The rise of augmented intelligence has brought about significant changes in the healthcare industry, making it essential for leaders to understand and embrace this transformative technology. AI combines human and artificial intelligence to enhance decision-making and improve patient outcomes. Healthcare leaders must recognize the potential in areas such as diagnosis, treatment planning, and patient monitoring. By leveraging new tech, health executives can also transform their processes, increase efficiency, and deliver personalized care at scale. However, leaders must address concerns and the impact on healthcare professionals’ roles. To navigate these challenges, healthcare leaders must stay informed about augmented intelligence in healthcare and collaborate with experts in the field to ensure its responsible implementation.
In recent years, the healthcare industry has observed significant adoption of advanced technologies, including augmented intelligence (AI), to improve patient outcomes and streamline processes. In the retail and marketing space, companies like Amazon and Netflix have turned to machine learning, augmented intelligence, and artificial intelligence tools to gain a competitive edge and enhance customer experiences and buyer decisions. These intelligent solutions are helping industry giants transform key areas of their business that may be lacking in accuracy and efficiency, such as customer service and fraud detection. Their applications in various domains may vary, but they undoubtedly shape our lives and industries. In e-commerce, algorithms and statistical models rapidly generate personalized content or recommendations based on users’ digital patterns and behaviors within an application. AI integration in healthcare is equally transformative.
Consumers leave digital breadcrumbs when they surf the internet, buy things online, and make virtual appointments. Similarly, a patient’s digital footprint is ever-expanding today as their healthcare experiences become more digitized. Clinicians now have access to extensive patient information—from electronic health records, medical exam results, wearable medical devices, and satisfaction surveys. Transitioning patient records from paper to electronic systems, coupled with the push for system interoperability, has resulted in a deep and vast reservoir of data points across healthcare systems.
Technological advancements have no doubt increased the amount of information available to healthcare providers and decision-makers for the better. But infinite data sources and a lack of effective healthcare analytics can obscure important facts about patient populations and the effectiveness of a healthcare system’s operations. Executives are left wondering how to make sense of this overwhelming volume of data. In response, some organizations will hastily ramp up their data-reporting efforts using manual methods or tools with limited functionality. Yet, many are finding that traditional business intelligence (BI) tools can’t keep pace. Their usual BI tools fail to meet today’s data processing and self-service analytics needs. These BI tools overlook hidden patterns or correlations that executives can swiftly act upon. Complicating matters are the healthcare system’s reluctance to adopt more sophisticated and robust tools, which burdens today’s healthcare workforce, who are already struggling with burnout and fatigue.
Could AI be applied in healthcare to influence decisions and behaviors, similar to its application in e-commerce? Many healthcare executives believe so and have demonstrated its efficacy, leveraging AI-powered solutions to enable better decision-making and improve health equity. By gaining new insights, healthcare leaders acknowledge that incorporating AI in healthcare is relatively simple and can yield remarkable improvements in outcomes.
While the concept of scouring big data to analyze, target, and predict future patterns and govern critical decisions isn’t groundbreaking, there is still much to discover about the application of augmented intelligence solutions in the healthcare industry.
The combination of augmented intelligence and human insights can assist healthcare organizations in extracting vital information from vast datasets to address business concerns related to revenue, costs, operations, and quality of care. However, it is imperative for executives and other stakeholders to distinguish between these tools since applications of machine learning, artificial intelligence, and augmented intelligence in this field are relatively new. Differentiating between the available tools can also lay the groundwork for their adoption by helping executives eliminate confusion and allay concerns.
Although artificial and augmented intelligence, both known as AI, share common objectives, they employ different methods to reach the user’s goal. Both technologies possess machine learning capabilities. Yet, artificial intelligence aims to replace human decisions entirely through a full-device approach, whereas augmented intelligence maintains the human aspect.
In other words, artificial intelligence refers to an application’s ability to perform tasks that typically require human intelligence, such as speech recognition or problem-solving, autonomously. One striking example is self-driving cars, which utilize sensors and complex algorithms to navigate roads without human involvement. On the other hand, augmented intelligence is a concept where human intelligence is enhanced or amplified by leveraging computational technologies. What’s more, augmented intelligence empowers individuals rather than renders them obsolete.
Gartner defines artificial intelligence as applying “advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take action.” Many clinicians and clinical executives are hesitant to adopt artificial intelligence in medical settings because it relies heavily on the machine’s ability to evaluate data and suggest treatment plans without considering other factors. Therefore, the fear that artificial intelligence would replace human decision-making is valid. Such sentiments, however, hinder widespread adoption and raise ethical concerns.
Gartner defines augmented intelligence as “a design pattern for a human-centered partnership model of people and artificial intelligence (AI) working together to enhance cognitive performance, including learning, decision making, and new experiences.” Ideally, technological solutions in healthcare should equip providers with forecasts and analysis while adhering to ethical guidelines and principles of high-value patient care. Augmented intelligence can complement the clinician’s awareness of, for instance, a patient’s social determinants of health or help them recall conversations during care visits. Speed, accuracy, and predictability are key differentiators for augmented intelligence, making this tool the preferred AI solution for healthcare entities.
