Stochastic programming for outpatient scheduling with flexible inpatient exam accommodation

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show only minor differences compared to those from the original model, thus proving the robustness of the scheduling solutions obtained from our optimal models against the impacts of emergency patient arrivals.

     

Value Driven Outcomes (VDO): a pragmatic,modular, and extensible software framework for understanding and improving health care costs and outcomes

Objective To develop expeditiously a pragmatic, modular, and extensible software framework for understanding and improving healthcare value (costs relative to outcomes).Materials and methods In 2012, a multidisciplinary team was assembled by the leadership of the University of Utah Health Sciences Center and charged with rapidly developing a pragmatic and actionable analytics framework for understanding and enhancing healthcare value. Based on an analysis of relevant prior work, a value analytics framework known as Value Driven Outcomes (VDO) was developed using an agile methodology. Evaluation consisted of measurement against project objectives, including implementation timeliness, system performance, completeness, accuracy, extensibility, adoption, satisfaction, and the ability to support value improvement.Results A modular, extensible framework was developed to allocate clinical care costs to individual patient encounters. For example, labor costs in a hospital unit are allocated to patients based on the hours they spent in the unit; actual medication acquisition costs are allocated to patients based on utilization; and radiology costs are allocated based on the minutes required for study performance. Relevant process and outcome measures are also available. A visualization layer facilitates the identification of value improvement opportunities, such as high-volume, high-cost case types with high variability in costs across providers. Initial implementation was completed within 6 months, and all project objectives were fulfilled. The framework has been improved iteratively and is now a foundational tool for delivering high-value care.Conclusions The framework described can be expeditiously implemented to provide a pragmatic, modular, and extensible approach to understanding and improving healthcare value.     

Exome sequencing in developmental eye disease leads to identification of causal variants in GJA8, CRYGC,PAX6 and CYP1B1

Developmental eye diseases, including cataract/microcornea, Peters anomaly and coloboma/microphthalmia/anophthalmia, are caused by mutations encoding many different signalling and structural proteins in the developing eye. All modes of Mendelian inheritance occur and many are sporadic cases, so provision of accurate recurrence risk information for families and affected individuals is highly challenging. Extreme genetic heterogeneity renders testing for all known disease genes clinically unavailable with traditional methods. We used whole-exome sequencing in 11 unrelated developmental eye disease patients, as it provides a strategy for assessment of multiple disease genes simultaneously. We identified five causative variants in four patients in four different disease genes, GJA8, CRYGC, PAX6 and CYP1B1. This detection rate (36%) is high for a group of patients where clinical testing is frequently not undertaken due to lack of availability and cost. The results affected clinical management in all cases. These variants were detected in the cataract/microcornea and Peters anomaly patients. In two patients with coloboma/microphthalmia, variants in ABCB6 and GDF3 were identified with incomplete penetrance, highlighting the complex inheritance pattern associated with this phenotype. In the coloboma/microphthalmia patients, four other variants were identified in CYP1B1, and CYP1B1 emerged as a candidate gene to be considered as a modifier in coloboma/microphthalmia.     

Ipsilateral keratoconus associated with long-standing primary hydatid cyst of the orbit

Hydatid cyst is a cyclozoonotic infection of the larvae form of a platyhelminthes Echinococcus granulosus. The majority of hydatid cysts appear in the liver (65%) and lungs (25%). Kidneys and brain are other less common sites for this disease. Only 1% to 2% cases are seen in the maxillofacial region. These commonly appear as cystic lesions located in the mandible, maxillary sinus, orbit, infratemporal fossa, pterygopalatine fossa, parapharyngeal space, tongue, and parotid and submandibular salivary gland. Hydatid cysts of the orbit are rare and account for 1% of all hydatid cysts. The article presents hydatid cyst of the orbit in a 10-year-old child. Clinical features, investigations, surgical approaches, and adjuvant medical management have been emphasized. We believe that the lateral orbital route allows excellent exposure and safe removal of an intraorbital hydatid cyst located posteriorly, superiorly, and laterally without damaging the surrounding important orbital structures. Upper blepharoplasty incision results in good cosmetic outcome.     

Rural-metropolitan disparities in ovarian cancer survival: a statewide population-based study

Purpose

To investigate rural-metropolitan disparities in ovarian cancer survival, we assessed ovarian cancer mortality and differences in prognostic factors by rural-metropolitan residence.

Quantitative structure–activity relationship (QSAR) studies as strategic approach in drug discovery

Drug design is a process which is driven by technological breakthroughs implying advanced experimental and computational methods. Nowadays, the techniques or the drug design methods are of paramount importance for prediction of biological profile, identification of hits, generation of leads, and moreover to accelerate the optimization of leads into drug candidates. Quantitative structure–activity relationship (QSAR) has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. From decades to recent research, QSAR methods have been applied in the development of relationship between properties of chemical substances and their biological activities to obtain a reliable statistical model for prediction of the activities of new chemical entities. Classical QSAR studies include ligands with their binding sites, inhibition constants, rate constants, and other biological end points, in addition molecular to properties such as lipophilicity, polarizability, electronic, and steric properties or with certain structural features. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free–Wilson approaches, which exploit the three-dimensional properties of the ligands to predict their biological activities using robust chemometric techniques such as PLS, G/PLS, and ANN. This paper provides an overview of 1-6 dimension-based developed QSAR methods and their approaches. In particular, we present various dimensional QSAR approaches, such as comparative molecular field analysis (CoMFA), comparative molecular similarity analysis, Topomer CoMFA, self-organizing molecular field analysis, comparative molecule/pseudo receptor interaction analysis, comparative molecular active site analysis, and FLUFF-BALL, 4D-QSAR, and G-QSAR approaches.