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AMP: An Autism Management Platform
To ease the burden of collecting and managing data in support of the treatment and education of those diagnosed with autism spectrum disorder, we have built AMP (Autism Management Platform), a health care and educational information system consisting of a mobile application, web client, secure data repository, and analytics engine. Together these components simplify the means by which multimedia data can be captured, disseminated, navigated, and analyzed by caregivers, educators, and clinicians. The result is a fully-integrated, intuitive system that aims to improve data sharing and facilitate data mining, with the ultimate goal of streamlining the treatment process for families and professionals alike. Because AMP has been designed as a modular platform, features can be easily augmented in response to new users, or new perspectives on treatment plans.
Objectives:
In designing AMP priority was given to producing a cohesive and integrated suite of tools capable of supporting multiple users with multiple roles within ASD treatment. In particular, a multi-modal interface that can easily deployed on tablets, smart phones, and desktop computers was a driving requirement in order to easily support in-the-field usage. This includes tracking IEP goals, collecting clinical data during therapy sessions, and analyzing data in real-time to identify pertinent trends.
While several commercial solutions exist that provide some of these functions individually, AMP is notable in the level of integration provided, as well as the fact that the system is based completely on free and open source software. The result is an affordable, evolvable system.
Methods:
The AMP system can be decomposed into several major subcomponents. A mobile app allows users involved in the day-to-day care of an individual, such as a parent or teacher, to capture data ``in the moment'' using a smart phone or tablet. A healthcare professional, such as a neurologist, can use the web client to monitor their patients’ data using a dashboard view, drilling down into items of interest, responding to feed items, or making private notes as they see fit. Finally, an analytics engine works in conjunction with a database to aggregate and mine patient data for patterns, the results of which can be visualized in the web client.
Results:
The subcomponents of AMP are fully implemented and capable of scaling to real-world clinical and educational environments. The mobile application has been tested for compatibility with a wide variety of Android tablets and smart phones. The system is currently in the user-testing phase, and recently completed a one-month pilot study in a high-school special day class supporting 10 students and 23 staff members. Initial feedback is positive with 80% of staff reporting an increase in the ease with which data is collected and tracked. We are currently transitioning to testing in a clinical setting.
Conclusions:
Though still in its infancy, AMP fills a gap in existing autism technologies, and shows promise for improving the ease with which data can be collected, shared, and analyzed to improve the treatment and education of those living with an ASD diagnosis.