Search

Background Explainability, the aspect of artificial intelligence-based decision support (ADS) systems that allows users to understand why predictions are made, offers many potential benefits. One common claim is that explainability increases user trust, yet this has not been established in healthcare contexts. For advanced algorithms such as artificial neural networks, the generation of explanations is not trivial, but requires the use of a second algorithm. The assumption of improved user trust should therefore be investigated to determine if it justifies the additional complexity. Methods Biochemistry staff completed a wrong blood in tube (WBIT) error identification task with the help of an ADS system. One-half of the volunteers were provided with both ADS predictions and explanations for those predictions, while the other half received predictions alone. The two groups were compared in terms of their rate of agreement with ADS predictions, as an index of user trust, and WBIT error detection performance. Since the AI model used to generate predictions was known to out-perform laboratory staff, increased trust was expected to improve user performance. Results Volunteers reviewed 1590 sets of results. The volunteers provided with explanations demonstrated no difference in their rate of agreement with the ADS system compared to volunteers receiving predictions alone (83.3% versus 81.8%, p = 0.46). The two volunteer groups were also equivalent in accuracy, sensitivity and specificity for WBIT error identification ( p-values >0.78). Conclusions For a WBIT error identification task, there was no evidence to justify the additional complexity of explainability on the grounds of increased user trust.

Many decision support methods and systems in pharmacovigilance are built without explicitly addressing specific challenges that jeopardize their eventual success. We describe two sets of challenges and appropriate strategies to address them. The first are data-related challenges, which include using extensive multi-source data of poor quality, incomplete information integration, and inefficient data visualization. The second are user-related challenges, which encompass users' overall expectations and their engagement in developing automated solutions. Pharmacovigilance decision support systems will need to rely on advanced methods, such as natural language processing and validated mathematical models, to resolve data-related issues and provide properly contextualized data. However, sophisticated approaches will not provide a complete solution if end-users do not actively participate in their development, which will ensure tools that efficiently complement existing processes without creating unnecessary resistance. Our group has already tackled these issues and applied the proposed strategies in multiple projects.

For years, avian influenza has influenced economies and human health around the world. The emergence and spread of avian influenza virus have been uncertain and sudden. The virus is likely to spread through several pathways such as poultry transportation and wild bird migration. The complicated and global spread of avian influenza calls for surveillance tools for timely and reliable prediction of disease events. These tools can increase situational awareness and lead to faster reaction to events. Here, we aimed to design and evaluate a decision support framework that aids decision makers by answering their questions regarding the future risk of events at various geographical scales. Risk patterns were driven from pre-built components and combined in a knowledge base. Subsequently, questions were answered by direct queries on the knowledge base or through a built-in algorithm. The evaluation of the system in detecting events resulted in average sensitivity and specificity of 69.70% and 85.50%, respectively. The presented framework here can support health care authorities by providing them with an opportunity for early control of emergency situations.

The COVID-19 pandemic presented health care organizations with a unique challenge in determining effective management of a large-scale incident across an extended time period.This report describes the response of a multisite integrated system to the COVID-19 pandemic through activation of the Hospital Incident Command System.A robust emergency response plan with multidisciplinary involvement can help to ensure clear lines of accountability and expedite decision-making. Consistent physician input across affected specialties allows for a robust understanding of impacted areas, peer-to-peer communication, and a sense of ownership across the medical staff. The necessity of effective communication with staff and patients during times of crisis cannot be understated. The potential for information overload in a pandemic is significant but can be overcome through consistent and transparent communication from leadership.Health systems should have a well-organized emergency response system prepared to launch in small-scale or large-scale situations. The threshold to implement the response system and accountability to make that decision must be a clearly defined organizational policy.

When healthcare providers review the results of a clinical trial study to understand its applicability to their practice, they typically analyze how well the characteristics of the study cohort correspond to those of the patients they see. We have previously created a study cohort ontology to standardize this information and make it accessible for knowledge-based decision support. The extraction of this information from research publications is challenging, however, given the wide variance in reporting cohort characteristics in a tabular representation. To address this issue, we have developed an ontology-enabled knowledge extraction pipeline for automatically constructing knowledge graphs from the cohort characteristics found in PDF-formatted research papers. We evaluated our approach using a training and test set of 41 research publications and found an overall accuracy of 83.3% in correctly assembling the knowledge graphs. Our research provides a promising approach for extracting knowledge more broadly from tabular information in research publications.

