Biomedical subjects

G Hripcsak

Publications and source records attributed to G Hripcsak.

At least 37 records · Page 2Linked to original sources

PubMed1998

Patients' acceptance of Internet-based home asthma telemonitoring.

We studied asthma patients from a low-income inner-city community without previous computer experience. The patients were given portable spirometers to perform spirometry tests and palmtop computers to enter symptoms in a diary, to exchange messages with physician and to review test results. The self-testing was performed at home on a daily basis. The results were transmitted to the hospital information system immediately after completion of each test. Physician could review results using an Internet Web browser from any location. A constantly active decision support server monitored all data traffic and dispatched alerts when certain clinical conditions were met. Seventeen patients, out of 19 invited, agreed to participate in the study and have been monitored for three weeks. They have been surveyed then using standardized questionnaire. Most of the patients (82.4%) characterized self-testing procedures as "not complicated at all." In 70.6% of cases self-testing did not interfere with usual activities, and 82.4% of patients felt the self-testing required a "very little" amount of their time. All patients stated that it is important for them to know that the results can be reviewed by professional staff in a timely manner. However, only 29.5% of patients reviewed their results at least once a week at home independently. The majority of the patients (94.1%) were strongly interested in using home asthma telemonitoring in the future. We concluded that Internet-based home asthma telemonitoring can be successfully implemented in the group of patients without previous computer background.

Adult↗

PubMed1998

Evolution of a knowledge base for a clinical decision support system encoded in the Arden Syntax.

Clinical decision support systems (CDSS) are being used increasingly in medical practice. Thus, long-term maintenance of the knowledge bases (KB) of such systems becomes important. To quantify changes that occur as a KB evolves, we studied the KB at the Columbia-Presbyterian Medical Center. This KB has a total of 229 Medical Logic Modules (MLMs) encoded in the Arden Syntax. Eliminating those never used in practice, we retrospectively analyzed 156 MLMs developed over 78 months. We noted 2020 distinct versions of these MLMs that included 5528 changed statements over time. These changes occurred primarily in the logic slot (38.7% of all changes), the action slot (17.8%), in queries (15.0%) and in the data slot exclusive of queries (12.4%). We conclude that long-term maintenance of a KB for a CDSS requires significant changes over time. We discuss the implications of these results for the design of KB editors for the Arden Syntax.

Artificial Intelligence↗

PubMed1998

Knowledge discovery and data mining to assist natural language understanding.

As natural language processing systems become more frequent in clinical use, methods for interpreting the output of these programs become increasingly important. These methods require the effort of a domain expert, who must build specific queries and rules for interpreting the processor output. Knowledge discovery and data mining tools can be used instead of a domain expert to automatically generate these queries and rules. C5.0, a decision tree generator, was used to create a rule base for a natural language understanding system. A general-purpose natural language processor using this rule base was tested on a set of 200 chest radiograph reports. When a small set of reports, classified by physicians, was used as the training set, the generated rule base performed as well as lay persons, but worse than physicians. When a larger set of reports, using ICD9 coding to classify the set, was used for training the system, the rule base performed worse than the physicians and lay persons. It appears that a larger, more accurate training set is needed to increase performance of the method.

Artificial Intelligence↗

PubMed1998

An evaluation of natural language processing methodologies.

Medical language processing (MLP) systems that codify information in textual patient reports have been developed to help solve the data entry problem. Some systems have been evaluated in order to assess performance, but there has been little evaluation of the underlying technology. Various methodologies are used by the different MLP systems but a comparison of the methods has not been performed although evaluations of MLP methodologies would be extremely beneficial to the field. This paper describes a study that evaluates different techniques. To accomplish this task an existing MLP system MedLEE was modified and results from a previous study were used. Based on confidence intervals and differences in sensitivity and specificity between each technique and all the others combined, the results showed that the two methods based on obtaining the largest well-formed segment within a sentence had significantly higher sensitivity than the others by 5% and 6%. The method based on recognizing a complete sentence had a significantly worse sensitivity than the others by 7% and a better specificity by .2%. None of the methods had significantly worse specificity.

Evaluation Studies as Topic↗

PubMed1997

Creating an environment for linking knowledge-based systems to a clinical database: a suite of tools.

