(01) Behavior, Behavioral Change, and Prevention

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(02) Bioethics

02-OD(OSP)-103*   Ethical Issues Associated with Electronic Sharing of Health Information. The development of an electronic health information infrastructure and the sharing of health information for patient care and research offer enormous promise to improve health care and promote scientific advances. However, the broad sharing of such data raises numerous ethical issues that may benefit from additional studies (e.g. those related to privacy and confidentiality). Examples include studies to assess risks associated with health information technology and the broad sharing of health information for research, and novel approaches for mitigating them. Examination could also include analysis of current oversight paradigms and suggestions for enhancements, as well as assessments of how privacy risks may change in the future. OD(OSP) Contact: Abigail Rives, 301-594-1976, rivesa@od.nih.gov; NLM Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

(03) Biomarker Discovery and Validation

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(04) Clinical Research

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(05) Comparative Effectiveness Research

05-LM-101*             Effect of "Information Prescriptions" on Improving Care by Increasing Compliance with Medication Protocol Given to Discharged Emergency Department Patients. A significant fraction of patients who are given a set of prescriptions, such as when they leave a physician office or the Emergency Department, are known to disregard or curtail recommended medications. Individually tailored information about risks, benefits, costs and treatment options are given by some clinicians as "information prescriptions", but the effectiveness of "information prescriptions" is not known. Studies in this area should determine value of such "information prescriptions" in improving patient compliance as contrasted to current discharge advice systems or standard office practices. Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

05-LM-102*             Ability of Decision Tools in an Electronic Health Care System to Increase Use of Generic Drugs. Although generic drugs are much less expensive than "brand name", clinicians commonly prescribe "brand name" drugs for a plethora of reasons often not related to belief that "brand name" drugs are more effective. Evaluation studies are needed to determine whether a Decision Support Tool that feeds information about generic options, presented to physicians prescribing "brand-name" drugs through a Computerized Physician Order Entry System (CPOE), would increase physician selection of less-expensive generic drugs. Studies should compare the use of such pre-decision feedback to situations where no feedback about generic options is provided. Contact: Dr. Hua-Chuan Sim, 301-594-4882, simh@mail.nih.gov.

05-LM-103*             Improving Compliance of School Children with Immunization Schedules. An increasing problem in inner city and some rural school systems is failure of pre-school children to complete immunization schedules for common illnesses as required by the school system. Some of the problem is caused by the discontinuity of record-keeping systems, and the absence of reminder systems in physician offices and clinics where students receive immunizations. Evaluation studies should compare completion of immunization schedules where clinics and physicians use tools specifically designed to record, share and manage progress of immunization for each child with completion rates of children where such tools are not used. Contact: Dr. Hua-Chuan Sim, 301-594-4882, simh@mail.nih.gov.

05-LM-104*             Value of "Virtual Reality" Interaction in Improving Compliance with Diabetic Regimen. Despite often intensive educational efforts, patients with diabetes commonly mismanage or undermanage their illness despite the known ability of optimal management to reduce complications and morbidity. Interactions between avatars in virtual reality environments such as Second Life are known to influence behavior. Studies should explore the effectiveness of periodic physician/nurse interaction with diabetic patients via a virtual reality environment in improving diabetic control, as compared to standard practice. Contact: Dr. Milton Corn, 301-496-4621, cornm@mail.nih.gov.

(06) Enabling Technologies

06-LM-101*             Intelligent Search Tool for Answering Clinical Questions. Develop new computational approaches to information retrieval that would allow a clinician or clinical researcher to pose a single query that would result in search of multiple data sources to produce a coherent response that highlights key relevant information which may signal new insights for clinical research or patient care. Information that could help a clinician diagnose or manage a health condition, or help a clinical researcher explore the significance of issues that arise during a clinical trial, is scattered across many different types of resources, such as paper or electronic charts, trial protocols, published biomedical articles, or best-practice guidelines for care. Develop artificial intelligence and information retrieval approaches that allow a clinician or researcher confronting complex patient problems to pose a single query that will result in a search that appears to "understand" the question, a search that inspects multiple databases and brings findings together into a useful answer. Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

06-LM-102*             Self-documenting encounters. Develop technologies, tools, and processes to achieve rapid and comprehensive electronic documentation of encounters with patients/research subjects. Clinicians & clinical researchers spend considerable time and effort in documenting clinical encounters (including using text to describe findings that are seen or heard) - often after the fact and with little immediate benefit to care of patients and clinical research subjects. Technologies and tools that could fully automate the capture of encounters and update electronic health records in real-time would support more effective and efficient health care and clinical research. Contact: Dr. Hua-Chuan Sim, 301-594-4882, simh@mail.nih.gov.

