Activating the Hospital Incident Command System Response in a Community Specialty Practice: The Mayo Clinic Experience

INTRODUCTION

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.

CASE PRESENTATION

This report describes the response of a multisite integrated system to the COVID-19 pandemic through activation of the Hospital Incident Command System.

DISCUSSION

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.

CONCLUSION

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.

Artificial Intelligence Decision Support for Medical Triage

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.

Knowledge Extraction of Cohort Characteristics in Research Publications

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.

A decision support framework for prediction of avian influenza

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.

Continuous Glucose Monitoring Sensors for Diabetes Management: A Review of Technologies and Applications

By providing blood glucose (BG) concentration measurements in an almost continuous-time fashion for several consecutive days, wearable minimally-invasive continuous glucose monitoring (CGM) sensors are revolutionizing diabetes management, and are becoming an increasingly adopted technology especially for diabetic individuals requiring insulin administrations. Indeed, by providing glucose real-time insights of BG dynamics and trend, and being equipped with visual and acoustic alarms for hypo- and hyperglycemia, CGM devices have been proved to improve safety and effectiveness of diabetes therapy, reduce hypoglycemia incidence and duration, and decrease glycemic variability. Furthermore, the real-time availability of BG values has been stimulating the realization of new tools to provide patients with decision support to improve insulin dosage tuning and infusion. The aim of this paper is to offer an overview of current literature and future possible developments regarding CGM technologies and applications. In particular, first, we outline the technological evolution of CGM devices through the last 20 years. Then, we discuss about the current use of CGM sensors from patients affected by diabetes, and, we report some works proving the beneficial impact provided by the adoption of CGM. Finally, we review some recent advanced applications for diabetes treatment based on CGM sensors.

Methodology and model-based DSS to managing the reallocation of inventory to orders in LHP situations. Application to the ceramics sector

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 model-based 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.

Ecological Interface Design: Thirty-Plus Years of Refinement, Progress, and Potential

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.

Directed forgetting in organisations: the positive effects of decision support systems on mental resources and well-being

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 N = 90 participants. Consistent with our assumptions, results revealed a higher recall of decision-unrelated information, higher decision quality and higher well-being when participants could use a DSS as compared to two Control conditions without a DSS. Moreover, directed forgetting effects were qualified by participants' trust in the DSS. This study provides the first evidence for directed forgetting effects cued by information systems in a business context. Practitioner summary: Information overload is an increasing challenge in modern business organisations. Extending findings from basic memory research, this study shows that availability of a computer-based decision support system triggers forgetting of decision-related background information, which in turn increases users' mental resources for additional tasks, decision quality, and well-being.

An ecological framework for informing permitting decisions on scientific activities in protected areas

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.

Decision support system for management of reactive nitrogen flows in wastewater system

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.

Water leakage management by district metered areas at water distribution networks

The aim of this study is to design a district metered area (DMA) at water distribution network (WDN) for determination and reduction of water losses in the city of Malatya, Turkey. In the application area, a pilot DMA zone was built by analyzing the existing WDN, topographic map, length of pipes, number of customers, service connections, and valves. In the DMA, International Water Association standard water balance was calculated considering inflow rates and billing records. The ratio of water losses in DMAs was determined as 82%. Moreover, 3124 water meters of 2805 customers were examined while 50% of water meters were detected as faulty. This study revealed that DMA application is useful for the determination of water loss rate in WDNs and identify a cost-effective leakage reduction program.

Management algorithms for cervical cancer screening and precancer treatment for resource-limited settings

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.

Application fields for the new Object Management Group (OMG) Standards Case Management Model and Notation (CMMN) and Decision Management Notation (DMN) in the perioperative field

PURPOSE

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.

Inclusion of social indicators in decision support tools for the selection of sustainable site remediation options

