Healthcare information exchange means sharing medical data between different systems. These include electronic health records (EHRs), payor databases, claims platforms, and provider networks. This sharing should make communication easy, but many problems still slow down data flow:
These problems cause the healthcare industry to spend billions every year on admin work. Meanwhile, patient care can slow down and become less coordinated.
AI is good at handling large amounts of data, finding patterns, and doing repetitive tasks automatically. Recent progress shows AI is helping healthcare data exchange in many ways:
Healthcare leaders agree that AI and data sharing are important to cut costs, improve operations, and help patient care work better across systems.
The US healthcare system spends more than $82.7 billion each year on back-office admin tasks. Costs went up by 50% in 2022 compared to 2021. Much of this money pays for manual phone calls between providers, payors, patients, and drug companies. These calls handle prior authorizations and disputes.
Each call usually lasts 20 to 30 minutes. Doing these calls by hand can make staff tired and slow down patient care. AI can automate these calls, helping lower costs and reduce staff work.
For example, Infinitus made an AI agent that makes these calls for healthcare providers. It uses a knowledge base from over 4 million previous calls to get answers right.
The AI agent handles complex talks but stays within strict rules to avoid mistakes or bias. Its AI helper, FastTrack, assists staff in navigating phone menus, shortening wait times before connecting to real people.
Good provider data is key for many healthcare tasks, from claims to member services. But many payors use broken data systems that create duplicates and wrong info.
The Public Employees Health Program (PEHP) had more than 7,000 data problems and 1,500 duplicate provider spots before using a new AI-based provider data management system.
Using HealthEdge’s platform, PEHP moved all provider data with 99.96% accuracy in just 3.5 hours. This took weeks of manual work before. Automation cut the need for employees to enter data by hand.
After using the system, PEHP saw a 13% to 15% jump in auto-approval rates for claims.
The platform does over 300 checks on addresses, provider IDs, and license statuses in real time. This not only makes claims more accurate but also follows rules like the No Surprises Act.
AI is not just for back-office jobs like claims. Front-office phone automation is growing in healthcare. Companies like Simbo AI are working in this area.
Front-office work includes answering phones, scheduling appointments, and answering patient questions. These tasks take a lot of time and involve repeating the same information.
Simbo AI uses AI to answer calls quickly, send them to the right place, and provide the needed info without a person. This lowers wait times and lessens work for medical office staff. It also collects and updates patient info right away to keep records accurate.
Using AI workflow tools can connect front-office, middle-office, and back-office jobs. This helps healthcare groups make their processes smoother and avoid working in silos. It fits a plan to make healthcare data easier to get, more correct, and useful.
Healthcare leaders say data interoperability—sharing and using data securely in real time—is very important. About 75% of them put it at the top of their list.
Healthcare data exchange means bringing together different systems like EHRs, claims platforms, payor databases, and pharmacies. AI helps by mixing different data forms and sorting unstructured data automatically.
Cloud-based APIs and standards like FHIR let data move quickly. For example, AVIZVA’s AI platform handles over 30 million claims each year. It keeps a fast sync between healthcare groups while keeping data safe and following rules.
This better interoperability helps patient care by letting providers see full and current health info. It avoids repeated tests and errors from missing data. It also speeds up claims and prior authorizations, which helps providers get paid faster.
Besides data sharing, AI built into admin systems changes healthcare money work:
HealthEdge reports over 80% of health plans now use AI in core admin work. AI could save $150 million to $300 million for every $10 billion in payer income.
As AI use grows, healthcare providers must keep privacy and security rules in mind. AI tools include strong encryption, role-based access, and constant security checks.
Companies like Infinitus put strict limits on AI conversations. This keeps talks inside safe topics and lowers risks of wrong or biased answers. HealthEdge follows rules like AI RMF 1.0 and CHAI to keep AI fair, clear, and ethical.
Training staff on AI and doing regular checks help keep rules followed and build trust in AI tools.
Medical practice administrators and IT managers should think about these AI advances:
By using these AI tools wisely, administrators can better control costs, improve worker satisfaction, and make patient care better.
The US healthcare industry spends at least $82.7 billion annually on back-office administrative work, highlighting significant operational inefficiencies.
In 2022, spending on healthcare administrative tasks increased by 50% compared to the previous year, demonstrating a rapidly growing issue.
Billions of manual phone calls occur annually among payors, providers, patients, and pharma companies to manage prior authorizations and dispute resolutions, often lasting 20–30 minutes each.
These calls lead to delays in patient access to treatments, higher healthcare costs, and increased employee burnout in call centers, affecting the wider healthcare ecosystem.
Infinitus uses an AI platform to automate healthcare phone calls and data gathering, accelerating patient care access, reducing employee turnover, and enhancing data quality.
The AI agent automates end-to-end phone calls for benefit verification which can take up to an hour manually, and updates patient records in real time.
FastTrack is an AI copilot that helps staff navigate complex IVR systems, waiting on hold before transferring callers to live payor agents, thereby reducing wait times and workload.
They implement strict AI guardrails through a coordination layer on top of large language models that restrict conversations to approved topics, minimizing hallucinations and bias.
Healthcare calls are lengthy and complex with a high accuracy bar; AI must parse unstructured data from payor policies and formularies to answer coverage questions precisely.
By accurately and efficiently processing vast data across multiple documents and entities, it streamlines communication within the healthcare ecosystem to improve patient care delivery.
Simbo is using AI agents to automate all phone related workflows. Connect now to know more.
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