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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September 30, 2019

AI & DMVs: An approach to improve customer interactions and minimize fraud/illegal activity

Artificial Intelligence (AI) technologies have various applications in the DMV space. In this blog, which is a detailed version of my session at AAMVA 2019 Region 1 Conference, I'll describe two of these - use of AI to improve customer engagement and to minimize fraud and illegal activity. I'll also discuss how motor vehicle and driver licensing agencies can navigate their AI adoption.

But before diving into these two use cases, allow me to outline my understanding of how an AI system works.

The system first discovers and ingests data from multiple sources, including:

  • Driver and vehicle data
  • Sensors
  • Internal systems
  • Partner systems
  • Real-time updates from any other system

Then, the machine learning models learns to identify various patterns from this data, such as:

  • Risk factors based on a driver's driving data or vehicle condition
  • Customers' preferred devices
  • Activities that might constitute fraud

The system then stores that knowledge in its database. When any transaction or event happens, (e.g., receiving a new application or an increase in traffic at a particular location) the system senses it and uses its modelling and knowledge repository to decide how to act.

AI-DMV.png

 

With this groundwork, let me dive into the two use cases that I mentioned above.

Using AI to improve customer interactions

Despite all the efforts that agencies have taken to address known customer pain points, we still see customers not looking forward to a DMV visit. They have to stand in long lines, fill out forms and get the forms validated by an agent, which requires the agent to log into the system, search for or create a customer, enter all sorts of data and process the payment. The entire process is long, tedious and prone to errors.

 

DMV-Manual-Form-Completion.png

An AI system removes all of the manual steps, relying instead on various inputs and its knowledge base to assist the customer with the steps needed. Image capture and algorithms ensure security and privacy.

A customer would use their mobile device to access the DMV. Using AI, the system would prompt them with a menu that either collects data for new users or presents available options to known customers. The customer could take pictures of their documents and the AI system would capture not only the image but the required data elements. It would prompt the customer to complete anything missing or incomplete. Validations would be handled by the AI system and the user would be notified of the next steps, with reminders and alerts as needed.


DMV-AI-Form-Completion.png

By using an AI-enabled wizard, the DMV can service many customers in a personalized manner 24/7, while optimizing valuable staff time. This will enable the staff to concentrate on the more complex operations and services provided by the DMV. Some of the benefits include:

  • Significant reduction in wait times for customers in DMV offices
  • Reduced customer service costs with faster throughput
  • Real-time feedback on performance and areas of concern that require managerial attention
  • Continuous learning from feedback on how complex queries are handled to reduce time-to-solve customer queries
  • Minimized errors and improved customer satisfaction

In addition to this, AI-based chatbot applications can help customers communicate with DMV on-line portals via text or voice or any messenger application. The virtual agent can answer customers' queries and execute various actions on behalf of the DMV agent, including appointment scheduling, registration renewals, wait time verifications and more. Instant response and 24/7 availability offered by an AI solution can enable DMVs to offer more services on-line, reduce the number of trips and wait time at the DMV offices and let their agents be more productive.

DMV-Chatbot.png

AI to minimize fraud and illegal activity

Internal fraud can take place anywhere, anytime and can last for years. There are very high costs associated with detection and investigations, yet criminal activities are not prevented. Gathering sufficient evidence for prosecution is difficult and the public's confidence in the security of their data can be undermined.

AI can detect and prevent fraudulent and illegal activities by ingesting and transforming alert and transaction data in real-time, using augmented models of behavior based on self-teaching algorithms. This enables the AI system to predict potential fraudulent transactions as soon as relevant activities are detected. The AI systems can stop or flag transactions for investigative purposes and incorporate adaptive self-learning from historical and ongoing transactions, data patterns and investigators' feedback to continuously and automatically update its models in near real-time.

