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ML-based system or why we use сomputer-aided systems in healthcare

May 03, 2022

15 minutes read

Healthcare companies — providers or payers — have historically relied on computers for administrative tasks. However, new use cases have emerged as technology matured and the industry digitized. Today, hardly any clinic operates without a fleet of computers to store and manage patient/facility data, monitor patients and equipment, perform operations, and research.

The advance in machine learning (ML) and artificial intelligence (AI) has brought about new changes to a firmly established healthcare system, which suffers from rising costs, low productivity, and inefficient care delivery. Implementing AI healthcare solutions can be challenging, but they have the potential to solve a lot of problems like:

  • increasing diagnosis efficiency
  • cutting triage and patient treatment time
  • reducing unnecessary hospital visits
  • creating administrative time savings
  • drastically improving clinical decision-making.

In this article, we will talk about computer-aided systems and look into specific AI-powered solutions that help physicians and administrative personnel drive efficiencies in healthcare.

What is a computer-aided system?

In healthcare, a computer-aided system is an integrative solution that assists doctors by partially automating their tasks or augmenting clinical support and decision-making.

Despite the term, these systems do not operate by just using a computer but are designed and built around such technologies as AI, machine learning, and deep learning.

Below are four areas (and a few use cases) in which today’s computer-aided systems and AI solutions are applied. Further, we will talk in detail about some of them.

Benefits of computer aided diagnosis:

  • better diagnostics 
  • less waiting time
  • less manual work
  • improved patient treatment 
  • no unnecessary hospital visits
  • optimized administrative time 
  • improved clinical decision-making

Computer-aided solutions in healthcare: approaches and software types

CADe/CADx systems

Computer-aided detection (CADe), also known as computer-aided diagnosis (CADx), are medical systems that help doctors interpret a wide range of medical images, including X-rays, MRIs, CTs, PETs, ultrasound images, etc.

CADe systems detect and characterize pathology in various tissues, such as tumors, lesions, and polyps. They are mostly applied in mammography, ultrasound imaging, magnetic resonance imaging, computer tomography, and tomosynthesis.

The idea of CADe is simple:

Because computer output, which is based on accurate and consistent diagnosis, is provided to doctors automatically, they can read images faster and more effectively. This not only scales medical imaging but drastically improves its affordability.
CADe systems demonstrate high diagnosis accuracy and impeccable efficiency since they use advanced radiology techniques (image analysis & processing) and augment them with computer vision and anomaly detection algorithms.

In this ecosystem, every image is pre-processed, then its features get extracted and classified. After that, the results are provided to a doctor for review. The system trains algorithms on large, representative datasets, and trained physicians verify the results to ensure accuracy. Often, human-in-the-loop (HITL) is an integral part of computer-aided diagnosis solutions.

The FDA must approve cADe systems.

Computer-aided prognosis systems

The computer-aided prognosis (CAP) is a sub-field of computer-aided diagnosis that combines medical image analysis and patient data analysis to help doctors predict disease outcomes and patient survival.

The concept of CAP is two-fold:

  • It enables physicians to effectively fuse all data sources about a patient like EHRs, medical images, tissue samples, and genome data (if available) to make it easier to analyze these heterogeneous data sources to predict and improve outcomes.
  • ​​More insight into patient data means that doctors can potentially determine the probability of individuals contracting certain diseases and responding to a specific treatment regimen.

CAP systems include image analysis technology, AI “brain” (i.e., ML/DL algorithms for anomaly detection and classification), data storage, and advanced analytics. These systems result from cooperation between computer and imaging scientists, clinicians, oncologists, radiologists, and pathologists.

As of now, computer-aided prognosis systems are more about theory than practice. The challenges of implementation range from lack of quality data and professionals with the right skill sets to privacy, security, and ethical concerns. And yet, companies have been exploring the capabilities of CAP systems for breast cancer treatment, lung cancer treatment, spinal cord injury prediction, and cell death categorization.

Clinical support systems

Computer-aided clinical support systems are a wide range of automated solutions to help healthcare professionals deliver patient care. In most cases, these solutions augment clinical support staff through more efficient data entry and patient records handling, financial reports preparation, triage, etc.

Given the description, clinical support systems are often part of larger-scale AI solutions. For instance, data conversion solutions always have a data entry component — scanned documents are prepared for processing before data is extracted, converted, and pushed for analysis. Or, they can complement hospital information systems (HIS) by giving symptom checkers easy and instant access to patient data to increase the accuracy of triage advice.

