Offering virtual visits via video, mobile apps, and text-based messaging has the merit of reducing physicians’ stress while maximizing the use of their time. With the flexible schedules made possible by telemedicine, doctors will have greater job satisfaction and better work/family balance.

Telemedicine is improving patient outcomes
Telemedicine also promises a broader reach into understaffed rural areas, giving patients remote access to specialists that are not available in isolated communities. Virtual scheduling capabilities also drastically reduce the number of cancelled or missed appointments because patients don’t have to leave the comfort of their homes. Driving down no-show numbers streamlines the system and makes better use of physicians’ precious time, enabling them to be more effective in their day-to-day activities.
Requiring physicians and senior nurses to perform data collection tasks, such as patient and symptom information, is inefficient and time-consuming. The compilation of data can be easily streamlined with interactive AI technologies prior to appointments, removing the need for standard intake questions, which senior staff usually complete. Transferring data-related tasks to digital system operations allows medical staff to maximize the application of their skill sets. The Mayo Clinic is currently piloting “Sense.Ly”, an interactive AI platform that uses a virtual nurse named Molly who, with dynamically generated speech designed to provide an emotional connection to patients, supplies symptom triage, gives advice and schedules appointments. AI systems like these lead to higher quality care, which in turn reduces costs for both patients and health care facilities. Virtual medical assistants can also process a higher volume of patients, thus improving processes and optimizing operations.

As annual visit rates climb to an all-time high, a greater focus is being placed on the way emergency departments are being used. Technology is one of the ways emergency overuse is being managed and reduced. When primary care appointments can’t be scheduled, Americans turn to the on-demand service provided by ER units. As a result, emergency departments are clogged with patients who should be directed toward other services. Patients often end up in the ER when faced with lack of insurance or a primary care physician’s limited availability. Normally, the addition of personnel would help compensate for this problem, but hospitals have shown equal or improved identification of patient outcomes when utilizing a machine learning based Electronic Triage System (ETS). The E-triage system calculates the emergency severity index (ESI) based on an algorithm that can “identify relationships between predictive data and patient outcomes.” The system rapidly assesses the patient and directs them to appropriate care, cutting down wait times and ER overuse.

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