Furthermore, augmented intelligence:
Carle Health, a world-class healthcare provider in Illinois, became an early adopter of augmented intelligence. Leadership outlined objectives to tackle the complexities of COVID-19, improve sepsis management, and accurately forecast patient outcomes and associated costs based on historical and current data. Carle Health laid a strong data foundation to support effective AI integration and healthcare analytics for near- and long-term success.
While early adoption stories such as this are becoming more common, ethical concerns still cause healthcare systems to stall efforts to adopt augmented intelligence.
Upholding regulatory compliance and ethical considerations are essential when implementing any new technology, especially emerging technologies like AI. Regulatory compliance ensures that the use of augmented intelligence in healthcare adheres to laws, regulations, and standards set by governing bodies and industry organizations. This includes safeguarding patient privacy, data security, and informed consent.
Ethical considerations must also align with principles that protect fairness, transparency, and accountability and avoid bias in the delivery of care . Therefore, it is critical to mitigate any potential harm caused by biased algorithms or discriminatory practices in AI-based decisions or processes. To ensure regulatory compliance and address ethical concerns, health systems must establish clear policies and guidelines for AI usage. They should conduct comprehensive risk assessments to identify potential legal or ethical challenges associated with using AI technologies before implementation. Additionally, health systems need to continuously monitor their technology usage and performance to detect any instances of non-compliance or unethical behavior.
Healthcare industry complexities, such as labor costs, overworked resources, and inadequate technology, are major concerns for healthcare leaders who strive to prioritize how data is accessed and managed across teams and departments. Aligning clinical and financial information at the organizational level to make strategic decisions is one of healthcare’s biggest challenges.
Historically, executives managed data using separate tools, including the following:
What’s missing from BI and advanced healthcare analytics tools today is the ability to extract data insights from a single clinical or business decision and apply these insights to the healthcare organization. That’s where AI becomes the linchpin of outcomes improvement. AI analyzes large datasets to extract the most relevant insights in the form of patterns, anomalies, and potential risks that fly under the radar, going unnoticed by clinicians, analysts, or data scientists. The healthcare industry has strived to enhance performance and quality for decades. Now, with the power of AI, healthcare leaders can make decisions with greater speed and accuracy.
AI capabilities enable rapid processing of incoming data – whether it’s operational, patient-generated, clinical, or administrative and billing. Such tools can calculate and analyze big data with precision and without bias in the fraction of the time it used to take, which could be months or even longer, which ties up precious resources. In addition, the cost savings that AI yields are an estimated $150 billion in 2026—an important driver for leadership buy-in.
When applied correctly, AI should act as a collaborator—not a replacement—in human decision-making so that the tools are ethically applied and leveraged correctly throughout the organization. Some healthcare provider organizations are eagerly embracing AI due to its seamless integration into their current technology infrastructure and effectively solving their unique clinical and financial challenges, including:
By combining human knowledge and skills with AI computations and analysis, AI creates endless opportunities for healthcare executives and providers to make an impact faster and with analytic precision.
Additionally, health systems need to continuously monitor their technology usage and performance to detect any instances of non-compliance or unethical behavior.
Healthcare providers navigate highly complicated challenges where the lives of patients are often in jeopardy. Yet, augmented intelligence functionality can be used in acute and chronic care settings. Legacy data analytics tools, compared to AI, are slow and cumbersome to use, often requiring manual input and analysis, which can be time consuming for healthcare professionals. Yet, AI allows health systems to answer meaningful questions about the quality of care and areas of improvement, such as:
In a word, augmented intelligence yields more meaningful, actionable data. However, leaders and providers must turn a keen eye toward the data required to apply AI in healthcare and believe the tools can advance healthcare objectives at multiple levels.
The benefits of augmented intelligence in healthcare abound.
#1: AI has the potential to greatly improve the accuracy and efficiency of diagnoses. By analyzing vast amounts of patient data and medical literature, AI algorithms can assist clinicians in making more informed decisions, leading to accurate diagnoses and personalized treatment plans.
#2: AI can automate mundane and repetitive tasks that often consume a significant amount of a healthcare provider’s time. Clinicians can focus on higher-value activities like patient interactions and critical thinking. By streamlining administrative processes, healthcare systems can also reduce costs and improve overall productivity.
#3: AI can adapt and evolve to a healthcare system’s needs over time. By continuously learning from new data inputs, real-world outcomes, and current best practices, AI solutions adjust to provide more relevant, up-to-date recommendations and analysis.
As a result, AI not only aids clinical decision-making but also contributes to ongoing professional development within the health system’s medical community and inform leaders’ strategic planning.
The following use cases explore how health systems have seamlessly integrated AI into their current technological infrastructure to produce a comprehensive analysis to improve healthcare coordination, enhance diagnostic accuracy, modify leadership compensation, and optimize resource allocation.
