Every healthcare initiative underpins mainly on the organizational ability to access trusted and secure data. They must be aware of the key strategic initiatives that share the potential of delivering improved patient outcomes, reduced enterprise risk, and increased efficiency.
Here are three ways healthcare can maximize their data usage while keeping its quality intact.
The first step for most health data management initiatives is to offer maximum access coupled with an ability to integrate it without difficulty. Having standards speed up this step, making access and the process of integration even more efficient. And so organizations can say goodbye to data that is inaccessible in data silos and gaps present in the data insights.
With the proliferation of electronic health records (EHR), healthcare units have access to more valuable data than ever before. Thanks to the initiatives driven by HL7, the path to accessing as well as bridging healthcare’s data silos has finally opened.
Further, with the introduction of artificial intelligence (AI) and machine learning (ML) along with self-service-based data integration solutions, the extent of handling voluminous information increases by leaps and bounds. Such data integration innovations leading to the rich landscape of data integration and interoperability.
With the emergence of self-service integration technology, healthcare units handle data without difficulty. These solutions have in-built functionalities such as pre-built application connectors, shared templates, monitoring dashboards, etc. incorporate data integration, B2B data exchange, application integration, along with a new focus on business self-service and user-friendly access to these techniques.
After integrating data, users must ensure that the data must be trustworthy. Data quality has been a challenge for quite long. It’s difficult to maintain data quality. However, it’s important since trustworthy data is integral to delivering safe, high-quality and cost-effective patient care.
Many data integration tools help organizations maintain data quality by providing an end-to-end encrypted environment that saves data from thefts and breaches. In this environment, only authenticated users are allowed to access data, and so the risk of breaches reduce to a minimum. These mature solutions are capable of serving real-time, proactive data quality requirements. And now with the cloud, artificial intelligence and machine learning driving significant innovations, the scale and automation of data quality are even greater than ever before.
Back in the day, it was difficult for healthcare units to master a single master domain (e.g., patient, member, location). Implementing these domains were fraught with risks and too often delivered less value than anticipated. Nowadays, it’s easier to do this and drive from the value of the single source of truth in a shorter time.
Healthcare centers can leverage AI and ML-powered technologies to associate all the interaction data that exists across social, internet of things (IoT) devices, and multiple other external sources with the internal transaction, reference, and master data. And this is what can finally deliver that elusive 360-degree view of virtually anything one needs to know.
With better data integration competencies, improved healthcare data standards, as well as technological innovations that span AI/ML, healthcare giants can bid goodbye to data frustration and embrace a path to data freedom!