Multi-step Mapping Research Articles

Mission-Profile-Based Reliability Evaluation of IGBT Modules for Wide-Speed Range Electric Vehicle Drive Using Fast Multistep Mapping Simulation Strategy

Mission-Profile-Based Reliability Evaluation of IGBT Modules for Wide-Speed Range Electric Vehicle Drive Using Fast Multistep Mapping Simulation Strategy

Leveraging PrOFILE data to illustrate and benchmark the Institute of Medicine quality aims in global pediatric oncology.

e18706 Background: PrOFILE is a dynamic 360o evaluation of health service delivery that helps care teams and institutions committed to increasing childhood cancer survival define an improvement strategy. PrOFILE stands for Pediatric Oncology Facility Integrated Local Evaluation. As of December 2021, PrOFILE has been utilized in over 130 facilities from over 25 countries around the globe. The standard outputs are presented by module, which reflect the patient-care continuum. The Institute of Medicine (IOM) has proposed six aims for improvement to drive fundamental change in health care delivery. The IOM Committee recommends periodically evaluating progress and developing strategies for overcoming barriers.However, a tool to assist facilities to set a baseline and periodically assess progress does not exist. We evaluated the feasibility of leveraging PrOFILE data to identify (1) the status of each quality aim as defined by IOM and (2) the status compared to facilities in the same country or region. Methods: This study employed a multi-step mapping approach. We first mapped the 1,279 PrOFILE variables to the six IOM quality aims. Mapping was first performed independently by two authors, then reviewed by PrOFILE experts to achieve consensus. Each variable could only contribute to one aim. We then validated the mapping by running the previously collected data available from two cohorts (one with 13 and one with 17 facilities) and benchmarking institutional data to cohort data. Quality improvement experts and local stakeholders provided feedback on further use of the illustrations and benchmarks. Results: We identified 305 PrOFILE variables that reflect the IOM quality aims (Table). More PrOFILE questions could be used as proxy for safety, effectiveness, and timeliness aims, than patient-centeredness, efficiency, and equity (Table), however, all domains could be represented. Benchmarking the institution results to the regional or country cohort results allowed for context-relevant insightful benchmarking. Framing PrOFILE results utilizing the IOM six aims allowed local healthcare teams and relevant stakeholders to evaluate their baseline and set targets for improvement. Conclusions: This exercise demonstrates the feasibility of leveraging PrOFILE data to assess institutional alignment with the IOM quality aims. The results allow illustrating, benchmarking, and engaging with stakeholders in conversations aimed at improving delivery of pediatric oncology care in global real-world settings utilizing this perspective. Future PrOFILE reports will incorporate this feature, supporting establishing a baseline and tracking of progress over time.[Table: see text]

Feasibility of using data collected through the St. Jude Pediatric Oncology Facility integrated local evaluation (PrOFILE) tool to determine previously defined levels of pediatric hematology-oncology care.

e13530 Background: Pediatric hematology and oncology (PHO) facilities are not all equally equipped to diagnose and treat the full range of childhood cancers. Proposed facility tier models have been published to facilitate the context-based adaptation of PHO diagnostic and treatment guidelines. However, a tool to assist facilities in identifying their level of PHO care does not exist. We assessed the feasibility of using the St. Jude Pediatric Oncology Facility Integrated Local Evaluation (PrOFILE) tool to identify (1) facility levels as defined by the International Society of Pediatric Oncology (SIOP) and the Lancet Oncology Commission (LOC) for sustainable care for children with cancer, and (2) treatment strata in the Adapted Resource and Implementation Application (ARIA) Guide. Methods: This study employed a multi-step mapping approach. We mapped the 1,279 PrOFILE tool variables to the nine SIOP service lines, ten LOC domains, and ten ARIA resource groups. Mapping was performed by two teams and carried out in two different rounds. The first round aimed to define if an indicator was fully or partially covered by PrOFILE. Multiple PrOFILE variables could be assigned to each concept to meet its content specificity. Various cut-offs were applied when necessary to assign mutually exclusive responses to each tier. The second round consisted of expert validation for each indicator previously identified. Results: Most (97%) of SIOP’s indicators were partially or fully covered in the PrOFILE tool (Table). Radiation therapy tools and appointment scheduling and call back system could not be mapped. Of the LOC indicators, 89% were covered (Table). Pathology technical staff, chemotherapy complexity (low, moderate, and high), lymph node biopsy, organ preservation, liver transplantation, intensity-modulated radiotherapy, and research infrastructure could not be mapped. Finally, 77% of the indicators used by the ARIA Guide were covered (Table). The ophthalmology resource group could not be fully mapped. Conclusions: This exercise demonstrates the feasibility of using PrOFILE tool variables to identify facility PHO care levels as defined by SIOP and LOC. The tool also contains most of the variables necessary to apply the ARIA Guide to diagnose, treat, and manage childhood cancer. Future PrOFILE versions will address concepts that are not covered or are partially covered in its current version.[Table: see text]