Enabling automation-led decision-making by using neural networks that span boundaries will lead to the creation of best practices and lessons learned without additional work by DMV staff. Some of the key benefits of using AI to combat fraud and illegal activities include:

  • Improved security
  • Improved investigative results and prosecutions
  • Reduced cost
  • Reduced errors
  • 24/7 availability
  • Detection and resolution at points of failure
  • Adherence to legal and regulatory restrictions

 

DMV-AI-Illegal-Activity.png

 

Navigating AI adoption

AI isn't just one model or a system, it is a continuum of different technologies or systems that can be adopted based on an organization's maturity. This is has been brilliantly articulated by my friends with Infosys Consulting in their paper on RPA & the insurance industry I've summarized and contextualized some points from that paper for DMVs.

The beginning of the continuum includes the first level of automation, where most DMVs are now, which is based on specific "if..., then . . ." rules programmed for each step in a particular process. These must be reworked as the rules change, a time-consuming effort that costs money and can introduce errors, including some that can be very public and require immediate, costly remediation.

The next stage represents robotic process automation, i.e. the automation of specifically defined repetitive, rule-based tasks carried out by humans over single or multiple applications, as an important step to achieving a seamless, digitized, end-to-end process. The digitization and automation of incoming paper mail, email and input from other channels is a good example of this stage. Robotic process automation is a crucial enabler in moving up the AI continuum.

Machine learning is another step up the continuum. Instead of manually formulating all of the rules to interpret data, algorithms allow a program to learn and determine the rules. The described development is based on experience gained in similar "if..., then . . ." cases. The more data from the past that is available, the better the learning curve will be. Machine-learning programs still require human intervention at this stage. They can present a variety of choices to consider but are usually not programmed to make decisions. Possible fields of application in the DMV domain include fraud detection or evaluating an application.

The future of AI in the DMV realm will require widespread usage of faster networks, such as those using 5G bandwidth, and real-time integration with the Internet of Things (IoT), which will provide greater data to facilitate machine learning. Hardware and software is being developed to take advantage of the speed and power that comes with AI adoption. This is available now for all to see in the commercial user interfaces being widely adopted.

DMV-AI-Adoption.png

There are some important things to consider when looking to the future and integrating the power of AI into your DMV systems. Education is key to understanding how components of the AI systems can be implemented. This includes establishing where your agency fits into the continuum and the best path to continuously improve the use and usefulness of AI, to allow you to determine which components will provide the most value and remedy your largest pain points.

By including AI in your upcoming procurements, you can ensure that the AI solutions are fully embedded into all of the architecture, systems, hardware and software that will support your modernization efforts.

More importantly, for the DMV domain as a whole, your outcomes and lessons learned will benefit other jurisdictions as AI moves to a full maturity model.


Reference

Robotic Process Automation - Putting AI to work in Insurance 

September 24, 2019

Treat the patient and not the disease

"Better late than never." It's good that we are realizing how our living environment, neighborhoods, and communities; schooling; quality of water and food; our income, access to transportation and our social interactions, relationships, behavior, habits and attitudinal aspects, etc., collectively termed as the "Social Determinants of Health" or SDoH, are influencing our state of health and outcomes. 

This growing awareness around SDoH, and its influence on the population health economy, is a national "call to action" for our health care system to reduce health disparities, mitigate patients' care gaps, improve their quality of life and health outcomes, and lower care costs. Along the same lines, in an effort to reduce expenditures and improve community health outcomes, the Centers for Medicare and Medicaid Services (CMS) is testing the Accountable Health Communities Model, which is the first model to include social determinants of health, to identify and address patient health related social needs through clinical and community-based settings.

As a physician, I can imagine a scenario where a patient's socio-behavioral, economic and attitudinal composites can drive better care decisions and patient wellbeing. Think of Julia (36/F), who comes to her primary care physician's (PCP) office and in a follow up discussion, shares how as a single mother (with a low salary job and without any alimony) she is stressed and struggling to support herself and her child with medications, food, transportation, better housing, etc.  Julia has lost around 10lb of weight and her blood sugar level has increased from past. She has started smoking cigarettes. She also missed her eye doctor and cardiology appointments in the last two months.

