Is Augmented Intelligence the Future of Healthcare Analytics?

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Today’s healthcare executives are under significant pressure from a variety of stakeholders to guarantee organizational effectiveness, efficiency, and profitability. However, according to the consulting firm WittKieffer, nearly 75 percent of healthcare executives in the U.S. are experiencing burnout, and most believe it negatively impacts their organizations. 

Fresh solutions that are simple to implement and yield instant and quantifiable outcomes are needed to address the job stress and burnout experienced by many healthcare executives. Augmented intelligence (AI) can relieve their burdens by automating tasks, streamlining operations, and monitoring waste and bottlenecks of healthcare analytics. 

Typical business intelligence (BI) tools aim to support decision-making in these areas, but augmented intelligence goes further. It utilizes advanced algorithms to analyze vast datasets to identify patterns and anomalies and generate previously undetected insights. In doing so, health leaders arrive at the correct decisions faster, without spending hours poring over spreadsheets or searching databases.

Augmented Intelligence Can More Precisely Inform Operational Decisions 

Uncovering insights through deep data analysis enables executives to make thoughtful decisions regarding relocating care delivery models, expanding service lines, and consolidating operations. More specifically, leaders can use AI algorithms to harness healthcare analytics to conduct the following:

• Compare the usage of facilities within the healthcare system against industry benchmarks or utilization rates within the same system. 
• Identify locations with significant deviations from expected usage levels. Make determinations based on historical usage patterns or factors such as available beds, equipment, and staffing levels.
• Gather and analyze patient-centric metrics like health outcomes, mortality rates, quality benchmarks, and patient satisfaction surveys. 
• Extrapolate data from EHRs, billing systems, and other sources to identify profitable and health-promoting services versus underperforming services. 
• Continuously monitor and evaluate service lines’ performance by analyzing performance metrics such as patient outcomes, length of stay, readmission rates, and financial indicators.

Act on Financial Data with AI-Driven Data Analysis

Experts have revealed that healthcare finance leaders are grappling with lower operating margins, labor shortages, high turnover, and rising supply chain expenses, which underpins the mounting stressors executives face. Leveraging AI can unveil opportunities to mitigate costs and more effectively manage existing workforces and operating margins, specifically in the following areas:

• Facilities & Equipment: Augmented intelligence can harness data from medical equipment and facilities to anticipate maintenance needs, minimize downtime, and optimize equipment utilization. This reduces unexpected repair costs, ensures equipment availability, and maximizes the lifespan of assets, contributing to cost management.
• Human Resources: Advanced algorithms can identify workflow bottlenecks and inefficiencies, including staff productivity. AI can also identify patterns and indicators of employee satisfaction, engagement, and turnover risks, allowing executives to target retention strategies, identify training needs, and improve employee satisfaction to mitigate staff shortages.
• Vendor Management: AI can analyze operational and financial data and healthcare analytics to identify cost-saving opportunities regarding medical supplies and equipment (i.e., surgical devices), technologies, transportation, and medications. By examining supply chain expenses in greater detail, AI can assist in optimizing inventory management, negotiating better contracts with suppliers, and identifying areas for cost reduction or process improvement to increase operating margins. 
• Drug Formulary: AI-powered data analysis could also inform drug selection. Utilizing comparative research, such applications can assess the effectiveness of high-cost drugs to alternative or generic therapies, considering clinical significance, adverse events, and patient-reported health outcomes.

Make Better Clinical Care Decisions by Integrating Augmented Intelligence in Medicine

By integrating augmented intelligence into the care process, healthcare professionals may garner real-time decision support. Indeed, pre-built algorithms can analyze extensive patient health records, more easily detect trends, and visualize historical data based on statistical models without human input.

Patient acuity is another area where AI can impact the care process. During the pandemic, America’s hospitals observed increasing numbers of patients requiring intensive care. On the other hand, individuals also delayed treatments for non-COVID-19 conditions due to capacity concerns, which applied tremendous pressure on health systems. According to an American Hospital Association report, various factors, including delayed treatments, have led to higher labor, drug, and medical supply expenses. Consequently, already stretched thin, healthcare leaders and hospitals faced unprecedented challenges. Health chiefs could address these challenges by leveraging AI to forecast clinical outcomes and assess disease progression before matters worsen.

Amplify Executive Decision-Making with Augmented Intelligence Tools

Regrettably, many BI tools lack the functionality to meet the requirements that enable swift and targeted executive decision-making. Healthcare.AI seamlessly integrates into a health system’s existing data analysis and reporting enterprise, thereby providing enhanced clarity and support for decision-making without replacing native tools. Such applications give healthcare leaders precise, comprehensive healthcare analytics to make strategic decisions for each department as it relates to:

• ED triage, length of stay, and discharge
• OR department volume, scheduling, and quality assurance
• Specialty departments (i.e., ICU and OB) structure and process improvement
• Morbidity and mortality assessments for quality upgrades

As health systems press on toward greater analytic complexity, new augmented intelligence-based applications can help foster a more effective and efficient healthcare ecosystem allowing executives to make better decisions that enhance revenues, patient engagement, operations, and continuous quality improvement.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

• AI in Healthcare: Hype, Hope, and Missed Opportunities
• Three Reasons Augmented Intelligence Is the Future of AI in Healthcare
• Leveraging Augmented Intelligence to Improve Health Equity
• Your Guide to Augmented Intelligence in Healthcare: Three Hows for AI Success
• How to Use AI in Healthcare to Unlock Four Secrets in Your Data