Search

To correlate a radiological assessment of MR motion artifacts with the incidence of repeated sequences and delays derived from modality log files (MLFs) and investigate the suitability of log files for quantifying the operational impact of patient motion.An experienced, blinded neuroradiologist retrospectively evaluated one full calendar month of sequentially obtained clinical MR exams of the head and/or brain for the presence of motion artifacts using a previously defined clinical grading scale. MLF data were analyzed to extract the occurrence of repeated sequences during the examinations. Statistical analysis included the determination of 95% confidence intervals for repetition ratios, and Welch's t-test to exclude the hypothesis of equal means for different groups of sequences.A total of 213 examinations were evaluated, comprising 1681 MLF-documented sequences, from which 1580 were archived. Radiological motion assessment scores (0, none to 4, severe) were assigned to each archived sequence. Higher motion scores correlated with a higher MLF-derived repetition probability, reflected by the average motion scores assigned to sequences that would be repeated (group 1, mean=2.5), those that are a repeat (group 2, mean=1.9), and those that are not repeated (group 3, mean=1.1) within an exam. The hypothesis of equal means was rejected with P = 5.9 × 10Log file data may help assess patterns of scanner and exam performance and may be useful in identifying pitfalls to diagnostic imaging in a clinical environment, particularly with respect to patient motion.

Objective The Liver Imaging Reporting and Data System (LI-RADS) has been widely applied to CT and MR liver observations in patients at high-risk for hepatocellular carcinoma (HCC). We investigated the impact of CT vs MR in upgrading LI-RADS 3 to LI-RADS 5 observations using a large cohort of high-risk patients. Methods We performed a retrospective, longitudinal study of CT and MR radiographic reports (June 2013 - February 2017) with an assigned LI-RADS category. A final population of 757 individual scans and 212 high-risk patients had at least one LI-RADS 3 observation. Differences in observation time to progression between modalities were determined using uni- and multivariable analysis. Results Of the 212 patients with a LI-RADS 3 observation, 52 (25%) had progression to LI-RADS 5. Tp ranged from 64 - 818 days (median: 196 days). One hundred and three patients (49%) had MR and 109 patients (51%) had CT as their index study. Twenty-four patients with an MR index exam progressed to LI-RADS 5 during the follow-up interval, with progression rates of 22% (CI:13%-30%) at 1 year and 29% (CI:17%-40%) at 2 years. Twenty-eight patients with a CT index exam progressed to LI-RADS 5 during follow-up, with progression rates of 26% (CI:16%-35%) at 1 year and 31% (CI:19%-41%) at 2 years. Progression rates were not significantly different between patients whose LI-RADS 3 observation was initially diagnosed on MR vs CT (HR: 0.81, P = 0.44). Discussion MR and CT modalities are comparable for demonstrating progression from LI-RADS 3 to 5 for high risk patients.

Radiology reports often contain follow-up imaging recommendations, but failure to comply with them in a timely manner can lead to delayed treatment, poor patient outcomes, complications, and legal liability. Using a dataset containing 2,972,164 exams for over 7 years, in this study we explored the association between recommendation specificity on follow-up rates. Our results suggest that explicitly mentioning the follow-up interval as part of a follow-up imaging recommendation has a significant impact on adherence making these recommendations 3 times more likely (95% CI: 2.95 - 3.05) to be followed-up, while explicit mentioning of the follow-up modality did not have a significant impact. Our findings can be incorporated into routine dictation macros so that the follow-up duration is explicitly mentioned whenever clinically applicable, and/or used as the basis for a quality improvement project focussed on improving adherence to follow-up imaging recommendations.

BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image degradation in the clinical application of MR imaging (MRI). Here we implement and validate an MRI motion-artifact correction method using a multiscale fully convolutional neural network. MATERIALS AND METHODS: The network was trained to identify motion artifacts in axial T2-weighted spin-echo images of the brain. Using an extensive data augmentation scheme and a motion artifact simulation pipeline, we created a synthetic training dataset of 93,600 images based on only 16 artifact-free clinical MRI cases. A blinded reader study using a unique test dataset of 28 additional clinical MRI cases with real patient motion was conducted to evaluate the performance of the network. RESULTS: Application of the network resulted in notably improved image quality without the loss of morphologic information. For synthetic test data, the average reduction in mean squared error was 41.84%. The blinded reader study on the real-world test data resulted in significant reduction in mean artifact scores across all cases (P

Purpose To evaluate the impact of environmental and socioeconomic factors on outpatient cancellations and “no-show visits” (NSVs) in radiology. Materials and Methods We conducted a retrospective analysis by collecting environmental factor data related to outpatient radiology visits occurring between 2000 and 2015 at our multihospital academic institution. Appointment attendance records were joined with daily weather observations from the National Oceanic and Atmospheric Administration and estimated median income from the US Census American Community Survey. A multivariate logistic regression model was built to examine relationships between NSV rate and median income, commute distance, maximum daily temperature, and daily snowfall. Results There were 270,574 (8.0%) cancellations and 87,407 (2.6%) NSVs among 3,379,947 scheduled outpatient radiology appointments and 575,206 unique patients from 2000 to 2015. Overall cancellation rates decreased from 14% to 8%, and NSV rates decreased from 6% to 1% as median income increased from $20,000 to $120,000 per year. In a multivariate model, the odds of NSV decreased 10.7% per $10,000 increase in median income (95% confidence interval [CI]: 10.3%-11.1%) and 2.0% per 10°F increase in maximum daily temperature (95% CI: 1.3%-1.6%). The odds of NSV increased 1.4% per 10-mile increase in commute distance (95% CI: 1.3%-1.6%) and 4.5% per 1-inch increase in daily snowfall (95% CI: 3.6%-5.3%). Commute distance was more strongly associated with NSV for those in the two lower tertiles of income than the highest tertile (P Conclusion Environmental factors are strongly associated with patients’ attendance at scheduled outpatient radiology examinations. Modeling of appointment failure risk based on environmental features can help increase the attendance of outpatient radiology appointments.

In this paper, we model the statistical properties of imaging exam durations using parametric probability distributions such as the Gaussian, Gamma, Weibull, lognormal, and log-logistic. We establish that in a majority of radiology procedures, the underlying distribution of exam durations is best modeled by a log-logistic distribution, while the Gaussian has the poorest fit among the candidates. Further, through illustrative examples, we show how business insights and workflow analytics can be significantly impacted by making the correct (log-logistic) versus incorrect (Gaussian) model choices.

Identifying areas for workflow improvement and growth is essential for an interventional radiology (IR) department to stay competitive. Deployment of traditional methods such as Lean and Six Sigma helped in reducing the waste in workflows at a strategic level. However, achieving efficient workflow needs both strategic and tactical approaches. Uncertainties about patient arrivals, staff availability, and variability in procedure durations pose hindrances to efficient workflow and lead to delayed patient care and staff overtime. We present an alternative approach to address both tactical and strategic needs using discrete event simulation (DES) and simulation based optimization methods. A comprehensive digital model of the patient workflow in a hospital-based IR department was modeled based on expert interviews with the incumbent personnel and analysis of 192 days’ worth of electronic medical record (EMR) data. Patient arrival patterns and process times were derived from 4393 individual patient appointments. Exactly 196 unique procedures were modeled, each with its own process time distribution and rule-based procedure-room mapping. Dynamic staff schedules for interventional radiologists, technologists, and nurses were incorporated in the model. Stochastic model simulation runs revealed the resource “computed tomography (CT) suite” as the major workflow bottleneck during the morning hours. This insight compelled the radiology department leadership to re-assign time blocks on a diagnostic CT scanner to the IR group. Moreover, this approach helped identify opportunities for additional appointments at times of lower diagnostic scanner utilization. Demand for interventional service from Outpatients during late hours of the day required the facility to extend hours of operations. Simulation-based optimization methods were used to model a new staff schedule, stretching the existing pool of resources to support the additional 2.5 h of daily operation. In conclusion, this study illustrates that the combination of workflow modeling, stochastic simulations, and optimization techniques is a viable and effective approach for identifying workflow inefficiencies and discovering and validating improvement options through what-if scenario testing.

