Navigating the realm of healthcare analytics is akin to embarking on a journey through the vast seas of data management.

In this digital ocean, two mighty vessels: the Data Lake and the Data Warehouse. They stand as stalwart ships, each offering unique strengths and capabilities to healthcare SaaS providers.

Just as sailors must choose the right vessel to weather the stormy seas, healthcare professionals must carefully select between the flexibility of the Data Lake and the structured efficiency of the Data Warehouse. All in the glory of charting a course toward insightful analytics and secure data management.

Healthcare SaaS Use Cases

Enough for the metaphors. The healthcare SaaS market size is big and keeps getting bigger. The Business Research Company expects growth to $48.78 billion in 2028 at a compound annual growth rate (CAGR) of 18.8%. 

That’s big.

While healthcare SaaS (software as a service) apps benefit healthcare providers, their use extends beyond that key audience. Let’s look at three use cases to highlight this point:

Improving Care in Hospitals

Many healthcare SaaS solutions can help manage patient flow and support clinical decision-making, improving patient care. Some healthcare solutions include electronic medical records, electronic health records, revenue cycle management, telemedicine, patient engagement, and practice management systems.

Boosting Efficiency for Payors, including Insurance Providers

Healthcare payors use SaaS apps for business operations. This includes:

  • policy administration
  • billing
  • claims administration
  • workflow automation
  • document management

Recent efforts to expand data standardization have increased data interoperability, enabling payors to make billing more accurate. This also means more systems are hosting on cloud services using expensive cloud computing.

Supporting Patients

Secure, user-friendly technology can deliver convenience to patients. These include logging into a portal to access personal files and get copies of records. Portals help patients access test results and easily send messages to healthcare providers without long wait times on hold.

Several technology trends are currently impacting healthcare including telemedicine. Virtual healthcare helps enhance health equity by delivering prompt treatment and clinical evaluations, particularly to the most marginalized communities. Over 30% of healthcare providers utilize patient monitoring and virtual care services to enhance patient care and engagement.

Healthcare technology experts think that AI will soon analyze a person’s entire genetic code. This will provide helpful information for diagnosing and treating illnesses. There are no rules for AI yet, so companies leading the way must be careful with sensitive data.

The Power of Custom Healthcare Data Collection & Analytics

All these use cases rely on data.

Researchers will develop and collect data in forms that no one has even imagined yet. Healthcare SaaS applications must be flexible when it comes to processing data.

This flexibility allows users to collect and analyze various types of data. The rapid pace of innovation in the healthcare industry will push today’s boundaries.

Lack of custom data analytics will impede research and development. Users must be able to analyze a wide variety of data such as patient surveys, images, and FHIR. 

A recent HIMSS (Healthcare Information and Management Systems Society) blog states, “There is now tremendous potential for analytics approaches like machine learning and artificial intelligence to help make sense of multifaceted health data.

“Health informatics, a multidisciplinary field that combines healthcare, information technology, and business, has experienced significant transformation with the integration of data analytics.This confluence has led to unprecedented opportunities for improving patient outcomes, optimizing clinical operations, and advancing medical research.”

The blog explains how predictive analytics can assist doctors. It does this by analyzing real-time data from ICU equipment. This helps doctors make informed decisions such as identifying early signs of patient deterioration.

Cybersecurity Risks Facing Healthcare SaaS

Healthcare providers regularly make life-saving decisions. When it comes to securing data, the stakes are also high.

The HIPAA (Health Insurance Portability and Accountability Act) privacy rule protects all “individually identifiable health information,” described as, “protected health information (PHI).” The federal law includes criminal penalties for a person who knowingly obtains or discloses PHI. This includes a fine of up to $50,000 and up to one year imprisonment.

Additionally, the healthcare industry is particularly susceptible to cybersecurity threats. While advantageous, the growing integrations between systems further increase risk. Threat actors can land and expand, infiltrating additional systems after breaching the first.

According to IBM, the global average data breach cost in 2023 was USD 4.45 million, a 15% increase over 3 years.

The average cost of a healthcare data breach was the highest among all industries at $10.93 million.

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Data Stores for Optimizing Healthcare Analytics

Advanced analytics transform data into insights, helping businesses make better decisions to achieve their numerous goals. SaaS providers can make their apps better by adding self-service analytics. This helps users see insights right in the apps they use.

However, to get accurate, complete insights, you need full access to all relevant data, including custom-generated data. All while maintaining security.

Healthcare SaaS apps rely on critical data stores for analytics and security.

Traditional data warehouses tend to be more popular than data lakes for healthcare SaaS companies. However, there is typically little cost savings with data warehouses given the expensive compute costs.

Here we’ll dive in (pun intended) and explore the pros and cons of both options.

What is a Data Lake?

