The Challenge: Providing an Information System for a Clinical Laboratory

1999 ◽  
pp. 357-365
Author(s):  
H. M. Aus ◽  
M. Haucke ◽  
A. Steimer
Keyword(s):  
Information System ◽  
Clinical Laboratory

Current Status of a Medical Information System

1970 ◽  
Vol 09 (03) ◽  
pp. 149-160 ◽  
Author(s):  
E. Van Brunt ◽  
L. S. Davis ◽  
J. F. Terdiman ◽  
S. Singer ◽  
E. Besag ◽  
...  
Keyword(s):  
Information System ◽  
Medical Records ◽  
Medical Information ◽  
Clinical Laboratory ◽  
Development Program ◽  
Medical Data ◽  
Direct Access ◽  
Medical Information System ◽  
Current Status ◽  
Central System

A pilot medical information system is being implemented and currently is providing services for limited categories of patient data. In one year, physicians’ diagnoses for 500,000 office visits, 300,000 drug prescriptions for outpatients, one million clinical laboratory tests, and 60,000 multiphasic screening examinations are being stored in and retrieved from integrated, direct access, patient computer medical records.This medical information system is a part of a long-term research and development program. Its major objective is the development of a multifacility computer-based system which will support eventually the medical data requirements of a population of one million persons and one thousand physicians. The strategy employed provides for modular development. The central system, the computer-stored medical records which are therein maintained, and a satellite pilot medical data system in one medical facility are described.

The development of a third generation system for entering microbiology data into a clinical laboratory information system

1988 ◽  
Vol 12 (6) ◽  
pp. 365-382 ◽  
Author(s):  
Arthur A. Eggert ◽  
Kenneth A. Emmerich ◽  
Carol A. Spiegel ◽  
Gary J. Smulka ◽  
Patricia A. Horstmeier ◽  
...  
Keyword(s):  
Information System ◽  
Clinical Laboratory ◽  
Third Generation ◽  
Laboratory Information System ◽  
Generation System

scholarly journals Effects of system tuning and RAM disk on the performance of a clinical laboratory information system

1989 ◽  
Vol 11 (3) ◽  
pp. 119-123 ◽  
Author(s):  
Arthur A. Eggert ◽  
Kenneth A. Emmerich ◽  
Thomas J. Blankenheim ◽  
Gary J. Smulka
Keyword(s):  
Operating System ◽  
Information System ◽  
Computer System ◽  
Clinical Laboratory ◽  
Storage Device ◽  
Laboratory Information System ◽  
Laboratory Computer ◽  
Before And After

Improvements in the performance of a laboratory computer system do not necessarily require the replacement of major portions of the system and may not require the acquisition of any hardware at all. Major bottlenecks may exist in the ways that the operating system manages its resources and the algorithm used for timesharing decisions. Moreover, significant throughput improvements may be attainable by switching to a faster storage device if substantial disk activity is performed. In this study the fractions of time used for each of the types of tasks a laboratory computer system performs (e.g. applications programs, disk transfer, queue cycler) are defined and measured. Methods for reducing the time fractions of the various types of overhead are evaluated by doing before and after studies. The combined results of the three studies indicated that a 50% improvement could be gained through system tuning and faster storage without replacement of the computer itself

scholarly journals Analisis dan Perancangan Sistem Informasi Laboratorium Rumah Sakit Kanker Dharmais Dengan Menggunakan Total Architecture Syntesis

2010 ◽  
Vol 1 (2) ◽  
pp. 561
Author(s):  
Michael Yoseph Ricky
Keyword(s):  
Information System ◽  
Design Methodology ◽  
Clinical Laboratory ◽  
Design Method ◽  
Current System ◽  
Laboratory Information System ◽  
Design And Implementation ◽  
Cancer Hospital ◽  
Analysis Design ◽  
Laboratory Information Systems

The purposes of this research are to design and implementation of laboratory information systems (LIS) at the Laboratory of Cancer Hospital Dharmais (Dharmais Cancer Hospital). The methods used are the method of analysis, design methodology using Total Architecture Synthesis (TAS) and database design methods. Methods include analysis of the survey directly into the clinical laboratory Dharmais Cancer Hospital, and interviews with users who running the current system in Dharmais Cancer Hospital Clinical Laboratory. The design method using TAS. The results of this study is a single integrated Laboratory Information System applications with other systems that exist in Dharmais Cancer Hospital and also the delivery of feature inspection results by using text service and email, in addition to be taken directly to Dharmais Cancer Hospital and sent to the address. The conclusions from this study are all the transactions contained in Dharmais Cancer Hospital Clinical Laboratory have been computerized and integrated. 

