The revolution of AI in healthcare

The pioneering benefits of AI in healthcare

A well-executed AI adoption strategy in healthcare will manifest in two distinct ways — enhanced patient care and improved operational efficiency that drive bottom-line benefits.

Optimizing patient care through AI

Here are several patient care benefits that come with a solid AI implementation:

1. Enhanced diagnostics

Traditional image recognition requires radiologists and other specialists to manually analyze X-rays, MRIs and CT scans — a process in which disease detection relies solely upon what’s visible to the human eye.

AI-powered diagnostics are enhancing accuracy and findings with the use of machine learning algorithms that are able to analyze large amounts of data from various sources (medical records, imaging scans, etc.) to identify subtle anomalies that may not have been noticed by physicians.

An emerging use case would be how AI-powered diagnostics are used in lung cancer screening. Studies have shown that AI-assisted reading greatly improves the accuracy and sensitivity of nodule detection in chest CTs. There was an actionable detection rate of 0.59% when aided by AI, compared to 0.25% without AI assistance.

AI’s impact in the world of diagnostics has branched out to enhancing cardiac imaging, classifying brain tumors more accurately and spotting vertebral fractures — all of which help medical practitioners augment their clinical decisions and arrive at a personalized treatment plan faster.

2. Personalized treatments

Personalized medicine — also commonly referred to as precision medicine — revolutionizes healthcare by tailoring treatment plans to each patient's unique needs. Unlike traditional approaches that rely solely on medical history and symptoms, precision medicine utilizes a range of data, including genetic information, biomarkers and other physiological indicators.

One way AI is leading the charge in precision medicine is through its approach to treating autoimmune diseases by analyzing genetic and molecular data.

For example, AI algorithms can analyze a patient's genetic makeup to identify specific variations in their genes associated with autoimmune diseases. By understanding these genetic predispositions, healthcare providers can develop personalized treatment plans that target the underlying mechanisms driving the disease.

This targeted approach improves treatment efficacy and reduces the risk of adverse effects commonly associated with conventional treatments.

3. Predictive analytics

Beyond detection, AI models can also be trained to help with disease prevention.

By analyzing vast amounts of medical data — think patient records, genetic information, environmental factors, etc. — AI predictive models can identify subtle patterns and correlations that humans might miss. This work can then be used to predict the likelihood of individuals developing certain diseases or conditions.

4. Remote patient monitoring

AI, coupled with other remote patient monitoring efforts, gives caregivers and medical practitioners more oversight over a patient’s vital signs and medication schedules — while facilitating a more seamless collection of the data.

In a study published in the peer-reviewed journal Nature Medicine, it was discovered that as many as 70% of patients fail to adhere to their insulin prescriptions.

To address this issue, an AI-powered tool akin to a Wi-Fi router can be implemented to monitor patients' use of insulin pens or inhalers in real time. This tool can passively analyze the patient's behavior and promptly flag any errors in medication administration, ensuring better adherence to treatment plans.

5. Smoother patient experiences

AI-powered content management systems continuously review and extract data from patient medical records. These systems automatically analyze new medical data, classify patient record type such as progress notes and lab results, and integrate it into the patient's electronic health record (EHR).

This ensures that healthcare providers have access to the most up-to-date information when making clinical decisions or interacting with patients, leading to more accurate diagnoses and treatment plans.

AI-powered chatbots can also help with patient questions without needing staff to step in yet. The chatbots gather initial details from patients and suggest solutions depending on predefined labels, but if a patient needs more help, the chatbot is able to help patients book an appointment.

Patients can also use chatbots to get help with insurance-related queries. While not AI, robotic process automation (RPA) technology can support routine manual tasks, such as gathering required documentation to support medical billing and claims.

Powering operational efficiency with AI in healthcare

AI has significantly bolstered operational efficiency, revolutionizing the way medical services are delivered and managed. Some notable benefits include:

1. Efficient workflows and reduced administrative burden

By leveraging AI, healthcare organizations can alleviate the burden of paperwork and administrative tasks that consume a significant portion of healthcare workers' time. AI can help with:

• Automating repetitive tasks such as appointment scheduling, verifying insurance information and classifying and extracting data to provide healthcare staff with more time to focus on complex patient care.
• Introducing intelligent content management processes and workflows by using specific algorithms to automatically organize and update patient information — done through analyzing and categorizing medical notes, lab results and imaging reports.
• Optimizing revenue cycles by using AI-driven tools to support validation of insurance coverage before the patient receives treatment and coding assistance tools to analyze clinical documentation to suggest accurate medical codes, reducing the likelihood of denials and billing errors.

2. Data management and analysis

EHR systems have long been a frustration for healthcare professionals, often leading to errors due to manual data entry. Medical data encompasses a wide array of formats, ranging from handwritten notes to digital records. Each format presents its own set of challenges when it comes to processing and categorizing the information effectively.

