June 02, 2022
14 minutes read
How does ML respond to the real needs of healthcare organizations?
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. Artificial intelligence (AI) and machine learning (ML) can decrease costs spent on such stuff.
Machine learning helps organize administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.
While no algorithm 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, a physician can determine a problem and react appropriately (e.g., review the patient’s record for missed complications or medication issues). So, a physician can apply the appropriate treatment and eliminate potential readmission.
The model can take all patients with outstanding debt and calculate their propensity to pay their bills or their risk of a payment default. This will allow financial services to avoid the lengthy and expensive process of unsuccessful collection efforts for patients who can not pay and 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 that financial services can focus collection efforts accordingly.
ML affects physicians and hospitals and plays a crucial role in clinical decision support, enabling earlier identification of disease and appropriate treatment plans to ensure optimal outcomes.
Doctors also can use ML to inform patients on potential outcomes and disease pathways after different treatment options. It can reduce the cost of care and treatment and impact hospitals and health systems in improving efficiency.
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:
It is crucial to replace slower, outmoded risk prediction rule sets with machine learning models.
As you can see, these days, machine learning solutions play a crucial role in many health-related realms, including the handling of patient data and records and the treatment of chronic diseases, and the development of new medical procedures. Our healthcare solutions are developed in this area as well.
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