Purpose Radiology reports often contain follow-up imaging recommendations. However, these recommendations are not always followed up by referring physicians and patients. Failure to comply in a timely manner can lead to delayed treatment, poor patient outcomes, unnecessary testing, lost revenue, and legal liability. Therefore, the primary objective of this research was to determine adherence rates to follow-up recommendations. Methods We extracted radiology examination–related data, including report text, for examinations performed between January 1, 2010, and February 28, 2017, from the radiology information system at an academic institution. The data set contained 2,972,164 examinations. The first 6 years were used as the period during which a follow-up recommendation was to be detected, allowing for a maximum of 14 months for a follow-up examination to be performed. Results At least one recommendation for follow-up imaging was present in 10.6% of radiology reports. Overall, the follow-up imaging adherence rate was 58.14%. Mammography had the highest follow-up adherence rate at 69.03%, followed by MRI at 67.54%. Of the modalities, nuclear medicine had the lowest adherence rate at 37.93%. Conclusions This study confirms that follow-up imaging adherence rates are inherently low and vary by modality and that appropriate interventions may be needed to improve compliance to follow-up imaging recommendations.

Objective Across the United States, there is a growing number of patients in Accountable Care Organizations and under risk contracts with commercial insurance. This is due to proliferation of new value-based payment models and care delivery reform efforts. In this context, the business model of radiology within a hospital or health system context is shifting from a primary profit-center to a cost-center with a goal of cost savings. Radiology departments need to increasingly understand how the transactional nature of the business relates to financial rewards. The main challenge with current reporting systems is that the information is presented only at an aggregated level, and often not broken down further, for instance, by type of exam. As such, the primary objective of this research is to provide better visibility into payments associated with individual radiology procedures in order to better calibrate expense/capital structure of the imaging enterprise to the actual revenue or value-add to the organization it belongs to. Materials and methods We propose a methodology that can be used to determine technical payments at a procedure level. We use a proportion based model to allocate payments to individual radiology procedures based on total charges (which also includes non-radiology related charges). Results Using a production dataset containing 424,250 radiology exams we calculated the overall average technical charge for Radiology to be $873.08 per procedure and the corresponding average payment to be $326.43 (range: $48.27 for XR and $2750.11 for PET/CT) resulting in an average payment percentage of 37.39% across all exams. Discussion We describe how charges associated with a procedure can be used to approximate technical payments at a more granular level with a focus on Radiology. The methodology is generalizable to approximate payment for other services as well. Understanding payments associated with each procedure can be useful during strategic practice planning. Conclusions Charge-to-total charge ratio can be used to approximate radiology payments at a procedure level.

This study is concerned with the determination of an optimal appointment schedule in an outpatient-inpatient hospital system where the inpatient exams can be cancelled based on certain rules while the outpatient exams cannot be cancelled. Stochastic programming models were formulated and solved to tackle the stochasticity in the procedure durations and patient arrival patterns. The first model, a two-stage stochastic programming model, is formulated to optimize the slot size. The second model further optimizes the inpatient block (IPB) placement and slot size simultaneously. A computational method is developed to solve the second optimization problem. A case study is conducted using the data from Magnetic Resonance Imaging (MRI) centers of Lahey Hospital and Medical Center (LHMC). The current schedule and the schedules obtained from the optimization models are evaluated and compared using simulation based on FlexSim Healthcare. Results indicate that the overall weighted cost can be reduced by 11.6% by optimizing the slot size and can be further reduced by an additional 12.6% by optimizing slot size and IPB placement simultaneously. Three commonly used sequencing rules (IPBEG, OPBEG, and a variant of ALTER rule) were also evaluated. The results showed that when optimization tools are not available, ALTER variant which evenly distributes the IPBs across the day has the best performance. Sensitivity analysis of weights for patient waiting time, machine idle time and exam cancellations further supports the superiority of ALTER variant sequencing rules compared to the other sequencing methods. A Pareto frontier was also developed and presented between patient waiting time and machine idle time to enable medical centers with different priorities to obtain solutions that accurately reflect their respective optimal tradeoffs. An extended optimization model was also developed to incorporate the emergency patient arrivals. The optimal schedules from the extended model show only minor differences compared to those from the original model, thus proving the robustness of the scheduling solutions obtained from our optimal models against the impacts of emergency patient arrivals.Timestamped operational data was analyzed to identify sources of uncertainty and delays. Stochastic programming models were developed to optimize slot size and inpatient block placement. A case study showed that the optimized schedules can reduce overall costs by 23%. Distributing inpatient and outpatient slots evenly throughout the day provides the best performance. A Pareto frontier was developed to allow practitioners to choose their own best tradeoffs between multiple objectives.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to delayed treatment, poor patient outcomes, complications, unnecessary testing, lost revenue, and legal liability. The objective of this study was to develop a scalable approach to automatically identify the completion of a follow-up imaging study recommended by a radiologist in a preceding report. We selected imaging-reports containing 559 follow-up imaging recommendations and all subsequent reports from a multi-hospital academic practice. Three radiologists identified appropriate follow-up examinations among the subsequent reports for the same patient, if any, to establish a ground-truth dataset. We then trained an Extremely Randomized Trees that uses recommendation attributes, study meta-data and text similarity of the radiology reports to determine the most likely follow-up examination for a preceding recommendation. Pairwise inter-annotator F-score ranged from 0.853 to 0.868; the corresponding F-score of the classifier in identifying follow-up exams was 0.807. Our study describes a methodology to automatically determine the most likely follow-up exam after a follow-up imaging recommendation. The accuracy of the algorithm suggests that automated methods can be integrated into a follow-up management application to improve adherence to follow-up imaging recommendations. Radiology administrators could use such a system to monitor follow-up compliance rates and proactively send reminders to primary care providers and/or patients to improve adherence.

