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The EHR Healthcare case study explicitly lists several business requirements, and from these statements, we can derive several facts about any solution.

 The company provides business-to-business services to insurance providers. The time it takes insurance providers to start using the system, known as onboarding, needs to be minimized.

 There is a mix of users, including medical offices, hospitals, and insurance providers. It is likely they all have different needs. For example, small medical offices may need more technical assistance when onboarding, while large insurance providers will likely have specialized data integration requirements.

 Medical records management services cannot have extended periods of downtime. These systems need to be available 99.9 percent of the time.

 Application performance is an issue. Latency needs to be reduced.

 Since the applications store and process protected health information such as medical history, maintaining data confidentiality is a top concern.

 The company is growing rapidly, and system administration costs cannot grow just because more infrastructure is added. System management practices should be designed to allow the organization to add infrastructure without needing to add staff to support it.

 The company wants to use its data to derive insights about industry trends.

This list of business requirements helps us start to understand or at least estimate some likely aspects of the technical requirements. Here are some examples of technical implications that should be considered based on the facts listed previously:

 Since the company is providing services to other businesses, customers will likely use public APIs.

 There are many legacy systems in the insurance industry, so there may be batch processing jobs as well.

 The need for availability calls for redundancy in infrastructure including compute, storage, and networking along with an architecture that prevents any single failure from making services unavailable.

 With a goal of deriving insights from data, the company will likely keep large amounts of data for extended periods of time. This coupled with the sensitivity of the data will require careful planning of access controls and data lifecycle management policies.

 Since the company serves customers in multiple nations and low latency is a goal, services and data will be served from multiple regions. For example, the EU's GDPR restricts the movement of records across national boundaries, which may have implications for region selection, storage strategy, and network topology.

 The adoption of managed services will likely lead to a decrease in infrastructure administration costs. AI and machine learning managed services will allow the company to start deriving insights from data faster than if they built ML models from scratch.

These are just possible requirements, but it helps to keep them in mind when analyzing a use case. This example shows how many possible requirements can be inferred from just a few statements about the business and product strategy. It also shows that architects need to draw on their knowledge of systems design to anticipate requirements that are not explicitly stated, such as the need to keep application response times low, which in turn may require replication of data across multiple regions.