Using UMLS for electronic health data standardization and database design.

Patients that undergo medical transfer represent 1 patient population that remains infrequently studied due to challenges in aggregating data across multiple domains and sources that are necessary to capture the entire episode of patient care. To facilitate access to and secondary use of transport patient data, we developed the Transport Data Repository that combines data from 3 separate domains and many sources within our health system. The repository is a relational database anchored by the Unified Medical Language System unique concept identifiers to integrate, map, and standardize the data into a common data model. Primary data domains included sending and receiving hospital encounters, medical transport record, and custom hospital transport log data. A 4-step mapping process was developed: 1) automatic source code match, 2) exact text match, 3) fuzzy matching, and 4) manual matching. 431090 total mappings were generated in the Transport Data Repository, consisting of 69010 unique concepts with 77% of the data being mapped automatically. Transport Source Data yielded significantly lower mapping results with only 8% of data entities automatically mapped and a significant amount (43%) remaining unmapped. The multistep mapping process resulted in a majority of data been automatically mapped. Poor matching of transport medical record data is due to the third-party vendor data being generated and stored in a nonstandardized format. The multistep mapping process developed and implemented is necessary to normalize electronic health data from multiple domains and sources into a common data model to support secondary use of data.

Maintaining automated measurement of Choosing Wisely adherence across the ICD 9 to 10 transition.

It remains unclear how to incorporate terminology changes, such as the International Classification of Disease (ICD) transition from ICD-9 to ICD-10, into established automated healthcare quality metrics. To evaluate whether general equivalence mapping (GEM) can apply ICD-9 based metrics to ICD-10 patient data. To develop and validate novel ICD-10 reference codesets. Retrospective analysis for eleven Choosing Wisely (CW) metrics was performed using three scripted algorithms on an institutional clinical data warehouse. ICD-10 data were compared against published ICD-9 based metric definitions using two equivalence mapping algorithms. A third algorithm implemented novel reference ICD-10 codes matching the original ICD-9 codes' intent for comparison with patient ICD-10 data. All adult patients seen at Vanderbilt University Medical Center, April - September 2016. The prevalence of eleven CW services during the six-month period. The three algorithms found similar prevalence of avoidable CW services, with an unweighted-mean of 8.4% (range: 0.16-65%), or approximately 20,000 CW services out of 240,000 potential cases in 515,406 unique patients. The algorithms' median sensitivity was 0.80 (interquartile range: 0.75-0.95), median specificity was 0.88 (IQR: 0.77-0.94), and median Rand accuracy was 0.84 (IQR: 0.79-0.89). The attributed waste of these eleven services for the period ranged from $871,049 to $951,829 between methods. Accuracy assessment demonstrated that the GEM-based methods suffered recall losses for metrics requiring multistep mapping due to incompleteness, while novel ICD-10 metric definitions avoided these challenges. Comprehensive mapping enables use of legacy metrics across ICD generations, but requires computational complexity that can be avoided with novel ICD-10 based metric definitions. Variation in the dollars attributed to waste due to ICD mapping introduces ambiguity that may affect quality-based reimbursement.

