Restructuring Routinely Collected Patient Data: ORCA Applied to Andrology

Hospital information systems do not always cover all required detail per specialty. This may lead to scattering of data over disparate systems and the paper record. The ORCA (Open Record for CAre) CPR offers a generic structure for record sharing, and record keeping tailored to specific needs. We studied whether a semantic integration of existing and new data was possible, using the ORCA structure. Existing andrology data, originating from separate sources, were utilized for this purpose. During normalization, validation and explication steps, latent problems in the source data were exposed and removed, followed by a merge with new data items. By conversion of source data to ORCA, a unique representation of medical concepts in the database was attained, facilitating retrieval of univocal data for multiple purposes. We conclude that the expansion to the andrology domain, including transparent integration of existing data, provides support for the generality of ORCA.

Keywords

Data Modeling - Computerized Medical Record Systems - Data Collection - Ambulatory Care Systems

⁴ The present address of T. Timmers is: Department of Medical Informatics, Academic Medical Center, Amsterdam, The Netherlands

• REFERENCES

• 1 Reiser SJ. The clinical record in medicine. Part 2: Reforming content and purpose. Ann Intern Med 1991; 114: 980-5.
• 2 Dick RS, Steen EB. eds. The Computer-based Patient Record: an Essential Technology/or Health Care. Committee on Improving the Patient Record. Division of Health Care Services. Institute of Medicine. Washington DC: National Academy Press; 1991
• 3 Trace D, Naeymi-Rad F, Haines D. et al. Intelligent Medical Record-entry (IMR-E). J Med Syst 1993; 17: 139-51.
• 4 Gouveia-Oliveira A, Salgado NC, Azevedo AP. et al. A unified approach to the design of clinical reporting systems. Meth Inform Med 1994; 33: 479-87.
• 5 Bernauer J. Conceptual graphs as an operational model for descriptive findings. In: Clayton PD. ed. Proceedings of the 15th Symposium on Computer Applications in Medical Care. . New York: McGraw-Hill Inc; 1991: 214-8.
• 6 Moorman PW, van Ginneken AM, van der Lei J, van Bemmel JH. A model for structured data entry based on explicit descriptional knowledge. Meth Inform Med 1994; 33: 454-63.
• 7 Dolin RH. Modeling the relational complexities of symptoms. Meth Inform Med 1994; 33: 448-53.
• 8 Bell DS, Greenes RA, Doubilet P. Form-based clinical input from a structured vocabulary: initial application in ultrasound reporting. In: Frisse ME. ed. Proceedings of the 16th Symposium on Computer Applications in Medical Care. . New York: McGraw-Hill Inc; 1992: 789-90.
• 9 Nygren E, Johnson M, Henriksson P. Reading the medical record. II. Design of a human-computer interface for basic reading of computerized medical records. . Comput Meth Prog Biomed 1992; 39: 13-25.
• 10 Nygren E, Henriksson P. Reading the medical record. I. Analysis of physicians’ ways of reading the medical record. Comput Methods Programs Biomed 1992; 39: 1-12.
• 11 Lemaitre D, Sauquet D, Fofol I, Tanguy L, Jean FC, Degoulet P. Legacy systems: managing evolution through integration in a distributed and object-oriented computing environment. In: Gardner RM. ed. Proceedings of the 19th Symposium on Computer Applications in Medical Care. . Philadelphia: Hanley & Belfus Inc; 1995: 132-6.
• 12 Timmers T, Van Mulligen EM, Van den Heuvel F. Integrating clinical databases in a medical workstation using knowledge-based modeling. In: Lun KC, Degoulet P, Piemme TE, Rienhoff O. eds. MEDINFO ‘92. . Elsevier: 1992: 478-82.
• 13 Van Mulligen EM, Timmers T, van Bemmel JH. A new architecture for integration of heterogeneous software components. Meth Inform Med 1993; 32: 292-301.
• 14 Barnett GO. The application of computer-based medical-record systems in ambulatory practice. N Engl J Med 1984; 310: 1643-50.
• 15 Frisse ME, Schnase JL, Metcalfe ES. Models for patient records. Acad Med 1994; 69: 546-50.
• 16 Van Ginneken AM, Stam H, Duisterhout JS. A powerful macro-model for the computer patient record. In: Ozbolt JG. ed. Proceedings of the 18th Symposium on Computer Applications in Medical Care. . Philadelphia: Hanley & Belfus Inc; 1994: 496-500.
• 17 Van Ginneken AM, Stam H, Moorman PW. A multi-strategy approach for medical records of specialists. Int J Biomed Comput 1996; 42: 21-6.
• 18 Van Ginneken AM, Stam H. Can one patient record accommodate the diversity of specialized care?. In: Gardner RM. ed. Proceedings of the 18th Symposium on Computer Applications in Medical Care. . Philadelphia: Hanley & Belfus Inc; 1995: 406-10.
• 19 Rector AL, Nowlan WA, Kay S. Foundations for an electronic medical record. Meth Inform Med 1991; 30: 179-86.
• 20 Fries JF. Time-oriented patient records and a computer databank. JAMA 1972; 222: 1536-42.
• 21 Safran C. Electronic patient records and clinical research. In: Van Bemmel JH, McCray AT. eds. IMIA Yearbook of Medical Informatics. . Stuttgart: Schattauer; 1995: 98-102.
• 22 Tansel AU, Clifford J, Gadia S, Jajodia S, Segev A, Snodgrass R. Temporal Databases: Theory, Design and Implementation . The Benjamin/Cummings Publ. Co; 1993
• 23 Snodgrass R, Ahn I. Temporal databases. IEEE Comput Sept 1986; 19: 35-42.
• 24 Date CJ. An Introduction to Database Systems. . Reading, Mass: Addison-Wesley; 1990
• 25 Codd EF. The relational model for database management. . Reading, Mass: Addison-Wesley; 1990
• 26 Safran C, Chute CG. Exploration and exploitation of clinical databases. Int J Biomed Comput 1995; 39: 151-6.
• 27 Clemmesen T, Sprechler M. Recording of patients with colorectal cancer on a database: results and advantages. Eur J Surg 1994; 160: 175-8.
• 28 Einbinder JS, Rury C, Safran C. Outcomes research using the electronic patient record: Beth Israel Hospital’s experience with anticoagulation. In: Gardner RM. ed. Proceedings of the 18th Symposium on Computer Applications in Medical Care. . Philadelphia: Hanley & Belfus Inc; 1995: 819-23.
• 29 Safran C. Using routinely collected data for clinical research. Stat Med 1991; 10: 559-64.
• 30 McDonald CJ, Hui SL. The analysis of humongous databases: problems and promises. Stat Med 1991; 10: 511-8.
• 31 Zwetsloot-Schonk JH, van Stiphout WA, Snitker P, van Es LA, Vandenbroucke JP. How to approach a hospital information system as sampling frame. Selection of patients with a percutaneous renal biopsy. Med Inf (Lond) 1991; 16: 287-98.
• 32 Safran C, Porter D, Lightfoot J. et al. ClinQuery: a system for online searching of data in a teaching hospital. Ann Intern Med 1989; 111: 751-6.