The prognosis for US healthcare costs is not good. Recent reports predict 2024 will be the most expensive year for healthcare in more than a decade. To make matters worse, the overall health of Americans is declining, with cases of diabetes, heart disease, and cancer on the rise.
Fixing the issue is complicated. One thing that is certain, however, is that healthcare costs need to be brought under control. Innovations that can reduce costs open the door to more effective care for a larger number of patients.
Artificial Intelligence has the potential to have a positive impact on healthcare costs — especially when applied to healthcare data management. The following are examples of ways AI can improve the efficiency and effectiveness of healthcare data management.
Using AI to Enhance the Patient Experience
Better patient experiences lead to better patient outcomes. With better outcomes, healthcare costs decline.
AI has the power to improve the patient experience by streamlining the care process. Starting with the onboarding process, AI can empower automated systems that reduce patient workload, while also promoting better patient engagement.
AI-powered chatbots can conduct natural language interviews to gather information, rather than requiring patients to fill out lengthy and complicated forms. AI-driven systems can also quickly draw upon client data from past interactions to facilitate personalized exchanges. By automating onboarding, AI also contributes to reduced wait times for patients.
Using AI to Optimize Healthcare Operations
Today’s healthcare organizations are drowning in data. According to recent studies, operations at the average hospital result in the production of roughly 50 petabytes of data every year, which is the equivalent of 25 trillion pages of standard printed text.
AI gives healthcare organizations the power they need to effectively collect, organize, and analyze the ever-increasing inflow of data in a way that drives better performance. By automating patient data collection, AI reduces workload for healthcare staff, while streamlining the process of populating databases. Clinical documentation and other workflows can also be streamlined by AI-powered tools.
Connecting business applications and processes to electronic medical records is another enhancement AI brings to healthcare operations. For example, integrating EMR data with insurance databases can automate the insurance eligibility verification process. EMR integrations can also assist with claims processing by identifying problems that lead to increased denial rates.
By fueling predictive analysis, AI can empower better capacity planning. This involves analyzing historical data and emerging healthcare trends to forecast potential needs. With predictive analysis, healthcare organizations can be better prepared for patient volumes, supply needs, and staffing needs.
AI can also leverage patient data to drive better preventive care. By identifying patients at a high risk of readmission, AI can alert healthcare providers to opportunities for proactive care interventions. It can also use data to create risk stratifications, allowing health practitioners to advise patients on potential health risks.
Using AI to Increase Revenue Cycle Velocity
AI-driven predictive analysis can also be applied to financial data to drive better revenue performance. This occurs when it is used to better predict cash collections.
Converting services provided to patients into collected payments is central to profitability in the healthcare sector. To optimize conversions, organizations must consider patient scheduling, clinical documentation, claims submissions, payment reconciliation, and more.
AI can optimize the processes that feed into the revenue cycle in a number of ways. Natural language processing can automate data capture, populating billing codes with information extracted from unstructured clinical notes. AI-powered reviews can identify errors and incomplete information on claims, reducing denial rates.
After the billing process is initiated, AI can be used to detect anomalies that may indicate billing issues, compliance issues, or fraud. It can also automate follow-up communication for overdue payments.
By revealing patterns in the revenue cycle, AI empowers healthcare organizations to make data-driven decisions about their finances. Data can pinpoint revenue leakage, denial patterns, coding gaps, and other dynamics that limit profitability.
Addressing the Ethical Implications of AI in Healthcare
Deploying AI in the management of healthcare data can raise ethical issues. Healthcare data is sensitive data. Patients want to be assured that information managed by AI remains secure and private.
The data analysis provided by AI-driven tools also has the potential for biases. Healthcare organizations must ensure that data being used to train AI is not biased, and audit for biases on an ongoing basis. Organizations must ensure that AI model outputs are subject to human review before using them to make decisions.
AI has quickly become a vital tool for any organization seeking to optimize data management. It has the potential to streamline data operations, eliminate redundant systems, automate tedious tasks, and extract insights from large data sets quickly and efficiently. Unleashing those abilities in the healthcare sector provides organizations with the power needed to improve the patient experience, optimize operations, and increase revenue cycle velocity.
The author, Sean Shahkarami is the CEO of Opilio, a leading AI company aimed at changing the way businesses mobilize their data.