Healthcare Solutions

There may be no more challenging environment that could benefit from AI decision support than healthcare where the number of variables to consider, the sometimes urgent need to make decisions when seconds matter, and with so much on the line. Qualetics’ blends the responsiveness of our AI Management System with expertise in AI and Machine Learning to solve challenging patient care challenges. Consider the examples below and how Qualetics can apply to deliver decision support to healthcare professionals and improve patient outcomes.

Healthcare Solutions

There may be no more challenging environment that could benefit from AI decision support than healthcare where the number of variables to consider, the sometimes urgent need to make decisions when seconds matter, and with so much on the line. Qualetics’ blends the responsiveness of our AI Management System with expertise in AI and Machine Learning to solve challenging patient care challenges. Consider the examples below and how Qualetics can apply to deliver decision support to healthcare professionals and improve patient outcomes.

Qualetics’ field of work in healthcare includes experience in a broad range of settings including remote healthcare delivery, clinical and hospital settings as well as the dental field.

The AI technologies we’ve employed include detailed Descriptive, Diagnostic, and Predictive Analytics, Deep Learning model development and training, Natural Language Processing and Image Analysis of the chest and dental X-rays applying complex logic and ML processes. 

Below is a summary of our Healthcare Experience:-

Objective: To improve decision support for doctors, to analyze a patient’s demographics, history, and symptoms to deliver a health risk prediction.

Patient Audience: Patients in remote areas where healthcare outreach efforts are essential, as well as in support of clinics for healthcare screenings.

Analyzed Result: An AI-generated patient risk diagnosis providing the status as well as any likely risks associated due to pre-existing conditions. Example: “The initial assessment suggests pre-existing cardiovascular & diabetes diseases, along with current conditions of novel coronavirus disease that may need further evaluation of risk and treatment.  The patient is predicted to be at medium risk as per risk assessment.”

Objective: To predict the likelihood of a patient being readmitted to a hospital for a recurring problem based on an evaluation of the patient’s condition at the time of hospital discharge.

Patient Audience: Patients being discharged from a hospital after treatment.

Analyzed Result: An AI-generated patient readmission risk diagnosis indicated by a score designed to the specification of the healthcare customer to indicate the level of risk on a numeric scale or a LIKERT scale (Ex. High, Low, Medium, Likely, Unlikely etc.)

Objective: Where a patient’s alignment issues may call for dental treatment and there was a need to apply the dental industry’s PAR index scoring measure (Peer Assessment Rating, a commonly accepted measure of orthodontic treatment) to determine the level of dental irregularity or malocclusion and assess relative improvement throughout treatment.

Patient Audience: Dental patients with alignment issues receiving corrective dental services

Analyzed Result: Scores measuring alignment and spacing of patient’s dental conditions over time.

Objective: To provide color-coded image decision support for doctors evaluating chest X-rays.  The analyzed result provides color-coded shading associated with such chest-related diseases as viral and bacterial pneumonia, tuberculosis, Covid-19, and other conditions.

Patient Audience: The algorithm was trained to provide the intended result for any human chest X-ray.

Analyzed Result: Initial training resulted in the algorithm achieving model accuracy of 85% with a 20% false positive rate and 19% false negative rate.

Objective: To analyze the success of two different diabetes treatments on an extensive patient population to understand treatment effectiveness relative to patient factors.

Patient Audience: Patients treated by a diabetes clinic network operated by a major Indian hospital network.

Analyzed Result: Presents treatment effectiveness on a time series graph correlating the duration of the treatment, treatments used, and changes in the condition of patients over time.

Objective: To provide a predictive early warning for incoming hospital patients. 

Patient Audience: In support of hospital or emergency room intake procedures this is designed to provide predictive insight providing staff early warning of a pending cardiac event before cardiac arrest occurs.

Analyzed Result: A Cardiac Risk prediction score designed to the specifications of the healthcare customer indicating the level of risk a patient has concerning heart disease.

Objective: To facilitate more accurate, and productive screening processes for a follow-on visit with the doctor, this algorithm is meant to predict disease possibilities and respond to the symptoms presented selectively prompting for other symptoms that would effectively exclude some disease conditions and narrow the possible diagnosis for the doctor’s attention.

Patient Audience: Underserved populations in remote locations where the patient rarely if ever can be evaluated or treated by medical professionals.

Analyzed Result: The models have been built and data is currently being collected to initiate the training and testing process.