Overcoming Healthcare’s Data Integration Challenges
How many people visit the doctor a year? While healthy individuals typically go once a year, patients with debilitating diseases might visit various specialists multiple times per day. This fact coupled with the world population hovering around 7.7 billion people leads to numerous healthcare records created each year.
The wealth of healthcare data waiting to be analyzed is enormous. Each visit, doctors and nurses capture a patient’s history, along with any allergies, past medical procedures, and medications. Not only that, new opportunities to collect healthcare data continues to grow. Patients are purchasing and interacting with wearables and other medical devices and have started to take advantage of telehealth services — healthcare appointments and information provided through telecommunication devices.
Analyzing this data can identify effective preventative strategies, eliminate inadequate care, and expose fraud.
Leveraging healthcare data is lucrative, so why are companies slow to start? Healthcare data is unstructured and often difficult to extract. This poses a real, pressing need for data integration in the healthcare space.
What’s at stake: Benefits of data integration for healthcare
Data can come from a multitude of sources, presenting a huge challenge for healthcare providers. Here is just one example:
A patient goes to three clinics in their area to get opinions on a medical issue. Each of these clinics has a different EMR system that stores data collected at each visit. After the last visit, the patient picks up a prescription at the drugstore — presenting a fourth data point. Any additional health data, such as heart rate records collected from a wearable device or virtual doctor visits, will create new data points housed in separate databases.
Connecting data points from multiple databases is cumbersome, yet consolidating the data would be extremely beneficial — even in this simple case. This is where data integration comes into play.
Data integration refers to the act of joining data from numerous sources into a unified set. During the data integration process, data is cleansed and transformed to ensure accurate analysis. Through data integration, disparate data sources are reviewed together to provide invaluable business intelligence.
What are the top data integration challenges for healthcare companies?
There is an obvious need to integrate data in healthcare, but there are considerable obstacles facing the industry today.
As described in the straightforward example above, healthcare data can come from a myriad of sources. One source is medical devices and wearables that upload data 24/7. While this trend is helping people become more engaged with their health, it produces an excessive amount of data and simultaneously increases compliance and privacy concerns.
Besides the processing power required to integrate all health-related data sources, the data must also be presented in a way that is accessible to doctors, nurses, medical researchers, and patients themselves.
These two major obstacles, in addition to the following, present significant challenges in data integration and, ultimately, providing healthcare providers a comprehensive picture of their patients.
- There is no way to standardize data formats
- Medical wearables create streaming data
- Data privacy and compliance regulations
- Healthcare needs more data integration processing power
- End users are not data scientists
1. There is no way to standardize data formats
Healthcare data is fragmented, coming from multiple sources in various formats. Healthcare technology must support images, texts, videos, as well as traditional EMR records.
Moreover, healthcare is known for its dated, expensive, on-premises data warehouses. These systems cannot “talk” to each other, nor to new data sources. Without a unified solution, healthcare IT departments are struggling to automate processes, keep up with swift changes in the marketplace, and offer consolidated data to decision makers.
One solution to this burgeoning problem is to construct a cloud-based data lake. Building a data warehouse on the cloud gives companies the ability to track a patient’s journey in real-time. Conventional ETL processes take far less time on the cloud, and storage is no longer problematic. Without these hurdles, healthcare organizations can cleanse, standardize, and segment data quickly and easily.
Getting all healthcare outlets (including hospitals, clinics, doctor’s offices, pharmacies, research labs, and telehealth providers) to adopt the same methodology might be unattainable. However, establishing cloud-based data lakes can significantly improve data quality, while enabling healthcare organizations to make fast, accurate decisions for their patients.
2. Medical wearables create streaming data
Medical wearables have revolutionized healthcare. Devices like pacemakers, insulin pumps, and fitness trackers are commonplace. This data is extremely useful to healthcare professionals, and make a difference in life or death situations.
Nevertheless, medical devices make data collection even more cluttered and unwieldy. Devices stream data nonstop. For now, the wearable trend shows no signs of slowing, putting a massive strain on IT departments that have to aggregate and process medical device data.
Real-time pipelines combine deployment and automation capabilities needed to achieve continuous data integration. These pipelines cleanse and condense medical wearable information and join it to EMR, claim, and provider data. In this way, real-time pipelines provide a holistic picture of a patient’s health while accommodating his or her modern lifestyle.
3. Data privacy and compliance regulations
With so much data on the internet, everyone is concerned about privacy and regulation. This concern becomes even more pronounced when the discussion comes to people’s health. Healthcare organizations need a comprehensive understanding of confidentiality laws to ensure that data is not inadvertently shared or stolen.
In the past, laws like HIPAA have caused companies to keep EMR, billing, and research data separate. Unintentionally, this formed departmental silos, causing data integration to be an even more arduous task.
