If we could save upwards of $30 billion a year on avoidable healthcare costs, why wouldn’t we? This is what the CDC estimates that hospital-acquired infections cost annually. 1 in 25 patients will suffer a hospital-acquired infection—many of these result in actual harm to the patient. For example, a central line or urinary catheter left in place too long causes an infection. Frustratingly, hospitals can also have these infections identified that may not be true infections, but fall into the NHSN criteria. These are costly in terms of unnecessary testing, financial penalties for hospitals, and lower publicly reported scores.

AI would alter the approach with an infection prediction with increased diagnostic accuracy. It could help discern acute inflammation from infection.

AI is poised perfectly to help us predict the patients that will get these infections through the use of predictive analytics scanning vast amounts of data combined with real time monitoring of the patient’s vital signs and other data points. This would allow us to mitigate those risks by removing or replacing the problematic lines. It can also predict multidrug-resistant organisms that could put a patient at risk. Multidisciplinary teams all have their roles in preventing these infections and AI suggestions and recommendations could be targeted towards the members of these teams.

AI would alter the approach with an infection prediction with increased diagnostic accuracy. It could help discern acute inflammation from infection.

While the risk of hospital-acquired infections depends on the hospital’s infection control practices, and those steps taken to reduce the risk, patient factors opposing these efforts are also at play which include immune status, recent antibiotic use, frequent visits to healthcare facilities, length of stay (LOS), major procedures, age, ventilatory support and intensive care stays. It seems quite plausible that artificial intelligence (AI) could identify risk factors and generate a score. Steps could be suggested and taken to mitigate infections by keeping devices out as much as possible and guiding clinician care decisions.

Cleveland Clinic investigators recently presented that AI could very accurately predict multi drug-resistant organisms days prior than a culture is available. It is exciting to think that we can use AI to predict and tailor antibiotics and isolation precautions towards these days ahead of a final culture. Taking antimicrobial stewardship to the next level to get patients appropriately treated earlier will save lives, time and money.

AI has recently been used to model new designs of urinary catheters to block the migration of bacteria towards the bladder. Catheters were made consistent with these designs, creating an obstacle course of geometric designs inside the catheter that blocked the migration of bacteria upstream. The design was optimized for E. coli, and testing showed that after 24 hours the bacterial burden was 1/100 of that of traditional Foley catheter design. This is exciting that we can use AI technology to predict the behavior of microbes and design ways to inhibit their growth and migration.

Machine learning (ML) algorithms have demonstrated value in predicting clostridium difficile infection with just 6 hours of data. With almost 30,000 deaths per year related to c. difficile infections, early diagnostics to treat, identify those at risk and isolate to prevent spread would be an incredible advance to saving lives.

CLABSI (central line-associated bloodstream infection) could be predicted allowing physicians to remove the lines prone to infection and avoid those consequences. Suggestions of treatment based on probability and risk would help discern true CLABSI from blood culture contamination.

The support for antimicrobial stewardship that AI could provide would adjust the approach toward treating infections. Currently, the physician evaluates data for the likelihood of an infection. Cultures are taken, the results of which will not be available for several days, and empiric antibiotics are started. When cultures and sensitivities are available, antibiotics are sometimes changed based on those results, or de-escalated. Have we then given a patient a toxic or broad-spectrum antibiotic for a few days that was unnecessary? Have we bred more resistance? AI would alter the approach with an infection prediction with increased diagnostic accuracy. It could help discern acute inflammation from infection. (Is this sepsis or something else?) The correct antibiotic could be chosen which would eliminate the need to de-escalate or change, and the best duration of therapy would be suggested.

Enhanced cleaning and sterilization practices could be suggested by algorithms to identify and mitigate risks with equipment and high touch areas in the healthcare setting.

Patient and caregiver education and engagement could be enhanced through AI based on their medical conditions or procedures to provide targeted and relevant material about their care and infection prevention practices. This would certainly foster a collaborative culture of safety, and mitigate the spread of infections.

I dream of a day when a patient is admitted to the hospital, using AI tools, we are able to get predictive scores on the likelihood of hospital-acquired infections or other complications. More informed decision-making can be made based on the probability instead of blindly pan-culturing when not needed or leaving devices in patients at high risk. Imagine that when a patient comes to the hospital, your risk of CAUTI, CLABSI or hospital-acquired pressure ulcer is present and available to the admitting team so that decision-making to reduce or eliminate this outcome altogether can be made. A most likely diagnosis and risks are presented along with the likely infection cause/ Is it MDRO or not? The right antibiotics are given and a short LOS gets the patient home safely and efficiently. Lives will be spared, and billions of dollars potentially saved.

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The integration of Artificial Intelligence (AI), automation, and data analytics into the revenue cycle management (RCM) of healthcare facilities marks a transformative leap toward operational excellence. In an era where financial sustainability is as crucial as clinical outcomes, these technologies are pivotal in optimizing processes from patient intake to final billing, ensuring that healthcare providers not only survive but thrive in a competitive market.

At our infectious disease lab, the implementation of AI and data analytics has revolutionized how we manage our revenue cycle. By automating routine tasks, we have freed up valuable time for our staff to focus on more complex, value-added activities. Automation of data entry and claims processing reduces the likelihood of errors and speeds up the turnaround time, directly impacting our cash flow and reducing the days in accounts receivable.

One of the most significant advantages of using AI is its ability to analyze vast amounts of data to identify trends and patterns that would be impossible for a human to discern. This capability allows us to anticipate issues before they become problematic, such as identifying which claims are likely to be denied based on historical data. With predictive analytics, we are proactive rather than reactive, which not only increases our revenue but also reduces the stress on our staff and improves our relationships with patients and insurers.