Combining Qualitative and Quantitative Methods in IS in Healthcare Revisited

2011 ◽  
pp. 198-211
Author(s):  
Bonnie Kaplan
Keyword(s):  
Information System ◽  
Quantitative Methods ◽  
Clinical Laboratory ◽  
Partial Information ◽  
Patient Record ◽  
Laboratory Information System ◽  
Multimethod Research ◽  
Qualitative And Quantitative ◽  
Qualitative And Quantitative Methods ◽  
Advisory System

Using a variety of research methods provides several advantages. Through multiple methods, different kinds of data are collected, each set of which might provide partial information needed for a complete picture, thereby strengthening the robustness of research results. Research that combines qualitative and quantitative methods, though rare, provides an example of the benefits of multimethod studies. In this chapter, I describe three evaluation research studies that used a combination of qualitative and quantitative methods. In these studies, researchers produced results they would not have achieved if they had not combined methods. The first study is of a pioneering computer-based patient record and clinical decision support system, PROMIS. The second study is of a clinical laboratory information system. The third, a more recent study, evaluated an automated telephone health behavior advisory system. The PROMIS study compared different groups using PROMIS and compared PROMIS users with those using a manual patient record. The laboratory information system and telephone advisory system studies explored differences among ostensibly the same users of the same technology, only to find that these users divided into groups that differed in their responses to the technologies. Such studies could point to considerations other than technology per se that are important in how individuals react to and use technologies. All three studies are examples of how multimethod research can produce significant results.

Corrections of Clinical Chemistry Test Results in a Laboratory Information System

2004 ◽  
Vol 128 (8) ◽  
pp. 890-892
Author(s):  
Sihe Wang ◽  
Virginia Ho
Keyword(s):  
Information System ◽  
Patient Safety ◽  
Error Detection ◽  
Clinical Laboratory ◽  
Improve Patient Safety ◽  
Clinical Chemistry ◽  
Laboratory Information System ◽  
Test Volume ◽  
Laboratory Errors ◽  
Made In

Abstract Context.—The recently released reports by the Institute of Medicine, To Err Is Human and Patient Safety, have received national attention because of their focus on the problem of medical errors. Although a small number of studies have reported on errors in general clinical laboratories, there are, to our knowledge, no reported studies that focus on errors in pediatric clinical laboratory testing. Objective.—To characterize the errors that have caused corrections to have to be made in pediatric clinical chemistry results in the laboratory information system, Misys. To provide initial data on the errors detected in pediatric clinical chemistry laboratories in order to improve patient safety in pediatric health care. Design.—All clinical chemistry staff members were informed of the study and were requested to report in writing when a correction was made in the laboratory information system, Misys. Errors were detected either by the clinicians (the results did not fit the patients' clinical conditions) or by the laboratory technologists (the results were double-checked, and the worksheets were carefully examined twice a day). No incident that was discovered before or during the final validation was included. On each Monday of the study, we generated a report from Misys that listed all of the corrections made during the previous week. We then categorized the corrections according to the types and stages of the incidents that led to the corrections. Results.—A total of 187 incidents were detected during the 10-month study, representing a 0.26% error detection rate per requisition. The distribution of the detected incidents included 31 (17%) preanalytic incidents, 46 (25%) analytic incidents, and 110 (59%) postanalytic incidents. The errors related to noninterfaced tests accounted for 50% of the total incidents and for 37% of the affected tests and orderable panels, while the noninterfaced tests and panels accounted for 17% of the total test volume in our laboratory. Conclusion.—This pilot study provided the rate and categories of errors detected in a pediatric clinical chemistry laboratory based on the corrections of results in the laboratory information system. A direct interface of the instruments to the laboratory information system showed that it had favorable effects on reducing laboratory errors.

A Relational Database Information System for a Small Clinical Laboratory

1989 ◽  
Vol 20 (8) ◽  
pp. 572-576
Author(s):  
Tsai F. Wu ◽  
Emmet J. Lamb
Keyword(s):  
Information System ◽  
Relational Database ◽  
Clinical Laboratory
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