Julia states that she and her child have to rely more on a nearby fast food shop, as returning late from work she neither gets time to cook regularly, nor does she have enough money to afford better, healthy diet. She has to space out her daily medications and often misses refiling them. She also finds it hard to make to her doctor's appointments, as she doesn't have a car and has to rely on public transportation, which is a long distance from her apartment.

These all are the SDoH that are influencing hidden risk factors and worsening health conditions for Julia and other people like her, daily. These social, economic, behavioral, environment and financial factors paint the 360-degree view of a patient that every physician or care giver needs to know to render the right care service, beyond their clinical assessment. 

For example, in the above situation, the PCP can now advise Julia to reduce her stress by exercising regularly, cut down on smoking and also prescribe low cost generic drugs to reduce her medication cost.   Free transportation assistance can be arranged to make sure Julia doesn't miss her doctor appointments.  She can also be connected to food resources/programs like the Supplemental Nutrition Assistance Program (SNAP) and the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC)) to help her feed herself and her family.

We have traditionally used claims, EMR, utilization and some health risk assessment (HRA) data to stratify patients and predict their clinical risks. But that data only provides a myopic view of a patient's life situation and hasn't been insightful enough to change their health trajectory by improving outcomes, quality of life and lowering care costs.

To optimally manage population health, we must look beyond the data that is traditionally captured in the system of records to obtain a wider perspective of the factors that influence our health conditions. SDoH data provides that wealth of insights to help us influence patient outreach, engagement, and improve health outcomes.  Robert Fields, M.D., SVP and CMO for population health at Mount Sinai Health System rightly said "When you start to get paid on outcomes and reductions in total cost of care then it makes it financially reasonable to invest upstream into infrastructure and preventive care. Many times, that preventive care looks a lot like closing social determinants gaps to avoid the downstream cost. The economics are changing...."

As we seek to foster innovation, rethink community health and find solutions to fight the threatening opioid, marijuana, e- cigarettes epidemic, etc. we need to treat patients as individuals in a broader societal framework and not only address their present(ing) disease state. We need to take into account the social determinants of health and recognize their impact on a patient's diseased state. These are the influencers, that if not checked early, will trigger the patient's movement in health risk corridors from low to high, increase population health disparities and generate a broader public health challenge.

Healthcare is more social than clinical. Let's harness the power of Social Determinants of Health and blend it with the clinical factors to create innovative healthcare solutions which can truly transform today's "Sick Care" to a real "Health Care" world and help us all to realize the World Health Organization (WHO)'s vision of "Health for All".

September 10, 2019

Digitizing Child Welfare Systems

Beyond the regulatory mandate of providing care and services to the most vulnerable citizens, investments in child welfare enable states to help the children succeed in life and contribute to a more productive society.

System applications used by states for administering their child welfare programs have evolved over time, however, caseworkers and agencies still face many challenges in delivering care and providing services to constituents.  These include:

  • Increasing caseloads
  • A stressful work environment
  • A high caseworker turnover (see 1 and 2 above)
  • Extensive travel and time in the field for caseworkers
  • Duplicate data and redundant processes that impact program efficiency and effectiveness
  • Lack of information to make critical informed decisions

Digitization of child welfare systems is an opportunity to address many of these challenges and enable agencies and case workers to effectively engage children and families, spend more time with them, use better information to make better decisions and provide superior service to their constituents.

 

Child welfare information system modernization options

The Comprehensive Child Welfare Information System (CCWIS) regulation, with its focus on modularity, technology optimization (1355.52(a) - efficient, economical, & effective), data governance and interoperability is helping agencies leverage technology to improve child welfare services and achieve better outcomes in terms of permanency and child safety.

When it comes to modernizing and digitizing the child welfare systems and implementing CCWIS, states can either choose to implement a commercial off the shelf (COTS) product, transfer a solution from another state, or build a solution from the grounds up. A hybrid option is another approach - with some modules provided through a COTS solution and the rest custom coded.

