ai-in-healthcare

How to Solve Waiting Rooms Pain in Health Care via AI?

Insane waiting rooms, tongue depressors, and the scent of disinfectant are many reasons to be unhappy when it comes to visiting the doctor. However, if you or someone close to you is down with illness, nothing is more important than having access to medical care. Making it easier for everyone to access healthcare is among the major problems of our times. While we have an inordinate amount of work to do, however, we are encouraged by how AI in health care is aiding in the fight against the disease. 

COVID-19 is forcing us to reduce social distance and face-to-face interaction everywhere, which includes between medical professionals and patients. This pandemic also puts huge pressure on the medical system and its resources. Remote and virtual health solutions are helping us face these kinds of problems.

Virtual intelligent, clever, and truly remote:

A telemedicine system was made available in only 10 days, meaning that patients living in remote areas and those who are unable to go to the medical facility in person can now access top-quality health care. The largest cancer clinic in India, treatment, HealthCare Global HCG has responded with Microsoft Teams to carry out virtual consultations. Through this, they’ve shown the potential of technology to aid in making cancer treatment easier to access and more affordable even for patients overseas. 

Patients can self-report their symptoms by recording audio like counting to 30, or saying”ah. “ah.” The algorithm analyses the recordings and notifies the doctor, who will then conduct an online meeting with the patient. In the end, the technology is focused on the patient’s security. Patients receive timely advice and correct treatments with no need for face-to-face contact.

Mixed Reality

Mixed Reality During ,the untethered mixed reality MR headset takes remote and virtual consulting to the next step. AI in health care technology, for instance, allows patients living who live on islands that are remote in can get real-time, virtual specialist medical treatment through the Brisk Logic. 

It’s as simple as that A doctor on the island using the headset is able to give a specialist at the hospital of the university a live 3D image of the subject, like the close-up of a hand that is affected by arthritis. In many instances, this kind of virtual examination can eliminate the requirement for patients to travel for a long distance to the hospital, or wait long to make an appointment. There is a similar system is being implemented. General practitioners, as well as nurses, use HoloLens on house calls together with specialists far away. This is effectively transforming a regular home visit to specialist consultation.

The COVID-19 epidemic has tested the health industry’s capacity to adapt and evolve. As healthcare becomes increasingly digital in terms of nature, the need for medical consultations in person could diminish in the years to be. In the meantime, wearable technology built upon AI as well as technology like the Internet of Things (IoT) could be able of monitoring patient health remotely constantly leaving doctors free to concentrate on urgent issues.

Three essential items for better accessibility:

Here are three important lessons for healthcare institutions in how they can use technology to improve their accessibility to patients:

Chatbots as well as Symptom Checker

Many health organizations have put a basic chatbot for Q&A on their websites to assist visitors who have general questions. A symptom checker is a sophisticated chatbot that is able to inquire about symptoms that a person has. Brisk Logic allows businesses to implement chatbots in a way that is scaled. Similar to the CDC’s Coronavirus Self-Checker. The self-checker will ask about the symptoms that are typical of COVID-19. If the user replies “yes” to any of the questions of them, they’ll be told how to proceed.

AI Models:

AI in health care can process and analyze huge amounts of data pertaining to the patient’s health, such as radiology, pathology MRI scans, and so on. This gives a doctor a complete overview of the patient’s condition during the health screening process, allowing a more precise treatment based on the Analytics/recommendations made by AI. 

For instance, in Australia, NSW Health Pathology provides a quicker diagnosis and treatment for patients, regardless of where they reside. In South Korea, Lunit has created INSIGHT CXR which is an AI software platform that is built on Azure that analyzes chest X-rays and immediately alerts doctors when it detects signs of COVID-19-related infection.

Connections and Teams: 

Connecting with your colleagues as well as patients can be essential in providing the highest quality of care that is possible during a pandemic. With the help of services such as Microsoft Teams, doctors and nurses can plan the best path of treatment for every patient, from any place. For example, “tumour board” meetings consisting of specialists who decide on the best way to treat cancer patients are usually difficult to schedule face-to-face. 

With a Teams conference doctors are able to meet online, making it much easier to make medical decisions. Virtually connecting can also be used to collaborate across borders. Holographic images taken by HoloLens are easily shared with medical experts around the world at any point to show clinical examinations and procedures at work. 

