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June 21, 2006
Organized by IBM Research Lab in Haifa, Israel
Information for Public Health Decision-Making: The Influenza Pandemic Case
Dr. Ran Balicer, Consultant, Pandemic influenza planning, Israel
Information technology is a fundamental component of modern public health. The risk of an emerging influenza virus that will rapidly spread from person to person causing an epidemic of global proportions, is considered among the most significant threats to public health worldwide. Early detection of the disease, rapid directed control measures, and judicious use of limited supplies of antiviral drugs and vaccines are crucial in tackling such a pandemic, and perhaps containing it in its early stages. Each of these measures is largely dependent on the availability of continuously updated data from medical records, laboratory results, epidemiological investigations, and logistical data gathered in the affected country as well as in other countries around the world. The impact of the availability of such timely data on the prospects of successful mitigation of a future pandemic will be discussed.
Today's US Public Health Lab and Common Reporting Challenges
Sondra R. Renly, IBM Almaden Research Lab
I have over eight years of experience working with US and Canadian public health labs through the support and development of their primary laboratory information system. I will describe the types of testing currently performed by a typical US public health laboratory and briefly differentiate their needs from clinical laboratories. I will also speak about the customer-driven focus for information system development, supporting very complex reporting needs for real-time, batch, and statistical reports.
Biomedical Information Integration for Clinical Genomics
Simona Cohen, IBM Haifa Labs
Clinical Genomics, the marriage of clinical information and knowledge about the human or pathogen genome, holds enormous promise to impact decisions on diagnosis, prognosis, treatment, and epidemiology. By understanding illnesses on the molecular level, including gene variations linked to disease or drug response, doctors may be able to make more precise diagnoses and tailored treatment decisions while utilizing personalized healthcare guidelines and protocols. Furthermore, the pharmaceutical companies can discover new targeted drugs and gene therapies with fewer side effects, at reduced cost, and with higher efficacy. The FDA recognizes this as a critical path for drug development and encourages the inclusion of surrogate endpoints, in addition to traditional clinical trial data, via voluntary submission of genomic data and the relevant imaging data.
A key enabler of clinical genomics is a sound standards-based biomedical information integration technology. This technology must be able to de-identify, integrate, and correlate clinical, clinical trial, genomic, and images metadata from the various systems, as well as correlate it to public data sources such as PubMed and GenBank. In this talk, we describe a standards-based biomedical information integration technology, and demonstrate its usage in EuResist, a project designed to integrate viral genomics with clinical data to predict the response to HIV treatment using some of the largest resistance databases in Europe. We also introduce requirements for new storage devices, which are needed to better support the integrated semi-structured data.
IBM's Global Pandemic Initiative (GPI) and the Spatio-Temporal Epidemiological Modeler (STEM)
Dr. James Kaufman and Daniel Ford, IBM Almaden Research Lab
On May 15, 2006, IBM announced the "Global Pandemic Initiative" (GPI), a collaborative effort with over 20 public-health institutions, including the World Health Organization and the U.S. Centers for Disease Control and Prevention. As part of its contribution to the GPI, IBM announced that it would release its disease modeling system called the "Spatio-Temporal Epidemiological Modeler" (STEM) as an open source project. A week later, on May 22, 2006, STEM was accepted as part of the Eclipse Open Healthcare Framework (http://www.eclipse.org/ohf) and will become the framework's first public healthcare component.
The Spatio-Temporal Epidemiological Modeler (STEM) is a system developed by IBM Research for modeling the spread of infectious diseases over time in any geographic area. It is designed to be a common, globally accessible tool that promotes open collaboration and joint development of disease models by different distributed researchers.
A significant innovation embodied in STEM is the ability to compose a disease model by combining the efforts of many different researchers. For instance, one might contribute a representation of migratory bird paths, while another might contribute data on cattle farms. Dynamic factors such as seasonal weather patterns can also be modeled by STEM.
These abilities give STEM unprecedented flexibility and extensibility and drive it towards the goal of facilitating global collaboration and cooperation in the development of effective and accurate disease models.
This talk will describe and demonstrate STEM.
