Biomedical subjects

Peter J Haug

Publications and source records attributed to Peter J Haug.

13 recordsLinked to original sources

PubMed2006

The contribution of nursing data to the development of a predictive model for the detection of acute pancreatitis.

The increasing use of information system has resulted in the accumulation of a large volume of nursing data in electronic medical records. These data have great potential for supporting the various clinical decisions made by physicians, nurses, and managers. However, how to re-use of nursing data remains largely an issue of informatics. The aim of this study was to demonstrate how these nursing data can be used and how much they could contribute to developing a predictive model for an expert system for early detection of acute pancreatitis. We employed a probability-based model consisting of a Bayesian network and trained this model with the patient data retrospectively retrieved from the enterprise data warehouse of a tertiary hospital. The performance of the predictive model was measured based on the error rate and the area under receiver operating characteristics curve, which were 13.89 % and 0.93, respectively. The sensitivity of the acute pancreatitis to the findings from each nursing data was measured using a test of sensitivity. The results showed that the role of nursing data is as important as laboratory data in formulating a model for an expert system.

Acute Disease↗

PubMed2005

Natural language processing to extract medical problems from electronic clinical documents: performance evaluation.

In this study, we evaluate the performance of a Natural Language Processing (NLP) application designed to extract medical problems from narrative text clinical documents. The documents come from a patient's electronic medical record and medical problems are proposed for inclusion in the patient's electronic problem list. This application has been developed to help maintain the problem list and make it more accurate, complete, and up-to-date. The NLP part of this system-analyzed in this study-uses the UMLS MetaMap Transfer (MMTx) application and a negation detection algorithm called NegEx to extract 80 different medical problems selected for their frequency of use in our institution. When using MMTx with its default data set, we measured a recall of 0.74 and a precision of 0.756. A custom data subset for MMTx was created, making it faster and significantly improving the recall to 0.896 with a non-significant reduction in precision.

Algorithms↗

PubMed2005

Automation of a problem list using natural language processing.

BACKGROUND: The medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained. METHODS: For this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list. RESULTS: The set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences. CONCLUSION: The global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.

Adult↗

PubMed2005

Classifying free-text triage chief complaints into syndromic categories with natural language processing.

OBJECTIVE: Develop and evaluate a natural language processing application for classifying chief complaints into syndromic categories for syndromic surveillance. INTRODUCTION: Much of the input data for artificial intelligence applications in the medical field are free-text patient medical records, including dictated medical reports and triage chief complaints. To be useful for automated systems, the free-text must be translated into encoded form. METHODS: We implemented a biosurveillance detection system from Pennsylvania to monitor the 2002 Winter Olympic Games. Because input data was in free-text format, we used a natural language processing text classifier to automatically classify free-text triage chief complaints into syndromic categories used by the biosurveillance system. The classifier was trained on 4700 chief complaints from Pennsylvania. We evaluated the ability of the classifier to classify free-text chief complaints into syndromic categories with a test set of 800 chief complaints from Utah. RESULTS: The classifier produced the following areas under the ROC curve: Constitutional = 0.95; Gastrointestinal = 0.97; Hemorrhagic = 0.99; Neurological = 0.96; Rash = 1.0; Respiratory = 0.99; Other = 0.96. Using information stored in the system's semantic model, we extracted from the Respiratory classifications lower respiratory complaints and lower respiratory complaints with fever with a precision of 0.97 and 0.96, respectively. CONCLUSION: Results suggest that a trainable natural language processing text classifier can accurately extract data from free-text chief complaints for biosurveillance.

Bayes Theorem↗

PubMed2005

Weaknesses in the classification criteria for antithrombotic-related major bleeding events.

We asked two physicians to review the medical records (electronic and paper) of 100 patients on antithrombotics. The physicians used published classification criteria to identify all of the bleeding events that the patients experienced. The goal of the review was to investigate whether the physicians would identify the same antithrombotic related major bleeding events (ARMBEs) for each patient. The correct identification and classification of multiple bleeding events is a prerequisite for studies of antithrombotic treatment practices during hospitalization that predispose patients to ARMBEs. In addition, we were interested in the reasons for disagreement between the physicians, so that we could find ways of improving their agreement. The reviewers identified 299 bleeding events for the 100 patients. They disagreed on whether 29 of the events represented an ARMBE occurring during hospitalization. With a kappa statistic of 0.49 (95% confidence interval, 0.31 to 0.66) the agreement was moderate. The reviewers most often disagreed either because they misinterpreted the data (12 events) or because the classification criteria for ARMBEs were not explicit enough (9 events). Disagreement took two main forms: either the reviewers disagreed on ARMBEs by not identifying the same bleeds (11 events) or by not applying the severity criteria appropriately (7 events). Because the main type of disagreement was not identifying the same bleeds, a study investigating the antithrombotic treatment practices that predispose patients to ARMBEs would be threatened. We therefore proposed supplementing the existing classification criteria with additional rules to avoid ambiguities in patients with multiple events.

