Soon after artificial intelligence (AI) was established as a field of research in the mid-1950s, researchers quickly discovered how to get machines to solve increasingly complex problems and automate a wide range of tasks. Advancements have only continued to accelerate, prompting further development and adoption across various industries. In healthcare, for example, AI is being used to streamline back office operations and improve patient care — benefits that, if widely adopted, could save 5% to 10% in the U.S.’s annual healthcare spending.

What Is AI?

AI is a branch of computer science that studies how to — and creates ways to — embed intelligence into machines so that they can perform tasks that typically require human intelligence. At its core, AI involves developing algorithms and models that allow computers to learn, reason and make decisions. A few important components of AI include machine learning (ML), which enables systems to learn from data; deep learning, a subset of ML using neural networks; natural language processing (NLP), which makes it possible for machines to understand and interpret human language; and generative AI, which creates new content in response to text prompts. (More on generative AI later.)

What Is AI in Healthcare?

In healthcare, as in other fields, AI is being used to quickly and accurately analyze and interpret data to help solve problems and make informed decisions. Though it mimics human cognition, AI isn’t meant to replace healthcare professionals. Rather, AI can enhance their expertise by quickly processing datasets too large for manual analysis and identifying patterns that may not be recognizable to the human eye. AI also automates repetitive workflows, reduces errors and makes more accurate predictions.

Key Takeaways

  • Healthcare organizations can use AI to automate workflows, support data analysis, boost productivity and improve patient care.
  • AI rapidly analyzes data to help healthcare professionals make more informed decisions across various healthcare applications, from inventory and supply chain to financials and diagnostics.
  • Successful AI integration addresses challenges related to data privacy, security and seamless integration with existing IT systems.
  • To avoid complex integration issues, it’s often more efficient to use a fully integrated data repository that includes built-in AI capabilities.

AI in Healthcare Explained

AI is transforming the healthcare industry by driving improvements across three key areas:

  • Administrative operations: AI-powered systems are automating tasks like billing, appointment scheduling and patient record management. By swiftly processing and analyzing large volumes of data, these systems can speed up documentation and minimize errors to help boost back-office efficiency.
  • Day-to-day operations: AI tools, including predictive analytics and ML models, help healthcare providers predict patient demand to determine optimal staffing levels, manage inventory based on usage trends and plan equipment maintenance schedules to reduce downtime.
  • Clinical care: AI is enhancing patient care by analyzing medical data and images to detect subtle abnormalities and spot diseases earlier. AI-powered robotics are increasingly being used for surgery and rehabilitation.

How Does AI in Healthcare Work?

AI in healthcare involves the processing and integration of a slew of healthcare-related data — claims information, operational metrics, patient records, medical images, lab results, etc. — that is typically collected by electronic health record (EHR) systems, practice management software, laboratory information systems and more. While these systems often operate in silos — making it challenging to access and analyze data across different platforms — cloud computing platforms help bridge this gap by providing a centralized data repository that AI algorithms and models can easily access. This integration is typically achieved through the use of application programming interfaces (APIs) and data pipelines, which enable a continuous, real-time data exchange among different systems and the cloud.

Once integrated, AI applications can leverage the vast amounts of data stored in the cloud to identify patterns, make predictions, automate tasks and support clinical decision-making.

Data privacy and security concerns should also be carefully addressed and rigorously maintained to comply with regulations like the Health Insurance Portability and Accountability Act (HIPAA) and the General Data Protection Regulation (GDPR). Cloud-based AI systems must incorporate robust security and data protection measures, such as encryption, multilayered access controls, audit trails and threat monitoring. Data anonymization and pseudonymization can also be employed to protect patient privacy, while still allowing for AI-driven analysis.

How AI Might Transform the Health Industry

AI is set to transform the healthcare industry by introducing sophisticated capabilities that go beyond “basic” automation. In healthcare financial management, for example, AI systems are beginning to predict reimbursement rates and identify potential claim denials before submission. This can significantly reduce revenue cycle inefficiencies. In workforce management, AI is making it possible to create dynamic staffing models that respond to real-time fluctuations in patient acuity and patient census. Meanwhile, NLP is evolving to automatically generate regulatory compliance reports from daily operations data, potentially saving countless hours of manual documentation.