By implementing advanced technologies like machine learning and predictive analytics, AI assists in the accuracy of diagnoses, personalized treatment plans, and improving outcomes for quality and financial gains. As a result, AI integration represents an important step toward improving patient outcomes. Unnecessary testing in healthcare inflates the cost of care for patients and the cost of care delivery for providers. For one example, overprescribing antibiotics for common infections, such as Group A strep pharyngitis or strep throat, is a well-known drain on a hospital’s economy.
To mitigate the accumulating costs of medical tests, WakeMed Health & Hospitals (WakeMed) established an evidence-based care pathway and sought a data-informed approach to reduce unnecessary testing, improve urgent care efficiency, and relieve providers of the burden of non-value-added work. With AI and other data governance tools, WakeMed realized the following results:
Health inequities have become more apparent today as widespread diseases, such as COVID-19, exacerbate and shed light on disparity gaps due to race, religion, socioeconomic status, and other demographics. Unfortunately, these inequities cause patients to experience unintentional bias in medical settings. What healthcare providers have been working to prove is that when bias is removed from the decision-making process, everyone involved benefits, regardless of whether the bias was intentional or not. Fortunately, health equity programs that leverage AI solutions produce promising results. ChristianaCare utilized AI to overcome disease testing disparities and increase access to care.
“Health equity and AI are interconnected. Technology and AI need to help reduce health disparities, not exacerbate them.”– Edward Ewen, MD, Director, Clinical Data and Analytics, Center for Strategic Information Management at ChristianaCare
A framework to achieve health equity success includes AI technology applied across an organization’s data foundation, a commitment to quality, and a collective leadership effort. Addressing three technical barriers can overcome health equity challenges through AI.
Similar to how Netflix provides recommendations to influence what users watch, AI can also be applied to modify clinician or executive behavior through analysis that considers data elements hidden from normal view. Moreover, AI tools, such as benchmarking, Observed/Expected (O/E) reporting, and forest plots, which are a graphical representation of the statistical similarities and differences between measures, groups, or other dimensions in order to better understand performance and identify opportunities for improvement. These features can transform vast data stores into actionable insights that leaders can use to identify goals to move their organizations forward and improve outcomes. For example, INTEGRIS Health collaborated with Health Catalyst to explore how augmented intelligence could assist in setting executive compensation plans. To achieve the desired results, the team listed the tasks that needed to be performed and then sorted them into three categories:
After clarifying the role of AI-assisted decision-making in leadership, INTEGRIS Health zeroed in on improvement opportunities and established equitable goals through AI-powered insights while increasing adoption through cascading training models.
UnityPoint Health recognized patients with complex chronic health conditions were undergoing excessive or unnecessary services, tests, and treatments, particularly when transitioning from hospital admission to an ambulatory care setting. Yet, despite having access to large volumes of data, clinicians lacked timely insight into care provided across ambulatory settings. To enhance healthcare utilization rates, they needed to enhance their healthcare data warehouse and harness AI to predict which patients would benefit from enrolling in a new care management program.
UnityPoint Health used numerous AI tools that incorporate chronic health data, social data, the probability of admission, and readmission risk after discharge from the hospital to risk-stratify patients and predict which patients are at high or rising risk for unnecessary healthcare utilization. In the 30 months since the care management program’s inception, UnityPoint Health substantially decreased unnecessary healthcare utilization and costs associated with caring for patients enrolled in their program. The organization reported the following outcomes:
These examples demonstrate that with accurate interpretation of analytics, healthcare leaders gain data-driven insight to know how to strategically improve and measure wins and losses. AI tools, like Healthcare.AI™ from Health Catalyst, make interpretation simple with pre-built algorithms for executives and data science leaders.
The fusion of machine learning technologies and human expertise will continue to solve quality and cost challenges for the healthcare industry. AI ensures that analytical insights are precise, accurate, and consistent while also facilitating swift integration of data science into existing healthcare analytics, strategies, and operations. Additionally, AI helps tackle the issue of scarce resources since pre-built algorithms simplify data extraction and interpretation for executives and data science leaders.
Healthcare.AI aims to help organizations wanting to expand the easy use of self-service AI in their mainstream analytics, improve success rates for advanced AI use cases, and use AI for purposes beyond predictive analytics. A collaborative partner provides health systems with a layer of accountability while upholding privacy, regulatory, and clinical quality standards, which could not be achieved with off-the-shelf BI tools or a standalone AI solution. With the Healthcare. AI offering, leaders can uncover additional opportunities for growth and improvements across their organization. Also, the use of AI-driven analyses promotes fair and equitable decision-making, leading to improved outcomes.
Health systems can streamline the adoption and utilization of AI tools by leveraging the strategies employed by similar organizations. Despite being traditionally slow in adopting technological advancements, forward-thinking executives and clinicians can adopt AI to demonstrate that such innovation is not exclusive to tech giants and that its benefits have significant and far-reaching impacts on healthcare systems and the communities they serve.
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