Health Status of Mytilus chilensis from Intensive Culture Areas in Chile Assessed by Molecular, Microbiological, and Histological Analyses.

Shellfish farming is a relevant economic activity in Chile, where the inner sea in Chiloé island concentrates 99% of the production of the mussel Mytilus chilensis. This area is characterized by the presence of numerous human activities, which could harm the quality of seawater. Additionally, the presence of potentially pathogenic microorganisms can influence the health status of mussels, which must be constantly monitored. To have a clear viewpoint of the health status of M. chilensis and to study its potential as a host species for exotic diseases, microbiological, molecular, and histological analyses were performed. This study was carried out in October 2018, where M. chilensis gut were studied for: presence of food-borne bacteria (Vibrio parahaemolyticus, Escherichia coli, Salmonella spp.), exotic bacteria ("Candidatus Xenohaliotis californiensis"), viruses (abalone and Ostreid herpes virus), and protozoa (Marteilia spp., Perkinsus spp. and Bonamia spp.). Additionally, 18S rDNA metabarcoding and histology analyses were included to have a complete evaluation of the health status of M. chilensis. Overall, despite the presence of risk factors, abnormal mortality rates were not reported during the monitoring period and the histological examination did not reveal significant lesions. Pathogens of mandatory notification to World Organization for Animal Health (OIE) and the Chilean National Fisheries and Aquaculture Service (SERNAPESCA) were not detected, which confirms that M. chilensis have a good health status, highlighting the importance of an integrated vision of different disciplines to ensure the sustainability of this important mussel industry in Chile.

A Quantitative Analysis of the Relationship Between Radiation Therapy Use and Travel Time.

PURPOSE: To model and quantify the relationship between radiation therapy (RT) use and travel time to RT services. METHODS AND MATERIALS: Population-based registries and databases were used to identify both incident cancer patient and patients receiving RT within 1 year of diagnosis (RT1y) in British Columbia, Canada, between 1992 and 2011. The effects of age, gender, diagnosis year, income, prevailing wait time, and travel duration for RT on RT1y were assessed. Significant factors from univariate analyses were included in a multivariable logistic regression model. The shape of the travel time-RT1y curve was represented by generalized additive and segmented regression models. Analyses were conducted for breast, lung, and genitourinary cancer separately and for all cancer sites combined. RESULTS: After adjustment for age, gender, diagnosis year, income, and prevailing wait times, increasing travel time to the closest RT facility had a negative impact RT1y. The shape of the travel time-RT1y curve varied with cancer type. For breast cancer, the odds of RT1y were constant for the first 2 driving hours and decreased at 17% per hour thereafter. For lung cancer, the odds of RT1y decreased by 16% after 20 minutes and then decreased at 6% per hour. Genitourinary cancer RT1y was relatively independent of travel time. For all cancer sites combined, the odds of RT1y were constant within the first 2 driving hours and decreased at 7% per hour thereafter. CONCLUSIONS: Travel time to receive RT has a different impact on RT1y for different tumor sites. The results provide evidence-based insights for the configuration of catchment areas for new and existing cancer centers providing RT.

Optimal location of radiation therapy centers with respect to geographic access.

PURPOSE: To develop a framework with which to evaluate locations of radiation therapy (RT) centers in a region based on geographic access. METHODS AND MATERIALS: Patient records were obtained for all external beam radiation therapy started in 2011 for the province of British Columbia, Canada. Two metrics of geographic access were defined. The primary analysis was percentage of patients (coverage) within a 90-minute drive from an RT center (C90), and the secondary analysis was the average drive time (ADT) to an RT center. An integer programming model was developed to determine optimal center locations, catchment areas, and capacity required under different scenarios. RESULTS: Records consisted of 11,096 courses of radiation corresponding to 161,616 fractions. Baseline geographic access was estimated at 102.5 minutes ADT (each way, per fraction) and 75.9% C90. Adding 2 and 3 new centers increased C90 to 88% and 92%, respectively, and decreased ADT by between 43% and 61%, respectively. A scenario in which RT was provided in every potential location that could support at least 1 fully utilized linear accelerator resulted in 35.3 minutes' ADT and 93.6% C90. CONCLUSIONS: The proposed framework and model provide a data-driven means to quantitatively evaluate alternative configurations of a regional RT system. Results suggest that the choice of location for future centers can significantly improve geographic access to RT.

Operations research methods improve chemotherapy patient appointment scheduling.