Objective: The objective is to provide a review of ecological interface design (EID), to illustrate its value to human factors/ergonomics, and to identify areas for future research and development. Background: EID uses mature interface technologies to provide decision making and problem solving support. A variety of theoretical concepts and analytical tools have been developed to meet the associated challenges. EID provides support that is simultaneously grounded in the practical realities of a work domain and tailored to human capabilities and limitations. Method: EID’s theoretical foundation is discussed briefly. Concrete examples of ecological and traditional interfaces are provided. Different categories of work domains are described, as well as the associated implications for interface design. A targeted literature review is conducted and the experimental outcomes are summarized. A representative evaluation is discussed, and interpretations of performance are provided. Results: The evidence reveals that EID has been remarkably successful in significantly improving performance for work domains with constraints that are law driven (e.g., process control). In contrast, work domains that are intent-driven (e.g., information retrieval) have, by and large, been ignored. Also, few studies have addressed nonvisual displays. Conclusion: EID has not yet realized its potential to improve safety and efficiency across the entire continuum of work domains. Application: EID provides a single integrated framework that is (a) sufficiently comprehensive to deal with complicated work domains and (b) capable of producing innovative support that will generalize to actual work settings.

In the domain of Home Health Care (HHC), precise decisions regarding patient's selection, staffing level, and scheduling of health care staff have a significant impact on the efficiency and effectiveness of the HHC system. However, decentralized planning, the absence of well defined decision rules, delayed decisions and lack of interactive tools typically lead towards low satisfaction level among all the stakeholders of the HHC system. In order to address these issues, we propose an integrated three phase decision support methodology for the HHC system. More specifically, the proposed methodology exploits the structure of the HHC problem and logistic regression based approaches to identify the decision rules for patient acceptance, staff hiring, and staff utilization. In the first phase, a mathematical model is constructed for the HHC scheduling and routing problem using Mixed-Integer Linear Programming (MILP). The mathematical model is solved with the MILP solver CPLEX and a Variable Neighbourhood Search (VNS) based method is used to find the heuristic solution for the HHC problem. The model considers the planning concerns related to compatibility, time restrictions, distance, and cost. In the second phase, Bender's method and Receiver Operating Characteristic (ROC) curves are implemented to identify the thresholds based on the CPLEX and VNS solution. While the third phase creates a fresh solution for the HHC problem with a new data set and validates the thresholds predicted in the second phase. The effectiveness of these thresholds is evaluated by utilizing performance measures of the widely-used confusion matrix. The evaluation of the thresholds shows that the ROC curves based thresholds of the first two parameters achieved 67% to 71% accuracy against the two considered solution methods. While the Bender's method based thresholds for the same parameters attained more than 70% accuracy in cases where probability value is small (p ≤ 0.5). The promising results indicate that the proposed methodology is applicable to define the decision rules for the HHC problem and beneficial to all the concerned stakeholders in making relevant decisions.

Objective Predicting patient outcomes using healthcare/genomics data is an increasingly popular/important area. However, some diseases are rare and require data from multiple institutions to construct generalizable models. To address institutional data protection policies, many distributed methods keep the data locally but rely on a central server for coordination, which introduces risks such as a single point of failure. We focus on providing an alternative based on a decentralized approach. We introduce the idea using blockchain technology for this purpose, with a brief description of its own potential advantages/disadvantages. Materials and Methods We explain how our proposed EXpectation Propagation LOgistic REgRession on Permissioned blockCHAIN (ExplorerChain) can achieve the same results when compared to a distributed model that uses a central server on 3 healthcare/genomic datasets, and what trade-offs need to be considered when using centralized/decentralized methods. We explain how the use of blockchain technology can help decrease some of the problems encountered in decentralized methods. Results We showed that the discrimination power of ExplorerChain can be statistically similar to its counterpart central server-based algorithm. While ExplorerChain inherited some benefits of blockchain, it had a small increased running time. Discussion ExplorerChain has the same prerequisites as a distributed model with a centralized server for coordination. In a manner similar to secure multi-party computation strategies, it assumes that participating institutions are honest, but “curious.” Conclusion When evaluated on relatively small datasets, results suggest that ExplorerChain, which combines artificial intelligence and blockchain technologies, performs as well as a central server-based method, and may avoid some risks at the cost of efficiency.

