According to Grand View Research, the global digital healthcare market was capped at $288.5 billion in 2024 and is expected to grow at a Compound Annual Growth Rate (CAGR) of 22.2% from 2025 to 2030 to reach $946.04 billion. This shows great promise for the rise of artificial intelligence (AI) market share in the healthcare ecosystem, and the need for integrating AI with EHR.
With North America dominating (revenue share of 37.7%) the global digital health market, there are many healthcare organizations in the US that are already the forerunners in integrating AI with their EHR systems. Some of them being:
Cleveland Clinic: Earlier in 2025, Cleveland announced the introduction of Ambience Healthcare’s AI Platform that helps in clinical documentation integrity (CDI), and point of care coding. Tomislav Mihaljevic MD, Cleveland Clinic CEO and President, said in a LinkedIn post: “…this AI scribe does not remove the human element of medicine. In fact, it enhances it. By lessening their administrative workload, clinicians will be more able to hold focused, personal conversations with patients.”
Geisinger Health System: Late in 2024, there was news about Geisinger partnering with Opmed.ai to enhance operating room (OR) management and streamline decision-making across its 10 hospital campuses in Pennsylvania. They have been using AI and robotics to improve care since the past decade. Geisinger launched its first bot through the Intelligent Automation Hub in December 2019. By 2020, the organization had implemented 21 automations and 20 bots across 31 business units, managing tasks like processing COVID-19 and other viral test results, issuing exposure alerts, handling Medicare Advantage vaccine billing, and submitting data to state health departments.
Mayo Clinic: Recently, Mayo Clinic partnered with Clearwater, Florida-based hellocare.ai, to co-develop ambient clinical intelligence to improve inpatient care and reduce clinician workloads. Mayo Clinic’s Department of Artificial Intelligence and Informatics brings together clinical and research faculty, along with operational staff, to harness informatics and digital technologies aimed at advancing patient care.
Kaiser Permanente: Towards the latter half of 2024, Kaiser Permanente partnered with Abridge to roll-out their medical note-taking AI available to more than 24,000 doctors across its system. Ramin Davidoff, M.D., executive medical director and chair of the board with the Southern California Permanente Medical Group said: “By reducing administrative tasks, we’re making it easier for our physicians to focus on patients and foster an environment where they can provide effective communication and transparency while meeting the individual needs of each patient who comes to them for care.”
Now let’s take a look at some of the benefits and challenges of integrating AI with EHR.
Benefits of AI-powered EHR Platforms
- Reduction in workload of manual documentation
The natural language processing (NLP) feature of AI scribe helps in reducing the time physicians spend on taking notes, which is said to be about 35% of normal working hours on any given day. This goes a long way in reducing physicians’ burden in clinical documentation.
- Optimization of administrative and workflow efficiency
AI-enabled EHRs can optimize healthcare administrative tasks like patient referrals, scheduling consultation appointments, viewing resource availability like beds or medical equipment, seamless sharing of patient information between medical professionals and much more. This in turn can ensure overall workflow optimization.
- Early detection of chronic diseases using AI
AI solutions for chronic disease detection through EHR is another great advantage. These AI models are programmed to detect slight changes in an individual’s biometric data and analyze complex clinical information. This enables prediction of the onset of chronic diseases like diabetes or cardiovascular diseases, which in turn helps physicians to make timely interventions that may prevent or slow down the progression of the condition, potentially saving lives and reducing long-term effects.
- Clinical Decision Support Systems (CDSS)
AI-powered EHRs can swiftly process extensive datasets—such as medical histories, lab reports, and genetic data—within seconds. This capability helps clinicians craft personalized treatment plans based on each patient’s unique profile. By delivering timely, data-driven insights, these AI tools support evidence-based care and promote early, proactive intervention.
- Improving billing and claims using AI in EHR
Apart from helping clinicians to give improved patient care, AI-driven EHRs can boost healthcare facilities’ revenue cycles by streamlining billing and claims processes. Automated tools help catch billing code errors early, ensuring accurate and timely claim submissions. AI also flags inconsistencies in insurance claims, reducing denial rates and speeding up reimbursements.
Challenges of Integrating AI with EHR
- Data privacy
Notwithstanding the many benefits, many cite data privacy concerns in AI-powered EHRs to be a major roadblock in its adoption. Healthcare organizations must adhere to strict regulations when it comes to data privacy and use secure AI tools compliant with HIPAA and HL7, and FHIR. This would make sure that patient data is stored securely and protected from unauthorized access.
- Ethical use and AI bias
AI is a continuous and ever-evolving learning process and it’s only as unbiased as the dataset used to train it. For instance, some algorithms designed to predict patient outcomes have unintentionally deprioritized care for minority groups because of skewed training data. To avoid this, organizations must use diverse and high-quality dataset, conduct regular audits, and monitor and evaluate AI algorithms to ensure fairness and equity.
- Incompatibility with Legacy EHRs
Integrating AI with older EHR systems, which many healthcare facilities still rely on, can become a major challenge as many of these modern AI models may be incompatible. However, experienced developers can help you seamlessly integrate these legacy systems with state-of-the-art AI models, ensuring smooth data flow and healthcare interoperability.
- Upskilling workforce
The effectiveness of even the most advanced AI-EHR system is only as much as the workforce using it. Burdened by busy workdays and, sometimes the tendency to reject or resist new technologies, healthcare staff often face challenges in investing time and effort to upskill themselves to evolving trends. Poor training results in these new AI models not being used to their full potential. Organizations can invest in systems with intuitive, user-friendly interface and comprehensive on-boarding programs to ease this transition.
- Cost
The cost of integrating AI into existing hospital EHR systems can prove to be quite expensive and many mid-size to smaller healthcare providers may not have the required budget or resources to tap into the full potential of digital healthcare.
Should We Approach AI in EHR with Open Hands or Caution?
The introduction of EHR in the early 2000 came with the promises of increasing efficiency, reducing healthcare costs, and most importantly – improving patient care. However, because of the haste in adopting a new technology, the early adopters overlooked many factors and the result was short of what was expected, and the focus fell more on healthcare administrative tasks. Beginning with the amount of time which had to be spent to feed raw data into the EHR systems to poorly designed interfaces gave rise to workflow disruptions and eventually frustrated the healthcare workforce.
Also, if the initial problem was the time consumed for entering patient data, the problem now is the overwhelming volume of information, which the clinicians have to process, and the complexity of the EHRs. This coupled with alert fatigue has resulted in clinicians struggling with missed diagnoses and care inefficiencies. While these models were developed to reduce errors, their widespread adoption has, at times, unintentionally undermined patient safety—the very goal they aimed to support.
Partnering with the right AI expert with a proven track record like DeepKnit AI can mitigate many of the challenges and help you with smart healthcare AI integration.
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