Each of these options have pros and cons and what works for one state may not be the right fit for another. Choosing the right solution and the right partner to implement it are critical to a successful digitization of child welfare systems and process.

Based on our experience with the implementation of large scale digital transformation programs for public sector and commercial clients, we recommend that states consider the following characteristics to identify the right child welfare information solution and how it can be implemented successfully. These characteristics correspond to the 3 dimensions of the Design Thinking approach -  desirability, viability and feasibility.

 

User experience (Desirability)

User experience is of paramount importance in digital transformation. Citizens expectations are being shaped by their experiences with organizations in financial, retail and high-technology sector. From ordering a cab through an app, paying bills with just a swipe, ordering food or depositing checks without going to bank, digitization is pervasive. Agency staff and constituents (affected families) expect the same level of experience when it comes to interactions with child welfare applications. Here are some factors agencies should consider when evaluating a solution for its user experience (desirability):

  • Modern and intuitive user interface: The system should navigate the user through the workflow and functionalities and not the other way round. It should be easy to learn with adequate contextual help, and have ability to save in-progress work. An intuitive system (easy to use and easy to learn) also simplifies change management during the new system roll-out and enables faster onboarding of new staff.
  • No duplication of data entry: Any data / information entered once should be pre-populated at all places where it is required, especially in forms and notices.
  • Real-time integration: The system should ensure that the data from interfacing systems is available at the time a case is processed and/or when a key decision needs to be made. It should also allow routing of tasks and workflows in a cross-functional setting. For example, integration with court systems.
  • Mobility: The system must have the ability to be rendered on various devices and accessed over internet while on the go. Caseworkers spend a significant part of their time on the road visiting children and families. They need the ability to perform some of the application tasks over a phone or tablet, use their phone camera to capture and upload photos and documents, and conduct a mobile search for information to make the right decision. For example, while removing a child from home in a remote location, the caseworker can quickly search for nearby temporary shelters or foster homes which are safe havens for the at risk child. As mobile internet reliability varies across locations, it is also imperative to have offline capabilities for some of the selected functionalities so that caseworkers can save their work while in the field and sync up the information when they are able to get back online. 
In addition, some other features like a scrapbook for children and families, self-service for the families, interfacing agencies, partners and third parties can ensure better community engagement, faster processing and reduced cost.

 

Automation and future proof technology (Viability)

The system should be built on the most viable technology that is available today and one that will continue to be relevant over the life of the application. Many agencies are taking a cloud-first or cloud-native approach for design and deployment of their modernized system. This will help in leveraging many modern technologies and enable the seamless integration capabilities available on the cloud, including advanced analytics and AI / Machine Learning.

AI capabilities like advanced analytics, intelligent search, image recognition, robotic process automation, natural language processing and machine learning should be considered as part of the system design as levers for workflow automation, process optimization and improved user experience. Application of these technologies could be in the areas of deep search (person, resources and providers), automating manual processes, data driven decision making, and determining the risk level of a child or family based on analysis of structured and unstructured data and images.

When it comes to evaluating options between platform customization vs. configuration, ground up development or an industry specific solution, agencies should consider the following:

  • Fit-for-purpose -  Is the platform / solution built to address the needs of a Child Welfare system or is it built on top of a platform that was designed for some other purpose?
  • Product / solution roadmap alignment - How to ensure that the solution and its underlying platform will not divert from the purpose of Child Welfare? Also, how does it take advantage of the latest technological advancements?
  • Modularity - Ease with which components can be replaced in the future either to leverage better technology, optimize cost or to accommodate business process changes.
  • Maintainability and total cost of ownership - Does the design of the system allow it to be maintained easily? Does it require costly niche skills to maintain and enhance the system? Does it lock an agency with a particular vendor for support? In addition to the technology itself, the modularity, configurability and product roadmap alignment impact the maintainability and cost of ownership significantly.

Agencies should choose a solution that can keep up with the expectations of users in a rapidly digitizing world, leveraging latest technologies like cloud, Micro-services, AI, RPA, Blockchain and best practices across multiple Industries.


