HHE Radiology and imaging supplement 2018 | Page 8

the field of artificial imaging ( AI ) promises opportunities to improve the speed , accuracy , and quality of image interpretation and diagnosis in radiology . 13 Surely AI will find its way into medical imaging ; however , to show how these AI products reduce costs and improve outcomes will require clinical translation and industrial-grade integration into routine workflow . 14

Value should always be defined around the patient , and in a well-functioning health care system , creating value for patients should determine the rewards for all other players in the system . 1 Creating value and contributing to patient outcome in radiology departments starts with well-organised utilisation plans , shorter waiting times , appropriateness criteria , structured and timely reporting and continuous research for better imaging , intervention and therapy . The contribution of radiologists should not be considered as a factory producing imaging examinations , and attention should not be focused on the volume of procedures performed . 4 Importantly , no false economic incentives should be set up , which primarily compensate for the volume of diagnostic or therapeutic measures .

There are different imaging value chain metrics to be considered . The most relevant ones that can be used in the daily practice could fit in five different categories , 15 namely :

• imaging appropriateness 1 , 2

• patient scheduling , preparation and protocol 3 modality operations 4

• reporting

• communication . 5

Guideline compliance , adherence to CDS tools and identification of redundancy are integral parts of the first group , while in the second category , the scheduler response time , the ease of access to scanning facility , compliance with protocols or radiation limits and staff service feedback are of utmost importance . The third category comprises metrics such as on time scanning , room time and contrast reactions or extravasations . In the reporting category , the adherence to ACR incidental finding criteria , the use of standard vocabulary , the time from examination completion to finalised report and the accuracy of report to final diagnosis are the most important metrics . Finally in the fifth category , time for report availability to patient , time for closed-loop critical results reporting , ease of report access by patient and report understood by or consulted to patient are the metrics to be used .

The most important questions regarding the outcome effects induced by imaging are : Did the referring physician find the report information useful ? Did results of imaging change diagnosis or therapy ? Did the use of imaging eliminate need for more invasive or expensive procedures ? Did the use of imaging reduce length of stay ? Complications , patient and referring physician satisfaction are also examination outcomes to be borne in mind .

Conclusions In conclusion , the goal is to achieve a sustainable and affordable care , creating value , better outcomes and satisfaction to both patients and all other players in the healthcare cycle , and of course reduce waste , keeping in mind not just stratospheric amounts of potential global or nationwide money waste ( like those $ 750 billion of waste spent on health care reported in US 16 ) but also those small amounts that we face daily in our practice , such as redundant or inappropriate emergent imaging exams , but summed on a year basis and even on a small hospital in a country such as Portugal 17 could easily equalise a radiologist annual salary . In this way , the role of radiology in healthcare management is pivotal , and radiologists , knowing the unique field of imaging as no one else , are at the forefront to become the master of a lean organisational structure .