The equation that keeps a diagnostic imaging center financially healthy has never been simple. On one side, equipment with high acquisition and maintenance costs, idleness that directly impacts the margin, and patients who spend less and less time inside the machine but expect increasingly faster answers. On the other side, physicians seek productivity, flexibility, and tools that allow them to work remotely without losing diagnostic quality.

This simultaneous pressure for operational efficiency and care quality is accelerating a structural shift in the sector. Technology has ceased to mean just a “better equipment” or a “better system” and has begun to redefine the entire operational model of radiology. The focus now is not only to generate better images, but to create smarter, integrated, distributed, and data-driven workflows. As AI stops being just a tool to assist diagnosis in isolated applications (such as one algorithm for mammography, another for stroke, etc.) and starts participating in the entire workflow, new opportunities and new challenges emerge.

 

The sector’s numbers help gauge the scale of this challenge. In 2024, Brazil surpassed the mark of 1 billion diagnostic tests performed in the year, a 15.7% growth compared to 2023, according to a survey by Abramed. Within this volume, imaging exams consolidated their position as the second largest category of procedures: the association’s members registered 24.4 million imaging exams, a 43.4% increase over the previous year.

The sector employs more than 307,000 professionals in total, with 50.2 thousand exclusively in diagnostic imaging services using ionizing radiation, and is present in 39.3 thousand health establishments across the country.

The larger the operational volume, the greater the need for systems capable of coordinating workflow, medical productivity, and patient experience without proportionally increasing operational complexity.

The Problem Begins Before the Patient Arrives

A large portion of inefficiencies in diagnostic imaging is not born in the examination room, nor in the report. They are born before the patient arrives.

If you think of it as a system, diagnostic imaging is a highly workflow-dependent operation. And a poorly designed workflow at the beginning generates:

• Idleness of expensive equipment
• Cascading delays
• Bottlenecks at the reception
• Decline in care quality
• Patient dissatisfaction
• Waste of medical capacity
  

It is necessary to understand that the process begins right at scheduling, which is, in practice, the “invisible engine” of operational efficiency. If it is poorly executed, you create a problem that no subsequent technology can correct.

To achieve truly efficient scheduling, one cannot think of it merely as a system for filling slots.

Several critical variables must be considered:

• Type of exam and protocol (with contrast, sedation, preparation)
• Patient profile (elderly, reduced mobility, pediatric)
• Real time variability
• Need for prior preparation
• Dependency on multiple resources (nursing, physician, specific equipment)
• Sequencing between exams
• Patient movement within the facility
• Exams performed in the same slot
• Intelligent control of fit-ins
• Among many, many others.
  

Technology plays a crucial role here. The most efficient players are those who treat scheduling as an operational optimization problem, not an administrative one, and seek to incorporate technologies that deliver this efficiency.

Adopting intelligent scheduling systems is a structural decision, with a major impact on current efforts and future profitability.

Furthermore, AI has the potential to transform how patients are served. The adoption of virtual agents for the heavy lifting of scheduling is a possibility. However, to become a reality in a complex context like that of imaging exams, it requires intelligence to be embedded in the system.

Web and AI as Infrastructure, Not as a Differentiator

The adoption of systems with a web architecture has ceased to be just a technological differentiator, in many scenarios, it has already become an operational requirement.

The logic is relatively simple: web systems eliminate a significant portion of the need for distributed infrastructure across each facility, reduce maintenance efforts, facilitate continuous updates, and allow remote access with the same resources available on-site.

For operations with multiple units, this completely changes the logic of distributing medical work. A radiologist can remotely report exams performed in different locations while maintaining access to history, clinical context, and advanced visualization resources without depending physically on the unit where the exam was performed.

