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A real-time program for obstetrical and neonatal data was developed in June 1986 at the maternity hospital in Haguenau. Used routinely, this program has raised comments and controversy. With investigative questionnaires, we have attempted to evaluate how the introduction of computer sciences was perceived by the personnel as well as the patients.
A major obstacle to establishing a computer-stored medical record is the lack of "standards" that would permit government, care providers, insurance companies, and medical computer system developers to share patient data easily. In this position paper, the Board of Directors of the American Medical Informatics Association recommends specific approaches to standardization in the areas of patient, provider, and site of care identifiers; computerized health care message exchange; medical record content and structure, and medical codes and terminologies. The key concept developed in this position paper is that developers and users of computer-stored medical records must embrace existing and tested approaches, despite their imperfections, to progress quickly. This approach to standardization is being coordinated with the American National Standards Institute's Health Informatics Standards Planning Panel. The development of standards is a long-term process involving continued refinement. The proposed standards are an important step toward the goal of better and more efficient health care.
The needs placed on today's physician to improve efficiency in the endoscopy unit are nowhere more apparent than in transcribing endoscopy reports. In spite of the constant progress made in currently available electronic endoscopy information systems, the systems lack efficiency and ease of use. Both of these problems could be addressed through the linkage of electronic endoscopy information systems to currently available accessory systems. W review how the linkage of tablet personal computers, voice recognition systems, and web connectivity software, which facilitates communication with electronic-based medical records, can be helpful. The final answer, however, will have to await clinical trials.
Information and communication systems in a health (care) environment are risky systems as the data processed, transmitted, stored and retrieved are person-related. An unjustified disclosure may compromise the individual's personal or social life. Therefore these systems must be subject to carefully designed and implemented protection procedures guaranteeing the correct use of those data, corresponding in the medical sphere with the ancient Oath of Hippokrates, as well as the preservation of their correctness, completeness etc., as requested by legal regulations valid for the location of the respective computer-aided information system. The same is true for the manual handling of person-related health data by conventional methods. In any case, the data subject's right of informational self determination must be taken into account.
The particular logistical problems of collecting cancer registration data in the South-Western region of England are discussed. The Regional Cancer Registry has endeavoured to overcome these through developing electronic links to hospital-based patient-information systems. Some of these involve data transfer on magnetic media, but there are also interrogation facilities which operate on a realtime, interactive basis, and complement these other facilities. A PC-based program (SNODEV5), which can accept data from histopathology computer systems, translate SNOMED codes to ICD-9, and produce files suitable for processing by the registry's Batch Data Entry system is described. Its contribution to the achievement and monitoring of equity in health care provision is discussed.
Major strides in both computer hardware and software with respect to costs, reliability and user friendliness have made computer systems more and more popular for information management. Although the acquisition and maintenance of patient medical records lend themselves extremely well to computerization, there are few, if any, descriptions of successful implementations of computerized medical records in a private clinical practice. This article examines the basic fundamentals of the medical record, compares paper and computerized records and describes a specific computerized office system installed in a private cardiovascular/internal medicine practice with specific emphasis on patient medical records.
An increasing number of health-care institutions are in the process of implementing clinical computing systems. The need for an accurate assessment of the clinical, administrative, social, and financial effects of such systems has been recognized. Techniques have been developed to evaluate these effects on the work patterns of health-care workers including: time-motion analysis, subjective evaluations, review of departmental statistics, personal activity records, and work-sampling. This study reviews these techniques, discusses both positive and negative aspects, and presents a step-by-step description of work-sampling.
Electronic patient records (EPR) can be regarded as an implicit source of clinical behaviour and problem-solving knowledge, systematically compiled by clinicians. We present an approach, together with its computational implementation, to pro-actively transform XML-based EPR into specialised Clinical Cases (CC) in the realm of Medical Case Base Systems. The 'correct' transformation of EPR to CC involves structural, terminological and conceptual standardisation, which is achieved by a confluence of techniques and resources, such as XML, UMLS (meta-thesaurus) and medical knowledge ontologies. We present below the functional architecture of a Medical Case-Base Reasoning Info-Structure (MCRIS) that features two distinct, yet related, functionalities: (1) a generic medical case-based reasoning system for decision-support activities; and (2) an EPR-CC transformation system to transform typical EPR's to CC.
The present paper deals with an experience of blackout of the hospital information system at our Medical Centre, trying to evaluate the impact on internal users (physicians, nurses, clerks) and patients population. Limited inconveniences have occurred to out-patients in terms of delay in collecting medical reports after diagnostic test execution. As regards direct users, impact was evaluated through a structured interview. Administrative personnel, that have been using computer-based system for at least ten years, have not declared particular inconveniences, accepting the overtime or the extra-work as simply unavoidable. On the contrary, health-care personnel reported a heavy negative impact of the system failure on their activity. After a great effort to achieve the system acceptance and direct physicians usage, the blackout of the system has pointed out that the situation has changed since a few years ago: now the HIS, and particularly its clinical core, is considered mission critical.