Background: To investigate opinions among employees at an Out-of-Hours general practitioner (OOH-GP) service and a safety alarm service about the establishment of a generic call-centre. Methods: Qualitative study using individual and group interviews with 14 employees and managers involved in preparation of a merge into a new generic call-centre. They were asked about their opinions towards establishing a generic call-centre where all contact about unplanned health inquiries from the public had to be done by telephone and how to solve more requests on the phone. Data was analysed thematically. Results: Participants who alternate between call handling and direct patient contact (personnel at the OOH-GP) believed that just handling calls would be monotonous, less challenging and provide poorer quality. This was not supported by those working at the safety alarm service. There were different opinions about introducing mandatory use of decision support system for all inquiries, but it was a common understanding that it would lead to more patients in need of face-to-face consultations due to over triage. To solve more requests on the phone participants believed a public information campaign was required, that GPs received more of the emergency requests within their ordinary working hours and having salaried doctors in the OOH-GP service. Conclusion: In the participants’ opinion, successful establishment of a generic call-centre depends on the employees’ possibility of direct patient contact, clarifications on the use of decision support system and good information to the population. © The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Background As a new application technology of the Internet of Things (IoT), intelligent medical treatment has attracted the attention of both nations and industries through its promotion of medical informatisation, modernisation, and intelligentisation. Faced with a wide variety of intelligent medical terminals, consumers may be affected by various factors when making purchase decisions. Purpose To examine and evaluate the key influential factors (and their interrelationships) of consumer adoption behavior for improving and promoting intelligent medical terminals toward achieving set aspiration level in each dimension and criterion. Method A hybrid modified Multiple Attribute Decision-Making (MADM) model was used for this study, based on three components: (1) the Decision-Making Trial and Evaluation Laboratory (DEMATEL) technique, to build an influential network relationship map (INRM) at both ‘dimensions’ and ‘criteria’ levels; (2) the DEMATEL-based analytic network process (DANP) method, to determine the interrelationships and influential weights among the criteria and identify the source-influential factors; and (3) the modified Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to evaluate and improve for reducing the performance gaps to meet the consumers’ needs for continuous improvement and sustainable products-development. First, a consensus on the influential factors affecting consumers’ adoption of intelligent medical terminals was collected from experts’ opinion in practical experience. Next, the interrelationships and influential weights of DANP among dimensions/criteria based on the DEMATEL technique were determined. Finally, two intelligent medicine bottles (AdhereTech, A1 alternative; and Audio/Visual Alerting Pillbox, A2 alternative) were reviewed as the terminal devices to verify the accuracy of the MADM model and evaluate its performance on each criterion for improving the total certification gaps by systematics according to the modified VIKOR method based on an INRM. Results In this paper, the criteria and dimensions used to improve the evaluation framework are validated. The systematic evaluation in index system is constructed on the basis of five dimensions and corresponding ten criteria. Influential weights of all criteria ranges from 0.037 to 0.152, which shows the rank of criteria importance. The evaluative framework were validated synthetically and scientifically. INRM (influential network relation map) was obtained from experts’ opinion through DEMATEL technique shows complex interrelationship among factors. At the dimension level, the environmental dimension influences other dimensions the most, whereas the security dimension is most influenced by others. So the improvement order of environmental dimension is prior to security dimension. The newly constructed approach was still further validated by the results of the empirical case, where performance gap improvement strategies were analyzed for decision-makers. The modified VIKOR method was especially validated for solving real-world problems in intelligent medical terminal improvement processes. For this paper, A1 performs better than A2, however, promotion mix, brand factor, and market environment are shortcomings faced by both A1 and A2. In addition, A2 should be improved in the wireless network technology, and the objective contact with a high degree of gaps. Conclusions Based on the evaluation index system and the integrated model proposed here, decision-makers in enterprises can identify gaps when promoting intelligent medical terminals, from which they can get valuable advice to improve consumer adoption. Additionally, an INRM and the influential weights of DANP can be combined using the modified VIKOR method as integrated weightings to determine how to reduce gaps and provide the best improvement strategies for reaching set aspiration levels.

Health information systems have been developed to help hospital managers steer daily operations, including key performance indicators (KPIs) for monitoring on a time-aggregated basis. Yet, current literature lacks in proposals of productivity dashboards to assist hospitals stakeholders. This research focuses on two related problems: (1) hospital organizations need access to productivity information to improve access to services; and (2) managers need productivity information to optimize resource allocation. This research consists in the development of dashboards to monitor information obtained from a hospital organization to support decision makers. To develop and evaluate the productivity dashboard, the Design Science Research (DSR) methodology was adopted. The dashboard was evaluated by stakeholders of a large Portuguese hospital who contributed to iteratively improving its design toward a useful decision support tool. Additionally, it was ascertained that monitoring productivity needs more study and that the dashboards on these themes are valuable assets at a monitoring level and subsequent decision-making process.