AI transforms data into actionable insights, enhancing efficiency and profitability in healthcare.

Moreover, machine learning (ML) models within our AI systems continuously learn from new data. As they become more sophisticated, they offer increasingly accurate forecasts and deeper insights into our lab’s financial operations. This ongoing learning process is crucial for adapting to the ever-changing landscape of healthcare regulations and insurance policies.

Our organization has also capitalized on data analytics to fine-tune our pricing strategies and to ensure compliance with billing regulations. By analyzing the outcomes of thousands of past transactions, we can set competitive prices that maximize our revenue without compromising patient care. Furthermore, compliance monitoring through AI-driven systems ensures we adhere to all billing regulations, reducing the risk of costly penalties and legal issues.

The integration of these technologies extends beyond internal operations to enhance patient interactions. Our patient portal, powered by AI, offers personalized experiences where patients can easily access their billing information, understand their payment options, and communicate with billing representatives seamlessly. This not only improves patient satisfaction but also expedites payments, positively affecting our cash flow.

In addition to these operational improvements, AI and data analytics significantly enhance our strategic decision-making capabilities. With access to real-time data and advanced analytical tools, our management team can make informed decisions quickly, addressing potential financial discrepancies and optimizing overall financial health.

Furthermore, the ability of AI to integrate with other technological advancements, such as electronic health records (EHRs), further streamlines our operations. This integration ensures that all patient data is synchronized across platforms, minimizing the risk of data silos, and ensuring that every department has access to the same accurate and updated information. This seamless integration helps in maintaining consistency in billing practices and patient care services.

Our commitment to leveraging AI extends to training our staff to effectively utilize these tools. By holding regular training sessions and workshops, we ensure that our team is not only comfortable but also proficient in using the latest technologies. This empowerment enables them to contribute actively to our ongoing efforts to refine and improve our revenue cycle processes.

Additionally, AI tools help us manage the complexities of insurance verification and eligibility checks with greater accuracy. By automating these processes, we reduce the instances of claim rejections due to coverage errors. This not only speeds up the billing process but also decreases the burden on our patients, who can be confident that their coverage is correctly verified at the outset of their healthcare journey.

Moreover, AI-driven analytics assist us in identifying inefficiencies in our billing and service delivery models, allowing us to make necessary adjustments. These adjustments are often predictive rather than reactive, positioning us to address potential issues before impacting our operations. This foresight saves time and resources and supports our strategic goals of maintaining financial health and patient satisfaction.

The adoption and continual advancement of these technologies in our revenue cycle processes illustrate a commitment to innovation and excellence in healthcare management. As these tools evolve, so too does our ability to meet the needs of the patients we serve and the staff we support, ensuring a future where healthcare and technology work hand in hand for the betterment of all involved. As we continue to harness these powerful technologies, we not only foresee a more robust financial footing for our lab but also a greater capacity to provide exceptional care to our patients.

Through ongoing investments in AI and data analytics, we not only optimize our current operations but also pave the way for future innovations. These technologies allow us to stay at the forefront of the healthcare industry, continually improving our services and outcomes. By embracing AI and automation, we not only enhance our operational efficiencies but also ensure a higher standard of care, which is the cornerstone of our mission in healthcare.

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In the realm of healthcare, decision support tools have long been heralded as essential aids for physicians in navigating complex medical scenarios and delivering optimal patient care. With the rapid advancement of artificial intelligence (AI) technologies, these tools are poised to undergo a transformative evolution, potentially assuming roles traditionally held by human physicians. As AI continues to mature, decision support tools are anticipated to become so proficient that they may effectively function as the primary healthcare provider, while physicians transition to roles of oversight and patient communication. This paradigm shift holds the promise of enhancing both the quality and efficiency of healthcare delivery.

Advancements in AI and Decision Support Tools

The integration of AI technologies into decision support tools has revolutionized the healthcare landscape by augmenting physicians’ diagnostic and treatment capabilities. AI algorithms can analyze vast amounts of patient data, including medical records, diagnostic images, and genetic information, to identify patterns and make recommendations with unprecedented speed and accuracy.

Recent studies have demonstrated the efficacy of AI-driven decision support tools in various medical domains, including radiology, pathology, and oncology. For instance, AI-powered imaging analysis systems have shown remarkable proficiency in detecting abnormalities and diagnosing diseases from medical scans, often outperforming human experts in certain tasks.

The Emergence of AI as the Primary Decision Maker

As AI algorithms continue to evolve and improve, there is a growing realization that these systems have the potential to assume a more prominent role in clinical decision-making. In the foreseeable future, decision support tools empowered by AI may become so adept at processing and interpreting medical data that they effectively serve as the primary healthcare provider.

Physicians, in turn, may transition to roles where they oversee and validate the recommendations generated by AI systems. Rather than solely being responsible for diagnosing and devising treatment plans, physicians may focus on interpreting AI-generated insights, providing context to patients, and ensuring the ethical and appropriate use of technology in healthcare delivery.

Implications for Healthcare Quality and Efficiency

The advent of AI-driven decision support tools holds profound implications for healthcare quality and efficiency. By harnessing the power of AI, healthcare providers can leverage data-driven insights to optimize clinical decision-making, minimize diagnostic errors, and tailor treatments to individual patient needs.

Moreover, AI-enabled decision support tools have the potential to streamline healthcare workflows, reduce administrative burdens, and enhance resource allocation within healthcare systems. With AI assuming routine tasks such as data analysis and risk assessment, physicians can devote more time to direct patient care, fostering deeper doctor-patient relationships and improving overall patient satisfaction.