In supply chain operations, AI algorithms can monitor inventory levels across entire hospital networks, while factoring in epidemiological trends and demographic data to help guarantee that critical supplies will always be on hand. In a similar vein, predictive maintenance AI can anticipate equipment failures and schedule proactive servicing to minimize care disruptions.

Looking ahead, AI-powered digital twins — virtual replicas of hospital operations that simulate real-time processes — could allow administrators to test operational changes, such as new patient flow protocols or service-line expansions, before implementation. These simulations make it possible for healthcare executives to forecast the impact of proposed changes, facilitating evidence-based optimization of human and material resources while mitigating the risks associated with implementing untested changes in clinical environments.

In clinical settings, AI’s utility extends far beyond diagnostic support and treatment planning. For one, it’s helping to accelerate biomedical research and drug discovery through complex data analysis. AI is also being used to help forecast disease outbreaks and community health concerns to support personalized risk assessments and improve public health. In surgical settings, AI-powered robotic systems are so precise that once-invasive procedures become minimally invasive, potentially improving postoperative outcomes and reducing the length of hospital stays.

Generative AI in Healthcare

Generative AI is a subset of AI that uses patterns in existing data to create new content, including text, images and audio. While it’s not a replacement for human creativity, generative AI produces content at a fraction of the time that humans can, making it a valuable tool to improve productivity and lighten workloads.

In healthcare, generative AI can apply NLP and ML algorithms to automatically create detailed administrative reports, financial summaries, operational analyses and clinical documentation. Although humans still need to verify the AI’s work, the time and effort required from healthcare managers are significantly reduced. In addition, generative AI can also be used to help draft internal communications, policy documents, training materials and other content tailored to specific roles within the organization. It can even be used to create synthetic datasets that mimic real-world scenarios, from changes in patient flow to financial scenario-planning, to inform and refine strategic plans.

Generative AI also plays a role in improving and refining healthcare-focused AI systems over time. By creating synthetic, yet realistic, medical data to augment existing datasets, for instance, generative AI outputs can be used to train other healthcare-related AI models and systems, such as NLP models and diagnostic imaging algorithms. This could prove to be an efficient way to increase the size and diversity of training data, address data scarcity issues for rare conditions or underrepresented patient populations, and reduce reliance on actual patient data, helping to protect patient privacy.

11 Applications of AI in Healthcare

From streamlining data management to enhancing operational efficiency, AI-powered tools are helping to transform healthcare through a wide range of practical applications. The following 11 use cases demonstrate AI’s impact across many facets of the healthcare industry, including supply chain optimization, inventory management, financial oversight and fraud detection, to showcase its potential to improve organizational performance and patient care.

  1. Healthcare Data Management

    AI-powered systems are transforming healthcare data management by enabling organizations to organize and analyze large volumes of diverse datasets effectively. These systems use sophisticated algorithms to automate the data processing from various disparate sources — EHRs, insurance claims, financial data, clinical notes, imaging studies, etc. — to create a comprehensive view of patient care and organizational performance in one unified database. These capabilities reduce the time and resources required to manage data while making sure data is consistently structured, clean and readily available for analysis.

  2. Administrative and Operational Efficiency

    AI-powered tools are increasingly automating routine administrative tasks in healthcare, such as appointment scheduling, billing and claims processing. These tools leverage NLP and ML algorithms to quickly and accurately process large volumes of data, which improves operational efficiency by scaling back the time needed for manual data entry and hands-on analysis. For instance, AI-based scheduling systems analyze historical patient data, provider availability and resource utilization patterns to generate optimal schedules that minimize wait times and maximize resource allocation. Similarly, AI-driven billing and claims management systems automatically code and process claims, while spotting billing discrepancies or potential fraud before they occur.

  3. Supply Chain Optimization

    AI systems that use advanced analytics and ML algorithms can analyze data from EHRs, inventory systems and supplier databases to identify supply chain issues and predict potential disruptions before they arise. Specifically, they can examine historical data on supply and demand patterns, lead times and transportation routes to inform decisions about supplier selection, order quantities and delivery schedules.

    For example, AI-powered demand forecasting tools can predict seasonal fluctuations in the need for flu vaccines, allowing hospitals to adjust their orders months in advance. Another application is in cold chain management, where AI algorithms monitor temperature-sensitive medications throughout the healthcare supply chain, alerting staff to possible problems before they result in product loss.

  4. Inventory Management

    AI-powered inventory management tools are helping behind-the-scenes healthcare inventory management teams more accurately forecast demand for medical supplies, pharmaceuticals, surgical equipment, cleaning and maintenance supplies, cafeteria goods and even office supplies. These tools can quickly analyze historical inventory data, purchasing records and patient-utilization patterns to inform purchasing and stocking decisions. With more data and details, hospitals and healthcare providers can maintain more appropriate stock levels, reducing the risk of critical shortages that could delay surgeries, interrupt treatments or compromise emergency care, while also minimizing costs due to overstocking, expired medications or obsolete equipment.

    When integrated with asset-tracking technologies like radio frequency identification (RFID) or Bluetooth Low Energy (BLE), AI-powered inventory tools can give managers a real-time view into the location and status of hospital equipment, such as computers, beds and diagnostic machines.

  5. Asset Management

    AI-based healthcare asset management systems analyze data from maintenance records, equipment usage logs, Internet of Things (IoT) devices and sensor readings to predict when equipment might fail or require servicing. This proactive approach allows healthcare organizations to schedule maintenance before problems occur, reducing unexpected downtime and guaranteeing critical equipment will be available when needed. For example, an AI system might detect an MRI machine’s performance deterioration based on changes in its operating temperature and vibration levels. The system would alert maintenance staff to these early warning signs, helping to prevent costly breakdowns and minimizing disruptions to patient care.

    AI algorithms also optimize maintenance schedules based on factors like equipment usage patterns and criticality, which can extend the lifespan of medical equipment and reduce overall maintenance costs. And by analyzing data on equipment performance, maintenance history and repair costs, AI tools can offer valuable insights into long-term asset life cycle management, with informed guidance on when to best repair, replace or retire equipment.

  6. Financial Management

    AI is making it easier for healthcare organizations to manage their finances, from billing to budgeting. For instance, AI-powered tools can quickly spot and fix errors in medical coding during billing, reducing the chance of claim denials and speeding up payments. These tools can also predict which claims are likely to be denied by analyzing past denial patterns and highlighting potential issues before submission, giving billing staff the chance to work out issues proactively. Additionally, AI tools can analyze troves of financial data — such as past payment patterns, patient demographics and shifts in economic conditions — to generate precise forecasts.

  7. ERP Integration and Automation

    AI-powered enterprise resource planning (ERP) systems help healthcare organizations connect and automate administrative functions, from finance and human resources to supply chain and patient data management. These integrated systems automate repetitive tasks, minimize manual data input and offer up-to-date views of organizationwide operations. Practically speaking, an ERP with AI functionality can perform basic tasks like automatic invoice routing. It can also help improve operational efficiency and patient care by analyzing cross-departmental data to identify bottlenecks, cost-saving opportunities and potential improvements in patient outcomes. This seamless data sharing and analysis breaks down silos to help strengthen collaboration among healthcare professionals.

  8. Workforce Management

    AI-powered workforce management and scheduling tools analyze historical patient volumes, staff skills and preferences to create optimal staff schedules that balance workload and maintain appropriate staffing ratios, while adapting to real-time changes like unexpected patient surges or staff absences. AI algorithms also forecast future staffing requirements by considering factors such as population health trends, seasonal variations and anticipated turnover. This predictive insight helps healthcare organizations proactively recruit and train staff with the right mix of skills and experience to meet evolving patient needs. AI can also help support retention efforts by analyzing employee data, such as engagement survey responses and performance metrics, to identify staff at risk of leaving and recommend targeted interventions, like additional training or mentorship opportunities.

  9. Risk Management and Compliance

    AI helps healthcare organizations manage risks and maintain regulatory compliance by continuously monitoring data to identify potential risks in real time or through predictive analytics. These AI-powered tools can detect patterns that may indicate medication errors, fraudulent activities or a higher likelihood of patient readmissions, for instance, allowing healthcare providers to take corrective actions, such as adjusting treatment plans or strengthening internal controls, before issues escalate.

    AI can also automate aspects of compliance monitoring and reporting, once relegated exclusively to human oversight. For example, AI systems can analyze medical records and claims data to help ensure compliance with coding and billing guidelines like ICD-10 and CPT, monitor electronic communications for potential HIPAA violations and breaches of patient privacy, and even generate compliance reports that highlight areas of noncompliance, while suggesting recommendations for remediation.