It gives information and information that are simple, precise, and may even save lives. It’s not difficult to see that COVID-19 has helped accelerate the adoption of technology across all industries and including healthcare. It has allowed telehealth services to become mainstream. Utilizing technology to boost the “throughput” in health facilities means that more patients will be treated at lower costs.

The process of AI in health care-assisted inpatient outpatient service:

We discuss the traditional outpatient process, and also the AI-based changes to it. Traditionally, patients have to sign up first, After registering they wait in the waiting room. When their turn comes around to go, they head into the consulting room to consult with a doctor. 

In most cases, a lab test or imaging exam is required in order to establish the cause. The patient must pay for them and make an appointment at the right place for examination or testing. 

After receiving the results the patient will have to wait to see their doctor, and possibly recommend a second examination or medication. In this research, Brisk Logic will focus on the process from registration through the examination or test.

  • The initial step of the AI in health care-assisted outpatient services is registration. 
  • The next step is to have patients tap on the WeChat application a similar social app to WhatsApp popular in phones. The unique numbers of patients’ outpatients are linked to a smart program that is based upon WeChat and the XIAO client-side. 
  • Client-side XIAOYI is the implementation of the algorithm discussed earlier that have clients for mobile phones as well as doctors’ computers. It is able to read the information about patients’ registration. 
  • Based on the primary problem, XIAO YI asks the patients various questions just like a real doctor would. 
  • The next question is then decided wisely, based on the answers to the question before. If XIAO YI believes it has obtained enough information and the inquiry is over. XIAO YI orders tests or examinations to be conducted to assist doctors in making the diagnosis. 
  • The tests and tests “prescribed” by XIAOYI are routine minor trauma and fairly inexpensive (e.g. the blood test). The patient makes the appropriate payment for these tests and is taken to the test rooms. 
  • If the patient is not satisfied with the test, they’ll undergo the normal procedure that involves waiting for a consultation with the doctor on human. 
  • Once the test or exam is finished and the report has been obtained and the report is ready, patients are taken to the doctor’s clinic to have a consultation.

Subjects to be Selected

During this time uniformly trained volunteers and nurses would announce XIAO Yi to the parents of children in the pediatric internal department, the gastroenterology department and respiratory department. They would also instruct the guardians on how to use it. 

With the assistance from volunteers, several parents utilized XIAO YI to request and take tests/examinations prior to when they were scheduled to see doctors, while other guardians adhered to the traditional approach of visiting doctors. Patients were then classified into two groups: namely the traditional outpatient group as well as the AI-assisted group (AI group) according to their personal preferences. Since the process of outpatient care that patients chose was similar to exposure, and because the length of waiting time was comparable to the result We conducted an observational study in a cohort. 

The two patient groups were first matched according to the registration time, mostly because the date of registration could be the most significant factor in determining the wait time of an outpatient, with the exception of the grouping. In general, the number of patients is higher during holidays than on days of work, and there are more patients during the morning hours than in the afternoon. 

How to solve this issue?

To avoid such issues We cleaned the data based on certain guidelines. We removed patients who did not have a lab test as the primary purpose for this AI was to request tests before consultation with a physician. 

Patients who waited more than 5 hours from registration to appointment were not included, nor were those who had to wait more than 8 hours after the time of registration to receive their medicine. Based on the experiences of numerous doctors in the hospital, these long wait times typically occurred because patients either needed appointments or were not on time for an appointment. Patients who had less than five minutes of waiting time were also excluded because they were probably committing mistakes.

Results:

The main result was the amount of time by the patient between registration to taking the laboratory test or exam, which is that is, the waiting time. The second result was the costs in the hospital. Therefore, we assessed the efficiency of the AI system from two perspectives.

Furthermore, patients from the AI-assisted group as well as the conventional group were divided into six subgroups, according to three clinic departments. These included those from the department of internal medicine, the gastroenterology department, and the respiratory department to analyze further the waiting time. Additionally, patients were divided according to the type of test (blood routine test routine urine test, routine urine test and the detection of flu A or B tests) or other tests (abdomen imaging as well as the chest radiograph).

In this study wait times were dramatically reduced with the AI in health care-assisted service. AI is not just able to improve the quality of medical care but can also have a significant impact on the development of procedures to improve the flow of patients.

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