A difficulty in using knowledge-based systems has been linking them to clinical databases. The challenge is in making a correct mapping from the data in the knowledge base to the data in the database. At Columbia-Presbyterian Medical Center, we have built a suite of tools developed to create queries that address this challenge. The tools were designed to allow users to easily retrieve data from the database without requiring the users have extensive database and vocabulary knowledge. The tools help users write correct queries (Query Builder), find correct terms in the clinical database (MED Browser), aggregate the resulting data into a useful form (Clinical Database Browser), and allow the user to test the query within the environment of the knowledge-based system (Event Playback). The tools have been in use for one year.

Artificial Intelligence↗

PubMed1996

Design of a clinical event monitor.

The issues and implementation of a clinical event monitor are described. An event monitor generates messages for providers, patients, and organizations based on clinical events and patient data. For example, an order for a medication might trigger the generation of a warning about a drug interaction. A model based on the active database literature has as its main components an event (which triggers a rule to fire), a condition (which tests whether an action ought to be performed), and an action (often the generation of a message). The details of implementing such a monitor are described, using as an example the Columbia-Presbyterian Medical Center clinical event monitor, which is based on the Arden Syntax for Medical Logic Modules.

Artificial Intelligence↗

PubMed1996

Identification of suspected tuberculosis patients based on natural language processing of chest radiograph reports.

Identification of eligible patients from electronically available patient data is a key difficulty in computerizing clinical practice guidelines because a large amount of the relevant data is stored as free text. We have been using MedLEE (Medical Language Extraction and Encoding System), a natural language processing system, to encode the clinical information in all chest radiograph and mammogram reports. This paper describes a retrospective study to determine if MedLEE can identify patients at risk for having tuberculosis (TB) based on their admission chest radiographs. Reports of 171 adult inpatients with culture-positive TB during 1992 and 1993 were manually coded (by a TB specialist) using seven terms suggestive of TB, and were also encoded by MedLEE. Using manual coding as the gold standard, MedLEE agreed on the classification of 152/171 (88.9%) reports--129/142 (90.8%) suspicious for TB and 23/29 (79.3%) not suspicious for TB; and 1072/1197 (89.6%) terms indicative of TB. Analysis showed that most of the discrepancies were caused by MedLEE not finding the location of the infiltrate. By ignoring the location of the infiltrate, the agreement became 157/171 (91.8%) reports and 946/1026 (92.2%) terms. Thus, natural language processing offers a practical alternative for using free-text reports to determine patient eligibility for computerized clinical practice guidelines.

Adult↗

PubMed1995

Unlocking clinical data from narrative reports: a study of natural language processing.

OBJECTIVE: To evaluate the automated detection of clinical conditions described in narrative reports. DESIGN: Automated methods and human experts detected the presence or absence of six clinical conditions in 200 admission chest radiograph reports. STUDY SUBJECTS: A computerized, general-purpose natural language processor; 6 internists; 6 radiologists; 6 lay persons; and 3 other computer methods. MAIN OUTCOME MEASURES: Intersubject disagreement was quantified by "distance" (the average number of clinical conditions per report on which two subjects disagreed) and by sensitivity and specificity with respect to the physicians. RESULTS: Using a majority vote, physicians detected 101 conditions in the 200 reports (0.51 per report); the most common condition was acute bacterial pneumonia (prevalence, 0.14), and the least common was chronic obstructive pulmonary disease (prevalence, 0.03). Pairs of physicians disagreed on the presence of at least 1 condition for an average of 20% of reports. The average intersubject distance among physicians was 0.24 (95% Cl, 0.19 to 0.29) out of a maximum possible distance of 6. No physician had a significantly greater distance than the average. The average distance of the natural language processor from the physicians was 0.26 (Cl, 0.21 to 0.32; not significantly greater than the average among physicians). Lay persons and alternative computer methods had significantly greater distance from the physicians (all > 0.5). The natural language processor had a sensitivity of 81% (Cl, 73% to 87%) and a specificity of 98% (Cl, 97% to 99%); physicians had an average sensitivity of 85% and an average specificity of 98%. CONCLUSIONS: Physicians disagreed on the interpretation of narrative reports, but this was not caused by outlier physicians or a consistent difference in the way internists and radiologists read reports. The natural language processor was not distinguishable from the physicians and was superior to all other comparison subjects. Although the domain of this study was restricted (six clinical conditions in chest radiographs), natural language processing seems to have the potential to extract clinical information from narrative reports in a manner that will support automated decision-support and clinical research.

