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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December 4, 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.

December 3, 2019

Navigating beyond insights with AI

I attended the Conference on Health IT and Analytics recently. The event sees participation from government health and human services (HHS) executives and includes discussions on the next big thing in HHS analytics. This year the focus was on leveraging AI (Artificial Intelligence)/Machine Learning (ML). Almost every person I spoke with mentioned that AI/ML-driven analytics is among the key initiatives that their agencies would like to explore in the near future.

This is not surprising. HHS agencies are looking for ways to arrest the escalating cost of healthcare and manage the impact of chronic diseases on the population and state resources. AI/ML-driven analytics can help.

AI/ML can enable agencies to become more proactive in the management of population health. Instead of reacting to an event, they can use these technologies to anticipate the likelihood of an event and act to prevent it.

HHS agencies have access to reams of data (both transactional and patient generated). However, getting accurate intelligence from this data, particularly the predictive and prescriptive insights, remains expensive, complex and time-consuming.

Advancements in data science and use of ML has helped cross-industry organizations create predictive models at a breakneck speed. HHS agencies can also use ML to solve the challenges outlined above and fast-track development of sophisticated analytical models to predict complex and unique public health scenarios such as:

  • Forecasting potential risk movers within a population subset
  • Predicting frequent service utilizers, particularly those who use high cost services like the emergency room and inpatient care
  • Identifying segments of the population susceptible to opioid abuse
  • Predicting high risk patients with a colonized bacterial condition that might pose a threat to other patients in an ICU
  • Predicting events of shock based on vital sign time series, etc.

Without ML, developing predictive models to address situations like the above would probably take months and require investments in expensive data science resources. With the tools and solutions that exist today, such models can be built, tested and deployed in a couple of days.

While quite a few HHS agencies are experimenting with AI/ML technologies to navigate the next in analytics, a couple of questions remain unanswered. Are AI/ML technologies only used to achieve faster and smarter predictions? How can these technologies enable agencies to go beyond mathematical data equations and solve business problems?

Let us consider an example - identifying patients who are likely to suffer severe asthmatic attacks during a dust storm and may have to visit an ER.

While predictive models can help agencies identify WHO these high-risk patients may be, the real value will be realized when they can also generate recommendations on WHY and HOW to manage these patients. This next-step in AI/ML-driven insights is what I call "Next-Best-Actions."

Such recommendations (Next-Best-Actions) obtained through augmented next level analytics will help the consumers of the information (for example, the care manager, provider etc.) proactively manage the patient population. 

In the example above, the ML output can arm care managers and other stakeholders (e.g., the provider) with intelligence that:

  • The patient's condition is going to get worse (and not just because of the weather condition), and;
  • The patient is likely to visit the ER because he/she ran out of the rescue inhaler or has not refilled the rescue inhaler in last six months and the dosage left is only good for next few days.

In addition to this intelligence, the models can also suggest the following set of proactive actions:

  • Recommend that the provider send out an eRx refill of the rescue inhaler to the pharmacy nearest to the patient, and;
  • Alert the care manager to contact the patient and to advise him/her to take the prescription

Recommendations such as these (i.e., Next-Best-Actions) can significantly improve care delivery, clinical outcomes and cost.

Therefore, when agencies look to use AI/ML for advanced analytics, they should look for solutions that not only generate predictive insights (WHO), but also generate Next-Best-Actions (WHAT, WHY, and HOW) to solve a complex health care problem end-to-end.

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