Habitat Mapping and Quality Assessment of NATURA 2000 Heathland Using Airborne Imaging Spectroscopy

Appropriate management of (semi-)natural areas requires detailed knowledge of the ecosystems present and their status. Remote sensing can provide a systematic, synoptic view at regular time intervals, and is therefore often suggested as a powerful tool to assist with the mapping and monitoring of protected habitats and vegetation. In this study, we present a multi-step mapping framework that enables detailed NATURA 2000 (N2000) heathland habitat patch mapping and the assessment of their conservation status at patch level. The method comprises three consecutive steps: (1) a hierarchical land/vegetation type (LVT) classification using airborne AHS imaging spectroscopy and field reference data; (2) a spatial re-classification to convert the LVT map to a patch map based on life forms; and (3) identification of the N2000 habitat type and conservation status parameters for each of the patches. Based on a multivariate analysis of 1325 vegetation reference plots acquired in 2006–2007, 24 LVT classes were identified that were considered relevant for the assessment of heathland conservation status. These labelled data were then used as ground reference for the supervised classification of the AHS image data to an LVT classification map, using Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Search feature selection. Overall classification accuracies for the LVT mapping varied from 83% to 92% (Kappa ≈ 0.82–0.91), depending on the level of detail in the hierarchical classification. After converting the LVT map to a N2000 habitat type patch map, an overall accuracy of 89% was obtained. By combining the N2000 habitat type patch map with the LVT map, two important conservation status parameters were directly deduced per patch: tree and shrub cover, and grass cover, showing a strong similarity to an independent dataset with estimates made in the field in 2009. The results of this study indicate the potential of imaging spectroscopy for detailed heathland habitat characterization of N2000 sites in a way that matches the current field-based workflows of the user.

Energy Consumption Oriented Network-on-Chip Mapping Method

In this paper, mapping algorithm has been mainly studied. The main work and contribution have been generalized as follows: Through the research of existing on-chip network mapping algorithm and global optimization algorithm, a multi-step mapping algorithm for low-power consumption have been designed, which is combined with the task allocation and the task scheduling. Compared with the traditional mapping algorithm, the algorithm in this paper takes the factors of task scheduling and allocation into account, mapping algorithm has three steps: task scheduling, IP core mapping and data block mapping. The simulation results show that the mapping method in this paper can effectively reduce Network-on-Chip (NoC) power consumption.

Topology optimization using multistep mapping from 2D photomask to 3D structure for designing reinforcing rib

We developed a topology optimization method for designing a two-dimensional (2D) photomask. The optimization employs multistep mapping compatible with micromachining processes to improve the characteristics of a three-dimensional (3D) MEMS fabricated by the photomask. We used this method in the development of an electrostatic micromirror to design the most effective reinforcing rib shape that reduces the deformation caused by internal residual stress. Employing a dot-array pattern as the initial topology, the optimization calculation converged after 300 iterations for predicted 79.1% reductions in maximum displacement. To verify this result, micromirror structures were fabricated and their deformations were experimentally measured. The experimental deformations were reduced by 87.1%. The calculated results agree well with the experimental results and demonstrate the effectiveness of the proposed method.

Visual Mental Rotation: Different Processes Used by Pilots

Air Force pilots and control subjects were tested on a visual “mental rotation” task. Nine of the 16 pilots, as well as all of the 16 control subjects, required more time to rotate greater angular distances. The performance of the other 7 pilots was unique: their response time did not increase with greater angular rotations. The results suggest that visual mental rotation can be accomplished by at least two different processes. One process involves incremental object rotations in a multi-step mapping –like an actual physical rotation of an object– going through intermediate stages. This process requires more time to rotate greater angular distances. The other process involves direct translation in a single-step mapping. In this process, the starting position transforms into the final position in one mapping without any intermediate steps, and thus does not require more time to rotate greater angular rotation. The lack of intermediate stages, which may allow small perturbations in location to be corrected, affects the accuracy of this process; this is particularly apparent when more complex stimuli are rotated. The pilots who did not show incremental rotation effects had different and distinct error patterns, their errors increased when rotating the more complex stimuli.