Moving health records into digital systems revolutionised patient care, and now another revolution in patient data is happening with the integration of artificial intelligence (AI) inside electronic health records (EHRs). Adding these computer science breakthroughs into the daily workflows of healthcare professionals (HCPs) has a vast impact on patient outcomes, reducing burnout, and cutting costs. While there are concerns with AI integration into EHRs, such as data quality and privacy challenges, bias, and transparency, the successful implementation of these tools must be pursued. This article focuses on how AI enhances EHRs
With the adoption of EHRs and other digital healthcare solutions, came an explosion of clinical data to analyse. Data is vital for HCPs to make informed decisions about patient care and to uncover treatment trends, but now there is simply too much health information to process for most humans – especially during record levels of HCP burnout. With 73% of patients in a recent survey reporting that the healthcare system fails to meet their needs, and 64% saying HCPs should understand their health journey better, there are obvious gaps between the information available and using it effectively to treat patients.
HCPs in a recent study identified critical areas of care that can be improved by implementing AI: predictive analysis, clinical decision support, data visualisation, and patient monitoring. Not only do patients and providers see room for improvement, but also the majority of healthcare executives agree that these technologies can positively impact global health challenges.
Integrating AI into EHRs can revolutionize data discovery, offering healthcare providers real-time personalised recommendations, patient satisfaction, and risk prediction analytics, equipping HCPs to understand the individual patient’s journey better. The market for AI inside healthcare was valued at $11 billion in 2021, and by 20230, it is projected to be at $187 billion, indicating that the industry will continue to invest in these technologies.
HCPs and patients manage healthcare data within EHRs, and these platforms bring together data from medical records, multiple providers, test results, and pharmaceutical orders. Bringing AI technology into EHR systems would unlock the potential of this data to reduce HCP burnout and improve patient outcomes. By improving the accessibility of patient data, AI can reduce the risk of human error and facilitate personalised treatment recommendations.
The average hospital produces roughly 50 petabytes of data each year, and a staggering 97% of that health data goes unused. Harnessing the power of AI to connect this fragmented data and enhance the quality of care can ultimately improve patient outcomes. Here are five ways AI can change the way HCPs interact with data inside of an EHR:
1. AI inside of an EHR can create patient overviews for their HCP by summarising test results, and patient history, and recommending diagnostic testing.
2. AI algorithms can interpret medical data more accurately and faster than HCPs alone. By analysing information available inside of an EHR, such as medical history, lifestyle, social factors, and genetics, these programs can improve test ordering and diagnosis.
3. AI tools can provide personalised care recommendations in real time using AI-triggered messaging.
4. These algorithms can simplify data input, reduce errors, and provide much-needed reliability to improve healthcare delivery.
5. Using predictive modelling, patient adherence can be predicted, and HCPs can proactively intervene by offering support and resources.
Patients often fail to adhere to a treatment protocol prescribed by their HCP, and this can be a barrier to improved health outcomes. Big data and AI can be used to discover these issues. One study found that 70% of patients fail to administer their insulin correctly. An AI monitoring system was developed to monitor patient drug administration and alert providers to errors.
Working with Google Health, Meditech uses AI to answer patient questions, auto-generate clinical notes, and create AI virtual assistance to search chart notes and place orders. Meditech sees these programs as ways to reduce physician burnout and improve the patient experience.
Many clinical trials struggle to recruit the right participants needed to determine a drug’s efficacy. Because patient data is disparate and siloed, HCPs struggle to connect the patients with the latest research that can improve not only that patient’s health outcomes, but thousands of patients who can benefit from a drug once it is fully developed. AI can be used to find potential patients and inform HCPs when patients qualify for clinical trials.
Using AI to identify suitable candidates based on individual health profiles and conditions creates a personalized approach to trial recruitment. This improved accuracy adds efficiency which can ensure access to the latest breakthrough treatments. With AI identification of participants, the HCPs who would otherwise need to manually search for clinical trials available to their patients can use their time to better inform patients about the trial process, and drug testing and development can be expedited.
Across the globe, the effectiveness of treatment plans is impacted by healthcare affordability. One in four Americans skipped necessary medical treatments because of the high cost in 2022. Yet only 15% of co-pay dollars aimed to lower patient costs for brand prescriptions are utilised.
Often the biggest hurdle to accessing these programs is awareness. More than 40% of HCPs self-report limited awareness of support programs for patient prescriptions, and this affordability gap has a direct impact on treatment adherence and therefore patient outcomes.
