How to juggle priorities? An interactive tool to provide quantitative support for strategic patient-mix decisions: an ophthalmology case

An interactive tool was developed for the ophthalmology department of the Academic Medical Center to quantitatively support management with strategic patient-mix decisions. The tool enables management to alter the number of patients in various patient groups and to see the consequences in terms of key performance indicators. In our case study, we focused on the bottleneck: the operating room. First, we performed a literature review to identify all factors that influence an operating room's utilization rate. Next, we decided which factors were relevant to our study. For these relevant factors, two quantitative methods were applied to quantify the impact of an individual factor: regression analysis and computer simulation. Finally, the average duration of an operation, the number of cancellations due to overrun of previous surgeries, and the waiting time target for elective patients all turned out to have significant impact. Accordingly, for the case study, the interactive tool was shown to offer management quantitative decision support to act proactively to expected alterations in patient-mix. Hence, management can anticipate the future situation, and either alter the expected patient-mix or expand capacity to ensure that the key performance indicators will be met in the future

Genome-Wide Association Study and Gene Expression Analysis Identifies CD84 as a Predictor of Response to Etanercept Therapy in Rheumatoid Arthritis

Anti-tumor necrosis factor alpha (anti-TNF) biologic therapy is a widely used treatment for rheumatoid arthritis (RA). It is unknown why some RA patients fail to respond adequately to anti-TNF therapy, which limits the development of clinical biomarkers to predict response or new drugs to target refractory cases. To understand the biological basis of response to anti-TNF therapy, we conducted a genome-wide association study (GWAS) meta-analysis of more than 2 million common variants in 2,706 RA patients from 13 different collections. Patients were treated with one of three anti-TNF medications: etanercept (n = 733), infliximab (n = 894), or adalimumab (n = 1,071). We identified a SNP (rs6427528) at the 1q23 locus that was associated with change in disease activity score (ΔDAS) in the etanercept subset of patients (P = 8×10-8), but not in the infliximab or adalimumab subsets (P>0.05). The SNP is predicted to disrupt transcription factor binding site motifs in the 3′ UTR of an immune-related gene, CD84, and the allele associated with better response to etanercept was associated with higher CD84 gene expression in peripheral blood mononuclear cells (P = 1×10-11 in 228 non-RA patients and P = 0.004 in 132 RA patients). Consistent with the genetic findings, higher CD84 gene expression correlated with lower cross-sectional DAS (P = 0.02, n = 210) and showed a non-significant trend for better ΔDAS in a subset of RA patients with gene expression data (n = 31, etanercept-treated). A small, multi-ethnic replication showed a non-significant trend towards an association among etanercept-treated RA patients of Portuguese ancestry (n = 139, P = 0.4), but no association among patients of Japanese ancestry (n = 151, P = 0.8). Our study demonstrates that an allele associated with response to etanercept therapy is also associated with CD84 gene expression, and further that CD84 expression correlates with disease activity. These findings support a model in which CD84 genotypes and/or expression may serve as a useful biomarker for response to etanercept treatment in RA patients of European ancestry. © 2013 Cui et al

Capacity management of nursing staff as a vehicle for organizational improvement

<p>Abstract</p> <p>Background</p> <p>Capacity management systems create insight into required resources like staff and equipment. For inpatient hospital care, capacity management requires information on beds and nursing staff capacity, on a daily as well as annual basis. This paper presents a comprehensive capacity model that gives insight into required nursing staff capacity and opportunities to improve capacity utilization on a ward level.</p> <p>Methods</p> <p>A capacity model was developed to calculate required nursing staff capacity. The model used historical bed utilization, nurse-patient ratios, and parameters concerning contract hours to calculate beds and nursing staff needed per shift and the number of nurses needed on an annual basis in a ward. The model was applied to three different capacity management problems on three separate groups of hospital wards. The problems entailed operational, tactical, and strategic management issues: optimizing working processes on pediatric wards, predicting the consequences of reducing length of stay on nursing staff required on a cardiology ward, and calculating the nursing staff consequences of merging two internal medicine wards.</p> <p>Results</p> <p>It was possible to build a model based on easily available data that calculate the nursing staff capacity needed daily and annually and that accommodate organizational improvements. Organizational improvement processes were initiated in three different groups of wards. For two pediatric wards, the most important improvements were found to be improving working processes so that the agreed nurse-patient ratios could be attained. In the second case, for a cardiology ward, what-if analyses with the model showed that workload could be substantially lowered by reducing length of stay. The third case demonstrated the possible savings in capacity that could be achieved by merging two small internal medicine wards.</p> <p>Conclusion</p> <p>A comprehensive capacity model was developed and successfully applied to support capacity decisions on operational, tactical, and strategic levels. It appeared to be a useful tool for supporting discussions between wards and hospital management by giving objective and quantitative insight into staff and bed requirements. Moreover, the model was applied to initiate organizational improvements, which resulted in more efficient capacity utilization.</p

Meta-Analysis of Genome-Wide Association Studies in Celiac Disease and Rheumatoid Arthritis Identifies Fourteen Non-HLA Shared Loci

Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease (CD) and rheumatoid arthritis (RA), but the extent of this sharing has not been systematically explored. Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci (out of 26 loci for each disease) are shared between both diseases. We hypothesized that there are additional shared risk alleles and that combining genome-wide association study (GWAS) data from each disease would increase power to identify these shared risk alleles. We performed a meta-analysis of two published GWAS on CD (4,533 cases and 10,750 controls) and RA (5,539 cases and 17,231 controls). After genotyping the top associated SNPs in 2,169 CD cases and 2,255 controls, and 2,845 RA cases and 4,944 controls, 8 additional SNPs demonstrated P<5×10−8 in a combined analysis of all 50,266 samples, including four SNPs that have not been previously confirmed in either disease: rs10892279 near the DDX6 gene (Pcombined = 1.2×10−12), rs864537 near CD247 (Pcombined = 2.2×10−11), rs2298428 near UBE2L3 (Pcombined = 2.5×10−10), and rs11203203 near UBASH3A (Pcombined = 1.1×10−8). We also confirmed that 4 gene loci previously established in either CD or RA are associated with the other autoimmune disease at combined P<5×10−8 (SH2B3, 8q24, STAT4, and TRAF1-C5). From the 14 shared gene loci, 7 SNPs showed a genome-wide significant effect on expression of one or more transcripts in the linkage disequilibrium (LD) block around the SNP. These associations implicate antigen presentation and T-cell activation as a shared mechanism of disease pathogenesis and underscore the utility of cross-disease meta-analysis for identification of genetic risk factors with pleiotropic effects between two clinically distinct diseases

A framework for performance and data quality assessment of Radio Frequency IDentification (RFID) systems in health care settings

Objective: RFID offers great opportunities to health care. Nevertheless, prior experiences also show that RFID systems have not been designed and tested in response to the particular needs of health care settings and might introduce new risks. The aim of this study is to present a framework that can be used to assess the performance of RFID systems particularly in health care settings. Methods: We developed a framework describing a systematic approach that can be used for assessing the feasibility of using an RFID technology in a particular healthcare setting; more specific for testing the impact of environmental factors on the quality of RFID generated data and vice versa. This framework is based on our own experiences with an RFID pilot implementation in an academic hospital in The Netherlands and a literature review concerning RFID test methods and current insights of RFID implementations in healthcare. The implementation of an RFID system within the blood transfusion chain inside a hospital setting was used as a show case to explain the different phases of the framework. Results: The framework consists of nine phases, including an implementation development plan, RFID and medical equipment interference tests, data accuracy- and data completeness tests to be run in laboratory, simulated field and real field settings. Conclusions: The potential risks that RFID technologies may bring to the healthcare setting should be thoroughly evaluated before they are introduced into a vital environment. The RFID performance assessment framework that we present can act as a reference model to start an RFID development, engineering, implementation and testing plan and more specific, to assess the potential risks of interference and to test the quality of the RFID generated data potentially influenced by physical objects in specific health care environments. (c) 2010 Elsevier Inc. All rights reserve

Usability Studies on Interactive Health Information Systems; Where Do We Stand?

This paper discusses the preliminary results of a systematic review of the literature on applied usability studies of health information systems in the period 1990 to 2006. Abstracts were included when they described an evaluation of the usability of a health information system. To gain insight into usability methods applied and their properties we constructed a framework to analyze the studies. The framework includes objectives, designs, number of participants, user-profiles, settings, medical domain, and type of health information systems evaluated. Fifty-two Papers were included in the review. Findings show that from 2002 an increasing trend can be observed of publication of usability studies. Most studies discuss summative usability results on working systems thereby focusing on systems' adoption problems. Formative usability studies lack a uniform way to describe how study results contributed to the system's iterative development cycl

Pre-Post Evaluation of Physicians' Satisfaction with a Redesigned Electronic Medical Record System

Physicians' acceptance of Electronic Medical Record Systems (EMRs) is closely related to their usability. Knowledge about end-users' opinions on usability of an EMR system may contribute to planning for the next phase of the usability cycle of the system. A demand for integration of new functionalities, such as computerized order entry and an electronic patient status led to redesign of our EMR system, which had been in use for over 8 years at the Academic Medical Center of Amsterdam. The aim of this study was to understand whether the redesigned EMR system was an improvement of the earlier EMR and which system aspects accounted for user satisfaction and which did not. We conducted a formative pre- and post usability evaluation of our former and redesigned EMR system. For the assessment of both system versions' usability, we distributed two standardized usability questionnaires among 150 clinicians who routinely had used the older EMR system and had been working with its newer version for 6 weeks. Though overall user satisfaction was relatively high for both EMR systems, screen layout and interaction structure proved less easy to work with in the newer EMR system. The new EMR system however was more appreciated because of its enhanced functionality, capabilities and likeable user-interface. The results point to a number of actions that might be useful in future usability improvement efforts of our EMR system and other EMR

Taxonomic classification of planning decisions in health care: a structured review of the state of the art in OR/MS FREE

We provide a comprehensive overview of the typical decisions to be made in resource capacity planning and control in health care, and a structured review of relevant articles from the field of Operations Research and Management Sciences (OR/MS) for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision makin

Adaptive resource allocation for efficient patient scheduling

Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible. We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam. We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacit