The Experts below are selected from a list of 2616 Experts worldwide ranked by ideXlab platform
Thorsten Winsemann - One of the best experts on this subject based on the ideXlab platform.
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a layered architecture for Enterprise Data Warehouse systems
Conference on Advanced Information Systems Engineering, 2012Co-Authors: Thorsten Winsemann, Veit Koppen, Gunter SaakeAbstract:The architecture of Data Warehouse systems is described on basis of so-called reference architectures. Today’s requirements to Enterprise Data Warehouses are often too complex to be satisfactorily achieved by the rather rough descriptions of this reference architecture. We describe an architecture of dedicated layers to face those complex requirements, and point out additional expenses and resulting advantages of our approach compared to the traditional one.
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kriterien fur datenpersistenz bei Enterprise Data Warehouse systemen auf in memory datenbanken
Grundlagen von Datenbanken, 2011Co-Authors: Thorsten Winsemann, Veit KoppenAbstract:Persistente Datenhaltung uber mehrere Schichten innerhalb eines Enterprise Data Warehouse Systems ist notwendig, um den dort vorhandenen, sehr grosen Datenbestand nutzen zu konnen, z.B. fur Reporting und Analyse. Die Pflege und Wartung solcher meist redundanten Daten ist jedoch sehr komplex und erfordert einen hohen Aufwand an Zeit und Ressourcen. Neueste In-MemoryTechnologien ermoglichen gute Performanz beim Datenzugriff, so dass sich die Frage stellt, welche Daten aus welchem Grund bzw. fur welchen Zweck uberhaupt noch persistent abgelegt werden mussen – und wie sich dies effizient entscheiden lasst. In diesem Papier prasentieren wir eine Ubersicht von Grunden fur Datenpersistenz, welche als Entscheidungsgrundlage bei der Problematik dient, Daten in Enterprise Data Warehouses auf InMemory Datenbanken zu speichern. Kategorien und Themenbeschreibung H.2.7 [Database Management]: Datenbank-Administration – Data Warehouse und Repository.
Matthias Kappelhoff - One of the best experts on this subject based on the ideXlab platform.
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machbarkeitsanalyse uber den aufbau eines Enterprise Data Warehouse auf basis von apache hadoop
2016Co-Authors: Matthias KappelhoffAbstract:Die vorliegende Masterthesis liefert eine Einfuhrung in die Themen Data Warehouse, Big Data und Apache Hadoop. Sie prasentiert grundlegende Data-Warehouse-Kon-zepte und uberpruft, inwieweit diese mit dem Apache Hadoop Software Framework zu realisieren sind. Hierbei wird sowohl eine technische Uberprufung vorgenommen als auch verschiedene Szenarien veranschaulicht, wie Hadoop inhaltlich sinnvoll in bestehende Systeme integriert werden kann. Inhaltlich wird uber das Thema Big Data an die Notwendigkeit einer solchen Uberprufung herangefuhrt.
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machbarkeitsanalyse uber den aufbau eines Enterprise Data Warehouse auf basis von apache hadoop
2016Co-Authors: Matthias KappelhoffAbstract:Die vorliegende Masterthesis liefert eine Einfuhrung in die Themen Data Warehouse, Big Data und Apache Hadoop. Sie prasentiert grundlegende Data-Warehouse-Kon-zepte und uberpruft, inwieweit diese mit dem Apache Hadoop Software Framework zu realisieren sind. Hierbei wird sowohl eine technische Uberprufung vorgenommen als auch verschiedene Szenarien veranschaulicht, wie Hadoop inhaltlich sinnvoll in bestehende Systeme integriert werden kann. Inhaltlich wird uber das Thema Big Data an die Notwendigkeit einer solchen Uberprufung herangefuhrt.
Kannan R Mutharasan - One of the best experts on this subject based on the ideXlab platform.