Humans↗

PubMed1995

Decision support in healthcare.

To address the recognized problems associated with information overload and limited human memory, computer-based systems which help healthcare providers use information to make better decisions have been developed and implemented. These decision aids are designed to improve the quality and reduce the cost of healthcare. Currently, the most widely used computer application is to simply provide needed facts about the patient in an organized and timely fashion. Additionally, healthcare workers can access literature, ask questions of aggregates of patient data for clinical or administrative decisions, receive warnings or suggestions when the patient's data satisfy certain logical rules receive critiques when proposing therapies or ordering diagnostic tests, receive guidelines for standards of care, access programs which analyze tradeoffs and likelihoods of alternative outcomes (decision analysis) and receive lists of differential diagnoses. Given this wonderful panoply of capabilities, the question becomes 'why aren't more people using these aids and what are the demonstrated benefits of such capabilities?' In this paper we review the types of decision aids which have been successfully implemented and the challenges to implementation (knowledge representation, connections to databases, need for comprehensive, coded databases and evaluation of benefits).

Decision Making, Computer-Assisted↗

PubMed1995

Medical decision support: experience with implementing the Arden Syntax at the Columbia-Presbyterian Medical Center.

We began implementation of a medical decision support system (MDSS) at the Columbia-Presbyterian Medical Center (CPMC) using the Arden Syntax in 1992. The Clinical Event Monitor which executes the Medical Logic Modules (MLMs) runs on a mainframe computer. Data are stored in a relational database and accessed via PL/I programs known as Data Access Modules (DAMs). Currently we have 18 clinical, 12 research and 10 administrative MLMs. On average, the clinical MLMs generate 50357 simple interpretations of laboratory data and 1080 alerts each month. The number of alerts actually read varies by subject of the MLM from 32.4% to 73.5%. Most simple interpretations are not read at all. A significant problem of MLMs is maintenance, and changes in laboratory testing and message output can impair MLM execution significantly. We are now using relational database technology and coded MLM output to study the process outcome of our MDSS.

Academic Medical Centers↗

PubMed1995

Using intermediate states to improve the ability of the Arden Syntax to implement care plans and reuse knowledge.

The Arden Syntax is one of a few knowledge representation languages currently in use for clinical decision support. While some of these languages are being used in active patient care settings, none have gained widespread acceptance as a clinical tool. Prior attempts to represent temporally complex care plans in the Arden Syntax have revealed difficulties in representing and tracking series of consecutive time-oriented events and recommendations, in sharing and reusing knowledge and in dealing with unobtainable data. In an attempt to improve Arden's ability to deal with these problems and demonstrate the importance of these factors, the clinical event monitor has been adapted to store coded data representing Intermediate States in the Columbia Presbyterian Medical Center (CPMC) central data repository. The Intermediate States define the current state of the patient as laid out in the care plan. Four care plans were constructed. The findings include an improved ability to track complex series of events and recommendations over long periods of time. The knowledge generated by the electronic care plans was able to be reused by the care plan that generated it, by other elements of the knowledge base and by non-decision support applications. Modular development, facilitated by the changes, simplified dealing with data not available to the central data repository by aiding the implementation of those parts of the care plan for which sufficient data is available.

Artificial Intelligence↗

PubMed1995

Managing vocabulary for a centralized clinical system.

The clinical computing environment at Columbia-Presbyterian Medical Center is organized around a centralized database of coded patient information collected from various ancillary sources. The Medical Entities Dictionary (MED) is the central repository for the controlled vocabulary used to encode the patient data. The MED is composed of terms used in the ancillary departments and, as such, changes in the source vocabularies must be maintained in the MED. The MED also contains some basic knowledge about the terms, and sophisticated maintenance tools have been developed that take advantage of this knowledge. This paper describes the success of the knowledge-based approach by describing the techniques used in two tasks: addition of a new vocabulary and maintenance of an existing one.

