Sarcouncil Journal of Multidisciplinary

Abstract: A basic paradox in healthcare delivery systems worldwide is that there is an astonishing level of technological innovation, and there remains a daily level of deep operational ineffectiveness, resulting in massive disparities between diagnosis and treatment, between what can be done and what gets done. The disintegration of clinical and administrative functions in healthcare organisations creates the accumulated inefficiencies that not only deteriorate the quality of patient care but also increase the cost of operations due to overlapping functions and manual workflow. This article introduces a radical model of healthcare operations by integrating artificial intelligence diagnosis engines into enterprise resource planning platforms systematically and without interference to create an autonomous ecosystem in which AI-generated diagnoses automatically activate downstream operational processes. This architecture takes advantage of cloud-native infrastructure in which advanced machine learning models operate on diagnostic data in standardised formats such as DICOM and HL7, and enterprise-grade middleware solutions can support secure real-time communication between AI engines and ERP systems. The presented features can be operationalized to be used to automatically create patient records, dynamically modify inventory, assign billing codes based on AI diagnostic findings, and automatically prescribe medications upon AI diagnostic work to remove manual handoffs that typically create delays and errors. The implementation of integrated systems in healthcare organisations is associated with significant gains, such as decreased administration processing time, a lower percentage of claim rejection due to high-quality coding, improved inventory optimization due to predictive analytics, and high medication errors due to automated prescription generating capabilities. However, implementation hurdles will have to address some challenging technical, organisational, and regulatory challenges (including the need to standardise data across systems of different types, plus a massive change management effort to encompass workforce accommodations and the increasingly shifting regulatory requirements surrounding the medical uses of AI).