Workshops International Workshop on Data Mining for Healthcare (DMH) The
potential of data mining on leading to advanced healthcare management
has been well recognized in industry as well as
academia. Discovering
patterns and trends from large amounts of complex data generated by
healthcare transactions to aid diagnosis, decisions and care
delivery has been of great interest. Data mining enhances several
aspects of healthcare management including disease diagnosis,
clinical decision-making,
medical fraud prevention and detection, fault detection of medical
devices, healthcare quality improvement strategies and privacy.
Data mining also helps to discover interesting business insights to
help make business decisions that can influence cost
efficiency without
affecting the quality of care. Natural language processing techniques
greatly enable achieving meaningful use of electronic
health records,
and integrating and formalizing several critical health resources and
clinical guidelines available on the Web. An increasing variety of
important data mining applications to healthcare strongly suggest the
need for a workshop that provides a common platform to
discuss longstanding
problems, discover new problems, and brainstorm potential
solutions
associated with large complex healthcare datasets. The Data Mining
for Healthcare (DMH) workshop will provide a critical and essential
forum for integrating various research challenges in this domain,
and promote collaboration among researchers from academia and industry
to enhance the state-of-art, and help design a vision for
future research. This workshop will facilitate collaboration among
multiple core disciplines including computer science,
medicine, public health, pharmacology, statistics, and social
sciences. Prasanna Desikan, Division of Applied Research, Allina Health Ritu Khare, National Center for Biotechnology Information, National Institutes of Health The DMH workshop program can be found here.The First Workshop on Mobile Cloud Computing in Healthcare (WMCCH) A mobile cloud computing for healthcare (MCCH) system is a specific type
of mobile cloud computing system that focuses on healthcare and
wellness applications. Examples of MCCH include body sensor networks for
long term healthcare monitoring, sensor-based elder care systems,
smartphone apps for Alzheimer’s. In these examples, cloud computing,
mobile computing and wireless networking are combined to build
successful solutions. The objective of this workshop is to provide a
forum for scientists, researchers, and practitioners working in the
areas of MCCH to share and exchange ideas, experiences, and lessons
leant. Chiu C. Tan, Temple University Mengjun Xie, University of Arkansas at Little Rock Workshop on Hospital Readmission Prediction and Clinical Risk Management (HRPCRM 2013) Managing the unexpected is essential in high-risk organizations such as
hospitals. This workshop aims to solicit participation by health
informatics researchers, public health professionals, and healthcare
administrators in the discussion of Hospital readmission and clinical
risk management.
Clinical risk management (CRM) plays a crucial role in enabling hospitals to identify, analyze, monitor, and manage risks related to patient safety. CRM focuses on clinical processes directly and indirectly related to the patient. Successful management strategies can prevent and control the risks and improve the quality and safety of healthcare. Hospital readmission (HR) refers to patient admission to a hospital after being discharged from an earlier hospital stay. 30-day readmission rate is considered as an indicator for evaluating the quality of care. In the U.S., starting from 2012, the Centers for Medicare & Medicaid Services (CMS) began to use readmission rates as a quality metric to determine the reimbursement to hospitals. Predicting hospital readmission risk helps identify which patients would benefit most from care transition interventions, such as arranging a visiting nurse for the patient after the discharge. The topic has received a great deal of attention recently among healthcare professionals. This imminent issue strongly suggests the need for a workshop to provide a common platform for discussion of this challenging problem and potential solutions. John W. Cromwell, Carver College of Medicine, University of Iowa Si-Chi Chin, Center for Web and Data Science, University of Washington – Tacoma https://sites.google.com/site/hrpcrm2013/ |
 | |
workshops |