Sustainable remediation requires a balanced decision-making process in which environmental, economic and social aspects of different remediation options are all considered together and the optimum remediation solution is selected. More attention has been paid to the evaluation of environmental and economic aspects, in particular to reduce the human and environmental risks and the remediation costs, to the exclusion of social aspects of remediation. This paper investigates how social aspects are currently considered in sustainability assessments of remediation projects. A selection of decision support tools (DSTs), used for the sustainability assessment of a remediation project, is analyzed to define how social aspects are considered in those tools. The social indicator categories of the Sustainable Remediation Forum - United Kingdom (SuRF-UK), are used as a basis for this evaluation. The consideration of social aspects in the investigated decision support tools is limited, but a clear increase is noticed in more recently developed tools. Among the five social indicator categories defined by SuRF-UK to facilitate a holistic consideration of social aspects of a remediation project only "Human health and safety" is systematically taken into account. "Neighbourhood and locality" is also often addressed, mostly emphasizing the potential disturbance caused by the remediation activities. However, the evaluation of 'Ethics and Equality', Communities and community involvement', and 'Uncertainty and evidence' is often neglected. Nevertheless, concrete examples can be found in some of the investigated tools. Specific legislation, standard procedures, and guidelines that have to be followed in a region or country are mainly been set up in the context of protecting human and ecosystem health, safety and prevention of nuisance. However, they sometimes already include some of the aspects addressed by the social indicators. In this perspective the use of DST to evaluate the sustainability of a site remediation project, should be tuned to the legislation, guidelines and procedures that are in force in a specific country or region.

Setting Objectives for Community and Systems Change: An Application of Concept Mapping for Planning a Statewide Health Improvement Initiative

The Hawaii Department of Health (HDOH) used concept mapping techniques to engage local stakeholders and national subject area experts in defining the community and system factors that affect individuals’ behaviors related to tobacco, nutrition, and physical activity. Over eight working days, project participants brainstormed 496 statements (edited to a final set of 90), which were then sorted and rated for their importance and feasibility. A sequence of multivariate statistical analyses, including multidimensional scaling and hierarchical cluster analysis, generated maps and figures that were then interpreted by project stakeholders. The results were immediately incorporated into an official plan, approved by the governor and state legislature, recommending how Hawaii’s tobacco settlement resources could be used to create sustainable changes in population health. The results also provide empirical support for the premise that both community and systems factors ought to be considered when planning comprehensive health improvement initiatives.

A randomised controlled trial of cognitive aids for emergency airway equipment preparation in a Paediatric Emergency Department

BACKGROUND

Safety of emergency intubation may be improved by standardising equipment preparation; the efficacy of cognitive aids is unknown.

METHODS

This randomised controlled trial compared no cognitive aid (control) with the use of a checklist or picture template for emergency airway equipment preparation in the Emergency Department of The Royal Children's Hospital, Melbourne.

RESULTS

Sixty-three participants were recruited, 21 randomised to each group. Equal numbers of nursing, junior medical, and senior medical staff were included in each group. Compared to controls, the checklist or template group had significantly lower equipment omission rates (median 30% IQR 20-40% control, median 10% IQR 5-10 % checklist, median 10% IQR 5-20% template; p < 0.05). The combined omission rate and sizing error rate was lower using a checklist or template (median 35 % IQR 30-45 % control, median 15% IQR 10-20% checklist, median 15% IQR 10-30% template; p < 0.05). The template group had less variation in equipment location compared to checklist or controls. There was no significant difference in preparation time in controls (mean 3 min 14 s sd 56 s) compared to checklist (mean 3 min 46 s sd 1 min 15 s) or template (mean 3 min 6 s sd 49 s; p = 0.06).

DISCUSSION

Template use reduces variation in airway equipment location during preparation foremergency intubation, with an equivalent reduction in equipment omission rate to the use of a checklist. The use of a template for equipment preparation and a checklist for team, patient, and monitoring preparation may provide the best combination of both cognitive aids.

CONCLUSIONS

The use of a cognitive aid for emergency airway equipment preparation reduces errors of omission. Template utilisation reduces variation in equipment location.

TRIAL REGISTRATION

Australian and New Zealand Trials Registry (ACTRN12615000541505).

A decision support framework for characterizing and managing dermal exposures to chemicals during Emergency Management and Operations

Emergency Management and Operations (EMO) personnel are in need of resources and tools to assist in understanding the health risks associated with dermal exposures during chemical incidents. This article reviews available resources and presents a conceptual framework for a decision support system (DSS) that assists in characterizing and managing risk during chemical emergencies involving dermal exposures. The framework merges principles of three decision-making techniques: 1) scenario planning, 2) risk analysis, and 3) multicriteria decision analysis (MCDA). This DSS facilitates dynamic decision making during each of the distinct life cycle phases of an emergency incident (ie, preparedness, response, or recovery) and identifies EMO needs. A checklist tool provides key questions intended to guide users through the complexities of conducting a dermal risk assessment. The questions define the scope of the framework for resource identification and application to support decision-making needs. The framework consists of three primary modules: 1) resource compilation, 2) prioritization, and 3) decision. The modules systematically identify, organize, and rank relevant information resources relating to the hazards of dermal exposures to chemicals and risk management strategies. Each module is subdivided into critical elements designed to further delineate the resources based on relevant incident phase and type of information. The DSS framework provides a much needed structure based on contemporary decision analysis principles for 1) documenting key questions for EMO problem formulation and 2) a method for systematically organizing, screening, and prioritizing information resources on dermal hazards, exposures, risk characterization, and management.

