🔎 There are numerous barriers for patients with mental health conditions to accessing and receiving timely, quality care: societal bias, self-stigmatization, cost, transportation, privacy concerns, repercussions at work, language, cultural and more.
🩺 There is an emerging niche of AI/ML algorithms that can help diagnose and monitor symptom changes among patients with mental health disorders or even predict those patients who are at potential risk for developing mental health conditions before symptoms manifest.
📱 In this use case, ML algorithms would be fed lots of data, not only from traditional claims data, but, importantly, also from real-time wearable devices and smartphones that can measure skin activity, phone calls, location and app usage, for example.
The goal of ML technology is to extract information from the algorithms and make it meaningful and actionable. This allows patients to access their evidence-based, personalized and timely information that will help manage their condition.
How will the machine-learning algorithms present their findings to patients??
Through a custom device, a smartphone app, or a method of notifying a predetermined doctor or family member of how best to support the patient (permission-based, of course).
Say for example, a custom wearable device ⌚️ detects that a patient has been sleeping less, staying inside their home more, and has a faster-than-usual heart rate. These subtle changes may go unnoticed by the patient or family members. ML algorithms can take these data, map them to the patient’s past experiences and the experiences of similar patients.
Insights from the algorithm may then be able to encourage the patient to contact their doctor, take medication as prescribed or take positive steps that have improved their well-being in the past.
Artificial intelligence and machine-learning algorithms can make connections and identify patterns in large datasets to augment human capabilities.
The healthcare industry in general, and the mental health sector in particular, must embrace the power of AI/ML to enhance both the patient experience, and ultimately, patient care outcomes.
Interested in how ML brought prestigious MIT and Mass General Hospital together?
Learn more here ➡️ https://lnkd.in/gPJKBUtP
Tell us, what are your thoughts on the upside/downside of using AI/ML to augment care decisions for mental health patients? 📝