AI Tools Will Improve Engagement For Patients And Clinicians
As the rise of big data in healthcare information technology (HIT) looms in the wake of global pandemic setbacks, the need for streamlined information exchange between clinicians and clinicians and patients becomes more vital than ever. So many obstacles in healthcare have surfaced since early 2020: high costs, supply and staff shortages, and new regulations. Add to these a rapidly aging generation along with a national epidemic of poor health, and the U.S. will face an urgent need for changes to all of healthcare. Provider burnout is a real effect of the current environment. The American Hospital Association recently linked staffing shortages to delays in patient discharges. A deficit of clinicians by 15 million is anticipated to leave acute healthcare by the end of the decade.
Existing Communication Barriers and Inequities
With all the imminent developments in healthcare, the prospect of well-developed communication within all points is crucial. There are currently several barriers to improved treatment and care due to communication inequities. Due to the amount of information to be communicated regarding diagnoses, treatment, and follow-up information, there is a clear need for technology platforms with the proper remote interpreting capabilities and bilingual and implicit bias training for patients. 
Digital and Health Literacy Imbalances
The two often go hand-in-hand: health literacy and digital literacy. Low-income patients sometimes need help understanding terminology related to their physicians’ diagnoses and treatment plans. While clinicians are practiced in interpreting the information they have to share with patients, sometimes specific terms cannot be expressed in further secular means. Even if a patient has an idea of their state of personal health, most typically do not research possible conclusions or treatment for their suspected ailment before meeting with their doctor. Limited health literacy also creates more work for clinicians, as patients may misread test results or diagnoses.
In the case of increasingly common and necessary virtual care, many patients—especially those of low income—lack the education and literacy required to comprehend information received via virtual care
Low-income and elderly patients are at greater risk of lacking the literacy needed to access digital information. There is a little-discussed matter of fear of technology in the elderly population (labeled technophobia). The rapidly aging baby boomer generation is faced with an onslaught of technology that is regularly upgrading, and there is a level of anxiety for these individuals accompanying the constant technological changes that can contribute to digital illiteracy in the group. Unfortunately, seniors may hesitate to use or attempt to understand language or processes related to healthcare technology for a number of reasons:
- Decreased cognition and decline in memory required for grasping digital concepts due to shrinkage of brain tissue or the slowing of neuron communication and blood flow to the brain
- Increased physical limitations of the body, including motor skills, sight, and hearing
- Distrust/skepticism and lack of understanding of the internet or social media
- Disinterest in learning new things
- Dislike of technology; conviction that it is unneeded or not beneficial
- Lack of confidence in using technology and devices
Communication gaps also impact limited or non-English speakers. As individuals migrate to the United States due to circumstances that may prevent them from preparing to have contact with numerous English-only speakers, there is a clear need for virtual healthcare services that are tailored to a considerably limited and non-English-speaking population.
Patients may require greater accessibility due to limitations in vision, hearing, or speaking. Technology must be accommodating for those facing such challenges. Screen readers, real-time captioning, remote ASL (American Sign Language), and speech-to-speech interpreters who can properly translate medical information are necessary.
Individuals with disabilities or specific sensory or cognitive impediments currently require in-person consultations and examinations, as virtual services can lack the capable interactive communication they need.
Barriers to Patient Records
When communicating with patients in-office, clinicians have traditionally faced the risk of missing content while inputting patients’ symptoms and other information due to typing while listening and absorbing what is said. Talking, listening, and recording all at the same time can present challenges and can result in incomplete patient records.
Technology That Is Making A Difference
AI (artificial intelligence) in healthcare is a method of applying machine learning (ML) algorithms and houses multiple cognitive technologies in medicine. When computers and machines perform the same cognitive tasks as humans, such as learning, thinking, decision making, and doing—that is AI in healthcare. Healthcare AI represents tools for use in the improvement of medical care safety. Amid staff shortages, AI can supplement onsite clinicians to prevent adverse events. Remote virtual observation is a sample of AI technology that allows for continuous and immediate communication between patients and their families, as well as companionship and the fostering of good relationships in a healthcare setting. Virtual observers take proactive care of patients, contributing to comfort and confidence. As virtual healthcare becomes more prevalent and simplified, individuals with difficulties using digital mediums will find it easier to use.
ML (machine learning) is one of the most common AI techniques used to deliver clinical risk prediction for improving patient safety. Machine learning has many versions and is a statistical technique for fitting models to data and education by training models with data. A common practice of traditional machine learning is precision medicine—predicting what treatment protocols may best work for a patient. Deep learning—neural network models with levels of variables to predict outcomes—is a type of machine learning that is increasingly used for speech recognition.
According to the National Library of Medicine, NLP (natural language processing) is a field of AI that “includes applications such as speech recognition, text analysis, translation, and other goals related to language. In healthcare, the dominant applications of NLP involve creating, understanding, and classifying clinical documentation and published research. NLP systems can analyze unstructured clinical notes on patients, prepare reports (e.g., radiology examinations), transcribe patient interactions, and conduct conversational AI.”
Natural language processing tools such as voice recognition are the answer to improving care delivery, provider workflow, and patient outcomes. NLP algorithms already show potential in simplifying clinical documentation for patient electronic health records (EHRs) and bringing voice-to-text dictation. Providers in some healthcare organizations already use voice-based dictation in their exam rooms with patients present. The process allows for discussion and recording to co-occur, resulting in improved documentation quality and accuracy and higher quality of time spent with the patient. Physicians can also devote more time to the patient due to records providing a more complete picture without duplicate information being discussed.
Additionally, patient health literacy is improved. A poll in 2016 showed that many patients who accessed their EHRs needed help understanding the information and were less likely to utilize it to make decisions on their medical care. More thorough EHRs also benefit patients when providers can better identify social and environmental determinants, both physical and behavioral health issues, and thus prevent some patients from falling through gaps due to mental and economic disadvantages.
Better Communication Overall
Without truly considering it, voice assistance is already so commonplace as to be utilized daily worldwide (Alexa, Siri, and others). They will become more critical with continuous upgrades to bring in additional languages. The global pandemic triggered the virtual workplace. Bringing AI technology into healthcare has already improved translation accuracy and speed while automating some manual processes for patients and professionals.
Machine learning enables computers containing vast amounts of data to make intelligent decisions. Thanks to NLP, language barriers are continually dissolving. Both forms of AI will continue to assist the currently overwhelmed healthcare system.
Healthcare data will continue to be improved and structured so that physicians can access patient records and bits of information more efficiently and effectively, and less time will be spent on documentation. As it stands, NLP software can already read and comprehend clinical notes and extract data from those to make them available. This technology will transform how healthcare is practiced and delivered as it becomes further available to healthcare providers nationwide, improving communication and patient care.
AI models are predicted to assist in narrowed, personalized care for individual patients in a way that can provide specific treatment to cater to genetic history, background, lifestyle, and environment. Experts say that legislation surrounding healthcare information technology AI will also improve to accommodate the vast amount of data continuing to be created and housed by healthcare organizations, and that those models will be developed with guidelines and inaccuracy mechanisms to follow regulations to prevent ethical concerns. AI models are anticipated to soon have the capacity for decreasing and removing discrepancies within health systems. AI is shaping up to be the answer for more efficient and accurate care, secure data, patient record efficiency, detail, and cost-effectiveness. It is estimated that by 2026, AI applications will cut U.S. healthcare costs by $150 billion.