How is Generative AI Transforming Healthcare Provider’s Engagement With EHR and EMR Systems?

Let’s see some captivating facts first. The generative AI market in healthcare peaked at over $1 billion in 2022. Forecasts show a growth of 36.7% from 2023 to 2030.

A study shows that administrative expenditure in the healthcare sector accounts for approximately 15–30% in the US. What’s more, around half of the expenses are borne by the hospitals.

Even these figures are too low because they do not account for non-dollar indirect costs experienced by patients and their families, such as time spent battling for insurance coverage and billing clarity.

Allowing artificial intelligence to break down barriers between insurers, hospitals, and customers would automate claims processing, prior authorization, payment planning, and collections, thus decreasing a significant strain on system performance.

Enhancing EHR/EHM capabilities via Generative AI

Generative AI is already capable of amazing things, such as processing large amounts of data to expedite digital health initiatives, improve patient experience, and even assist physicians in making more informed decisions.

Organizations will place an even greater emphasis on data warehouse consulting services as the healthcare industry adopts technology. It will result in a change in the role of data scientists.

Here are some of the few advantages:

1. Better Information Sharing

AI is an expert at safe data sharing in addition to automation. It exchanges insightful information and knowledge between various healthcare organizations using cutting-edge methods like federated learning and differential privacy, all without ever disclosing private patient data.

2. Generation of Natural Language

Generative AI can create reports and summaries from structured EHR data. This functionality could allow healthcare providers to swiftly summarize and sort through large volumes of EHR patient data. In the healthcare system, it can enhance team communication, enhancing care coordination.

3. Enhanced Processes

There are many benefits to integrating generative AI into your existing electronic health record (EHR) software. Doctors can devote more of their free time to patient care because it can handle jobs like paperwork, coding, and even less exciting administrative labor.

4. Medical Imaging

Medical image quality can be significantly improved by generative AI using methods such as Generative Adversarial Networks (GANs). Handling little information makes a big difference in producing higher-quality photos. It improves image resolution and is essential for AI algorithm training.

5. Precision and Efficiency

Generative models can analyze vast amounts of historical data to identify patterns and trends, leading to more accurate diagnoses and treatment recommendations. Additionally, AI can anticipate future needs and generate preventive measures, streamlining workflows and improving efficiency.

Use cases of EHR/EHM systems using Generative AI

The availability of Chat-GPT and other chatbot interfaces has made generative AI a popular topic in many areas, including healthcare. Several EHR providers are utilizing technology to expedite administrative procedures to allow more time for physicians to provide patient-centered care.

1. Epic

Automating repetitive and administrative activities is the ideal use of generative AI, according to Phil Lindemann, vice president of data and analytics at Epic. Consider replying to messages on the patient portal.

In essence, the support staff or clinician must take all of the information in their minds and translate it into a human reaction narrative. An answer can be drafted by generative AI, which the practitioner can then evaluate, edit, and email to the patient.

The new Epic function, which attempts to shorten message response times and free up more time for doctors to engage with patients, is presently being tested by a group of early-adopter health systems. But Epic is working on 60 generative AI projects, of which this functionality is just one.

A tool to compile fresh information since a provider’s last appointment with a patient is another example. Rather than requiring to go through a patient’s medical history to find out what has transpired since their last appointment, the program compiles data to give doctors an overview.

2. Oracle Health

A generative AI-powered chatbot for healthcare workers is being beta-tested by Oracle Health to streamline administrative procedures.

The Oracle Digital Assistant, a platform for developing chatbots or conversational interfaces, acted as the foundation for the EHR vendor’s tool.

Clinical professionals can review patient charts using voice commands thanks to one of the Clinical Digital Assistant tool’s many primary features: summarizing patient data.

Asking the system questions like “What does my day look like?” or “Tell me a little bit about my next patient,” prompts the tool to provide pertinent data.

The technology also makes automatic note-taking easier by listening to conversations between patients and clinicians. The technology generates clinical notes automatically for provider review after a visit.

3. eClinical Works

Additionally, generative AI-based technology is being used by EHR provider eClinicalWorks to assist in automating clinical documentation. eClinicalWorks and health IT provider sunoh.ai collaborated in October 2023 to incorporate ambient listening technologies into the EHR.

The EHR integration generates draft clinical documentation through ambient listening. Before the system may document the information, providers must either accept the note or make any necessary changes.

Sunoh.ai also suggests specific highlights and next steps for the practitioner to examine and approve based on their chat with the patient. For example, the integration records lab, imaging, prescription orders, referrals, and follow-up appointment information.

Early adopters are now using the EHR integration, which will be widely available to customers in early 2024.

Final Thoughts

Generative AI enables healthcare practitioners to leverage data-driven insights and develop more individualized care plans. Furthermore, generative AI can improve the quality of data recorded in electronic health records.

EHRs are generally unstructured and lacking in standards, making it difficult for clinicians to find specific data. Generative AI helps by automatically arranging EHR data, making it more accessible and structured for healthcare practitioners.

Despite the challenges and potential dangers, successful and ethical AI integration in medicine holds great promise for custom healthcare software development firms looking to improve the efficiency and precision of their healthcare systems.

Originally posted on OpenDataScience.com

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