Augmented Reality and Virtual Reality Transforming Spinal Imaging Landscape: A Feasibility Study

Importance Increasing value requires improving quality or decreasing costs. In surgery, estimates for the cost of 1 minute of operating room (OR) time vary widely. No benchmark exists for the cost of OR time, nor has there been a comprehensive assessment of what contributes to OR cost. Objectives To calculate the cost of 1 minute of OR time, assess cost by setting and facility characteristics, and ascertain the proportion of costs that are direct and indirect. Design, Setting, and Participants This cross-sectional and longitudinal analysis examined annual financial disclosure documents from all comparable short-term general and specialty care hospitals in California from fiscal year (FY) 2005 to FY2014 (N = 3044; FY2014, n = 302). The analysis focused on 2 revenue centers: (1) surgery and recovery and (2) ambulatory surgery. Main Outcomes and Measures Mean cost of 1 minute of OR time, stratified by setting (inpatient vs ambulatory), teaching status, and hospital ownership. The proportion of cost attributable to indirect and direct expenses was identified; direct expenses were further divided into salary, benefits, supplies, and other direct expenses. Results In FY2014, a total of 175 of 302 facilities (57.9%) were not for profit, 78 (25.8%) were for profit, and 49 (16.2%) were government owned. Thirty facilities (9.9%) were teaching hospitals. The mean (SD) cost for 1 minute of OR time across California hospitals was $37.45 ($16.04) in the inpatient setting and $36.14 ($19.53) in the ambulatory setting ( P  = .65). There were no differences in mean expenditures when stratifying by ownership or teaching status except that teaching hospitals had lower mean (SD) expenditures than nonteaching hospitals in the inpatient setting ($29.88 [$9.06] vs $38.29 [$16.43]; P  = .006). Direct expenses accounted for 54.6% of total expenses ($20.40 of $37.37) in the inpatient setting and 59.1% of total expenses ($20.90 of $35.39) in the ambulatory setting. Wages and benefits accounted for approximately two-thirds of direct expenses (inpatient, $14.00 of $20.40; ambulatory, $14.35 of $20.90), with nonbillable supplies accounting for less than 10% of total expenses (inpatient, $2.55 of $37.37; ambulatory, $3.33 of $35.39). From FY2005 to FY2014, expenses in the OR have increased faster than the consumer price index and medical consumer price index. Teaching hospitals had slower growth in costs than nonteaching hospitals. Over time, the proportion of expenses dedicated to indirect costs has increased, while the proportion attributable to salary and supplies has decreased. Conclusions and Relevance The mean cost of OR time is $36 to $37 per minute, using financial data from California’s short-term general and specialty hospitals in FY2014. These statewide data provide a generalizable benchmark for the value of OR time. Furthermore, understanding the composition of costs will allow those interested in value improvement to identify high-yield targets. This cross-sectional analysis of annual financial disclosure documents calculates the cost of 1 minute of operating room time, assesses cost by setting and facility characteristics, and ascertains the proportion of costs that are direct and indirect. Questions What is the cost of 1 minute of operating room time, and what contributes to this cost? Findings In this cross-sectional analysis, the mean cost of operating room time in fiscal year 2014 for California’s acute care hospitals was $36 to $37 per minute; $20 to $21 of this amount is direct cost, with $13 to $14 attributable to wages and benefits and $2.50 to $3.50 attributable to surgical supplies. Meaning These numbers are the first standardized estimates of operating room cost; understanding the composition of costs will allow those interested in value improvement to identify high-yield targets.

Background Computer-based applications are increasingly used to support the training of medical professionals. Augmented reality applications (ARAs) render an interactive virtual layer on top of reality. The use of ARAs is of real interest to medical education because they blend digital elements with the physical learning environment. This will result in new educational opportunities. The aim of this systematic review is to investigate to which extent augmented reality applications are currently used to validly support medical professionals training. Methods PubMed, Embase, INSPEC and PsychInfo were searched using predefined inclusion criteria for relevant articles up to August 2015. All study types were considered eligible. Articles concerning AR applications used to train or educate medical professionals were evaluated. Results Twenty-seven studies were found relevant, describing a total of seven augmented reality applications. Applications were assigned to three different categories. The first category is directed toward laparoscopic surgical training, the second category toward mixed reality training of neurosurgical procedures and the third category toward training echocardiography. Statistical pooling of data could not be performed due to heterogeneity of study designs. Face-, construct- and concurrent validity was proven for two applications directed at laparoscopic training, face- and construct validity for neurosurgical procedures and face-, content- and construct validity in echocardiography training. In the literature, none of the ARAs completed a full validation process for the purpose of use. Conclusion Augmented reality applications that support blended learning in medical training have gained public and scientific interest. In order to be of value, applications must be able to transfer information to the user. Although promising, the literature to date is lacking to support such evidence.

Arriving at a medical diagnosis is a highly complex process that is extremely error prone. Missed or delayed diagnoses often lead to patient harm and missed opportunities for treatment. Since medical imaging is a major contributor to the overall diagnostic process, it is also a major potential source of diagnostic error. Although some diagnoses may be missed because of the technical or physical limitations of the imaging modality, including image resolution, intrinsic or extrinsic contrast, and signal-to-noise ratio, most missed radiologic diagnoses are attributable to image interpretation errors by radiologists. Radiologic interpretation cannot be mechanized or automated; it is a human enterprise based on complex psychophysiologic and cognitive processes and is itself subject to a wide variety of error types, including perceptual errors (those in which an important abnormality is simply not seen on the images) and cognitive errors (those in which the abnormality is visually detected but the meaning or importance of the finding is not correctly understood or appreciated). The overall prevalence of radiologists' errors in practice does not appear to have changed since it was first estimated in the 1960s. The authors review the epidemiology of errors in diagnostic radiology, including a recently proposed taxonomy of radiologists' errors, as well as research findings, in an attempt to elucidate possible underlying causes of these errors. The authors also propose strategies for error reduction in radiology. On the basis of current understanding, specific suggestions are offered as to how radiologists can improve their performance in practice.