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AI applications in healthcare smooth providers' operations

Healthcare providers are embracing AI systems at an increasing rate as the potential benefits for both patient care and operational management become more apparent.

Artificial intelligence and machine learning are transforming many areas of the healthcare industry, ranging from patient-facing and customer service activities to improvements in overall care, diagnosis and treatment.

Many of the opportunities from AI applications in healthcare relate to the sheer quantity of data produced by healthcare providers and the opportunity to identify patterns and augment the capabilities of existing physicians, clinicians and staff.

Intelligent bots provide an extra pair of hands and eyes

Over the past few decades, healthcare facilities have seen increasing adoption of robotic technology in hospitals and treatment facilities, as well as in surgical and operating rooms. Providing extreme precision, skill, dexterity and control to surgeons, robotic-assisted surgeries enhance operating room capabilities, resulting in fewer mistakes and complications as a result of more precise incisions and tool placement. This can also produce smaller scars, less damage to body parts, reduced inflammation and quicker recovery time for patients.

In addition, AI is seeing broader adoption as part of medical imagery and diagnosis. Through the use of trained image recognition and machine learning-based diagnosis cognitive technology, AI-enabled systems can provide assistance to analyze medical images from a range of radiology sources. AI-enabled systems are improving accuracy rates in detecting early signs of cancerous formations in the body, determining and identifying skin ailments, and other analyses.

These intelligent systems are also speeding up the time required to analyze certain medical tests, such as bacterial swabs, and can provide recommendations for treatment, medication and dosage. As a result, medical diagnostic facilities are seeing fewer errors and misdiagnoses, reduced operational costs, and faster turnaround time on lab results.

Furthermore, by using machine learning-powered analysis that can identify patterns and spot anomalies, healthcare facilities can provide proactive care and treatment. The Cleveland Clinic, for example, is using predictive analytics to identify potential at-risk patients under ICU care.

Through a partnership with Microsoft, the Cleveland Clinic has been able to use historical data collected from ICU patients to make predictions about whether current patients will need vasopressors to prevent the occurrence of cardiac failure. This has enabled doctors and nurses to use technology to become proactive and intervene with patients before situations get too serious.

Other hospitals have used predictive analytics to speed up bed assignments in the ER, dispatch ambulances faster, minimize the risk of infections and falls, streamline administrative tasks for doctors, and provide more efficient discharges.

AI furthering the telemedicine revolution

Adequate medical care is inaccessible to many people throughout the world. These individuals might not be in a location where high-quality care is available or might not have access to reliable transportation, may be too sick to travel, may not have the time to go or may be otherwise unmotivated to seek the care they need.

The evolution of telemedicine and other forms of virtual medical care has made care more accessible to these chronically sick patients. AI applications in healthcare have enabled new levels of care through the use of chatbots and intelligent assistants that are available to patients on-demand 24/7, providing immediate answers to medical questions, personalized care responses and escalation to remote physicians that can step in to handle immediate care issues. These technologies have enabled improvements to overall quality of care, patient satisfaction and treatment outcome.

The expanded use of wearable devices has further enhanced the ability to provide personalized, on-demand and more effective treatment to individuals. Further enhancing these technologies, a number of companies are using AI-enabled pattern matching to extract actionable insights from heart rate, daily exercise, sleep pattern and daily food consumption data.

The combination of data quantity and quality paired with AI-enabled insights and pattern matching are giving healthcare professionals the ability to better understand the day-to-day behaviors of their patients, the effectiveness of treatments and the ability to individualize care for patients.

AI improving healthcare operations

In the healthcare back office, AI and machine learning are making a big impact on improving facility efficiency and eliminating medical billing-related problems. AI-enabled systems are being used to spot fraud and billing errors that previously required manual reviews of medical claims -- a time-consuming and error-prone process. These AI systems are able to audit and examine all the billing data to spot coding errors and anomalies and identify suspect charges or false claims with greater accuracy and speed than humans.

Machine learning algorithms are trained on historical claims data to recognize medical terms and billing codes. The algorithms can then make sure new incoming claims are filed correctly, speeding up payment and reducing errors. As a result, providers are applying machine learning and natural language processing to streamline workflows, improve coding accuracy and potentially increase revenue.

Furthermore, healthcare providers are using AI systems to spot billing fraud and abuse before claims are paid. Traditionally, fraud is spotted after the fact either by audits or someone tipping off investigators. By analyzing large amounts of historical claims, systems can learn what looks normal and more quickly spot anomalies. By using advanced analytics, investigators can flag potentially fraudulent reports in near real-time.

Through the combined use of AI applications in healthcare, diagnosis, treatment and customer care, hospitals, care practitioners, health insurers and the health industry as a whole hope to dramatically reduce the cost of care while improving treatment outcomes, reducing risk and increasing overall patient satisfaction.

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