Artificial Intelligence in Health Information Systems Essay.

Artificial intelligence describes the ability of a machine to learn the way a human learns, for example, through image recognition and discovering patterns in complex situations. AI in health care is changing the way information gets collected, analyzed and developed for patient care.

The people who work in patient diagnoses, health logistics, radiology and administration are increasingly vital to the operation of any sizable health care provider. Artificial Intelligence in Health Information Systems Essay.Professionals who earn a Master of Science in Health Informatics are uniquely well positioned to step into important roles in these areas, and their services are shaping the ways AI in health care is changing the patient experience.

1. Deep Learning to Diagnose Diseases

One of the areas where AI in health care has shown the most promise is in diagnostics. Early diagnosis is one of the most important factors in the ultimate outcome of a patient’s care. AI deep-learning algorithms are being used to shave down the time it takes to diagnose serious illnesses. The way AI rapidly processes large amounts of information and arrives at likely causes for symptoms can drastically reduce the diagnosis-treatment-recovery cycle for many patients. The effects of this are already being felt in several areas.

In the article “Assessment of Machine Learning Detection of Environmental Enteropathy and Celiac Disease in Children” published in the Journal of the American Medical Association, the authors describe a machine learning tool developed at the University of Virginia School of Medicine that can rapidly analyze thousands of images from children’s biopsies and distinguish between environmental enteropathy and celiac disease more reliably than human doctors, at least at the earliest stages when the two disorders are easily confused. It is hoped this can speed up children’s treatment and reduce rates of stunted growth that can be the result of delayed diagnosis.

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2. Machine Learning and Radiology

A team of researchers at Osaka University has developed a deep-learning algorithm that can reliably diagnose many neurological diseases, including epilepsy. The program scans patients’ magnetoencephalography results, comparing their images with tens of thousands of other scans from healthy patients. It then identifies potential lesions and other abnormal regions in the brain. Since epilepsy often spreads across the brain, identifying abnormal scans as early as possible is crucial to improving patients’ treatment options and ultimate outcomes.Artificial Intelligence in Health Information Systems Essay.

3. Automating Administrative Tasks

While patient care is always the top priority of health care providers, they still have to operate as businesses, which means expensive and time-consuming overhead and administrative costs. AI in health care administration is drastically reducing these expenses, and it’s helping health care providers devote more of their limited resources to the patient care that really matters.

The American Hospital Association estimates that hospitals spend $39 billion annually to comply with the vast requirements of health care laws. Regulatory compliance is a full-time job for an average of 59 employees per medium-sized hospital, and meeting regulatory burdens occupies roughly 25% of doctors’ and nurses’ working hours. AI in health care is radically shifting that time and money back toward patient care. Some examples are intelligent checklists that automatically enforce patient privacy rules and automate the production of disclosures and check-in systems that remind hospital workers to wash their hands and replace equipment on legally mandated schedules.

About 25% of health care costs in the United States go toward billing, usually for insurance. Even in countries with public health insurance, such as Canada, the cost of medical billers can be as high as 12% of the total health care budget. AI in health care billing applications uses smart algorithms to analyze and assign costs, as well as to correctly structure invoice requests and even negotiate with some insurers. Applications such as 1Desk can coordinate the workflow between patients, insurance companies, hospitals, specialists and banks to keep human intervention to a bare minimum in the billing process.

Streamlining the billing process may be one of the most important, if underrated, benefits promised by the increasing use of AI in health care. Billing AI, such as 1Desk, is potentially very attractive for recent graduates in health informatics. The generally nonclinical nature of the program lets practitioners apply their skills virtually anywhere that uses automated billing programs, such as insurance and government offices.

4. Reducing Operational Costs

The operational costs of running a health care organization can be staggering. This is as true for private practice providers as it is for sprawling HMOs with tens of thousands of employees. AI in health care is cutting those costs and letting providers at every level extend their budget further. This results in more resources for the practice, more time for the patient and significantly improved outcomes for many.

One example of how AI is improving health care delivery by cutting operating costs is in the field of joint replacement therapy. An intelligent program known as PeerWell helps patients prep for total joint replacement by guiding the course of pre-op physical therapy. As reported in Healthcare Finance, a study in the Annals of Translational Medicine reported that patients using the AI saw a reduction in surgery costs of $1,215.

Even better, the intelligent prep work the program recommended got so many patients into such good shape prior to their surgeries that the test group saw a reduction of 25% in the time patients spent in the hospital, 80% in the need for post-op home care and a staggering 91% in the number of patients discharged to nursing home care after their operations.

The providers saw similar reductions in operating costs because patients no longer required pre-op care from clinicians. This not only saves time for doctors and expense for patients and their insurers, it also dramatically reduces demand for post-op and rehab facilities, which frees up resources for the remaining patients and improves outcomes for nearly everyone.Artificial Intelligence in Health Information Systems Essay.