Applying state-of-the-art machine learning and natural language processing on approximately one million of teleconsultation records, we developed a triage system, now certified and in use at the largest European telemedicine provider. The system evaluates care alternatives through interactions with patients via a mobile application. Reasoning on an initial set of provided symptoms, the triage application generates AI-powered, personalized questions to better characterize the problem and recommends the most appropriate point of care and time frame for a consultation. The underlying technology was developed to meet the needs for performance, transparency, user acceptance and ease of use, central aspects to the adoption of AI-based decision support systems. Providing such remote guidance at the beginning of the chain of care has significant potential for improving cost efficiency, patient experience and outcomes. Being remote, always available and highly scalable, this service is fundamental in high demand situations, such as the current COVID-19 outbreak., Comment: 10 pages, 5 figures, accepted to AMIA 2020

A decision support system using recent data about work hours and real-time data about relief events was developed to guide anesthesiologist end-of-shift relief decisions in an effort to promote a relief order that prioritized relief for those who had recently worked later than others. After system implementation, there were fewer deviations from this idealized order of relief, and early relief was more evenly distributed.

The U.S. Department of Defense (USDOD) service members are at risk of exposure to ionizing radiation due to radiation accidents, terrorist attacks and national defense activities. The use of biodosimetry is a standard of care for the triage and treatment of radiation injuries. Resources and procedures need to be established to implement a multiple-parameter biodosimetry system coupled with expert medial guidance to provide an integrated radiation diagnostic system to meet USDOD requirements. Current USDOD biodosimetry capabilities were identified and recommendations to fill the identified gaps are provided. A USDOD Multi-parametric Biodosimetry Network, based on the expertise that resides at the Armed Forces Radiobiology Research Institute and the Naval Dosimetry Center, was designed. This network based on the use of multiple biodosimetry modalities would provide diagnostic and triage capabilities needed to meet USDOD requirements. These are not available with sufficient capacity elsewhere but could be needed urgently after a major radiological/nuclear event.

The public health and medical response to a radiological or nuclear incident requires the capability to sort, assess, treat, triage and to ultimately discharge, refer or transport people to their next step in medical care. The size of the incident and scarcity of resources at the location of each medical decision point will determine how patients are triaged and treated. This will be a rapidly evolving situation impacting medical responders at regional, national and international levels. As capabilities, diagnostics and medical countermeasures improve, a dynamic system-based approach is needed to plan for and manage the incident, and to adapt effectively in real time. In that the concepts and terms can be unfamiliar and possibly confusing, resources and a concept of operations must be considered well in advance. An essential underlying tenet is that medical evaluation and care will be managed by healthcare professionals with biodosimetry assays providing critical supporting data.

Background Appropriate thromboprophylaxis for patients with atrial fibrillation (AF) remains a national challenge. Methods We hypothesized that provision of decision support in the form of an Atrial Fibrillation Decision Support Tool (AFDST) would improve thromboprophylaxis for AF patients. We conducted a cluster randomized trial involving 15 primary care practices and 1,493 adults with nonvalvular AF in an integrated health care system between April 2014 and February 2015. Physicians in the intervention group received patient-level treatment recommendations made by the AFDST. Our primary outcome was the proportion of patients with antithrombotic therapy that was discordant from AFDST recommendation. Results Treatment was discordant in 42% of 801 patients in the intervention group. Physicians reviewed reports for 240 patients. Among these patients, thromboprophylaxis was discordant in 63%, decreasing to 59% 1 year later ( P = .02). In nonstratified analyses, changes in discordant care were not significantly different between the intervention group and control groups. In multivariate regression models, assignment to the intervention group resulted in a nonsignificant trend toward decreased discordance ( P = .29), and being a patient of a resident physician ( P = .02) and a higher HAS-BLED score predicted decreased discordance ( P = .03), whereas female gender ( P = .01) and a higher CHADSVASc score ( P = .10) predicted increased discordance. Conclusions Among patients whose physicians reviewed recommendations of the decision support tool discordant therapy decreased significantly over 1 year. However, in nonstratified analyses, the intervention did not result in significant improvements in discordant antithrombotic therapy.

Decision makers in organisations are often overtaxed by huge amounts of information in daily business processes. As a potential support strategy, this study examined 'directed forgetting' (Bjork, 1970) in a simulated sales planning scenario. We assumed that the availability of a computer-based decision support system (DSS) triggers the forgetting of decision-related background information. Such directed forgetting should not only release memory capacities for additional tasks but also enhance decision quality and decrease strain of decision makers. Assumptions were tested in an experimental study with

The current prevalence of mental disorders demands improved ways of the management and planning of mental health (MH) services. Relative technical efficiency (RTE) is an appropriate and robust indicator to support decision-making in health care, but it has not been applied significantly in MH. This article systematically reviews the empirical background of RTE in MH services following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Finally, 13 studies were included, and the findings provide new standard classifications of RTE variables, efficiency determinants and strategies to improve MH management and planning.

