Machine learning and the AI field in general has amazing potential to revolutionize the mental health field. The mental health field is riddled with fatigue, burnout, and turnover. Much of this can be helped with more robust tools in the hands of mental health professionals. But the key to machine learning is data, good data. And the greatest risk to technological advances in the mental health field may be a lack of good data.
A proven way to improve data quality is to make the data meaningful to those collecting it. This can pose a unique challenge for those in the mental health field. The nature of the practice of mental health work often involves trauma, crisis, and bursts of breakthrough amidst patient and persistent therapy work. How do numbers and graphs and multiple choice forms stand a chance of feeling valuable or important in those spaces?
But the mental health field is also unique in the compassion of care and the commitment of practice. Practitioners are ready to do whatever it takes to empower recovery and healing in the lives of their clients. Therefore the more useful an assessment is in helping clients, the more value it will have.
As the use of data analysis tools and machine learning algorithms grows, so does the value of the assessment. Visual data analytics help the practitioner and the client to communicate about change over time, highlighting areas of success and opportunities for growth. Machine learning algorithms built at the local level help programs better understand their client population, what works for whom and where the highest areas of need exist.
So you might say, the best way to get good data for the technology of the future, is to interact with the data you have now, as much as possible. How will you use your data today?