Rapid and cost effective implementation (Feasibility)

Even the most desirable and viable solution may not produce the expected level of outcome if the implementation is not done effectively. A solution becomes the most feasible one when the following three elements are balanced:

  • Schedule or speed of delivery - how quickly can the users use new features and functions, how quickly can their changes/updates be incorporated
  • Cost - what's the total cost of ownership which will include cost to implement, maintain and support, and upgrade/change
  • Quality - quality of the delivered product(s), program management, effectiveness of training and OCM

Listed below are some of the key areas of focus for modernization of child welfare systems information systems:

  • Methodology - Agile is gaining ground as the preferred approach. However, agile implementation requires a high level of collaboration across departments as well as between the system integrator and the various groups within the agency - PMO, Business, Program Areas, QA/IV&V etc. Agencies should budget adequate effort for staff to participate in the process as well as enough time to orient their teams to the agile methodology. Agile also requires continuous prioritization at every level of the agile team organization. There are certain governance processes and review gates in any large public sector implementation which are not fully aligned with the agile way of delivery. Agencies and SI partners should work collaboratively to define a framework which can align the agile delivery with the governance processes at the state and federal level. There are various flavors of agile methodologies available today to choose form. We suggest using SAFe, which widely followed and caters to various scales of agile organization - project, program, portfolio etc.

  • Quality Management - It has two aspects (1) quality control (QC), which is more about detecting deficiencies in the work product before it moves to the next stage of delivery and (2) quality assurance (QA), which ensures that the right processes exist to prevent deficiencies from occurring and when they occur, detecting them as early as possible.  Quality control is achieved through an adequate level of review and testing. In agile methodology, the feedback and testing provides quality feedback real time as part of each sprint. Quality assurance is addressed through training & enablement, process implementation and process adherence tracking, defect analysis and corrective action planning. In the agile way of working, many of these are addressed during the retrospective and program increment (PI) planning. The implementation team should continuously strive to detect deficiencies earlier and even prevent them from happening leveraging various 'shift-left' levers like - automated code review, peer review, standards / guidelines / checklists, automated regression testing etc.

  • Training and OCM - Key to successful adoption of any major system is effective training and change management. Users should be engaged early in the process - they should be given preview of the work in progress application and the opportunity to provide feedback right from the requirements and design phase. Well defined planning, a dedicated training environment with adequate technical support, detailed training material and effective training delivery are key to a well prepared implementation team. Training delivery is also largely digitized these days, thanks to various available training platforms and technologies. Also, the on-demand computer based training can be integrated with the application, providing contextual help at the time when users need it. Change management is another important aspect in overall program success. The right level of stakeholder engagement, proactive communication and help for users and staff through the process are all cornerstones of a successful OCM program. Agencies should identify a team with the expertise and experience required to help them navigate through the change process.

  • Program management - It is undoubtedly one of the most important aspects of a successful implementation. Program management integrates various teams, work streams and processes to ensure that the program delivers the intended outcome. Program management is a vast subject and we will cover key tenets of successful program management in a separate white paper.

We have been using these principles to help our clients successfully digitize their child welfare systems. And, we have leveraged this experience to develop our Child Welfare solution. You can learn more about our solution and experience here.


A reference model for service-based/modular Integrated Eligibility

In my last blog, I discussed about Service-based or Modular Integrated Eligibility system and how it can help states build an integrated yet flexible technology landscape supporting all health and human services programs.

Let's look at the key blocks of a service-based IES (View image) that can help design a common reference HHS architecture.