Background: Crowding in emergency departments (EDs) is a challenge globally. To counteract crowding in day-to-day operations, better tools to improve monitoring of the patient flow in the ED is needed. The objective of this study was the development of a continuously updated monitoring system to forecast emergency department (ED)arrivals on a short time-horizon incorporating data from prehospital services. Methods: Time of notification and ED arrival was obtained for all 191,939 arrivals at the ED of a Norwegian university hospital from 2010 to 2018. An arrival notification was an automatically captured time stamp which indicated the first time the ED was notified of an arriving patient, typically by a call from an ambulance to the emergency service communication center. A Poisson time-series regression model for forecasting the number of arrivals on a 1-, 2- and 3-h horizon with continuous weekly and yearly cyclic effects was implemented. We incorporated time of arrival notification by modelling time to arrival as a time varying hazard function. We validated the model on the last full year of data. Results: In our data, 20% of the arrivals had been notified more than 1 hour prior to arrival. By incorporating time of notification into the forecasting model, we saw a substantial improvement in forecasting accuracy, especially on a one-hour horizon. In terms of mean absolute prediction error, we observed around a six percentage-point decrease compared to a simplified prediction model. The increase in accuracy was particularly large for periods with large inflow. Conclusions: The proposed model shows increased predictability in ED patient inflow when incorporating data on patient notifications. This approach to forecasting arrivals can be a valuable tool for logistic, decision making and ED resource management. © The Author(s). 2019Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

A fundamental activity in hospital operations is patient assignment, which we define as the process of assigning hospital patients to specific physician services and clinical units based on their diagnosis. When the preferred assignment is not possible, typically due to capacity limits, hospitals often allow for overflow, which is the assignment of patients to other services and/or units. Overflow accelerates assignment, but can also reduce care quality and increase length of stay. This paper develops a discrete-event simulation model to evaluate different assignment strategies. Using a simulation-based optimization approach, we evaluate and heuristically optimize these strategies accounting for expected hospital and physician profit, care quality and patient waiting time. We apply the model using data from the University of Chicago Medical Center. We find that the strategies that use heuristically optimized designation of overflow services and units increase expected profit relative to the capacity-based strategy in which overflow patients are assigned to a service and unit with the most available capacity. We also find further improvement in the strategy that uses heuristically optimized overflow services and units as well as a holding unit that holds patients until a bed in their primary or secondary unit becomes available. Additionally, we demonstrate the effects of these strategies on other performance measures such as patient concentration, waiting time, and outcomes.

Background Variation in hospital management of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) may prolong length of stay, increasing the risk of hospital-acquired complications and worsening quality of life. We sought to determine whether an evidence-based computerized AECOPD admission order set could improve quality and reduce length of stay. Methods The order set was designed by a provincial COPD working group and implemented voluntarily among three physician groups in a Canadian tertiary-care teaching hospital. The primary outcome was length of stay for patients admitted during order set implementation period, compared to the previous 12 months. Secondary outcomes included length of stay of patients admitted with and without order set after implementation, all-cause readmissions, and emergency department visits. Results There were 556 admissions prior to and 857 admissions after order set implementation, for which the order set was used in 47%. There was no difference in overall length of stay after implementation (median 6.37 days (95% confidence interval 5.94, 6.81) pre-implementation vs. 6.02 days (95% confidence interval 5.59, 6.46) post-implementation, p = 0.26). In the post-implementation period, order set use was associated with a 1.15-day reduction in length of stay (95% confidence interval − 0.5, − 1.81, p = 0.001) compared to patients admitted without the order set. There was no difference in readmissions. Conclusions Use of a computerized guidelines-based admission order set for COPD exacerbations reduced hospital length of stay without increasing readmissions. Interventions to increase order set use could lead to greater improvements in length of stay and quality of care. Electronic supplementary material The online version of this article (10.1186/s12890-018-0657-x) contains supplementary material, which is available to authorized users.

Fee for services (FFS) is the prevailing method of payment in most Chinese public hospitals. Under this retrospective payment system, medical care providers are paid based on medical services and tend to over-treat to maximize their income, thereby contributing to rising medical costs and uncontrollable health expenditures to a large extent. Payment reform needs to be promptly implemented to move to a prospective payment plan. The diagnosis-related group (DRG)-based case-mix payment system, with its superior efficiency and containment of costs, has garnered increased attention and it represents a promising alternative. This article briefly describes the DRG-based case-mix payment system, it comparatively analyzes differences between FFS and case-mix funding systems, and it describes the implementation of DRGs in China. China's social and economic conditions differ across regions, so establishment of a national payment standard will take time and involve difficulties. No single method of provider payment is perfect. Measures to monitor and minimize the negative ethical implications and unintended effects of a DRG-based case-mix payment system are essential to ensuring the lasting social benefits of payment reform in Chinese public hospitals.

