# Bridging Care Gaps: AI Solutions in Healthcare

By Arpita Hazra · Published 2024-03-06 · Updated 2026-03-19 · Healthcare on MarketScale
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> Automated clinical decision support tools powered by machine learning are reshaping how healthcare providers identify and prevent diagnostic gaps

## Key points

- Automated clinical decision support tools powered by machine learning are reshaping how healthcare providers identify and prevent diagnostic gaps

How can AI solutions in healthcare transform patient care by enhancing clinical decision-making and reducing diagnostic errors? Dr. Arpita Hazra, a Clinical Patient Data Safety Specialist, offers compelling insight into integrating artificial intelligence and machine learning models with Electronic Medical Records (EMR) to bridge care gaps in fast-paced healthcare environments. "Artificial intelligence and machine learning models can be used to create clinical decision-making support tools that can integrate into the EMR, which can help fill care gaps in busy healthcare settings. This will reduce the incidence of incorrect diagnosis, reduce the delay in diagnosis and delay in treatment for patients, help the providers in ordering the correct diagnostic tests, and help escalate the patient's care when needed," Hazra said. Artificial intelligence and machine learning models can be used to create clinical decision-making support tools that can integrate into the EMR, which can help fill care gaps in busy healthcare settings.— Dr. Arpita Hazra, Clinical Patient Data Safety Specialist

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