Algorithms↗

PubMed2005

Evaluation of Medical Problem Extraction from Electronic Clinical Documents Using MetaMap Transfer (MMTx).

To improve the use and quality of the electronic Problem List, which is at the heart of the problem-oriented medical record in development in our institution (Intermountain Health Care, Utah, U.S.), we developed an Automated Problem List system using Natural Language Processing (NLP) technologies. A key part of this system is a module that automatically extracts potential medical problems from free-text clinical documents. The NLP module uses MMTx, developed at the U.S. National Library of Medicine. Negation detection was added to this application by adapting a negation detection algorithm called NegEx. To evaluate the adequacy of the performance of the NLP module for our Automated Problem List system, we evaluated it with 160 electronic clinical documents of different types. Two different data sets for MMTx were used: the default full UMLS data set and a customised subset adapted to detect the set of 80 medical problems we are interested in. With the default data set, we measured a recall of 0.74 (95% CI 0.68-0.8) and a precision of 0.76 (0.69-0.82). The customised subset had a significantly better recall of 0.9 (0.85-0.94), and a non-significantly different precision of 0.69 (0.63-0.75).

Algorithms↗

PubMed2005

Comparing natural language processing tools to extract medical problems from narrative text.

To help maintain a complete, accurate and timely Problem List, we are developing a system to automatically retrieve medical problems from free-text documents. This system uses Natural Language Processing to analyze all electronic narrative text documents in a patient's record. Here we evaluate and compare 3 different applications of NLP technology in our system: the first using MMTx (MetaMap Transfer) with a negation detection algorithm (NegEx), the second using an alpha version of a locally developed NLP application called MPLUS2, and the third using keyword searching. They were adapted and trained to extract medical problems from a set of 80 problems of diagnosis type. The version using MMTx and NegEx was improved by adding some disambiguation and modifying the negation detection algorithm, and these modifications significantly improved recall and precision. The different versions of the NLP module were compared, and showed the following recall / precision results: standard MMTx with NegEx version 0.775 / 0.398; improved MMTx with NegEx version 0.892 / 0.753; MPLUS2 version 0.693 / 0.402; and keyword searching version 0.575 / 0.807. Average results for the reviewers were a recall of 0.788 and a precision of 0.912.

Algorithms↗

PubMed2004

Electronic screening of dictated reports to identify patients with do-not-resuscitate status.

OBJECTIVE: Do-not-resuscitate (DNR) orders and advance directives are increasingly prevalent and may affect medical interventions and outcomes. Simple, automated techniques to identify patients with DNR orders do not currently exist but could help avoid costly and time-consuming chart review. This study hypothesized that a decision to withhold cardiopulmonary resuscitation would be included in a patient's dictated reports. The authors developed and validated a simple computerized search method, which screens dictated reports to detect patients with DNR status. METHODS: A list of concepts related to DNR order documentation was developed using emergency department, hospital admission, consult, and hospital discharge reports of 665 consecutive, hospitalized pneumonia patients during a four-year period (1995-1999). The list was validated in an independent group of 190 consecutive inpatients with pneumonia during a five-month period (1999-2000). The reference standard for the presence of DNR orders was manual chart review of all study patients. Sensitivity, specificity, predictive values, and nonerror rates were calculated for individual and combined concepts. RESULTS: The list of concepts included: DNR, Do Not Attempt to Resuscitate (DNAR), DNI, NCR, advanced directive, living will, power of attorney, Cardiopulmonary Resuscitation (CPR), defibrillation, arrest, resuscitate, code, and comfort care. As determined by manual chart review, a DNR order was written for 32.6% of patients in the derivation and for 31.6% in the validation group. Dictated reports included DNR order-related information for 74.5% of patients in the derivation and 73% in the validation group. If mentioned in the dictated report, the combined keyword search had a sensitivity of 74.2% in the derivation group (70.0% in the validation group), a specificity of 91.5% (81.5%), a positive predictive value of 80.9% (63.6%), a negative predictive value of 88.0% (85.5%), and a nonerror rate of 85.9% (77.9%). DNR and resuscitate were the most frequently used and power of attorney and advanced directives the least frequently used terms. CONCLUSION: Dictated hospital reports frequently contained DNR order-related information for patients with a written DNR order. Using an uncomplicated keyword search, electronic screening of dictated reports yielded good accuracy for identifying patients with DNR order information.