  10. Fraud Detection and Prevention

    Healthcare fraud costs tens of billions of dollars annually. It also undermines patient-care quality by diverting resources from legitimate treatments, potentially exposing patients to unnecessary or harmful procedures and eroding trust in the healthcare system. AI’s ability to automate much of the fraud-detection process is becoming a powerful tool to help reduce financial losses, reinforce patient trust and lessen the burden on human investigators, allowing them to focus on complex, high-risk cases.

    AI-powered fraud-detection systems can scrutinize medical claims data to flag suspicious billing patterns, such as providers consistently billing for expensive procedures or submitting an unusually high number of claims for certain services. These systems can also identify claims for treatments inconsistent with a patient’s diagnosis or medical history and monitor financial transactions and activities to detect unusual patterns that suggest fraudulent behavior. Suspicious provider-referral patterns, such as high referral volumes to specific facilities or providers, for instance, can indicate kickback schemes.

  11. Document and Contract Management

    Healthcare organizations generate and manage a vast array of documents, from patient records and medical reports to provider contracts, vendor agreements and legal documents. Manually processing and categorizing these documents can be time-consuming and prone to human error, leading to delays and potential compliance issues. But AI-powered document processing and categorization tools can automatically scan, analyze and classify large volumes of documents based on their content and structure. For example, these tools can identify and extract key information from patient records, such as diagnoses, medications and treatment plans, and categorize them according to predefined parameters.

    Similarly, AI-powered contract management and analysis tools can automatically scan and analyze contracts to extract payment terms, expiration dates and other key information to confirm compliance with terms, automate contract renewals and track obligations. These tools can also identify potential risks or compliance issues, such as missing clauses or conflicting provisions, and alert relevant stakeholders so they can take corrective action before serious legal or financial repercussions arise.

Benefits of AI in Healthcare

AI is increasingly benefitting various aspects of healthcare operations, helping to address inefficiencies and improve overall performance. The following list outlines key areas where AI is making practical impacts, from automating routine tasks to enhancing data analysis and management.

  • Workflow automation: AI can automate repetitive and time-consuming business processes, such as appointment scheduling, data entry, inventory management, claims processing, medical coding and staff scheduling. This frees up healthcare professionals to focus on higher-value activities, such as patient care and strategic planning.
  • Better data management: AI tools can efficiently collect, organize and store vast amounts of structured and unstructured health data from EHRs, medical devices, clinical notes and other sources. This provides healthcare organizations with accurate, accessible and up-to-date data at all times, resulting in informed decision-making and operational improvements.
  • Superior data analysis: AI algorithms can quickly identify patterns and insights in complex healthcare data that may be difficult for humans to discern. This can help healthcare providers make more accurate diagnoses, predict patient outcomes and identify potential health risks at both the individual and population levels. AI-powered healthcare dashboards and other data visualization tools further simplify data analysis and interpretation to help make data more accessible to all stakeholders and support swift decision-making.
  • Improved productivity: AI enhances healthcare productivity not only by automating routine tasks and providing decision support through data analysis, it also helps with the timely availability of staff, equipment and supplies based on predictive needs. It can also automatically share patient updates and flag critical information to facilitate real-time coordination among healthcare teams. As a result, healthcare professionals can work more efficiently, reduce administrative burdens and lower burnout.

Addressing Challenges in Healthcare AI Integration

AI is not a silver bullet solution. Implementing these systems requires significant investments in data collection, security and integration. Additionally, AI algorithms are only as good as the data they are trained on, so organizations must ensure that they have high-quality, reliable data to support their initiatives. Two major AI-related healthcare industry challenges are:

