Improving Billing Accuracy Through Enterprise-Wide Standardized Structured Reporting With Cross-Divisional Shared Templates.

OBJECTIVE: We describe our experience in implementing enterprise-wide standardized structured reporting for chest radiographs (CXRs) via change management strategies and assess the economic impact of structured template adoption. METHODS: Enterprise-wide standardized structured CXR reporting was implemented in a large urban health care enterprise in two phases from September 2016 to March 2019: initial implementation of division-specific structured templates followed by introduction of auto launching cross-divisional consensus structured templates. Usage was tracked over time, and potential radiologist time savings were estimated. Correct-to-bill (CTB) rates were collected between January 2018 and May 2019 for radiography. RESULTS: CXR structured template adoption increased from 46% to 92% in phase 1 and to 96.2% in phase 2, resulting in an estimated 8.5 hours per month of radiologist time saved. CTB rates for both radiographs and all radiology reports showed a linearly increasing trend postintervention with radiography CTB rate showing greater absolute values with an average difference of 20% throughout the sampling period. The CTB rate for all modalities increased by 12%, and the rate for radiography increased by 8%. DISCUSSION: Change management strategies prompted adoption of division-specific structured templates, and exposure via auto launching enforced widespread adoption of consensus templates. Standardized structured reporting resulted in both economic gains and projected radiologist time saved.

Assessing Contribution of Higher Order Clinical Risk Factors to Prediction of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients.

The goal of this study was to investigate the application of machine learning models capable of capturing multiplica tive and temporal clinical risk factors for outcome prediction inpatients with aneurysmal subarachnoid hemorrhage (aSAH). We examined a cohort of 575 aSAH patients from Emory Healthcare, identified via digital subtraction angiog- raphy. The outcome measure was the modified Ranking Scale (mRS) after 90 days. Predictions were performed with longitudinal clinical and imaging risk factors as inputs into a regularized Logistic Regression, a feedforward Neural Network and a multivariate time-series prediction model known as the long short-term memory (LSTM) architecture. Through extraction of higher-order risk factors, the LSTM model achieved an AUC of 0.89 eight days into hospitaliza tion, outperforming other techniques. Our preliminary findings indicate the proposed model has the potential to aid treatment decisions and effective imaging resource utilization in high-risk patients by providing actionable predictions prior to the development of neurological deterioration.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains.

Digitizing an Analog Radiography Teaching File Under Time Constraint: Trade-Offs in Efficiency and Image Quality.

We digitized the radiography teaching file at Black Lion Hospital (Addis Ababa, Ethiopia) during a recent trip, using a standard digital camera and a fluorescent light box. Our goal was to photograph every radiograph in the existing library while optimizing the final image size to the maximum resolution of a high quality tablet computer, preserving the contrast resolution of the radiographs, and minimizing total library file size. A secondary important goal was to minimize the cost and time required to take and process the images. Three workers were able to efficiently remove the radiographs from their storage folders, hang them on the light box, operate the camera, catalog the image, and repack the radiographs back to the storage folder. Zoom, focal length, and film speed were fixed, while aperture and shutter speed were manually adjusted for each image, allowing for efficiency and flexibility in image acquisition. Keeping zoom and focal length fixed, which kept the view box at the same relative position in all of the images acquired during a single photography session, allowed unused space to be batch-cropped, saving considerable time in post-processing, at the expense of final image resolution. We present an analysis of the trade-offs in workflow efficiency and final image quality, and demonstrate that a few people with minimal equipment can efficiently digitize a teaching file library.

Factors Associated With Condom Breakage During Anal Intercourse: A Cross-Sectional Study of Men Who Have Sex With Men Recruited in an Online Survey.

BACKGROUND: Within the United States, HIV affects men who have sex with men (MSM) disproportionally compared to the general population. In 2011, MSM represented nearly two-thirds of all new HIV infections while representing less than 2% of the US male population. Condoms continue to be the foundation of many HIV prevention programs; however, the failure rate of condoms during anal intercourse among MSM is estimated to be 0.5% to 8%, and condom breakages leave those affected at high risk for HIV transmission. OBJECTIVE: Estimate the frequency of condom breakage and associated demographic and behavioral factors during last act of anal intercourse using data from a national online HIV prevention survey of MSM. METHODS: From March 19 to April 16, 2009, data were collected on 9005 MSM through an online survey of US MSM recruited through a social networking site. Using multivariable logistic regression and controlling for several demographic and sexual risk behaviors, we estimated odds ratios of the association between condom breakage and several risk factors. RESULTS: In the study, 8063 participants reported having at least one male sexual partner in the last 12 months. The median age of participants was 21 years (range 18-65). More than two-thirds (68.2%, 5498/8063) reported anal intercourse during last sex and 16.90% (927/5498) reported using a condom during last anal intercourse act. Condom breakage was reported by 4.4% (28/635) participants who engaged in receptive anal intercourse and 3.5% (16/459) participants who engaged in insertive anal intercourse, with an overall failure rate of 4.0% (95% CI 3.2%-6.0%). Age (adjusted odds ratio [aOR] per 5 years: 0.53 (95% CI 0.30-0.94), number of male sex partners (aOR per 5 sex partners: 1.03 (95% CI 1.00-1.08), and being high or buzzed during sex with a casual sex partner (aOR: 3.14, 95% CI 1.02-9.60) were associated with condom breakage. CONCLUSIONS: Our results indicate condom breakage is an important problem for MSM that may be more common for younger men, for men with more partners, and during sex with casual partners after alcohol consumption or drug use. A better understanding of why condom breakage occurs more often in these groups is needed to improve educational efforts. Further, during this time of expanded interest in new condom designs, consideration should be given to how condom design might minimize breakage during anal sex.

Big Data and the Future of Radiology Informatics.

Rapid growth in the amount of data that is electronically recorded as part of routine clinical operations has generated great interest in the use of Big Data methodologies to address clinical and research questions. These methods can efficiently analyze and deliver insights from high-volume, high-variety, and high-growth rate datasets generated across the continuum of care, thereby forgoing the time, cost, and effort of more focused and controlled hypothesis-driven research. By virtue of an existing robust information technology infrastructure and years of archived digital data, radiology departments are particularly well positioned to take advantage of emerging Big Data techniques. In this review, we describe four areas in which Big Data is poised to have an immediate impact on radiology practice, research, and operations. In addition, we provide an overview of the Big Data adoption cycle and describe how academic radiology departments can promote Big Data development.