Scheduling non-urgent patient transportation while maximizing emergency coverage

Many ambulance providers operate both advanced life support (ALS) and basic life support (BLS) ambulances. Typically, only an ALS ambulance can respond to an emergency call, whereas non-urgent patient transportation requests can be served by either an ALS or a BLS ambulance. The total capacity of BLS ambulances is usually not enough to fulfill all non-urgent transportation requests. The remaining transportation requests then have to be performed by ALS ambulances, which reduces the coverage for emergency calls. We present a model that determines the routes for BLS ambulances while maximizing the remaining coverage by ALS ambulances. Different from the classical dial-a-ride problem, only one patient can be transported at a time, and not all requests are known in advance. Throughout the day, new requests arrive, and we present an online model to deal with these requests

Linear formulation for the Maximum Expected Coverage Location Model with fractional coverage

Since ambulance providers are responsible for life-saving medical care at the scene in emergency situations and since response times are important in these situations, it is crucial that ambulances are located in such a way that good coverage is provided throughout the region. Most models that are developed to determine good base locations assume strict 0-1 coverage given a fixed base location and demand point. However, multiple applications require fractional coverage. Examples include stochastic, instead of fixed, response times and survival probabilities. Straightforward adaption of the well-studied MEXCLP to allow for coverage probabilities results in a non-linear formulation in integer variables, limiting the size of instances that can be solved by the model. In this paper, we present a linear integer programming formulation for the problem. We show that the computation time of the linear formulation is significantly shorter than that for the non-linear formulation. As a consequence, we are able to solve larger instances. Finally, we will apply the model, in the setting of stochastic response times, to the region of Amsterdam, the Netherlands

Benchmarking online dispatch algorithms for Emergency Medical Services

Providers of Emergency Medical Services (EMS) face the online ambulance dispatch problem, in which they decide which ambulance to send to an incoming incident. Their objective is to minimize the fraction of arrivals later than a target time. Today, the gap between existing solutions and the optimum is unknown, and we provide a bound for this gap.Motivated by this, we propose a benchmark model (referred to as the offline model) to calculate the optimal dispatch decisions assuming that all incidents are known in advance. For this model, we introduce and implement three different methods to compute the optimal offline dispatch policy for problems with a finite number of incidents. The performance of the offline optimal solution serves as a bound for the performance of an - unknown - optimal online dispatching policy.We show that the competitive ratio (i.e., the worst case performance ratio between the optimal online and the optimal offline solution) of the dispatch problem is infinitely large; that is, even an optimal online dispatch algorithm can perform arbitrarily bad compared to the offline solution. Then, we performed benchmark experiments for a large ambulance provider in the Netherlands. The results show that for this realistic EMS system, when dispatching the closest idle vehicle to every incident, one obtains a fraction of late arrivals that is approximately 2.7 times that of the optimal offline policy. We also analyze another online dispatch heuristic, that manages to reduce this gap to approximately 1.9. This constitutes the first quantification of the gap between online and offline dispatch policies

A simulation model shows how individual differences affect major life decisions

Individuals are faced with a number of major decisions throughout their lives, including the choice of a suitable education, career, and life partner. Making such ‘major life decisions’ is challenging, as is evidenced by substantial rates of divorce and drop-out from higher education. Although poor major life decisions can lead to considerable costs for both individuals and society, little is known about how people make these decisions. This is because major life decisions are not simple short-term weighings of options – they are strongly intertwined with identity development. Here, we present a simulation model of major life decisions that integrates the short-term perspective of decision science with the long-term perspective of identity theory. We model major life decisions as a process comprising many explorations of available options, resulting in changing commitments, and eventually leading to a decision. Using our model, we run a large-scale in silico experiment, systematically simulating how three key individual characteristics affect the choice process and the quality of the decision: (1) exploration tendency (broad vs in-depth), (2) accuracy in assessing how well options fit, and (3) selectiveness. We identify the types of individuals who are at risk of exhibiting ‘maladaptive’ decision dynamics, including ruminative exploration and rash decision making, and conclude that these features often, but not always, lead to bad decisions. Our simulation results generate concrete predictions that can be empirically tested and may eventually result in individually tailored tools to aid individuals in making major life decisions

A putative serine protease, SpSsp1, from Saprolegnia parasitica is recognised by sera of rainbow trout, Oncorhynchus mykiss

Acknowledgements Our work was supported by the BBSRC (BB/C518457/1, BB/G012075/1, BB/J018333/1) (K.L.M., C.J.S., J.S.C., K.S.D., and P.v.W.), the University of Aberdeen (V.L.A., C.J.S., and P.v.W.), MSD Animal Health (J.S.C., K.S.D., and A.H.v.d.B), and The Royal Society (P.v.W.). This work was also supported by a Marie Curie Initial Training Networks with the SAPRO (sustainable approaches to reduce Oomycete (Saprolegnia) infections in aquacultures) grant PITN-GA-2009-238550 (A.H.v.d.B., L.L., C.J.S., P.v.W.). We would like to acknowledge Aberdeen Proteomics for carrying out LC–MS/MS and Laura Grenville-Briggs for valuable discussion and technical help. We are grateful to the Broad Institute (Carsten Russ, Rays Jiang, Brian Haas, and Chad Nusbaum), Brett Tyler (VBI), and P.v.W. for early release of draft supercontigs of the genome sequence of isolate CBS233.65, which helped us identify SpSsp1.Peer reviewedPublisher PD

