Overcoming Data Silos in Healthcare with Strategies for Enhancing Integration and Interoperability to Improve Clinical and Operational Efficiency

Abstract

Data silos in healthcare limit access to complete patient records, leading to delays, duplicate tests, and increased risk of errors. They also reduce operational efficiency by complicating resource coordination and technology adoption. These silos arise from fragmented IT systems, legacy infrastructure, and inconsistent data standards, which prevent the exchange of information across departments and healthcare platforms. The resulting data isolation negatively impacts patient outcomes, increases operational inefficiencies, and hinders the adoption of advanced technologies such as artificial intelligence and predictive analytics. This paper examines the root causes of data silos, including technical and organizational barriers, and explores their effects on healthcare delivery. It proposes a framework to overcome these challenges featuring centralized data lakes, interoperable health information systems (HIS) based on standards such as HL7 FHIR, and the standardization of data formats. The framework emphasizes robust data governance practices and cross-departmental collaboration to support effective data sharing. This study argues that healthcare organizations can achieve greater interoperability, improve clinical outcomes, streamline operations, and unlock the potential of data-driven technologies by implementing these strategies.