The Role of Free/Libre and Open Source Software in Learning Health Systems

OBJECTIVE: To give an overview of the role of Free/Libre and Open Source Software (FLOSS) in the context of secondary use of patient data to enable Learning Health Systems (LHSs). METHODS: We conducted an environmental scan of the academic and grey literature utilising the MedFLOSS database of open source systems in healthcare to inform a discussion of the role of open source in developing LHSs that reuse patient data for research and quality improvement. RESULTS: A wide range of FLOSS is identified that contributes to the information technology (IT) infrastructure of LHSs including operating systems, databases, frameworks, interoperability software, and mobile and web apps. The recent literature around the development and use of key clinical data management tools is also reviewed. CONCLUSIONS: FLOSS already plays a critical role in modern health IT infrastructure for the collection, storage, and analysis of patient data. The nature of FLOSS systems to be collaborative, modular, and modifiable may make open source approaches appropriate for building the digital infrastructure for a LHS.</p

Health services research in the public healthcare system in Hong Kong: An analysis of over 1 million antihypertensive prescriptions between 2004-2007 as an example of the potential and pitfalls of using routinely collected electronic patient data

&lt;b&gt;Objectives&lt;/b&gt; Increasing use is being made of routinely collected electronic patient data in health services research. The aim of the present study was to evaluate the potential usefulness of a comprehensive database used routinely in the public healthcare system in Hong Kong, using antihypertensive drug prescriptions in primary care as an example.&lt;p&gt;&lt;/p&gt; &lt;b&gt;Methods&lt;/b&gt; Data on antihypertensive drug prescriptions were retrieved from the electronic Clinical Management System (e-CMS) of all primary care clinics run by the Health Authority (HA) in the New Territory East (NTE) cluster of Hong Kong between January 2004 and June 2007. Information was also retrieved on patients’ demographic and socioeconomic characteristics, visit type (new or follow-up), and relevant diseases (International Classification of Primary Care, ICPC codes). &lt;p&gt;&lt;/p&gt; &lt;b&gt;Results&lt;/b&gt; 1,096,282 visit episodes were accessed, representing 93,450 patients. Patients’ demographic and socio-economic details were recorded in all cases. Prescription details for anti-hypertensive drugs were missing in only 18 patients (0.02%). However, ICPC-code was missing for 36,409 patients (39%). Significant independent predictors of whether disease codes were applied included patient age &gt; 70 years (OR 2.18), female gender (OR 1.20), district of residence (range of ORs in more rural districts; 0.32-0.41), type of clinic (OR in Family Medicine Specialist Clinics; 1.45) and type of visit (OR follow-up visit; 2.39). &lt;p&gt;&lt;/p&gt; In the 57,041 patients with an ICPC-code, uncomplicated hypertension (ICPC K86) was recorded in 45,859 patients (82.1%). The characteristics of these patients were very similar to those of the non-coded group, suggesting that most non-coded patients on antihypertensive drugs are likely to have uncomplicated hypertension. &lt;p&gt;&lt;/p&gt; &lt;b&gt;Conclusion&lt;/b&gt; The e-CMS database of the HA in Hong Kong varies in quality in terms of recorded information. Potential future health services research using demographic and prescription information is highly feasible but for disease-specific research dependant on ICPC codes some caution is warranted. In the case of uncomplicated hypertension, future research on pharmaco-epidemiology (such as prescription patterns) and clinical issues (such as side-effects of medications on metabolic parameters) seems feasible given the large size of the data set and the comparability of coded and non-coded patients

EnzyMiner: automatic identification of protein level mutations and their impact on target enzymes from PubMed abstracts

