Smart Data Solutions, in collaboration with Everest Group, had an insightful discussion on how a strategic approach to data interoperability can help bring people and systems into alignment and ultimately deliver on the promise of technology in healthcare. Watch the LinkedIn Live session here.
Rapid enhancements in technology and increasing traction from healthcare enterprises around technology-enabled initiatives have significantly improved the way patients and members receive healthcare-related services. This has led to a surge in member/patient data wherein enterprises are actively looking to leverage those datasets and turn them into a strategic asset.
One of the prerequisites to best utilize data is to have complete access and make it interoperable so that a 360-degree view of a member/patient can be built. However, enterprises will have to navigate challenges that hinder them from exchanging data seamlessly.
Some of the prominent challenges are:
- Lack of standardized data structure – Diverse systems using unique data formats and protocols pose data interpretation and exchange challenges. Therefore, achieving consensus on common standards is a complex and time-consuming process.
- Legacy systems – Legacy systems lack interoperability features, creating challenges due to outdated technologies and limited extensibility. Integrating them with modern, interoperable solutions would consume a lot of effort and cost.
- Data privacy – Interoperability involves sharing sensitive data across systems, raising concerns about data security and privacy. Safeguarding data during transmission and storage, and complying with regulations like HIPAA or GDPR, can be challenging.
- Change management – Outside of technology challenges, key stakeholders within the firm must be educated around the benefits and processes of implementing interoperability. Change management is a challenge as there is some resistance among few stakeholders in sharing data with other stakeholders in the healthcare ecosystem
How is industry solving these challenges?
Solving interoperability requires increased collaboration among various enterprise groups, including payers, TPAs, providers, PBMs, and pharmacies.
While HL7 standards and the CMS interoperability mandate are steps in the right direction in terms of standardizing data formats and enhancing collaboration among enterprises, enterprises are relying on interoperable data platforms to store data, transform data into HL7 formats, and exchange data seamlessly. These data platforms are also eliminating security risks, as most of these platforms are HIPAA compliant to ensure data can be exchanged across the ecosystem in a secure manner.
While traditionally, data platforms were leveraged to perform basic data management tasks, with the increase in data growth, healthcare enterprises are trying to tap into the analytics and automation capabilities of these platforms to enhance clinical and business outcomes.
Role of automation in interoperability
From a technology standpoint, besides data management and sharing, automation continues to be one of the prominent building blocks for a robust interoperability data platform. Automation led-processes such as data extraction through optical character recognition, automated file conversion, and scheduled data sharing play a major role in identifying, preparing, and sharing the data sets to implement interoperability. Furthermore, beyond data extraction and sharing, automation plays a key role in enabling some of the healthcare-specific business use cases.
Automation-led healthcare use cases
With use cases spanning across both payer and provider functions, enterprises are looking to adopt interoperability data platforms and leverage its automation capabilities to streamline data exchange and enhance some of their existing business functions.
Moving forward, how will this space evolve?
Automation and interoperability will continue to work hand-in-hand as enterprises will look to transform some of their business functions, especially the front-office functions such as patient scheduling and member onboarding, as demand for hyper-personalization is growing by the minute. Other technologies such as analytics and AI, will also play an integral role in the interoperability and automation story, as enterprises would look to generate actionable insights from the patient/member data being exchanged across the ecosystem, to enhance their clinical and business outcomes.
We will be closing track this space to see how enterprises leverage these existing technologies and tie it to their business processes as they try to achieve the trifecta of cost, quality, and experience.