Report: Biomedical, patient data network needed for better classification
A new data network that integrates emerging research on the molecular makeup of diseases with clinical data on individual patients could drive the development of a more accurate classification of disease and ultimately enhance diagnosis and treatment, according to a new report from the National Research Council (NRC).
The taxonomy that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms, according to the report, which added that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights.
“This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia,” the Washington, D.C.-based NRC stated.
The International Classification of Diseases (ICD) approach, where disease classifications are based on signs and symptoms, may have been adequate in an era when treatments were largely directed toward symptoms rather than underlying causes, but diagnosis based on traditional signs and symptoms alone carries the risk of missing or misclassifying diseases, the authors stated. “For instance, symptoms in patients are often nonspecific and rarely identify a disease unambiguously, and numerous diseases, such as cancer and HIV infection, are asymptomatic in the early stages. Moreover, many subgroups of certain diseases have diverse molecular causes and are classified as one disease and, conversely, multiple diseases share a common molecular cause and are not categorized in the same disease classification.”
The authors recommended a modernization and reorientation of the information systems used by researchers and healthcare providers to attain the new taxonomy and move toward precision medicine. They suggested a framework for creating a "knowledge network of disease" that integrates the range of information on what causes diseases and allows researchers, healthcare providers and the public to share and update this information. The first stage in developing the network would involve creating an "information commons" that links layers of molecular data and medical histories, including information on social and physical environments, the authors stated.
The second stage would construct the network and require data mining of the information commons to highlight the data's interconnectedness and integrate it with evolving research. “Fundamentally, data would be continuously deposited by the research community and extracted directly from the medical records of participating patients.”
To acquire information for the knowledge network, the authors recommended designing strategies to collect and integrate disease-relevant information; implementing pilot studies to assess the feasibility of integrating molecular parameters with medical histories in the ordinary course of care; and gradually eliminating institutional, cultural and regulatory barriers to widespread sharing of individuals' molecular profiles and health histories while still protecting patients' rights.
"Much of the initial work necessary to develop the information commons should take the form of observational studies, which would collect molecular and other patient data during treatment,” they wrote. “Having this access at point of care could reduce the cost of research, make scientific advances relevant to real-life medicine and facilitate the use of EHRs.”
The authors concluded that moving toward individualized medicine will require that researchers and healthcare providers have access to very large sets of health and disease-related data linked to individual patients. “These data are also critical for developing the information commons, the knowledge network of disease and ultimately the new taxonomy,” they wrote.
The study was sponsored by the National Institutes of Health.
The taxonomy that emerges would define diseases by their underlying molecular causes and other factors in addition to their traditional physical signs and symptoms, according to the report, which added that the new data network could also improve biomedical research by enabling scientists to access patients' information during treatment while still protecting their rights.
“This would allow the marriage of molecular research and clinical data at the point of care, as opposed to research information continuing to reside primarily in academia,” the Washington, D.C.-based NRC stated.
The International Classification of Diseases (ICD) approach, where disease classifications are based on signs and symptoms, may have been adequate in an era when treatments were largely directed toward symptoms rather than underlying causes, but diagnosis based on traditional signs and symptoms alone carries the risk of missing or misclassifying diseases, the authors stated. “For instance, symptoms in patients are often nonspecific and rarely identify a disease unambiguously, and numerous diseases, such as cancer and HIV infection, are asymptomatic in the early stages. Moreover, many subgroups of certain diseases have diverse molecular causes and are classified as one disease and, conversely, multiple diseases share a common molecular cause and are not categorized in the same disease classification.”
The authors recommended a modernization and reorientation of the information systems used by researchers and healthcare providers to attain the new taxonomy and move toward precision medicine. They suggested a framework for creating a "knowledge network of disease" that integrates the range of information on what causes diseases and allows researchers, healthcare providers and the public to share and update this information. The first stage in developing the network would involve creating an "information commons" that links layers of molecular data and medical histories, including information on social and physical environments, the authors stated.
The second stage would construct the network and require data mining of the information commons to highlight the data's interconnectedness and integrate it with evolving research. “Fundamentally, data would be continuously deposited by the research community and extracted directly from the medical records of participating patients.”
To acquire information for the knowledge network, the authors recommended designing strategies to collect and integrate disease-relevant information; implementing pilot studies to assess the feasibility of integrating molecular parameters with medical histories in the ordinary course of care; and gradually eliminating institutional, cultural and regulatory barriers to widespread sharing of individuals' molecular profiles and health histories while still protecting patients' rights.
"Much of the initial work necessary to develop the information commons should take the form of observational studies, which would collect molecular and other patient data during treatment,” they wrote. “Having this access at point of care could reduce the cost of research, make scientific advances relevant to real-life medicine and facilitate the use of EHRs.”
The authors concluded that moving toward individualized medicine will require that researchers and healthcare providers have access to very large sets of health and disease-related data linked to individual patients. “These data are also critical for developing the information commons, the knowledge network of disease and ultimately the new taxonomy,” they wrote.
The study was sponsored by the National Institutes of Health.