Governments are overwhelmed balancing consumer expectations, aging workforce, regulations, rapid technology change and fiscal deficits. This blog gathers a community of SMEs who discuss trends and outline how public sector organizations can leverage relevant best practices to drive their software-led transformation and build the future of technology – today!

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How technology can help solve the Opioid crisis

Recent developments have finally provided funding to address the Opioid crisis. It is important to recognize that the crisis is a policy problem that will be solved with programs and people. But, technology can be an enabler to identify those at risk, define and execute the right interventions, and measure effectiveness of those interventions.

Much like the focus on the social determinants of health, identifying those at risk and defining targeted prevention programs can be enhanced by looking at a spectrum of data. Analyzing demographics - such as age, location, and income - and non-traditional data sources like social media will help identify those in need of help. Just as smoking prevention programs are more cost effective and desirable than smoking cessation programs, identifying those at risk and creating effective interventions is a better approach to solve the Opioid crisis.

After you have determined the population that may be at risk, the next step is to identify those that may be abusing opioids, or enabling their abuse. Tools to identify multiple prescriptions or fraudulent purchases exist, but can be enhanced, for example by extending the data set to cross state boundaries. Prescription databases exist, and standards such as NCPDP enable semantic normalization and integration of cross-provider and cross-pharmacy data. Challenges exist when drugs cross state borders: both policy challenges of sharing data, and integration issues of sharing data from various repositories. These can be solved by integration tools that focus on federation and use of meta-data dictionaries. The more technology can accommodate data as it is - structured or unstructured - the more actionable data can be generated for the people to enable programs.

Lastly, effective programs can be identified and fast-tracked through modeling. As initial program results are reported, those can be combined with historical program effectiveness data and predictive analysis, enhanced by AI tools, to both assess the effectiveness of programs and identify those programs or tools that would work best moving forward. Like most AI tools, the more data you feed it, the better the result. Again, focusing on federated data and enabling semantic integration through meta-data dictionaries will lessen the burden on data providers and enable a large, unified data set for use.

This is how technology can support the people and programs to solve a heart-breaking problem to today's society - a tool to enable knowledge sharing and support outcomes, a means to an end. 

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