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Healthcare terminologies SNOMED and the Read Codes have been around for a long time. Now, a new terminology combining the two promises to take the industry one step closer to realizing the vision of an electronic patient record. Here 's a sneak peek at SNOMED CT.
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.
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.
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.
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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.
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.
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.
To improve the functionality of our MAternal Record System (MARS), we reviewed the clinical problems entered into the MARS database from approximately 2800 patient records and interviewed representative clinicians about their use of the Clinical Comments and Problem List sections of MARS. Problems were assigned a unique term that corrected for spelling, spacing, synonyms, and abbreviation variations. Analysis of these terms suggested design changes that would increase the number of unique entries into the Problem List, including (1) modifications in the automated problem entry functions within MARS and (2) integration of the Problem List and Clinical Comments sections.
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.
Coded problem lists will be increasingly used for many purposes in healthcare. The usefulness of coded problem lists may be limited by 1) how consistently clinicians enumerate patients' problems and 2) how consistently clinicians choose a given concept from a controlled terminology to represent a given problem. In this study, 10 physicians reviewed the same 5 clinical cases and created a coded problem list for each case using UMLS as a controlled terminology. We assessed inter-rater agreement for coded problem lists by computing the average pair-wise positive specific agreement for each case for all 10 reviewers. We also standardized problems to common terms across reviewers' lists for a given case, adjusting sequentially for synonymy, granularity, and general concept representation. Our results suggest that inter-rater agreement in unstandardized problem lists is moderate at best; standardization improves agreement, but much variability may be attributable to differences in clinicians' style and the inherent fuzziness of medical diagnosis.
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.
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.
We propose a framework for a problem-oriented patient record for general practice 1 and defend that the problem-oriented medical record represents an intuitive way to organize the patient record. By adding a layer of knowledge to the electronic patient record the record system is able to better utilize the information stored in the record. If a record system is process aware, having knowledge of work processes and is able to distinguish between different contexts in use, the system can provide relevant and useful information during the handling of patients' medical problems. Information is ranked according to its relevancy in a given context by using action patterns - traces. Traces give valuable indications of what is going on during the process of patient care. Decision frames represents relevant contexts based on the information in the record. Both decision frames and traces provide an environment in which more optimal medical decisions can be made.
OBJECTIVE: To implement a computerized system to gather and transmit medical record information from six sites to a centralized database for two cancer prevention studies. METHODS: Microsoft Access 97 was selected as the application for the system. Sites purchased Access and hardware meeting technical specifications required for the system. A developer worked with the lead investigator and medical record abstractors to develop a 'back-end' database to hold the desired data while maintaining a user-friendly 'front-end' interface. Abstractors trained on a paper version of the abstraction form were then trained to use the system. Meeting minutes and technical notes were used in summarizing the approach and process. Observations were collected through discussions. RESULTS: We overcame multiple obstacles to develop computerized systems supporting medical record data collection at multiple sites. Although system development slowed implementation of the study, the system produced data for cleaning and analysis immediately. Overall the approach decreased the time from study implementation to manuscript submission. Development time for a second system was substantially reduced. CONCLUSIONS: Computerized systems for medical record abstraction at multiple sites convey substantial benefit. We present a schematic approach to facilitate development of similar systems in the future.
Although the Institute of Medicine states that a patient problem list should have a prominent place in the computer-based patient record, the design and function of the problem list is not a matter of universal agreement. Developer experience with implementation has been inconsistent, in part because of confusion on data standards, uncertain user acceptance of data entry, and minimal rewards for the clinician. I propose that necessary features of the problem list include: 1) clinical focus, 2) codification of problems, 3) support for problem resolution, 4) historicity of problems, 5) support for multiple clinical views, 6) integration of maintenance functions with workflow, 7) support for administrative reporting, and 8) integration with useful clinical tools. I describe the strategies that we employed to meet these goals while implementing the problem list in a computerized patient record serving a large, complex clinical enterprise. I further report the successful achievement of those goals based upon audits six months after implementation.
It is conservatively estimated that 8 percent of inmates in U.S. prisons have significant psychiatric impairment. Correctional mental health systems are often unable to provide adequate treatment to such inmates because of staff recruitment problems. Collaboration between universities and departments of corrections can help solve recruitment problems, and needs assessment studies and computerized tracking systems can help obtain needed resources. A national survey collected data on collaborations between universities and corrections departments and on needs assessment and computerized tracking systems in prisons. Results indicated that such collaborations exist in more than half of state prison systems. Of 42 states responding, only ten states reported having systemwide computerized tracking systems, but others are in the process of developing such systems. Two-thirds of the correctional mental health systems did not have an ongoing needs assessment program.
OBJECTIVES: Clinical use of computer systems has been hampered by their poorly designed user interfaces. The objective of this study was to design a user interface for a pediatric oncologists' computerized patient record with great consideration of their working behavior and of human computer interfacing principles so as to contribute to oncologists' efficiency and satisfaction in interaction with the system. METHODS: The think aloud method was used in combination with video recording to get a deep understanding of the way in which four pediatric oncologists searched through the paper-based patient record in preparing a patient visit. Protocol and video analyses was used to develop a cognitive task model reflecting pediatric oncologists' task behavior. This model was input for a prototype user interface, which was subsequently evaluated by eight other pediatric oncologists. RESULTS: The resulting computerized medical record system proved to meet pediatric oncologists' information needs and task behavior patterns. The design of the user interface minimized pediatric oncologists' work load and was highly efficient in supporting the pediatric oncologists in preparing their patient visits. The pediatric oncologists were very much satisfied with the computer system. CONCLUSIONS: It is argued that early involvement of cognitive engineering methods in the system design process may be of great help in designing systems that fully support health care professionals' work practices. The think aloud method, if applied under prescribed conditions, is a valuable information source of human task-behavior and as such a useful technique for requirements analysis in designing clinical computer systems.