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buffer or suffer redesigning heart failure postdischarge clinic using queuing theory
Circulation-cardiovascular Quality and Outcomes, 2018Co-Authors: Kannan R Mutharasan, Faraz S Ahmad, Itai Gurvich, Hannah Alphs Jackson, Jan A Van Mieghem, Clyde W YancyAbstract:Timely follow-up in clinic after heart failure hospitalization represents an evidence-based intervention associated with reduced rehospitalization.1 Major cardiovascular societies endorse a 7-day follow-up visit as an appropriate target for quality.2 Yet the rate of scheduled follow-up visits remains relatively low, at ≈65% in 2012 by registry Data.3 Even more striking is the rate of arrived follow-up visits: 30% in 7 days.3 This represents a substantial missed opportunity to address and a likely explanation for ongoing avoidable readmissions. We took an unconventional approach to improving clinic scheduling policies by collaborating with our colleagues at the Northwestern University Kellogg School of Management to implement queuing theory as a novel approach to address a previously unyielding problem. In 2015, as part of a multidisciplinary intervention to improve outcomes for hospitalized heart failure patients, we systematically identified all patients within our hospital at risk for heart failure-related readmissions through a daily Enterprise Data Warehouse screen4; developed a multidisciplinary bridge and transition team to engage patients during the index hospitalization; and then deployed queuing theory to first assess and then improve clinic follow-up. Queuing theory is the mathematical study of waiting times.5,6 With roots in the telecommunications field, it has widespread applications in several processes such as understanding supermarket lines and managing factory inventory. A particularly powerful insight arising from queuing theory is the notion that extra capacity, or a capacity buffer, is necessary to ensure system performance when variable demand arises, such as for a hospital discharge clinic. We opted to use queuing theory to analyze hospital discharge load and understand the capacity needed in clinic to reduce wait times and improve access. Here, we provide our mathematical analysis based on real-world practice; the results of our intervention; and an online calculator (http://www.hfresearch.org) for other …
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abstract 161 heart failure care transitions a queuing theory approach to match variable hospital discharge rate with outpatient clinic capacity
Circulation-cardiovascular Quality and Outcomes, 2016Co-Authors: Kannan R Mutharasan, Itai Gurvich, Hannah Alphs Jackson, Preeti Kansal, Michael Abecassis, Allen S Anderson, Corrine Benacka, Jillian Berry A Jaeker, Charles J Davidson, Daniel NavarroAbstract:Background: Heart failure (HF) readmissions remain a major driver of cost and health care utilization. Timely follow-up of patients post-discharge represents an evidence-based intervention proven to reduce readmission rates. A previously unexplored characteristic of hospital discharges is variability in discharge caseload. This variability thwarts the timeliness of follow-up, negates the benefit of transition care planning and may lead to a higher risk of HF readmissions. Queuing theory is the mathematical study of waiting times. We opted to use queuing theory to determine if caseload can be determined more precisely in a manner that sufficiently accommodates HF discharge variability. Objective: To analyze the impact of hospital discharge rate variability on outpatient clinic capacity needs using HF hospitalization discharge Data and operations management approaches. Methods: Higher risk hospitalizations requiring active transitional care heart failure management were detected using an Enterprise Data Warehouse-supported process over the study period. Queuing theory approaches were used to model the impact of HF discharge clinic capacity on wait time to an appointment. Discharge clinic was modeled as a single 7-day follow-up appointment, with an acceptable scheduling window of 5 to 9 days post-discharge. Results: During the study period of 100 days, 566 HF discharges were made, for a median of 5.66 discharges daily, or 39.6 discharges weekly. The distribution of daily discharges was skewed rightward (mode = 3, range = 0 to 18, standard deviation = 3.3, coefficient of variation = 0.58). Current clinic design: Providing one discharge slot for every hospital discharge (100% utilization) leads to an average wait of 18.3 days prior to an appointment, with only 31.9% of appointments scheduled within 7 days, and 38.9% of appointments scheduled within 9 days. Clinic re-design (queuing theory): Providing five extra discharge appointment slots per week (88% utilization or 13.6% excess capacity) reduces the expected waiting period to 1.1 days, with 99.8% of patients seen within 7 days, and virtually all patients seen within 9 days of discharge. Conclusions: Deployment of queuing theory allows for a more precise quantification of needed clinical capacity to accomplish appropriate HF follow-up with a reasonable degree of certainty. Our simplified model demonstrates that variability in hospital discharge rates leads to excessive clinic wait times in the absence of a modest capacity buffer and consequently exposes patients to a higher risk of HF readmission. We show using single center HF discharge Data that a 10-15% increase in capacity is needed to ensure an adequate follow-up service level. Ongoing process of care work will demonstrate if optimization of clinic load yields a significant reduction in HF readmissions.
