Artificial intelligence and ML-based solutions are changing medical care. Healthcare AI solutions are a reality in many fields and specialties of medical help. Machine learning (ML), deep learning (DL), natural language processing (NLP), and AI identify healthcare needs and help work faster, and with more accuracy. They use data patterns to make informed medical or business decisions quickly.
It is still a lot to overcome in achieving an AI-dependent healthcare industry. Most notably fears of mismanaged medical help due to machine error and lack of human oversight and data privacy concerns. Tech companies, governments, and healthcare providers are willing to invest and test out AI solutions for healthcare.
Now virtual nurses are available 24/7. They can answer questions, monitor patients, and provide quick answers, saving the healthcare industry a lot of money. Most virtual nursing assistants applications allow more regular communication between patients and care providers to prevent unnecessary office visits and hospital readmissions.
AI can analyze many medical images, clinical research trials, medical claims, and can identify patterns and insights often undetectable by manual human skillsets, helping doctors make more precise diagnoses. Clinicians can improve and customize patient care by combing medical data to predict or diagnose disease faster.
Technology such as voice-to-text transcriptions could help prescribe medications, order tests, and write chart notes.
Currently, image analysis is very time-consuming for human providers, but a machine-learning algorithm can analyze faster than humans. AI image analysis could support remote areas and even make telemedicine more effective. Patients can send pics of rashes, cuts, or bruises to determine necessary care.
VITech has experience in creating software that can detect and classify lung diseases promptly with computer-aided assistance, get the results of the diagnostic in minutes, not hours or weeks.
Nowadays, medical staff worldwide is under great pressure due to a significant rise in the number of patients. As proper diagnosis is crucial for further treatment and pandemic decline, healthcare providers struggle to diagnose patients quickly and correctly. Even in normal conditions, doctors don’t have enough time for each patient and deep diagnostics, and each case is unique. In high patient flow, precious doctors’ time should be released for better patient care and decision-making. Hence a computer-aided assistant is an excellent solution for that challenge.
AI-based solutions for computer-assisted COVID-19 and pneumonia diagnosis are developed to speed up the diagnostic process by classifying illnesses and calculating the probability.
The solution serves as an assistant, aiming to reduce the pressure on medical staff during the COVID-19 pandemic. It reduces the time and effort doctors spend on diagnostic, leaving more space for decision-making and treatment.
The solutions are trained to analyze images and DICOM files. They can classify such illnesses as pneumothorax, pneumonia, bacterial pneumonia, viral pneumonia, COVID-19.
The solutions analyze images and, as an output, represent the probability of each disease it has detected. In this way, it helps doctors understand the risk and make decisions about treatment and observation in time.
Life science organizations are looking to monitor Personal Protective Equipment (PPE) compliance more efficiently and detect missing PPE. Occupational Safety and Health Act (OSHA) Personal Protective Equipment (PPE) standards (in general industry, 29 CFR 1910 Subpart I) require the use of eye and face protection, gloves, and respiratory protection.
VITech developed AI-based software that can help control PPE compliance in real-time, detect safety violations automatically and avoid injuries up to 95% for a safer working environment.
PPE monitoring software is developed to help healthcare providers during the COVID-19 pandemic. It allows organizations to enforce safety regulations in organizations. Powered by computer vision and image analysis, such solutions processes live video footage from cameras mounted in working areas to detect and classify cases of PPE non-compliance in real-time.
Capable of detecting different types of laboratory attire, AI-based software for PPE monitoring automatically sends safety violation reports to a responsible engineer if any required item is missing.
Worksites are monitored in real-time for instant violation reporting, which means better safety. Injuries, laboratory substances contamination, and infection transmission are reduced to the minimum.
As safety is monitored in real-time, any case of injury can be researched to identify what caused it.
High detection accuracy and automated alerts allow safety managers to cut down on manual inspections and focus more on safety drills.
Safety engineers can analyze all video footage for the most common violations to develop more efficient safety drills with all video footage collected.
Centralized, connected camera-based automated monitoring lets you reduce the burden of manual safety monitoring.
Real-time, around-the-clock analytics. Compatible with existing high-resolution cameras.
Related: Benefits of implementing AI in medical
The PPE detection makes it easy to instantly detect and report on PPE violations as it captures and processes CCTV streams for real-time analysis. The solutions see various PPE objects, find their absence, and alert a safety engineer.
Powered by computer vision and image analysis, those solutions processes live video footage from cameras mounted on-premises to detect and classify cases of PPE non-compliance in real-time.
On the one hand, these classes ensure employee safety in hazardous environments. On the other hand, they protect laboratory substances from micro-contaminants and particles that can be brought from outer backgrounds. At the client’s request, any other object may be added to the solution.
The detector automatically measures body temperature if cameras have a thermal imager function. The notification is automatically sent to the responsible manager if a high temperature is detected.
