The Use of Medical Images in Planning and Delivery of Radiation TherapyKalet, Ira J.; Austin-Seymour, Mary M.
doi: 10.1136/jamia.1997.0040327pmid: 9292839
AbstractThe authors provide a survey of how images are used in radiation therapy to improve the precision of radiation therapy plans, and delivery of radiation treatment. In contrast to diagnostic radiology, where the focus is on interpretation of the images to decide if disease is present, radiation therapy quantifies the extent of the region to be treated, and relates it to the proposed treatment using a quantitative modeling system called a radiation treatment planning (RTP) system. This necessitates several requirements of image display and manipulation in radiation therapy that are not usually important in diagnosis. The images must have uniform spatial fidelity: i.e., the pixel size must be known and consistent throughout individual images, and between spatially related sets. The exact spatial relation of images in a set must be known. Radiation oncologists draw on images to define target volumes; dosimetrists use RTP systems to superimpose quantitative models of radiation beams and radiation dose distributions on the images and on the sets of organ and target contours derived from them. While this mainly uses transverse cross-sectional images, projected images are also important, both those produced by the radiation treatment simulator and the treatment machines, and so-called “digital reconstructed radiographs,” computed from spatially related sets of cross-sectional images. These requirements are not typically met by software produced for radiologists but are addressed by RTP systems. This review briefly summarizes ongoing work on software development in this area at the University of Washington Department of Radiation Oncology.
Accuracy of Data in Computer-based Patient RecordsHogan, William R.; Wagner, Michael M.
doi: 10.1136/jamia.1997.0040342pmid: 9292840
AbstractData in computer-based patient records (CPRs) have many uses beyond their primary role in patient care, including research and health-system management. Although the accuracy of CPR data directly affects these applications, there has been only sporadic interest in, and no previous review of, data accuracy in CPRs. This paper reviews the published studies of data accuracy in CPRs. These studies report highly variable levels of accuracy. This variability stems from differences in study design, in types of data studied, and in the CPRs themselves. These differences confound interpretation of this literature. We conclude that our knowledge of data accuracy in CPRs is not commensurate with its importance and further studies are needed. We propose methodological guidelines for studying accuracy that address shortcomings of the current literature. As CPR data are used increasingly for research, methods used in research databases to continuously monitor and improve accuracy should be applied to CPRs.
A Randomized Trial of “Corollary Orders” to Prevent Errors of OmissionOverhage, J. Marc; Tierney, William M.; Zhou, Xiao-Hua; McDonald, Clement J.
doi: 10.1136/jamia.1997.0040364pmid: 9292842
AbstractObjective: Errors of omission are a common cause of systems failures. Physicians often fail to order tests or treatments needed to monitor/ameliorate the effects of other tests or treatments. The authors hypothesized that automated, guideline-based reminders to physicians, provided as they wrote orders, could reduce these omissions.Design: The study was performed on the inpatient general medicine ward of a public teaching hospital. Faculty and housestaff from the Indiana University School of Medicine, who used computer workstations to write orders, were randomized to intervention and control groups. As intervention physicians wrote orders for 1 of 87 selected tests or treatments, the computer suggested corollary orders needed to detect or ameliorate adverse reactions to the trigger orders. The physicians could accept or reject these suggestions.Results: During the 6-month trial, reminders about corollary orders were presented to 48 intervention physicians and withheld from 41 control physicians. Intervention physicians ordered the suggested corollary orders in 46.3% of instances when they received a reminder, compared with 21.9% compliance by control physicians (p < 0.0001). Physicians discriminated in their acceptance of suggested orders, readily accepting some while rejecting others. There were one third fewer interventions initiated by pharmacists with physicians in the intervention than control groups.Conclusion: This study demonstrates that physician workstations, linked to a comprehensive electronic medical record, can be an efficient means for decreasing errors of omissions and improving adherence to practice guidelines.
Automated Tuberculosis DetectionHripcsak, George; Knirsch, Charles A.; Jain, Nilesh L.; Pablos-Mendez, Ariel
doi: 10.1136/jamia.1997.0040376pmid: 9292843
AbstractObjective: To measure the accuracy of automated tuberculosis case detection.Setting: An inner-city medical center.Intervention: An electronic medical record and a clinical event monitor with a natural language processor were used to detect tuberculosis cases according to Centers for Disease Control criteria.Measurement: Cases identified by the automated system were compared to the local health department's tuberculosis registry, and positive predictive value and sensitivity were calculated.Results: The best automated rule was based on tuberculosis cultures; it had a sensitivity of .89 (95% CI. 75–.96) and a positive predictive value of .96 (.89–.99). All other rules had a positive predictive value less than .20. A rule based on chest radiographs had a sensitivity of .41 (.26–.57) and a positive predictive value of .03 (.02–.05), and a rule that represented the overall Centers for Disease Control criteria had a sensitivity of .91 (.78–.97) and a positive predictive value of .15 (.12–.18). The culture-based rule was the most useful rule for automated case reporting to the health department, and the chest radiograph-based rule was the most useful rule for improving tuberculosis respiratory isolation compliance.Conclusions: Automated tuberculosis case detection is feasible and useful, although the predictive value of most of the clinical rules was low. The usefulness of an individual rule depends on the context in which it is used. The major challenge facing automated detection is the availability and accuracy of electronic clinical data.
Representation of Clinical Practice Guidelines in Conventional and Augmented Decision TablesShiffman, Richard N.
doi: 10.1136/jamia.1997.0040382pmid: 9292844
AbstractObjective: To develop a knowledge representation model for clinical practice guidelines that is linguistically adequate, comprehensible, reusable, and maintainable.Design: Decision tables provide the basic framework for the proposed knowledge representation model. Guideline logic is represented as rules in conventional decision tables. These tables are augmented by layers where collateral information is recorded in slots beneath the logic.Results: Decision tables organize rules into cohesive rule sets wherein complex logic is clarified. Decision table rule sets may be verified to assure completeness and consistency. Optimization and display of rule sets as sequential decision trees may enhance the comprehensibility of the logic. The modularity of the rule formats may facilitate maintenance. The augmentation layers provide links to descriptive language, information sources, decision variable characteristics, costs and expected values of policies, and evidence sources and quality.Conclusion: Augmented decision tables can serve as a unifying knowledge representation for developers and implementers of clinical practice guidelines.