Addressing EMR (electronic medical record) issues head-on.
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Classification↗
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Results for “Medical Records Systems, Computerized”
Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.
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Cross-institutional reuse of a problem statement knowledge base.
This article describes client and server applications for a problem statement knowledge base derived from a large corpus of provider entered terminology. The current status and potential for integration of the server into the Vanderbilt University Medical Center computing environment are discussed. Finally, an experiment in multiple dimensions of reuse for problem list terms is introduced, and possible strategies to mediate between free text and coded data are examined.
Artificial Intelligence↗
Intelligent Medical Record--entry (IMR-E).
This paper describes an automated medical record designed to allow providers to enter patient data at the point of care. The system runs on PCs and Macintoshes and uses a graphical user interface and object-oriented programming to take advantage of current mouse and pen technologies. The provider acquires all relevant patient data by pointing and clicking at selections on input screens, many of which contain anatomical drawings to help the provider quickly and accurately describe patient findings. The system also generates a grammatically correct progress note using the problem-oriented structure. Furthermore, items identified in the assessment and plans portion of the program can be ported to expert systems for medical decisions assistance or to billing systems. The system allows the provider to obtain the necessary information on a focused patient visit in less than 5 min or to enter a complete history and physical.
Artificial Intelligence↗
Empirical derivation of an electronic clinically useful problem statement system.
Problem lists are tools to improve patient management. In the medical record, they connect diagnoses to therapy, prognosis, and psychosocial issues. Computer-based problem lists enhance paper-based approaches by enabling cost-containment and quality assurance applications, but they require clinically expressive controlled vocabularies. Because existing controlled vocabularies do not represent problem statements at a clinically useful level, we derived a new canonical problem statement vocabulary through semi-automated analysis and distillation of provider-entered problem lists collected over 6 years from 74,696 patients. We combined automated and manual methods to condense 891,770 problem statements entered by 1961 care providers at Grady Memorial Hospital in Atlanta, Georgia, to 15,534 Canonical Clinical Problem Statement System (CCPSS) terms. The nature and frequency of problem statements were characterized, interrelations among them were enumerated, and a database capturing the epidemiology of problems was created. The authors identified 23,503 problem relations (co-occurrences, sign-symptom complexes, and differential diagnoses) and 22,690 modifier words that further categorized "canonical" problems. To assess completeness, CCPSS content was compared with that of the 1997 Unified Medical Language System Metathesaurus (containing terms from 44 clinical vocabularies). Unified Medical Language System terms expressed 25% of individual CCPSS terms exactly (71% of problems by frequency), 27% partially, and 48% poorly or not at all. Clinicians judged that CCPSS terms completely captured their clinical intent for 84% of 686 randomly selected free-text problem statements. The CCPSS represents clinical concepts at a level exceeding that of previous approaches. A similar national approach could create a standardized, useful, shared resource for clinical practice.
Humans↗
Fourth Global Congress on Patient Cards & Computerisation of Health Records, Berlin, Germany 25-28 May, 1992.
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Europe↗
Extracting medical knowledge for a coded problem list vocabulary from the UMLS Knowledge Sources.
INTRODUCTION: The Unified Medical Language System (UMLS) Knowledge Sources embody a rich source of medical knowledge. We sought to extract a portion of this knowledge by incorporating information about relationships between UMLS concepts into an existing problem list vocabulary. METHODS: We matched terms from the coded problem list of The Medical Record (TMR), a computer-based patient record system, with those found in the UMLS Metathesaurus. Those UMLS concepts that participate in 'parent' relationships with the matched TMR concepts were translated back into TMR codes and the relationship information was retained for integration into the coded problem list of TMR. RESULTS: Of the coded problems currently in use in TMR, 67% (1627/2436) could be matched by normalized string matches to the UMLS Knowledge Sources. Of these matched TMR concepts, 91% (1488/1627) participated in at least one UMLS-identified parent relationship but only 28% of the matched concepts (454/1627) participated in parent relationships that already matched to a TMR code. As a result, although 67% of TMR codes were matched to UMLS concepts, only 19% of our original problem list (454/2436) could be augmented by relationship information contained in UMLS without improving the rate of matches or adding additional UMLS concepts as coded problems in TMR. CONCLUSION: This study illustrates the rapid decline in overall rates of matching that result from a multiplicative effect of successive matches of terms to concepts, concepts to relationships and concepts back to entry terms. This effect will hamper any effort to extract relationship knowledge from the UMLS for incorporation into an entry vocabulary that is not already one of the source vocabularies of the UMLS Metathesaurus.