Conclusion: A Future of Collaborative Healthcare

In conclusion, the evolution of decision support tools powered by AI heralds a new era of collaborative healthcare, where human expertise synergizes with machine intelligence to deliver superior patient outcomes. While the role of physicians may evolve in response to technological advancements, their fundamental commitment to patient care remains unwavering.

As AI continues to advance, decision support tools will become indispensable allies for physicians, empowering them to make informed decisions and deliver personalized care. By embracing the potential of AI-driven innovation, healthcare systems can unlock new possibilities for improving healthcare quality, efficiency, and accessibility in the years to come.

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Introduction

AI is rapidly becoming a game changer with its next generation technologies in improvising clinical workflows, promising enhanced diagnostics, personalized treatment plans and optimized healthcare operations. From automation in diagnostics to predictive analytics, AI has the potential to revolutionize every aspect of patient care delivery.

AI can mean different things, from specific kinds of AI, like machine learning, to the possible AI that has awareness and feelings.

AI in Clinical Diagnosis and Decision-Making

The use of AI algorithms is quite versatile in automatic diagnostics of different diseases simply by analyzing medical images like X-rays, MRIs, and CT scans. These AI systems can now discover patterns and anomalies that may not be visible to the naked eye. It leads to detecting cancer, cardiovascular problems and also neurological disorders earlier.

AI in breast cancer diagnosis is one of the common instances. AI algorithms have been built that can read a mammogram and detect cancers with a high accuracy, often better than human radiologists. For example, an AI system has been able to detect cancer of the breast with 90% accuracy, which is more than 87% compared to radiologists.

AI will be the challenge of humans who should show wisdom and willingness to define the AI’s role in twenty-first-century healthcare and to determine when AI helps humanity and when it hurts it.

AI clinical decision-support is a kind of medical application, which is based on the integration of big data and machine learning (ML) to give personalized medical advice. Employing patients’ data, including history of illness, genetics and determining the response, is the AI system that recognizes risk factors, predicts outcomes and provides personalized treatment plans. The AI breaking into electronic health records (EHRs) is a source of data upload, input and retrieval. It is also an analysis that helps healthcare providers in making decisions and this can be done quickly.

“AI will transform the healthcare sector, particularly diagnosis in the field of medical imaging.”

AI in Treatment Planning and Delivery

AI provides automated dosage calculations and medication management through algorithms that determine the most suitable dosages for patients based on their unique features and medical history. By having automated calculations, AI contributes to reducing the risk of dosage errors committed by people, helping to guarantee that patients are given the exact medications in adequate amounts. 

AI-empowered robotic systems are driving a paradigm shift in surgery through increased accuracy, stability, and reduced people mistakes during surgeries. These robotic systems help surgeons see better and enter into areas of the body that are impossible to reach for normal human beings, thus making it possible to conduct further complicated surgeries using a minimally invasive approach.

AI-infused wearable devices and sensors allow healthcare providers to monitor patients at a distance and provide telehealth services by gathering continuous data regarding patient’s health status and intervening with this data as needed. These gadgets can track or measure vital signs, activity level, drug compliance, and other health metrics and give experts a way to control patients remotely.

A world-renowned company in cancer treatment technologies, has introduced an AI-powered treatment planning product. This product employs machine learning (ML) algorithms to design effective radiation therapy treatment plans based on patient’s past information, thereby substantially reducing planning time and degrees of inaccuracy.

AI in Clinical Workflow Optimization

AI can, in many ways, speed up insurance claims and billing processes through automating the verification of insurance coverage, accurately coding medical procedures, and submitting claims to the insurance company. AI algorithms can utilize medical records, treatment codes, and insurance policies for claim processing and ensure the accuracy that may lead to claim denials or delays.

AI predictive analytics plays a major role in forecasting patient volume, staffing needs, and healthcare facilities as far as resource allocation is concerned. By investigating historical data, patient trends, and external factors, AI algorithms help to predict future demand for health services. Thus, healthcare organizations can reallocate staff, optimize resource usage and improve operations planning.  
For instance, a leading AI-based workflow solution is capable of automating and optimizing several radiology workflows. It deploys machine learning (ML) for automating functions such as image work, finding lesions and report creation, improving radiologists’ efficiency and productivity.

Challenges and Considerations

“AI will be the challenge of humans who should show wisdom and willingness to define the AI’s role in twenty-first century healthcare and to determine when AI helps humanity and when it hurts it”.

The key challenges and considerations in implementing AI in healthcare, including data security and privacy, are the most essential aspects of consideration, as patient’s data can be leaked and AI processing these data leads to the loss of patient trust. In addition to adopting regulations like HIPAA, clinical settings must guarantee the secrecy and safety of private patient information. Clinical decision-making entails the ethical use of AI in AI deployment, which is also a very complex issue. The visible and honest ways of reaching the decision by AI systems must be guaranteed, and it is important to get rid of bias and prevent unfairness in such scenarios to avoid discriminating results. The complex integration of AI technologies into the present healthcare system and workflow flows is the key technical and organizational problem to resolve. Interoperability between AI systems and EHRs gets involved in effective and integrated healthcare provision.

Conclusion

Finally, the use of AI along with clinical workflows presses several benefits in providing patients the best care, improving operations, and advancing medical outcomes in the long run. Yet, the challenges that need to be thoroughly considered are data security, ethical issues, and systemic integration. These are vital factors that must not be disregarded in order to fully harness AI in healthcare.

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By Dr. Sowmya Viswanathan, Chief Physician Executive, Marlene Besnoff, CDI Program Director and Dr. Laura Arline, Chief Quality Officer, BayCare Health System

In recent years, the healthcare industry has witnessed significant advancements in technology, particularly in the field of AI. AI is revolutionizing all aspects of healthcare, including Clinical Documentation Integrity (CDI). CDI programs play a critical role in ensuring accurate and comprehensive clinical documentation, which is essential for optimal patient care and reimbursement. By leveraging AI, CDI programs can enhance their effectiveness and efficiency.