Cost-effectiveness of counseling and pedometer use to increase physical activity in the Netherlands: a modeling study

Background: Counseling in combination with pedometer use has proven to be effective in increasing physical activity and improving health outcomes. We investigated the cost-effectiveness of this intervention targeted at one million insufficiently active adults who visit their general practitioner in the Netherlands.Methods: We used the RIVM chronic disease model to estimate the long-term effects of increased physical activity on the future health care costs and quality adjusted life years (QALY) gained, from a health care perspective.Results: The intervention resulted in almost 6000 people shifting to more favorable physical-activity levels, and in 5100 life years and 6100 QALYs gained, at an additional total cost of EUR 67.6 million. The incremental cost-effectiveness ratio (ICER) was EUR 13,200 per life year gained and EUR 11,100 per QALY gained. The intervention has a probability of 0.66 to be cost-effective if a QALY gained is valued at the Dutch informal threshold for cost-effectiveness of preventive intervention of EUR 20,000. A sensitivity analysis showed substantial uncertainty of ICER values.Conclusion: Counseling in combination with pedometer use aiming to increase physical activity may be a cost-effective intervention. However, the intervention only yields relatively small health benefits in the Netherlands

Antithrombotic therapy in patients undergoing TAVI: An overview of Dutch hospitals

Purpose To assess current antithrombotic treatment strategies in the Netherlands in patients undergoing transcatheter aortic valve implantation (TAVI). Methods For every Dutch hospital performing TAVI (n =14) an interventional cardiologist experienced in performing TAVI was interviewed concerning heparin, aspirin, thienopyridine and oral anticoagulation treatment in patients undergoing TAVI. Results The response rate was 100 %. In every centre, a protocol for antithrombotic treatment after TAVI was available. Aspirin was prescribed in all centres, concomitant clopidogrel was prescribed 13 of the 14 centres. Duration of concomitant clopidogrel was 3 months in over twothirds of cases. In 2 centres, duration of concomitant clopidogrel was based upon type of prosthesis: 6 months versus 3 months for supra-annular and intra-annular prostheses, respectively. Conclusions Leaning on a small basis of evidence and recommendations, the antithrombotic policy for patients undergoing TAVI is highly variable in the Netherlands. As a standardised regimen might further reduce haemorrhagic complications, large randomised clinical trials may help to establish the most appropriate approach

International Guillain-Barré Syndrome Outcome Study (IGOS): protocol of a prospective observational cohort study on clinical and biological predictors of disease course and outcome in Guillain-Barré syndrome

Guillain-Barré syndrome (GBS) is an acute polyradiculoneuropathy with a highly variable clinical presentation, course, and outcome. The factors that determine the clinical variation of GBS are poorly understood which complicates the care and treatment of individual patients. The protocol of the ongoing International GBS Outcome Study (IGOS), a prospective, observational, multi-centre cohort study that aims to identify the clinical and biological determinants and predictors of disease onset, subtype, course and outcome of GBS is presented here. Patients fulfilling the diagnostic criteria for GBS, regardless of age, disease severity, variant forms, or treatment, can participate if included within two weeks after onset of weakness. Information about demography, preceding infections, clinical features, diagnostic findings, treatment, course and outcome is collected. In addition, cerebrospinal fluid and serial blood samples for serum and DNA is collected at standard time points. The original aim was to include at least 1000 patients with a follow-up of 1-3 years. Data are collected via a web-based data entry system and stored anonymously. IGOS started in May 2012 and by January 2017 included more than 1400 participants from 143 active centres in 19 countries across 5 continents. The IGOS data/biobank is available for research projects conducted by expertise groups focusing on specific topics including epidemiology, diagnostic criteria, clinimetrics, electrophysiology, antecedent events, antibodies, genetics, prognostic modelling, treatment effects and long-term outcome of GBS. The IGOS will help to standardize the international collection of data and biosamples for future research of GBS. ClinicalTrials.gov Identifier: NCT01582763

Identification of Two Protein-Signaling States Delineating Transcriptionally Heterogeneous Human Medulloblastoma

Summary: The brain cancer medulloblastoma consists of different transcriptional subgroups. To characterize medulloblastoma at the phosphoprotein-signaling level, we performed high-throughput peptide phosphorylation profiling on a large cohort of SHH (Sonic Hedgehog), group 3, and group 4 medulloblastomas. We identified two major protein-signaling profiles. One profile was associated with rapid death post-recurrence and resembled MYC-like signaling for which MYC lesions are sufficient but not necessary. The second profile showed enrichment for DNA damage, as well as apoptotic and neuronal signaling. Integrative analysis demonstrated that heterogeneous transcriptional input converges on these protein-signaling profiles: all SHH and a subset of group 3 patients exhibited the MYC-like protein-signaling profile; the majority of the other group 3 subset and group 4 patients displayed the DNA damage/apoptotic/neuronal signaling profile. Functional analysis of enriched pathways highlighted cell-cycle progression and protein synthesis as therapeutic targets for MYC-like medulloblastoma. : Using peptide phosphorylation profiling, Zomerman et al. identify two medulloblastoma phosphoprotein-signaling profiles that have prognostic value and are potentially targetable. They find that these profiles extend across transcriptome-based subgroup borders. This suggests that diverse genetic information converges on common protein-signaling pathways and highlights protein-signaling as a unique information layer. Keywords: medulloblastoma, protein-signaling, protein synthesis, MYC, TP53, proteome, phosphoproteom