Humans↗
[Electronic organization of patient records as a component of integrated medical informatics].
Conventional patient records are not only inconvenient to store, their structure is often not consistent and faulty, research is difficult and quality control is not possible. One solution to this problem is the computerized patient record. It contains all necessary data beginning with the first medical statements and ending with the final results of treatment. At any time and from any with the system connected computer terminal it allows immediate access to all medical records. In addition to medical information also administrative data are stored. The here presented system of a computerized medical record has proven to be an effective tool in the daily work as well for clinical, administrative and research tasks.
Humans↗
Effect of a computerized ambulatory medical record system on the validity of claims data.
Relationships were compared between claims data and charts data in a medical practice when a paper chart and manually prepared claims forms were used and after implementation of a computerized medical record system (COSTAR) in which claims data were derived automatically from the medical database. Claims data and chart data resembled each other more closely when the computer system was used, suggesting that claims data derived in this manner may have particular value in health care planning and research.
Ambulatory Care↗
An evaluation of UMLS as a controlled terminology for the Problem List Toolkit.
We are developing a set of software components--the Problem List Toolkit (PL-Tk)--to support operations on clinical problem labels. An adaptation of the National Library of Medicine's Unified Medical Language System (UMLS) provides general vocabulary services to domain-specific software components. Our initial investigation centers on the inclusion in UMLS of problem labels used in the Beth Israel Deaconess Medical Center's Online Medical Record (OMR). We also explore the semantic typing of problem labels matched in UMLS. We have operationally defined a clinical problem to derive its semantic type from classes of terms representing findings or processes typically requiring diagnostic evaluation or therapeutic management in clinical practice. Of 1262 unique OMR problem labels, 999 terms (79%) have matches in UMLS. 986 of 999 terms (99%) map to the UMLS concept of the corresponding lexical match. 952 of 999 terms (95%) have semantic types that comply with our operational definition of clinical problems. These 952 terms (75%) constitute Version 1.0 of the problem list vocabulary B196. Matching terms with inappropriate semantic types raise issues regarding requirements for PL-Tk, typing of existing UMLS terms, and the adequacy of our operational definition for clinical problems. UMLS provides a large repertoire of pre-coordinated terms that are used as problem labels in a heavily used computer-based patient record system. The semantic type hierarchy provides a framework for the consistent use of clinical concepts in problem lists such that clinical problem labels represent "good" clinical problems.
Evaluation Studies as Topic↗
How useful is the UMLS metathesaurus in developing a controlled vocabulary for an automated problem list?
We are developing a set of problem list phrases to be used in the automated problem list of a prototype clinical computing system. Because of the large number of terms in the Unified Medical Language System (UMLS) and the links between them, we are experimenting with the use of the UMLS as the foundation for our problem list phrase set. We have found the UMLS to be very useful for this project, but that it lacks many phases clinicians wish to include in the problem list. Internal linkages between phrases provided in the UMLS are not well suited to our needs. We plan to continue our use of the UMLS but to add problem list phrases and linkages between phrases to support browsing and decision support applications.
Ambulatory Care Information Systems↗
Standardized problem list generation, utilizing the Mayo canonical vocabulary embedded within the Unified Medical Language System.