Nowadays, most activities on controlled medical vocabularies focus on the provision of a sufficient atomic-level granularity for representing clinical data. Amongst others, clinical vocabularies should be concept oriented, compositional and should also reject "Not Elsewhere Classified". We strongly share the opinion that there is a need to deal with serious deficits of existing manually created vocabularies and with new demands for computer-based advanced processing and exchange of medical language data. However, we do not share the opinion that methodological requirements like observational and structural comparability needed for sound statistics should not be included in desiderata of controlled medical vocabularies. Statistical-oriented classifications are not developed for representing detailed clinical data but for providing purpose-dependent classes where cases of interest are assigned uniquely. Either statistical classifications are not included into the set of controlled medical vocabularies in the sense of Cimino, or his desiderata are misleading. We argue that statistical classifications should be linked to (formal) concept systems, but again this linkage does not change their different natures. With this article we continue the "classification versus nomenclature" controversy referring to Coté.
BACKGROUND: New terminology continuously enters the biomedical literature. How can curators identify new terms that can be added to existing nomenclatures? The most direct method, and one that has served well, involves reading the current literature. The scholarly curator adds new terms as they are encountered. Present-day scholars are severely challenged by the enormous volume of biomedical literature. Curators of medical nomenclatures need computational assistance if they hope to keep their terminologies current. The purpose of this paper is to describe a method of rapidly extracting new, candidate terms from huge volumes of biomedical text. The resulting lists of terms can be quickly reviewed by curators and added to nomenclatures, if appropriate. The candidate term extractor uses a variation of the previously described doublet coding method. The algorithm, which operates on virtually any nomenclature, derives from the observation that most terms within a knowledge domain are composed entirely of word combinations found in other terms from the same knowledge domain. Terms can be expressed as sequences of overlapping word doublets that have more specific meaning than the individual words that compose the term. The algorithm parses through text, finding contiguous sequences of word doublets that are known to occur somewhere in the reference nomenclature. When a sequence of matching word doublets is encountered, it is compared with whole terms already included in the nomenclature. If the doublet sequence is not already in the nomenclature, it is extracted as a candidate new term. Candidate new terms can be reviewed by a curator to determine if they should be added to the nomenclature. An implementation of the algorithm is demonstrated, using a corpus of published abstracts obtained through the National Library of Medicine's PubMed query service and using "The developmental lineage classification and taxonomy of neoplasms" as a reference nomenclature. RESULTS: A 31+ Megabyte corpus of pathology journal abstracts was parsed using the doublet extraction method. This corpus consisted of 4,289 records, each containing an abstract title. The total number of words included in the abstract titles was 50,547. New candidate terms for the nomenclature were automatically extracted from the titles of abstracts in the corpus. Total execution time on a desktop computer with CPU speed of 2.79 GHz was 2 seconds. The resulting output consisted of 313 new candidate terms, each consisting of concatenated doublets found in the reference nomenclature. Human review of the 313 candidate terms yielded a list of 285 terms approved by a curator. A final automatic extraction of duplicate terms yielded a final list of 222 new terms (71% of the original 313 extracted candidate terms) that could be added to the reference nomenclature. CONCLUSION: The doublet method for automatically extracting candidate nomenclature terms can be used to quickly find new terms from vast amounts of text. The method can be immediately adapted for virtually any text and any nomenclature. An implementation of the algorithm, in the Perl programming language, is provided with this article.
Patient descriptors, or "problems," such as "brain metastases of melanoma" are an effective way for caregivers to describe patients. But most problems, e.g., "cubital tunnel syndrome" or "ulnar nerve compression," found in problem lists in an Electronic Medical Record (EMR) are not comparable computationally--in general, a computer cannot determine whether they describe the same or a related problem, or whether the user would have preferred "ulnar nerve compression syndrome." Metaphrase is a scalable, middleware component designed to be accessed from problem-manager applications in EMR systems. In response to caregivers' informal descriptors it suggests potentially equivalent, authoritative, and more formally comparable descriptors. Metaphrase contains a clinical subset of the 1997 UMLS Metathesaurus and some 10,000 "problems" from the Mayo Clinic and Harvard Beth Israel Hospital. Word and term completion, spelling correction, and semantic navigation, all combine to ease the burden of problem conceptualization, entry and formalization.