BayCare CDI Program Director, Marlene Besnoff explains, “The skills of our CDI specialists, combined with AI technology, is the future of medical record integrity. This partnership is key to capturing accurate and complete documentation, which describes the outstanding care delivered by the healthcare team and tells the correct patients’ stories as they move through the healthcare system.”

A patient’s healthcare story is told in two languages: 1) clinical chart documentation and 2) the codes submitted on claims to payers based on this documentation. CDI Specialists translate the chart documentation into billing codes and may clarify imprecise and incomplete physician/advanced practice provider (APP) clinical documentation. The role of CDI programs is to ensure the correct translation of this clinical documentation into coding language that reflects the patient’s severity of illness and the complexity of care provided. Telling the patient’s story in both languages is critical to high-quality outcomes, patient safety, and care team coordination.

AI can streamline and augment the work of CDI specialists, making the process more efficient, as technology combs through data much quicker than humans. One of the key challenges faced by CDI programs is the manual review of patient records to identify documentation gaps. This process can be time-consuming and is very detail-oriented. Through natural language processing (NLP) technology, large volumes of unstructured data (such as clinical notes) are analyzed, and relevant information is abstracted. Some mature AI programs examine more than 30,000 data points per chart to arrive at suggested differential diagnoses based on notes, labs, medications, orders, vital signs, imaging, and other studies. These algorithms can identify missing diagnoses, clarify ambiguous or conflicting terms, and suggest appropriate documentation based on the comprehensive data in the patient’s chart.

AI has already started to revolutionize the field of CDI and continues to develop. By leveraging AI-powered tools, CDI programs will continue to streamline and augment their work.

Many AI-powered CDI tools allow for real-time feedback to physicians and other healthcare professionals. CAPD (computer-assisted physician documentation) AI tools analyze the clinical data entered by physicians and other healthcare professionals and nudge them to consider potential documentation inconsistencies. By providing immediate feedback, AI-enabled CDI tools can help improve documentation quality at the point of care, leading to more accurate and comprehensive patient records. This, in turn, leads to enhanced communication within the interdisciplinary team for safer patient care.

Another area where AI supports CDI programs is prioritization of charts for review. AI software can be customized to prioritize the CDI team’s review, scanning the record content ahead of time and triaging charts with the most opportunity. Healthcare organizations can choose their level of priority and focus of reviews based on patient population, specific diagnoses, procedures, or other relevant data elements. AI algorithms flag clinical conditions that may have been missed or under-documented and prompt CDI specialists to investigate further.

AI can also play a significant role in predicting and preventing documentation-related concerns. By analyzing historical data and patterns, AI algorithms can identify documentation trends such as coding errors and missing documentation. CDI programs can use this information to proactively educate healthcare professionals, implement targeted interventions, and develop strategies to mitigate documentation-related concerns. This proactive approach can help prevent adverse patient outcomes and financial losses.

AI is still not human – While AI offers tremendous potential to CDI programs, it is important to recognize that it is not meant to replace human expertise. Rather, AI should be seen as a powerful tool that complements and enhances the work of CDI specialists. Many CDI specialists are nurses with years of experience and clinical judgment, and they play a critical role in the interpretation and validation of clinical information. Critical thinking skills are essential in determining whether the information set forth by the computer is appropriate and fitting the patient’s description of their condition. AI can assist in automating certain tasks, providing real-time nudges and feedback, extracting relevant information from copious amounts of data, and identifying potential issues, but the trained eyes of the CDI Specialists lead to the decision towards optimization of the documentation.

AI has already started to revolutionize the field of CDI and continues to develop. By leveraging AI-powered tools, CDI programs will continue to streamline and augment their work. The impact of capturing accurate language for diagnoses and patient conditions in the health record ensures all clinicians, patients and their families understand the care rendered. The partnership of our CDI teams with evolving AI technology leads the path to higher quality care, patient safety, and improved financial performance. From automating data extraction and analysis, to providing real-time feedback to healthcare professionals, AI offers numerous benefits to CDI programs. However, it is important to remember that AI is a tool, and human expertise and judgment are indispensable in ensuring accurate and comprehensive clinical documentation. Our patients are unique, and the documentation published in their medical record tells their story. By harnessing the power of AI while leveraging the skills of CDI specialists, healthcare organizations can achieve optimal CDI and better depict patient care.

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In the bustling corridors of healthcare, nurses epitomize dedication and compassion.They tirelessly navigate long shifts, demanding tasks, and emotional strains. Their profession’s relentless pace highlights the overlooked necessity of self-care in their daily lives. Too often, their commitment transcends the boundaries of the profession, embodying a profound commitment to the well-being of others, and self-care frequently becomes a casualty.

But there’s a glimmer of hope on the horizon: AI-driven self-care tools. These cutting-edge technologies are not just tools; they are beacons of relief, poised to transform how nurses prioritize their mental health and overall well-being, offering a new level of support and empowerment.

At the dawn of AI-enabled self-care tools, a beacon of hope shines amidst healthcare’s chaos. Among these innovations are

1. An AI-powered chatbot that delivers cognitive-behavioral therapy techniques. It empowers nurses to manage stress effectively.
2. A meditation and mindfulness app that offers guided sessions, providing solace for nurses amid their busy schedules.
3. A fitness and lifestyle app that tracks sleep patterns, aiding rest and recovery
4. A collaboration app that fosters community and collaboration among nurses.

These tools are more than innovations; they’re lifelines, offering personalized support for nurses’ well-being. They empower nurses to prioritize self-care and foster a sense of community, ensuring they can continue delivering top-notch care while nurturing their well-being, knowing they’re not alone in their journey.