Cross-sectoral optimization and visualization of transformation processes in urban water infrastructures in rural areas

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.

Comparing the performance of expert user heuristics and an integer linear program in aircraft carrier deck operations

Planning operations across a number of domains can be considered as resource allocation problems with timing constraints. An unexplored instance of such a problem domain is the aircraft carrier flight deck, where, in current operations, replanning is done without the aid of any computerized decision support. Rather, veteran operators employ a set of experience-based heuristics to quickly generate new operating schedules. These expert user heuristics are neither codified nor evaluated by the United States Navy; they have grown solely from the convergent experiences of supervisory staff. As unmanned aerial vehicles (UAVs) are introduced in the aircraft carrier domain, these heuristics may require alterations due to differing capabilities. The inclusion of UAVs also allows for new opportunities for on-line planning and control, providing an alternative to the current heuristic-based replanning methodology. To investigate these issues formally, we have developed a decision support system for flight deck operations that utilizes a conventional integer linear program-based planning algorithm. In this system, a human operator sets both the goals and constraints for the algorithm, which then returns a proposed schedule for operator approval. As a part of validating this system, the performance of this collaborative human-automation planner was compared with that of the expert user heuristics over a set of test scenarios. The resulting analysis shows that human heuristics often outperform the plans produced by an optimization algorithm, but are also often more conservative.

Simulation and modeling efforts to support decision making in healthcare supply chain management

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.

A framework to support human factors of automation in railway intelligent infrastructure

Technological and organisational advances have increased the potential for remote access and proactive monitoring of the infrastructure in various domains and sectors - water and sewage, oil and gas and transport. Intelligent Infrastructure (II) is an architecture that potentially enables the generation of timely and relevant information about the state of any type of infrastructure asset, providing a basis for reliable decision-making. This paper reports an exploratory study to understand the concepts and human factors associated with II in the railway, largely drawing from structured interviews with key industry decision-makers and attachment to pilot projects. Outputs from the study include a data-processing framework defining the key human factors at different levels of the data structure within a railway II system and a system-level representation. The framework and other study findings will form a basis for human factors contributions to systems design elements such as information interfaces and role specifications.

Measuring best value from 'refurb' projects

Research at Aberdeen's Robert Gordon University has identified the requirement for the development of a 'Decision Support Model' to 'facilitate and measure the selection of main elements and sub-elements within refurbishment and maintenance projects'. One of the major drivers, explains Grant Wilson, a completing PhD researcher with the University's Institute for Innovation, Design and Sustainability (IDEAS), was to provide a mechanism via which NHS estates managers, design teams, and contractors, could 'evidence and demonstrate' that best value-for-money had been pursued, 'specific to the facility in question, and in the context of its unique business case requirements'.

Healthcare performance turned into decision support

PURPOSE

The 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/APPROACH

The 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.

FINDINGS

Four 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/IMPLICATIONS

Since 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 Tillaegspension (ATP) showed no deviation from the results in the healthcare sector.

PRACTICAL IMPLICATIONS

The 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/VALUE

Gathering 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.

Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: method, implementation and case study

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an integration of geographical information systems (GIS) and multiple criteria decision analysis (MCDA) methods for incorporating conflicting objectives and decision makers' (DMs') preferences into spatial decision models. This article presents a raster-based MC-SDSS that combines the analytic hierarchy process (AHP) and compromise programming methods, such as TOPSIS (technique for order preference by similarity to the ideal solution) and Ideal Point Methods. To the best of our knowledge it is the first time that a synergy of AHP and compromise programming methods is implemented in raster-driven GIS-based landfill suitability analysis. This procedure is supported by a spatial decision support system (SDSS) that was developed within a widely used commercial GIS software package. A real case study in the Thrace region in northeast Greece serves as a guide on how to conduct a suitability analysis for a MSW landfill site with the proposed MC-SDSS. Moreover, the procedure for identifying MSW disposal sites is accomplished by performing four computational models for synthesizing the DMs per criterion preferential system. Based on the case study results, a comparison analysis is performed according to suitability index estimations. According to them Euclidean distance metric and TOPSIS present strong similarities. When compared with Euclidean distance metric, TOPSIS seems to generate results closer to that derived by Manhattan distance metric. The comparison of Chebychev distance metric with all the other approaches revealed the greatest deviations.

