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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March 26, 2020

Making emergency social program delivery effective

The COVID-19 pandemic has not only put a brake on life in the fast lane for most of us but has also shown how poorly prepared we are to deal with natural disasters and pandemics. Other than the obvious impact on health and wellbeing, loss of livelihood and associated income for a significant portion of the population is a major ripple effect. This, in turn, puts the spotlight on the social safety net and programs offered by the federal, state and county governments. Some lessons have been learned from Katrina and Sandy, however the scale of COVID-19 is unprecedented and will require solutions that require quick and flexible deployments.

The big ideas to solve big problems are also the simplest. If we breakdown what is exactly needed, it comes down to:

  • Easy Access
  • Real time response
  • Quick Delivery

During an emergency like the one we are facing now, people should have remote easy access to look up the available emergency programs and apply for themselves and their family. 

Systems developed as a result of the Affordable Care Act (ACA) provide a view of what is possible.  The ACA allowed anybody with a device and an internet connection to quickly 'apply' for MAGI Medicaid and/or select a plan from the market place with insurance assistance.

The entire process starting with 'Screening' for eligibility, creating an application, real-time verification and eligibility determination, selecting an MCO and/or plans and actually paying the first month premium - all part of a single transaction, took less than 20 minutes from start to finish.  The infrastructure that was built for the ACA needs to be expanded beyond MAGI Medicaid to all social programs, especially for any emergency programs.

Just like the Federal Hub, SSA, DOL, and DMV provided real-time verifications, and applications and cases became 'no-touch' or 'quick-touch'.  All emergency programs have to follow suit. Support requirements in times of emergency vary greatly -- from basic needs like food and shelter to direct cash, heat/energy payments and transportation passes.  Use of bar coded emails to avail services, and commercial cash cards like VISA/AMEX instead of EBTs, will make delivery instant.

There already are programs in place like Disaster SNAP (D-SNAP) and Transitional and Migrant Worker Assistance, however, the different departments that handle these programs both at the federal and state level need to collaborate to present a unified front that simplifies the process for both the citizen who is seeking assistance and the workers who administer the services.

For example, all the HHS agencies like CMS, FNS, OCYF should have a data sharing agreement.  Data verified by one program (like MAGI) should be used by other programs. Although the 'no wrong door' approach is a good move, behind the scenes, most of the states have a bunch of disintegrated systems that keep the wheels moving, albeit slowly. A collaborative approach to data will also help ease the operational challenges of administering emergency programs. The IT systems will naturally follow the state and federal policies, and with incredible strides in technology and scalability, will be able to help the state to provide the necessary safety net for its most vulnerable citizens.

March 18, 2020

Arresting the spread of COVID-19 with AI

From its beginnings in Wuhan, China and now dispersed to more than 120 countries, the novel Coronavirus (COVID-19) has infected over 211,000 people with a death toll of over 8,300 thus far.  This makes it one of the deadliest outbreaks that the world has ever faced.

Compared to the double-stranded RNA viruses like HIV, Ebola, and Influenza, COVID-19, a single-strand RNA virus, mutates rapidly. This makes its spread more challenging to arrest. The virus is proving to be extremely difficult to analyze as well. Even if scientists develop a vaccine (which might take as much as 18 months or so), there is a chance that it may not be completely effective.

This is where AI can help. With the power and potential of next generation data science techniques and emerging technologies, healthcare agencies can effectively analyze, track and arrest the spread of coronaviruses like the COVID-19.

AI/machine learning algorithms can help fast-track genome sequence analysis, the drug development process, and development of an effective therapeutic regimen.

On the preventive side (which is the need of the hour), platforms that use advanced data science technologies can help agencies detect cases faster, predict new cases more accurately, monitor status, plan quarantines more effectively and arrest the spread of COVID-19. Here are some of the key components of such platforms.

Polyglot, non-model data storage & automated polymorphic data aggregation

Data sources like IoT devices, systems that capture travel history and health symptoms at airports/ports of entry, social media updates, immigration information, public health data systems, transportation data, health insurance database, medical questionnaires, lab and radiological diagnostics and EHR can provide reams of data about people. This data can be key to generate the right intelligence to reduce the spread of COVID 19 or other epidemics. But how to manage this multi-format and multi-type data?

Agencies can automatically aggregate and store all this data using non-model databases. These databases fast-track data capture, ensure high operational speed, and provide greater analytical flexibility. Through process automation techniques, agencies can clean and harmonize all of this data in real-time, developing an integrated, unambiguous "golden record" of a person and the population quickly.

This data set can be further enriched with social determinants of health (SDOH) attributes. A person's relationships, economic-behavioral- attitudinal composites (e.g. income, housing, state of hygiene, nature of work, access to care, propensity to engage with community, health awareness and engagement media preferences, etc.) shape health and disease distribution in the population. In the situations like COVID-19, SDOH data can help unearth additional information about a person/population, generating better intelligence to minimize the spread and raise awareness, particularly among the most vulnerable populations.

Automated predictive modeling tools

This SDOH enriched and harmonized data set can then be fed into an automated predictive modeling engine that can analyze various scenarios to proactively identify, assess and/or predict the at-risk people/population. The tool can generate proactive alerts and tailored recommendations to the care community. This analytical output can be also fed into a Chatbot/Avatar/Virtual Care Assistant solution or similar online communication tools to disseminate health education, preventive measure, quarantine, clinical advisory Q&As to the population and the care community. 

Dynamic visualization tools, cloud & next-best-action generators

Dynamic visualizations tools can make it easier for agencies to understand all this data. Agencies can visualize geo-clustering and heat mapping to track cases, events and locations related to the coronavirus. All this information can be further analyzed by advanced AI models to generate next-best-actions; i.e., automated recommendations for healthcare professionals in the care continuum to proactively determine the right interventions for the right people, just in time.

FHIR (Fast Healthcare Interoperability Resources) compatibility and an API (Application Programming Interface) first approach will make the data and intelligence easily shareable across agencies and healthcare organizations for easy, near real time interventions and enable coordinated preventive measures for the population in need.

Cloud technology can host all these different components, making the platform easily accessible to the people in the field and scalable to meet changing requirements while staying compliant with strict healthcare regulations, including HIPAA and HITECH.


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Conclusion

AI-based solutions like these can amplify the ability for an agency to more effectively fight deadly outbreaks like the novel coronavirus (COVID-19) and to ensure that the infected people receive the right care at the right time. It's time for agencies to accelerate the adoption of AI-based technologies to fight pandemics like COVID-19 so that societies can more effectively deal with and control these outbreaks.


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