But how do nurses find the time to incorporate these tools into their already-packed schedules? It’s a valid concern, considering the scarcity of time in their daily lives. However, with some creativity and intentionality, integrating AI-driven self-care tools into their routines is not just feasible; it’s empowering. Meet Monica, a dedicated ICU nurse with a few minutes between patient rounds. Instead of idly scrolling through her phone, she opens her favorite meditation app and spends five minutes practicing deep breathing exercises. In those fleeting moments, she feels a sense of calm wash over her, grounding her amidst the chaos of the unit. During shift report handoffs, Monica takes the opportunity to share her experiences with AI-driven self-care tools with her colleagues. They discuss their favorite apps, tips for managing stress, and the impact these tools have had on their well-being. In that moment of connection, they realize they’re not alone in their struggles—they have each other for support. During her commute home, Monica tunes into a relaxation podcast recommended by her colleague. She listens intently as she navigates the bustling streets, soaking in the soothing words and gentle melodies. By the time she arrives home, she feels refreshed and ready to unwind, thanks to those precious moments of self-care during her journey. As bedtime approaches, Monica slips on a sleek device that tracks her sleep patterns and provides insights to improve sleep quality. She reviews her sleep data from the previous night, noting any trends or patterns that may affect her rest. Armed with this knowledge, she adjusts her bedtime routine, such as dimming the lights and avoiding screen time, hoping to achieve a more restful night’s sleep. As Monica drifts off to sleep, she reflects on the day’s events, feeling grateful for the moments of calm amidst the chaos. With AI-enabled self-care tools by her side, she knows she’s equipped to face whatever challenges tomorrow may bring. In the journey towards well-being, every small step forward is a victory worth celebrating, and these tools make those steps feel not just possible, but easy.

Healthcare organizations experience reduced turnover rates, increased productivity, and improved quality of care when nurses prioritize their mental health.

Through these simple yet intentional actions, nurses like Monica reclaim control over their mental health and well-being, one moment at a time. Integrating AI-driven self-care tools into their daily routines marks a transformative shift in nursing culture that prioritizes self-care as an essential component of professional practice. When nurses prioritize their mental well-being, the benefits extend beyond their health. Patients and their families benefit from receiving care from nurses who are present, attentive, and compassionate. Improved patient outcomes, increased satisfaction, and better communication are some positive impacts observed when nurses are mentally well. Furthermore, healthcare organizations experience reduced turnover rates, increased productivity, and improved quality of care when nurses prioritize their mental health. A positive work environment, fostered by self-care initiatives, promotes collaboration, innovation, and employee engagement, driving organizational success and enhancing the healthcare experience for all involved.

The dawn of AI-driven self-care tools heralds a new era of empowerment for nurses across the globe. With these innovative technologies at their fingertips, nurses like Monica stand poised to revolutionize the healthcare landscape by prioritizing their mental well-being. As they harness the power of AI to cultivate resilience and balance in their lives, nurses not only enhance their quality of life but also elevate the standard of care for their patients. In healthcare, where dedication and compassion are the guiding lights, integrating AI-driven self-care tools represents more than just a technological advancement—it signifies a paradigm shift in how nurses approach their profession. By embracing these tools, nurses reclaim control over their wellness journey, paving the way for a future where self-care is not just an afterthought but an integral part of professional practice.

As we bid farewell to the days when burnout and exhaustion were accepted as inevitable nursing consequences, let us embrace this new chapter with open arms. By proactively adopting AI-enabled self-care tools, nurses safeguard their well-being and foster a culture of resilience, compassion, and excellence within healthcare organizations.

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A baby girl comes to the pediatric ophthalmologist. She bounces happily in her mother’s lap but has a pronounced crossed eye. Infantile esotropia is usually a surgical condition. However, there is always a small chance of an underlying neurologic cause. The doctor asks questions about onset, progression and family history, then finds an otherwise unremarkable exam. The risk is of a brain problem is low, but how low? Is the next step surgery or an MRI?

Similar questions play out in doctors’ offices thousands of times every day. Artificial intelligence (AI) is suddenly everywhere these days, and the hype keeps building. For healthcare, an industry under constant pressure to do more with less and do it better, AI seems like the right tool at the right time. Can we use this technology to help us do a better job caring for our patients?

In the broadest sense, AI is a computer-driven supplement to human decision-making. With alerts built into our electronic medical record (EMR), we have been using this type of AI for years, although most would agree that these alerts are not very intelligent Artificial Intelligence. Recently, Large Language Models (LLMs) have achieved the success that had seemed years, if not decades away. LLMs work by predicting the next word in a sentence, and when given a hugely sophisticated algorithm and essentially all the data on the internet, they can produce output that feels very human-like. At MultiCare, a twelve hospital healthcare system in Washington State, we have been focusing a how to use this seemingly magical technology to improve performance.

We have heard the promise of technology in healthcare before. A dozen years ago, healthcare systems across the country adopted EMRs in response to the HITECH Act and Meaningful Use. Things did not go as planned. While most clinicians would not go back to paper charts, the EMR came with unintended consequences and unfulfilled promises, including the promise to help doctors make better decisions. This is exactly where AI could help. But before we rush to incorporate AI into clinical workflows, we should apply the hard-earned lessons we learned from EMR clinical decision support (CDS) implementation.

For one thing, it is not enough for an AI to be “right”. While it is impressive to see LLMs pass standardized medical exams, this alone does not make them helpful. For AI to add value and help phycians (rather than replace them) the AI must be correct when the clinician would otherwise be wrong. While we certainly make mistakes, we usually get  things correct. Many EMR alerts are overridden more than 90% of the time and do little more than create a cognitive burden. If a busy doctor sees too many “I already know that” suggestions from an LLM, they will click right through them.

