Continuous Testing to Deliver Results
Developing the right test engineering environment helps create roadmaps for scalable frameworks and continuous testing that is modular, reusable, and cost-effective.
A health tech start-up required Prakat’s assistance to improve the quality of care by tracking patient data and facilitating routine diagnostic procedures at the correct intervals.
The required data the primary care physician needed included a combination of age, gender, ethnicity, family history, and more. Data tracking is needed for proactive treatment of health issues and requires legal compliance to protect patient identity.
Medicare and other private insurance providers offer several preventive care services to patients; some are assessments, and others are diagnostic procedures that occur at specific intervals.
Many of these assessments required telehealth for administration during the pandemic, further complicating the process.
The founders shared their vision to develop a solution and design a roadmap in a stakeholders’ meeting.
The envisioned product solution included work and product flows, multiple screens, data structures, personas, and designs that were approved by the founders.
Prakat developed a data solution that integrated with Medicare’s Content Management System (CMS) and other insurance systems.
After rigorous testing, the modernized software then required beta testing by physicians to fine-tune the user features.
The patient data-tracking product was deployed in production, remaining within HIPAA compliance.
The final product was released, fully integrated with several Electronic Medical Record (EMR) systems and more than 2,000 insurance systems, including Medicare.
Prototyping development approach allowed incremental hypothesis testing and validation.
Solution fully integrates with Medicare and other insurance providers for HIPAA-compliant access to patient data.
Workflows now include notifications, telehealth services, and seamless co-pay processes.
Development of artificial intelligence-based engine to analyze patient data from multiple origins, referencing dynamic guidance data sources.