Utilizing data about pharmacy benefits coverage, pharmaceutical co-pay assistance programs, and the individual patient, AI-triggered technology can connect prescribers to details financial programs available to their patient. By using AI to connect HCPs to this information in real time, EHRs can help patients save on healthcare expenses and empower HCPs to improve patient engagement through personalised recommendations.
The advanced data analysis of AI tools creates opportunities to improve healthcare costs outside of connecting patients to affordability programs as well. According to the CDC, chronic health conditions account for 90% of annual healthcare costs. Helping patients with chronic conditions manage their health can reduce overall healthcare costs. With AI assistance, providers can examine factors from economic status to social support and predict which patients are most likely to miss scheduled treatments and intervene. Harvard’s School of Public Health found that diagnoses made with AI reduced treatment costs by up to 50%.
When implementing an AI tool within an EHR system it is important to think through specific use cases. Consider which tasks are time-consuming, repetitive, and causing burnout, and prioritize AI solutions that can clearly demonstrate value. EHR software managers should create governance teams to oversee AI software solutions and ensure the ethical execution of these tools, protect patient information, and optimize program deployment.
Many existing EHR partners may offer AI solutions that can be easily adapted or developed to meet a specific need. Look for partners who are excited about the potential of AI and understand the need for human oversight.
As EHRs add AI functionality to their software offering, challenges can and will surface. Here are five key concerns to consider when developing or integrating an AI-powered solution:
1. AI tools rely on quality data and the lack of standardization between tools can impact the functionality and output of these algorithms. To effectively integrate an AI solution, high-quality, consistent, and interoperable data must be accessible. When data is complex or ambiguous, it can be difficult for today’s AI algorithms to understand.
2. Keeping sensitive patient data secure is not only a concern for complying with regulations like the Health Insurance Portability and Accountability Act (HIPAA), but it is also important for building trust with health systems and HCPs. When training AI software or adding an AI-powered integration, EHR systems must follow robust security and privacy protocols to reduce the risk of data misuse or breaches. HIPAA applies to protected health information (PHI), and AI software that learns from this data requires consent and enhanced security measures.
3. Beyond HIPAA concerns, EHR leadership should consider other regulatory processes and understand that this is an evolving space where regulatory guidelines are trying to keep up with technological breakthroughs. The FDA may need to approve AI tools that make diagnosis recommendations. Gaining FDA approval can be a lengthy process, and to stay on the forefront EHRs should look for partners who have successfully launched AI tools to provide guidance.
4. While HCPs will ultimately benefit from AI-enhanced EHRs, many have not been trained on these tools or fully understand how to use the new functionality. EHR leadership teams should work with hospitals and health systems to provide the right training and messaging to end users to ensure tools are used effectively.
5. Outside of training, EHR systems will also need to inform HCPs and health systems about how AI tools work to build trust and ensure transparency. Concerns about bias and legal implications require collaboration between the EHR system and the health systems using these tools. When AI tools provide diagnosis recommendations, there’s potential for liability risks particularly when there are errors or adverse effects. Highlighting the importance of human oversight and intervention.
Clearly, advancements in AI are driving the core goal of the healthcare industry: Delivering superior treatment outcomes. These technologies enhance how physicians interact with data, reshape the EHR ecosystem, and improve patient outcomes. From enhanced access to tests, medical histories, and treatment recommendations, AI-powered technology is assisting diagnoses, treatment recommendations, and risk assessments.
By taking on the tasks of transcribing patient conversations, AI tools allow HCPs to focus on their patients. This EHR enhancement can reduce provider burnout and burdens on understaffed teams. By creating summaries and intuitive visualisations of complex data, AI can help HCPs identify important trends in health data.
With trend recognition power, AI systems can connect patients to affordability programs and connect researchers to clinical trial participants to improve drug discovery.
From personalised medication recommendations to improved test ordering, there is no limit to the EHR enhancements now available through AI. As patient outcomes improve and HCP burnout improves, the entire healthcare system will feel relief.
Adapala is a seasoned executive with 25 years of experience in healthcare marketing, ad tech, digital media, and consumer tech. At Doceree, Adapala oversees the end-to-end global supply partnerships with healthcare platforms and publishers. Prior to joining Doceree, Adapala served as the GM at Amazon Publisher Services for web and mobile app publishers, where he led the strategic vision, successfully launched and scaled Amazon's ad tech solutions, and spearheaded global expansion strategies. Prior to Amazon, he held leadership positions at Yahoo and Motorola. He holds an MBA from Kellogg School of Management, Northwestern University, and a BSEE from Purdue University.