Healthcare companies moving to a cloud-based data lake should implement a robust data governance practice to enforce security. Data governance allows database administrators to permit access to certain data at certain points in time. This ensures compliance, but also keeps data transparent to people who have a need to view and analyze it.
4. Healthcare needs more data integration processing power
Since the emergence of cloud technologies and social media, many industries have failed to scale. They cannot process data as quickly as data is generated.
Healthcare has arguably more data than any other industry, and the on-premises data warehouses used at most healthcare companies cannot rival the rate of data influx. As a result, healthcare organizations miss significant insights and overlook opportunities to improve patient care. Healthcare companies are starting to rely on cloud-based integration solutions to close the gap.
Many healthcare organizations are turning to cloud-based platforms that can communicate with multiple tools and languages, such as: SQL, Apache Spark, and Tableau. These tools can be used to enrich and analyze data with meaning and context. Artificial intelligence and machine learning are also becoming popular solutions because of their ability to handle and analyze petabytes-worth of data.
5. End users are not data scientists
Patients, doctors, nurses, researchers, and even medical device inventors all benefit from healthcare data. Data can be used to make more effective diagnoses, improve medical devices, study patient behavior, and measure pharmaceutical success.
While healthcare data can be extremely powerful, raw data is easy to misinterpret without guidance from data scientists. What is more, data can be misused if it ends up in the wrong hands. Due to these risks, IT predominantly manages healthcare database access. That said, resolving help desk tickets on and end user-by-end user basis is burdensome.
One remedy is developing a self-service access model. These models are instituted by the IT team, and involve hierarchies to preserve confidential information. Moreover, self-service platforms contain production-ready data, lessening end user extrapolation and interpretation.
The cloud and the future of data integration in healthcare
Modern healthcare is deeply intertwined with the cloud. Patients can now engage with providers and wellness programs online, allowing them to become more independent, aware, and empowered. By the same token, audio, video, and other files accumulate and need to be incorporated into already-complex datasets.
The cloud is becoming indispensable with the rising need for data storage, but it is also critical in the effort to promote patient engagement and assess patient populations on the whole. Furthermore, the healthcare industry is undergoing more mergers and acquisitions than ever before, forcing them to decide on a new data storage and integration strategy.
Cloud-based data integration can generate insights from these increasingly large healthcare datasets. Agile, cloud-based platforms can associate previously disjointed or siloed datasets and create a more universal reporting methodology. The continued growth of the cloud and development of cloud tech, will surely drive healthcare technology innovations.
Getting started with data integration for healthcare
As the number of healthcare data sources and types increases, it becomes more and more difficult to gain a comprehensive picture of an individual’s health and produce better patient outcomes.
Data integration is critical to healthcare success. Integration of massive datasets can: identify methods of disease prevention, limit the number of inaccurate diagnoses, introduce more personalized care, and reduce costs by detecting fraud and avoidable overuse. Companies are using cloud-based or hybrid infrastructures to house and interpret this ever-growing data.
One of these foundational cloud-based platforms is Talend Data Fabric. Talend Data Fabric amasses and integrates data across systems and sources, transforms data into usable formats, and governs data to ensure proper compliance. Using Talend Data Fabric, a healthcare organization can selectively share data with internal and external stakeholders to make life-saving decisions.
There is a wealth of health information out there waiting to be analyzed. Become a leading provider and explore Talend Data Fabric today.
Ready to get started with Talend?
More related articles
- What are Data Silos?
- What is Data Extraction? Definition and Examples
- What is Customer Data Integration (CDI)?
- Talend Job Design Patterns and Best Practices: Part 4
- Talend Job Design Patterns and Best Practices: Part 3
- What is Streaming Data?
- What is Data Migration?
- What is Data Mapping?
- What is Database Integration?
- What is Data Integration?
- Understanding Data Migration: Strategy and Best Practices
- Talend Job Design Patterns and Best Practices: Part 2
- Talend Job Design Patterns and Best Practices: Part 1
- Change Data Capture
- Experience the magic of shuffling columns in Talend Dynamic Schema
- Day-in-the-Life of a Data Integration Developer: How to Build Your First Talend Job
- An Informatica PowerCenter Developers’ Guide to Talend: Part 3
- An Informatica PowerCenter Developers’ Guide to Talend: Part 2
- 5 Data Integration Methods and Strategies
- An Informatica PowerCenter Developers' Guide to Talend: Part 1
- Best Practices for Using Context Variables with Talend: Part 2
- Best Practices for Using Context Variables with Talend: Part 3
- Best Practices for Using Context Variables with Talend: Part 4
- Best Practices for Using Context Variables with Talend: Part 1