In addition, the solution is capable of face recognition. On request, this optional function can be activated to identify epidemiological safety rules violators among the staff and automatically report this to management.
Our professionals can help you to develop such AI-based solutions that can help understand and improve patient experiences by gleaning insights from B2C communication channels & customer feedback.
The sentiment analysis solutions enable organizations in healthcare to analyze patient feedback — be it reviews, support requests, or phone calls.
Powered by NLP, they analyze texts and converted audio-to-text data to assess message sentiment as positive, neutral, or negative and assign it to a given entity. While scanning through the text, the solutions detect such keywords as brand titles, types of services, contact data, etc.
The data processed is stored in a database for further use in ML applications, or it can be displayed in a dashboard for analysis and review by customer support teams.
Sentiment analysis tools allow looking into patients’ needs to determine how their challenges are resolved by personnel and customer support.
Medical personnel and customer support no longer have to look through and prioritize dozens of incoming messages and requests per day.
More efficient processing of incoming messages means that doctors access patient insights faster to provide higher-quality care.
With sentiment analysis solutions, customer support teams can resolve more requests at a higher rate.
Such solutions can convert unstructured medical data for more innovative analytics and reporting, automatically transforming unstructured data efficiently and without errors.
The plain-text to FHIR/CCDA converters offer a fast and reliable way to convert unstructured ‘text’ data into structured objects.
The solutions developed by VITech can process TXT or HL7 messages with relevant TX/NTE segments to extract useful information (e.g., patient medical history, claims, etc.) and then encode it based on the range/format of measurements.
The extracted data points are provided as CCDA or FHIR, with the original text referred to as a source. Non-digitized documents can be processed after scanning and OCR.
The solutions can identify various data types in unstructured texts efficiently. They enable and accelerate text to CCDA/FHIR conversion.
Powered by machine learning, the solutions can extract and convert data from unstructured documents with an accuracy of up to 100%.
The combination of high accuracy, automation, and efficiency-driven of machine learning allows you to reduce document processing costs.
Drive analytics initiatives like readmission rate prediction and patient deterioration rate prediction using PHI and ePHI in structured formats.
Do you need help by developing software that can detect early signs of diabetic retinopathy faster, achieve earlier disease detection and faster diagnosis through computer-aided, AI-driven screening systems. We are ready to share our experience with you.
The solutions for diabetic retinopathy diagnosis analyze images to detect anomalies and subtle morphological changes in the fundus of the eye. Those, if unchecked, cause diabetic retinopathy and blindness. The manual inspection of fundus images is time-consuming, and it overburdens the ophthalmologists unnecessarily. They inspect multiple fundus screens (most of which display no signs of diabetic retinopathy) to diagnose the condition before critical damage to the optic disk, exudates, and blood vessels is caused.
Those solutions assist ophthalmologists in diagnosing diabetic retinopathy by detecting the first signs of the condition in dozens of thousands of images per minute and arranging “healthy” and “unhealthy” screens in separate folders. It gives the doctors more time to look into disputable cases to diagnose more accurately and efficiently to prevent lifelong blindness in patients at scale.
Trained on thousands of eye screens by expert ophthalmologists, the ML-based solutions detect and classify pathology accurately.
By scaling the expertise of ophthalmologists, the solutions reduce eye screening costs, helping address the needs of low-income families.
The combination of faster screening and more accurate diagnosis allows to spend precious doctor time more efficiently and cuts triage costs.
With deep/iterative learning on board, the solutions learn new features automatically extracted from eye screens to improve outcomes.
Such solutions can predict avoidable after-discharge readmission cases of heart failure, and reduce 30, 60, and 90 days readmission risk with an ML-based risk management model.
Readmission risk prediction ML solutions help healthcare providers focus on high-risk patients who can avoid readmission if additional care is provided, thereby noticeably reducing the risk for 30-, 60-, and 90-day readmissions.
Among our projects we developed solutions that are usually designed to predict heart failure readmission risk, rank patients by their risk level, and identify potentially avoidable causes. The software analyzes a wide range of clinical factors and identifies predictable readmission risk as early as possible.
If there’s a proper custom dataset of non-clinical socio-economic factors, it can be included in the solution’s database for significantly more accurate prediction.
AI-based healthcare applications can help provide faster service, improve diagnostics and analyze information to identify genetic information or trends that would predispose someone to a particular disease. When saving minutes can mean saving lives, AI and machine learning can transform healthcare and every patient.
We specialize in machine learning and data science for healthcare technology organizations and engineering teams working for healthcare. Pre-packed and trained, our machine learning solutions are ready to be implemented in weeks, not months, or customized to fit your request. We can help you to improve your business. Contact us to know more.
Tell us about your project and we’ll be glad to help.
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