 

Design principles

  • Compliance - Compliant with government regulation and standards like NIST SP800-53, CMS TRA
  • Modularity - Decouples business functionality from monolithic architecture. Decomposes business processes to lowest level, preferably micro-services
  • Scalability - Supports increases in capacity on an episodic and long term basis
  • Portability - Enables movement from one platform to another platform and/or on- premise to hybrid to cloud without a need for any redevelopment or re-eingeering
  • Platform Agnostic - Not completely technology agnostic as CMS/HHS have declared technologies that should be used, such as an external rules engine, but platform agnostic and implementation agnostic

 

Standards alignment

  • MITA alignment - Offers a layer that corresponds to the MITA access channels and uses a service gateway and multi module configuration to enable a services infrastructure, with discrete business services (workflows, rules, and processes) enabled by specific technologies (e.g. rules engine). Provides interoperable technical services through a series of frameworks (Notices, Document Management, etc). And, delivers interoperability services as another access channel, enabling a unified security approach and avoiding a "side door" interoperability into the application layer
  • NHSIA alignment - NHSIA focuses on shared business processes across HHS systems such as Eligibility, HIE, etc. Service-based IES should enable this through the concept of modular business processes in its application layer, which can be as granular as an identity matching micro-service or as large as a case management business process, which can be linked through services to provide the required application functionality. Shared infrastructure is supported at the data layer through the use of an Operational Data Store, integration through master data technologies, and aggregation into a data warehouse with data marts to support the operational and strategic intelligence needs of the agency. Security is supported through a security layer enabling Identity and Access Management as the gatekeeper to the access layer.

 

Layers

  • Security - Service-based IES puts the focus on security as the gatekeeper and helps focus implementation on that as a primary goal, along with depth of security throughout in the use of zoned firewalls, FIPS-140 compliant encryption of data in transit and at rest, and sufficient technologies to defend the system. In particular, Service-based IES calls for the use of an Intrusion Protection System to mediate the effects of zero-day exploits and a security information and event management system to enable auditing, problem resolution, and accountability.
  • Access - The system can be accessed from two channels: Internet and intranet. Both classified as external to the trusted system and pass through the security layer. This enables organizations to mitigate the effects of intranet systems being the source of security failures. Access can be broadly classified as EDI (Electronic Data Interchange), which can be an exchange automated by an internal or external actor, and portal based, which provides humans with access to the system. EDI can be sophisticated web-services gateways exchanging data in advanced formats such as National Information Exchange Model (NIEM), or as old-fashioned as faxed documents. The abstract treatment is the same - pass through security and reside in a DMZ waiting for the application layer to decide what is to be done. The whole access layer is firewalled from the rest of the system.
  • Application - The application layer contains the largest number of components and the most complicated architecture. At the highest level, it provides business services that can be consumed by a portal technology, or shared via EDI gateways. Underlying these logical business services is an API gateway that provides the technical services. Below the gateway, modules from various applications, vendors, and platforms provide business processes that can be orchestrated via the API gateway or passed to the portal as business services. The logic of the business processes is externalized through Workflow Management, Business Process Management, and Rules Engines. Items susceptible to real world change, such as workflow or rules, are externalized. Underlying the business processes, and supporting MITA-style technical services, are a variety of frameworks that enable and present a vendor agnostic layer to enable the use of the technology of choice for areas like document management, notices, batch, etc.
  • Interoperability - Interoperability is a function tightly coupled with the application processes. Meta data and service registries enable interoperability between systems both at a transport and semantic level: data or services can be universally located and harmonized via interoperability services. Data abstraction below an interoperability level assures incremental, modular data stores. Data stores may operate as natively required, but the data integrity and meaning will be mapped as the business requires. Underlying the data abstraction and meta services is the Enterprise Services Bus (ESB) that enables transformation services.
  • Data - Data is firewalled from the application layer to facilitate access control and minimize risk. Individual business modules can store data as required to perform their function. The data is aggregated and normalized into an Operational Data Store to enable a single source of truth for the enterprise. Operational data is transformed and transferred to a data warehouse, where it can be used for operational and strategic decision support. An arbitrary number of data marts can be created to facilitate data use for a specific purpose, by specific classes of users, or to enable more timely views with a mind to performance.

Practical use of reference architecture

Service-based IES can serve as a benchmark to design any HHS system. By following the patterns contained in the Service-based IES, HHS agencies can ensure alignment with federal standards and guidelines such as NIST, NHSIA, and MITA. Modularity, including multi-vendor and multi-platform, is supported at the data and application layers, with the service gateway and Access Layer providing the glue to present a user-centric methodology.

