Doctors have always been skeptical of artificial intelligence. Most people believe that artificial intelligence and machine learning technologies are exaggerated and cannot solve clinical problems in real life.
Doctors often don’t like machines dictating their decisions. They are more willing to rely on their clinical acuity and judgment to diagnose and make clinical decisions.
But in today’s changing nursing service environment and consumers demand better participation and nursing experience, doctors are rethinking how to improve nursing services.
Empower decision-making and improve health outcomes
The problem has never been about artificial intelligence and doctors’ clinical decision-making. As Atul Gawande said in his best-selling book Complications, “No matter what measures are taken, doctors sometimes hesitate to ask us to be perfect. It is unreasonable to ask us to never stop taking it as a the goal.”
Gawande provides real anecdotes about the mistakes made by surgeons and doctors in this book.
With the support of artificial intelligence and machine learning assistants, the assistant filters historical data and draws similarities and relevant insights in cases. The decision-making process can greatly speed up the diagnosis and decision-making process.
Consider the diagnosis of sepsis. AI algorithms are widely used in intensive care units to diagnose sepsis. The sepsis sniffer algorithm will alert the doctor at least 3 to 4 hours before an escalation event that causes severe sepsis occurs. This can reduce mortality.
Early indicators are given by an algorithm that works in the background to collect all the data generated from the bedside and the patient’s laboratory, and produce intermittent results to alert doctors of an impending crisis.
Saw it at the hospital Average The in-hospital mortality rate was reduced by 39.5%, the length of hospital stay was reduced by 32.3%, and the hospital stay of sepsis-related patients was reduced by 30 days, and the readmission rate was 22.7%.
Artificial intelligence is a lever that can be used as a second opinion to get a diagnosis and strive for perfection in complex situations.
Patient participation and care experience
In today’s scenario where virtual care is combined with face-to-face care, doctors supporting artificial intelligence can delegate daily and ordinary tasks with the support of artificial intelligence algorithms, such as sending educational materials, ordering prescription drugs, and responding to patient queries.
In larger facilities, by using artificial intelligence tools such as symptom checkers to classify patients, doctors can further optimize the functions of their clinics or departments. Using artificial intelligence chatbots to answer daily questions, appointments are other uses of artificial intelligence to help improve the patient experience.
Artificial intelligence algorithms are helping to identify patients with chronic diseases, sending them medication reminders, educational materials, and alerting doctors to vital signs or any changes in the laboratory when using connected devices. Overall, make patients more engaged and responsible for their health.
Choose use cases that can successfully implement artificial intelligence
It is important to select use cases where AI algorithms can have a measurable impact in the clinical field. Some areas where artificial intelligence has been successfully implemented are radiology, internal medicine, neurology, and cardiology.
In all these areas, algorithms work quietly in the background and help doctors do their jobs, sometimes by providing second opinions or simply by alerting to any impending crisis. Artificial intelligence cannot hide the existence of doctors anywhere.
Patients always prefer to hear their diagnosis from the doctor. In terms of imaging, today, artificial intelligence models are helping to automatically draw the contours of healthy tissues and organs from tumors, formulate adaptive doses and treatment plans for radiation therapy, diagnose cancer at an early stage, diagnose large blood vessel blockages in stroke, and recognize images The disease pattern. It is then reviewed by physicians and radiologists who understand the patient’s overall clinical, social, and psychological conditions.
Machine learning has algorithmic deviations, and a slogan or disclaimer is always attached: “Clinical relevance is required”. However, artificial intelligence replaced by clinical interventions by experts who understand the human aspects of patients is a good solution for integrating machine and human care models.
Many other use cases have been implemented or are in development to aid diagnosis at the bedside. Recently, technologies such as natural language programming for reading unstructured information in doctor’s notes and voice assistants for predicting emotion and behavior characteristics are being studied.
Artificial intelligence has made significant progress in the administrative and operational fields of healthcare, and has achieved significant results in increasing the revenue of large-scale medical systems.
However, artificial intelligence has also experienced a series of failures in the clinical field, resulting in a lack of actual deployment of machine learning algorithms in mainstream clinical practice. IBM Watson’s failure in cancer diagnosis and treatment, and Google’s failure to detect diabetic retinopathy through deep learning models of patient eye images are recent examples.
So far, the potential of artificial intelligence in the field of healthcare has not been realized. The number of clinical and cost-benefit reports on the actual use of AI algorithms in clinical practice is limited.
Although slow, artificial intelligence in the clinical field is steadily picking up, but it needs to fulfill its promise to make a difference at the point of care.
As health systems and hospitals undergo digital transformation to improve care services and patient experience, doctors cannot be left behind. They must also change and contribute to making this transition a more positive experience for themselves and their patients.
Dr. Joyoti Goswami is the chief consultant of Dharma Consulting.