 

In other words, the traditional on-premise RIS/PACS is undergoing an important transition to cloud-native models. This changes a lot of things:

• Scalability
• Expansion cost
• Disaster recovery
• Exam sharing
• Teleradiology
• Geographic distribution
  

Today, there is a clear movement toward:

 

• Distributed reading
• Multi-clinic/multi-hospital operation
• Centralized storage
• Secure remote access
• Universal web viewer

In Brazil, this directly addresses:

• Scarcity of radiologists in certain regions
• Expansion of national groups
• Consolidation of the diagnostic market
  

AI plays a structural role in this transformation. AI algorithms can be used to optimize the screening and distribution of cases, as they can automatically detect critical findings, organize the radiologist’s queue, perform quantitative operational analyses, and generate insights.

In addition to immediate operational gains, SaaS models add an important layer of scalability. Centers that grow through geographic expansion, acquisition, or increased demand can evolve operationally without needing to rebuild their technological architecture with each new growth cycle.

AI in Distribution: The Next Operational Move

While artificial intelligence for diagnostic support is already part of the operational routine of many services, it is now beginning to advance to a different layer of the process: intelligent case distribution.

The logic is simple in theory and quite complex in practice. Different exams have different levels of urgency, complexity, and need for medical specialization. Directing each case to the most appropriate professional, balancing productivity, operational cost, and diagnostic quality, becomes an increasingly strategic decision.

When incorporated into the workflow and reporting system, this intelligence reduces the time between performing the exam and delivering the result, improves the utilization of the available clinical staff, and contributes to more efficient models of management by medical productivity.

Voice Reporting is Becoming the Standard

Voice recognition in the clinical environment is not new, what has changed is the level of integration of these tools with the operational workflow of diagnostics.

When voice recognition functions natively within the reporting system, the physician can draft the report without switching applications or losing clinical context during the process. This reduces operational friction and consistently improves medical productivity.

At the same time, concern within the sector regarding the attraction and retention of specialists is growing. Radiologists value autonomy, flexibility, and stable remote access without sacrificing advanced diagnostic capabilities; web platforms integrated with PACS, offering access to the complete patient history and centralized workflow, are beginning to respond directly to this need.

The Great Enabler: Interoperability

This is probably the least “sexy” trend, but the most important one for managers. Historically, radiology became a mosaic:

• RIS from one vendor, PACS from another, and HIS from another
• Separate viewer
• Separate patient portal
• Separate AI
  

While adopting the best of each of these worlds is often the best option, and sometimes even inevitable, effort and planning are required so that the result is not an expensive, fragile environment full of rework. In this regard, the importance of interoperability stands out.

Real interoperability is an ecosystem of integrations that works.

Now the market is moving strongly in this direction, based on:

• DICOM
• HL7
• FHIR
• Open APIs
  

The goal is to create connected ecosystems among:

• HIS
• RIS
• PACS
• AI platforms
• Electronic medical records
• Service platforms/CRM
• Digital patient journey
  

This integration already appears as one of the central priorities of the sector.

Therefore, as a technology manager, it is essential to demand from vendors compliance with the principles of interoperability.

In Brazil, this is particularly relevant because many groups grew through acquisitions and ended up inheriting extremely heterogeneous architectures.

Ultimately, the combination of an integrated workflow, web reporting, intelligent distribution, and a connected digital journey reorganizes the entire operational dynamic of diagnostic imaging.

In a sector where differentiation increasingly depends on operational capacity rather than just equipment, the technology that organizes the workflow can become as strategic as the machine performing the exam itself.

MotionCoreImage, from Touch Health, was designed to function in a centralized manner, with integration into RIS, HIS, PACS, and AI algorithms, remote access via a virtual reporting center, configurable templates, and voice recognition integrated into the workflow. The system also includes audit features, biometrics with Live Finger Detection technology, and compliance with accreditation requirements such as ONA.

 

If your operation’s challenge today involves medical productivity, system integration, reduction of operational friction, and the evolution of the patient’s digital journey, speak with our specialists and understand how we can support your institution: https://conteudo.touchhealth.com.br/links