We don’t need AI to tell us what we already know. What we need is an AI to give us the information we need to do a better job.

Further, making a medical diagnosis is more than finding a single correct answer. Early in the process, what matters is generating a reasonable list of possible diagnoses—a differential diagnosis—and then working through that appropriately. Improving this process could reduce medical errors since doctors will not work up what they do not consider.

While premature closure is a concern, common problems are, well, common. Generating a lengthy differential is a medical student game. When the condition is straightforward, working through an exhaustive list with the extra labs and imaging studies that entails could unnecessarily increase cost and documentation burden. Again, the question is not whether the AI is right, but whether it adds value.

What could be helpful is an AI to help us identify those uncommon diagnoses we otherwise would have otherwise missed. But the laws of statistics make it hard to predict rare events. When the probability of a diagnosis is very low, even an accurate test still results in many false positives. When an AI tries to predict rare diagnoses, we can expect a lot of useless alerts, which could even be harmful if they lead to unnecessary invasive tests.

There are some ways AI could help. One is as a consultant: “Hey AI, can you read this patient’s chart and see if I’m missing anything?” Rather than firing unhelpful suggestions, an on-demand AI might add value in situations when a doctor is uncertain. The doctor would need to think to ask the AI, and once the novelty wears off, they would need to get valuable insight consistently, not just recommendations to order more low-yield tests.

AI, in its current form, may struggle to add value, but it will not be in its current form for long. One active area of development is retrieval augmented generation, where the AI uses its understanding of language to query a separate data source. Rather than just a differential diagnosis, LLMs could find information on the appropriate work up of a condition and the cost of each test. Knowing the most cost-effective way to work up a problem, one that minimizes both cost and risk to the patient, would not only help us provide better care, but improve efficiency. Instructing the LLM to limit its responses to data in the database could even reduce the risk of hallucinations. We don’t need AI to tell us what we already know. What we need is an AI to give us the information we need to do a better job.

Doctors do not routinely access risk and cost databases, but the data are there. The limiting factor has been integration into clinical workflows. LLMs, with their ability to make sense of clinical scenarios, may be the bridge that allows doctors to make truly informed clinical decisions. The real benefit of AI may come not by supporting current processes, but by helping us do things differently and better. What is the risk of a brain problem in a baby with strabismus? The doctor and family may be able to make a data-driven decision.

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In 2020, hospitals across the world were in the throes of the COVID-19 pandemic. Burnout was rampant among administrative and clinical staff. The promise of the EHR to reduce physicians’ and nurses’ burnout and administrative burden had failed to be fulfilled during a crisis in which time was paramount, with lives hanging in the balance. The pandemic caused innovation began to bubble up from hospitals’ leaders and frontline staff, ranging from hospital-at-home to automating clinical decision-making processes within the EHR to alleviate clinicians’ administrative burden and allow them to practice at the top of their licenses. However, the abatement of the pandemic did not end the quest for innovation in care delivery.

One of the ways that LifeBridge Health, a previous health system in which I worked, innovated was by creating a clinical decision-making AI algorithm in the Cerner EHR for patients who were potential palliative care candidates. The algorithm was comprised of clinical criteria, such as the primary diagnosis and the number of admissions in the past 30 days. The algorithm created a popup each time it identified patients as palliative care-eligible. If clinicians agreed that the patient was eligible then the algorithm automatically created a palliative care consult order.

Several steps were involved in creating the algorithm. First, the palliative care team (physicians, nurse practitioners, and the VP of Palliative Care) identified a need to facilitate deciding which patients were eligible for palliative care in the EHR. The team then contacted the team of data analysts and account managers at Cerner to brainstorm a solution. Together, they decided upon an algorithm that identified patients eligible to receive palliative care. The multidisciplinary team customized the algorithm to Baltimore and Carroll County’s unique patient populations.

Additionally, LifeBridge’s data analysts, database administrators, and clinical informaticists were included to design metrics to track the algorithm’s effectiveness. As a result, the number of palliative care patients increased substantially, while the administrative burden of clinicians to place a consult order and identify potential palliative care patients was reduced. Not only were patients identified as palliative care-eligible more quickly, but length-of-stay decreased. Operations and the discharge process were streamlined so that palliative care patients were moved in a timelier fashion to different units and post-acute facilities to receive optimal care. Patients’ loved ones appreciated that their family members were being cared for. Stories of the palliative care teams’ high-quality care abounded in LifeBridge’s internal monthly magazine, on the LifeBridge website, and during awards ceremonies. Involving a multidisciplinary team, especially physicians and users, early in the algorithm’s creation was necessary for their success.

At Lifepoint Health, we include multidisciplinary teams in creating algorithms and technology solutions for clinicians. Our performance improvement and technology implementation projects are closely aligned because operations and technology are closely related. The faster and higher-quality decisions are made, the more streamlined operations are. When a need is identified, our team goes to the site in-person to map the current process. Throughout the site visit, we ask clinicians about their workflow and gather their suggestions to provide the best recommendations and technology solutions.

One of our successful projects involved streamlining the prior authorization process at Lifepoint’s primary care practices. The prior authorization process was cumbersome for physicians and administrative staff. Our team first identified pain points, researched best practices, and then found an automated solution using a vendor. We received opinions from primary care physicians and administrative staff and pilot-tested the solution in several practices for proof of concept. We received and implemented positive, constructive feedback from physicians and administrative staff. We are exploring the expansion of the platform. The automated technology has enabled the task to be shifted to a centralized administrative team, increasing physicians’ clinical capacity.

