Current problems in diagnostic radiology最新文献

Objective: To segregate imaging expenditures from claims data by resource utilization bands (RUBs) and underlying conditions to create an "expenditure map" of pediatric imaging costs.

Methods: A Claims data for children enrolled in a commercial value-based plan were categorized by RUB 0 non-user, 1 healthy user, 2 low morbidity, 3 moderate morbidity, 4 high morbidity, & 5 very high morbidity. The per member per year (PMPY) expense, total imaging spend, and imaging modality with the highest spend were assessed for each RUB. Diagnosis categories associated with high imaging costs were also evaluated.

Results: There were 40,022 pediatric plan members. 14% had imaging-related claims accounting for approximately $2.8 million in expenditures. Member distribution and mean PMPY expenditure RUB was respectively: RUB 0 (3,037, $0), RUB 1 (6,604, $7), RUB 2 - 13,698, $27), RUB 3 - 13,341, $87), RUB 4 (2,810, $268), RUB 5 (532, $841). RUB 3 had the largest total imaging costs at $1,159,523. The imaging modality with the greatest mean PMPY expense varied by RUB with radiography highest in lower RUBs and MRI highest in higher RUBs. The top 3 diagnoses associated with the highest total imaging costs were developmental disorders ($443,980), asthma ($388,797), and congenital heart disease ($294,977) and greatest mean PMPY imaging expenditures malignancy/leukemia ($3,100), transplant ($2,639), and tracheostomy ($1,661).

Discussion: Expense mapping using claims data allows for a better understanding of the distribution of imaging costs across a covered pediatric population. This tool may assist organizations in planning effective cost-reduction initiatives and learning how imaging utilization varies by patient complexity in their system.

Rationale and objectives: Historically radiology resident education has taken the form of workstation and didactic teaching. Due to increasing clinical demand and administrative burden for academic radiologists, the need for more efficient and effective teaching has increased. Flipped classroom teaching, where trainees independently learn material prior to interactive teaching sessions with faculty, is a possible alternative. While the use of flipped teaching in radiology has been studied in the medical student setting, its use in the radiology residency setting has been less published.

Materials and methods: At two academic institutions (University of Washington and Northwestern), exam scores from five PGY-2 Core rotations were collected. Flipped teaching was used for one rotation at the University of Washington (FR). The influence of teaching method, rotation, and institution on exam score was examined. Resident surveys were also collected to understand perceptions of flipped classroom teaching.

Results: At the University of Washington, the mean exam score for the flipped rotation was significantly higher than the majority of other rotations utilizing traditional teaching (p<0.05). Between the University of Washington and Northwestern, there was no difference in exam scores when comparing comparable rotations. Among residents at the University of Washington, the flipped teaching rotation was perceived as more educationally valuable than traditional teaching rotations.

Conclusion: Flipped classroom teaching is at least as effective as the traditional teaching model and associated with better performance on standardized exams at one institution. Among residents, flipped learning is also associated with higher perceived educational value.

To improve awareness and understanding of cybersecurity threats to radiology practice and better equip healthcare practices to manage cybersecurity risks associated with medical imaging, this article reviews topics related to cybersecurity in healthcare, with emphasis on common vulnerabilities in radiology operations. This review is intended to assist radiologists and radiology administrators who are not information technology specialists to attain an updated overview of relevant cybersecurity concepts and concerns relevant to safe and effective practice of radiology and provides a succinct reference for individuals interested in learning about imaging-related vulnerabilities in healthcare settings. As cybersecurity incidents have become increasingly common in healthcare, we first review common cybersecurity threats in healthcare and provide updates on incidence of healthcare data breaches, with emphasis on the impact to radiology. Next, we discuss practical considerations on how to respond to a healthcare data breach, including notification and disclosure requirements, and elaborate on a variety of technical, organizational, and individual actions that can be adopted to minimize cybersecurity risks applicable to radiology professionals and administrators. While emphasis is placed on specific vulnerabilities within radiology workflow, many of the preventive or mitigating strategies are also relevant to cybersecurity within the larger digital healthcare arena. We anticipate that readers, upon completing this review article, will gain a better appreciation of cybersecurity issues relevant to radiology practice and be better equipped to mitigate cybersecurity risks associated with medical imaging.

