Study of collective intelligence form a clinical data warehouse as a potential model for clinical decision support

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.Includes bibliographical references.Clinical decision support systems (CDSS) are developed primarily from knowledge gleaned from evidence-based research, guidelines, trusted resources and domain experts. While these resources generally represent information that is research proven, time-tested and consistent with current medical knowledge, they lack some qualities that would be desirable in a CDSS. For instance, the information is presented as generalized recommendations that are not specific to particular patients and may not consider certain subpopulations. In addition, the knowledge base that produces the guidelines may be outdated and may not reflect real-world practice. Ideally, resources for decision support should be timely, patient-specific, and represent current practice. Patient-oriented clinical decision support is particularly important in the practice of pediatrics because it addresses a population in constant flux. Every age represents a different set of physiological and developmental concerns and considerations, especially in medication dosing patterns. Patient clinical data warehouses (CDW) may be able to bridge the knowledge gap. CDWs contain the collective intelligence of various contributors (i.e. clinicians, administrators, etc.) where each data entry provides information regarding medical care for a patient in the real world. CDWs have the potential to provide information as current as the latest upload, be focused to specific subpopulations and reflect current clinical practice. In this paper, I study the potential of a well-known patient clinical data warehouse to provide information regarding pediatric levothyroxine dosing as a form of clinical decision support. I study the state of the stored data, the necessary data transformations and options for representing the data to effectively summarize and communicate the findings.(cont.) I also compare the resulting transformed data, representing actual practice within this population, against established dosing recommendations. Of the transformed records, 728 of the 854 (85.2%, [95% confidence interval 82.7:87.6]) medication records contained doses that were under the published recommended range for levothyroxine. As demonstrated by these results, real world practice can diverge from established recommendations. Delivering this information on real-world peer practice medication dosing to clinicians in real-time offers the potential to provide a valuable supplement to established dosing guidelines, enhancing the general and sometimes static dosing recommendations.by Elisabeth Lee Scheufele.S.M

Collaborative Cloud Computing Framework for Health Data with Open Source Technologies

The proliferation of sensor technologies and advancements in data collection methods have enabled the accumulation of very large amounts of data. Increasingly, these datasets are considered for scientific research. However, the design of the system architecture to achieve high performance in terms of parallelization, query processing time, aggregation of heterogeneous data types (e.g., time series, images, structured data, among others), and difficulty in reproducing scientific research remain a major challenge. This is specifically true for health sciences research, where the systems must be i) easy to use with the flexibility to manipulate data at the most granular level, ii) agnostic of programming language kernel, iii) scalable, and iv) compliant with the HIPAA privacy law. In this paper, we review the existing literature for such big data systems for scientific research in health sciences and identify the gaps of the current system landscape. We propose a novel architecture for software-hardware-data ecosystem using open source technologies such as Apache Hadoop, Kubernetes and JupyterHub in a distributed environment. We also evaluate the system using a large clinical data set of 69M patients.Comment: This paper is accepted in ACM-BCB 202

Population Segmentation Using a Novel Socio-Demographic Dataset

Appending market segmentation data to a national healthcare knowledge, attitude and behavior survey and medical claims by geocode can provide valuable insight for providers, payers and public health entities to better understand populations at a hyperlocal level and develop cohort-specific strategies for health improvement. A prolonged use case investigates population factors, including social determinants of health, in depression and develops cohort-level management strategies, utilizing market segmentation and survey data. Survey response scores for each segment were normalized against the average national score and appended to claims data to identify at-risk segment whose scores were compared with three socio-demographically comparable but not at-risk segments via Nonparametric Mann-Whitney U test to identify specific risk factors for intervention. The marketing segment, New Melting Point (NMP), was identified as at-risk. The median scores of three comparable segments differed from NMP in “Inability to Pay For Basic Needs” (121% vs 123%), “Lack of Transportation” (112% vs 153%), “Utilities Threatened” (103% vs 239%), “Delay Visiting MD” (67% vs 181%), “Delay/Not Fill Prescription” (117% vs 182%), “Depressed: All/Most Time” (127% vs 150%), and “Internet: Virtual Visit” (55% vs 130%) (all with p<0.001). The appended dataset illustrates NMP as having many stressors (e.g., difficult social situations, delaying seeking medical care). Strategies to improve depression management in NMP could employ virtual visits, or pharmacy incentives. Insights gleaned from appending market segmentation and healthcare utilization survey data can fill in knowledge gaps from claims-based data and provide practical and actionable insights for use by providers, payers and public health entities

tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform

The tranSMART knowledge management and high-content analysis platform is a flexible software framework featuring novel research capabilities. It enables analysis of integrated data for the purposes of hypothesis generation, hypothesis validation, and cohort discovery in translational research. tranSMART bridges the prolific world of basic science and clinical practice data at the point of care by merging multiple types of data from disparate sources into a common environment. The application supports data harmonization and integration with analytical pipelines. The application code was released into the open source community in January 2012, with 32 instances in operation. tranSMART’s extensible data model and corresponding data integration processes, rapid data analysis features, and open source nature make it an indispensable tool in translational or clinical research

Anatomie von Kommunikationsrollen. Methoden zur Identifizierung von Akteursrollen in gerichteten Netzwerken.

Die Identifizierung von generalisierbaren Akteursrollen in sozialen Systemen ist seit jeher ein zentrales Anliegen der Sozialwissenschaften. Dies gilt insbesondere für die Identifizierung von Kommunikationsrollen, um die überaus komplexen Prozesse der interpersonalen und massenmedialen Kommunikation systematisch zu beschreiben und zu verstehen. Der vorliegende Beitrag zeigt auf, welche theoretischen und methodischen Überlegungen bei der Operationalisierung von Akteursrollen in gerichteten Netzwerken zu berücksichtigen sind. Basierend auf einer netzwerkanalytischen Betrachtung werden zum einen Unzulänglichkeiten in bestehenden Operationalisierungen von Kommunikationsrollen aufgezeigt und zum anderen neue Konzepte vorgeschlagen. Die diskutierten Konzepte lassen sich zwei unterschiedlichen Ansätzen zuordnen. Einerseits können Mikrostrukturen wie dyadische und triadische Ego Rollen als Basis für die Operationalisierung verwendet werden und andererseits kann man von der Gesamtstruktur des Netzwerks ausgehen. Für den ersten Fall werden unterschiedliche Aggregationsregeln diskutiert, welche eine Anwendung in komplexeren Netzwerken ermöglichen. Beim zweiten Ansatz, der Berücksichtigung der Gesamtstruktur, werden die Eignung unterschiedlicher Zentralitätsmaße, das Konzept des Blockmodelling und die hierarchische Strukturanalyse besprochen. Zwecks Anschaulichkeit beschränkt sich dieser Beitrag auf Rollen in Kommunikationsnetzwerken. Die vorgestellten Konzepte sind jedoch auch auf andere Netzwerke übertragbar, die aus gerichteten Beziehungen bestehen. ----------------------------------------------------------- The identification of generalizable roles in social systems has been one of the most central issues in social science ever since. Particularly in the field of interpersonal communication, the notion of communication roles has been used to describe and better understand the complex processes in social groups and society. This contribution identifies the theoretical and methodological concepts that have to be considered when roles in directed networks are operationalized. Based on a network analytic approach and critically evaluating the shortcomings of some widely used models, this study aims to propose new concepts for the operationalization of communication roles. These concepts can be divided into two distinctive approaches. One possibility is to use microstructures like dyadic and triadic communication roles as basic units. For this approach, different aggregation rules are discussed which are necessary for their application in more complex networks. The second approach takes the overall structure of a network into ac-count and identifies different roles by applying centrality measures, blockmodelling or hierarchical structure analysis tools. For illustrative reasons, this study limits its focus on the operationalization of communication roles as a prominent object in social science research. The concepts presented, however, are applicable for directed graphs in general