Insurance payment for artificial intelligence technology: Methods used by a stroke artificial intelligence system and strategies to qualify for the new technology add-on payment

The first ever insurance reimbursement for an artificial intelligence (AI) system, which expedites triage of acute stroke, occurred in 2020 when the Centers for Medicare and Medicaid Services (CMS) granted approval for a New Technology Add-on Payment (NTAP). Key aspects of the AI system that led to its approval by the CMS included its unique mechanism of action, use of robotic process automation, and clear linkage of the system’s output to clinical outcomes. The specific strategies employed encompass a first-case scenario of proving reimbursable value for improved stroke outcomes using AI. Given the rapid change in utilization of AI technology in stroke care, we describe the economic drivers of stroke AI systems in healthcare, focusing on concepts of reimbursement for value added by AI to the stroke care system. This report reviews (1) the successful approach used by the first NTAP-approved AI system, (2) economic variables in insurance reimbursement for AI, and (3) resultant strategies that may be utilized to facilitate qualification for NTAP reimbursement, which may be adopted by other AI systems used in stroke care.

Applications of a geographic information system–based business intelligence system for decision making at Coffee Refresh

This case is designed to illustrate how to utilize a business intelligence framework and a geographic information system to make better decisions on franchise opening requests. The case started when Jong, the senior certified analytics professional, and his team were drafting a proposal presentation on a new franchise approval process to present to Tony, the director of Coffee Refresh. Currently, Coffee Refresh was experiencing a significant delay in approving franchise opening requests. In addition, the current approval process was relied on the appraisal teams’ evaluations, which involved self-judgment as to the accessibility and visibility of the store location in each franchise opening request and a manual count of the potential targeted customers at each location, which impacted how the appraisal teams estimated the expected revenue for each location. During the first meeting with Jong and his team, Tony was frustrated by the current evaluation process, resulting in a significant backlog of requests pending approval, which was far behind the target for its franchise branch expansion strategy. It had also been reported that the branches that opened during the past few years had a relatively low success ratio (losses between 4.8% and 16.5%) and that the rate of branch closures had increased from 2% to 7.5%. Currently, eight franchise opening requests were pending, and Jong had to provide a recommendation for which requests should be approved. Coffee Refresh could approve all of them instantly, approve some of them, or even decline all of them. This was a great opportunity for Jong and his team to revisit the current franchise approval process and to demonstrate how business intelligence systems could improve the request approval process as well as address the issues of the current franchises’ underperformance and the closure rates.

Ethical Considerations of Using VR in Mental Health Training

Mental health students, who are still undergoing training, might find it challenging to visualise and fully understand what their patients experience. For this reason, the authors created a virtual reality simulator which mimics the symptoms of a person suffering from schizophrenia at a virtual workplace. The simulation is managed by an artificial intelligence system which asks the user to attempt simple tasks, while simultaneously facing both visual and auditory hallucinations. The AI also adapts the storyline and character behaviour dynamically to increase the immersiveness of the experience. A pilot study was carried out, and the initial results were very encouraging. In fact, the absolute majority of the users stated that the simulation has helped increase their understanding of schizophrenia. In this chapter, the authors evaluate this experiment but from a different perspective. They focus mainly on the use of emerging technologies such as AI and VR and discuss the ethical considerations of their use within the field of mental health.

Developing a Conceptual Framework for Zakat Collection and Distribution Impact on Social Welfare Through Implications of SDGs

Although Islamic social finance including zakat has witnessed an upsurge in development in quantum, which reached US$2 trillion in 2015 and is projected to exceed US$3 trillion by 2020, due to a lack of transparency, trust, and timely disclosure to public has resulted in inefficient of zakat collection and ineffective disbursement for the wellbeing of recipients. The reason for this could be neglecting adoption of financial technology specifically blockchain and artificial intelligence system in zakat management to enhance proper collection and efficient distributions timely and effectively reporting to public. Therefore, this research proposes an ‘Islamic Social Welfare Financial Technology' (ISW FinTech) as an innovative framework that assesses zakat institutions operational efficiency of zakat collection, transparency, and effective distribution that would lead to wellbeing of zakat recipients. This framework is based on new six clusters according to their needs and priority, which convert them from being zakat recipients into zakat payers.

Improving Skin cancer Management with ARTificial Intelligence (SMARTI): protocol for a preintervention/postintervention trial of an artificial intelligence system used as a diagnostic aid for skin cancer management in a specialist dermatology setting

IntroductionConvolutional neural networks (CNNs) can diagnose skin cancers with impressive accuracy in experimental settings, however, their performance in the real-world clinical setting, including comparison to teledermatology services, has not been validated in prospective clinical studies.Methods and analysisParticipants will be recruited from dermatology clinics at the Alfred Hospital and Skin Health Institute, Melbourne. Skin lesions will be imaged using a proprietary dermoscopic camera. The artificial intelligence (AI) algorithm, a CNN developed by MoleMap Ltd and Monash eResearch, classifies lesions as benign, malignant or uncertain. This is a preintervention/postintervention study. In the preintervention period, treating doctors are blinded to AI lesion assessment. In the postintervention period, treating doctors review the AI lesion assessment in real time, and have the opportunity to then change their diagnosis and management. Any skin lesions of concern and at least two benign lesions will be selected for imaging. Each participant’s lesions will be examined by a registrar, the treating consultant dermatologist and later by a teledermatologist. At the conclusion of the preintervention period, the safety of the AI algorithm will be evaluated in a primary analysis by measuring its sensitivity, specificity and agreement with histopathology where available, or the treating consultant dermatologists’ classification. At trial completion, AI classifications will be compared with those of the teledermatologist, registrar, treating dermatologist and histopathology. The impact of the AI algorithm on diagnostic and management decisions will be evaluated by: (1) comparing the initial management decision of the registrar with their AI-assisted decision and (2) comparing the benign to malignant ratio (for lesions biopsied) between the preintervention and postintervention periods.Ethics and disseminationHuman Research Ethics Committee (HREC) approval received from the Alfred Hospital Ethics Committee on 14 February 2019 (HREC/48865/Alfred-2018). Findings from this study will be disseminated through peer-reviewed publications, non-peer reviewed media and conferences.Trial registration numberNCT04040114.