With the advent of AI-powered content management systems in healthcare, managing this diverse data landscape gets considerably more efficient.

For instance, AI algorithms can be trained to recognize and extract relevant information from handwritten notes, such as patient symptoms, treatment plans and medication prescriptions.

By utilizing natural language processing (NLP) techniques, these algorithms can parse through the text, identify key phrases and categorize the data accordingly.

3. Cost savings

When it comes to bottom-line improvements, AI shines by facilitating the automation of administrative tasks and optimizing resource use. Through machine learning, AI automates content classification and extraction, integrating data into the EHR and other core systems.

One example of how AI helps reduce costs is through predictive analytics for hospital bed management. AI algorithms analyze historical patient data, current patient conditions and various other factors to predict everything from patient admissions to discharges.

By managing bed availability in advance, hospitals can avoid overcrowding and reduce patient wait times. This improves patient satisfaction and saves costs by minimizing the need for additional staff or facilities to handle overflow situations.

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AI in healthcare use cases

AI applications in healthcare are multifaceted and expansive, spanning from advanced medical imaging to reducing administrative burden. These innovations contribute to cost savings and enhance insights to elevate the quality of care provided, which can be seen via these real-life case studies:

Yale New Haven Health

Yale New Haven Health — a nonprofit healthcare system — performs 1.3 million imaging studies annually across multiple departments, necessitating an enterprise medical imaging strategy for consolidating acquired hospitals and improving data access for clinicians.

To do so, the organization harnessed AI and machine learning technology to analyze large volumes of imaging data in a rapid manner. This was done through APIs that were developed to pull data directly from the Hyland vendor neutral archive (VNA) solution, Acuo VNA, and run it through AI algorithms to get data to third-party systems.

Today, Yale New Haven relies on this “central ingestion” point to consolidate all clinical data onto a single platform and facilitate easy access for clinicians.

Asante

Asante — a healthcare provider with facilities throughout Oregon and California — deployed the Hyland Intelligent MedRecords solution for process improvement, workflow alignment and to reduce four-plus months of backlogged tasks.

The system leverages machine learning technology to automatically classify and extract fields from paper and digital patient records, routing the classified data into Asante’s EHR. Within the first year of using the system, Asante was able to realize about $200,000 in labor cost savings alone, while speeding access to medical records to support timely care decisions.

Sharp HealthCare

Sharp HealthCare — a San Diego-based nonprofit healthcare group with more than 18,000 employees — selected Hyland’s OnBase content services platform integrated with Brainware, an intelligent capture solution that utilizes machine learning, to streamline their accounts payable (AP) processes.

With OnBase, Sharp HealthCare was able to streamline more than 150 workflows across 54 departments, encompassing both clinical and non-clinical domains.

Sharp’s AP automation initiative with Brainware has significantly optimized resource allocation and operational effectiveness — leading to a 26% reduction in staffing requirements, faster batch processing times (from hours to minutes) and a greater ability to secure early payment discounts through AP automation.

Addressing ethical challenges with AI adoption

Studies by the Pew Research Center state that 60% of Americans are less than comfortable with the idea that their healthcare providers use AI in diagnosing diseases and recommending treatment plans.

Despite the widespread adoption of AI in healthcare, concerns about the technology range from ethical dilemmas to privacy issues. These could look like:

• A lack of transparency about why AI is necessary. Without understanding the “why” and “how” behind AI-driven outcomes, it remains a complex, opaque technology that both patients and providers may distrust. By demystifying AI and emphasizing its tangible advantages, trust can be built by fostering a clearer understanding of its role in enhancing healthcare delivery.
• Fear of replacing human judgment. It becomes the responsibility of healthcare providers to emphasize that AI is merely meant to augment, not replace, human touch or intuition, and to spotlight use cases of AI aiding physicians with making more informed decisions.
• Data governance and patient privacy. Past instances of data breaches or exaggerated claims about AI capabilities can contribute to skepticism. Upcoming regulations should tackle conversations like who owns and can use healthcare data, how AI medical devices and software are approved and where the liability lies between medical providers and AI developers.
  
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Navigating the AI-enabled healthcare frontier

Exciting advancements in AI are reshaping the industry, offering promising tools to improve diagnostics, treatment and overall patient care. These emerging technologies include:

• Drug discovery and development: For instance, Amgen — a biopharmaceutical company in California — has developed ATOMIC, a machine learning platform, with a goal to shave two years off drug development times. Many other healthcare organizations are also leveraging AI to reduce the time and costs it takes to develop new drugs.
• AI-powered telepresence robots are growing in popularity: The telepresence robot market is projected to grow at a 17% CAGR, reaching $1.6 billion by 2033, according to Fact.MR.

At Hyland, AI, machine learning and intelligent automation technology allow healthcare providers to address interoperability gaps and enhance clinical and operational decision-making — ensuring your organization remains adaptable for the future.