Advance Directives↗

PubMed2003

Decision support in medicine: lessons from the HELP system.

PURPOSE: This report describes an ongoing transition from the HELP Hospital Information System to HELP II, a replacement Health Information System built to manage clinical information captured in a variety of medical settings. The focus of the article is on the medical decision support provided by this system and studied by researchers at the University of Utah and Intermountain Health Care (IHC), a large health care organization in Utah, for many years. METHODS: Select success features of the original HELP system's decision support environment are identified and lessons learned are related. Plans for transferring these features to HELP II are discussed. RESULTS: The article focuses on four features: (1) the importance of easy access to patient data essential for decision support, (2) the commitment to continued measurement and revision of both the logic and the interventional strategy in a decision support application, (3) experience with data mining as a tool for developing decision support tools, and (4) the role of clinical reports in supporting the decision making process.

Decision Support Systems, Clinical↗

PubMed2003

Medical problem and document model for natural language understanding.

We are developing tools to help maintain a complete, accurate and timely problem list within a general purpose Electronic Medical Record system. As a part of this project, we have designed a system to automatically retrieve medical problems from free-text documents. Here we describe an information model based on XML (eXtensible Markup Language) and compliant with the CDA (Clinical Document Architecture). This model is used to ease the exchange of clinical data between the Natural Language Understanding application that retrieves potential problems from narrative document, and the problem list management application.

Computer Simulation↗

PubMed2002

Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic Winter Games.

The key to minimizing the effects of an intentionally caused disease outbreak is early detection of the attack and rapid identification of the affected individuals. The Bush administration's leadership in advocating for biosurveillance systems capable of monitoring for bioterrorism attacks suggests that we should move quickly to establish a nationwide early warning biosurveillance system as a defense against this threat. The spirit of collaboration and unity inspired by the events of 9-11 and the 2002 Olympic Winter Games in Salt Lake City provided the opportunity to demonstrate how a prototypic biosurveillance system could be rapidly deployed. In seven weeks we were able to implement an automated, real-time disease outbreak detection system in the State of Utah and monitored 80,684 acute care visits occurring during a 28-day period spanning the Olympics. No trends of immediate public health concern were identified.

Bioterrorism↗

PubMedDate not supplied

Automation of performance measures reporting.

This article describes efforts to design and automate a balanced performance indicator report to meet the needs of hospital and physician leaders. Indicator measurement reports provide clinical information but often do not provide other administrative data. An automation team developed business rules, standardized definitions, and developed a Web-based electronic application to report quarterly indicators. The efficiency and effectiveness of the automation project were measured and included report production time, data sources, indicators included, and statistical significance of indicator rates. Significant improvement in efficiency of report preparation and a decrease in resources used were demonstrated through this automation project, although variation existed among clinical services. Generic performance measures reports encompassing clinical and administrative data can improve the consistency and quality of reporting to the medical staff. The process provided insight for expanding the prototype.

Data Collection↗

PubMedDate not supplied

Accuracy of administrative data for identifying patients with pneumonia.

The goal of this study was to determine the accuracy and the impact of 5 different claims-based pneumonia definitions. Three International Classification of Diseases, Version 9, (ICD-9), and 2 diagnosis-related group (DRG)-based case identification algorithms were compared against an independent, clinical pneumonia reference standard. Among 10748 patients, 272 (2.5%) had pneumonia verified by the reference standard. The sensitivity of claims-based algorithms ranged from 47.8% to 66.2%. The positive predictive values ranged from 72.6% to 80.8%. Patient-related variables were not significantly different from the reference standard among the 3 ICD-9-based algorithms. DRG-based algorithms had significantly lower hospital admission rates (57% and 65% vs 73.2%), lower 30-day mortality (5.0% and 5.8% vs 10.7%), shorter length of stay (3.9 and 4.1 days vs 5.6 days), and lower costs (USD $4543 and USD $5159 vs USD $8585). Claims-based identification algorithms for defining pneumonia in administrative databases are imprecise. ICD-9-based algorithms did not influence patient variables in our population. Identifying pneumonia patients with DRG codes is significantly less precise.

Aged↗