Clinical, administrative and analytics software for healthcare facilities
LGI Healthcare Solutions builds administrative, clinical and BI software for hospitals, running a layered stack that spans Delphi, C++ and Windows Forms alongside .NET Core, Angular, React and Kubernetes. That mix explains why legacy modernization and HR/payroll web rebuilds dominate its job posts, while RAG and Cursor signal AI-assisted development entering the workflow. Engineering-heavy, senior-skewed hiring plus a Microsoft Defender and Intune buildout point to a parallel security hardening push.
LGI Healthcare Solutions is a privately held software company headquartered in Montreal, Quebec, founded in 1982. It provides a portfolio of administrative, clinical-administrative and business intelligence solutions for healthcare facilities, including eReferral, staff scheduling and analytics. Beyond its Canadian base, it serves facilities in the United States, Australia, the United Kingdom and France. The company employs 201–500 people and integrates with clinical systems through standards such as HL7 v2.
LGI runs C#, .NET, .NET Core, Java, Delphi, C++ and Windows Forms, with Angular and React front ends, Oracle and PL/SQL data, Kubernetes, Dynatrace, and Selenium, TestComplete and JMeter for testing.
Yes. Engineering leads its hiring at a steady pace, mostly senior and mid-level roles in Canada, with additional openings in product, security and support.
Job posts point to clinical data integration pipelines, AI-augmented feature development, modernizing HR and payroll web solutions, and a Microsoft 365 security rollout using Intune, Conditional Access and Defender XDR.
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LGI Healthcare Solutions's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.