Hospital readmissions result from incomplete care provided by healthcare providers, complications, or because of ill-planned post-discharge treatments. Such readmissions are not beneficial to both the patients and the healthcare systems as they increase healthcare costs, strain resources, and negatively impact patient contentment. Here are 5 ways AI is reducing hospital readmissions.
1. Monitoring Patients Remotely
Remote Monitoring has been a part of healthcare for a very long time, but with the incorporation of AI, it has reached new heights. New advanced IoT devices, such as wearable watches, sensors, and trackers, can record patient statistics and vital signs and report back to the doctors in charge as everything is logged onto an app.
These devices can detect temperature, heart rate, blood sugar levels, etcetera. A spike in these statistics means that there is some abnormality or that the patient needs medical intervention. Since doctors have access to the current condition of the patient, they can meet them remotely to confirm further how they are feeling and provide immediate care. In this way, the worsening of the condition can be prevented, and the problem can be solved there and then.
Every 1 in 5 patients in the United States is readmitted within a month, and this amounts to a staggering 41 million readmissions. AI techniques like remote monitoring can avoid this.
2. Improved Follow-Up and Communication
Chatbots and virtual health assistants (VHAs) can respond to frequently asked patient queries. Not only that, but they can also be used to set reminders for taking medicine, provide patients with post-discharge help, and support them in taking care of themselves by adhering to post-treatment plans. That is an easy way for patients to optimize their treatments, make the best out of it, and avoid any chance of readmission to hospitals.
Furthermore, doctors are not too far away now. If the VHAs are not able to solve the problem, patients can easily contact their doctors remotely on an online platform.
3. Personalizing Post-Discharge Plans
AI solutions for hospitals can now create personalized post-discharge plans. AI can learn from data and use it to make recommendations. After treatment, patients need some post-discharge care to maintain their effects and to ensure that there are no side effects.
Such plans tell patients about the medicines they should take and at what times, the type of exercises they should do, the types of foods they should eat and avoid, follow-up care, lifestyle modifications, etc. Such AI-powered plans are tailored to specific patients and consider medical backgrounds before making suggestions.
Personalized treatment plans are a milestone in healthcare as patients feel safe and supported. The use of VHAs and chatbots further enhances the effectiveness of such a solution, as worried patients can ask chatbots as many questions as they like. Chatbots and VHAs use multitudes of data, are trained on commonly asked questions, and can easily answer all questions.
4. Predicting High-Risk Patients
AI algorithms train on available data and learn to make predictions about this data. So, when exposed to patient data, like their lab reports, genomic data, medical history, etc., the AI algorithms can detect patterns and use real-time data to find similar cases with the same conditions and symptoms. It can predict diseases that a patient might have. Such is an excellent example of how AI helps reduce hospital readmissions and admissions in the first place.
If people know what disease they may suffer from, they can take all preventative measures to avoid getting the disease. Such precautionary actions turn out to be very fruitful in the long run as they save the patient a lot of time, money, and health in case they contract the disease.
5. Optimizing Patient Care
Since patients receive personalized post-charge care, and since they also have VHAs and chatbots to help them and alert them with medical reminders, it keeps them updated and involved in their treatment process. It optimizes care in this way and lets both the patients and the healthcare providers be stress-free. Since everything is being monitored remotely, all information is openly available. Medical intervention can take place immediately when required, and such care avoids emergency room visits and unnecessary hospital stays.
Conclusion
AI in healthcare is improving efficiency while lowering readmission rates. Hospitals now can maximize resource allocation and raise the general standard of patient care with AI approaches like predictive analysis and remote monitoring.