Systems approach to assessing and improving local human research Institutional Review Board performance.

ObjectiveTo quantifying the interdependency within the regulatory environment governing human subject research, including Institutional Review Boards (IRBs), federally mandated Medicare coverage analysis and contract negotiations.MethodsOver 8000 IRB, coverage analysis and contract applications initiated between 2013 and 2016 were analyzed using traditional and machine learning analytics for a quality improvement effort to improve the time required to authorize the start of human research studies.ResultsStaffing ratios, study characteristics such as the number of arms, source of funding and number and type of ancillary reviews significantly influenced the timelines. Using key variables, a predictive algorithm identified outliers for a workflow distinct from the standard process. Improved communication between regulatory units, integration of common functions, and education outreach improved the regulatory approval process.ConclusionsUnderstanding and improving the interdependencies between IRB, coverage analysis and contract negotiation offices requires a systems approach and might benefit from predictive analytics

Achieving Efficiency: Lessons From Four Top-Performing Hospitals

Synthesizes lessons from case studies of how four hospitals achieved greater efficiency, including pursuing quality and access, customizing technology, emphasizing communications, standardizing processes, and integrating care, systems, and providers

Using Data Analytics to Predict an Individual Lawyer\u27s Legal Malpractice Risk Profile: Becoming an LPL Precog

The power of data analytics is revolutionizing the way that business is conducted in nearly every industry. The medical industry, the consumer/retail space, and the banking and financial industries are taking their business operations to the next level by leveraging the power of big data. Despite radical transformations in nearly every other aspect of the legal industry, though, the approach to preventing, predicting, assessing, and resolving malpractice claims hasn\u27t really changed. Malpractice insurers and their law firm clients continue to take an old-fashioned approach when it comes to legal professional liability. Unlike the insurers pricing automobile policies, the vast opportunity that LPL insurers could use hasn\u27t been used well--at least not yet. LPL industry experts have confirmed that most legal malpractice insurers aren\u27t leveraging advancements in technology and legal analytics in order to predict risk areas. Instead, LPL carriers primarily are reacting to actual events or using the broad brush of simple demographics to set rates. Consequently, the all-in malpractice costs for insurers and law firms continue to escalate, even though risk and costs should both be decreasing. This is the wrong result for everyone directly or peripherally involved in the legal industry and, more specifically, the wrong result for the LPL industry as a whole. Our paper posits that a progressive, data-driven approach to legal professional liability will reduce the overall cost of malpractice claims, thus helping law firms to recognize potential pressure points before those intimations of problems become full-blown blisters. Part I analyzes the underpinnings of malpractice claims. Part II discusses how malpractice insurers and their law firm clients have historically assessed, underwritten, and resolved malpractice claims. Part III explains why historical malpractice metrics fall short. And Part IV proposes a new data-driven analytic schema by which malpractice claims might be predicted, managed, assessed, and resolved

Performance Management: A Guide for City Leaders

We are pleased to present, Performance Management: A Guide for City Leaders, a report written and published as a service to NLC members and all cities. This guide presents an overview of existing performance management best practices with an eye toward the future of service delivery in cities. We also aim to empower more city leaders to launch performance management programs in their own cities.Performance management and data analytics in general are key aspects of the continued shift toward data-driven decision-making in cities nationwide. Data-driven decisions help local governments provide city services that are efficient, effective and driven by community priorities. The value of making data- driven decisions is imperative as many cities continue to face the post-recession realities of decreased city revenues, limited intergovernmental aid and reduced municipal workforces. At the same time, there is a growing trend towards openness and making the inner workings of municipal governments more accountable and transparent.Looking to the future, as advanced data analytics and open data become more prevalent in cities, there will be more opportunities to prepare and predict service needs of constituents. We plan to continue highlighting the importance of this epochal shift in city governance through our City of the Future initiative that seeks to advise cities on coming trends and opportunities. Within the Center for City Solutions and Applied Research we strive to strengthen communities, transform and improve cities and assist city leaders.Performance Management: A Guide for City Leaders was developed through staff interviews and surveys with a cross-section of large cities across the United States. This work was supported financially by a grant to the National League of Cities Institute by The Pew Charitable Trusts. We join the authors in thanking the city officials who helped make this work possible, and welcome comments and thoughts from readers, as we continue to work to help city leaders lead

A Case Study Perspective toward Data-driven Process Improvement for Balanced Perioperative Workflow

Based on a 143-month longitudinal study of an academic medical center, this paper examines operations management practices of continuous improvement, workflow balancing, benchmarking, and process reengineering within a hospital’s perioperative operations. Specifically, this paper highlights data-driven efforts within perioperative sub-processes to balance overall patient workflow by eliminating bottlenecks, delays, and inefficiencies. This paper illustrates how dynamic technological activities of analysis, evaluation, and synthesis applied to internal and external organizational data can highlight complex relationships within integrated processes to identify process limitations and potential process capabilities, ultimately yielding balanced workflow and improvement. Study implications and/or limitations are also included

Application of Incident Command Structure to clinical trial management in the academic setting: principles and lessons learned