Predicted demographic, climatic and socio-economic changes will require adaptations of existing water supply and wastewater disposal systems. Especially in rural areas, these new challenges will affect the functionality of the present systems. This paper presents a joint interdisciplinary research project with the objective of developing an innovative software-based optimization and decision support system for the implementation of long-term transformations of existing infrastructures of water supply, wastewater and energy. The concept of the decision support and optimization tool is described and visualization methods for the presentation of results are illustrated. The model is tested in a rural case study region in the Southwest of Germany. A transformation strategy for a decentralized wastewater treatment concept and its visualization are presented for a model village.

There are numerous reasons to conduct scientific research within protected areas, but research activities may also negatively impact organisms and habitats, and thus conflict with a protected area's conservation goals. We developed a quantitative ecological decision-support framework that estimates these potential impacts so managers can weigh costs and benefits of proposed research projects and make informed permitting decisions. The framework generates quantitative estimates of the ecological impacts of the project and the cumulative impacts of the proposed project and all other projects in the protected area, and then compares the estimated cumulative impacts of all projects with policy-based acceptable impact thresholds. We use a series of simplified equations (models) to assess the impacts of proposed research to: a) the population of any targeted species, b) the major ecological assemblages that make up the community, and c) the physical habitat that supports protected area biota. These models consider both targeted and incidental impacts to the ecosystem and include consideration of the vulnerability of targeted species, assemblages, and habitats, based on their recovery time and ecological role. We parameterized the models for a wide variety of potential research activities that regularly occur in the study area using a combination of literature review and expert judgment with a precautionary approach to uncertainty. We also conducted sensitivity analyses to examine the relationships between model input parameters and estimated impacts to understand the dominant drivers of the ecological impact estimates. Although the decision-support framework was designed for and adopted by the California Department of Fish and Wildlife for permitting scientific studies in the state-wide network of marine protected areas (MPAs), the framework can readily be adapted for terrestrial and freshwater protected areas.

Summary Background Thyrolipomas seem to be a rare occurrence and until now their prevalence has only been reported in two studies. Because of the known significant geographic variations of thyroid disease, generalizability of these findings is uncertain as well as the management of found thyrolipomas. Material/Methods Retrospective study of 107 computed tomography scans of the chest of a European inpatient population. A literature review was performed and considerations for pragmatic management were proposed. Results Thyrolipomas were found in 2.8% of patients. All thyrolipomas were smaller than 15 mm. In all patients these were incidental findings unrelated to patients symptoms. No secondary signs of malignancy could be detected. Conclusions Thyrolipomas are a common finding on cross sectional imaging. Like in this study they seem to be incidental findings lacking clinical relevance. Because of the rare possibility of an thyroid cancer with inclusion of mature fat, sonographic follow-up of incidentally discovered thyrolipomas larger than 15 mm should be suggested.

Management algorithms for screen-positive women in cervical cancer prevention programs have undergone substantial changes in recent years. The WHO strongly recommends human papillomavirus (HPV) testing for primary screening, if affordable, or if not, then visual inspection with acetic acid (VIA), and promotes treatment directly following screening through the screen-and-treat approach (one or two clinic visits). While VIA-positive women can be offered immediate ablative treatment based on certain eligibility criteria, HPV-positive women need to undergo subsequent VIA to determine their eligibility. Simpler ablative methods of treatment such as cryotherapy and thermal coagulation have been demonstrated to be effective and to have excellent safety profiles, and these have become integral parts of new management algorithms. The challenges faced by low-resource countries are many and include, from the management perspective, identifying an affordable point-of-care HPV detection test, minimizing over-treatment, and installing an effective information system to ensure high compliance to treatment and follow-up.

The change in nitrogen balance causes many environmental and socioeconomic impacts. In relation to food production and nitrogen release in wastewater systems, wastewater and sludge discharge and mineral fertilizer use intensify nitrogen imbalance of a region. The replacement of mineral fertilizer by nitrogen from treated wastewater, biosolids, and treated urine is a promising alternative. This work presents a model to support decision taking for the management of reactive nitrogen flows in wastewater systems based on system dynamics. Six scenarios were simulated for nitrogen flows in wastewater systems and related components.