Radiology reports often contain follow-up imaging recommendations. Failure to comply with these recommendations in a timely manner can lead to poor patient outcomes, complications, and legal liability. As such, the primary objective of this research was to determine adherence rates to follow-up recommendations.Radiology-related examination data, including report text, for examinations performed between June 1, 2015, and July 31, 2017, were extracted from the radiology departments at the University of Washington (UW) and Lahey Hospital and Medical Center (LHMC). The UW dataset contained 923,885 examinations, and the LHMC dataset contained 763,059 examinations. A 1-year period was used for detection of imaging recommendations and up to 14-months for the follow-up examination to be performed.On the basis of an algorithm with 97.9% detection accuracy, the follow-up imaging recommendation rate was 11.4% at UW and 20.9% at LHMC. Excluding mammography examinations, the overall follow-up imaging adherence rate was 51.9% at UW (range, 44.4% for nuclear medicine to 63.0% for MRI) and 52.0% at LHMC (range, 30.1% for fluoroscopy to 63.2% for ultrasound) using a matcher algorithm with 76.5% accuracy.This study suggests that follow-up imaging adherence rates vary by modality and between sites. Adherence rates can be influenced by various legitimate factors. Having the capability to identify patients who can benefit from patient engagement initiatives is important to improve overall adherence rates. Monitoring of follow-up adherence rates over time and critical evaluation of variation in recommendation patterns across the practice can inform measures to standardize and help mitigate risk.

To understand why patients "no-show" for imaging appointments, and to provide new insights for improving resource utilization.We conducted a retrospective analysis of nearly 2.9 million outpatient examinations in our radiology information system from 2000 to 2015 at our multihospital academic institution. No-show visits were identified by the "reason code" entry "NOSHOW" in our radiology information system. We restricted data to radiography, CT, mammography, MRI, ultrasound, and nuclear medicine examinations that included all studied variables. These variables included modality, patient age, appointment time, day of week, and scheduling lead time. Multivariate logistic regression was used to identify factors associated with no-show visits.Out of 2,893,626 patient visits that met our inclusion criteria, there were 94,096 no-shows during the 16-year period. Rates of no-show visits varied from 3.36% in 2000 to 2.26% in 2015. The effect size for no-shows was strongest for modality and scheduling lead time. Mammography had the highest modality no-show visit rate of 6.99% (odds ratio [OR] 5.38, P.001) compared with the lowest modality rate of 1.25% in radiography. Scheduling lead time greater than 6 months was associated with more no-show visits than scheduling within 1 week (OR 3.18, P.001). Patients 60 years and older were less likely to miss imaging appointments than patients under 40 (OR 0.70, P.001). Mondays and Saturdays had significantly higher rates of no-show than Sundays (OR 1.52 and 1.51, P .001).Modality type and scheduling lead time were the most predictive factors of no-show. This may be used to guide new interventions such as targeted reminders and flexible scheduling.

