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Forbes Healthcare Summit
2017-11-29 - 2017-11-30    
All Day
ForbesLive leverages unique access to the world’s most influential leaders, policy-makers, entrepreneurs, and artists—uniting these global forces to harness their collective knowledge, address today’s critical [...]
29th Annual National Forum on Quality Improvement in Health Care
2017-12-10 - 2017-12-13    
All Day
PROGRAM OVERVIEW The IHI National Forum on December 10–13​, 2017, will bring more than 5,000 brilliant minds in health care to Orla​​ndo, Florida, to find meaningful connections [...]
Dallas Health IT Summit
2017-12-14 - 2017-12-15    
All Day
About Health IT Summits U.S. healthcare is at an inflection point right now, as policy mandates and internal healthcare system reform begin to take hold, [...]
Events on 2017-11-29
Forbes Healthcare Summit
29 Nov 17
New York
Events on 2017-12-14
Dallas Health IT Summit
14 Dec 17
Dallas
Articles Latest News

Pediatric EHR Software Market Size and Projections, 2025 to 2034

EMR Industry

The global pediatric electronic health records (EHR) software market was valued at USD 40.42 billion in 2024 and is projected to grow from USD 43.39 billion in 2025 to approximately USD 82.15 billion by 2034, registering a compound annual growth rate (CAGR) of 7.35% over the forecast period. This growth is primarily driven by the healthcare sector’s ongoing shift toward digitalization. Additionally, the increasing volume of medical records is fueling demand for EHR software, further contributing to market expansion.

Key Highlights of the Pediatric Electronic Health Records (EHR) Software Market

  • The global pediatric EHR software market was valued at USD 40.42 billion in 2024.
  • It is projected to grow to USD 82.15 billion by 2034, expanding at a CAGR of 7.35% from 2025 to 2034.
  • North America led the market in 2024, holding the largest share of global revenue.
  • The Asia Pacific region is expected to register the fastest CAGR during the forecast period.
  • Software services dominated the market by component in 2024 and are expected to retain the leading position through 2034.
  • In terms of deployment, the cloud-based segment accounted for the largest market share in 2024.
  • The on-premises deployment segment is forecast to grow steadily at a notable CAGR between 2025 and 2034.
  • By application, clinical documentation held a significant share of the market in 2024.
  • The patient management segment is expected to witness considerable growth over the forecast period.
  • Based on enterprise size, large enterprises contributed the highest share in 2024.
  • Meanwhile, small and medium enterprises (SMEs) are projected to expand at a strong CAGR from 2025 to 2034.
  • Among end-users, hospitals and clinics generated the majority of the market revenue in 2024.
  • The diagnostic centers segment is expected to grow substantially during the forecast period.

How AI Is Transforming Pediatric Electronic Health Records (EHR) Software
Artificial Intelligence (AI) is significantly reshaping pediatric electronic health records software by streamlining administrative workflows, enhancing diagnostic precision, and improving the overall quality of patient care. AI-powered analytics enable early detection of subtle disease indicators, supporting more informed clinical decision-making.

When integrated with pediatric EHR systems, AI can deliver real-time, evidence-based recommendations tailored to pediatric care. These intelligent insights contribute to more accurate diagnoses and optimized treatment plans. Although pediatric EHR systems are often complex, AI simplifies the interface and supports real-time decision-making, even in intricate clinical scenarios. Beyond improving clinical outcomes and operational efficiency, AI also plays a key role in increasing patient and family engagement.

A notable example is ChatEHR, an AI tool developed at Stanford Medicine, which is currently being piloted by a group of 33 physicians, nurses, and physician assistants. This innovative software allows users to interact with medical records through natural language queries powered by large language models such as GPT-4—making access to critical patient information more intuitive and efficient.