Laboratory Information Management Systems for Forensic Laboratories: A White Paper for Directors and Decision Makers

Modern, forensics laboratories need Laboratory Information Management Systems (LIMS) implementations that allow the lab to track evidentiary items through their examination lifecycle and also serve all pertinent laboratory personnel. The research presented here presents LIMS core requirements as viewed by respondents serving in different forensic laboratory capacities as well as different forensic laboratory environments. A product-development methodology was employed to evaluate the relative value of the key features that constitute a LIMS, in order to develop a set of relative values for these features and the specifics of their implementation. In addition to the results of the product development analysis, this paper also provides an extensive review of LIMS and provides an overview of the preparation and planning process for the successful upgrade or implementation of a LIMS. Analysis of the data indicate that the relative value of LIMS components are viewed differently depending upon respondents' job roles (i.e., evidence technicians, scientists, and lab management), as well as by laboratory size. Specifically, the data show that: (1) Evidence technicians place the most value on chain of evidence capabilities and on chain of custody tracking; (2) Scientists generally place greatest value on report writing and generation, and on tracking daughter evidence more » that develops during their analyses; (3) Lab. Managers place the greatest value on chain of custody, daughter evidence, and not surprisingly, management reporting capabilities; and (4) Lab size affects LIMS preference in that, while all labs place daughter evidence tracking, chain of custody, and management and analyst report generation as their top three priorities, the order of this prioritization is size dependent. « less

[1]  Pradeep K. Korgaonkar,et al.  An Empirical Comparison of the Predictive Validity of Self-Explicated, Huber-Hybrid, Traditional Conjoint, and Hybrid Conjoint Models , 1983 .

[2]  Dick R. Wittink,et al.  Commercial use of conjoint analysis in Europe: Results and critical reflections , 1994 .

[3]  I. P. Akaah.  Predictive performance of self-explicated, traditional conjoint, and hybrid conjoint models under alternative data collection modes , 1991 .

[4]  J. Hauser,et al.  Profit Maximizing Perceptual Positions: An Integrated Theory for the Selection of Product Features and Price , 1981 .

[5]  Paul E. Green,et al.  Product design strategies for target-market positioning , 1991 .

[6]  V. Mahajan,et al.  New product models: Practice, shortcomings and desired improvements , 1992 .

[7]  Jerry Wind,et al.  Courtyard by Marriott: Designing a Hotel Facility with Consumer-Based Marketing Models , 1989 .

[8]  Manoj K. Agarwal,et al.  Adaptive conjoint analysis versus selfexplicated models: Some empirical results , 1991 .

[9]  D. Wittink,et al.  Commercial Use of Conjoint Analysis: A Survey , 1982 .

[10]  Richard M. Johnson.  Comment on “Adaptive Conjoint Analysis: Some Caveats and Suggestions”7 , 1991 .

[11]  A. Page,et al.  Redesigning product lines with conjoint analysis: How sunbeam does it , 1987 .

[12]  Paul E. Green,et al.  Adaptive Conjoint Analysis: Some Caveats and Suggestions , 1991 .

[13]  R. Verma,et al.  Using conjoint analysis to help design product platforms , 1999 .

[14]  Kenneth C. Laudon,et al.  Management Information Systems: New Approaches to Organization and Technology , 1997 .