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How machine learning reduces costs spent on treatment and care

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.​

  • 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 spending on commodity supplies such as surgical drapes, needles, and labels can be decreased by about 18%.
  • Another example – hospitals spend an average of $13,286 on medical and surgical supplies like bone nails and grafts, aortic stents, and tracheal tubes used in moderately invasive procedures. This amount accounts for more than a quarter of total spending.
  • Except for it, AI and ML can save costs for spinal rod implants and tibial knee prosthetics, so-called provider preference items. Medical facilities tend to spend more than half of their supply budgets on these things.

Machine learning helps organize administrative processes in hospitals, map and treat infectious diseases and personalize medical treatments.

Hospitals often need to lower readmission rates

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.

As well, machine learning has solutions for financial decision support

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 in healthcare can help us benefit in the following areas:

  • Disease identification and diagnosis become more exact because trained machine learning models perform automated scanning processes.
  • Every patient expects a better cure, more attention paid, and more effective prescribed medicines. It is why personalized medical treatment is one of the most critical industry challenges.
  • Medical imaging enables visual representations of organs and tissues on the cell level, contributing to prognostication and disease identification.
  • Innovative health records demand both security and accessibility: a machine remembers and stores all data, readying it for global research.
  • Drug discovery and manufacturing process attempt to be low-cost, practical, not harmful, and low risk of side effects.
  • Disease prediction is all about the quality of life improvements and the social impact of medicine.

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

It is crucial to replace slower, outmoded risk prediction rule sets with machine learning models.

The most common use cases of ML solutions in healthcare:

  • Using machine learning, we can optimize and standardize how EMR [electronic medical records] systems are designed, which will lower the supporting cost. 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 enhance health information management and exchange of health information by facilitating access to clinical data, upgrading workflows, and improving the accuracy and flow of health information.
  • ML models can help pathologists identify patients who might benefit from new treatments or therapies. ML helps also make quicker and more accurate diagnoses.
  • Improve the accuracy and speed of breast cancer diagnosis. ​
  • Analyze oncology data, providing insights that allow pharmaceutical companies, oncologists, payers, and providers to practice precision medicine and health.
  • Machine learning solutions can help to differentiate between healthy anatomy and tumors. It uses 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 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. It improves accuracy and eliminates time-consuming tasks that humans typically do in different healthcare industry sectors, such as biopharmaceuticals, precision medicine, technology, hospitals, and health systems.
  • Doctors can use machine learning for disease mapping and treatments in oncology, neurology, and other rare conditions. Such solutions use biology and patient data and allow healthcare providers to take a more predictive approach rather than trial-and-error.
  • Machine learning models can be used as an automated, 24/7 “concierge for healthcare” via text, email, Slack, and video-conferencing. It may help insurers and employers save money and time on healthcare by making it easier for people to see their advantages, enabling employees or members to understand their benefits and find the lowest cost providers by locating the least expensive providers.

Readily available solutions

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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