Complete and accurate collection of clinical data in the course of health care is a long-standing goal that has not been achieved either by manual record-keeping or through electronic record systems. This proposed project addresses the problem from the beginning of the clinical process, by aiming to improve the capture of relevant medical facts during the face-to-face interaction between a patient and provider. Instead of relying on the provider's fallible memory to record facts after the visit, the proposed system will """"""""listen"""""""" to the conversation, use automatic speech recognition to produce an (imperfect) record of what was said, and apply a variety of text analysis and extraction methods to create a draft record of the encounter. Further, it will provide an interface that should permit patients and providers to examine the facts that were recorded and to correct and complete them, also using speech as the primary interface. The projects aims are to develop and integrate the components needed to accomplish this goal, to create a testbed in collaboration with researchers at the environmental health clinic of a children's hos- pital in which experiments can guide system development and assess progress, and to conduct a series of evaluations that assess a series of objectives. First, the research will characterize the ability of the speech recognition, information extraction and information organization components to process the target conversations. Second, it will evaluate the hypothesis that this system can collect a more complete and accurate record than what is routinely collected. Subsequently, it will evaluate the time taken by clinicians to use the system, the extent to which the system is seen to disrupt the patient-provider encounter, the ability of patients to use the system to make additions and corrections to their records, and the subjective response of both patients and providers to use of the system. Success in this effort should lead to better clinical care that is based on more complete and accurate data. In addition, clinical data are also becoming an important resource in the conduct of translational medicine research, where improved data are obviously highly valuable. Project Narrative A well-organized, complete and accurate record of clinical encounters can form the bedrock of data on which both clinical care and clinical and biomedical research can rest. This project applies state of the art and novel technologies to """"""""listen"""""""" to and interpret encounters between patients and health care providers to create such records. Its success should lead to better health care and greater possibilities for using clinical data in medical research.

Agency
National Institute of Health (NIH)
Institute
National Library of Medicine (NLM)
Type
Research Project (R01)
Project #
3R01LM009723-02S1
Application #
7934200
Study Section
Biomedical Library and Informatics Review Committee (BLR)
Program Officer
Sim, Hua-Chuan
Project Start
2008-09-30
Project End
2010-09-29
Budget Start
2009-09-30
Budget End
2010-09-29
Support Year
2
Fiscal Year
2009
Total Cost
$153,012
Indirect Cost
Name
Massachusetts Institute of Technology
Department
Type
Organized Research Units
DUNS #
001425594
City
Cambridge
State
MA
Country
United States
Zip Code
02139
Klann, Jeffrey G; Szolovits, Peter (2009) An intelligent listening framework for capturing encounter notes from a doctor-patient dialog. BMC Med Inform Decis Mak 9 Suppl 1:S3