BACKGROUND: Clinical complexity, scheduling restrictions, and outdated manual booking processes resulted in frequent clerical rework, long waitlists for treatment, and late appointment notification for patients at a chemotherapy clinic in a large cancer center in British Columbia, Canada. A 17-month study was conducted to address booking, scheduling and workload issues and to develop, implement, and evaluate solutions. METHODS: A review of scheduling practices included process observation and mapping, analysis of historical appointment data, creation of a new performance metric (final appointment notification lead time), and a baseline patient satisfaction survey. Process improvement involved discrete event simulation to evaluate alternative booking practice scenarios, development of an optimization-based scheduling tool to improve scheduling efficiency, and change management for implementation of process changes. Results were evaluated through analysis of appointment data, a follow-up patient survey, and staff surveys. RESULTS: Process review revealed a two-stage scheduling process. Long waitlists and late notification resulted from an inflexible first-stage process. The second-stage process was time consuming and tedious. After a revised, more flexible first-stage process and an automated second-stage process were implemented, the median percentage of appointments exceeding the final appointment notification lead time target of one week was reduced by 57% and median waitlist size decreased by 83%. Patient surveys confirmed increased satisfaction while staff feedback reported reduced stress levels. CONCLUSION: Significant operational improvements can be achieved through process redesign combined with operations research methods.

Acute care utilization due to hospitalizations for pediatric lower respiratory tract infections in British Columbia, Canada.

BACKGROUND: Pediatric LRTI hospitalizations are a significant burden on patients, families, and healthcare systems. This study determined the burden of pediatric LRTIs on hospital settings in British Columbia and the benefits of prevention strategies as they relate to healthcare resource demand. METHODS: LRTI inpatient episodes for patients <19 years of age during 2008-2010 were extracted from the BC Discharge Abstract Database. The annual number of acute care beds required to treat pediatric LRTIs was estimated. Sub-analyses determined the burden due to infants <1 year of age and high-risk infants. Population projections were used to forecast LRTI hospitalizations and the effectiveness of public health initiatives to reduce the incidence of LRTIs to 2020 and 2030. RESULTS: During 2008-2010, LRTI as the primary diagnosis accounted for 32.0 and 75.9% hospitalizations for diseases of the respiratory system in children <19 years of age and infants <1 year of age, respectively. Infants <1 year of age accounted for 47 and 77% hospitalizations due to pediatric LRTIs and pediatric LRTI hospitalizations specifically due to respiratory syncytial virus (RSV), respectively. The average length of stay was 3.1 days for otherwise healthy infants <1 year of age and 9.1 days for high-risk infants (P <0.0001). 73.1% pediatric LRTI hospitalizations occurred between November and April. Over the study timeframe, 19.6 acute care beds were required on average to care for pediatric LRTIs which increased to 64.0 beds at the peak of LRTI hospitalizations. Increases in LRTI bed-days of 5.5 and 16.2% among <19 year olds by 2020 and 2030, respectively, were predicted. Implementation of appropriate prevention strategies could cause 307 and 338 less LRTI hospitalizations in <19 year olds in 2020 and 2030, respectively. CONCLUSION: Pediatric LRTI hospitalizations require significant use of acute care infrastructure particularly between November and April. Population projections show the burden may increase in the next 20 years, but implementation of effective public health prevention strategies may contribute to reducing the acute care demand and to supporting efforts for overall pediatric healthcare sustainability.

Process data: a means to measure operational performance and implement advanced analytical models.

We present the case of an ambulatory clinic in which an operational review was conducted to identify opportunities for efficiency in appointment scheduling and capacity allocation. We required process data to compare that which was planned to that which actually happened and to develop advanced analytical models. Similar to other health care studies, these data proved to be limited or non-existent. Consequently we had to conduct a time-consuming collection of operational metrics. We make recommendations for the perpetual collection of process data for modeling and simulation.

Reducing patient wait times and improving resource utilization at British Columbia Cancer Agency's ambulatory care unit through simulation.

We consider an ambulatory care unit (ACU) in a large cancer centre, where operational and resource utilization challenges led to overcrowding, excessive delays, and concerns regarding safety of critical patient care duties. We use simulation to analyze the simultaneous impact of operations, scheduling, and resource allocation on patient wait time, clinic overtime, and resource utilization. The impact of these factors has been studied before, but usually in isolation. Further, our model considers multiple clinics operating concurrently, and includes the extra burden of training residents and medical students during patient consults. Through scenario analyses we found that the best outcomes were obtained when not one but multiple changes were implemented simultaneously. We developed configurations that achieve a reduction of up to 70% in patient wait times and 25% in physical space requirements, with the same appointment volume. The key findings of the study are the importance of on time clinic start, the need for improved patient scheduling; and the potential improvements from allocating examination rooms flexibly and dynamically among individual clinics within each of the oncology programs. These findings are currently being evaluated for implementation by senior management.

Surgical block scheduling in a system of hospitals: an application to resource and wait list management in a British Columbia health authority.

Scheduling surgical specialties in a medical facility is a very complex process. The choice of schedules and resource availability impact directly on the number of patients treated by specialty, cancellations, wait times, and the overall performance of the system. In this paper we present a system-wide model developed to allow management to explore tradeoffs between OR availability, bed capacity, surgeons' booking privileges, and wait lists. We developed a mixed integer programming model to schedule surgical blocks for each specialty into ORs and applied it to the hospitals in a British Columbia Health Authority, considering OR time availability and post-surgical resource constraints. The results offer promising insights into resource optimization and wait list management, showing that without increasing post-surgical resources hospitals could handle more cases by scheduling specialties differently.