UNLABELLED: VOCABULARY: The Mayo problem list vocabulary is a clinically derived lexicon created from the entries made to the Mayo Clinic's Master Sheet Index and the problem list entries made to the Impression/ Report/Plan section of the Clinical Notes System over the last three years. The vocabulary was reduced by eliminating repetition including lexical variants, spelling errors, and qualifiers (Administrative or Operational terms). Qualifiers are re-coordinated with other terms, at run-time, which greatly increased the number of input strings which our system is capable of recognizing. IMPLEMENTATION: The Problem Manager is implemented using standard windows tools in a Windows NT environment. The interface is designed using Object Pascal. HTTP calls are passed over the World Wide Web to a UNIX based vocabulary server. The server returns a document, which is read into Object Pascal structures, parsed, filtered and displayed. STUDY: This paper reports the results of a recent Usability Trial focused on assessing the viability of this mechanism for standardized problem entry. Eight clinicians engaged in eleven scenarios and responded as to their satisfaction with the systems performance. These responses were observed, videotaped and tabulated. Clinicians in this study were able to find acceptable diagnoses in 91.1% of the scenarios. The response time was acceptable in 92.5% of the scenarios. The presentation of related terms was stated to be useful in at least one scenario by seven of the eight participants. All clinicians wanted to make use of shortcuts which would minimize the amount of typing necessary to encode the concept they were searching for (e.g. Abbreviations, Word Completion). CONCLUSIONS: Clinicians are willing to choose a canonical term from a suggested list (as opposed to their own wording). Clinicians want an "intelligent" system, which would suggest terms within a category (e.g. Types of "Migraine"). They are able to make functional use of our system, in its current state of development. Finally, all clinicians appreciate the value of encoding their problems in a standardized vocabulary, toward improved research, education and practice.
Computer Communication Networks↗
[Aspects of electronic patient records in radiology].
PURPOSE: The computerized patient record must provide patient- and problem-oriented access to all relevant patient data for health care professionals. The aspects of such systems will be analyzed in the light of the radiologist's needs. METHODS: Integration of the computerized patient record in a hospital communication network allows automated data exchange with the ancillary systems. Accessibility of electronic textbooks supports case-based learning during routine work. RESULTS: The computerized patient record not only supports routine clinical tasks but also training, education and research. The workflow in a hospital can be supported by the computerized patient record. Studying the tasks of a radiology department shows that both clinicians and radiologists will benefit from such a system. DISCUSSION: Current implementations of clinical computerized workstations offer only a fraction of these features. Advances in technology and increasing demands at the point of care will promote the development of new information systems of this kind.
Computer Systems↗
Information technology and work flow reengineering improves and revitalizes primary care.
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Ambulatory Care Information Systems↗
International transfer of the Johns Hopkins Oncology Center clinical information system.
The transfer of the Johns Hopkins Oncology Center clinical information system to an Australian tertiary care center began in 1982. The converted system was installed for use by the hospital administration in 1991, and it is now used extensively in management and patient care. This article discusses the original software, the applications implemented, and the problems encountered and overcome, as well as the role of the hospital administration in the development and subsequent freezing of the system.
Baltimore↗
A prototype of a computerized patient record.
Computerized medical record systems (CPRS) should present user and problem oriented views of the patient file. Problem lists, clinical course, medication profiles and results of examinations have to be recorded in a computerized patient record. Patient review screens should give a synopsis of the patient data to inform whenever the patient record is opened. Several different types of data have to be stored in a patient record. Qualitative and quantitative measurements, narratives and images are such examples. Therefore, a CPR must also be able to handle these different data types. New methods and concepts appear frequently in medicine. Thus a CPRS must be flexible enough to cope with coming demands. We developed a prototype of a computer based patient record with a graphical user interface on a SUN workstation. The basis of the system are a dynamic data dictionary, an interpreter language and a large set of basic functions. This approach gives optimal flexibility to the system. A lot of different data types are already supported. Extensions are easily possible. There is also almost no limit concerning the number of medical concepts that can be handled by our prototype. Several applications were built on this platform. Some of them are presented to exemplify the patient and problem oriented handling of the CPR.
