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Computer-supported weight-based drug infusion concentrations in the neonatal intensive care unit.

This article addresses the development of a computerized provider order entry (CPOE)-embedded solution for weight-based neonatal drug infusion developed during the transition from a legacy CPOE system to a customized application of a neonatal CPOE product during a hospital-wide information system transition. The importance of accurate fluid management in the neonate is reviewed. The process of tailoring the system that eventually resulted in the successful development of a computer application enabling weight-based medication infusion calculation for neonates within the CPOE information system is explored. In addition, the article provides guidelines on how to customize a vendor solution for hospitals with neonatal intensive care unit.

Body Weight↗

Investigation and evaluation of the change in the outpatients flow before and after the move of the hospital.

In order to estimate the changes in the service for outpatients with hospital movement, patient flow investigation has been performed at both old and new hospitals. IC memory card was handed out to each outpatient, and timestamps and places were recorded on the card at the places that the patient have been by using card writers set at each department. By comparing with two results, old and new hospitals, patient-waiting time at payment counter was remarkably shortened because of the effect of order-entry system. However, there were few changes in stay time at clinical departments. From the results, it is found that optimization of booking system was required for improvement of patients flow at clinical departments.

Appointments and Schedules↗

Automatic lexeme acquisition for a multilingual medical subword thesaurus.

PURPOSE: We present a method for the automated acquisition of a multilingual medical lexicon (for Spanish, French and Swedish) to be used within the framework of a medical cross-language text retrieval system. METHODS: For the lexical acquisition process, we incorporate seed lexicons and lists of trusted term translations derived from the UMLS Metathesaurus. The seed lexicons for Spanish, French and Swedish are automatically generated from (previously manually constructed) Portuguese, German and English sources by simple string transformations. Lexical and semantic hypotheses are then validated by processing pairs of term translations. In a last step, we use the cleaned list of "approved" translations in order to augment, step by step, the target dictionaries by processing the parallel corpora in terms of co-occurrence patterns of hypothesized translation equivalents which cannot be derived by simple character substitutions. RESULTS: An existing multilingual lexicon for the medical domain with about 60,000 entries for English, German, and Portuguese was automatically augmented by more then 17,000 new lexemes for Spanish, French, and Swedish. CONCLUSIONS: Our approach constitutes a promising method for the automated creation of new lexicon entries and their linkage to semantic identifiers.

Electronic Data Processing↗

Database technology for medical records.

Paper truncation may be the first goal that comes to mind when people think of computerized medical records, but this is probably the least realistic goal at present. Structuring a medical records system to improve record quality and to open the door to data analysis are two more realistic goals. Of the many significant barriers to computerized medical records, the most serious is the lack of agreement in the medical community regarding what should go into a medical record. Even small steps in that area should bring major improvements in available medical records software. Choosing a database for medical records requires familiarity with database principles and a detailed understanding of data needs. The safe choice is an SQL-based relational database, but a smaller system will suffice if present and future medical records challenges are small enough. The rate of change in the computer world is increasing all the time. Emerging and maturing technologies that can have a significant impact on medical records software include data entry tools such as the mouse, pen, and voice; better data retrieval tools such as GUIs and 4GLs; and software tools that use rules, pattern-matching, hypermedia links, and fuzzy logic to create systems that seem to think like humans. While computerized medical records systems are not yet comparable with medical office management systems, they should be someday. In the meantime, smart physicians will begin exploring possibilities on their own in order to reap some benefits at an early stage and to be better prepared when the tide finally turns.

Databases, Factual↗

Facsimile machines for medication order transmission.

The use of facsimile machines for medication order transmission has improved turnaround time and provided benefits for the Department of Pharmacy Services and the hospital. As a result of this program pharmacists have more time and more opportunities to improve drug therapy because they no longer have to travel to patient care units or handle patient charts for the purpose of order entry. Pharmacists will have even more time when technicians have been fully trained to carry out medication order entry. Important also is the ability for nurses to FAX rather than hand carry urgently needed orders to the pharmacy satellites. The use of FAX machines in this manner lowers the cost of patient care and makes this technology available for other purposes as well. It is becoming increasingly clear that pharmacists, as well as others, can utilize modern technology such as this to maintain and improve services in the presence of a cost-containment environment.