Adherence rates for timely imaging follow-up are usually low due to low rates of diligence by referring physicians and/or patients with following recommendations for follow-up imaging. This can lead to delayed treatment, poor patient outcomes, unnecessary testing, and legal liability. Existing follow-up recommendation detection methods are often disease- and modality-specific. To address some of these limitations, we present a generic radiology report processing pipeline that can be used to extract follow-up imaging recommendations by anatomy using an ontology-based approach. Using a large dataset from three hospitals, we discuss our methodology in the context of identifying follow-up imaging recommendations that are related to lung, adrenal and/or thyroid conditions. The algorithm has 99% accuracy (95% CI: 95.8-99%). We also present an interactive dashboard that can be used to understand trends related to follow-up recommendations.

No-show appointments are a troublesome, but frequent, occurrence in radiology hospital departments and private practice. Prior work in medical appointment no-show prediction has focused on general practice and has not considered features specific to the radiology environment. We collect data from 16 years of outpatient examinations in a multi-site hospital radiology department. Data from the radiology information system (RIS) are fused with patient income estimated from U.S. Census data. Features were categorized into three groups: Patient, Exam, and Scheduling. Models based on the total feature set and separately on each feature group were developed using logistic regression to assess the per-appointment likelihood of no-show. After five-fold cross-validation, no-show prediction using the total feature set from 554,611 appointments yielded an area under the curve (AUC) of 0.770 ± 0.003. Feature groups that were most informative in the prediction of no-show appointments were those based on the type of exam and on scheduling attributes such as the lead time of scheduling the appointment. A data-driven no-show prediction model like the one presented here could be useful to schedulers in the implementation of an automated scheduling policy or the assignment of examinations with a high risk of no-show to lower impact appointment slots.

Persons living with HIV (PLWH) are living longer, remaining sexually active, and may continue risky sexual behaviors. As such, it is crucial for providers to ask all HIV-positive patients about behaviors related to HIV transmission and STD acquisition. The “Ask, Screen, Intervene” (ASI) curriculum was developed to increase provider knowledge, skills, and motivation to incorporate risk assessment and prevention services into the care of PLWH. The ASI curriculum was delivered to 2558 HIV-care providers at 137 sites between September 30, 2007 and December 31, 2010. Immediately post-training, participants self-reported significant gains in perceived confidence to demonstrate ASI knowledge and skills (p

With ongoing healthcare payment reforms in the USA, radiology is moving from its current state of a revenue generating department to a new reality of a cost-center. Under bundled payment methods, radiology does not get reimbursed for each and every inpatient procedure, but rather, the hospital gets reimbursed for the entire hospital stay under an applicable diagnosis-related group code. The hospital case mix index (CMI) metric, as defined by the Centers for Medicare and Medicaid Services, has a significant impact on how much hospitals get reimbursed for an inpatient stay. Oftentimes, patients with the highest disease acuity are treated in tertiary care radiology departments. Therefore, the average hospital CMI based on the entire inpatient population may not be adequate to determine department-level resource utilization, such as the number of technologists and nurses, as case length and staffing intensity gets quite high for sicker patients. In this study, we determine CMI for the overall radiology department in a tertiary care setting based on inpatients undergoing radiology procedures. Between April and September 2015, CMI for radiology was 1.93. With an average of 2.81, interventional neuroradiology had the highest CMI out of the ten radiology sections. CMI was consistently higher across seven of the radiology sections than the average hospital CMI of 1.81. Our results suggest that inpatients undergoing radiology procedures were on average more complex in this hospital setting during the time period considered. This finding is relevant for accurate calculation of labor analytics and other predictive resource utilization tools.