Creating a transparent and dynamic view of staffing as a foundation for improving quality and efficiency

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.

Value-based resource management: a model for best value nursing care

With the health care environment shifting to a value-based payment system, Catholic Health Initiatives nursing leadership spearheaded an initiative with 14 hospitals to establish best nursing care at a lower cost. The implementation of technology-enabled business processes at point of care led to a new model for best value nursing care: Value-Based Resource Management. The new model integrates clinical patient data from the electronic medical record and embeds the new information in care team workflows for actionable real-time decision support and predictive forecasting. The participating hospitals reported increased patient satisfaction and cost savings in the reduction of overtime and improvement in length of stay management. New data generated by the initiative on nursing hours and cost by patient and by population (Medicare severity diagnosis-related groups), and patient health status outcomes across the acute care continuum expanded business intelligence for a value-based population health system.

Discrete event simulation for healthcare organizations: a tool for decision making

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.

Quest for business intelligence in health care

In an era of reform, providers are examining more forward-thinking business intelligence strategies, according to a recent study. Enterprise business intelligence tool sets offer a breadth of design and functionality that often are capable of serving the enterprise. One limitation of broader tool sets is that they may lack needed application-specific functionality or prebuilt healthcare content for a specific department.

Healthcare security staffing for smaller facilities: where science meets art

Obtaining effective security resourcing and staffing for smaller healthcare facilities presents many difficulties, according to the author In this article, he provides guidance to security practitioners on taking existing data and translating it into a language that administration will understand and appreciate.

Delivering antibiotic resistance information specifically tailored to location and time

Antibiotic resistance poses a significant threat to humanity. Hundred years since the beginning of the era of antibacterial drugs, we are facing increasing numbers of infections with multi-resistant pathogens. The current approach of distributing information on antibiotic resistance in printed form in the clinics has disadvantages with respect to the actuality of the data and the regional heterogeneity of resistance patterns. We developed an application named qRe using representational state transfer as a communication standard to deliver antibiotic resistance percentage information to the end user. The data is selected specifically for his/her geographic location. The user can display the information using either the application for Android smart phones or the web application. With the presented software we show the technical feasibility of delivering antibiotic resistance information specifically tailored to location and time. A short evaluation of the software showed an overall positive response from physicians. Based on recommendations of previous investigations, we expect a measurable clinical impact.

How business intelligence can improve value

Case studies of three healthcare organizations reinforce the premise that business intelligence--the ability to convert data into actionable information for decision making--is critical to demonstrating improved value.

ARDEN2BYTECODE: a one-pass Arden Syntax compiler for service-oriented decision support systems based on the OSGi platform

Patient empowerment might be one key to reduce the pressure on health care systems challenged by the expected demographic changes. Knowledge based systems can, in combination with automated sensor measurements, improve the patients' ability to review their state of health and make informed decisions. The Arden Syntax as a standardized language to represent medical knowledge can be used to express the corresponding decision rules. In this paper we introduce ARDEN2BYTECODE, a newly developed open source compiler for the Arden Syntax. ARDEN2BYTECODE runs on Java Virtual Machines (JVM) and translates Arden Syntax directly to Java Bytecode (JBC) executable on JVMs. ARDEN2BYTECODE easily integrates into service oriented architectures, like the Open Services Gateway Initiative (OSGi) platform. Apart from an evaluation of compilation performance and execution times, ARDEN2BYTECODE was integrated into an existing knowledge supported exercise training system and recorded training sessions have been used to check the implementation.

Business process modeling in healthcare

The importance of the process point of view is not restricted to a specific enterprise sector. In the field of health, as a result of the nature of the service offered, health institutions' processes are also the basis for decision making which is focused on achieving their objective of providing quality medical assistance. In this chapter the application of business process modelling - using the Business Process Modelling Notation (BPMN) standard is described. Main challenges of business process modelling in healthcare are the definition of healthcare processes, the multi-disciplinary nature of healthcare, the flexibility and variability of the activities involved in health care processes, the need of interoperability between multiple information systems, and the continuous updating of scientific knowledge in healthcare.