Competitive Bidding↗

Mechanization of library procedures in the medium-sized medical library. IX. Holding statements in PHILSOM: a study of their activity.

A study was made of the serial holding statements in PHILSOM over a six-month period, in order to determine the desirability of printing the complete serial holding statements monthly. Attention was given to the frequency of internal and update changes in both active and dead entries. The results indicate that while sufficient activity is observed in active serial entries to warrant their monthly updating, dead serial entries remain constant over this period. This indicates that a large group of PHILSOM entries can be easily identified and isolated, facilitating division and independent updating of the resultant lists. The desirability of such a division, however, must also take into consideration the user's ease in handling such a segmented listing.

Electronic Data Processing↗

Error classification in community optometric practice - a pilot project.

OBJECTIVE: A pilot study was conducted to undertake a baseline assessment of errors reported in community optometric practice. The feasibility and acceptability of a method for recording staff-reported errors in optometric community practice was investigated. DESIGN: An anonymous self-reporting system was introduced in order to collect information regarding errors/untoward events in community optometric practice. SETTING: UK community optometric practice. MAIN OUTCOME MEASURES: Classification of errors according to a previously published study pertaining to general medical practice in the same geographical area. RESULTS: Thirty-six notebooks were distributed to 10 participating community optometric practices. At the end of the 1 month study period the note books were returned and the 439 entries made were classified into seven categories: optical prescriptions (18.2%), communication (35.5%), administrative (15%), appointments (2.3%), equipment (11.9%), clinical (10.3%) and other (6.9%). CONCLUSION: A previously developed classification of errors in general medical practice was found to be equally applicable to community optometric practice. This study forms the basis of providing an acceptable and practical methodology, which can be applied by Primary Care Trusts (PCTs) when developing their risk management strategy to include optometry.

Appointments and Schedules↗

Clinical decision support for physician order-entry: design challenges.

We report on a joint development effort between ALLTEL Information Services Health Care Division and IBM Worldwide Healthcare Industry to demonstrate concurrent clinical decision support using Arden Syntax at order-entry time. The goal of the partnership is to build a high performance CDS toolkit that may be easily customized for multiple health care enterprises. Our work uses and promotes open technologies and health care standards while building a generalizable interface to a legacy patient-care system and clinical database. This paper identifies four areas of design challenges and solutions unique to a concurrent order-entry environment: the clinical information model, the currency of the patient virtual chart, the granularity of event triggers and rule evaluation context, and performance.

Artificial Intelligence↗

Development and deployment of a web-based physician order entry system.

The computer-based Physician Order Entry System (POES) has been employed in many clinical institutes in Taiwan. Most of the POES systems are developed in the two-tier client-server architecture, and a large portion of the systems are constructed on a mainframe or even a single PC. The exponential growth of the Internet has had a tremendous impact on our society in recent years. In consideration of the future user interface and system architecture, we have developed a three-tier web-based Physician Order Entry System and successfully deployed it in the Wang-Fang Hospital in Taipei. The system is the first POES based on three-tier and World Wide Web (WWW) in Taiwan. The system provides the Subjective, Objective, Assessment, and Plan (SOAP) structure for the physician to enter subject, object, diagnoses, medicine dosage, treatment and laboratory test request, and prints out the prescription and necessary document. The doctor can also retrieve the patient's medical record on the system. One of the special characteristics of the system is its personalized design. The doctor can define their own diagnosis, medicine and treatment database and any combination of these to facilitate their clinical work. The system has been reviewed since February 1999. The result shows that the clinical procedure has become more efficient, and the chances of omission have been reduced. The system is very stable and the Open Database Connectivity (ODBC) database access did not show any delay in the network. Since we have incorporated many new web-programming techniques, the progress of the techniques will improve the system performance in the future.

Computer Systems↗

Computerized physician order entry: a prescription for patient safety.

Clinician adoption of CPOE/CDS solutions is crucial to helping caregivers reduce medical errors and enhance patient safety. The LeapFrog Group CPOE/CDS report can be a helpful guide, but as clinicians concerned about the quality of health care and the well-being of our patients, we must play an active role in the successful adoption of these solutions by: 1. Making sure that your institution is committed to having the appropriate people involved in the entire process, including nurse leaders. 2. Selecting a vendor that has the knowledge and clearly understands the importance of implementing this type of system. 3. Ensuring that your organization is selecting a system that actually meets the criteria defined by the LeapFrog Group.