The magic number, or number of ranks needed to achieve a greater than 90 % chance of matching, has not been investigated for diagnostic radiology (DR). Somewhat reflective of a field's changing competitiveness, this individual metric can be useful for reassuring applicants or identifying a need to reach out to mentors. The NRMP's Charting Outcomes in the Match was accessed over the previous 10 cycles to assess changes to magic number and other match-related metrics. Over the last 10 cycles, there has been an increase in magic number for prospective radiologists. Based on the most 2022 recent report, the magic number was 14 compared to 5 and 2 in 2014 and 2016 respectively. Compared to the average US MD senior, those applying into DR were significantly more likely to match in 2014, 2016 and 2020 (p < 0.01 for all), and significantly less likely to match in 2018 and 2022 (p = 0.03 and p < 0.01, respectively). This trend has had important consequences for applicants and programs as the incentive to apply more widely grows. The increasing magic number demonstrates increasing competitiveness in the field, which might be due to a positive job market, changing medical student preferences, or increased access to radiology electives and mentors. The 2024 Charting Outcomes document will be the first to include data from a class almost entirely affected by the change to a pass/fail Step1 and the new preference signaling supplement. It is currently unclear how either change will affect the overall competitiveness of the field and the magic number.

The Anti-Kickback Statute was passed by Congress in the 1970s to reduce the overuse of government-reimbursed medical services. It attempts to eliminate fraud, abuse, and waste of medical services by outlawing the incentive of personal gain when referring patients for government-funded services. Although safe harbors were written into the law to maintain transactions beneficial to society, they require strict adherence. Anti-Kickback Statute violations are subject to the whistleblower provision of the False Claims Act, and violations can yield significant civil and criminal penalties.

Problem description: Musculoskeletal (MSK) anatomy and pathology from a radiology perspective can be difficult to conceptualize and understand due to the challenge of visualizing 3D structures in stacks of 2D imaging. Consequently, trainees may benefit from inexpensive methods that can help trainees better visualize MSK anatomy and pathology. The purpose of this study is to provide proof of concept for inexpensive methodology to help learners such as radiology residents quickly and inexpensively understand musculoskeletal anatomy and pathology. This can help trainees become better at applying musculoskeletal knowledge to clinical practice.

Institutional methodology: Soft-modeling compounds such as Play-Doh® was utilized in a variety of colors with pottery tools to recreate 3D models of challenging MSK anatomy and pathology for trainees. Qualitative feedback from the residents was collected.

Results: Eighteen different pathological conditions across six major bone structures were modeled with a soft modeling compound. Residents qualitatively identified the experience as educational in terms of helping them better understand MSK pathology and positive in terms of making learning fun, less stressful, and memorable due to uniqueness of the learning modality. Residents report challenges modeling complex anatomical features and pathology via this methodology.

Conclusion: Radiology residents and other learners can enhance their knowledge of musculoskeletal anatomy and pathology via utilization of inexpensive soft modeling compounds. This may offer a cheaper and more time sensitive alternative to current 3-dimensional hardware and software technologies being developed for educational purposes. Additional work needs to be done to examine the utility of this methodology across larger and diverse groups of learners.

Radiology has usually been the field of medicine that has been at the forefront of technological advances, often being the first to wholeheartedly embrace them. Whether it's from digitization to cloud side architecture, radiology has led the way for adopting the latest advances. With the advent of large language models (LLMs), especially with the unprecedented explosion of freely available ChatGPT, time is ripe for radiology and radiologists to find novel ways to use the technology to improve their workflow. Towards this, we believe these LLMs have a key role in the radiology reading room not only to expedite processes, simplify mundane and archaic tasks, but also to increase the radiologist's and radiologist trainee's knowledge base at a far faster pace. In this article, we discuss some of the ways we believe ChatGPT, and the likes can be harnessed in the reading room.