Humans↗
Overcoming the limitations of proprietary computerized billing systems to enhance patient care.
Most physician offices have proprietary computerized billing systems, but these are not designed for monitoring utilization or addressing patient care issues, and they are difficult or impossible to modify. These systems do, however, contain valuable diagnosis and demographic information. An open-ended, relational x-base system is described that downloads this billing information and combines it with additional input to provide the practitioner with: current problem lists; medication and allergy lists; health screening reminders that are age, sex and disease specific; and commonly used demographic information. Several popular query/reporting tools are used to generate standard reports and ad hoc inquiries that relate directly to patient care. Two studies, one involving alerting physicians to possible adverse medication effects on specific patients, and one investigating appropriate use and billing of stool occult blood testing are summarized. In the constantly evolving arenas of utilization, outcomes research and cost efficiency, such an open ended, time efficient system has unlimited potential to improve patient care.
Drug-Related Side Effects and Adverse Reactions↗
Validation of clinical problems using a UMLS-based semantic parser.
The capture and symbolization of data from the clinical problem list facilitates the creation of high-fidelity patient resumes for use in aggregate analysis and decision support. We report on the development of a UMLS-based semantic parser and present a preliminary evaluation of the parser in the recognition and validation of disease-related clinical problems. We randomly sampled 20% of the 26,858 unique non-dictionary clinical problems entered into OMR (Online Medical Record) between 1989 and August, 1997, and eliminated a series of qualified problem labels, e.g., history-of, to obtain a dataset of 4122 problem labels. Within this dataset, the authors identified 2810 labels (68.2%) as referring to a broad range of disease-related processes. The parser correctly recognized and validated 1398 of the 2810 disease-related labels (49.8 +/- 1.9%) and correctly excluded 1220 of 1312 non-disease-related labels (93.0 +/- 1.4%). 812 of the 1181 match failures (68.8%) were caused by terms either absent from UMLS or modifiers not accepted by the parser; 369 match failures (31.2%) were caused by labels having patterns not recognized by the parser. By enriching the UMLS lexicon with terms commonly found in provider-entered labels, it appears that performance of the parser can be significantly enhanced over a few subsequent iterations. This initial evaluation provides a foundation from which to make principled additions to the UMLS lexicon locally for use in symbolizing clinical data; further research is necessary to determine applicability to other health care settings.
Medical Records Systems, Computerized↗
Categorization of free-text problem lists: an effective method of capturing clinical data.
Problem lists assist in organizing patient information in computer based medical records. However, in order to use problem lists for billing, research, decision support and standardization, a categorization of the problems entered is required. We describe the problem list component of our computerized patient record, the On-line Medical Record (OMR), which combines a free-text entry mechanism with a categorization scheme, using a dictionary containing 846 terms. All 118,040 problems entered during the system's six years of use have been analyzed, 477 clinicians have entered a mean +/- S.D. of 238 +/- 604 problems into 22,311 patient records. The average number of problems in each patient's file was 5.1 +/- 3.9. Comments were typed for 80,281 (68%) of the problems, ranging in length from 1 to 2456 characters, with a mean length of 98 +/- 110 characters. Half the problems were entered on the day of the encounter with the patient. Overall, 66% of all problems were categorized in relation to terms from the problem dictionary. Lexical analysis of all problem names showed that 80% could be mapped to Meta 1.4, Snomed 3.0 or a pre-release version of Read 3.0. We conclude that a problem list entry scheme combining free-text entry and optional categorization using a dictionary can result in a high proportion of problems being categorized as desired. Improvement of the system by elimination of unused dictionary terms and addition of 1000 terms identified by the lexical analysis is likely to result in even higher categorization rates.
Humans↗