OBJECTIVES: It is necessary to improve the pathology workflow. A workflow task analysis was performed using a pathology picture archiving and communication system (pathology PACS) in order to propose a user interface for the Pathology PACS considering user experience. METHODS: An interface analysis of the Pathology PACS in Seoul National University Hospital and a task analysis of the pathology workflow were performed by observing recorded video. Based on obtained results, a user interface for the Pathology PACS was proposed. RESULTS: Hierarchical task analysis of Pathology PACS was classified into 17 tasks including 1) pre-operation, 2) text, 3) images, 4) medical record viewer, 5) screen transition, 6) pathology identification number input, 7) admission date input, 8) diagnosis doctor, 9) diagnosis code, 10) diagnosis, 11) pathology identification number check box, 12) presence or absence of images, 13) search, 14) clear, 15) Excel save, 16) search results, and 17) re-search. And frequently used menu items were identified and schematized. CONCLUSIONS: A user interface for the Pathology PACS considering user experience could be proposed as a preliminary step, and this study may contribute to the development of medical information systems based on user experience and usability.
Diagnosis ; Information Systems ; Medical Records ; Pathology* ; Radiology Information Systems ; Seoul ; Task Performance and Analysis ; User-Computer Interface ; Workflow

INTRODUCTIONThere is interest in surveillance systems for outbreak detection at stages where clinical presentation would still be undifferentiated. Such systems focus on detecting clusters of syndromes in excess of baseline levels, which may indicate an outbreak. We model the detection limits of a potential system based on primary care consults for the detection of an outbreak of severe acute respiratory syndrome (SARS).

MATERIALS AND METHODSData from an averaged-sized medical centre were extracted from the Patient Care Enhancement System (PACES) [the electronic medical records system serving the Singapore Armed Forces (SAF)]. Thresholds were set to 3 or more cases presenting with particular syndromes and a temperature reading of >or=38oC (T >or=38). Monte Carlo simulation was used to insert simulated SARS outbreaks of various sizes onto the background incidence of febrile cases, accounting for distribution of SARS incubation period, delay from onset to first consult, and likelihood of presenting with T >or=38 to the SAF medical centre.

RESULTSValid temperature data was available for 2,012 out of 2,305 eligible syndromic consults (87.2%). T >or=38 was observed in 166 consults (8.3%). Simulated outbreaks would peak 7 days after exposure, but, on average, signals at their peak would consist of 10.9% of entire outbreak size. Under baseline assumptions, the system has a higher than 90% chance of detecting an outbreak only with 20 or more cases.

CONCLUSIONSSurveillance based on clusters of cases with T >or=38 helps reduce background noise in primary care data, but the major limitation of such systems is that they are still only able to confidently detect large outbreaks.

Adult ; Body Temperature ; Cluster Analysis ; Communicable Diseases, Emerging ; epidemiology ; Computer Simulation ; Fever ; diagnosis ; Hospitals, Military ; utilization ; Humans ; Medical Records Systems, Computerized ; Middle Aged ; Military Medicine ; Military Personnel ; statistics & numerical data ; Monte Carlo Method ; Personnel, Hospital ; statistics & numerical data ; Primary Health Care ; statistics & numerical data ; Referral and Consultation ; statistics & numerical data ; Sentinel Surveillance ; Severe Acute Respiratory Syndrome ; diagnosis ; epidemiology ; Singapore ; epidemiology

The Monte Carlo method cannot have been used for routine treatment planning because of heavy time consumption for the acceptable accuracy. Since calculation time is proportional to particle histories, we can save time by decreasing the number of histories. However, a small number of histories can cause serious uncertainties. In this study, we proposed Monte Carlo dose computation time and uncertainty reduction method using specially designed filters and adaptive denoising process. Proposed algorithm was applied to 6 MV photon and 21 MeV electron dose calculations in homogeneous and heterogeneous phantoms. Filtering time was negligible comparing to Monte Carlo simulation time. The accuracy was improved dramatically in all situations and the simulation of 1% to 10% number of histories of benchmark in photon and electron dose calculation showed the most beneficial result. The empirical reduction of necessary histories was about a factor of ten to fifty from the result.
Monte Carlo Method ; Uncertainty

The photon energy spectrum of KRISS 60Co irradiation system (AECL Eldorado 8) was calculated by means of the Monte Carlo method. The collimators were modeled realistically, and the material and dimensions of the 60Co sealed source were extracted from the source certificate given by the manufacturer. It was confirmed that the photon energy spectrum of KRISS 60Co irradiation system was in similar shape with those of NRC and BIPM.
Monte Carlo Method

Within six months of the discovery of X-ray in 1895, the technology was used to scan the interior of the human body, paving the way for many innovations in the field of medicine, including an ultrasound device in 1950, a CT scanner in 1972, and MRI in 1980. More recent decades have witnessed developments such as digital imaging using a picture archiving and communication system, computer-aided detection/diagnosis, organ-specific workstations, and molecular, functional, and quantitative imaging. One of the latest technical breakthrough in the field of radiology has been imaging genomics and robotic interventions for biopsy and theragnosis. This review provides an engineering perspective on these developments and several other megatrends in radiology.
Biological Markers/analysis ; Biomedical Engineering ; Diagnosis, Computer-Assisted/*trends ; Diagnostic Imaging/*trends ; Equipment Design ; Genomics ; Humans ; Image Processing, Computer-Assisted/*trends ; Radiology Information Systems/*trends ; Robotics ; Systems Integration ; User-Computer Interface

OBJECTIVETo study population pharmacokinetics of phenytoin in pediatric patients by using sparse data.

