BackgroundDiabetes self-management involves adherence to healthy daily habits typically involving blood glucose monitoring, medication, exercise, and diet. To support self-management, some providers have begun testing remote interventions for monitoring and assisting patients between clinic visits. Although some studies have shown success, there are barriers to widespread adoption.ObjectiveThe objective of our study was to identify and classify barriers to adoption of remote health for management of type 2 diabetes.MethodsThe following 6 electronic databases were searched for articles published from 2010 to 2015: MEDLINE (Ovid), Embase (Ovid), CINAHL, Cochrane Central, Northern Light Life Sciences Conference Abstracts, and Scopus (Elsevier). The search identified studies involving remote technologies for type 2 diabetes self-management. Reviewers worked in teams of 2 to review and extract data from identified papers. Information collected included study characteristics, outcomes, dropout rates, technologies used, and barriers identified.ResultsA total of 53 publications on 41 studies met the specified criteria. Lack of data accuracy due to input bias (32%, 13/41), limitations on scalability (24%, 10/41), and technology illiteracy (24%, 10/41) were the most commonly cited barriers. Technology illiteracy was most prominent in low-income populations, whereas limitations on scalability were more prominent in mid-income populations. Barriers identified were applied to a conceptual model of successful remote health, which includes patient engagement, patient technology accessibility, quality of care, system technology cost, and provider productivity. In total, 40.5% (60/148) of identified barrier instances impeded patient engagement, which is manifest in the large dropout rates cited (up to 57%).ConclusionsThe barriers identified represent major challenges in the design of remote health interventions for diabetes. Breakthrough technologies and systems are needed to alleviate the barriers identified so far, particularly those associated with patient engagement. Monitoring devices that provide objective and reliable data streams on medication, exercise, diet, and glucose monitoring will be essential for widespread effectiveness. Additional work is needed to understand root causes of high dropout rates, and new interventions are needed to identify and assist those at the greatest risk of dropout. Finally, future studies must quantify costs and benefits to determine financial sustainability.

This article presents the state of the art of Lean principles applied in Emergency Departments through a systematic literature review. Our article extends previous work found in the literature to respond to the following questions: (i) What research problems in emergency departments can Lean principles help overcome? (ii) What Lean approaches and tools are used most often in this environment? (iii) What are the results and benefits obtained by these practices? and (iv) What research opportunities appear as gaps in the current state of the art on the subject? A six-step systematic review was performed following the guidance of the PRISMA method. The review analysis identified six main research problems where Lean was applied in Emergency Departments: (i) High Waiting Time and High Length of Hospital Stay; (ii) Health Safety; (iii) Process redesign; (iv) Management and Lessons Learned; (v) High Patient Flow; (vi) Cost Analysis. The six research problems’ main approaches identified were Lean Thinking, Multidisciplinary, Statistics, and Six Sigma. The leading Lean tools and methodologies were VSM, Teamwork, DMAIC, and Kaizen. The main benefits of applying Lean Principles were (a) reductions in waiting time, costs, length of hospital stay, patient flow, and procedure times; and (b) improvements in patient satisfaction, efficiency, productivity, standardization, relationships, safety, quality, and cost savings. Multidisciplinary integration of managers and work teams often yields good results. Finally, this study identifies knowledge gaps and new opportunities to study Lean best practices in healthcare organizations.

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy under limited resources, such as nurses and chemotherapy chairs. Chemotherapy is a cancer treatment method that is administered orally or intravenously at an outpatient oncology clinic. Chemotherapy patients require a treatment regimen, which is a series of appointments over several weeks or months prescribed by the oncologist. The timing of these appointments is critical to the effectiveness of the chemotherapy treatment on cancer. This motivates the need for new methods for making efficient appointment schedules and for assessing clinic operation performance from both patient and management perspectives. This work uses a classic modeling approach based on systems theory to develop a discrete event system specification (DEVS) simulation model for oncology clinic operations called DEVS-CHEMO. DEVS-CHEMO is configurable to any oncology clinic and provides several capabilities for oncology clinic managers. For example, it can simulate scheduling of chemotherapy patients, clinic resources, and the arrival process of the patients to the clinic on the day of their appointment. This model simulates oncology clinic operations as patients receive chemotherapy treatments and thus allows for assessing scheduling algorithms using both patient and management perspectives. DEVS-CHEMO has been tested and validated using historical data from a real outpatient oncology clinic and the simulation results reported in this paper provide several insights regarding oncology clinic operations management.