BACKGROUND: A better understanding of the mechanisms of an enzyme's functionality and stability, as well as knowledge and impact of mutations is crucial for researchers working with enzymes. Though, several of the enzymes' databases are currently available, scientific literature still remains at large for up-to-date source of learning the effects of a mutation on an enzyme. However, going through vast amounts of scientific documents to extract the information on desired mutation has always been a time consuming process. In this paper, therefore, we describe an unique method, termed as EnzyMiner, which automatically identifies the PubMed abstracts that contain information on the impact of a protein level mutation on the stability and/or the activity of a given enzyme. RESULTS: We present an automated system which identifies the abstracts that contain an amino-acid-level mutation and then classifies them according to the mutation's effect on the enzyme. In the case of mutation identification, MuGeX, an automated mutation-gene extraction system has an accuracy of 93.1% with a 91.5 F-measure. For impact analysis, document classification is performed to identify the abstracts that contain a change in enzyme's stability or activity resulting from the mutation. The system was trained on lipases and tested on amylases with an accuracy of 85%. CONCLUSION: EnzyMiner identifies the abstracts that contain a protein mutation for a given enzyme and checks whether the abstract is related to a disease with the help of information extraction and machine learning techniques. For disease related abstracts, the mutation list and direct links to the abstracts are retrieved from the system and displayed on the Web. For those abstracts that are related to non-diseases, in addition to having the mutation list, the abstracts are also categorized into two groups. These two groups determine whether the mutation has an effect on the enzyme's stability or functionality followed by displaying these on the web

Integrative modeling of transcriptional regulation in response to antirheumatic therapy

<p>Abstract</p> <p>Background</p> <p>The investigation of gene regulatory networks is an important issue in molecular systems biology and significant progress has been made by combining different types of biological data. The purpose of this study was to characterize the transcriptional program induced by etanercept therapy in patients with rheumatoid arthritis (RA). Etanercept is known to reduce disease symptoms and progression in RA, but the underlying molecular mechanisms have not been fully elucidated.</p> <p>Results</p> <p>Using a DNA microarray dataset providing genome-wide expression profiles of 19 RA patients within the first week of therapy we identified significant transcriptional changes in 83 genes. Most of these genes are known to control the human body's immune response. A novel algorithm called TILAR was then applied to construct a linear network model of the genes' regulatory interactions. The inference method derives a model from the data based on the Least Angle Regression while incorporating DNA-binding site information. As a result we obtained a scale-free network that exhibits a self-regulating and highly parallel architecture, and reflects the pleiotropic immunological role of the therapeutic target TNF-alpha. Moreover, we could show that our integrative modeling strategy performs much better than algorithms using gene expression data alone.</p> <p>Conclusion</p> <p>We present TILAR, a method to deduce gene regulatory interactions from gene expression data by integrating information on transcription factor binding sites. The inferred network uncovers gene regulatory effects in response to etanercept and thus provides useful hypotheses about the drug's mechanisms of action.</p

The role of free/libre and open source software in learning health systems

OBJECTIVE: To give an overview of the role of Free/Libre and Open Source Software (FLOSS) in the context of secondary use of patient data to enable Learning Health Systems (LHSs). METHODS: We conducted an environmental scan of the academic and grey literature utilising the MedFLOSS database of open source systems in healthcare to inform a discussion of the role of open source in developing LHSs that reuse patient data for research and quality improvement. RESULTS: A wide range of FLOSS is identified that contributes to the information technology (IT) infrastructure of LHSs including operating systems, databases, frameworks, interoperability software, and mobile and web apps. The recent literature around the development and use of key clinical data management tools is also reviewed. CONCLUSIONS: FLOSS already plays a critical role in modern health IT infrastructure for the collection, storage, and analysis of patient data. The nature of FLOSS systems to be collaborative, modular, and modifiable may make open source approaches appropriate for building the digital infrastructure for a LHS.</p

Medfloss.org &ndash; Ein umfassendes und offenes Informationsportal zu Open Source im Gesundheitswesen.