Decision support system to divide a large network into suitable District Metered Areas

This paper presents a new approach to divide large Water Distribution Networks (WDN) into suitable District Metered Areas (DMAs). It uses a hydraulic simulator and two operational models to identify the optimal number of DMAs, their entry points and boundary valves, and the network reinforcement/replacement needs throughout the project plan. The first model divides the WDN into suitable DMAs based on graph theory concepts and some user-defined criteria. The second model uses a simulated annealing algorithm to identify the optimal number and location of entry points and boundary valves, and the pipes reinforcement/replacement, necessary to meet the velocity and pressure requirements. The objective function is the difference between the economic benefits in terms of water loss reduction (arising from the average pressure reduction) and the cost of implementing the DMAs. To illustrate the proposed methodology, the results from a hypothetical case study are presented and discussed.

State of the art in pathology business process analysis, modeling, design and optimization

For analyzing current workflows and processes, for improving them, for quality management and quality assurance, for integrating hardware and software components, but also for education, training and communication between different domains' experts, modeling business process in a pathology department is inevitable. The authors highlight three main processes in pathology: general diagnostic, cytology diagnostic, and autopsy. In this chapter, those processes are formally modeled and described in detail. Finally, specialized processes such as immunohistochemistry and frozen section have been considered.

Model-based knowledge acquisition in environmental decision support system for wastewater integrated management

The main goal of the Water Framework Directive is to achieve good chemical and ecological status of water bodies by 2015. The implementation of integrated river basin management, including sewer systems, wastewater treatment plants and receiving water bodies, is essential to accomplishing this objective. Integrated management is complex and therefore the implementation of control systems and the development of decision support systems are needed to facilitate the work of urban wastewater system (UWS) managers. Within this context, the objective of this paper is to apply integrated modelling of an UWS to simulate and analyse the behaviour of the 'Congost' UWS in Spain, and to optimize its performance against different types of perturbations. This analysis results in optimal operating set-points for each perturbation, improves river water quality, minimizes combined sewer overflows and optimizes flow lamination from storm water tanks. This is achieved by running Monte Carlo simulations and applying global sensitivity analysis. The set-points will become part of the knowledge base composed of a set of IF-THEN rules of the environmental decision support system being developed for this case study.

Use of decision analysis interviews to support the sustainable use of the forests in Finnish Upper Lapland

Controversy between alternative uses of forests in Finnish Upper Lapland has been going on for decades, and in recent years it has been escalated to a serious conflict. The core of the conflict is the adverse impacts of forestry on old forests which are important grazing areas for reindeer and which are regarded as intact nature and wilderness areas. This paper describes the experiences of applying multi-criteria decision analysis interview approach on this conflict. The approach provides tools for structuring the problem and preferences of the stakeholders as well as for analyzing the effects of different alternatives in a common framework. We focus on the practical experiences gained from the application of this approach in this context. Multi-criteria decision analysis was found to be a useful approach to evaluate the economic, ecological and cultural aspects of this intense conflict. The obtained experiences also support the view that the approach works best when tightly integrated into the planning process.

Effect of descriptive information and experience on automation reliance

OBJECTIVE

The present research addresses the issue of reliance on decision support systems for the long-term (DSSLT), which help users develop decision-making strategies and long-term planning. It is argued that providing information about a system's future performance in an experiential manner, as compared with a descriptive manner, encourages users to increase their reliance level.

BACKGROUND

Establishing appropriate reliance on DSSLT is contingent on the system developer's ability to provide users with information about the system's future performance.

METHOD

A sequence of three studies contrasts the effect on automation reliance of providing descriptive information versus experience for DSSLT with two different positive expected values of recommendations.

RESULTS

Study I demonstrated that when automation reliance was determined solely on the basis of description, it was relatively low, but it increased significantly when a decision was made after experience with 50 training simulations. Participants were able to learn to increase their automation reliance levels when they encountered the same type of recommendation again. Study 2 showed that the absence of preliminary descriptive information did not affect the automation reliance levels obtained after experience. Study 3 demonstrated that participants were able to generalize their learning about increasing reliance levels to new recommendations.

CONCLUSION

Using experience rather than description to give users information about future performance in DSSLT can help increase automation reliance levels.