Clinical Pharmacy Information Systems↗

Traversing the paperless frontier: using information technology to improve quality care.

When it comes to information technology (IT), healthcare has lagged behind. Most healthcare transactions still are conducted via a paper route. Only a fraction of hospitals and physicians' offices have implemented a comprehensive electronic health record. With computerized provider order entry systems, the story has been much the same, with only about 10% of hospitals using them to transmit patient orders. But this may be changing soon. This issue of The Quality Letter for Healthcare Leaders looks at what is in store for healthcare IT and what these trends will mean to the healthcare community.

Diffusion of Innovation↗

Web-based data integration and annotation in the intensive care unit.

Integrating patient data to facilitate review and annotation is a challenging task in the intensive care unit (ICU), partially due to the wide variety of proprietary systems and types of data involved. This paper describes SIMON-Web, a Java-based user interface to integrate data from an existing bedside monitoring system and a clinical information system (CIS). SIMON-Web displays graphical data from physiologic monitors and other bedside devices as well as information from the clinical laboratory and physician order-entry systems, and allows users to annotate the data using a point-and-click or free-text interface. By continuing to add functionality to SIMON-Web's extensible user interface, we hope to continue to augment and eventually replace manual care provider data charting in the ICU. As of March, 1997, SIMON-Web has been implemented on two bedside personal computer workstations in the cardiac care unit (CCU) at Vanderbilt University Medical Center.

Computer Communication Networks↗

An efficient fault-tolerant order entry management information system based on special distributed client/server architecture.

An automatic order entry system is very important for the processing of out-patient information, not only helping doctors to enter their orders directly but also reducing errors of communication. Many hospitals are anxious to set up a direct order entry system but are concerned about possible system failures. In this paper we report on an effective and efficient fault-tolerant order entry management system which satisfies the requirements for out-patient order entry. From the results of experiments on a prototype we found that the system was user friendly and reduced the time taken. Doctors are able to enter their orders more easily, accurately and quickly by selecting from the standardized and personalized menus to be found in the system.

Computer Systems↗

Automatic extraction of linguistic knowledge from an international classification.

Automatic extraction of knowledge from large corpus of texts is an essential step toward linguistic knowledge acquisition in the medical domain. The current situation shows a lack of computer-readable large medical lexicons, with a partial exception for the English language. Moreover, multilingual lexicons with versatility for multiple languages applications are far from reach as long as only manual extraction is considered. Computer-assisted linguistic knowledge acquisition is a must. A multilingual lexicon differs from a monolingual one by the necessity to bridge the words in different languages. A kind of interlingua has to be built under the form of concepts to which the specific entries are attached. In the present approach, the authors have developed an intelligent rule-based tool in order to focus on a multilingual source of medical knowledge, like the International Classification of Disease (ICD) which contains a vocabulary of some 20,000 words, translated in numerous languages.

Disease↗

The MeSH translation maintenance system: structure, interface design, and implementation.

The National Library of Medicine (NLM) produces annual editions of the Medical Subject Headings (MeSH). Translations of MeSH are often done to make the vocabulary useful for non-English users. However, MeSH translators have encountered difficulties with entry vocabulary as they maintain and update their translation. Tracking MeSH changes and updating their translations in a reasonable time frame is cumbersome. NLM has developed and implemented a concept-centered vocabulary maintenance system for MeSH. This system has been extended to create an interlingual database of translations, the MeSH Translation Maintenance System (MTMS). This database allows continual updating of the translations, as well as facilitating tracking of the changes within MeSH from one year to another. The MTMS interface uses a Web-based design with multiple colors and fonts to indicate concepts needing translation or review. Concepts for which there is no exact English equivalent can be added. The system software encourages compliance with the Unicode standard in order to ensure that character sets with native alphabets and full orthography are used consistently.

Databases as Topic↗

Description and advantages of an index-driven medical knowledge base.

