Nascent regional system for alerting infection preventionists about patients with multidrug-resistant gram-negative bacteria: implementation and initial results.

Objective: To build and to begin evaluating a regional automated system to notify infection preventionists (IPs) when a patient with a history of gram-negative rod multidrug-resistant organism (GNRMDRO) is admitted to an emergency department (ED) or inpatient setting.

Design: Observational, retrospective study.

Setting: Twenty-seven hospitals, mostly in the Indianapolis metropolitan area, in a health information exchange (HIE).

Generalizability and comparison of automatic clinical text de-identification methods and resources.

In this paper, we present an evaluation of the hybrid best-of-breed automated VHA (Veteran's Health Administration) clinical text de-identification system, nicknamed BoB, developed within the VHA Consortium for Healthcare Informatics Research. We also evaluate two available machine learning-based text de-identifications systems: MIST and HIDE. Two different clinical corpora were used for this evaluation: a manually annotated VHA corpus, and the 2006 i2b2 de-identification challenge corpus.

Consistency in the safety labeling of bioequivalent medications.

Purpose: Bioequivalent medications are required by the Food and Drug Administration to have identical warnings on their labels. This requirement has both clinical and legal importance, yet has never been validated. We sought to determine the real-world consistency of electronic labeling for bioequivalent drugs from different manufacturers.

An evaluation of the UMLS in representing corpus derived clinical concepts.

We performed an evaluation of the Unified Medical Language System (UMLS) in representing concepts derived from medical narrative documents from three domains: chest x-ray reports, discharge summaries and admission notes. We detected concepts in these documents by identifying noun phrases (NPs) and N-grams, including unigrams (single words), bigrams (word pairs) and trigrams (word triples). After removing NPs and N-grams that did not represent discrete clinical concepts, we processed the remaining with the UMLS MetaMap program.

Practical challenges in the secondary use of real-world data: the notifiable condition detector.

The interoperability specifications for electronic laboratory reporting specify the use of HL7, LOINC, SNOMED CT and UCUM. We explored the degree to which health care transactions comply with these standards by evaluating laboratory data captured in a health information exchange to support automated detection of public health notifiable diseases. We studied the NCD's ability to detect and report Lead, Influenza and MRSA.

Natural language processing to extract follow-up provider information from hospital discharge summaries.

Objective: We evaluate the performance of a Natural Language Processing (NLP) application designed to extract follow-up provider information from free-text discharge summaries at two hospitals.

Evaluation: We compare performance by the NLP application, called the Regenstrief EXtracion tool (REX), to performance by three physician reviewers at extracting follow-up provider names, phone/fax numbers and location information. Precision, recall, and F-measures are reported, with 95% CI for pairwise comparisons.

Comparing methods for identifying pancreatic cancer patients using electronic data sources.

We sought to determine the accuracy of two electronic methods of identifying pancreatic cancer in a cohort of pancreatic cyst patients, and to examine the reasons for identification failure. We used the International Classification of Diseases, 9(th) Edition (ICD-9) codes and natural language processing (NLP) technology to identify pancreatic cancer in these patients. We compared both methods to a human-validated gold-standard surgical database.

ADESSA: A Real-Time Decision Support Service for Delivery of Semantically Coded Adverse Drug Event Data.

Evaluating medications for potential adverse events is a time-consuming process, typically involving manual lookup of information by physicians. This process can be expedited by CDS systems that support dynamic retrieval and filtering of adverse drug events (ADE's), but such systems require a source of semantically-coded ADE data. We created a two-component system that addresses this need.

Natural language processing for the development of a clinical registry: a validation study in intraductal papillary mucinous neoplasms.

Background: Medical natural language processing (NLP) systems have been developed to identify, extract and encode information within clinical narrative text. However, the role of NLP in clinical research and patient care remains limited. Pancreatic cysts are common.

An evaluation of medical knowledge contained in Wikipedia and its use in the LOINC database.

The logical observation identifiers names and codes (LOINC) database contains 55 000 terms consisting of more atomic components called parts. LOINC carries more than 18 000 distinct parts. It is necessary to have definitions/descriptions for each of these parts to assist users in mapping local laboratory codes to LOINC.

A comparison of automated methicillin-resistant Staphylococcus aureus identification with current infection control practice.

Infections with Methicillin-Resistant Staphylococcus aureus (MRSA) account for almost 20,000 deaths per year. Early identification of patients with MRSA infection or colonization aids in stopping spread. We compared automated identification of MRSA using HL7 lab result messages to current manual infection control practices at a local hospital during July-September 2008.

Inductive creation of an annotation schema and a reference standard for de-identification of VA electronic clinical notes.

Accessing both structured and unstructured clinical data is a high priority for research efforts. However, HIPAA requires that data meet or exceed a deidentification standard to assure that protected health information (PHI) is removed. This is a particularly difficult problem in the case of unstructured clinical free text and natural language processing (NLP) systems can be trained to automatically de-identify clinical text.

Using natural language processing to improve accuracy of automated notifiable disease reporting.

We examined whether using a natural language processing (NLP) system results in improved accuracy and completeness of automated electronic laboratory reporting (ELR) of notifiable conditions. We used data from a community-wide health information exchange that has automated ELR functionality. We focused on methicillin-resistant Staphylococcus Aureus (MRSA), a reportable infection found in unstructured, free-text culture result reports.

Details of a successful clinical decision support system.

Computerized physician order entry (CPOE) with clinical decision support (CDS) is regarded as one of the most effective ways to improve the quality of health care and increase patient safety. As electronic medical records become more available, such systems will increasingly become the method of choice to achieve these goals. Creating a CPOE/CDS system is a complex task, and some fail despite time consuming and expensive development.

A software tool for removing patient identifying information from clinical documents.

We created a software tool that accurately removes all patient identifying information from various kinds of clinical data documents, including laboratory and narrative reports. We created the Medical De-identification System (MeDS), a software tool that de-identifies clinical documents, and performed 2 evaluations. Our first evaluation used 2,400 Health Level Seven (HL7) messages from 10 different HL7 message producers.

Using a natural language processing system to extract and code family history data from admission reports.

We developed a rule-based natural language processing (NLP) system for extracting and coding clinical data from free text reports. We studied the systems ability to accurately extract and code family history data from hospital admission notes. The system searches the family history for 12 diseases (and relative degree).

A natural language processing system to extract and code concepts relating to congestive heart failure from chest radiology reports.

We have developed a natural language processing system for extracting and coding clinical data from free text reports. The system is designed to be easily modified and adapted to a variety of free text clinical reports such as admission notes, radiology and pathology reports, and discharge summaries. This report presents the results of this system to extract and code clinical concepts related to congestive heart failure from 39,000 chest radiology reports.