In each of these cases, the algorithms and automated solutions could not have succeeded without the feedback of the clinicians, data specialists, and administrators. Each of our teams had the following takeaways from these projects:

• Involve representatives from different departments from the beginning to the maintenance phase of the projects. Representatives include physicians, nurses, data analysts, and operations experts.
• The project never truly ends. Team members should expect to be involved for several months, or perhaps years, as the algorithm and automated solutions are refined based on evolving patient and clinician needs. Expect the metrics to evolve as additional opportunities for improvement are identified.
• Communication is paramount to success. Each of our teams continues to meet regularly to review metrics, celebrate successes, and identify opportunities for improvement. We met more frequently at the beginning of the projects to discuss scope, roles and responsibilities, metrics, and potential impacts on clinical decision-making and non-clinical operations. Identifying the opportunity costs was important in decision-making, and ensuring that all team members’ voices were heard engaged them and allowed us to make the best, data-driven decisions for physicians, nurses, patients, and the organizations.
• Identify early adopters and allow them to conduct a pilot to ensure proof of concept. Physicians are often open to new ideas and solutions but would want to be involved early in the decision-making process and algorithm creation. Play to physicians’ autonomy to gain their buy-in, and explain solutions and algorithms so that they can connect how the solutions contribute to higher-quality patient care.

By including a multidisciplinary team in creating AI algorithms and technology to automate clinical decision-making, clinicians can return to what they entered medicine for: caring for patients as they would their own loved ones.

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Gastroenterology is a unique specialty using endoscopy to manage hepatic, pancreatic, and gastrointestinal diseases. Advanced gastroenterology, also known as advanced endoscopy or interventional endoscopy, is a subset of gastroenterology focused on providing more complex endoscopic procedures. Although gastroenterologists traditionally use endoscopy to diagnose and treat multiple gastrointestinal diseases, technological advances have provided new tools for gastroenterologists to influence patient care. Of these recent advances is the artificial intelligence (AI). Artificial intelligence is a computer-based system analyzing medical images through algorithms triggered by pattern recognition. Such medical images may be interpreted during an endoscopic procedure, clinical photographs, or video. Therefore, gastroenterologists could use artificial intelligence as an adjunct to help discover subtle changes and differentiate pre-cancerous from cancerous lesions.

One of the most common endoscopic procedures gastroenterologists perform is a colonoscopy. Although colonoscopy is used to help diagnose and treat multiple gastrointestinal diseases, colonoscopy is also used to screen for colon cancer. Gastroenterologists can use colonoscopies to help discover both precancers and cancers in the colon. However, precancers can be missed by gastroenterologists using standard colonoscopy. It is estimated that up to 15-20% of colonic precancers can be missed following a screening colonoscopy.

As technology continues to grow, the implementation of novel inventions into endoscopy has and will continue to improve patient care and outcomes.

Furthermore, with the recent recommendations by the United States Preventative Task Force, lowering the initial age of colon cancer screening from 50 to 45, more than a million Americans are eligible for a colonoscopy screening. Conversely, the number of gastroenterologists has not changed; therefore, overworked gastroenterologists may add extra colonoscopies to their already double-booked schedule. To address these practice gaps, recent literature investigated the role of artificial intelligence as a colonoscope adjunct to improve precancer detection rate during a screening colonoscopy. In two systematic reviews and meta-analyses published in 2021, both studies reported an increase in precancer detection rate by 44% using artificial intelligence-assisted colonoscopy. Furthermore, two other clinical trials using artificial intelligence called GI-Genius (Medtronic, Minneapolis, Minnesota, United States of America) reported an increase in precancer detection rate by 15% and 45%.

Despite an increase in precancer detection rate using artificial intelligence-assisted screening colonoscopy, one may argue that a high precancer detection rate may not decrease a gastroenterologist’s rate of missing a precancer. To address this concern, an additional article recently published in July 2022 by Wallace et al. reported an improvement in precancer miss rates using artificial intelligence-assisted colonoscopy compared to standard colonoscopy alone. Therefore, artificial intelligence could be used as an adjunct to aid overworked gastroenterologists in accurately detecting precancerous lesions during screening colonoscopies.

Although advanced gastroenterologists perform complex therapeutic procedures endoscopically, they also perform advanced diagnostic techniques to help diagnose and prognosticate multiple conditions, including malignancy. One of these diagnostic techniques is endoscopic ultrasonography, characterized by a modified conventional endoscope with an ultrasound probe at the distal end of the endoscope. This special endoscope creates an ultrasonographic image transmitted to a monitor for advanced gastroenterologists to visualize both anatomical and pathologic findings while performing endoscopy in real time. Although advanced endoscopy is a useful tool for healthcare providers, it requires a lot of training and may only be found in larger tertiary care centers. Therefore, limited exposure may cause advanced gastroenterologists to misinterpret or miss a lesion. To address this hurdle, artificial intelligence could be used as a tool to improve misinterpretation and detection among gastroenterologists. In a meta-analysis published in June 2022, artificial intelligence-assisted endoscopic ultrasonography was able to detect early esophageal cancer with an accuracy of 98% and a sensitivity and specificity of 95%.

In contrast, artificial intelligence-assisted endoscopic ultrasound was able to detect early gastric cancer with an accuracy of 94%, a sensitivity of 87% and a specificity of 88%. Another challenge advanced gastroenterologists may face is the differentiation of different pancreatic cystic lesions under endoscopic ultrasonography. In a study assessing artificial intelligence-assisted endoscopic ultrasonography’s ability to detect malignant potential in a pancreatic cyst, artificial intelligence was able to report malignant potential with an accuracy of 94% compared to a human interpretation accuracy of 56%. Such differentiation is important as artificial intelligence could help physicians risk-stratify which patients require monitoring and which patients require a surgical intervention. By improving ultrasonographic interpretation and detection, advanced gastroenterologists could rely on artificial intelligence to strengthen patient care and outcomes. 

