Certain diseases and symptoms carry an overrepresentation of cancer. To be able to measure the strength of such an association it is necessary to be able to predict cancer development in the group being observed. A computer program for computers running under the MS DOS operating system has been developed for this purpose. The program is written in the CLIPPER programming language. The estimates are based on incidence and prevalence data from the Swedish Cancer Registry for the years 1958 to 1986. The program also computes confidence intervals based on the Poisson distribution. The results can be printed out or exported to other programs for further analysis.
EHR systems are widely used in hospitals and primary care centres but it is usually difficult to share information and to collect patient data for clinical research. This is partly due to the different proprietary information models and inconsistent data quality. Our objective was to provide a more flexible solution enabling the clinicians to define which data to be recorded and shared for both routine documentation and clinical studies. The data should be possible to reuse through a common set of variable definitions providing a consistent nomenclature and validation of data. Another objective was that the templates used for the data entry and presentation should be possible to use in combination with the existing EHR systems.
We have designed and developed a template based system (called Julius) that was integrated with existing EHR systems. The system is driven by the medical domain knowledge defined by clinicians in the form of templates and variable definitions stored in a common data repository. The system architecture consists of three layers. The presentation layer is purely web-based, which facilitates integration with existing EHR products. The domain layer consists of the template design system, a variable/clinical concept definition system, the transformation and validation logic all implemented in Java. The data source layer utilizes an object relational mapping tool and a relational database.
The Julius system has been implemented, tested and deployed to three health care units in Stockholm, Sweden. The initial responses from the pilot users were positive. The template system facilitates patient data collection in many ways. The experience of using the template system suggests that enabling the clinicians to be in control of the system, is a good way to add supplementary functionality to the present EHR systems.
The approach of the template system in combination with various local EHR systems can facilitate the sharing and reuse of validated clinical information from different health care units. However, future system developments for these purposes should consider using the openEHR/CEN models with shareable archetypes.
There is a growing interest in using care maps in ICU. So far, the emphasis has been on developing the critical path, problem/outcome, and variance reporting for specific diagnoses. This paper presents a conceptual knowledge-based care protocol system design for the ICU. It is based on the manual care map currently in use for managing myocardial infarction in the ICU of the Sturgeon General Hospital in Alberta. The proposed design uses expert rules, object schemas, case-based reasoning, and quantitative models as sources of its knowledge. Also being developed is a decision model with explicit linkages for outcome-process-measure from the care map. The resulting system is intended as a bedside charting and decision-support tool for caregivers. Proposed usage includes charting by acknowledgment, generation of alerts, and critiques on variances/events recorded, recommendations for planned interventions, and comparison with historical cases. Currently, a prototype is being developed on a PC-based network with Visual Basic, Level-Expert Object, and xBase. A clinical trial is also planned to evaluate whether this knowledge-based care protocol can reduce the length of stay of patients with myocardial infarction in the ICU.
Situations managed by clinical practice guidelines (CPGs) usually correspond to general descriptions of theoretical patients that suffer from only one disease in addition to the specific pathology CPGs focus on. The lack of decision support for complex multiple-disease patients is usually transferred to computer-based systems. Starting from the GEM-encoded instance of CPGs, we developed a module that automatically generated IF-THEN-WITH decision rules. A two-stage unification process has been implemented. All the rules whose IF-part is in partial matching with a patient clinical profile were triggered. A synthesis of triggered rules has then been performed to eliminate redundancies and incoherences. All remaining, eventually contradictory, recommendations were displayed to physicians leaving them the responsibility of handling the controversy and thus the opportunity to control the therapeutic decision.
The authors analyze the use of computers in x-ray examinations of gynecologic patients. X-ray signs of infertility, endometritis, tuberculosis, myoma, malignant tumors, etc. were formalized. A total of 131 patients were examined and a council of physicians for these cases was computer-simulated. Variants of computer-processed x-ray diagnoses are presented, their informativeness indexes ranging from 0 to 100%. Programmed processing may be realized via SM-4, SM-1420, IZOT-1016C, Electronika 100-25 computers. The FORTRAN program language was employed to make up the programs.
This paper reports on the results of a large scale mapping of SNOMED CT on scientific medical corpora. The aim is to automatically access the validity, reliability and coverage of the Swedish SNOMED-CT translation, the largest, most extensive available resource of medical terminology. The method described here is based on the generation of predominantly safe harbor term variants which together with simple linguistic processing and the already available SNOMED term content are mapped to large corpora. The results show that term variations are very frequent and this may have implication on technological applications (such as indexing and information retrieval, decision support systems, text mining) using SNOMED CT. Naïve approaches to terminology mapping and indexing would critically affect the performance, success and results of such applications. SNOMED CT appears not well-suited for automatically capturing the enormous variety of concepts in scientific corpora (only 6,3% of all SNOMED terms could be directly matched to the corpus) unless extensive variant forms are generated and fuzzy and partial matching techniques are applied with the risk of allowing the recognition of a large number of false positives and spurious results.