References:
Keynote: Biological Threats: The Need for Cooperation, Collaboration, and Transparency
Mark S. Smolinski, M.D., M.P.H. Director, Global Health and Security Initiative, Vice President, Biological Programs, NTI
Infectious diseases continue to be a serious burden around the world, in developing and industrialized countries alike. Whether naturally occurring or intentionally inflicted, microbial agents can cause illness, disability, and death in individuals while disrupting entire populations, economies, and governments. In the highly interconnected and readily traversed "global village" of our time, one nation's problem soon becomes every nation's problem as geographical and political boundaries offer trivial impediments to such threats. Factors relating to society, the environment, and our increasing global interconnectedness actually enhance the likelihood of disease emergence and spread. Dramatic advances in science, technology, and medicine have enabled us to make great strides forward in our struggle to prevent and control infectious diseases, yet we cannot fall prey to an illusory complacency. The magnitude of the problem requires renewed commitment. We must do more to improve our ability to prevent, detect, and control microbial threats to health.
Using Information Technology for Epidemiological Research of Infectious Diseases
Tomer Ziv, MHA, MBA, PhD Candidate, Tel Aviv University
This talk presents three studies that focus on the epidemiology of infectious diseases (one is complete, another is currently underway, and a third has just begun). The studies address the following issues:
- Transfer of antimicrobial-resistant nosocomial infections from one hospital to another (a general hospital and a rehabilitative institution) on the same campus
- Skewed calculations of morbidity rates for diarrhea-comparing population surveys, physician surveys, and information systems
- Comparing mortality rates for antimicrobial-resistant infections versus standard infections
HL7 V3 Standards for Public Health and the Clinical-Genomics Standard for the CDC NHANES Nutrition Project
Dr. Amnon Shabo, IBM Haifa Labs
Historically, human genomic data has not been routinely obtained in public health investigations. However, recent developments, including the completion of the Human Genome Project, may soon change this fact. In order to facilitate the transmission of human genomic data in healthcare, the Health Level Seven (HL7) Clinical Genomics Special Interest Group (CG SIG) develops data standards for patient-specific genomic data associated with the patient clinical data.
The CDC's Public Health Information Network (PHIN) has adopted HL7 as the standard for electronic messages, as well as the HL7 V3 Reference Information Model (RIM) as the foundation for the PHIN Logical Data Model. The challenge is how healthcare information standards can be utilized in the field of public health genomics, and how human genomics data can be integrated into PHIN. In particular, the HL7 Clinical Genomics specification was explored as a standard way to transmit genotype information in the National Health and Nutritional Examination Study (NHANES). The information is captured in the NHANES III DNA Bank, which helps contributing researchers examine the prevalence of genetic variants described as important to public health.
Based on joint work with the CDC's Tom Savel, Bruce Lin, and Geraldine M. McQuillan, presented at the PHIN 05 Conference in Atlanta.
Public Health System Based on Present and Future IHE Initiatives
Boaz Carmeli, IBM Haifa Labs, and Sarah Knoop, IBM Almaden Research Lab
The 21st century public health environment is full of processes, technologies, time latent data, and proprietary systems unable to quickly detect emerging infectious threats. Coupled with a complex ecosystem and the lack of a global surveillance system, the public health system seeks standardized technologies and services to automate processes, transmit adverse event data in real-time, detect events before they happen, communicate across jurisdictions, and respond to incidents in a timely and efficient manner.
In order for interoperability and data exchange to become a reality, both industry best practices and open standards must be adopted. HL7 is probably the most important standard in the healthcare domain for defining messages and data formats. The Integrating the Healthcare Enterprise initiative (IHE) defines the interoperability approach by articulating a set of integration profiles for cross-enterprise document sharing. In this talk, we describe an ongoing effort to utilize present and future IHE concepts for the public health domain.
Similar to the Clinical Affinity Domain (CAD) term of the IHE Cross-enterprise Document Sharing (XDS) profile, we define a Public Health Affinity Domain (PHAD) as a group of care delivery and public health organizations working together using a common set of policies and centralized services for enhancing public health to the benefit of the entire community and population. Based on the layered structure of the public health domain, we propose a hierarchical architecture of PHADs. Each public health organization exposes only part of its information, as metadata, to a higher level, while the real data is not exposed but available for query and retrieval.
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