Influencing Decisions With Clinical Artificial Intelligence

An HL7 FHIR trained professional with certifications in TOGAF 9 level 1 and level 2 solution architect, professional academy for healthcare management, RABQSA and six sigma green belt, Abarna at present is leading the offshore solutions team in healthcare which focuses on population health and care management, management of utilization, integrity payment solutions, products of life sciences and analytical services.

The long awaited true reformation in Healthcare is happening now involuntarily amidst the Pandemic. Whilst there seems to be a strong assertion towards Artificial Intelligence transforming many aspects of patient care, Clinical Artificial Intelligence can help fitment of the fundamental blocks in the Patient Care Continuum and Transition of care.

Disrupting the Traditional Approach
The AI era has penetrated the Healthcare Informatics world with key categories of applications involving diagnosis and treatment recommendations, patient engagement and adherence, and administrative activities. Today, deep learning algorithms are spotting abnormalities in CT scans, MRIs powering intelligent screenings and also questioning Radiologists' medical perspectives. While, focusing on the foundational aspects can augment the smart/intelligent medical systems with better outcomes, Hospitals need to successfully innovate with strong AI Infrastructure supplemented by the right team construct, Resources, Policies and the Domain acumen to yield best benefits.

Disruption in the clinical workflow with AI will be appreciated only if it can allow seamless transitions, and perhaps reduce clinician burnout.

Building the Clinical AI Team
Currently, the physician systems and teams are able to build intelligent workflows, however in reality the disintegrated data, teams, process and systems can often by accompanied by data design issues, models not being calibrated to appropriate cohorts, limited retrospective insights, short algorithm circles, unavailability of most relevant and real time patient data . This state can potentially transform in to a clinically powered Health AI Community with interoperable systems, optimized model fitting patient & physician point of care needs, easy integration in to existing workflows and a framework for continuously learning algorithms. Evidently, organizations need to build a team of AI experts who are knowledgeable about translating insights from clinical AI technologies to actionable steps at the point of care.

The Team of Clinical AI Experts should:
-> Understand the current state of Health Systems & Workflows
-> Create concepts/algorithms that's simplistic and practically applicable to Specialty/Department requirements
-> Involve information technology specialists to play an essential role in the implementation, utilization and enhancement of the infrastructure
-> Deploy the Deep Learning algorithms and acquire the feedback from the systems to recalibrate the models continuously
-> Standardize the best practices with assessment of the model, measure performance and gauge patient and physician experience factors

In the Physicians world, Departmentalization of Medical Practices definitely helps in gaining granular focus. However, a true disruption in Healthcare practices can be influenced by a clinically powerful AI expert team supporting the Clinicians with interoperable systems and infrastructure. This can truly enable realizing the expected clinical and financial outcomes more effectively.