The use of ultrasound contrast agents as local drug delivery systems Continues to grow. Current limitations are the amount of drug that can be incorporated as well as the efficiency of drug release Upon insonification. This Study focuses oil the synthesis and characterisation of novel polymeric microcapsules for ultrasound-triggered delivery of lipophilic drugs. Microcapsules with a shell Of fluorinated end-capped poly(L-lactic acid) were made through pre-mix membrane emulsification and contained, apart from a gaseous phase, different amounts of hexadecane oil as a drug-carrier reservoir. Mean number weighted diameters were between 1.22 mu m and 1.31 mu m. High-speed imaging at similar to 10 million fames per second showed that for low acoustic pressures (1 MHz, 0.24 MPa) microcapsules compressed but remained intact. At higher diagnostic pressures of 0.51 MPa, microcapsules cracked, thereby releasing the encapsulated gas and model lipophilic drug, U-Sing conventional ultrasound B-mode imaging at a frequency of 2.5 MHz, a marked enhancement of scatter intensity over a tissue-mimicking phantom was observed for all differently loaded microcapsules. The partially oil-filled microcapsules with high drug loads and well-defined acoustic activation thresholds have great potential for ultrasound-triggered local delivery of lipophilic drugs under ultrasound image-guidance. (C) 2008 Elsevier B.V. All rights reserved.

Intravascular transplantation of bone marrow-derived mesenchymal stem cells (MSCs) is a promising therapeutic approach after acute myocardial infarction. Efficacy and targeting of myocardial cell engraftment are crucial variables determining the therapeutic value of MSC transplantation. Highly focused ultrasound-mediated stimulation of microbubbles (hf-UMS) allows locoregional pre-treatment of target tissue. In a "proof of concept" study, we investigated augmentation of site-targeted MSC engraftment with hf-UMS. We further evaluated the ability of transplanted MSCs to transmigrate across the endothelial barrier into non-ischemic and post-ischemic myocardium in vivo. After acute myocardial ischemia and reperfusion, rats received hf-UMS focused on the anterior left-ventricular wall followed by intravascular transplantation of MSCs. Global and regional myocardial engraftment of MSCs was quantified by means of confocal laser-scanning microscopy; endothelial adhesion, transendothelial migration and invasion of basement membrane were distinguished. Targeted myocardium exhibited higher amount of transplanted MSCs vs. non-targeted tissue. The rate of transendothelial migration was lowest in non-ischemic (41.2+/-2%) compared to post-ischemic myocardium (53+/-5.7%, p

Liver disease is a leading cause of mortality for HIV-infected individuals in the antiretroviral therapy era, and hepatitis C virus (HCV) coinfection is a major factor [1, 2]. The prevalence of HCV coinfection is estimated to be 15%–40% among HIV-infected individuals [3–5]. However, higher rates of HCV coinfection have been reported in specific groups, including injection drug users [6]. Given the clinical significance of HCV coinfection, national guidelines recommend HCV antibody screening for all HIV-infected patients [7, 8]. However, false-negative results of serologic tests for HCV can occur in persons with acute HCV infection(during the period from exposure to HCV antibody seroconversion) and in those with chronic HCV infection. Chronic HCV viremia, or having detectable HCV RNA in serum (HCV RNA positivity) in the absence of detectable HCV antibodies (anti-HCV negativity), has been reported in HIV-uninfected [9–12] and HIV-infected individuals [13–17]. Experimental HCV infection of chimpanzees has also resulted in chronic HCV viremia without the development of HCV antibodies [18]. HIV-infected individuals may be at particular risk for seronegative HCV infection, possibly as a result of immunosuppression, with resultant failure to mount or maintain HCV antibody titers for detection by standard serodiagnostic tests [14, 17, 19]. The reported prevalence of chronic seronegative HCV infection among anti-HCV–negative, HIV-infected patients has varied between 0% and 13.2% [13, 15, 16, 20], depending upon the population studied and the HCV EIA used. Because the total reported number of HIV-infected patients with seronegative HCV infection is small, it has been difficult to establish the risk factors for seronegative HCV infection. We determined the prevalence of chronic seronegative HCV infection in a large, multicenter, nationally representative cohort of HIV-infected men and women. Using data combined from that cohort and data from 3 previously described cohorts, we analyzed the predictors of HCV RNA positivity among anti-HCV–negative, HIV-infected subjects to determine which groups of anti-HCV–negative subjects are at risk for having HCV RNA positivity.

