How artificial intelligence can improve patient care


AI’s ability to quickly process and interpret large quantities of health data is a valuable tool for physicians.

The U.S. health care system is at a crossroads. An excess of challenges—both structural and financial—hamper present-day operations and threaten future stability. Among these:

These realities emphasize the critical need to better address and improve our health care delivery system’s efficiency. The antiquated fee-for-service model is weighing down a system already plagued by overwhelming challenges, and long-term trends will only increase the pressure on it.

In contrast, comprehensive and holistic value-based care aligns diverse interests—including payers, providers, self-funded employers, third-party administrators, brokers, consultants, and patients. Value-based care drives improved patient outcomes while meaningfully managing health care costs. And advanced technology platforms leveraging forms of artificial intelligence (AI) are well-positioned to support and increase the productivity of health care providers while improving patient outcomes with the ability to identify those at most risk.

Improved patient care

AI represents a set of technologies that consist of automated systems able to perform tasks including visual perceptions, augmenting diagnostics and predictions, and seamlessly processing large quantities of data. Tasks performed by AI have promising applications in value-based care, including strengthening patient care and enhancing health outcomes.

Data overload is an escalating problem afflicting health care systems across the continuum. Interpretable AI processes can simplify colossal amounts of complex data and synthesize key facets of the data for analysis by the proper specialist with recommendations and insights. This ability to digest and streamline data maximizes the valuable time a doctor can spend with patients.

Interpretable AI allows providers to access medical data immediately, review medical history, identify patterns, and recommend interventions. These features assist in targeting unique symptoms and stratifying risk severity for each patient while keeping a focus on the patient’s well-being and quality of care.

Accessible data brings numerous efficiencies

While interpretable AI has many capabilities, it does not replace human expertise, as feedback from specialists and physicians is essential to building a connection with the patient. AI can be thought of as serving as an extension of the care team, augmenting the capacity of experts to be more precise while maximizing resources.

A distinct attraction for adopting interpretable AI is its ability to accurately and immediately complete tasks that formerly required extensive hours of manual data parsing—streamlining compliance workflows and locating and resolving anomalous data outliers, all within any of the 60 million electronic health record queries that occur in the average-sized hospital. To take on this task by hand, a hospital employee would have to review more than 6 million electronic health records, roughly 300,000, every day.

Interpretable AI can help providers expedite responses to interventions, streamline workflow, and allow employees to spend less time on lengthy processes and manual tasks. Additionally, increased efficiencies address the growing and considerable financial expenditures and help lower costs. This ultimately leads to more hours dedicated to patient care, efficient hospital administration, and reduced stress for physicians and all medical staff.

Improved population health

Interpretable AI can look at populations and increase data availability on broad community-related factors influencing population health, along with improved capacity to link sources to individual, patient-level data to help predict future outcomes. AI-insights data can provide doctors with progress updates, detailed histories, and other information related to the patient. It has the potential to match a physician’s observations with data that provide targeted insight into surrounding circumstances, helping identify missing gaps in care for individuals and communities.

By identifying emerging or high-risk patients grouped by specific clinical conditions, co-morbidities, or driven by predictive risk models, patients with the greatest need can be targeted earlier with effective interventions. The result translates into fewer, less severe interventions and reduced hospitalization. The goal is a continuous decrease in expensive hospitalizations, readmission rates, and avoidance of emergent, intense interventions, all while improving patient health.

AI-enabled solutions are transforming the way health care is delivered. These solutions are streamlining diagnostic and treatment processes, enabling focus on quality care, and offering innovative solutions to relieve an overburdened system—a system that sees the importance and efficiency of value-based care but is in transition.

Interpretable AI is poised to evolve and play a growing and essential role in supporting holistic clinical and health care operations. While delivering proven benefits today, AI’s potential to shape the future holds boundless potential in the form of a health care system focused on improved efficiencies, lower costs, and a structure that educates, engages, and empowers patients.

Stephen Zander is chief analytics officer at Cedar Gate Technologies

Originally published on our sister brand, Medical Economics.

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