Technological advancements in conjunction with innovative thinking have led to changes in the radiology industry. Today, innovative solutions offer advantages to radiologists and patients. These solutions allow radiologists to deliver higher-quality services, faster than ever before. In addition, some of these advancements can help prevent the radiologists themselves from experiencing burnout. However, determining which technologies to add to your diagnostic toolbox can be a challenge. Therefore, to take full advantage of these benefits, radiologists need to become educated as to what is available and then equip themselves with the new technologies and skills that will provide them with the most benefit. These are the radiologists who will have the ability to compete on price and value, while offering patients a higher quality of service than those who do not adopt these innovative practices can.
2020 Radiology Industry Technology Trends
Hyperautomation refers to the use of technology to automate tasks. By applying advanced technologies, such as machine learning (ML) and artificial intelligence (AI), processes that previously required humans can be completed automatically. This technology can be used to automate numerous tools, which is essential because a single tool cannot successfully replace a human being. Hyperautomation requires the implementation of a combination of tools.
This combination includes:
- Robotic process automation (RPA).
- Artificial intelligence.
- Intelligent business management software (iBPMS).
The end goal of hyperautomation is to increase AI-driven decisions.
Artificial Intelligence and Machine Learning
Thanks to the combination of AI and ML, diagnostic tools can now be trained to read radiologic scans and tissue samples. Radiologists who decide to take advantage of this cutting-edge technology can expedite patient diagnoses.
AI Assistance to Improve Diagnostics
Through AIs ability to automate monotonous tasks and assist with prioritizing cases or secondary reads according to tumor detection, radiologists will be able to provide patients with a more precise diagnosis. Several pathology and radiology departments in Sweden are already using AI assistance for these purposes.
AI Will Allow Continuous Training
Artificial intelligence will allow health professionals to train, continuously. For the most part, this is accomplished through the creation of feedback loops. For example, if a radiologist is notified of a pathology and/or surgery outcome, he or she can easily access previous cases to learn more.
In 2019, nearly four out of five of the industry cybersecurity breaches that occurred involved a healthcare organization. By the end of 2019, healthcare data breaches for the year will total $4 billion, and it is anticipated that the cost of 2020’s data breaches may be even higher.
According to a Black Book Market Research survey, 96 percent of the IT professionals who took the survey agree that the data attackers are outpacing the medical enterprises. Thus, leaving providers vulnerable to cybersecurity attacks. In addition, survey answers indicate that more than 50 percent of the 2019 attacks occurred due to external hacking.
Although hyperautomation brings with it many benefits, it can also lead to vulnerabilities; therefore, radiologists must implement an effective AI security solution that can understand patterns and protect data.
The key perspectives of an artificial intelligence security system include:
- Protecting all AI-powered systems — training data, ML models and training pipelines.
- Leveraging AI to improve security defense — use ML as a means to understand patterns, uncover attacks and use automation in parts of the cybersecurity process.
- Anticipating nefarious use of artificial intelligence — identify attacks and protect the data.
Currently, some artificial intelligence assisted workflow management pilots are in the testing phase and there are others still being developed.
The artificial intelligence automatic reallocation of exams needs to be based on the radiologist’s workload and be capable of:
- Planning and organizing exams automatically by using various factors such as time of day, department, specialty, etc.
- Balancing workloads automatically by matching the radiology exam workload with reading capacity.
- Automatically escalate and assign studies based on the availability of the radiologist, location, subspecialties, time of day, etc.
- Sending specific exams to be analyzed by artificial intelligence first. Specific exams may be based on the type of exam and the request. For example, AI-assisted pre-analysis may be requested for an exam that detects a lung embolism.
- Prioritizing radiologists’ reading queues and setting configurable deadlines for escalating and monitoring service level agreement (SLA) exams.
In the future, AI-based workflow systems will have the ability to use and apply data as well as to assist with operations management. For example, these systems will be able to recognize bottlenecks. The system will use that data to route exams to the radiologists that are available; thus, reducing waiting times and streamlining the delivery of care. Implementing an AI-based workflow orchestration system early-on allows radiologists to get a head start by increasing their productivity and overall reading workflow now.
In the near future, forecasting capabilities and predictive analytics will become available. With the ability to forecast capabilities based on patient data generated from electronic medical records (EMR) and other systems, this feature will be an essential component of tomorrow’s workflow system.
Cross-Discipline Collaboration with the Pathology Department
To provide optimal care, radiology has become increasingly dependent on collaborating with other specialists; therefore, radiologists have the ability to take the lead in the management of cross-disciplinary workflows. Since radiology is one of the most IT-knowledgeable disciplines within the healthcare industry, radiologists will have no problem applying integrated diagnostics using digital technology. Now that pathologists are moving towards reviewing images digitally, a cross-discipline collaboration is on the horizon.