Oncology clinics are often burdened with scheduling large volumes of cancer patients for chemotherapy treatments under limited resources such as the number of nurses and chairs. These cancer patients require a series of appointments over several weeks or months and the timing of these appointments is critical to the treatment's effectiveness. Additionally, the appointment duration, the acuity levels of each appointment, and the availability of clinic nurses are uncertain. The timing constraints, stochastic parameters, rising treatment costs, and increased demand of outpatient oncology clinic services motivate the need for efficient appointment schedules and clinic operations. In this paper, we develop three mean-risk stochastic integer programming (SIP) models, referred to as SIP-CHEMO, for the problem of scheduling individual chemotherapy patient appointments and resources. These mean-risk models are presented and an algorithm is devised to improve computational speed. Computational results were conducted using a simulation model and results indicate that the risk-averse SIP-CHEMO model with the expected excess mean-risk measure can decrease patient waiting times and nurse overtime when compared to deterministic scheduling algorithms by 42 % and 27 %, respectively.

The integration of condition monitoring with queueing systems to support decision making is not well explored. This work addresses the impact of condition monitoring of the server on the system level performance experienced by entities in a queueing system. The system consists of a queue with a single server subject to Markovian degradation. The model assumes a Poisson arrival process with service times and repair times according to general distributions. We develop stability conditions and perform steady state analysis to obtain performance measures (average queue length, average degradation, etc.). We propose minimizing an objective function involving four types of costs: repair, catastrophic failure, quality and holding. The queue performance measures derived from steady state analysis are bench-marked and compared to those from a discrete event simulation model. After verifying the queuing model, a sensitivity analysis is performed to determine the relationships between system performance and model parameters. Further, we explore the impact of stochastic service times on degradation and cost coefficients on optimal repair policy.Results indicate that the total cost function is convex and thus subject to an optimal repair policy.The model is sensitive to service time, quality costs, and failure costs for late-stage policy repairs decisions and sensitive to expected repair times and repair costs for early-stage policy repair decisions. The stochastic nature of service times drastically increases the likelihood of catastrophic failure of the server, and substantially increases holding costs. However, quality degrades more for constant service times while repair costs are minimally impacted.

Background Two barriers to effective enrollment decisions are low health insurance literacy and lack of knowledge about how to choose a plan. To remedy these issues, digital decision aids have been used to increase the knowledge of plan options and to guide the decision process. Previous research has shown that the way information is presented in a decision aid can impact consumer choice, and existing health insurance decision aids vary in their design, content, and layout. Commercial virtual benefits counselors (VBCs) are digital decision aids that provide decision support by mimicking the guidance provided by an in-person human resources (HR) counselor, whereas more traditional HR websites provide information that requires self-directed navigation through the system. However, few studies have compared how decision processes are impacted by these different methods of providing information. Objective This study aims to examine how individuals interact with two different types of health insurance decision aids (guided VBCs that mimic conversations with a real HR counselor and self-directed HR websites that provide a broad range of detailed information) to make employer-provided health insurance decisions. Methods In total, 16 employees from a local state university completed a user study in which they made mock employer-provided health insurance decisions using 1 of 2 systems (VBC vs HR website). Participants took part in a retrospective think-aloud interview, cued using eye-tracking data to understand decision aid interactions. In addition, pre- and postexperiment measures of literacy and knowledge and decision conflict and usability of the system were also examined. Results Both the VBC and HR website had positive benefits for health insurance knowledge and literacy. Previous health insurance knowledge also impacted how individuals used decision aids. Individuals who scored lower on the pre-experiment knowledge test focused on different decision factors and were more conflicted about their final enrollment decisions than those with higher knowledge test scores. Although both decision aids resulted in similar changes in the Health Insurance Literacy Measure and knowledge test scores, perceived usability differed. Website navigation was not intuitive, and it took longer to locate information, although users appreciated that it had more details; the VBC website was easier to use but had limited information. Lower knowledge participants, in particular, found the website to be less useful and harder to use than those with higher health insurance knowledge. Finally, out-of-pocket cost estimation tools can lead to confusion when they do not highlight the factors that contribute to the cost estimate. Conclusions This study showed that health insurance decision aids help individuals improve their confidence in selecting and using health insurance plans. However, previous health insurance knowledge plays a significant role in how users interact with and benefit from decision aids, even when information is presented in different formats.

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