Layers can be abstracted, through use of on-premise, Platform-as-a-Service, or integration of cloud platforms, as needed, replacing modules or the application and data layers. Scenarios where all three zones are cloud based in whole or in part are possible, with the integration points being at the data and access channel boundaries.

Devolving the Service-based IES into an application, information, or data architecture becomes an exercise of zooming focus on a Service-based IES component, providing the detail needed to develop code or configure a tool. It should not require the addition of a component as the Service-based IES is meant to be a holistic approach to the requirements of all HHS systems.

 

Conclusion

Service-based IES offers a powerful architecture to develop a modern, modular, loosely-coupled and scalable HHS IT landscape that enables agencies to shift focus from IT to their core mission of improving outcomes and care and reducing cost.


Implementation of Service-based IES relies on modern technologies that are used in the commercial world today: platforms, data federation and master data management, externalized business rules and externalized business processes. A base set of functionality exists, but can be easily replaced or overwritten as needed. The core is truly loosely coupled, which enables each layer and program to operate independently, but integrated with the data for the citizen. 

At the ISM 2019 conference, we will be discussing this model in detail and outline how agencies can leverage it to navigate their transformation. Drop by our booth 431 if you'd like to know how your agency can adopt this model.

Service-based/Modular Integrated Eligibility

Traditional Integrated Eligibility systems encapsulate complex rules for the determination of program participation and manage the lifecycle of the program participant -- enrollment, change in circumstances, reenrollment, etc. -- through case management software. These systems are tightly integrated between programs and technologies. Older systems include business logic in all the three application architecture layers: User Interfaces, Business Logic, and Data Storage. This leads to long-duration implementations as dependencies must be carefully documented and the impact of any changes must be carefully assessed and tested in detail.

This is a major barrier to enabling the business processes ideal for an organization's specific requirements and the regulatory environment. It is often heard that the organization must fit the system's "out of the box" functionality. Change is feared, expensive to implement, and results in errors in production that give rise to lawsuits, worker frustration, and degraded service. Regulatory change is not implemented in the systems and the citizen's interest deferred to technology complexity. This is not appropriate or desirable.

 

Potential solutions to improve agility

A variety of approaches have been taken to solve the problem of rigidity in systems which prevent change and optimization.

Dis-integrating eligibility - allowing departments and their programs to implement their own systems. Some states include a central repository to link citizen ID across programs. This increases flexibility and reduces complexity for any given program, but results in multiple systems serving the same population which increases the chances of fraud, reduces program coordination and negatively impacts service delivery.

One system for everything - traditional eligibility, child welfare, adult protective services and all social programs. This approach assures tight program integration and maximizes dollar investment, but at the cost of flexibility and the associated risk of error while incorporation of any change. Any variance for a particular location, such as a county, must be hard coded into existing rules and programs.

Old-HHS-System.png


Designing a Service-based integrated eligibility system

A preferred solution is to modularize components, both in the application architecture (user interface, business logic, data storage) as well as the program and government or organizational unit (e.g., state, county, city, service center). This solution balances program independence and integration to ensure maximum citizen value.  Each program shares a common platform and a set of tools, but is also able to implement the rules and processes required for its particular circumstances. We call this a service-based integrated eligibility system (IES).

Service-based-IES.png

Designing a Service-based IES

A Service-based IES is a classic N-tier architecture that includes specific technologies (e.g., rules engine, workflow engine) where federal preferences or requirements exist. It includes the classic presentation/application/persistence model while incorporating requirements for loosely coupled systems and modularity.

This can help develop a reference architecture for Health and Human Services (HHS) and provide a unified technology view of a diverse set of federal design guidance and requirements. While there are state-specific and/or program-specific requirements, quite a few common tools and patterns exist that can be reused by multiple states. By developing a unified HHS architecture, states can take advantage of economies of scale and reuse, enabling higher efficiencies, reducing training costs, maximizing the use of technology investments, and accelerating implementations.