Background Clinical trial success depends on appropriate management, but practical guidance to trial organisation and planning is lacking. The Incident Command System (ICS) is the ‘gold standard’ management system developed for managing diverse operations in major incident and public health arenas. It enables effective and flexible management through integration of personnel, procedures, resources, and communications within a common hierarchical organisational structure. Conventional ICS organisation consists of five function modules: Command, Planning, Operations, Logistics, and Finance/Administration. Large clinical trials will require a separate Regulatory Administrative arm, and an Information arm, consisting of dedicated data management and information technology staff. We applied ICS principles to organisation and management of the Prehospital Use of Plasma in Traumatic Haemorrhage (PUPTH) trial. This trial was a multidepartmental, multiagency, randomised clinical trial investigating prehospital administration of thawed plasma on mortality and coagulation response in severely injured trauma patients. We describe the ICS system as it would apply to large clinical trials in general, and the benefits, barriers, and lessons learned in utilising ICS principles to reorganise and coordinate the PUPTH trial. Results Without a formal trial management structure, early stages of the trial were characterised by inertia and organisational confusion. Implementing ICS improved organisation, coordination, and communication between multiple agencies and service groups, and greatly streamlined regulatory compliance administration. However, unfamiliarity of clinicians with ICS culture, conflicting resource allocation priorities, and communication bottlenecks were significant barriers. Conclusions ICS is a flexible and powerful organisational tool for managing large complex clinical trials. However, for successful implementation the cultural, psychological, and social environment of trial participants must be accounted for, and personnel need to be educated in the basics of ICS

Developing a data-driven approach for improving operating room scheduling processes

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 52).In the current healthcare environment, the cost of delivering patient care is an important concern for hospitals. As a result, healthcare organizations are being driven to maximize their existing resources, both in terms of infrastructure and human capital. Using a data-driven approach with analytical techniques from operations management can contribute towards this goal. More specifically, this thesis shows, drawing from a recent project at Beth Israel Deaconess Medical Center (BIDMC), that predictive modeling can be applied to operating room (OR) scheduling in order to effectively increase capacity. By examining the current usage of the existing block schedule system at BIDMC and developing a linear regression model, OR time that is expected to go unused can be instead identified in advance and freed for use. Sample model results show that it is expected to be operationally effective by capturing a large enough portion of OR time for a pooled set of blocks to be useful for advanced scheduling purposes. This analytically determined free time represents an improvement in how the current block system is employed, especially in terms of the nominal block release time. This thesis makes the argument that such a model can integrate into a scheduling system with more efficient and flexible processes, ultimately resulting in more effective usage of existing resources.by Gregory C. Sham.S.M.M.B.A

CARES Act Unemployment Insurance Program Implementation in Kentucky: A Case Study

In March of 2020, the COVID-19 pandemic arrived in the United States. The deadly virus filled hospitals to capacity and caused approximately 375,000 deaths nationwide over the next nine months (Ahmad et al., 2021). In an effort to slow the spread of the Coronavirus, federal and state governments closed businesses and agencies within a matter of weeks. Almost as quickly as the threat to human health changed the lives of Americans, the secondary threat of economic disruption began to unfold. Nationwide unemployment rates increased from approximately 4% to 15% between February 2020 and April 2020 (U.S. Bureau of Labor Statistics, 2019). In response, the Coronavirus Aid, Relief, and Economic Security Act (CARES Act) was passed in March of 2020. It provided needed funding for relief to businesses and citizens who were impacted. One provision of the CARES Act was to help states provide unemployment insurance (UI) to workers who were impacted during the pandemic including those who were not traditionally eligible for unemployment insurance such as small business and those who are self-employed. The CARES Act passed quickly because of the emergency circumstances the pandemic presented. Consequently, it had a short timeframe for policy development. The legislation relied on states to implement the programs but provided insufficient guidance. Meanwhile, states faced public pressure to distribute the payments quickly. Expedited implementation, high volume of recipients, and scarcity of staff resources in government offices during the pandemic caused challenges to states in distributing the benefits to their entitled recipients. This report focuses on how the state of Kentucky dealt with the challenges of implementing the CARES UI program implementation. Laws, government reports, news articles, and audits are examined to provide context and a general understanding about the CARES Act and its provisions for unemployment. The research will examine how Kentucky dealt with challenges common to many states as well as how it handled its own unique UI program implementation challenges. The purpose of doing a case study is to provide an in-depth analysis of successes and failures of CARES Act UI programs in Kentucky. The circumstances and reasons for the outcomes that occurred can provide valuable lessons about UI public policy in Kentucky. The conclusions from this study can be used to develop more sound public financial management practices and to inform policy planning for emergency health and economic crises. Applicability of this analysis may be beneficial on the federal level where future policy is written, and on the state level, where states can design more robust unemployment insurance programs

Predicting Failure Rate of Oil & Gas Equipment Using ML

Value of time has become an important perspective in business application ranging from day to day working to big businesses. The value of time is more important in the case of refinery business which has become of paramount importance with increasing energy needs. The main point of contention in refinery operations is the periodic maintenance of the pipelines which consumes of valuable time and resources. With a proper solution which can cater the time requirements of the lead time. The fact is that time consumption is extremely critical for the operations of refinery. Therefore, the application of machine learning is implemented in the prediction of when and how the equipment will be needing preventive maintenance all of this can be accomplished by using available open-source data which will help us in the designing the algorithm and also in the learning of the same. This model has allowed us to investigate different outcomes and planning strategies that are possible through the prediction models and the estimated timings for the maintenance of the pipelines. This predictive maintenance system has allowed for more intelligent and smart planning and has reduced the down time significantly allowing for more revenues

Proceedings of the Project Management Innovation Annual Conference

The Project Management Innovation Conference brings together professionals, leaders, visionaries, researchers, and students to engage in the topics related to the future of project management. Attendees represent all fields and industries, including government, corporate, and private. Attendee demographics include practitioners, front-line staff, and executive management