AI For Health And Hope: How Machine Learning Is Being Used In Hospitals

Have you ever found yourself sitting for hours in a busy waiting room at the ER? It’s stressful, you’re in physical pain and everyone is rushing around you, focused on the massive number of other patients waiting, too.

Luckily, hospitals are beginning to use machine learning solutions to streamline the waiting room process. That’s not all. The healthcare industry is introducing AI to combat many issues in the field.

AI in hospitals can not only ease hospital patient flow, but it can also help develop pharmaceutical drugs, keep and analyze data and patient records, and even help diagnose illnesses like cancer.

When people spend less time in the ER waiting room, more people can be treated in a timely manner. When life-saving drugs are developed and proceed through clinical trials efficiently, more people can access these cures more quickly. When AI can decrease human error in cancer diagnoses, cancer patients have a much better chance of survival.

AI in hospitals can not only ease hospital patient flow, but it can also help develop pharmaceutical drugs, keep and analyze data and patient records, and even help diagnose illnesses like cancer.

When people spend less time in the ER waiting room, more people can be treated in a timely manner. When life-saving drugs are developed and proceed through clinical trials efficiently, more people can access these cures more quickly. When AI can decrease human error in cancer diagnoses, cancer patients have a much better chance of survival.

These are some of the most influential uses for machine learning technology in hospitals and throughout the healthcare field.

Record Keeping

Record keeping AI in hospitals improves the management of electronic health records (EHRs). It saves healthcare workers’ time and serves as an expert analytical tool.

Some hospital machine learning technology recognizes and scans handwritten forms quickly. This streamlines the process of transferring paper documents to an online platform. Other AI listens to conversations between a doctor and their patient and converts them to written text. They use natural language processing technology.

These are some of the most influential uses for machine learning technology in hospitals and throughout the healthcare field.

Record Keeping

Record keeping AI in hospitals improves the management of electronic health records (EHRs). It saves healthcare workers’ time and serves as an expert analytical tool.

Some hospital machine learning technology recognizes and scans handwritten forms quickly. This streamlines the process of transferring paper documents to an online platform. Other AI listens to conversations between a doctor and their patient and converts them to written text. They use natural language processing technology.

Clinical research is another area of medicine that is significantly affected by the use of AI in hospitals.

AI improves the registration process. It helps doctors and clinical researchers reach out to potential participants for the trial. That’s because machine learning technologies can access hospital databases of patient EHRs. Then they find people who fit the conditions for a particular trial.

Disease Diagnosis

When it comes to new AI innovations in the healthcare field, machine learning diagnostic tools are some of the most exciting and life-changing recent discoveries.

Research suggests that approximately one-third of all AI SaaS companies in the healthcare space focus most, if not all of their resources, on diagnostics. AI in hospitals can now assist doctors as an integral diagnostic tool that will change the outcome of many patients.

Experts developed AI that can scan a patient’s data for significant symptoms and patterns that could point to a serious illness or condition that is otherwise hard to detect, better yet diagnose.

AI is also an increasingly important tool in the field of oncology. Treating and diagnosing some forms of cancer is notoriously tricky. That’s why researchers are building machine learning software that can examine tissue for cancerous cells with precision.

In fact, one study on breast cancer diagnostics and AI produced results that show machine learning decreased the human error diagnosis rate by a whopping 85%.

The study was conducted by a team of expert AI developers from the Beth Israel Deaconess Medical Center (BIDMC) at Harvard Medical School, who created a computational system designed to detect metastatic cells in lymph nodes. 92% of the time, the system was accurate and successful in its analyses.

Hospital Patient Flow

To see as many patients as efficiently as possible, a hospital must have a streamlined hospital patient flow. This ensures that every patient is treated promptly and the nurses and doctors don’t get burnt out.

If a hospital’s patient flow is knocked out of balance by a large influx of patients or a staffing issue, the whole treatment process is knocked off-kilter. Patients end up spending too much time in the waiting room while other surgeries and procedures get backed up and even canceled. It is stressful at worst and life-threatening at best.

Thankfully, hospital machine learning technology can facilitate a smooth and efficient hospital patient flow that maintains a healthy balance. It solves many problems that hospitals faced in the past when the patient flow is backed up.

It’s perfect for managing space, and the healthcare workers can easily track available rooms and beds, which helps reduce surgery postponement and cancellations. Hospital patient flow AI can conduct analyses that determine, on average, when the hospital is extra busy. Then, it will arrange for more staff to be on shift at those specific times.

Pharmaceutical Innovation

AI is revolutionizing the pharmaceutical industry. That’s because it helps with drug discovery, formulation, screening, manufacturing and even assists all the way through to drug distribution.

Here’s one example of how scientists formulate new drugs to treat various illnesses: AI, like the quantitative structure-activity relationship (QSAR)-based computational model, can formulate predictions of a drug’s target protein and potential interactions between the drug and the protein.

That’s just the first step. These machine learning techniques can help pharmacists synthesize new drugs by predicting potential chemical reactions and the limits of a drug’s reaction yield.

Drugs must also be screened for safety and to prove functionality. AI in healthcare can help determine the bioactivity and toxicity of a drug before it’s tested on a patient. It can also help assemble patients for clinical drug research trials. Researchers can find potential patients by searching through hospital databases for people who match the trial’s criteria.

Conclusion

Machine learning is changing the world of medicine for the better every day. It helps doctors diagnose more accurately, nurses admit patients more quickly, and pharmacists develop drugs more efficiently and safely.

 

Original post: https://www.forbes.com/sites/forbestechcouncil/2022/02/16/ai-for-health-and-hope-how-machine-learning-is-being-used-in-hospitals/

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