The increasing uptake of information technology in the healthcare domain has resulted in a large volume of digital health data being generated on a regular basis. Most of the health information systems exchange information using HL7 messages making HL7 a good source for data collection. Despite the large volume of data that is generated within the treatment facilities, sharing this data for research and development purposes is not straightforward due to patient privacy protection legislation. In this paper we present an HL7 data processing pipeline that extracts information from complex HL7 data structures, de-identifies fields containing identifiable patient information, and reconstructs HL7 messages using the de-identified data, while providing a secure mechanism for re-identification. Using a dataset of 149,647 production HL7 messages, we demonstrate how the system has over 99% accuracy, suggesting that this is a feasible approach to de-identify patient data.

Purpose: To characterize the group of providers delivering medical care to HIV and hepatitis C (HCV) co-infected homeless and marginally housed individuals in San Francisco and to assess factors affecting provider decisions to initiate HCV treatment in this population. Subjects and methods: The Research in Access to Care for the Homeless (REACH) cohort is a representative sample of HIV-infected homeless and marginally housed individuals identified from single room occupancy hotels, homeless shelters and free lunch programs in San Francisco. Primary care providers (PCP) for active, HIV/HCV co-infected REACH cohort participants were administered face-to-face, semi-structured interviews. REACH participants were administered quarterly face-to-face structured interviews. Results: 52/62 (83.9%) providers were interviewed regarding 133/155 (85.8%) active, HIV/HCV co-infected patients. Providers classified 94/133 (70.7%) patients as ineligible for HCV treatment. The mean number of reasons for ineligibility was 3.2. Most frequent reasons for provider determination of ineligibility included likelihood of poor medication adherence, depression, active injection drug use and patient disinterest in treatment. In addition, structural barriers to treatment included poor access to testing, delays in evaluation by a gastroenterologist and exclusion from treatment of patients with comorbidities. Conclusions: While HCV infection is common, HCV treatment is rare in the HIV/HCV coinfected urban poor. On average, the PCP in this study are experienced and are familiar with this patient population. There are many reasons for providers classifying patients as ineligible for HCV treatment. While these reasons indicate that treatment is difficult given chaotic lifestyle and concurrent medical conditions of this population, they are not insurmountable barriers. New treatments and strategies are necessary to treat this population with high rates of hepatitis C infection.

We describe characterization of digital signals using analogs of thermodynamic quantities: the topological entropy, Shannon entropy, thermodynamic energy, partition function, specific heat at constant volume, and an idealized version of Shannon entropy in the limit of digitizing with infinite dynamic range and sampling rate. We show that analysis based on these quantities is capable of detecting differences between digital signals that are undetectable by conventional methods of characterization based on peak-to-peak amplitude or signal energy. We report the results of applying thermodynamic quantities to a problem from nondestructive materials evaluation: detection of foreign objects (FO) embedded near the surface of thin graphite/epoxy laminates using backscattered waveforms obtained by C-scanning the laminate. The characterization problem was to distinguish waveforms acquired from the region containing the FO from those acquired outside. In all cases the thermodynamic analogs exhibit significant increases (up to 20-fold) in contrast and for certain types of FO materials permit detection when energy or amplitude methods fail altogether.

Objective As are the attenuation coefficient and sound speed, the backscatter coefficient is a fundamental ultrasonic property that has been used to characterize many tissues. Unfortunately, there is currently far less standardization for the ultrasonic backscatter measurement than for the other two, as evidenced by a previous American Institute of Ultrasound in Medicine (AIUM)-sponsored interlaboratory comparison of ultrasonic backscatter, attenuation, and speed measurements (J Ultrasound Med 1999; 18:615-631). To explore reasons for these disparities, the AIUM Endowment for Education and Research recently supported this second interlaboratory comparison, which extends the upper limit of the frequency range from 7 to 9 MHz. Methods Eleven laboratories were provided with standard test objects designed and manufactured at the University of Wisconsin (Madison, WI). Each laboratory was asked to perform ultrasonic measurements of sound speed, attenuation coefficients, and backscatter coefficients. Each laboratory was blinded to the values of the ultrasonic properties of the test objects at the time the measurements were performed. Results Eight of the 11 laboratories submitted results. The range of variation of absolute magnitude of backscatter coefficient measurements was about 2 orders of magnitude. If the results of 1 outlier laboratory are excluded, then the range is reduced to about 1 order of magnitude. Agreement regarding frequency dependence of backscatter was better than reported in the previous interlaboratory comparison. For example, when scatterers were small compared with the ultrasonic wavelength, experimental frequency-dependent backscatter coefficient data obtained by the participating laboratories were usually consistent with the expected Rayleigh scattering behavior (proportional to frequency to the fourth power). Conclusions Greater standardization of backscatter measurement methods is needed. Measurements of frequency dependence of backscatter are more consistent than measurements of absolute magnitude.

