This SBIR Phase I proposal aims to fund research and development for a new, multitenant secure cloud-based platform specifically tailored to provide local governmental agencies with tools to share datasets and link them accurately, at high quality and low cost. The OpenLattice platform will focus on reducing drug overdoses and making drug treatment less fractured. Individual-level datasets linked across medical providers and law enforcement can support analyses of prescribing pathways and treatment trajectories that precede opioid overdose, entry into treatment, disruption, and recovery. However, linking data at the individual level has proven to be a difficult and resource-intensive endeavor compared to use of aggregate-level data, with issues with deduplication plaguing many institutional databases. With 91 American deaths recorded daily from opioid overdoses and systems of care spread across multiple institutions, the need for greater and high-quality data sharing is undeniable. Our test partner for assessing the efficacy of proposed innovations is the Greater Portland Addiction Collaborative (GPAC) in Maine, a partnership of hospitals, a police department, jail, detox treatment centers and halfway houses already working together to reduce drug overdoses. This proposal aims to demonstrate proof of concept for (i) scaling high-quality data integrations across multiple governmental domains via a standardized entity data model, and (ii) improving record linkage using neural networks. Firstly, OpenLattice is developing an open source ontology and integration scripts to standardize integration of datasets into OpenLattice's database. As the individual customization requirements decline for onboarding customers and integrating new data into the platform, costs will be greatly slashed, removing a significant barrier to data solutions for smaller counties and cities across the country, who have historically faced custom integrations, system updates, data storage fees and add-ons at high cost. The OpenLattice platform also enables use of existing ETL tools and seamless integration with police dispatch systems, emergency medical calls, healthcare records, and online prescription systems across partners who have committed to data sharing and collaboration. Secondly, OpenLattice is developing a new, proprietary algorithm for record linkage that employs a promising but as-yet commercially untested technique: a multilayer perceptron neural network, more commonly known as deep learning. In pilot research, the linking algorithm has already demonstrated success rivaling?and sometimes exceeding?current state of the art linking technologies. In Phase I, OpenLattice will continue to improve ontologies, integration tools, and the deep learning neural network, and test on publicly available datasets with dissimilar data types and formats, with manual confirmation of results. When successful, these innovations will address critical barriers to improving clinical practice in treating opioid addiction by enabling a more comprehensive continuum of care for those in treatment.
Large-scale and coordinated responses to several of the US?s hot-button public health and criminal justice issues, such as the opioid epidemic and mass incarceration, are complicated by poor resource sharing and the US government?s highly fractured jurisdictional authority. This Small Business Innovation Research Phase I project aims to develop an efficient, scalable, cloud- based platform for hosting and linking highly sensitive state and local government databases at low cost, using (i) innovative data integration scripts and ontologies that standardize and scale capacity and (ii) technical advances in record de-duplication for linking databases. Data solutions would have tremendous societal impact on understandings of public health and the opioid epidemic by making drug treatment less fractured, saving lives and dramatically broadening contextual information, once data is broken out of silos.