  • Data privacy and security: Healthcare data is highly sensitive, and organizations must adhere to strict regulations like HIPAA and GDPR to protect patient privacy. AI systems often require access to extensive datasets to learn and improve, but this data must be carefully managed and secured. Adding to the challenge, some AI point solutions require data to be copied into their own systems, rather than allowing direct access from the organization’s data repository, increasing the risk of inconsistencies and security breaches. To address these concerns, healthcare organizations should implement strong data management and governance frameworks, verify that any external AI vendors comply with relevant regulations and prioritize solutions that can seamlessly integrate with their existing data management systems.
  • Integration with existing IT systems: Healthcare organizations often rely on a complex web of legacy systems and disparate data sources, complicating AI integration. Siloed data and system incompatibility can limit AI’s effectiveness, burden IT teams and pose data privacy and security risks. Smooth systems integration hinges on interoperability and modern, flexible IT infrastructure that can easily accommodate new technologies. This may involve developing APIs and data pipelines to enable seamless data exchange among systems, as well as close collaboration with AI vendors. Alternatively, adopting a fully integrated suite of AI-powered applications built on a common platform can eliminate the need for complex integrations, providing the consistent, accessible and secure systemwide data needed to fully reap AI’s benefits.

The Future of AI in Healthcare

The future of healthcare operations looks promising. Predictive analytics are expected to evolve to forecast patient care demands in real time, making it possible for teams to anticipate surges in specific treatments or procedures. Similarly, up-to-the-minute data can be leveraged to adjust staffing levels and allocate equipment on the fly, reducing delays in patient care. On the administrative side, robotic process automation (RPA) is poised to streamline everything from billing and coding to medical records management. This can alleviate one of the biggest pain points in healthcare operations, as well as dramatically improve accuracy and compliance.

While AI is already analyzing medical images like X-rays, MRIs and CT scans to assist in diagnoses, the tech may soon outperform human radiologists in detecting certain conditions. Still, AI should be expected to augment, not replace, human expertise to help clinicians make faster and more accurate decisions. Meanwhile, as AI systems continue to learn from larger datasets, personalized treatment plans based on individual genetic, clinical and lifestyle factors will become even more precise. Telemedicine will also likely see significant advances, with AI-powered wearable devices and sensors expanding access to quality care, regardless of location.

In addition, AI’s impact will extend to drug discovery and development. With impressive amounts of data at their disposal, AI-driven solutions can identify new drug candidates, predict efficacy and simulate interactions. This could dramatically cut the time and costs associated with bringing new therapies to market, potentially accelerating healthcare professionals’ abilities to respond to public health challenges.

Integrate AI for Better Healthcare Management with NetSuite

NetSuite SuiteSuccess Healthcare Edition offers a comprehensive, cloud-based solution tailored specifically for the healthcare industry, making it easier for organizations to improve financial management, enhance operational workflows and ultimately refine patient care. A natively integrated platform, NetSuite breaks down data silos to support a holistic approach to healthcare management. This makes it possible to easily adapt to changing market dynamics and make quick data-driven decisions, all thanks to an accurate, real-time data repository. In other words, organizations can manage everything from financials and supply chain to patient engagement and regulatory compliance within a single, unified system — eliminating the need for complex integration initiatives.

NetSuite’s AI capabilities further enhance the platform’s value to healthcare organizations looking to modernize operations and set a foundation for the future. For example, NetSuite’s intelligent automation tools can simplify routine tasks, such as invoice processing and inventory management, while AI-powered analytics provide nuanced insights into financial performance, patient trends and operational efficiency metrics. AI-driven supply chain management tools take oversight to the next level, enabling healthcare organizations to optimize inventory levels, reduce waste and guarantee that critical supplies are always on hand.

From automating administrative tasks and managing resource allocation to enabling personalized treatment plans, AI is beginning to influence every aspect of the healthcare industry. But as organizations adopt AI technologies, they’ll need to address data privacy, security and system integration challenges to reinforce patient trust, maintain regulatory compliance and prevent operational disturbances. Making life easier are integrated, AI-powered platforms that make it possible to securely harness data and streamline workflows to improve patient outcomes.

AI in Healthcare FAQs

How does AI help nurses?

AI helps nurses by automating documentation, assisting with patient monitoring, providing clinical decision support and improving task prioritization. In turn, these tools can reduce nurses’ workload to reduce burnout, while allowing nurses to spend less time at the computer and more time on direct patient care.

How popular is AI in healthcare?

AI adoption in healthcare is growing rapidly. A 2024 survey by McKinsey revealed that more than 70% of healthcare organization respondents are pursuing or have already implemented generative AI — and that figure doesn’t include adoption rates of other types of AI technology. What’s more, the global market size of AI in healthcare is forecasted to reach a projected revenue of more than $208 billion by 2030, up from just over $22 billion in 2023.