Original article was published by Dr. Monica on Artificial Intelligence on Medium
Future AI systems are ramped up into medical research and evolving the best practices in the healthcare sector. With constant improvement in a different paradigm, patient-centered service is a leading focus. This aims for improving between good health care and the healthcare that people actually receive. Using advanced human-generated data devices, multiple office visits for checkups, and routine treatment are replaced with remote monitoring. Providers can save time and increase the accuracy of their diagnoses when this tracking is combined with online consultations, driven by AI. From eye and CT scans, X-rays and mammograms, automation and robotics not only speed up medical procedures but also boost performance, to predicting ovulation cycles.
Not all procedures require human involvement and understanding of daily healthcare delivery. Under this premise, continuing to function puts a growing strain on the current structures of care delivery. Not only do AI, virtual reality, and robotics allow for faster remote diagnosis and tracking, but they can also be specifically used to treat anxiety, stress, and pain relief. The more open this “virtual” healthcare becomes to patients, the greater the amount in which it is possible to help within the given time frame.
What is the Patient-centric approach?
Patient-centered treatment is about treating the client, not the disorder. In decision making for his or her own health, a patient should be involved. It is crucial that understandable knowledge, both about the disease and the treatment options, is available to achieve this. In addition, making the entire diagnosis and recovery journey as seamless as possible for a patient is of great benefits, such as by prompt access to care, quick transfers from one healthcare facility to a healthcare facility, Or by providing the right amount of emotional and physical support during the whole process.
Patient-centered care and the role of AI
AI can be merged with the following perspectives which will provide patient-centric approaches:
Coordination and integration of care
AI in healthcare is best suited to enhancing patient management. For example, an algorithm that can predict peak hours in an emergency room can be trained and assisted by admission coordination. What about applications that can flag and prioritize these patients in the most acute cases? Great opportunities are also provided by AI-powered integration of treatment. For example, AI may play a major role in streamlining the sharing of information between departments by automatically collecting reports from various patient evaluations and displaying them cohesively in a dashboard to be used during multidisciplinary team meetings.
Information, engagement, and education
Patients want to learn and understand what is happening in their bodies and how their medication functions specifically. This includes awareness, but also ample communication to inform the patient about his or her condition. Across the board, AI can provide assistance. For patients, AI might generate extra information that will enable them to understand their condition.
Management of pain, assistance with a daily living, hospital environment
Managing the suffering of a patient is also an important part of the trajectory of treatment. Nevertheless, it is not always an easy exercise to settle on drugs that will most certainly lead to the best result. Huge datasets that contain treatment options and their effects can be used in the Big Data era to train algorithms that predict what drug is most likely to help a particular patient. Personalized healthcare, driven by AI, would be able to greatly contribute to the realization of patient-centered treatment.
Access to care in a timely manner
The acceleration of existing healthcare systems, resulting in shorter waiting times, is a major contribution AI can offer. A patient has to wait for weeks before he or she can get an MRI. It is not unusual. AI has the ability to overhaul scan protocols, shorten the scan time, and thus allow radiographers to scan more patients every day.
These are the different sectors where AI can really contribute to making an efficient, timely, and patient-centric pattern in the field of healthcare.