In the FRAMEMED system design, the inherent attributes of its concepts are expressed in the hierarchical lists of its 26 Elements (e.g., Agents, Clinical Manifestations, Diseases, Tests, etc.). These concepts, contained in regular structures, are then alphabetized by phrase (and synonym), forming a combined index in which the user may quickly find a concept either alphabetically or hierarchically. Stored in the structures of the index are pointers to four types of knowledge records: 1) Descriptive (definition); 2) Relational (incidental attributes); 3) Conditional (rules); and 4) Procedural (how to). In contrast to the index which is stored in regular structures for rapid access (like relational databases), the knowledge records are stored in free text (variable length) and may include pointers to imaging and audio records. A particular feature of the FRAMEMED system is careful attention to modifiers, an aspect usually not emphasized in other systems. In trying to structure the free text describing a patient encounter, for example, the major concepts such as cough, fever, stiff neck, etc., are relatively easy to code (although a common system has not yet been agreed upon). The devil lies in the modifiers such as 'history of', 'severe,' 'constant,' 'absent,' 'left,' 'abnormal,' etc., particularly when there is concatenation of modifiers modifying modifiers. Our Relational records (in our knowledge base) and our Chronological Medical Records (CMR) in our patient record have the same format, namely, a title, several related items, and a date/author. For example, our disease profile (Relational record) for 'Influenza' might include 'cough,' 'fever,' and 'stiff neck.' The CMR of a particular patient encounter might include the same items. The only differences would be the title (disease name for the disease profile, date for the CMR, and the omission of the redundant date in the date/author line of the CMR). Each related item in either of these records is expressed in a four-part string, namely: 1) Relation; 2) Code; 3) Phrase; and 4) Comment. Modifiers (common ASCII symbols) are structured into each of these parts. For example, if the patient did not have 'cough,' the default '+' in the Relation would be edited to a '-', while 'history of' cough would be '>'. Each Relation can be graded (on a 5-level scale) for both importance and frequency. The Code for a test can carry the result suffix, '+ positive/high,' '-negative/low,' '# abnormal (qualitatively)', or '1 unremarkable/normal.' Topological information, such as '/left,' can be appended to a Code. If the cough is getting worse, its code can have the suffix, '<'. The standardized Phrases associated with the Codes come from the hierarchical lists of the index section described earlier. Phrases are not stored, being rematched to the codes as needed for user display. This practice not only saves memory space but allows a CMR encounter recorded in one language to be displayed in another second language subsequently, requiring only the existence of the hierarchical code/phrase in the second language. A free-text Comment is allowed for any related item in a Relational record or CMR, to allow the doctor to add important nuances such as 'worse on arising' or for a numeric result such as a test result or a thermometer reading. Some structuring can be accommodated in the Comment by introducing symbols such as '> relieved by,' followed by a list containing entries such as 'antacids.' Time can be sturctured through symbol lists such as '@-2 mo' representing '2 months previously.' Because Relational records in the knowledge ase and patient encoutner records in the CMR both display findings in hierarchical order; all similar items (e.g., Agents, Clinical Manifestations, Tests, Procedures, etc.) occur together and in an unique order. (abstract truncated)

Abstracting and Indexing↗

Evaluating the impact of computerized clinical documentation.

A computerized system for care planning and documentation of patient care was initiated at a western teaching hospital, using the framework of Nursing Interventions Classification and Nursing Outcomes Classification standardized languages. The software integrates care planning and documentation, and includes both order entry as well as a charting application. Prior to initiating the project, a study was conducted to evaluate staff attitude toward computerization, time needed for documentation, and comprehensiveness of charting entries. Data from staff surveys, observations, and chart audits conducted pre- and post-computer project implementation demonstrated that the staff attitudes toward computers were less positive, the time required for charting was unchanged, and there were improvements in how completely the nurses documented charting elements.

Adult↗

Evolution of health information management and information technology in emergency medicine.

The implementation of information technology will continue to have important effects on the practice of emergency medicine. Patient registration and tracking systems, telemedicine electronic order entry systems, and the advent of electronic health records are some examples of applications that influence patient care in emergency departments. We review the literature regarding information technology advances in emergency medicine and outline the important role that emergency physicians play in developing and implementing a national health information technology strategy.

Australasia↗