Introduction: Historically, there has been no objective method of measuring the time required for radiologists to produce reports during normal work. We have created a technique for semi-automated measurement of radiologist reporting time, and through it produced a robust set of absolute time requirements and relative value units for consultant reporting of diagnostic examinations in our hospital. Methods: A large sample of reporting times, recorded automatically by the Radiology Information System (COMRAD, Software Innovations, Christchurch, New Zealand) along with the description of each examination being reported, was placed in a database. Analysis was confined to diagnostic reporting by consultant radiologists. A spreadsheet was produced, listing the total number and the frequency of reporting times of each distinct examination. Outliers with exceptionally long report times (more than 10 min for plain radiography, 30 min for ultrasound, or 60 min for CT or MRI with some exceptions) were culled; this removed 9.5% of the total. Complex CTs requiring separate workstation time were assigned times by consensus. The median time for the remainder of each sample was the assigned absolute reporting time in minutes and seconds. Relative value units were calculated using the reporting time for a single view department chest X-ray of 1 min 38 s including verifying a report made using speech recognition software. Results: A schedule of absolute and relative values, based on over 179 000 reports, forms Table 2 of this paper. Conclusions:The technique provides a schedule of reporting times with reduced subjective input, which is more robust than existing systems for measuring reporting time.

The RANZCR 2016 study ascribable times are ready for use by the Australian and New Zealand radiologist and nuclear medicine specialist community. We hope these times will also stimulate further data collection in our two countries towards a robust, bi-national study ascribable times database.

Introduction: Accurate and transparent measurement and monitoring of radiologist workload is highly desirable for management of daily workflow in a radiology department, and for informing decisions on department staffing needs. It offers the potential for benchmarking between departments and assessing future national workforce and training requirements. We describe a technique for quantifying, with minimum subjectivity, all the work carried out by radiologists in a tertiary department. Methods: Six broad categories of clinical activities contributing to radiologist workload were identified: reporting, procedures, trainee supervision, clinical conferences and teaching, informal case discussions, and administration related to referral forms. Time required for reporting was measured using data from the radiology information system. Other activities were measured by observation and timing by observers, and based on these results and extensive consultation, the time requirements and frequency of each activity was agreed on. An activity list was created to record this information and to calculate the total clinical hours required to meet the demand for radiologist services. Results: Diagnostic reporting accounted for approximately 35% of radiologist clinical time; procedures, 23%; trainee supervision, 15%; conferences and tutorials, 14%; informal case discussions, 10%; and referral-related administration, 3%. The derived data have been proven reliable for workload planning over the past 3 years. Conclusions: A transparent and robust method of measuring radiologists' workload has been developed, with subjective assessments kept to a minimum. The technique has value for daily workload and longer term planning. It could be adapted for widespread use.

Background: Understanding and being able to measure constraints within a health system is crucial if outcomes are to be improved. Current systems lack the ability to capture decision making with regard to tasks performed within a patient journey. The aim of this study was to assess the impact of a mobile task management tool on clinical workflow within an acute general surgical service by analysing data capture and usability of the application tool. Methods: The Cortex iOS application was developed to digitize patient flow and provide real-time visibility over clinical decision making and task performance. Study outcomes measured were workflow data capture for patient and staff events. Usability was assessed using an electronic survey. Results: There were 449 unique patient journeys tracked with a total of 3072 patient events recorded. The results repository was accessed 7792 times. The participants reported that the application sped up decision making, reduced redundancy of work and improved team communication. The mode of the estimated time the application saved participants was 5-9 min/h of work. Of the 14 respondents, nine discarded their analogue methods of tracking tasks by the end of the study period. Conclusion: The introduction of a mobile task management system improved the working efficiency of junior clinical staff. The application allowed capture of data not previously available to hospital systems. In the future, such data will contribute to the accurate mapping of patient journeys through the health system.