Check out my next blog to know about the key building blocks of a service-based or modular IES.


September 9, 2019

HHS technology vision for the future - navigating your next to #DigitizeHHS

To a great extent, the future of Health and Human Services (HHS) technology is the present of the commercial sector: modular, agile, loosely coupled, and best-of-breed.  The difference is the speed of implementation and complexity.

Government systems need to meet a number of constitutional obligations and operate in a regulatory environment more complex than what commercial sector systems face. Government has many more stakeholders, from government agencies, interest groups, to each and every citizen, both taxpayer and benefit recipient. Achieving consensus while meeting regulatory requirements is always more complicated and a tedious process.

The commercial sector today

The commercial world today is being driven by a need to simplify an organization's management of technology. Today's cloud enabled world removes the need and the extra expense of managing individual servers. Abstracting computing through containerization or technologies such as Amazon's Lambda enables organizations to reduce cost (cloud and platform technology providers achieve economies of scale that are hard to match in even the largest environments) and focus on the business. Platforms (Salesforce, Microsoft Dynamics, Pega, etc.) abstract the layers needed to provide business services, and discrete tools abstract processes thus avoiding green field development to solve a business problem.

Another characteristic of today's commercial technology world is the startup culture. Even large-scale, foundational technology organizations (e.g., Google) incubate small, agile firms that focus on smaller problems with niche solutions, but do it better than all others. These small, niche solutions can be integrated through services and APIs, often times available as services in the cloud. There are start-ups that focus on items such as citizen portals; it is possible to imagine start-ups that focus on small increments, for example rules for a single program.

The HHS world of the future

HHS has taken to the cloud in earnest in just the last five years. There is a large scale "lift and shift" of existing infrastructure to the cloud. Standalone servers are being virtualized, virtual servers moved to the cloud. The future will see greater abstraction - virtual servers will be containerized, services will be taken from the traditional stack and cloud services will be utilized.

Solutions will be built by niche-players who provide best-in-class applications. Risk, always a paramount concern in the public sector, will be mitigated by reducing the criticality of any given component. The "too big to fail" model of single vendor implementations, already fading with the focus on agile procurements and modular solutions, will disappear faster than anybody thinks. States will realize that picking a small company with the best fitting solution for today is a safe bet because in the growing world of startup culture and open technologies, there is always another start up creating even better technology that can be plugged into a modular platform.

The role of the systems integrator

Traditionally, large scale systems integrators (SI) brought the process and technology capabilities needed to create and implement solutions. Additionally, the large scale and concurrent financial resources enabled a perception of comfort and lower risk - a big SI could be sued to perform and had the money to get the job done. History has shown, however, that the large SI is better at counter-suing and threatening Armageddon if more money is not provided to finish the project. This isn't just a public sector problem- the rise of modular, small solutions is driven, in part, by the commercial sector's desire to reduce risk and maintain leverage over its service providers.

In today's world, and in the future, a SI will be vital. They still provide a set of process and technical resources to augment capabilities, share knowledge, and reduce risk. But now states don't have to contract the whole project to one (or a few) solution providers, betting that it will work. They can use agile procurements and niche solutions based on a service-based architecture to build the solution they need. The SI can (and should) be the glue that ensure it all sticks together - PMO, Architecture Team, QA etc.

It is a rare, perhaps even nonexistent, to find a government agency that has enough scale to build cross-industry knowledge base of best practices to drive its own modular projects and ensure the niche solution "bricks" make a house, not a pile of rubble. The role of the SI is changing, but they are not going away.

The outlines of the future of HHS technology can be seen in the commercial world today - understanding today's technology will point to how HHS agencies will transform their operations and systems to meet their unique regulatory and operating environments.

At the ISM 2019 conference, we will be discussing instances when HHS agencies learnt and adapted what their commercial sector peers did and the benefits that they realized. Drop by our booth 431 if you are attending. Would be great to exchange ideas on the future of HHS technology.