Artificial Intelligence and healthcare
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Supercharge your healthcare data with medical artificial intelligence
With nearly a decade of experience helping healthcare teams optimize software systems, our AI Practice experts can guide stakeholders through the process of planning, implementing, and analyzing cutting-edge technologies that enhance patient care, optimize operations, and drive groundbreaking advancements. Examples include:
NLP in healthcare
Natural language processing-powered chatbots, virtual assistants, and AI augmented health coaches, streamline patient interactions, provide medical information, and offer appointment scheduling assistance.
Analyze patient data to create personalized treatment plans, taking into account genetic information, medical history, current health status, and any other relevant structured or unstructured data.
Leverage AI-driven algorithms to enhance diagnostic accuracy, identify patterns, and provide doctors with invaluable insights for more informed decisions.
AI Predictive Analytics in Healthcare
Anticipate disease outbreaks, patient admission rates, patient acuity, and resource utilization through predictive models, enabling proactive planning and resource allocation.
Remote Patient Monitoring Services
Enhance patient care with wearable devices and AI analytics, allowing real-time monitoring and early intervention for high-risk patients.
Clinical Trials Optimization
AI optimizes the recruitment process for clinical trials by identifying suitable candidates from large patient databases, expediting research timelines.
Implement digital diagnostics or other AI based Software as Medical Device (SaMD) based on analysis of images or other medical device data.
A HUMAN-CENTERED APPROACH
Medical AI: HTD’s vision
Human-centered AI: AI healthcare solutions should exist to improve the lives of patients and to make clinical work easier
Deep domain knowledge: Successful implementation depends on a deep understanding of the clinical workflows and health IT systems
Careful evaluation: AI systems need to be carefully evaluated in multiple ways. Computing performance metrics on test data are necessary but not sufficient.
Rigorous testing: When moving models to the bedside, the underlying AI infrastructure needs to be diligently tested and instrumented. Monitoring care processes is the way to assess if these models are making a positive impact.
AI in healthcare: Examples
Learn more about the use cases, opportunities, and risks for medical AI. HTD's AI Practice Lead Erkin Otles describes the challenges facing AI/ML implementation including lack of technical and people infrastructure in legacy care delivery organizations, and the high-risk nature of AI-assisted diagnosis and treatment.
Jul 25, 2023
11 min read