A data lake is a central storage for various types of data. Typically, data lakes store data in its original form. Unlike a traditional data warehouse, which stores preprocessed data, a data lake is a central repository for raw data. A data lake can consume, retain, and manipulate structured, semi-structured, and unstructured data.

Advantages of a Data Lake

A data lake stores raw data from operational systems, keeping large amounts of data in its original format. This requires less processing and transformation.

According to AWS, a data lake is a good fit for many types of big data analytics on public clouds. This is especially true for machine learning / artificial intelligence (AI) for data scientists and analysts.

The advantages of a data lake for healthcare include:

1) Scalability

AWS writes that a data lake, “allows you to store any data at any scale.”

2) Flexibility

You’re free from the obstacle of requiring data to be in a specific format. A data lake can hold various types of data from different sources. These sources include databases, files, social media, logs, XML, multimedia, sensor data, and even chat.

This flexibility is particularly important considering the complexity of medical data. As HIMSS stated, “Developing data analysis skills and deploying analytics optimized for the intricate nature of medical data will be key priorities for health informatics professionals going forward.” Healthcare SaaS apps must consider this complexity and empower users to analyze custom data.

3) Cost-effectiveness

For embedded analytics use cases, data lakes are generally more cost-effective.

Data lakes require less effort to build, have very low latency, and can support data analysis. By contrast, total costs for data warehouses like Snowflake often grow out of control because of concurrent querying. The compute demands on a healthcare SaaS platform are different than an internal analytics reporting tool.

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What is a Data Warehouse?

A data warehouse is a data store of primarily transformed, curated, and modeled data from upstream systems. Data warehouses use a structured data format. They also require the work of a data engineer to transform the data lake into a data warehouse. 

Advantages of a Data Warehouse

1) Optimized for structured data

Data warehouses use a structured, or relational, data format for data storage. Data warehousing requires you to design your schema before saving the data, so you can only load structured data.

2) Better for single tenant and internal use cases

Structured data in a data warehouse helps users quickly generate reports using internal business data. In this use case, user-specific data security is much easier to implement.

Challenges of a Data Warehouse

1) Multi-tenancy requires engineering effort

Most data warehouses store large volumes of data, but generally not for multi-tenant analytics. If you use a data warehouse to power your multi-tenant analytics, the proper approach is vital. Snowflake and Redshift are useful for organizing and storing data. However, they can be challenging when it comes to analyzing data from multiple tenants.

Data warehouses for multi-tenant analytics require significant modeling and engineering up front, resulting in substantially higher costs. Not to mention the complete lack of a semantic layer to implement user permissions.

Check out our 3 reasons why companies struggle with Snowflake for multi-tenant analytics.

2) Lack of multi-tenant security logic

Securing data in multi-tenant SaaS apps can be particularly difficult. This is particularly the case when connecting charts directly to the data warehouse.

Data management and governance require in-house built software.

This includes metatable tables, user access controls, and a semantic layer that manages data security and permissions.

Connecting to your data warehouse requires building another semantic layer. This component will translate your front-end web application multi-tenant logic back into the data warehouse logic. Unfortunately, this process can be particularly cumbersome.

3) Expensive computing costs

Leveraging a data warehouse for your multi-tenant analytics can lead to substantial recurring expenses. The computational expense of per-query fees significantly increases on a multi-tenant platform.

4) Scalability

With a SaaS app, you can’t control when users will choose to conduct their analyses. You must make analytics available nearly instantly to everyone. SaaS providers need to ensureOur platform offers an API for receiving live data in real-time. It is compatible with JSON and semi-structured information, including FHIR data.

that a data warehouse can seamlessly scale up with the growth in the number of tenants. Constantly adding to the concurrency burden presents a challenge for balancing cost and performance.

Why is a Data Lake Better for Embedded Analytics in a Healthcare SaaS Application?

There are a few ways in which a data lake is the best choice for embedded analytics in a multi-tenant SaaS app.

1) Simple scalability healthcare SaaS applications

Consolidating storage, compute, and administration overhead into shared infrastructure significantly reduces costs for both providers and tenant subscribers as user bases grow. Data lakes are also advantageous for tenant data isolation. With tenants accessing the same instance, strict access controls prevent visibility into other tenants’ data, ensuring compliance with HIPAA.

Our goal is to assist software product teams in improving their analytics capabilities quickly and cost-effectively. We provide a range of data integration solutions to meet different requirements. Qrvey enables real-time connections to existing databases and the intake of data into its own data repository.

This cloud-native approach enhances performance and reduces costs for intricate analytics queries. Moreover, the platform automatically standardizes data upon intake, making it suitable for multi-user analysis and reporting.

Qrvey is also compatible with popular databases and data storage systems such as Redshift, Snowflake, MongoDB, Postgres, and more. It also supports the intake of data from cloud storage services like AWS S3 buckets. Often unstructured data types such as documents, text, and images use S3 buckets for storage.

