Romexsoft Blog

Network penetration testing is a strategic practice that moves beyond mere compliance as it validates your system’s real-world security posture and significantly reduces the risk of system vulnerabilities. When executed effectively, it inherently prepares your team for robust incident response. In this article, we share actionable insights on how to implement penetration testing to achieve these security outcomes.

The blog gives an overview of:

• key elements of a strong network pentest
• best timing and reasons to run a pentest
• what results and reports to expect
• how to prepare your team and systems
• common testing issues and how to fix them
• signs of a low-value pentest.

This article explores versions of HL7 standards along with their similarities and differences. In particular, it focuses on the differences between FHIR and HL7.

In addition, it explains why FHIR is the best option for modern solutions while highlighting the challenges of its implementation and reasoning behind using more outdated HL7 V2, HL7 V3, and CDA.

The blog highlights:

• definitions of HL7 and FHIR
• FHIR vs HL7 comparison
• which is the better option
• what are FHIR adoption challenges
• future of HL7 healthcare data standards

Embracing regression testing as an integrated discipline, rather than a reactive task, is the key to achieving continuous delivery and business continuity. Ignoring robust regression testing is a significant risk, as ITIC’s 2024 survey notes that in 90% of cases, mid-size and big enterprises face over $300,000 for an hour of unplanned downtime. In this article, we provide actionable insights to help you stabilize releases and scale your QA effectively.

The blog gives an overview of:

• why regression testing still matters
• types of regression testing explained
• regression vs. retesting vs. continuous
• manual, automated, or managed testing
• high-impact regression strategy essentials
• modern regression testing lifecycle

SaaS applications demand a unique testing strategy tailored for such features as multi-tenancy, continuous delivery, and immense scale. Rapid development often results in critical bugs, customer dissatisfaction, and operational issues without a solid SaaS testing process. This article provides a comprehensive guide to help CTOs and product leaders overcome key quality assurance challenges. It outlines scalable SaaS testing strategies, highlights crucial SaaS testing types, and emphasizes when to seek specialized support to ensure a sustainable and reliable SaaS application.

The blog discusses:

• why reliable SaaS testing matters
• key SaaS QA challenges
• building a test plan
• growth strategies
• quick issue-fix table
• the choice of the right tools
• when to hire outside QA

The global mobile healthcare application market is growing fast. Understanding what kind of applications succeed in this lucrative field and how to develop one is key if you plan to invest in this type of product.

This article provides a detailed analysis of mobile healthcare application types and a step-by-step guide for developing one, augmented with a list of best practices that can help ensure the success of your product.

The blog discusses:

• what healthcare mobile apps are
• benefits of using such applications
• types of mHealth solutions
• must-have features for mobile health apps
• steps to build a healthcare app
• best practices for app development

Effective medical imaging and patient data management rely on specialized healthcare IT systems: PACS and RIS. The purpose of these foundational technologies is to streamline radiology operations, with RIS handling administrative tasks and PACS managing critical image storage and sharing. This article explores the distinct role of each system and how their integration transforms clinical workflows, enhances diagnostic accuracy, and improves patient safety.

The blog highlights:

• what is RIS and PACS
• key differences
• integration benefits
• integration challenges
• implementation and upgrade guide
• emerging trends

Predictive modeling is transforming the health sector by utilizing vast data, AI, and machine learning to anticipate patient needs. This shift from reactive care to proactive forecasting holds immense potential, as it enhances care quality and minimizes expenses. This article explores how this technology functions, its key benefits for patient care, and the real-world challenges it helps overcome.

The blog discusses:

• definition of data analytics in healthcare
• role in the industry
• types of health data
• use cases in healthcare
• barriers and fixes
• emerging trends

Powered by AI and machine learning, predictive modeling is emerging as a key strategy for improving various aspects of modern healthcare. By leveraging EHRs and other health data, this approach allows medical professionals to forecast risks, personalize treatment, streamline operations, and ultimately enhance outcomes while reducing costs. In this article, we explore this proactive method, its proven gains and challenges, and highlight the reasons behind its rapid, industry-wide adoption.

The blog discusses:

• definition of predictive modeling in healthcare
• why it is important in healthcare
• how predictive modeling works
• types of data used
• adoption challenges and solutions
• predictive modeling use cases
• how ML is used in predictive modeling
• trends to shape predictive modeling