Process Technology for Achieving Government Online Dispute Resolution (NSF/NMB)
Dispute resolution is a fundamental and pervasive activity of government. It is essential that it be done in as efficient, effective and fair a manner as possible. This initiative applies process technology to developing and evaluating dispute resolution processes through online delivery. It has been able to improve dispute resolution, and deepen understanding of how to be more successful in developing and evaluating processes with the stringent requirements of public governance. The project based its efforts on the dispute resolution processes and approaches used by the National Mediation Board (NMB). NMB is the independent Federal agency charged with facilitating harmonious labor-management relations within two of the nation's key transportation modes--the railroads and airlines. The effort builds on UMass Amherst research both in process technology and in dispute resolution held by the National Center for Technology and Dispute Resolution. The work has resulted in a system in use by NMB.
In the first phase of the work, EEI demonstrated that formal process definitions could be the basis for effective application of computer and communications automation, paving the way for introduction of Online Dispute Resolution into NMB. In the early phases of the project, EEI developed STORM, a prototype providing rudimentary support for NMB dispute resolution. NMB uses STORM as a training aid for new mediators and parties and to support some actual dispute resolution sessions, thus demonstrating the feasibility of using ODR at the NMB.
The second phase of the work focused on how to create such a family of ODR support systems and did so through the development of STORM2, a platform capable of generating a family of STORM-like systems. STORM2 employs a process-based approach, in which an explicit process specification precisely defines how the ODR tasks are to be performed, and specifies which are to be done by which different agents, human or automated. STORM2 was implemented using the Little-JIL process definition language developed at UMass Amherst that has well-defined semantics. Thus a process defined using Little-JIL can be executed and used as the basis for analyses aimed at inferring whether the process has desired properties of correctness, privacy, etc. STORM2 is now in use at NMB and through its efforts being considered for use throughout the Federal Government.
Information on STORM and STORM2 can be obtained by contacting the EEI.
Patient and Provider Interaction to Improve the Quality of Data contained in Electronic Patient Records(ONC/NSF/NMB)
The EEI and UMass Amherst’s National Center for Technology and Dispute Resolution (NCTDR) organized a workshop to identify disputes that are likely to arise in connection with the growing use of Electronic Health Records. Representatives from the White House and various federal government agencies, as well as researchers in areas such as health databases, healthcare processes, and health informatics met and discussed the issues suggesting follow-on work. The EEI and the NCTDR joined NORC at the University of Chicago to continue the exploration under the sponsorship of the Office of the National Coordinator for Health Information technology (ONC). This began by a survey of Best Practices for patient engagement aimed at data quality improvement that did not show significant attention to the issues. ONC then established an experiment study currently under way at Geisinger Health System testing an approach based on the EEI’s and NCTDR’s previous research.
The initiative is showing that process definition and analysis technologies can be used to reason about the vulnerability of election processes with respect to incorrect or fraudulent behaviors by election officials. The Little-JIL language is used to model example election processes, and various election worker fraudulent behaviors. The FLAVERS finite-state verification system is then used to determine whether different combinations of election worker behaviors cause the process to produce incorrect election results or whether protective actions can be used to thwart these threats. In addition, given the process model and a potential undesirable event, or hazard, a fault tree is automatically derived. Fault tree analysis is then used to automatically identify combinations of failures that might allow the selected potential hazard to occur. Once these combinations have been identified, EEI iteratively improved the process model to increase the robustness of the election process against those combinations that seem the most likely to occur.
Process Modeling as the Basis for Digitizing Government Processes (NSF)
Much of the work that governments are entrusted with performing can be viewed as the efficient, faithful execution of carefully prescribed processes. To perform these processes, governments generally assign key tasks to new and/or existing agencies and officials. The EEI envisions the possibility that government processes can be programmed in a special purpose language, so that computers can provide material assistance to their execution, and analyzers can be employed to spot key defects and omissions. Similarly, policy makers and administrators will be able to see clearly how the automated process would be realized and the requirements placed on their organizations. Once vetted, these processes would then be compiled into specifications of how various agencies and individuals would coordinate their efforts. Computers would be assigned key computational, communication, storage, and visualization tasks in support of the other process performers. This project determined the features and characteristics that are needed in a process definition language capable of supporting effective government process analysis and execution support.
Electronic Enterprise Institute executed two separate efforts: the application of process modeling to the License Renewal Analysis, and a survey analysis of practices of states other than Massachusetts regarding online license renewal and related activities. Several questions that immediately emerge within the initial stage of this project include:
The Collective Mind: Techniques for early warning of systematic failures of aerospace components (DARPA/USAF/US Navy)
Fleets of aerospace equipment are managed through carefully controlled supply chain processes. When any of the planning assumptions are no longer valid, for example, due to a change in the optempo or a delivery of an order of out-of-spec parts or the fielding of an ill-conceived maintenance procedure, an unexpected demand on maintenance and supply can develop and the availability of equipment decrease. Detection of early warning signals in maintenance and supply activities is critical to dealing with such problems proactively. This initiative aimed to show how concurrent developments of embedded computing and associated sensor systems along with the connections of equipment to the computer based communications, popularly referred to as the Internet of Things, allows for new approaches to achieving early warning of problems.
The EEI initiated the effort to develop the technology to respond to this opportunity by recruiting a consortium of universities and research laboratories to conduct a DARPA feasibility study that showed the ideas were achievable to some degree by using data leading industries now collect. We with our partner, the Auton Lab at the Carnegie Mellon University, were then challenged by the US Air Force to prove the ideas on data routinely collected by them from maintenance and logistics activity. This proved so successful that we were allowed to transition the tool into regular use on the largest fleet of US Air Force planes. Plans are now underway to use the ideas throughout the Air Force. The US Navy challenged our team to meet an even greater challenge by including in the analysis built-in-test data and sensor data from their V-22 Osprey fleet.
Social Security Administration Electronic Service Provision: A Strategic Assessment (NAS CSTB)
The Social Security Administration (SSA) faces significant ongoing change in technology, demographics, and public expectations as it carries out its activities, services, and interactions with a variety of user communities. The Social Security Administration Electronic Service Provision: A Strategic Assessment report examines the SSA’s proposed e-government strategy and provides advice on how the SSA can best deliver services to its user communities in the future.
The assessment by the Committee on the Social Security Administration’s E-Government Strategy and Planning for the Future, lead by Professor Osterweil, was based on (1) its examination of the SSA’s current e-government strategy, including technological assumptions, performance measures and targets, planned operational capabilities, strategic requirements, and future goals; (2) its consideration of strategies, assumptions, and technical and operational requirements in comparable public- and private-sector institutions; and (3) its consideration of the larger organizational, societal, and technological context in which the SSA operates.
The report is available from the National Academies.
Planning Meeting on Information Technology and the States: Public Policy and Public Interests (NAS CSTB/Ford Foundation)
With the staff of the National Research Council's Computer Science and Telecommunications Board (CSTB) EEI conducted background research into the nature of state information technology (IT) planning and investment, consulted widely with experts across the country and in a range of disciplines, and focused on experiences in a few states.