APPLICATIONS

Implications for designing DSSLT and decision support systems in general are discussed.

GeoCREV: veterinary geographical information system and the development of a practical sub-national spatial data infrastructure

This paper illustrates and discusses the key issues of the geographical information system (GIS) developed by the Unit of Veterinary Epidemiology of the Veneto region (CREV), defined according to user needs, spatial data (availability, accessibility and applicability), development, technical aspects, inter-institutional relationships, constraints and policies. GeoCREV, the support system for decision-making, was designed to integrate geographic information and veterinary laboratory data with the main aim to develop a sub-national, spatial data infrastructure (SDI) for the veterinary services of the Veneto region in north-eastern Italy. Its implementation required (i) collection of data and information; (ii) building a geodatabase; and (iii) development of a WebGIS application. Tools for the management, collection, validation and dissemination of the results (public access and limited access) were developed. The modular concept facilitates the updating and development of the system according to user needs and data availability. The GIS management practices that were followed to develop the system are outlined, followed by a detailed discussion of the key elements of the GIS implementation process (data model, technical aspects, inter-institutional relationship, user dimension and institutional framework). Problems encountered in organising the non-spatial data and the future work directions are also described.

Devils lake emergency outlet diversion conflict

The Devils Lake Emergency Outlet Diversion conflict is systematically studied from a strategic viewpoint using the Graph Model for Conflict Resolution in order to obtain insights about the resolution of this nagging international dispute. By modelling the conflict for the situation existing as of July 2005, just before the project began operation, the dispute is put into proper perspective and, subsequently, a stability analysis is carried out to obtain potential resolutions or equilibria. The results of a sensitivity analysis accurately predict the deal which actually took place when Canada and the American state of North Dakota reached a negotiated settlement. Finally, suggestions are put forward for improving the Boundary Waters Treaty of 1909 between Canada and the United States.

Finding the sweet spot: how to get the right staffing for variable workloads

All too often, hospital department managers set their staff schedules too much in anticipation of high levels of demand for services, leading to higher-than-necessary staffing costs when demand is lower than expected. The opposite approach of scheduling too few staff to meet demand, then relying on on-call or callback staff to address the shortage, also results in higher-than-necessary costs due to the premium wages that such staff must be paid. A staffing and workload simulation tool allows hospital departments to find the right balance between these extremes.

Computerised Decision Support Systems for the management of freshwater radioecological emergencies: assessment of the state-of-the-art with respect to the experiences and needs of end-users

Assessment of the environmental and radiological consequences of a nuclear accident requires the management of a great deal of data and information as well as the use of predictive models. Computerised Decision Support Systems (CDSS) are essential tools for this kind of complex assessment and for assisting experts with a rational decision process. The present work focuses on the assessment of the main features of selected state-of-the-art CDSS for off-site management of freshwater ecosystems contaminated by radionuclides. This study involved both developers and end-users of the assessed CDSS and was based on practical customisation exercises, installation and application of the decision systems. Potential end-users can benefit from the availability of several ready-to-use CDSS that allow one to run different kinds of models aimed at predicting the behaviour of radionuclides in aquatic ecosystems, evaluating doses to humans, assessing the effectiveness of different kinds of environmental management interventions and ranking these interventions, accounting for their social, economic and environmental impacts. As a result of the present assessment, the importance of CDSS "integration" became apparent: in many circumstances, different CDSS can be used as complementary tools for the decision-making process. The results of this assessment can also be useful for the future development and improvement of the CDSS.

A knowledge-based control system for air-scour optimisation in membrane bioreactors

Although membrane bioreactors (MBRs) technology is still a growing sector, its progressive implementation all over the world, together with great technical achievements, has allowed it to reach a mature degree, just comparable to other more conventional wastewater treatment technologies. With current energy requirements around 0.6-1.1 kWh/m3 of treated wastewater and investment costs similar to conventional treatment plants, main market niche for MBRs can be areas with very high restrictive discharge limits, where treatment plants have to be compact or where water reuse is necessary. Operational costs are higher than for conventional treatments; consequently there is still a need and possibilities for energy saving and optimisation. This paper presents the development of a knowledge-based decision support system (DSS) for the integrated operation and remote control of the biological and physical (filtration and backwashing or relaxation) processes in MBRs. The core of the DSS is a knowledge-based control module for air-scour consumption automation and energy consumption minimisation.