Computerized Decision Support Tool Research Articles

An Evaluative Tool for Solid Waste Management

Computer-assisted decision making in the public policy arena has become more prevalent in recent years as policymakers have faced increasing demands for accountability. Numerous support tools have been presented in the literature and applied in practice. This paper presents a new computerized decision support tool customized for a particular public policy issue—solid waste management. The model, Multiple Attribute Decision System, differs from models previously presented in the decision support and solid waste modeling literature by its comprehensive, systems approach. Solid waste systems are shown to be large, complex, multidimensional,\Isquishy\N systems. The comprehensive approach of the Multiple Attribute Decision System addresses these issues by considering human factors as well as the mechanics of solid waste management, and economic and environmental impacts are assessed. This paper uses the solid waste example to highlight the importance of this study’s approach for public policy problems.

Assessing the quality of clinical data in a computer-based record for calculating the pneumonia severity index.

This study examined whether clinical data routinely available in a computerized patient record (CPR) can be used to drive a complex guideline that supports physicians in real time and at the point of care in assessing the risk of mortality for patients with community-acquired pneumonia. Emergency department of a tertiary-care hospital. Retrospective analysis with medical chart review. All 241 inpatients during a 17-month period (Jun 1995 to Nov 1996) who presented to the emergency department and had a primary discharge diagnosis of community-acquired pneumonia. METHODS/MAIN OUTCOME MEASURES: The 20 guideline variables were extracted from the CPR (HELP System) and the paper chart. The risk score and the risk class of the Pneumonia Severity Index were computed using data from the CPR alone and from a reference standard of all data available in the paper chart and the CPR at the time of the emergency department encounters. Availability and concordance were quantified to determine data quality. The type and cause of errors were analyzed depending on the source and format of the clinical variables. Of the 20 guideline variables, 12 variables were required to be present for every computer-charted emergency department patient, seven variables were required for selected patients only, and one variable was not typically available in the HELP System during a patient's encounter. The risk class was identical for 86.7 percent of the patients. The majority of patients with different risk classes were assigned too low a risk class. The risk scores were identical for 72.1 percent of the patients. The average availability was 0.99 for the data elements that were required to be present and 0.79 for the data elements that were not required to be present. The average concordance was 0.98 when all a patient's variables were taken into account. The cause of error was attributed to the nurse charting in 77 percent of the cases and to the computerized evaluation in 23 percent. The type of error originated from the free-text fields in 64 percent, from coded fields in 21 percent, from vital signs in 14 percent, and from laboratory results in 1 percent. From a clinical perspective, the current level of data quality in the HELP System supports the automation and the prospective evaluation of the Pneumonia Severity Index as a computerized decision support tool.

An empirical investigation of judgment feedback and computerized decision support in a prediction task

This study examines the effects on judgment accuracy of cognitive and outcome feedback provided using a computerized decision support tool. Five feedback conditions were examined in a two-stage experiment utilizing 294 participants: an outcome feedback condition, two cognitive feedback conditions (judgment policy feedback and model predictions feedback), and two joint feedback conditions (judgment policy plus outcome feedback, and model predictions plus outcome feedback). In the first stage, decision makers specified the judgment policies (i.e. cue weights and function forms) that they believed they would use in making their earnings predictions. They were then asked to forecast earnings per share for several companies based on average earnings for the last three years, current year gross margin percentage, quick ratio and eamings yield. Using appropriately modified end-user software, feedback was then provided to all participants, except those receiving outcome feedback only. Judgment policy feedback consisted of informing decision makers of the cue weights and function forms underlying their actual predictions, while model predictions feedback consisted of earnings predictions generated from the decision makers' stated judgment policies. In the second stage, decision makers revised or retained their original judgment policies and then made another set of earnings predictions. Outcome feedback, consisting of information about the actual earnings attained by the companies, was then provided to participants in the outcome feedback and joint feedback conditions. This process was then repeated for a new set of companies to determine how the various forms of feedback influenced judgment accuracy. Results indicated that providing decision makers with either type of cognitive feedback, relative to providing outcome feedback, contributed to improvements in judgment accuracy. There were no significant differences between the judgment accuracy of the cognitive feedback conditions and of the respective joint feedback conditions, indicating that adding outcome feedback did not enhance judgment accuracy. Results also suggested that model predictions feedback may be more effective than judgment policy feedback, which in turn is superior to outcome feedback. All cognitive feedback conditions, relative to outcome feedback only, also demonstrated convergence between stated model predictions and actual predictions. These results are discussed in terms of implications for the design of decision support systems for individual judgment tasks.

Clinical utility of an international thyroid database

Assessment of thyroid function is prone to errors from several sources. Confusion is most often due to inappropriate use of tests, especially in patients with acute non-thyroid illness. With these problems in mind we have designed an international prospective thyroid database in three countries, which registers clinical and laboratory data on new patients with suspected thyroid disease. An additional aim was to assess the use of a computerised decision support tool to interpret thyroid data. The database permits rapid access to temporal trends in thyroid tests, which is useful in monitoring therapy and in follow up for hyperthyroidism. Marked contrasts in local clinical practices have highlighted the challenge in providing a valid decision tool to serve all clinical needs. Experience with multi-centre databases such as this hold promise in the drive to coordinate the disciplines of laboratory analysis and clinical decision making.