6 Best Practices for Seamless Clinical Data Integration

Clinical data integration refers to the collection of information from various medical departments, third-party vendors, and payers, and to enable seamless data exchange. An integrated healthcare organization has the power to improve the quality of care and streamline workflows.

According to a recent report by Deloitte, in the forthcoming years, the healthcare landscape will be dominated by preventive and wellness approaches, interoperable data, and consumer-driven initiatives. Apart from this, McKinsey reveals that digital and data capabilities are honing the future of healthcare, thus ameliorating care delivery.

These reports signify the role played by clinical data integration. A flawless data integration results in seamless interoperability, ultimately leading to better patient outcomes.

To ensure effective clinical data integration, here are top-notch best practices that every healthcare organizations can implement.

1. Detect Data Sources

So, before jumping into clinical data integration, start by defining types of data and its sources. This helps the data management teams to comprehend the data requirements for providers.

Healthcare data is spread across multiple on-premise and cloud locations, so organizations can lose track of the data, resulting in higher storage cost and data silos. Conducting clinical data integration without understanding data sources is like sailing without a compass.

So, start with these questions:

  1. How are the healthcare providers using the systems? What’s their ultimate aim: decrease operational costs, quality of care, or to give medical resources?
  2.  Which needs aren’t met by the existing healthcare systems?
  3. How is the data consumed by different users and applications?

2. Enable Data Privacy and Security

Ensure to have the best data privacy and security measures in place during clinical data integration. Here are some of the data privacy and security measures that organizations can implement:

  1. Risk Assessment: Conduct security risk assessment. This involves detecting potential data risks, threats, and vulnerabilities, and outlining a plan to counter these threats. It also involves determining levels of sensitive data, so precise actions such as data encryption, access control, and compliance are taken to protect the health information.
  2.  Audit Trials and Monitoring: Audit trials capture details such as who has accessed the data, what actions were taken, and when did it occur? This helps in identifying unauthorized data access and suspicious activities. It also helps to comply with regulatory standards. Furthermore, continuous monitoring detects security breaches and anomalies in real-time, thereby maintaining proactive security measures.
  3. Compliance Standards: Essential compliance standards are HIPAA, and data standards such as HL7 and FHIR. These regulations contain specific guidelines for handling sensitive healthcare data. Data standards assure that the information is exchanged seamlessly between multiple systems.

3. Leverage Healthcare Data Analytical Tools

With vast amount of information coming via clinical data integration, it is paramount for providers to leverage healthcare data analytical tools because of the following reasons:

  1. Analyzing Complex Healthcare Data: Research reveals that hospitals generate around 50 petabytes of data every year. This data is highly complex, so to extract useful insights from this data, healthcare organizations can use analytical tools. These tools can extract, process, and analyze complex data, and perform predictive, cognitive, and comparative modeling. These insights ultimately help providers to make informed decisions and improve patient outcomes.
  2. Gain Real-Time Insights: Healthcare data analytical tools capture real-time data from wearables and remote monitoring software. This data is immediately sent to the providers in a readable format.
  3. Enhanced Patient Care: By extracting meaningful insights from analytical tools, providers can identify at-risk patients and start early intervention and treatment. Proactive care can nip the illness before it evolves into something else. Data insights help providers to devise customized treatment plans that is tailored to patients’ conditions.

4. Use Healthcare Data Warehousing

With over 50 petabytes of data being generated, there’s a need for a vast storage space. This is where healthcare data warehousing comes into the picture. Clinical data integration connects several systems together, so the data obtained has to be stored in a secured place.

Healthcare data warehouse is a repository for storing structured data. It aggregates data from multiple sources, converts it into a standard format, and stores it in a way that is easy to analyze and retrieve.

Data warehouses use the ETL (Extract, Transform, and Load) process to move data from varied sources to the warehouse. It also uses the OLAP (Online Analytical Processing) method for analysis.

5. Utilize Data Lake

Apart from data warehouse, data lake is another way to store data. It is a cloud repository that stores raw data in its native format till it is required for analysis. A data lake hosts structured, semi-structured, and unstructured data at the same time.

Furthermore, data lake is designed to support machine learning, data analytics, and other applications that need surplus raw data. Unlike data warehouse, data lakes provide instant accessibility. They are also cost-effective than warehouse and highly scalable, thus making them a more attractive solution. Conversely, data warehouse works better if looking to incorporate business intelligence tools for healthcare data.

6. Use APIs to Extract Data from Legacy Systems

One major issue faced during clinical data integration is incompatibility with the legacy systems. An on-premise ERP is incompatible with cloud applications and IoMT. This incompatibility is due to different data formats and the inability of apps to handle information supported by new SaaS apps.

To make data accessible from legacy systems, use a layer of API abstraction. Try to separate and wrap legacy systems with RESTful APIs. You can also try to build access to constituent web services via APIs. Alternatively, rearchitect the underlying system code as a microservice and expose it via API.

APIs are cost-effective than replacing legacy systems while developing new data integrations. An API management platform helps to incorporate optimal policies such as throttling and rate limiting to safeguard the legacy systems from receiving many API calls and resulting in downtime.

Want an effective clinical data integration? Just get in touch with the experts in the field. We at Arkenea have over 14 years of experience in developing healthcare software, plus we also provide data integration services. To know more connect with us for a consultation call.