How Machine Learning reduces costs spent
on treatment and care
How does ML respond to the real needs of Healthcare organizations
Numbers we know from researches
According to research made by Syft in 2018, hospitals spend over $25 billion more than necessary in their supply chains despite having the ability to save an average of 17.7% in their total supply expenses. Now artificial intelligence (AI) and machine learning (ML) can decrease costs spent for such stuff.
AI can provide physicians and medical staff with tools that give them near-real-time statistics about how certain supplies perform. For example, a typical hospital spends for commodity supplies such as surgical drapes, needles, and labels can be decreased for about 18% .
Another example - the medical and surgical supplies like bone nails and grafts, aortic stents and tracheal tubes, used in moderately invasive procedures. Hospitals spend an average of $13,286 on these items, with the amount accounting for more than a quarter of total spending.
Except of it, AI and ML can save costs for so-called provider preference items, such as spinal rod implants and tibial knee prosthetics. Medical facilities tend to spend more than half of their supply budgets on these things.
Machine learning helps to organize administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.
Hospitals often need to lower readmission rates. While there is no algorithm that eliminates readmissions, it is possible to implement a machine learning model that takes a patient’s data and calculates a risk of readmission based on historical data of similar patient types. Assuming the risk score is very high, so a physician can determine a problem and react properly (e.g., review the patient’s record for a missed complication or medication issue). So, a physician can apply the appropriate treatment and eliminate a potential readmission.
As well, machine learning has solutions for financial decision support. Model can take all patients with outstanding debt and calculate their propensity to pay their bills or their risk of payment default. This will allow financial services to avoid the lengthy and expensive process of unsuccessful collection efforts for patients who are determined unable to pay and, instead, flags them for charity care.
Such models help to forecast demand on limited charity care resources. At the same time, they may define those who can pay, so financial services can focus collection efforts accordingly.
ML affects physicians and hospitals and plays a key role in clinical decision support, enabling earlier identification of a disease and appropriate treatment plans to ensure optimal outcomes.
It can also be used to show and educate patients on potential disease pathways and outcomes given different treatment options. It can impact hospitals and health systems in improving efficiency, while reducing the cost of care and treatment.
Machine learning in healthcare can help us benefit in the following areas:
Disease identification and diagnosis becomes more exact because of automated scanning processes performed by trained machine learning models
Personalized medical treatment is one of the most important industry challenges because every patient expects for a better cure, more attention paid, as well as more effective prescribed medicines.
Medical imaging gives visual representations of organs and tissues on the cell level, which contributes to prognostication and disease identifying.
Smart health records demand both security and accessibility: a machine remembers and stores all data, readying it for global research;
Drug discovering and manufacturing process attempts to be low-cost, effective, not harmful, and with a low risk of side effects;
Disease prediction is all about the social impact of medicine and quality of life improvements.
Machine learning is predicted to be used in administrative, financial, operational, and clinical areas to improve monitoring of people’s health, assist in disease predictions, and protect data. Let’s see how it works.
ML models can change the way doctors practice, enhance their current role and help professionals in their everyday routine like:
finding effective personal medical treatment diagnostics
providing health records
predicting drug effects
storing and securing patients’ data
managing working time at hospitals
The most important thing at the moment is to replace slower, outmoded risk prediction rulesets with machine learning models.
The most common use cases of ML solutions in Healthcare:
Using machine learning we can reduce the cost of supporting EMR [electronic medical records] systems by optimizing and standardizing the way those systems are designed. The ultimate goal in this case is improved care at a lower price.
Machine learning solutions can also be used to predict illness and treatment to help physicians and payers predict population health risk by identifying patterns and surfacing high risk markers and model disease progression and more.
Machine learning can help to enhance health information management and exchange of health information, by upgrading workflows, facilitating access to clinical data and improving the accuracy and flow of health information.
ML models can help pathologists make quicker and more accurate diagnoses and identify patients that might benefit from new types of treatments or therapies.
Improve the speed and accuracy of breast cancer diagnosis.
Analyze oncology data, providing insights that allow oncologists, pharmaceutical companies, payers and providers to practice precision medicine and health.
Machine learning solutions can help to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning, among other things.
Machine learning and data science combined with appropriate laboratory technology can help to develop drugs for faster treatment of patients at a lower cost.
Via its machine learning platforms we can perform automated ML and data pre-processing, which improves accuracy and eliminates time-consuming tasks that are typically done by humans in different sectors of the healthcare industry, such as biopharmaceuticals, precision medicine, technology, hospitals and health systems.
Machine learning can be used for disease mapping and treatments in oncology, neurology and other rare conditions. Using biology and patient data, such solutions allow healthcare providers to take a more predictive approach rather than relying on trial-and-error.
Machine learning models can be used as an automated, 24/7 concierge for healthcare” via text, email, Slack, video-conferencing. It may help employers and insurers save time and money on healthcare by making it easier for people to understand their benefits, locate the least expensive providers, enabling employees or members to understand their benefits and find lowest cost providers.
Readily available solutions
As you can see, these days machine learning solutions play a key role in many health-related realms, including the development of new medical procedures, the handling of patient data and records and the treatment of chronic diseases. Our healthcare solutions are developed in this area as well. Some of them are described below.
Detector for COVID-19 and other lung diseases is an application based on the maсhine learning model, which can identify and classify 14 types of inflammatory lung diseases such as Bacterial Pneumonia, COVID-19, Chlamydophila, Fungal Pneumonia, Klebsiella, Legionella, Pneumocystis and other viral types of pneumonia, ARDS, Pneumothorax, and Hydrothorax.
The web application installed on your computer reads and analyzes the images obtained from the digital X-ray machine in png, jpeg and DICOM files formats. The algorithm identifies the infected areas that the physician should pay attention to for diagnosis. Based on the reference information provided in the development the algorithm shows the likelihood of a particular disease as a percentage. As well, users have the ability to share the received data with colleagues through the network for consultations which reduces the probability of making the wrong diagnosis. In fact, the application is a physician's assistant. And its goal is to reduce the burden on the radiologists when the healthcare systems are operating beyond their capabilities and to reduce the likelihood of a diagnostic error caused by overwork and psychological pressure.
The Computer-Aided Diagnosis of Retinopathy solution for diabetic retinopathy diagnosis is based on image analysis.This application is employed to diagnose diabetic retinopathy at an early stage. The problem is, that diabetic retinopathy can long remain unrecognized because of a lack of symptoms. It is difficult to cure it at the late stage. And it causes critical damage to optic disk, exudates, blood vessels, and blindness. So, that is critical to diagnose it at an early stage.
The Computer-Aided Diagnosis of Retinopathy solution for diabetic retinopathy diagnosis helps to detect subtle morphological changes in the fundus of the eye that, if unchecked, causes diabetic retinopathy and blindness. The manual inspection of fundus images is not so efficient, takes a lot of time and needs a lot of manual work and concentration. A physician has to look over multiple fundus screens, most of which display no signs of diabetic retinopathy.
It helps the ophthalmologist to diagnose diabetic retinopathy by comparing dozens of thousands of images per minute. ‘Healthy’ and ‘Unhealthy’ screens will be stored in separate folders, which helps physicians to diagnose more accurately and efficiently and can save more time.
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