AI/ML Trends Expected to Rule Healthcare in 2022

Introduction

As per the recent research, by 2025, around 50% of healthcare organizations will implement some kind of artificial intelligence into their operations globally. In the US alone, McKinsey estimates that leveraging these technologies in the pharmaceutical industry will generate $100B annually.
AI and ML are being leveraged for R&D and drug discovery in the industry, and they will continue to gain traction as digital transformation capabilities are accelerating globally.

CVS Pharmacy, for example, uses Machine Learning personalization to increase prescription refill rates and reduce gaps in treatment. Furthermore, a start-up from Toronto, Canada is able to scan more than a hundred thousand media sources in over 65 different languages every single day with their application “BlueDot” that helps them to predict dangerous disease outbreaks. ML has also helped identify protein fragments, allowing vaccine development for Covid-19 at a record pace. Artificial intelligence and machine learning are already finding their ways and usage in healthcare to have a huge impact in battling covid-19 and saving human lives.

The healthcare industry can be classified overall into the following 5 sectors, each of which can benefit greatly from AI/ML.

• Healthcare providers and facilities like hospitals, surgery centers, nursing homes, or doctors and physicians
• Medical equipment like diagnostic equipment, orthopedic devices, medical instruments
• Distributors and wholesalers, like pharmacies, distributors of equipment to healthcare providers
• Health insurance and managed care
• Pharma, life sciences, and biotechnology

Healthcare software development companies are developing solutions in order to enable organizations involved in the manufacture and distribution of drugs to use technologies that allow greater efficiency, better outcomes in the patients, and overall patient safety by trying to predict reactions between certain types of drugs. This blog will discuss a few trendy use cases that integrate some form of these technologies, and what is the future for these use cases.

Combatting Deficit in Drug Availability using Machine Learning

As seen in the recent pandemic, life-saving prescription drugs can fall short on availability and fail to meet the demand. To be able to combat this, custom healthcare software development companies develop solutions that leverage machine learning techniques such as linear regression to predict the shortage of drugs on the basis of the current supply trend.

If drug availability is nearing shortage, drug distributors can be intimated. The leadership can make decisions around sourcing the medicines elsewhere, or provide alternatives to the drug. The custom healthcare software solution can be configured to suggest alternative drugs using algorithms that find drugs with the same or similar components.

Tracking Inventory to avoid Drug Diversion and Pharmaceutical Logistics

Similar to availability, there is an ongoing problem of drugs being “diverted” to other non-intended places of use, arising from theft by internal staff members. There are significant risks of illegally sourcing drugs in this manner, aside from the shortage resulting from it, as intimated by CDC. This can be averted by using custom healthcare software solutions that leverage machine learning techniques such as data matching. Drug diversion cannot easily go undetected when such a solution is in place. Furthermore, uality control, material waste reduction, improved production reuse, and predictive maintenance are all possible with AI. Machine learning can assist in forecasting and preventing over- and under-demand, as well as resolving supply chain issues and production line breakdowns.

Predicting Patient Adherence

Based on historical data, the adherence level of a patient can be calculated to predict the likelihood of patients, especially chronic patients, to pick up their prescription drugs. Furthermore, based on such solutions, intervention protocols by the healthcare provider can be set up depending on the risk of ill effects of non-adherence on the patient.

Repurposing Existing Drugs

Artificial Intelligence has great potential to be able to identify drug compounds and their effects on pathogens. Machine learning techniques use supervised learning algorithms, which use historical data to predict future effects. This can be useful in swiftly finding drugs with compounds that can be effective against new threats.

Automated Drug Prescription

If a healthcare provider is able to identify symptoms in a patient and feed those into a system leveraging machine learning techniques, supervised ML algorithms can automatically prescribe the drugs in a prescription signed by the healthcare provider. This can be integrated into telemedicine solutions, where during the consultations the healthcare provider can identify and feed the symptom information into the custom healthcare software solution.

Clinical Trials

By enhancing sample screening processes for clinical trials, AI can affect the future of pharmaceuticals. AI can help ensure trial adoption by swiftly evaluating patients and determining the best prospects for a given trial. Additionally, the technology aids in the removal of samples that may obstruct clinical trials, reducing the need to compensate for those factors with a large trial group.

Global healthcare leaders have gained tangible insights from the pandemic, and how to avert the situations it posed in front of us. This includes averting unforeseen developments in the pharmaceutical industry operations. As per the use cases mentioned above, among a plethora of others, it is important to find avenues to leverage AI/ML techniques for life-saving operations, based on the problems identified during the pandemic and other healthcare crises. If your organization has faced problems similar to the above, leverage the expertise of a custom healthcare software development provider

Pratiti Technologies has been providing custom healthcare solutions using technologies such as IoT, machine learning, cloud, and edge to solve problems such as the above. It is important to delegate discrete technology implementation to a technology partner with tangible and proven industry experience. Contact us today to begin your digital transformation journey.

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