BACKGROUND: Multiplication of data sources within heterogeneous healthcare information systems always results in redundant information, split among multiple databases. Our objective is to detect exact and approximate duplicates within identity records, in order to attain a better quality of information and to permit cross-linkage among stand-alone and clustered databases. Furthermore, we need to assist human decision making, by computing a value reflecting identity proximity. METHODS: The proposed method is in three steps. The first step is to standardise and to index elementary identity fields, using blocking variables, in order to speed up information analysis. The second is to match similar pair records, relying on a global similarity value taken from the Porter-Jaro-Winkler algorithm. And the third is to create clusters of coherent related records, using graph drawing, agglomerative clustering methods and partitioning methods. RESULTS: The batch analysis of 300,000 "supposedly" distinct identities isolates 240,000 true unique records, 24,000 duplicates (clusters composed of 2 records) and 3,000 clusters whose size is greater than or equal to 3 records. CONCLUSION: Duplicate-free databases, used in conjunction with relevant indexes and similarity values, allow immediate (i.e. real-time) proximity detection when inserting a new identity.
The French health policy PMSI project (Program for a Medical Information System) is mainly aimed at development of cost analysis based on diagnosis related groups. An indicator of the relative costs of anaesthetics, the relative complexity index (ICR beta), was defined as the result of a computation of different indexes of surgical and anaesthetic procedures such as duration, patients ASA status, degree of emergency, stay in the recovery room. The aims of this study were 1) to analyze relative part of each index in ICR beta value and in ICR beta variance and 2) to assess the value of ICR beta as a cost or a complexity index. The study included 14,435 anaesthetics analyzed with the Dunn and Clarks multiple linear regression. Mean ICR beta was 267.28. The duration of the procedure accounted for 46% of the ICR beta value and surgery for 25%. The anaesthesia and the recovery accounted for 15% of the ICR beta value, but only for 0.0341 and 0.0347 respectively of the ICR beta variance. The product of the surgical procedure index by the ASA status index accounted for 0.259 of the ICR beta variance and the duration index for 0.650. The comparison of homogeneous groups with regard to the surgical procedure shows that intravenous anaesthesia accounts for 12.4% of the ICR beta value, inhalational anaesthesia for 13.84%, but exceeds 15% for regional anaesthesia (44.5% for caudal anaesthesia). For appendectomies (n = 114) duration index is still an important parameter in ICR beta variance (delta R2 = 0.354) but equivalent to the emergency index (delta R2 = 0.363).(ABSTRACT TRUNCATED AT 250 WORDS)
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Conventional relational database management systems fail to address three features of statistical data management in a biomedical/clinical database, namely, that (1) statistical and medical data (SMD) require a great deal of space and need to be stored in a reduced form with minimal duplication; indeed, SMD have many derived/calculated and summary statistics that make the number of attributes in a relation (i.e., a set of records) grow rapidly and dynamically; (2) most SMD have hierarchical structures that are difficult to manage using the relational data model since SMD are stored in separate relations for duplication and space considerations; and (3) the management of SMD is made easier if it is possible to reorganize relations or group data, a capability lacking in conventional relational database management systems. In this paper, we (1) introduce five extended relational operators, (lattice) NEST, (lattice) UNNEST, MERGE, SPREAD, and GEN, to reorganize relations; (2) integrate the extended operators with conventional relational algebra and introduce the concept of the lattice relational model; and (3) give applications of the extended relational operators and the lattice relational model in solving the problems of statistical data manipulation in medical databases.
Activity theory suggests three principles for contextual analysis of clinical cognition: orientation to objects in cognition, role of contradictions in cognition, and the importance of collaboration in cognition. Focusing on the objects of cognition calls attention to differences across medical work settings. There is an interconnection between the type of the object encountered, the physician's generalized conception of the object, and the physician's choice of linearization or lateralization as cognitive strategy. In a consultation the object of medical cognition is locally constructed through a series of mediated actions. Identification of contradictions at the level of the institutional activity system is crucial for the understanding of failures and innovations in actions of medical cognition. A conceptual model for analyzing such contradictions is presented. It is demonstrated that medical cognition is a collaborative achievement between the physician and the patient. Patients use a variety of strategies to turn their experienced health problems into manageable problems. The patient's strategy can and often does influence the physician's strategy and the outcome of the consultation.
Clinical repositories containing large amounts of biological, clinical, and administrative data are increasingly becoming available as health care systems integrate patient information for research and utilization objectives. To investigate the potential value of searching these databases for novel insights, we applied a new data mining approach, HealthMiner, to a large cohort of 667,000 inpatient and outpatient digital records from an academic medical system. HealthMiner approaches knowledge discovery using three unsupervised methods: CliniMiner, Predictive Analysis, and Pattern Discovery. The initial results from this study suggest that these approaches have the potential to expand research capabilities through identification of potentially novel clinical disease associations.