© 2019 Oltra-Badenes et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Lack of homogeneity in the product (LHP) is a problem when customers require homogeneous units of a single product. In such cases, the optimal allocation of inventory to orders becomes much more complex. Furthermore, in an MTS environment, an optimal initial allocation may become less than ideal over time, due to different circumstances. This problem occurs in the ceramics sector, where the final product varies in tone and calibre. This paper proposes a methodology for the reallocation of inventory to orders in LHP situation (MERIO-LHP) and a model-based decision-support system (DSS) to support the methodology, which enables an optimal reallocation of inventory to order lines to be carried out in real businesses environments in which LHP is inherent. The proposed methodology and modelbased DSS were validated by applying it to a real case at a ceramics company. The analysis of the results indicates that considerable improvements can be obtained with regard to the quantity of orders fulfilled and sales turnover.

Recently, most healthcare organizations focus their attention on reducing the cost of their supply chain management (SCM) by improving the decision making pertaining processes’ efficiencies. The availability of products through healthcare SCM is often a matter of life or death to the patient; therefore, trial and error approaches are not an option in this environment. Simulation and modeling (SM) has been presented as an alternative approach for supply chain managers in healthcare organizations to test solutions and to support decision making processes associated with various SCM problems. This paper presents and analyzes past SM efforts to support decision making in healthcare SCM and identifies the key challenges associated with healthcare SCM modeling. We also present and discuss emerging technologies to meet these challenges.

PurposeWhen introducing new health technologies, decision makers must integrate research evidence with local operational management information to guide decisions about whether and under what conditions the technology will be used. Multi‐criteria decision analysis can support the adoption or prioritization of health interventions by using criteria to explicitly articulate the health organization's needs, limitations, and values in addition to evaluating evidence for safety and effectiveness. This paper seeks to describe the development of a framework to create agreed‐upon criteria and decision tools to enhance a pre‐existing local health technology assessment (HTA) decision support program.Design/methodology/approachThe authors compiled a list of published criteria from the literature, consulted with experts to refine the criteria list, and used a modified Delphi process with a group of key stakeholders to review, modify, and validate each criterion. In a workshop setting, the criteria were used to create decision tools.FindingsA set of user‐validated criteria for new health technology evaluation and adoption was developed and integrated into the local HTA decision support program. Technology evaluation and decision guideline tools were created using these criteria to ensure that the decision process is systematic, consistent, and transparent.Practical implicationsThis framework can be used by others to develop decision‐making criteria and tools to enhance similar technology adoption programs.Originality/valueThe development of clear, user‐validated criteria for evaluating new technologies adds a critical element to improve decision‐making on technology adoption, and the decision tools ensure consistency, transparency, and real‐world relevance.

As the United States moves into health care reform, there will be renewed debate on staffing models and ratios. The state of Massachusetts has developed a dashboard that will prepare them for nursing care models of the future while demonstrating that a variety of staffing templates can result in quality patient care today. Nursing leaders, legislators, and the public have access to information that demonstrates that staffing is complex and cannot be based on precise nursing ratios in all hospital settings.

Medical processes can be modeled using different methods and notations. Currently used modeling systems like Business Process Model and Notation (BPMN) are not capable of describing the highly flexible and variable medical processes in sufficient detail. We combined two modeling systems, Business Process Management (BPM) and Adaptive Case Management (ACM), to be able to model non-deterministic medical processes. We used the new Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN). First, we explain how CMMN, DMN and BPMN could be used to model non-deterministic medical processes. We applied this methodology to model 79 cataract operations provided by University Hospital Leipzig, Germany, and four cataract operations provided by University Eye Hospital Tuebingen, Germany. Our model consists of 85 tasks and about 20 decisions in BPMN. We were able to expand the system with more complex situations that might appear during an intervention. An effective modeling of the cataract intervention is possible using the combination of BPM and ACM. The combination gives the possibility to depict complex processes with complex decisions. This combination allows a significant advantage for modeling perioperative processes.

