AI technologies such as NLP (natural language processing) and computer vision can facilitate the data digitization process. NLP supports speech-to-text and vice versa, document and data conversions, patient notes, processing of unstructured data, and query support systems. Computer vision includes augmented reality (AR), virtual reality (VR), telehealth, and digital radiology.

ML algorithms can improve error detection in billing and coding, leading to fewer claims denials. These algorithms also can optimize the pharmaceutical supply chain.


Deep learning and cognitive computing tools accelerate the processing of huge data sets, helping to inform precise and comprehensive risk forecasting and providing recommended actions that improve patient outcomes.

Flexible and scalable data infrastructure
A robust patient LHR isn’t possible without a robust data engineering framework. Given the dearth of standards for the 18 data sets mentioned above, it is critical that a data engineering framework can:
1. Process standard (HL7, FHIR, etc.) and non-standard data sets
2. Process external data sets
3. Support unstructured data
4. Implement a data digitization process that tags and merges this data with the rest of the data while using proper categorization
5. Deploy an EMPI algorithm to tie disparate patient data records into a unified patient LHR
6. Utilize APIs both to expose this data using DaaS (Data as a Service) and in the platform infrastructure through a Platform as a Service (PaaS)/Software as a Service (SaaS) model running on a modern application stack that offers microservices.

Such a data infrastructure makes it possible to digitize data, synthesize different kinds of data sources (and address their inconsistencies), help identify errors or misreporting, and seamlessly integrate credible new feeds.

The right data engineering framework can serve up different forms of applications in different ways. This could be done via a series of APIs under a DaaS model in which the partners/clients can pull in data and utilize it within their existing infrastructures. It also can be accomplished through a PaaS model where a set of microservices exposes the different data sets to build out different workflow-based applications or enhance existing applications.

In addition, applications can be used in a traditional SaaS model in which preconfigured workflow-based apps are built on top of these microservices for end users. Integration of such applications across the partner continuum can be achieved easily through single sign-on (SSO) to ensure a seamless end-user experience.

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