Clinical Laboratory Information Systems↗

PubMed1994

Rationale for the Arden Syntax.

The Arden Syntax, a language designed for writing and sharing task-specific knowledge for Medical Logic Modules (MLMs), has been recently accepted as a standard by the ASTM. The syntax is concerned with the critical task of sharing medical knowledge bases across many institutions. Because of the relative lack of agreement on vocabularies and data standards and because of the many other obstacles, the developers of the Arden Syntax took a pragmatic, straightforward approach that has borne fruit in a very short period of time. The syntax provides a vehicle for the health care community to begin sharing, so that we can see what works and what does not work, and we can begin to address the critical obstacles. In designing a language like the Arden Syntax, the authors make many decisions--but the final document gives only the result of these decisions without any explanation. By writing down the rationale behind the design of the syntax, we hope to aid users of the language, implementors of the language, and future designers of new languages.

Artificial Intelligence↗

PubMed1994

Writing Arden Syntax Medical Logic Modules.

The Arden Syntax for Medical Logic Modules is a language for encoding medical knowledge bases that consist of independent modules. The Arden Syntax has been used to generate clinical alerts, diagnostic interpretations, management messages, and screening for research studies and quality assurance. An Arden Syntax knowledge base consists of rules called Medical Logic Modules (MLMs), which are stored as simple ASCII files that can be written on any text editor. An MLM is made of slots grouped into three categories: maintenance information, library information, and the actual medical knowledge. Most MLMs are triggered by clinical events, evaluate medical criteria, and, if appropriate, perform an action such as sending a message to a health care provider. This paper provides a detailed tutorial on how to write MLMs.

Artificial Intelligence↗

PubMed1994

Encoding a post-operative coronary artery bypass surgery care plan in the Arden Syntax.

The Arden Syntax for medical logic modules (Arden) was used to test the feasibility of encoding large, complex care plans. The critical portions of an existing paper-based care plan for the management of patients following coronary artery bypass graft (CABG) surgery were encoded in Arden and an X-windows user-interface was developed. The Arden Syntax proved adequate for encoding all of the necessary functions of the care plan. The limitations of the current Arden Syntax and possible additions to Arden are discussed.

Artificial Intelligence↗

PubMed1994

IAIMS and sharing.

The Integrated Academic Information Management System (IAIMS) concept is about sharing resources and information, and about improving the decision-making ability of health care professionals by integrating information. At Columbia-Presbyterian Medical Center, the IAIMS project has established an information architecture based on common, shared computing and networking resources. The institutional computing culture has been changed with increased sharing of information and, consequently, improved quality of information. Several classes of information in the areas of clinical, scholarly, administrative, basic research, and core resources have been identified for better understanding of information responsibility. Technical problems such as heterogeneity on workstation platforms and lack of universal syntactic and semantic standards for health care information exchange still impede inter-institutional sharing of information.

Artificial Intelligence↗

PubMed1994

Accessing the Columbia Clinical Repository.

The Columbia Clinical Repository is the foundation of the Clinical Information System at the Columbia Presbyterian Medical Center (CPMC). The Repository is implemented as a relational database on an IBM mainframe, using a generic design that employs a small number of tables. Client applications on remote platforms send and receive data through Database Access Modules (DAMs), which support the HL7 protocol, while applications on the mainframe manipulate data through DAMs supporting a locally defined "query template". Implementation using static (compiled) SQL is compared to dynamic (ad hoc) SQL in terms of efficiency and flexibility.

Clinical Laboratory Information Systems↗

PubMed1994

User comments on a clinical event monitor.

Columbia-Presbyterian Medical Center's health care providers have access to alerts and interpretations generated by an Arden Syntax-based clinical event monitor. They have the opportunity to send comments to the clinical information services staff. Over a period of 26 months, they sent 126 comments. The comments were analyzed using the critical incident technique, resulting in a hierarchy of categories that summarizes user concerns. The majority of comments (65) indicated that the messages were actually (8) or at least potentially useful (57). A minority (28) indicated that they were unhelpful (27) or actually harmful (1). Another group (27) made suggestions or asked questions. The comments have been very helpful for the maintenance of Medical Logic Modules (MLMs) and the clinical event monitor itself.

Academic Medical Centers↗