PurposeThe purpose of this study is to first create an overview of relevant factors directly influencing employee absence in the healthcare sector. The overview is used to further investigate the factors identified using employee satisfaction survey scores exclusively. The result of the overall objective is a management framework that allows managers to gain insight into the current status of risk factors with high influence on employee absence levels.Design/methodology/approachThe research consists of a quantitative literature study supported by formal and semi‐formal interviews conducted at the case organisations. Employee satisfaction surveys were applied to analyse the development over time of selected factors correlated with concurrent employee absence rates. Checking for causal results, comparisons with the included published literature findings were also carried out.FindingsFour major clustered factors, three of which constitute the term “social capital”, showed a high degree of connection with employee absence rates. The factors are general satisfaction, fairness, reliance and co‐operation. Integrating the four elements in a management framework will provide valuable and holistic information about the determinants with regard to current levels of employee absence. The framework will be a valuable support for leaders with the authority to alter the determinants of employee absence.Research limitations/implicationsSince a great part of the empirical material is supplied from the healthcare sector, the results obtained could be restricted to this sector. Inclusion of data from Arbejdsmarkedets Tillægspension (ATP) showed no deviation from the results in the healthcare sector.Practical implicationsThe product of the study is a decision support tool for leaders to cope with levels of employee absence. The framework is holistic and can prove to be a valuable tool to take a bearing of where to focus future initiatives.Originality/valueGathering former observational studies in a complete overview embracing many relevant factors that influence sickness absence has not yet been attempted. Hospital management is provided with valuable information when given insight into the factors that control employee absence behaviour. Having this insight will enable the managers to promote a healthy working environment, thus lowering employee absence rates to a minimum.

Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling procedures, and (5) using ancillary resources (e.g., labs, pharmacies). This article describes applicable scenarios, outlines DES concepts, and describes the steps required for development. An original DES model developed to examine crowding and patient flow for staffing decision making at an urban academic emergency department serves as a practical example.

Long length of stay and overcrowding in emergency departments (EDs) are two common problems in the healthcare industry. To decrease the average length of stay (ALOS) and tackle overcrowding, numerous resources, including the number of doctors, nurses and receptionists need to be adjusted, while a number of constraints are to be considered at the same time. In this study, an efficient method based on agent-based simulation, machine learning and the genetic algorithm (GA) is presented to determine optimum resource allocation in emergency departments. GA can effectively explore the entire domain of all 19 variables and identify the optimum resource allocation through evolution and mimicking the survival of the fittest concept. A chaotic mutation operator is used in this study to boost GA performance. A model of the system needs to be run several thousand times through the GA evolution process to evaluate each solution, hence the process is computationally expensive. To overcome this drawback, a robust metamodel is initially constructed based on an agent-based system simulation. The simulation exhibits ED performance with various resource allocations and trains the metamodel. The metamodel is created with an ensemble of the adaptive neuro-fuzzy inference system (ANFIS), feedforward neural network (FFNN) and recurrent neural network (RNN) using the adaptive boosting (AdaBoost) ensemble algorithm. The proposed GA-based optimization approach is tested in a public ED, and it is shown to decrease the ALOS in this ED case study by 14%. Additionally, the proposed metamodel shows a 26.6% improvement compared to the average results of ANFIS, FFNN and RNN in terms of mean absolute percentage error (MAPE).

To analyse the healthcare monitoring practices of the local government and its interfaces with nursing.This is a descriptive, exploratory, and qualitative study conducted in six municipalities in the 10th health region of the state of Rio Grande do Sul. Data were collected through semi-structured interviews with six healthcare managers and one adviser, and subjected to content analysis.The results led to the final categories, "Monitoring practices in municipal healthcare management" and "Difficulties of managers in implementing monitoring".The managers pointed out potentialities and weaknesses in the monitoring practices of municipal healthcare. This process is critical for the practice of healthcare workers, especially nurses, since it encourages the use of new tools and innovations that support decision making.

Background Reliable and timely information on disease-specific treatment burdens within a health system is critical for the planning and monitoring of service provision. Health management information systems (HMIS) exist to address this need at national scales across Africa but are failing to deliver adequate data because of widespread underreporting by health facilities. Faced with this inadequacy, vital public health decisions often rely on crudely adjusted regional and national estimates of treatment burdens. Methods and Findings This study has taken the example of presumed malaria in outpatients within the largely incomplete Kenyan HMIS database and has defined a geostatistical modelling framework that can predict values for all data that are missing through space and time. The resulting complete set can then be used to define treatment burdens for presumed malaria at any level of spatial and temporal aggregation. Validation of the model has shown that these burdens are quantified to an acceptable level of accuracy at the district, provincial, and national scale. Conclusions The modelling framework presented here provides, to our knowledge for the first time, reliable information from imperfect HMIS data to support evidence-based decision-making at national and sub-national levels., When adopted to health systems reporting, space-time kriging can estimate large numbers of missing data to acceptable levels of accuracy, and thus allow evidence-based decision making and allocation of resources.

To construct a Decision support system of nursing human resources allocation in general wards based on Hospital information system (HIS).Time series prediction model and Information technical method were used based on data of HIS in West China Hospital, Sichuan University (Chengdu, P.R. China).This study completed the function design and system implementation of the nursing human resources allocation decision support system.The system would help nursing managers choose the optimal scheme and make scientific decisions in combination with "the actual" situation but more empirical studies are needed.