ABSTRACT
Translating evidence into clinical practice is a complex process that depends on the availability of evidence, the environment into which the research evidence is translated, and the system that facilitates the translation. This paper presents InfoBot, a system designed for automatic delivery of patient-specific information from evidence-based resources. A prototype system has been implemented to support development of individualized patient care plans. The prototype explores possibilities to automatically extract patients problems from the interdisciplinary team notes and query evidence-based resources using the extracted terms. Using 4,335 de-identified interdisciplinary team notes for 525 patients, the system automatically extracted biomedical terminology from 4,219 notes and linked resources to 260 patient records. Sixty of those records (15 each for Pediatrics, Oncology & Hematology, Medical & Surgical, and Behavioral Health units) have been selected for an ongoing evaluation of the quality of automatically proactively delivered evidence and its usefulness in development of care plans.
Subject(s)
Dictionaries as Topic , Evidence-Based Medicine/methods , Information Dissemination/methods , Medical Records Systems, Computerized/organization & administration , National Library of Medicine (U.S.) , Vocabulary, Controlled , Writing , Pilot Projects , Semantics , Translating , United StatesABSTRACT
OBJECTIVE: To evaluate: (1) the effectiveness of wireless handheld computers for online information retrieval in clinical settings; (2) the role of MEDLINE in answering clinical questions raised at the point of care. DESIGN: A prospective single-cohort study: accompanying medical teams on teaching rounds, five internal medicine residents used and evaluated MD on Tap, an application for handheld computers, to seek answers in real time to clinical questions arising at the point of care. MEASUREMENTS: All transactions were stored by an intermediate server. Evaluators recorded clinical scenarios and questions, identified MEDLINE citations that answered the questions, and submitted daily and summative reports of their experience. A senior medical librarian corroborated the relevance of the selected citation to each scenario and question. RESULTS: Evaluators answered 68% of 363 background and foreground clinical questions during rounding sessions using a variety of MD on Tap features in an average session length of less than four minutes. The evaluator, the number and quality of query terms, the total number of citations found for a query, and the use of auto-spellcheck significantly contributed to the probability of query success. CONCLUSION: Handheld computers with Internet access are useful tools for healthcare providers to access MEDLINE in real time. MEDLINE citations can answer specific clinical questions when several medical terms are used to form a query. The MD on Tap application is an effective interface to MEDLINE in clinical settings, allowing clinicians to quickly find relevant citations.
Subject(s)
Attitude to Computers , Computers, Handheld , Information Storage and Retrieval/methods , MEDLINE , Point-of-Care Systems , Attitude of Health Personnel , Humans , Medical Subject Headings , User-Computer InterfaceABSTRACT
OBJECTIVE: Understanding the effect of a given intervention on the patient's health outcome is one of the key elements in providing optimal patient care. This study presents a methodology for automatic identification of outcomes-related information in medical text and evaluates its potential in satisfying clinical information needs related to health care outcomes. DESIGN: An annotation scheme based on an evidence-based medicine model for critical appraisal of evidence was developed and used to annotate 633 MEDLINE citations. Textual, structural, and meta-information features essential to outcome identification were learned from the created collection and used to develop an automatic system. Accuracy of automatic outcome identification was assessed in an intrinsic evaluation and in an extrinsic evaluation, in which ranking of MEDLINE search results obtained using PubMed Clinical Queries relied on identified outcome statements. MEASUREMENTS: The accuracy and positive predictive value of outcome identification were calculated. Effectiveness of the outcome-based ranking was measured using mean average precision and precision at rank 10. RESULTS: Automatic outcome identification achieved 88% to 93% accuracy. The positive predictive value of individual sentences identified as outcomes ranged from 30% to 37%. Outcome-based ranking improved retrieval accuracy, tripling mean average precision and achieving 389% improvement in precision at rank 10. CONCLUSION: Preliminary results in outcome-based document ranking show potential validity of the evidence-based medicine-model approach in timely delivery of information critical to clinical decision support at the point of service.
Subject(s)
Artificial Intelligence , Information Storage and Retrieval/methods , MEDLINE , Outcome Assessment, Health Care , Evidence-Based Medicine , HumansABSTRACT
Clinicians increasingly use handheld devices to support evidence-based practice and for clinical decision support. However, support of clinical decisions through information retrieval from MEDLINE(R) and other databases lags behind popular daily activities such as patient information or drug formulary look-up. The objective of the current study is to determine whether relevant information can be retrieved from MEDLINE to answer clinical questions using a handheld device at the point of care. Analysis of search and retrieval results for 108 clinical questions asked by members of clinical teams during 28 daily rounds in a 12-bed intensive care unit confirm MEDLINE as a potentially valuable resource for just-in-time answers to clinical questions. Answers to 93 (86%) questions were found in MEDLINE by two resident physicians using handheld devices. The majority of answers, 88.9% and 97.7% respectively, were found during rounds. Strategies that facilitated timely retrieval of results include using PubMed(R) Clinical Queries and Related Articles, spell check, and organizing retrieval results into topical clusters. Further possible improvements in organization of retrieval results such as automatic semantic clustering and providing patient outcome information along with the titles of the retrieved articles are discussed.
Subject(s)
Computers, Handheld , Information Storage and Retrieval , MEDLINE , Point-of-Care Systems , Abstracting and Indexing , Humans , User-Computer InterfaceABSTRACT
Medical resident physicians used MD on Tap in real time to search for MEDLINE citations relevant to clinical questions using three search engines: Essie, Entrez and Google, in order of performance.
Subject(s)
Computers, Handheld , Information Storage and Retrieval/methods , MEDLINE , Internship and Residency , Point-of-Care SystemsABSTRACT
High OCR error rates encountered in author affiliations increase the manual labor needed to verify MEDLINE citations automatically created from scanned journal articles. This is due to poor OCR recognition of the small text and italics frequently used in printed affiliations. Using author-affiliation relationships found in existing MEDLINE records, the SeekAffiliation (SA) program automatically finds potentially correct and complete affiliations, thereby reducing manual effort and increasing the efficiency of creating the citations.
Subject(s)
Electronic Data Processing , MEDLINE , AuthorshipABSTRACT
MD on Tap, a PDA application that searches and retrieves biomedical literature, is specifically designed for use by mobile healthcare professionals. With the goal of improving the usability of the application, a preliminary comparison was made of two search engines (PubMed and Essie) to determine which provided most efficient path to the desired clinically-relevant information.
Subject(s)
Computers, Handheld , Information Storage and Retrieval/methods , PubMed , MEDLINEABSTRACT
Answers to clinical questions occurring during healthcare practitioner/patient interaction can be often found in National Library of Medicine's (NLM) databases. The recent advances in wireless handheld computers promise to make them a widely used tool to deliver needed information to the practitioner at the point of service. This paper addresses challenges in organizing and presenting information obtained from NLM's MEDLINE database of indexed citations in a way that will help practitioners reduce literature search time on handheld computers. We study two clustering algorithms and two methods of labeling document clusters.
Subject(s)
Computers, Handheld , Decision Making, Computer-Assisted , Information Storage and Retrieval , MEDLINE , Algorithms , Data Display , Medical Subject Headings , PubMedABSTRACT
Online access to biomedical information from handheld computers will be a valuable adjunct to other popular medical applications if information delivery systems are designed with handheld computers in mind. The goal of this project is to discover design principles to facilitate practitioners' access to online medical information at the point-of-care. A prototype system was developed to serve as a testbed for this research. Using the testbed, an initial evaluation has yielded several user interface design principles. Continued research is expected to discover additional user interface design principles as well as guidelines for results organization and system performance