Hintergrund: Freie und Open Source (OS) Software bezeichnet im allgemeinen Software, deren Source Code frei zug&auml;nglich ist und f&uuml;r beliebige Weiterverwendung kopiert und modifiziert werden darf. Oft verfolgen OS Projekte eine kollaborative Herangehensweise, durch Wiederverwendung und Erweiterung bestehender Software kann im Ganzen mehr erreicht werden und verf&uuml;gbare Ressourcen k&ouml;nnen effizienter genutzt werden, als bei Neuentwicklungen. Auch f&uuml;r das Gesundheitswesen existiert eine gro&szlig;e Vielfalt an fachspezifischen OS Software L&ouml;sungen. Die Verf&uuml;gbarkeit ausgereifter OS Applikationen f&uuml;r ann&auml;hernd jedes Anwendungsgebiet ist nur wenig bekannt, eine zentrale und umfassende Informationsquelle war bisher nicht verf&uuml;gbar. Dieser Sachverhalt spiegelt sich auch in bisher kaum praktizierten Wiederverwendung bestehender OS Software und dem geringen Verbreitungsgrad wieder. Mit Medfloss.org wurde erstmals ein zentrales Verzeichnis verf&uuml;gbarer Projekte und weiterer relevanter Ressourcen wie Dienstleistungsanbieter und wissenschaftliche Ver&ouml;ffentlichungen geschaffen. Methoden: Bestehende Informationsquellen im Internet, wie zum Beispiel Wikipedia und Sourceforge.net, wurden in Bezug auf medizinische OS Software analysiert. Des Weiteren wurde eine Literaturrecherche durchgef&uuml;hrt. Die Webseite basiert auf dem CMS Drupal, verschiedenste Informationen zu den Projekten sind strukturiert aufbereitet und werden in einheitlicher Form bereitgestellt. Unterschiedlichste Kategorietypen stehen zur Projektcharakterisierung zur Verf&uuml;gung, wie zum Beispiel Applikationstyp, unterst&uuml;tze Unternehmensfunktionen und Standards. Damit k&ouml;nnen relevante Projekte je nach Fragestellung in k&uuml;rzester Zeit identifizieren werden. Die Projektbeschreibung umfasst auch Verweise auf Homepage, Dienstleister, Literatur, &auml;hnliche Projekte und externe Informationsquellen. Medfloss.org wurde bewusst als offenes Informationsportal gestaltet, in dem jeder Benutzer Inhalt aktualisieren und hinzuf&uuml;gen kann. Auf Grund der Anzahl und Vielfalt der Projekt kann nur durch die aktive Mitwirkung die Aktualit&auml;t und Korrektheit der Informationen gew&auml;hrleistet werden. Ergebnisse: Medfloss.org wurde Februar 2010 mit damals 120 erfassten Projekten online gestellt. Mittlerweile beinhaltet die Datenbank mehr als 230 Software Projekte, mehr als 75 Dienstleister und mehr als 110 Publikationen. Innerhalb der letzten 6 Monate wurde die Seite von &uuml;ber 24.000 Besuchern aufgerufen. Die Initiative wird mittlerweile von der IMIA OS WG, der EFMI LIFOSS WG, dem Helmholtz Zentrum M&uuml;nchen und dem Universit&auml;tsklinikum Heidelberg unterst&uuml;tzt. Diskussion: Die Anzahl der Besucher verdeutlicht, dass die Plattform gro&szlig;e Akzeptanz findet und reges Interesse an dieser Thematik besteht. Obwohl neue Eintr&auml;ge von Besuchern selbst hinzugef&uuml;gt und bestehende Informationen erweitert und aktualisiert wurden, wird aktuell der Gro&szlig;teil der inhaltlichen Arbeiten noch durch die Autoren selbst durchgef&uuml;hrt. Die Verf&uuml;gbarkeit von strukturierten Informationen erm&ouml;glicht erstmals eine analytische Auswertung der verf&uuml;gbaren Daten. Dadurch werden auch umfassendere Analysen m&ouml;glich, die eine Evaluation der Eigenschaften von OS Software f&uuml;r das Gesundheitswesen unterst&uuml;tzen. &nbsp