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