With the advent of artificial intelligence in gastroenterology, both gastroenterologists and Interventional Endoscopists may be able to improve patient care by increasing the detection of polyps, differentiating cancerous from non-cancerous lesions, and more. As technology continues to grow, the implementation of novel inventions into endoscopy has and will continue to improve patient care and outcomes. Artificial intelligence will serve as a valuable diagnostic and therapeutic tool in the endoscopic world of gastroenterology.

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Additionally, the platform allows early recognition of patient deterioration and improvement to enable clinical teams to optimize the patient journey across the care continuum, which can reduce length of stay (LOS) and help address overflow in critical care units.

The comprehensive AI-driven platform is used in 100+ ICUs worldwide and has demonstrated clinical outcomes, like decreased hospital lengths of stay, decreased ICU re-admissions, and reduced time on mechanical ventilation and other invasive treatments, such as vasoactive support. The outcomes impact not only the quality of patient care but also curb medical staff burnout.

The advanced AI algorithms powering the Etiometry platform provide early warning signs of clinical deterioration to prompt care teams to escalate and de-escalate care. These risk indices can interact with the platform’s clinical pathway automation capability, which essentially automates the hospital’s specific workflows or protocols.

Keeping Chaos Out of the ICUs

“The ICU is the most complex, expensive, and unpredictable care setting in healthcare, requiring rapid decision-making, often using snapshots of information,” says Shane Cooke, Etiometry’s President and CEO. Etiometry’s technologies and its team are driven by the passion for improving the lives of patients and their caregivers. Shane’s vision fosters a culture where customers are partners to the company. And working closely to understand their specific clinical situations helps identify ways to enhance their clinical and operational workflows. It helps optimize the care team’s communication.

For a decade, Etiometry’s cutting-edge platform has been a game-changer in the industry. The platform has secured nine FDA clearances, supported more than 150 clinical studies and provided unparalleled insight into patient physiology to help clinical teams deliver care that enhances quality and reduces costs.

Shane has over two decades of experience working with large enterprises and start-ups. He leads a diverse team at Etiometry, which includes software engineers, data scientists, ICU clinicians, and medical device and software industry veterans. Collectively, they work towards Etiometry’s core business philosophy and development efforts to automate the care escalation and de-escalation workflows. The most recent research and development initiative involves workflow automation, starting from patient admission to discharge, to provide the best-in-class clinical decision support.

Unlike conventional patient health monitoring modalities, Etiometry provides in-depth information on longitudinal trends using patient physiology to enable a personalized and proactive approach to clinical decisions. Coupled with the chaos in the ICU environment and typical hospital resource constraints, including staffing, critical care units are at risk of suboptimal care and patient outcomes. The overarching goal of Etiometry is to replace chaos with important data elements and in-depth, real-time insights into patient physiology.

Driving Outcomes with Interlinked Systems and High-fidelity Data

For a decade, Etiometry’s cutting-edge platform has been a game-changer in the industry. The platform has secured nine FDA clearances. To date, it has been deployed across 150 clinical studies. Additionally, the platform uses algorithms that allow early risk detection or patient conditions to support care decisions. These FDA-cleared algorithms, or risk indices, were developed utilizing more than 150 million hours of de-identified patient data. The indices guide treatment to help clinicians get ahead of patient deterioration, by providing an objective data-driven continuous assessment of the risk of patient deterioration, facilitating both efficient escalation and safe de-escalation decisions.

In addition, the Etiometry Quality Improvement App (QIA), is a tremendous value-add to clinical researchers and quality improvement professionals. It provides high-fidelity data for studies to improve patient care. The QIA is a separate API providing very detailed data in which researchers can assess outcomes for patients and evaluate adherence to important protocols. It also helps test clinical hypotheses. Moreover, the QIA is a searchable, normalized database that supports both single-center and multi-center research. Etiometry has supported more than 150 clinical studies to date.

Elaborating on the functionality within the Etiometry platform, Shane highlighted its improved representation of clinical data compared to EHR data. The features enable seamless extension of the EHR for effective patient care in high acuity settings. The feature allows integration of high-frequency device data and EHR information to provide a single source of truth about patient information for the clinical teams. Bridging the information gap between bedside monitoring and EHR powers the clinical decision support with high-fidelity data.

Building A Pathway for Successful Clinical Outcomes

One of Etiometry’s customer success stories involves the automation of an extubation readiness protocol. The result was that it led to a 19% reduction in hospital length of stay and a 22% reduction in time on ventilation. A typical extubation process is challenging and driven by several clinical factors. Additionally, respiratory therapists at the customer’s hospital had many patients to manage and detailed protocols to follow to determine the right time for patient extubation. When combined, these factors tend to result in patients remaining on ventilators longer than needed. Etiometry worked with an ICU to assess its current extubation protocol by retrospectively analyzing the data collected for thousands of patients. Based on this analysis, changes were made to the protocol parameters, and a real-time alert was developed to flag patients for extubation readiness tests and to track compliance with the chosen criteria.

The Etiometry platform was used to identify patients with active alerts for being ready for an extubation test and to quickly assess the outcome of the test to make more informed ventilation decisions. This automated pathway was utilized with ~750 patients.

After driving several advancements and innovations in the realm of critical care, Etiometry is poised to invest the current year in development efforts to expand its waveform functionality, as well as its automated clinical pathways to cover more disease states and patient populations. In 2024, Etiometry will continue to enhance EHR integration with more hospital systems and platforms to aid informed clinical decisions in hospitals and critical care units.

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