Note: Do not confuse clinical support systems with clinical decision support systems (CDSS). The first is management tools for healthcare assistants used in routine care delivery, while the latter are software-based AI tools for better decision-making.

Healthcare facility management systems

Computer-aided facility management systems are quickly becoming a go-to solution in the medical field to improve maintenance, increase performance, reduce risk, and cut costs.

Facility management solutions have a lot in common with hospital information systems (HIS). They are used as the interface where all the data about aspects of facility activities are displayed. The main difference is that computer-aided facility management systems feature an AI-enabled decision-support component to help staff look into core factors that affect healthcare delivery. For example, facility age, occupancy, energy consumption, and maintenance costs can be analyzed for insights.

Alternatively, healthcare facility management systems can help medical personnel stay in sync and work more efficiently. For instance, a study by the KPI Institute found that the ideal hospital bed occupancy rate is 85-90%. Any rate higher than 90% may cause overcrowding, forcing hospitals to turn away patients and postpone care provision. With all the data stored in the system, doctors, symptom checkers, and clinical support workers can regulate how many patients they can admit to treating effectively.

Computer-assisted history taking systems

According to Statista, about 33% of US physicians spend 17-24 minutes with their patients.

In the meantime, history taking and examination can take up to 22 minutes for a patient.

A computer-assisted history-taking system (CAHTS) can help doctors gather data from patients much faster, drastically reducing the time from admission to diagnosis to treatment plan.

Systems are powered by speech recognition and Natural Language Processing (NLP). They take a doctor-to-patient conversation as an input, process and analyze it using algorithms, and then display it digitally for a doctor’s review. Alternatively, doctors can dictate their notes to store in HIS or update EHRs.

Medical transcription (along with CAD) is one of the most high-potential applications of AI and machine learning in healthcare. The medical transcription market is projected to reach more than $70 billion by 2026. The technology offers such significant advantages as time and cost savings, more effective data gathering, and more fast and efficient care delivery.

Let’s pass to the actual solutions

Computer diagnostics of diabetic retinopathy and computer diagnostics of pneumonia are the most popular intelligent healthcare solutions.

Computer-aided disease screening and diagnosis systems are a new frontier for ophthalmology. By using AI and machine learning, medical providers make it easy for eye doctors to detect and diagnose various conditions at high speed and scale. Eventually, they seek to scale doctors’ expertise and make screening more effective and affordable.

Diabetic retinopathy is one of many conditions that can be easily prevented if diagnosed and treated in time. It belongs to the leading cause of blindness in adults, but it is still often misdiagnosed due to the lack of trained ophthalmologists. It is critical to free up doctors with expertise from routine tasks like a time-consuming review of eye screens through automation.

Related: Benefits of implementing AI in medical imaging

The CADx solution for diabetic retinopathy diagnosis utilizes anomaly detection and image analysis to automate and scale the detection of subtle morphological changes in the funds of the eye. Since the system sifts out “healthy” screens, ophthalmologists get more time to look into cases with the first signs of damage to optic disks, exudates, and blood vessels. They can diagnose more accurately and efficiently to prevent lifelong blindness at scale.

During the coronavirus pandemic, healthcare systems globally have to operate over their capacity. Doctors and medical personnel are overworked and overstressed, and they can neither diagnose nor treat patients as fast as the disease progresses. They want to be able to diagnose faster to focus on patients who need help.

Easily integrated with CT or MRT, the solution can process dozens of thousands of medical images per day to identify the most severe cases for medical professionals to address first. Because doctors spend less time on diagnostics, they can focus on decision-making and treatment of patients.

Conclusion

Computers are no longer machines that medical staff use to handle patient and facility data. Today, they encompass the broadest range of use cases, from EHR handling to AI-based decision-making.

Computer-aided systems have transformed the US healthcare system for less than a few decades. Still, artificial intelligence and machine learning have propelled a revolution in how providers deliver care, manage their facilities and staff, and make healthcare more efficient cost- and resource-wise.

At VITech, we design and build AI healthcare solutions to help businesses and patients take advantage of the novel technologies. We strive for efficiency, effectiveness, and affordability, aiming to renovate healthcare and address global challenges like the COVID-19 pandemic.

Do you want to learn more about our possibilities to help healthcare organizations drive AI transformations? Contact us at info@vitechteam.com for details!

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