To measure Hepatitis C Virus (HCV) prevalence, incidence, and initiation of HCV therapy in a representative HIV-infected cohort of the urban poor.Cohort analysis.The Research and Access to Care for the Homeless (REACH) Cohort is a systematic sample of HIV-infected marginally housed individuals identified from single-room occupancy hotels, homeless shelters, and free lunch programs in San Francisco.Two hundred forty-nine participants with 28.9 months (median) of follow-up were studied. Mean age was 44 (range 24 to 75, standard deviation 8.4) years. Eighty-two percent were male, 43% were African-American, 64% were lifetime injection drug users, and 24% had been on the street or in a shelter in the prior month.We measured HCV testing and treatment history with structured interviews; additionally, participants were tested for HCV antibodies (EIA-2) with RNA viral load confirmation.At baseline, 172 (69.1%) were HCV-positive and 182 (73.1%) were HCV-positive at follow-up, including 155 (62.2%) with viremia. HCV-positive status was associated with having injected drugs, elevated serum alanine aminotransferase, homelessness in the last 1 year, and more severe depressive symptoms. The incidence of new HCV infection was 4.63% per person-year (ppy; 95% confidence interval, 2.31 to 8.13) in the entire cohort and 16.77% ppy among injection drug users. The prevalence of HCV antibody-negative HCV-viremia was 13.2% (10/76). Nonwhites were less likely to receive HCV testing and subspecialty referral, controlled for drug use and other confounders. Sixty-eight percent (123/182) were aware treatment was available; however, only 3.8% (7/182) or 1.16% ppy received HCV treatment.While HCV infection is common, HCV treatment is rare in the HIV-HCV coinfected urban poor. Urban poor, nonwhite individuals are less likely to receive HCV testing and subspecialty referral than their white counterparts. Antibody-negative infection may complicate screening and diagnosis in HIV-infected persons. J

Syphilis has re-emerged in the United States and elsewhere, and clinicians caring for HIV-infected patients are challenged with syphilis diagnosis and management decisions. HIV alters the natural history of syphilis to an extent that is poorly understood, and initial presentation may be more varied in coinfected patients. Although commonly available diagnostic assays for syphilis should be interpreted as usual, such tests rely on antibody measurement and may be an imperfect indicator of active infection. Assessment of all available clinical and risk behavior data remains critically important in the diagnosis of syphilis in coinfected patients. Treatment of syphilis in such patients requires stage-appropriate therapy, with careful serologic monitoring to assess response. Clinicians must have heightened appreciation of the role of frequent risk assessment, serologic screening, symptom recognition, and follow-up of treated patients, as well as an understanding of public health functions such as sex partner treatment and communicable disease reporting.

To define the impact of age on microscopic structural remodeling after myocardial infarction, the physical properties of infarct scar tissue and viable remote zone tissues in young (3 months) and older adult (18 months) Fischer rats were quantified with the use of high-frequency (50 MHz) high-resolution acoustic microscopy 3 months after coronary artery occlusion. We observed that integrated backscatter increased by 100% in the viable zones of old animals after infarction, but remained relatively unaffected in the same regions of younger animals. Mathematical models of myocardial scattering behavior indicated that a 25% increase in stiffness of the extracellular matrix materials in viable zones likely occurred in the older animals. Alterations in gross tissue collagen content were not responsible for this increased stiffness. These observations are compatible with the hypothesis that progressive age-related changes in the quality of the collagen (e.g., excessive age-related crosslinking) rather than its amount per se may have altered the stiffness of the extracellular matrix of remodeled viable tissue in older animals.