METHODSWe used routinely collected therapeutic drug monitoring data, derived from the steady state serum concentrations of phenytoin in 42 pediatric outpatients with epilepsy. Depending on whether the patients were administered with phenytoin alone or coadministered with phenobabital or clonazepam, the subjects were divided into two groups: phenytoin group and coadministration group. The population parameter and individual parameter of phenytoin in children were estimated using Monte Carlo method.

RESULTSThe children's phenytoin population pharmacokinetic parameters Vm and Km were 9.8 mg.kg(-1).d(-1) and 2.73 mg/L in phenytoin group; and 9.2 mg.kg(-1).d(-1) and 3.24 mg/L in coadministration group. There were good relationship between predicted and determined concentrations with correlation coefficient of 0.999 and 0.984, respectively.

CONCLUSIONThe coadministration of phenobarbital or clonazepam obviously affected the pharmacokinetics of phenytoin. The population pharmacokinetics of phenytoin in children may provide a usefull index for individualization of dosage regimen.

Adolescent ; Child ; Female ; Humans ; Male ; Monte Carlo Method ; Phenytoin ; pharmacokinetics

Monte Carlo method was used for calculation of finite-diameter laser distribution in tissues through convolution operation. Photo-thermal ablation model was set up on the basis of Pennes bioheat equation, and tissue temperature distribution was simulated by using finite element method by ANSYS through the model. The simulation result is helpful for clinical application of laser.
Algorithms ; Finite Element Analysis ; Laser Therapy ; Monte Carlo Method ; Temperature

The dose verification methods in advanced radiotherapy are elaborated in this paper. The usage and application results for various dosimeters in dose verification are explained. As a theoretical method, Monte Carlo simulation, which has been developed greatly in recent years based on the technical progress in computer science, can be also used in dose verification with unique advantages. On the other hand, the principle of dose verification on proton and heavy-ion therapy is discussed briefly. Finally, the evaluation criteria for verification and the future development for dose verification are presented.
Humans ; Monte Carlo Method ; Radiotherapy Dosage ; Radiotherapy Planning, Computer-Assisted

OBJECTIVE: To explore the relationship between sample size in the groups and statistical power of ANOVA and Kruskal-Wallis test with an imbalanced design. METHODS: The sample sizes of the two tests were estimated by SAS program with given parameter settings, and Monte Carlo simulation was used to examine the changes in power when the total sample size varied or remained fixed. RESULTS: In ANOVA, when the total sample size was fixed, increasing the sample size in the group with a larger mean square error improved the statistical power, but an excessively large difference in the sample sizes between groups led to reduced power. When the total sample size was not fixed, a larger mean square error in the group with increased sample size was associated with a greater increase of the statistical power. In Kruskal-wallis test, when the total sample size was fixed, increasing the sample size in groups with large mean square errors increased the statistical power irrespective of the sample size difference between the groups; when total sample size was not fixed, a larger mean square error in the group with increased sample size resulted in an increased statistical power, and the increment was similar to that for a fixed total sample size. CONCLUSIONS The relationship between statistical power and sample size in groups is affected by the mean square error, and increasing the sample size in a group with a large mean square error increases the statistical power. In Kruskal-Wallis test, increasing the sample size in a group with a large mean square error is more cost- effective than increasing the total sample size to improve the statistical power.
Computer Simulation ; Models, Statistical ; Monte Carlo Method ; Sample Size

OBJECTIVE: To compare different methods for calculating sample size based on confidence interval estimation for a single proportion with different event incidences and precisions. METHODS: We compared 7 methods, namely Wald, AgrestiCoull add z2, Agresti-Coull add 4, Wilson Score, Clopper-Pearson, Mid-p, and Jefferys, for confidence interval estimation for a single proportion. The sample size was calculated using the search method with different parameter settings (proportion of specified events and half width of the confidence interval [ω=0.05, 0.1]). With Monte Carlo simulation, the estimated sample size was used to simulate and compare the width of the confidence interval, the coverage of the confidence interval and the ratio of the noncoverage probability. RESULTS: For a high accuracy requirement (ω =0.05), the Mid-p method and Clopper Pearson method performed better when the incidence of events was low (P < 0.15). In other settings, the performance of the 7 methods did not differ significantly except for a poor symmetry of the Wald method. In the setting of ω=0.1 with a very low p (0.01-0.05), failure of iteration occurred with nearly all the methods except for the Clopper-Pearson method. CONCLUSION Different sample size determination methods based on confidence interval estimation should be selected for single proportions with different parameter settings.
Confidence Intervals ; Sample Size ; Computer Simulation ; Monte Carlo Method ; Probability