(07) Enhancing Clinical Trials

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(08) Genomics

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(09) Health Disparities

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(10) Information Technology for Processing Health Care Data for Research

10-LM-101*             Informatics for post-marketing surveillance. Use computational data mining (artificial intelligence and natural language processing, among other techniques) of a large longitudinal medical records database to perform post-marketing surveillance (Phase 4 Clinical Trial). Large clinical data repositories exist that contain longitudinal health records for millions of people. Advanced computational techniques can be used to mine clinical notes, test data and abnormal images to undertake an in silico Phase 4 Clinical Trial, by searching for possible adverse drug events and side effects of drugs already in use. Contact: Dr. Milton Corn, 301-496-4621, cornm@mail.nih.gov.

10-LM-102*             Advanced decision support for complex clinical decisions. Use artificial intelligence techniques to provide practical support for complex decision making in health care and clinical research contexts. Most electronic data about patients and clinical research subjects exists at the level of raw data, individual test results and observations, and individual encounters. This mass of data obscures the view of the patient as a whole, hides key facts that deserve attention, and complicates the delivery of relevant electronic knowledge to improve decisions or identify candidate research subjects. Advanced computational techniques should be useful in generating a higher level picture of the patient that can support more effective clinical decision support. Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

(11) Regenerative Medicine

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(12) Science, Technology, Engineering and Mathematics (STEM) Education

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(13) Smart Biomaterials - Theranostics

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(14) Stem Cells

For this RFA, there is no NLM-specific Challenge Topic in this Challenge Area.

(15) Translational Science

15-LM-101*             Presenting genome information in electronic health records. Develop approaches for presenting relevant genomic information in an understandable way, in the context of a patient's electronic health record. As genomic data becomes available for more individuals, these data must be integrated into electronic health records in ways that: help clinicians and patients to understand the significance of the data; provide an avenue for alerting clinicians and patients when new knowledge from GWAS, etc. rises to the level of potential clinical impact; and enable linking to effective decision support. Contact: Dr. Jane Ye, 301-594-4882, yej@mail.nih.gov.

15-LM-102             Computational hypothesis generation for biology and medicine. Employing two or more sources, use advanced computational approaches to generate a new and meaningful hypothesis in biomedical science, capable of being tested by bench or clinical research. One source must be full-text published biomedical literature; the other source should be either (1) a database storing primary data from basic biomedical research or (2) data drawn from the electronic health records used for routine clinical care or from the data accumulated for a clinical research project. The user interface of an integrated hypothesis generation system should support easy use by the intended users (i.e., by biomedical researchers or clinicians). Mining techniques should involve minimal human intervention. Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

15-LM-103             In silico hypothesis testing for biology and medicine. Employing two or more sources, use advanced computational approaches to test rigorously in silico a new and meaningful scientific hypothesis in biomedicine, one which otherwise would require laboratory or clinical verification. One source must be full-text published biomedical literature; the other source should be either (1) a database storing primary data from basic biomedical research or (2) data drawn from the electronic health records used for routine clinical care or the from the data accumulated for a clinical research project. The approach should involve minimal human intervention. Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.

15-TW-101             Models to predict health effects of climate change. Quantitative and predictive models of effects of climate change on disease burden and health outcomes are needed. Approaches may include statistical, spatial or other modeling methods to quantify the current impacts of climate on a diversity of communicable or non-communicable diseases, or project impacts of different climate and socio-economic scenarios on health. For example, new and innovative approaches to develop projections of changes in disease burden in specific regions or populations will facilitate public health planning. Existing databases on population and environmental variables, such as air quality and climatologic episodes should be used to test the utility of these models where possible. Contact: Dr. Joshua Rosenthal, 301-496-1653, joshua_rosenthal@nih.gov; Contact: Dr. Valerie Florance, 301-594-4882, florancev@mail.nih.gov.