2) Can handle different data formats

Data types are increasing. Data lakes open up analytics options. When semi-structured data is in play, databases like MongoDB become easier to store in a data lake. With unstructured data options, you can even offer text analytics for customer service use cases.

3) Scalability for multiple tenants

Achieving multi-tenancy with a data warehouse requires significant development effort to build additional infrastructure. Engineering teams must build components that serve as a bridge between the user-facing application and the database.

4) Data isolation and security

As HIMSS noted, data privacy and security are among the challenges facing data analytics in health informatics. Data warehouses struggle with data privacy and security, particularly with row-level security in multi-tenant environments. 

5) Cost advantages

Data lakes are easier to scale. Additionally, they often require less compute. This is a major reason we power our multi-tenant data lake with Elasticsearch

Challenges of Implementing a Data Lake

Creating a data lake can also be challenging. Typically, it requires specific data engineering skills that software engineers may not have.

Qrvey removes the need for data engineers as we focus on software engineers.

To analyze data from multiple sources, SaaS providers must build independent data pipelines to integrate with existing systems. But we have the solution here too.

Qrvey eliminates the need for separate ETL processes for data collection. SaaS companies using Qrvey don’t need the assistance of data engineers to create self-service analytics functions.

Without Qrvey, teams end up building a separate data pipeline and ETL process for each source. That’s a big waste of time.

Qrvey addresses this challenge with a turnkey data management layer with a unified data pipeline that offers:

  • A single API to ingest any data type
  • Pre-built data connectors to common databases and data warehouses
  • A transformation rules engine
  • A data lake optimized for scale and security requirements that include multi-tenancy when required

Read more about best practices for using a data lake multi-tenant analytics.

The Need for Data Literacy in Healthcare

A recent HIMSS blog states, “The rate of change for the increasing awareness of data dependency for clinical decision-making has increased exponentially over the last decade.”

The blog discusses why nurses need to understand data and the “DIKW model.” This model explains information processing. Nurses should be skilled in understanding this model. 

“In nursing, this model is often applied to describe the process of transforming raw data into meaningful information and, ultimately into wise decision-making.” 

To reach “wisdom,” you need to turn raw data into useful information. This involves using knowledge and experience to solve problems effectively. The data lake is the ideal foundation of the pyramid DIKW model.

Why a Hosted Solution is Ideal for Securing Healthcare SaaS Data

Maintaining security within your own cloud environment is challenging, but if your data leaves your cloud, complexity multiplies. BI vendors create an unnecessary security risk by requiring your data in their cloud for analytics.

By contrast, with a self-hosted solution like Qrvey, your data never leaves your cloud environment. Your analytics can run entirely inside your environment, inheriting your security policies already in place. This is optimal for SaaS applications. It makes your solution not only secure but easier and faster to install, develop, test, and deploy.

AppOmni, provider of SaaS Security Posture Management (SSPM), outlined the 3 most common ways threat actors steal PHI stored in healthcare systems:

  1. Improper identity privileges and access permissions
  2. Poor configuration in 3rd party apps
  3. IoT devices

Healthcare SaaS app developers should not worry about their users’ sending data to a third-party cloud.

Securely Ingesting & Analyzing FHIR Data

With a solution like Qrvey’s unified data lake approach to embedded analytics, analyzing FHIR data is within reach. We support real-time analytics with a push API into our data pipeline. This supports JSON and semi-structured data, AKA FHIR data.

FHIR analytics data is difficult for both humans and machines to understand and integrate into applications and analysis. This interoperability opportunity enables greater insights from analytics, which can drive improved patient outcomes.

The FHIR format enables seamless data exchange between different healthcare systems. It drastically reduces the need for complex data conversions and integrations. FHIR also empowers patients to access their medical information easily and securely.

Additionally, the improved data portability enables patients to transfer their medical records between providers easily.

Finally, eliminating the need for custom data integrations reduces operational costs. 

For SaaS providers, this presents an opportunity. Offering analytics across FHIR patient data is still not a common product feature. Saas leaders would be keen to learn more about FHIR analytics to differentiate their products.

Embedded Analytics Platform for Multi-Tenant SaaS Apps

At Qrvey, we understand that analytics starts with data. That’s why we focused on the use of a data lake.

The Qrvey embedded analytics platform is specifically for multi-tenant analytics software within SaaS applications. Our goal is to enable development teams to offer better analytics while building less software in-house.

It all starts with our unified data pipeline for any type of data. Qrvey allows for live connections to existing databases as well as ingesting data into its built-in data lake. This cloud data lake approach optimizes performance and cost-efficiency for complex analytics queries. Additionally, the system automatically normalizes data during ingestion so it’s ready for multi-tenant analysis and reporting. 

Qrvey supports connections to common databases and data warehouses like Redshift, Snowflake, MongoDB, Postgres, and more. You can also ingest data from cloud storage like S3 buckets, and unstructured data like documents, text, and images.

See a demo today.

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