Patient Records: from Single Events to Elements for Health Planning

Abstract:

Data collected in patient records are not only the kernel of a ward information system, but also the groundwork for planning and evaluating services in health care. The aim of this study was to analyze the problem of aggregate data generation starting from separate items in patient records. After describing the different uses of patient record data, we outline the process which generates aggregate data starting from individual records. This process leads to the definition of the “view on aggregation” as an intermediate step between patient records and aggregate data.

A simplified schema is presented based on the Entity-Relationship model representing a conceptual model of the integration of aggregate data and patient record items. Finally, the role is discussed of automation in this process and the perspectives for its implementation.

Keywords:

Patient Records - Health Care Planning - Statistical Databases

• REFERENCES

• 1 Consorti F. et al. Specification of the Medical Requirements. Commission of European Communities, Advanced Informatics in Medicine, MILORD Project (A-2024), Report A2; 1992
• 2 Nowlan WA, Rector AL, Kay S. et al. PEN & PAD: A doctors’ workstation with intelligent data entry and summaries. In: Proceedings of the 14th Symposium on Computer Applications in Medical Care. Los Alamitos: IEEE CS Press; 1990: 941-2.
• 3 De Zegher I. et al. IRHIS: Intelligent, adaptive information retrieval system as Hospital Information System front end. In: Christensen P. et al. eds. Advances in Medical Informatics, Results of the AIM Exploratory Action. Amsterdam: IOS Press; 1992
• 4 Van Ginneken AM, Liem EB, Moorman PW. Integrating QMR with a computer-based patient record. In: Safran C. ed. Proceedings of the 17th SCAMC. New York: Mc Graw-Hill; 1993: 98-102.
• 5 Ahlsen M, Consorti F, Ferri F. et al. Multimedia patient folders: computerized support for clinical activities in a federated information system. In: Reichert A. et al. eds. Proceedings of MIE 93. London: Freund Publishing House; 1993: 574-8.
• 6 Pryor TA, Gardner RM, Clayton PD, Warner HR. The HELP System. J Med Syst 1983; 07: 87-102.
• 7 Clayton PD, Pryor TA, Wigertz OB, Hrip-csak GM. Issues and structures for sharing knowledge among decision-making systems: The 1989 Arden Homestead Retreat. In: Kingsland LC. ed. Proceedings of the 13th SCAMC. New York: IEEE Computer Society Press; 1989: 116-21.
• 8 American Society for Testing and Materials (ASTM). E 1460: Arden Syntax for Medical Logic Modules, v. 14.01. Philadelphia: ASTM; 1992
• 9 Johansson B, Bergqvist Y. Integrating decision support, based on the Arden Syntax, in a clinical laboratory environment. In: Safran C. ed. Proceedings of the 17th SCAMC. New York: Mc Graw-Hill; 1993: 394-8.
• 10 Pryor TA, Hripcsak C. Sharing MLM’s: An Experiment between Columbia-Presbyterian and LDS Hospital. In: Safran C. ed. Proceedings of the 17th SCAMC. New York: Mc Graw-Hill; 1993: 399-403.
• 11 Shoshani A, Wong HKT. Statistical and scientific database issues. IEEE Trans Softw Engin 1985; 11: 1040-7.
• 12 Hand DJ. Microdata, macrodata and metadata. In: Proceedings COMPSTAT 92, vol. II. Berlin: Physica-Verlag; 1992: 325-40.
• 13 Klug A. Equivalence of relational algebra and relational calculus query language having aggregate functions. J ACM 1982; 29: 699-717.
• 14 Ozsoyoglu G, Ozsoyoglu ZM, Matos V. Extending relational algebra and relational calculus with set-valued attributes and aggregate functions. ACM Transaction on Database Systems 1987; 12: 566-92.
• 15 SISU, Swedish Institute for Systems Development. Handbook Business Modeler, Version 2.1. 1992
• 16 Codd EF. A relational model of data for large shared data bank. Comm ACM 1970; 13: 377-87.
• 17 Rafanelli M, Ricci FL. Mefisto: a functional model for statistical entities. IEEE Trans Knowl Data Engin 1993; 05: 670-81.
• 18 Meo-Evoli L, Ricci FL, Shoshani A. On the | semantic completeness of macro-data operators for statistical aggregation. In: Proceedings of the VII International Working Conference on Scientific and Statistical Database Management. Berlin: Springer Verlag; 1992: 234-58.
• 19 Meo-Evoli T, Rafanelli M, Ricci FL. The relational model and the statistical tables. Statistical Software Newsletter 1990; 18: 78-84.
• 20 Chen PPS. The Entity Relationship Model: Toward a unifying view of data. ACM Trans Database Systems 1976; 01: 9-36.
• 21 Falcitelli G, Pisanelli DM, Rafanelli M, Ricci FL. Expert interface for epidemiologic data management. In: O’Moore RR. et al. Proceedings of MIE 90. Berlin: Springer Verlag; 1990: 580-7.
• 22 Raschetti R. EPIAIM: artificial intelligence and epidemiology. In: Noothoven van Goor J, Christensen JP. eds. Advances in Medical Informatics, Results of the AIM Exploratory Action. Amsterdam: IOS Press; 1992: 102-7.
• 23 Pisanelli DM, Rossi AMori, Coacci V. GIA-NO: a decision aid to represent the semantics of medical terms. In: Adlassnig KP. et al eds. Proceedings of MIE 91. Berlin: Springer Verlag; 1991: 744-8.
• 24 Baud RH, Rassinoux AM, Scherrer JR. Natural language processing and semantical representation of medical texts. Meth Inform Med 1992; 31: 117-25.
• 25 Rector AL, Nowland WA, Kay S. Foundations of an electronic medical record. Methods of Information in Medicine 1991; 30 (02) 179-86.
• 26 Ferri F, Grifoni P, Meo-Evoli L, Pisanelli DM, Ricci FL. ADAMS: aggregate data management system for epidemiologists and health-care managers. Comput Meth Progr Biomed 1993; 40: 43-53.
• 27 Ozsoyoglu G, Ozsoyoglu ZM. Statistical database query language. IEEE Trans Softw Engin 1985; 11: 1071-81.