Artificial Intelligence in Healthcare – use cases
Healthcare companies can use Machine Learning to increase their bottom lines through gaining competitive advantages, reducing expenses and improving efficiency. They can optimize all areas of their businesses, starting from optimization of logistics processes to risk analysis, to make data-driven decisions that lead to increased profitability.
1) DIRECT MARKETING
To build a successful business in healthcare, the key is to maximize ROI. You can do this by boosting marketing response rates and minimizing misdirected communication. Current state-of-the-art algorithms give the best results, but they also require a specific, deep domain (subject-matter) expertise in Machine Learning.
Marketing to prospects is expensive and is going to be more difficult with the coming of GDPR regulations. There is no point in using default schemas for direct emails and telemarketing, as it is not going to help you target the right customers. Incorrect targeting hurts your brand and makes your prospects feel like they are being spammed, resulting in low response rates that lead to a high cost-per-lead.
Our cutting edge algorithms included in the Data-Driven Sales Optimization System can deliver information about which people are most likely to purchase your product or service and help you yield a higher ROI. To achieve your goals, maintain a favorable image of your company with targeted communication to save time and money.
Our Machine Learning systems will make your marketing campaigns more effective. You don’t have to be an AI expert – that’s our job.
2) HOSPITAL READMISSION RISK
Identify hospital re-admittance and increase the quality of care, decreasing costs and improving the lives of patients at the same time.
Once a patient leaves the hospital, it is extremely hard to monitor and impact his/her health, many patients are difficult to contact and influence. When patients are readmitted to the hospital, their health level has usually dropped even further.
A solution that identifies patients that are likely to return to the hospital. Our algorithms are designed to utilize information about diagnosis, previous medical records and lengths of stay, as well as age and other patient data to provide info about potential physical downturns and take action before the patient is discharged to save costs and time and improve the overall quality of treatment.
We use Machine Learning and Deep Learning to make the whole process of identifying at-risk patients easy for hospitals and other caregivers. You don’t have to be an AI expert – that’s our job.
3) ESTIMATING DISEASE RISK
Many diseases occur suddenly and have a life-threatening impact. It is important to identify patients most at-risk for developing those diseases to make a difference between life and death.
Many different diseases are life-threatening conditions that can occur in patients with certain types of injuries.
We use our precisely crafted algorithms included in Pattern Finder and the Information Extraction System to find patients at-risk for developing particular diseases. With the usage of patient data and historical data, we’ve created an efficient predictive model that learns from historical data to make it all automatic and effective.
We identify disease patterns and rank patients by risk of developing a specific disease using the power of Machine Learning. You don’t have to be an AI expert – that’s our job.
4) MODELING ICU OCCUPANCY
Analyzing and optimizing ICU occupancy means calculating the number of beds needed based on event data and past admission informations, without staffing empty beds.
Patients can not be admitted when an ICU hits capacity. It does not matter whether it is by staffing or the bed space, it results in delays and transfers that impact the quality of patient care and can even have financial consequences.
Our solutions, like the Data Driven Sales Optimization System and Pattern Finder, utilize your current and historical data about patients, events and environment. With this information, we can predict the ICU usage, as well as which current patients are likely transfer to the ICU, which ones can be discharged, as well as the number of new ER patients. With these solutions, you can be sure that each patient gets the best care they need and you do not need to pay for unnecessary resources.
We use Machine Learning to make the whole hospital a more well-organized place and estimate the movements of patients to help you prepare for the future events. You don’t have to be an AI expert – that’s our job.
5) DISEASE PROPENSITY
Technology helps you to identify patients with a high likelihood for a particular disease. Waiting until they seek care results in higher costs and poorer outcomes for everyone.
Identifying patients with a higher likelihood of a particular disease is imperative to managing both disease and costs. While you can treat a patient when they seek care, it is best for all involved to take pre-emptive action.
Our cutting edge algorithms included in the Data-Driven Sales Optimization System, Pattern Finder and Distributed Knowledge System can deliver information about at-risk people. It allows you to better target your marketing efforts and drive response rates much higher, producing better outcomes.
Our solutions use efficient algorithms to let you use demographic data to identify at-risk populations and target them. You don’t have to be an AI expert – that’s our job.
6) DRUG DELIVERY OPTIMIZATION
Pharmaceutical and medical companies ship dozens of millions of drug samples to hospitals and doctors to increase product adoption. When the same location requests many drug samples, the orders can be consolidated. The solution helps to optimize supply chain delivery processes.
To acquire new, early adopters, pharmaceutical and medical companies ship many drug samples and spend significant amount of money. The delivery and supply chain processes must be optimized to avoid unnecessary costs.
A system that utilizes drug delivery data and processes it real-time to fit in the model that predicts whether a given drug sample order should be consolidated with another one to the same location and department. The solution also allows you to minimize the shipping and storage costs with accurate predictions of which samples should be retained in the warehouse.
Our systems analyze very diverse data about the drugs, including time and characteristics, geographical data, as well as information such as doctor characteristics. Our algorithms and technologies generate accurate predictions efficiently. You don’t have to be an AI expert – that’s our job.