IDAMAP 2011 - INTELLIGENT DATA ANALYSIS FOR QUALITY AND SAFETY IN HEALTHCARE

INTELLIGENT DATA ANALYSIS FOR QUALITY AND SAFETY IN HEALTHCARE

6 July 2011 - Bled, Slovenia
Organized in conjunction with the 13th Conference on Artificial Intelligence in Medicine (AIME'11)

Niels Peek, John Holmes, Allan Tucker, Riccardo Bellazzi

http://idamap.org/idamap2011

BACKGROUND

Since the publication of two landmarks reports by the Institute of Medicine 2000 and 2001, quality improvement and patient safety have dominated the healthcare research agenda. The reports led to wide awareness that preventable medical errors are responsible for substantial numbers of adverse events in routine care, leading to increased costs and poor efficiency of the healthcare system. In the U.S. between 44,000 to 98,000 people die each year as a result of preventable medical errors. This is more than the numbers of deaths caused by motor vehicle crashes (43,000) and by incidents involving firearms (20,000). The Institute of Medicine attributed these errors not to failures of individual healthcare professionals, but to inabilities of the healthcare system to manage the growing complexity of care, inadequate translation of knowledge into practice, and failures to apply new technology safely and appropriately. The Institute of Medicine called for a comprehensive effort by researchers, healthcare providers, governments, and care consumers, to reduce the number of errors by 50% within five years. Similar targets for improvement were formulated in many other countries. Although not officially quantified, it is broadly agreed that these ambitious goals have yet to be met.

Over the past several years, there has been increasing interest in, and evidence, of using intelligent data analysis methods in patient safety research and practice. A number of methods have been developed for identifying adverse drug events in post-marketing surveillance as well as near-real time detection of such events in spontaneous report databases. However, there is an urgent need to new approaches that address the limitations in such data, especially the lack of robust denominators, reporting accuracy and data quality. These analytic methods should address ongoing clinical needs, such as pharmacovigilance, real-time drug interaction detection, and provider performance, guideline compliance, and safety audits conducted in a variety of patient care settings. In addition, there is a pressing need in the clinical research domain for these analytic methods, especially with the increasing interest in distributed research networks and other approaches to sharing clinical data on a wide scale. Of particular interest is the detection of adverse events that may be rare or unexpected. IDAMAP 2011 will provide the opportunity for researchers and developers to present their work which addresses these critical issues.

CALL FOR PAPERS

Contributions are sollicited regarding theory and application of data analysis methods for improvement of quality and patient safety in healthcare, building on methods and insights from the fields of machine learning, data mining, statistics, and artifical intelligence. Contributions to theory should present or analyse of the properties of novel data analysis methods. Papers on techniques and methodologies should describe the development or the extension of existing methods and their implementation, and discuss the assumptions and limitations of the proposed methods and their novelty with respect to the state of the art. Papers addressing applications or systems should describe the implementation of new methods, and discuss their specific suitability and effectiveness for improvement of healthcare quality and patient safety. In particular, empirical studies are sought that have evaluated the impact of such data analysis methods on care.

Topics include, but are not limited to:

- data analysis methods for adverse event detection

- prognostic models for case-mix adjustment in quality measurement

- data analysis methods for feedback and audit of care quality

- pharmacovigilance methods and systems

- outcomes-based benchmarking and institutional/provider comparison

- statistical process control methods

- measurement and analysis of guideline compliance

- data analysis for clinical decision support

- strategies for risk measurement and adverse event prevention

IDAMAP

The IDAMAP workshop series is devoted to computational methods for data analysis in medicine, biology and pharmacology that present results of analysis in the form communicable to domain experts and that somehow exploit knowledge of the problem domain. Such knowledge may be available at different stages of the data-analysis and model-building process. Typical methods include data visualization, data exploration, machine learning, and data mining.

Gathering in an informal setting, workshop participants will have the opportunity to meet and discuss selected topics in an atmosphere which fosters the active exchange of ideas among researchers and practitioners. The workshop is intended to be a genuinely interactive event and not a mini-conference, thus ample time will be allotted for general discussion.

The IDAMAP workshops are organized in collaboration with the working group on Intelligent Data Analysis & Data Mining of the International Medical Informatics Association, and the working group on Knowledge Discovery & Data Mining of the American Medical Informatics Association.

SUBMISSION OF PAPERS

We invite submissions of either short papers (up to 5 pages, leading to a short presentation at the meeting) or full papers (up to 10 pages, leading to a long presentation at the meeting). Papers should be written in English. Papers should be formatted according to Springer's LNCS format (see www.springeronline.com/lncs or www.springer.de/comp/lncs/authors.html).

Authors should send an electronic submission in PDF to Niels Peek n.b.peek-AT-amc.uva.nl). Please use "IDAMAP SUBMISSION YOUR_NAME" as a subject, where YOUR_NAME is the surname of the first author. Submissions should be received no later than April 14, 2011.

Submissions will be reviewed by at least two people of the programme committee. Authors will be notified of acceptance/rejection by May 8, 2011. Accepted papers will appear in colloquium notes that will only be distributed among registered participants. A selection of revised and expanded papers from the workshop will appear in an international, peer-reviewed scientific journal.

IMPORTANT DATES

Submission: April 14, 2011
Notification: May 8, 2011
Camera-ready: June 8, 2011

REGISTRATION

Registration fee for the workshop will be 90 euros. At least one co-author of each IDAMAP paper should also register to AIME (in addition to registration to the workshop). Registration fee for AIME will be 500 euros (regular), 450 euros (early-bird, until May 20), or 350 euros (students). See http://www.aimedicine.info/aime11 for details.