The digitization of healthcare platforms has made the sector more competitive. Consumers are inclined toward healthcare that is well-established in the digital ecosystem. Continued economic pressure has made internal process efficiency a vital step in establishing such ecosystems. An agile organization with digital delivery capabilities, scalable IT platforms, and at-scale data processing can sustain the current landscape.

In this scenario, data has become the main limiting factor. To succeed in building a sturdy architecture around data, cloud solutions, scalable technology and granular and automated management of consent should be aimed for. Data lakes are a major component in ensuring this framework.

Automated consent and permission management in healthcare platforms

Health data is the most vulnerable form of data for a user and requires high levels of regulation to protect patient information from loss, attacks, corruption, etc. This has caused some hesitation among healthcare payers. Hence

data management should be done in such a way that each data point should be linked to a clear purpose.

For instance, an email address will always be used for billing but adverts must not be sent unless the individual opts in explicitly. Data deletion is also a valuable aspect of data management. Data of a patient is usually spread across multiple systems and deletion protocols assure the patient that their data is used solely for medical health records. A well-established permission management system should be tailored to specific healthcare situations.

Scaling technology and developing on cloud

Data storage solutions should be upgraded, including optimization of master data management to improve data quality and conduct compliance testing. Data lakes and modern streaming can be used to process live data streams or unstructured data. Modern analytics like open-source tools and evaluation techniques help fill the gap between data generation and data management.

Access to data insights requires flexibility in process design and developing a capability to embed analytical models seamlessly. The usage of cloud solutions can significantly speed up this transition. Deployment of API layers, scalable architecture, and leveraging dedicated technological solutions like knowledge graphs to handle specific problems are a few examples of cloud usage for data management. Modern integration tools can help perform these tasks in an automated manner.

A clearly defined scope and the right technical and organizational measures allow organizations to deploy cloud solutions successfully.

Access to healthcare platforms

There are primarily two kinds of ecosystems in the healthcare domain.

First, a traditional healthcare system that is integrated with electronic health approaches. This enables providers and payers to be better connected and allows ease in the flow of medical health data.

Second, digital healthcare through telemedicine. Medical apps, fitness trackers, and other similar devices fall under the umbrella of such an ecosystem. Transfer of data along healthcare journeys is a critical task.

  • Cross-party identity management and single sign-on tools can help reduce hurdles in data movement.
  • The transfer of data supported by modern API management solutions enables different services to exchange information.
  • Standardization of internal and external APIs establishes a maximum level of consistency, enabling external services to seamlessly participate in the user’s journey.

Targeted enhancements to tangible use cases, measures to counter private data exposures, and steps to utilize broader ecosystems can improve data-driven insights in the entire domain of healthcare.

Thus, a 360-degree view of the consumer journey for an omnichannel service, a data layer integration to support ecosystems, and process tracking and mining to enhance internal efficiency are three vital steps of a digital agenda of data processing.

Healthcare systems have an abundance of data but cannot make it accessible and amenable for analysis purposes. Following a strict regime of cloud-based solutions inclined towards data-lakes forms the most optimal solution to this data problem.