Telehealth EMR software adoption rates have been on the rise since the recent pandemic, healthcare professionals and patients are seeing it as a convenient option to receive care remotely. Artificial Intelligence (AI) plays an important role in the telehealth domain. Advancements in machine learning and AI technologies help to make telehealth EMR software solutions a more accurate inpatient diagnosis and cost reductions have also been realized. Both AI and telehealth are a great combination in the healthcare sector.

In How Many Ways AI is used for Telehealth EMR Software

AI applications are emerging to enhance telehealth capabilities in the healthcare industry. AI is used for telehealth in two ways.

  • Virtual Consultations – healthcare providers use machine learning to help analyze patient’s clinical data in the EMR software system to provide better patient treatment and recommendations.
  • Diagnostic Support – This is when companies develop algorithms for chatbots. These medical chatbots provide a diagnosis to patients according to their symptoms by using AI and machine learning.

How AI is Enhancing Telehealth EMR Software

Let’s look at the four main ways of how Artificial Intelligence (AI) is enhancing and improving telemedicine EMR software.

  1. Makes Better Patient Diagnosis – By combining remote monitoring with machine learning clinicians can better diagnose with less specialty labor. For example, machine learning algorithms can be trained to identify rare diseases by looking at patient’s faces and photographs. Patients with rare genetic disorders must on average visit seven doctors or opinions or to be sure what they have. By sending their face photographs to an algorithm through the telemedicine platform can eliminate the visit to the healthcare provider.
  2. Treatment Recommendations – Treatment plans can be recommended using machine learning algorithms for example, IBM Watson Health is recommending cure and care plans to cancer patients using machine learning technology. An algorithm can track every treatment of strep throat and then question the patients as to how long it took them to recover. This information could then be used to recommend effective treatment plans based on previous success rates.
  3. Reducing Long Hospital Wait Times – Artificial Intelligence can be used to solve many logistical challenges faced by patients and other administrative issues. Hospitals like John Hopkins and GE Healthcare are using predictive analysis to enhance patient flow. Predictive analysis can also be used to help find doctors faster for telemedicine patients.
  4. Avoiding Physician Burnout – For many doctors, the telemedicine platform helps to prevent physician burnout. When AI technologies combine with telemedicine the platform becomes more robust and powerful. It was noted that 40% of the clinician’s time is spent on entering notes in the electronic medical records software system. With AI physician burnout can be prevented due to less screen time. This way the doctor is in a better position to completely focus on the patient as the number of clicks and typing is reduced.

Conclusion

AI offers great potential to improve care delivery through the telehealth platform. AI-enabled telehealth EMR software system provides enhancements in existing practice and helps to increase patient outcome levels. Telemedicine and Artificial Intelligence both go hand in hand and it is important to consider that AI implementation in this module is still quite new. Further research is required to determine its sustainability in the long run.