Logo

Hevo is an automated, no-code data pipeline, that allows you to extract data from 150+ sources, and load it to your preferred destination in near real-time, without writing a single line of code. It not only provides a fully managed platform that enriches your data and preserves its integrity, but also provides a host of pre-load transformation options. Hevo’s fault-tolerant architecture ensures that there is no data loss, even in those rare instances when things go south. 


ETL in Healthcare: Talend Logo
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Talend is an open-source tool that has a wide range of in-built connectors to extract data from multiple healthcare data sources. It offers a wide range of connectors to extract data from various healthcare data sources and load it to your desired destination.

Informatica PowerCenter

ETL in Healthcare: Informatica Logo
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Informatica PowerCenter is an enterprise-level data integration tool that supports ETL and ELT workflows. It supports real-time data processing which helps healthcare providers in making prompt critical decisions regarding treatment and care. Also, its visual interface helps transform raw data into valuable insights.

IBM InfoSphere DataStage

ETL in Healthcare: IBM InfoSphere Logo
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IBM InfoSphere DataStage is another powerful tool that offers users a graphical framework to design data pipelines. It provides a wide range of data transformation functions and can be used for both batch and real-time data processing.

Challenges Faced by the Healthcare Industry to Build Secure and Scalable Data Pipelines

Data pipelines in the healthcare industry can help in improving the quality of patient care, treatment, managing operations, and more. Having said that, with digitization of medical data becoming commonplace and the pressing need for sharing said data across systems, it becomes imperative to address the challenges of building secure and scalable data pipelines. 

Designing such healthcare ETL tools comes with impediments such as ensuring data privacy, data integration and credibility, ensuring real-time data availability, and minimizing the downtime of data pipelines. Let’s take a closer look at these challenges.

Data Privacy and Compliance in Healthcare

One of the major concerns that most healthcare organizations have while opting for an automated data pipeline is patient data privacy and the pipeline’s compliance with important healthcare regulations. Hackers often target healthcare data as a multitude of crimes can be committed using a patient’s personal information, like Social Security Number, date of birth, etc. Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), and Fast Healthcare Interoperability Resources (FHIR) are a few regulations that the healthcare industry must be compliant with. Building data pipelines that comply with these regulations can be challenging as these healthcare standards and privacy laws are quite complex and the healthcare industry is in a constant state of flux. 

Data Integration and Credibility

Medical data extracted from sources like EHRs, medical devices and patient-generated data can have different formats, standards and terminologies. Unifying data from all these sources to provide a single source of truth, without compromising on its credibility can be incredibly challenging. More often than not, replicating and transforming this data could lead to data inaccuracies, redundancies and inconsistencies, resulting in serious repercussions for a patient. 

Real-Time Data Availability

In order to make quick decisions about a patient’s health, healthcare providers need data to be processed in real-time. But with the healthcare industry collecting and moving loads of data every day, building data pipelines that can process large volumes of data could be cumbersome.

Minimizing Downtime of Data Pipelines

Downtime and system failures of healthcare data pipelines can severely impact a patient’s care and treatment, and lead to significant security breaches and operational inefficiencies. For instance, if a data pipeline fails to deliver notifications and alerts to healthcare providers about a patient’s condition on time, it could result in delayed interventions and even adverse effects. 

The Solution?

One solution to this could be to build a data pipeline in-house. However, a dedicated engineering team to build such a solution might not be the optimum use of resources as it involves spending a significant amount of money. 

Opting for an automated data pipeline platform like Hevo Data, that is compliant with healthcare data security and privacy regulations, like HIPAA, and processes data in real-time, can alleviate much of the stress. 

What Does the Right Healthcare Data Pipeline Look Like?

We’ve examined the challenges of building data pipelines for the healthcare industry in great depth thus far. And that is why, it now begs the question–what does the right healthcare data pipeline look like? 

In the simplest words, a good medical data pipeline must allow healthcare providers access to fresh and updated information for timely and accurate diagnosis of a patient’s symptoms. But that’s not all! Let’s take a look at the measures that must be taken while setting up a high-quality healthcare data pipeline:

  • A secure and scalable healthcare data pipeline must be able to ingest data from various disparate sources.
  • A medical data pipeline must be compliant with all the healthcare privacy standards and regulations, like HIPAA, GDPR, and FHIR. Access to data should be controlled and monitored to prevent security breaches.
  • A data governance framework should be put in place to ensure that the data is accurate, credible and consistent. 
  • A healthcare data pipeline should be subjected to testing and validation to ensure that it is functioning optimally and without any significant downtime.
  • Finally, a healthcare data pipeline must be idempotent. An idempotent data pipeline ensures that you get the same results when it extracts data from a source and loads it to the destination multiple times. In other words, idempotence sees to it that there is no data duplication if the system fails and there is a need of backfilling.

Parting Thoughts

The healthcare landscape is continually evolving. Healthcare providers need to leverage the huge volumes of data optimally to make crucial decisions. And a secure and high-quality data pipeline can automate the process of data ingestion, processing and delivery, allowing healthcare providers the opportunity to enhance the quality of patient care and optimize their operations and resources. We’ve examined the role of ETL in the healthcare industry, and also talked about how building a data pipeline that fits the bill could be challenging. However, we’ve also seen how these challenges can be addressed, especially with the use of automated data pipelines, like Hevo Data, that offer seamless ELT solutions for all your data management and integration needs. 

With Hevo, you can integrate data from a host of sources into a destination of your choice. ELT your data without any worries in just a few minutes.

Visit our Website to Explore Hevo

It has pre-built integrations with 150+ sources. You can connect your SaaS platforms, databases, etc. to any data warehouse of your choice, without writing any code or worrying about maintenance. If you are interested, you can try Hevo by signing up for the 14-day free trial, and check out our transparent pricing to make an informed decision.

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