1 Introduction Several informatics endeavors related to the automated structuring of data are perti nent here. Electronic collections of validated questionnaires are being created, formally defining pertinent positive/negative questions and responses(eg, see the National Insti- tutes of Health(NIH) PROMIS project [7] and related efforts by the National Cancer Institute, NCD). Such databases provide a foundation from which chief complaints and symptoms can be objectified and quantified with specificity: duration, severity, timing, and activities that either trigger or relieve the symptom can be asked. Likewise, existing diagnostic guidelines intended for non-physicians, such as the American Medical Asso- ciation Family Medical Guide 5], can be turned into online, interactive modules with ecision trees to guide a patient through the response process. Markedly, an inherent issue with such questionnaires is determining how best to elicit responses from patients: aspects of visualization and human-computer interaction(HCD) thus also come into play (see Chapter 4). Apart from structured formats, more complicated methods such as medical natural language processing(NLP) can be applied to structure the statement by vides an overview of NLP research and applications Assessing the patient. The chief complaint provides a basis for beginning to under- stand the problem, but a clinician will still require additional background to establish potential reasons for the knee pain. For example, does the patient have a history of a previous condition that may explain the current problem? Has this specific problem occurred before (ie, is it chronic) or did any specific past event cause this issue(e.g trauma to the knee)? The answers to these questions are all gleaned from questioning the patient further and an exploration of the medical record An array of medical and imaging informatics research is ongoing to enrich the elec tronic medical records(EMR) functionality and to bring new capabilities to the point of care. A longstanding pursuit of the EMR is to provide an automated set of relevant information and a readily searchable index to patient data: rather than manually in spect past reports and results, the system should locate germane documents, if not permit the physician to pose a simple query to find key points. Informatics work in distributed information systems concentrates on the problems of data representation and connectivity in an increasingly geographically dispersed, multidisciplinary health care environment. Patients are commonly seen by several physicians, who are often at different physical locations and institutions. As such, a patient's medical history may be segmented across several disparate databases: a core challenge of informatics is to find effective ways to integrate such information in a secure and timely fashion (see Chapter 3). For imaging, past exams should be made available; but instead of the whole study, only(annotated) sentinel image slices that detail a problem could be re called. Although manual image capture and markup is presently used, automated tech- niques are being investigated to identify anatomical regions and uncover potential abnormalities on an image(e.g, CAD); and to segment and quantify disease based on domain knowledge(see Chapter 5). For textual data, such as generated from notes and consults(e.g, a radiology report), NLP techniques are being developed to facilitate content indexing(see Chapter 6). To aggregate the information into a useful tool data model that matches the expectations of the clinician must be used to organize the extracted patient data(see Chapter 7), and it must then be presented in a way con- ducive to thinking about the problem(see Chapter 4) Specifying the study. Based on the patient 's responses and review of her record, the PCP wishes to differentiate between degenerative joint disease and a meniscal tear. If1 Introduction 7 Several informatics endeavors related to the automated structuring of data are perti￾nent here. Electronic collections of validated questionnaires are being created, formally defining pertinent positive/negative questions and responses (e.g., see the National Insti￾tutes of Health (NIH) PROMIS project [7] and related efforts by the National Cancer Institute, NCI). Such databases provide a foundation from which chief complaints and symptoms can be objectified and quantified with specificity: duration, severity, timing, and activities that either trigger or relieve the symptom can be asked. Likewise, existing diagnostic guidelines intended for non-physicians, such as the American Medical Asso￾ciation Family Medical Guide [5], can be turned into online, interactive modules with decision trees to guide a patient through the response process. Markedly, an inherent issue with such questionnaires is determining how best to elicit responses from patients; aspects of visualization and human-computer interaction (HCI) thus also come into play (see Chapter 4). Apart from structured formats, more complicated methods such as medical natural language processing (NLP) can be applied to structure the statement by the patient, identifying and codifying the chief complaint automatically. Chapter 6 pro￾vides an overview of NLP research and applications. Assessing the patient. The chief complaint provides a basis for beginning to under￾stand the problem, but a clinician will still require additional background to establish potential reasons for the knee pain. For example, does the patient have a history of a previous condition that may explain the current problem? Has this specific problem occurred before (i.e., is it chronic) or did any specific past event cause this issue (e.g., trauma to the knee)? The answers to these questions are all gleaned from questioning the patient further and an exploration of the medical record. An array of medical and imaging informatics research is ongoing to enrich the elec￾tronic medical record’s (EMR) functionality and to bring new capabilities to the point of care. A longstanding pursuit of the EMR is to provide an automated set of relevant information and a readily searchable index to patient data: rather than manually in￾spect past reports and results, the system should locate germane documents, if not permit the physician to pose a simple query to find key points. Informatics work in distributed information systems concentrates on the problems of data representation and connectivity in an increasingly geographically dispersed, multidisciplinary health￾care environment. Patients are commonly seen by several physicians, who are often at different physical locations and institutions. As such, a patient’s medical history may be segmented across several disparate databases: a core challenge of informatics is to find effective ways to integrate such information in a secure and timely fashion (see Chapter 3). For imaging, past exams should be made available; but instead of the whole study, only (annotated) sentinel image slices that detail a problem could be re￾called. Although manual image capture and markup is presently used, automated tech￾domain knowledge (see Chapter 5). For textual data, such as generated from notes and consults (e.g., a radiology report), NLP techniques are being developed to facilitate content indexing (see Chapter 6). To aggregate the information into a useful tool, a data model that matches the expectations of the clinician must be used to organize the extracted patient data (see Chapter 7), and it must then be presented in a way con￾ducive to thinking about the problem (see Chapter 4). Specifying the study. Based on the patient’s responses and review of her record, the PCP wishes to differentiate between degenerative joint disease and a meniscal tear. If abnormalities on an image (e.g., CAD); and to segment and quantify disease based on niques are being investigated to identify anatomical regions and uncover potential