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Anticipated Impact on Healthcare of Recent AI Developments

May 28, 2019

Artificial Intelligence AdvantagesJust a couple of years ago, artificial intelligence (AI) as it applies to healthcare was considered a promising but distant goal on the horizon. Even Partners Health Care as recently as 2018 set a 10-year time frame for the AI technologies poised to revolutionize care within a decade.

Within just the past 12 months, however, research has made enough progress to roll out the following developments that will impact how we provide healthcare for patients and run practices from the top down.

Sixty of these Partners members, among others, have narrowed down the twelve technological tools that are expected to have benefits immediately for both providers and their patients. Among them are:

Gaps in mental healthcare are expected to narrow

Among the most troublesome mental health crisis of our times is the opioid-abuse epidemic, one of numerous mental health issues affecting almost 20 percent of Americans. Treatment for these disorders and illnesses requires that new approaches stress the ability to connect patients with services. Among these are smart speakers using natural language processing (NLP) in cognitive and group therapy for treating depression, substance abuse and eating disorders. Randomized clinical trials that back AI-based tools have shown good results so far.

Increasing access to acute stroke care, especially in rural settings

One of the biggest benefits of AI is the ability to close gaps in high-quality imaging that identify both clot and bleeding location and the type of stroke. AI-driven tools currently under research will be able to automate stroke detection, supporting decision-making for appropriate treatment.

Especially in rural settings or where care resources are low, using these algorithms can compensate for lack of on-site specialists while improving the chances of the best treatment and recovery.

Streamlining clinical process workflows with voice-first technologies

Natural language processing (NLP) is already used in clinical workflows but new applications for smart speakers will capture free-form conversations and translate them into more structured documentation, thus relieving providers of a huge EHR reporting burden

With some companies such as Amazon’s consumer-facing products already HIPAA-compliant, patients may soon have voice-first options for managing chronic disease.

Identifying persons at high risk of victimization by domestic violence

One of the most frustrating aspects of treating domestic violence victims is underreporting, according to radiologist Bharti Kurana, MD, of Brigham’s Women’s Health (BWH). By using AI to identify and flag injury patterns or mismatched patient-reporting histories compared to types of fractures showing up on x-rays, providers can learn when a more exploratory conversation is needed.  

Reducing providers’ administrative burdens and tasks

With data from the Center for American Progress showing that medical billing for insurance costs most providers close to $282 billion annually, it’s not surprising that medical coding and billing are perfect for using NLP and machine learning. Translating into free-text directly into standardized code can reduce the complexity of the process and thereby mistakes as well as the need for intense regulatory oversight, as many healthcare organizations have already learned by adopting the technology. 

Sharing of health data through more fluid and rapid exchange of information

Fast Healthcare Interoperability Resources (FHIR) is another technology developed to standardize information in the exchange of health information among providers and other professionals, and includes clinical, diagnostic, and related healthcare and administrative data. Precautions need to be in place to protect data privacy when moving across varying systems, but benefits should outweigh risks.

Improving access to eye health and detecting ocular disease

Ophthalmology is expected to undergo major changes with the introduction more robust and accurate AI algorithms. Conditions ranging from diabetic retinopathy to glaucoma can be more readily assessed by giving physicians better tools for identifying and treating ocular diseases sooner, particularly for those in areas with less access to regular eye care.

Monitoring brain health in real-time

Whether predicting epileptic and other types of seizures through EEG test readings to earlier identification of dementia, AI permits physicians access to more continuous and detailed brain health measurements and data. Secrets of cognitive function and neurological disease are interpreted by AI resulting in improved quality of life for more patients.

Improved malaria detection within developing countries

With nearly half a million people dying in 2017 alone from mosquito-borne malaria, deep learning tools can automate and improve the detection of parasites in blood samples for those providers who must work without pathology experts. Software which can run on a smartphone connected to a microscope-mounted camera can expand expert-level diagnosis and hasten treatment.

Integrating and augmenting diagnostics and decision-making

Pathology is a central component of the diagnostic process, and in turn, the underpinning of much healthcare. AI helps triage and prioritize cases to speed up the care process and automate the more routine tasks to ensure that key information isn’t missed within the large volumes of test and clinical data.

Predicting suicide risks

Identifying high-risk patients from self-harm is a difficult and imprecise task, due to complex socioeconomic and mental health factors. As the tenth leading cause of death in the US, using AI’s NLP and other methodologies to check electronic health records, social media posts, and other free-text documents to identify potential risks of harm and permit timely intervention.

Revolutionizing medical imaging

Radiology is already benefiting from more accurate readings of complex images, leading to earlier disease detection offered by AI’s machine learning. While some challenges in the form of understanding algorithms remain among radiologists, making testing less expensive and more accessible to a wider population to equalize desperately-needed care around the world.

AI, medical billing and practice management services

When it comes to utilizing the latest technologies in coding and related tasks, M-Scribe remains in the forefront of new developments that make medical claims billing and follow-up faster and more accurate by our experienced personnel using state-of-the art technologies. To learn more about how our services can work for you, regardless of your practice’s size or specialty, contact us at 770-666-0470 or by email for a free confidential analysis of your practice’s needs and goals.

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