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abstract 152 Enterprise Data Warehouse supported early identification of acute decompensated heart failure admissions for efficient and multidisciplinary transitional care team interventions
Circulation-cardiovascular Quality and Outcomes, 2016Co-Authors: Kannan R Mutharasan, Hannah Alphs Jackson, Preeti Kansal, Michael Abecassis, Allen S Anderson, Corrine Benacka, Jillian Berry A Jaeker, Charles J Davidson, Daniel Navarro, Itai GurvichAbstract:Background: Multidisciplinary transitional care teams represent a model for reducing heart failure readmissions. Within this context, early identification of patients hospitalized with acute decompensated heart failure (ADHF) permits meaningful transitional care plan development. Improving the efficiency of early identification of the higher risk ADHF patient represents an area not well studied in hospitalized heart failure (HF). Objective: To validate the sensitivity and specificity of an Enterprise Data Warehouse (EDW)-based strategy for early identification of patients with ADHF. Methods: An EDW query was constructed to identify patients with ADHF based on clinical and diagnosis-related parameters, including BNP level and administration of intravenous diuretics. The EDW query was run daily; expert clinicians verified the diagnosis of ADHF based on comprehensive chart review. This classification was used to determine specificity of the query for ADHF. We computed the sensitivity of the EDW-based approach by matching query results to heart failure diagnosis related group (DRG) Data and primary discharge diagnosis Data from separate hospital systems. Results: During the study period of 70 days, a total of 2354 charts were screened (33.6 charts per day). A total of 410 patients were identified by chart review as having heart failure requiring active management, for a specificity of 17.4%. Sensitivity was computed using both heart failure DRG Data and primary discharge diagnosis Data. Of the 114 patients discharged with a heart failure DRG (291, 292, or 293), all 114 were detected a priori by the admission EDW screen, for a sensitivity of 100%. A similar analysis conducted using HF principal diagnoses, which includes cardiac surgery-related admissions, yielded a sensitivity of 97.2%. Conclusions: EDW-based screening of patients based on simple clinical parameters early in the hospitalization is highly sensitive for detection of ADHF hospitalizations, but specificity is low. Brief chart review by expert clinicians is rapid, and identifies a specific cohort of patients that can be targeted for multidisciplinary HF transitional care. A better delineation of risk has broad outpatient workflow implications. Ongoing process improvements will demonstrate if early identification of at-risk patients yields significant reduction in HF readmissions.
Veit Koppen - One of the best experts on this subject based on the ideXlab platform.
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a layered architecture for Enterprise Data Warehouse systems
Conference on Advanced Information Systems Engineering, 2012Co-Authors: Thorsten Winsemann, Veit Koppen, Gunter SaakeAbstract:The architecture of Data Warehouse systems is described on basis of so-called reference architectures. Today’s requirements to Enterprise Data Warehouses are often too complex to be satisfactorily achieved by the rather rough descriptions of this reference architecture. We describe an architecture of dedicated layers to face those complex requirements, and point out additional expenses and resulting advantages of our approach compared to the traditional one.
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kriterien fur datenpersistenz bei Enterprise Data Warehouse systemen auf in memory datenbanken
Grundlagen von Datenbanken, 2011Co-Authors: Thorsten Winsemann, Veit KoppenAbstract:Persistente Datenhaltung uber mehrere Schichten innerhalb eines Enterprise Data Warehouse Systems ist notwendig, um den dort vorhandenen, sehr grosen Datenbestand nutzen zu konnen, z.B. fur Reporting und Analyse. Die Pflege und Wartung solcher meist redundanten Daten ist jedoch sehr komplex und erfordert einen hohen Aufwand an Zeit und Ressourcen. Neueste In-MemoryTechnologien ermoglichen gute Performanz beim Datenzugriff, so dass sich die Frage stellt, welche Daten aus welchem Grund bzw. fur welchen Zweck uberhaupt noch persistent abgelegt werden mussen – und wie sich dies effizient entscheiden lasst. In diesem Papier prasentieren wir eine Ubersicht von Grunden fur Datenpersistenz, welche als Entscheidungsgrundlage bei der Problematik dient, Daten in Enterprise Data Warehouses auf InMemory Datenbanken zu speichern. Kategorien und Themenbeschreibung H.2.7 [Database Management]: Datenbank-Administration – Data Warehouse und Repository.
Gunter Saake - One of the best experts on this subject based on the ideXlab platform.
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a layered architecture for Enterprise Data Warehouse systems
Conference on Advanced Information Systems Engineering, 2012Co-Authors: Thorsten Winsemann, Veit Koppen, Gunter SaakeAbstract:The architecture of Data Warehouse systems is described on basis of so-called reference architectures. Today’s requirements to Enterprise Data Warehouses are often too complex to be satisfactorily achieved by the rather rough descriptions of this reference architecture. We describe an architecture of dedicated layers to face those complex requirements, and point out additional expenses and resulting advantages of our approach compared to the traditional one.