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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The integration of Clinical Decision Support Systems (CDSS) into Electronic Health Records (EHRs) was anticipated to revolutionize healthcare delivery, offering promises of enhanced patient safety, improved clinical outcomes, and streamlined care processes through evidence-based recommendations. Despite notable successes, the journey from promise to practice has been challenged by various obstacles, hindering widespread adoption and adding burdens to clinical staff.

CDSS systems enhance clinical decision-making by delivering timely, patient-specific data at the point of care, aiming to better health outcomes and healthcare efficiency. By filtering data, automating tasks like issue flagging, and recommending guideline-based treatments, they improve patient safety, guideline adherence, reduce mortality, and increase care cost-effectiveness. Additionally, CDSS boosts healthcare team collaboration, communication and fosters patient involvement in care.

The successful adoption of CDSS hinges on building trust among clinical users, addressing barriers to adoption, and leveraging AI responsibly.

CDSS adoption has been hindered by early versions’ lack of transparency, insufficient training, interoperability challenges, and doubts about information accuracy and relevance. Clinicians worry about CDSS undermining their judgment or offering advice that doesn’t align with their expertise, experiences, or patient preferences. Alert fatigue from too many redundant and low-priority alerts has also fueled skepticism and decreased attention to critical notifications. Moreover, poor integration into clinical workflows has prompted inefficiencies and clinician workarounds, detracting from CDSS’s intended benefits.

Integrating Artificial Intelligence (AI) into CDSS holds significant potential to transform healthcare decision-making. By leveraging the capabilities of AI to analyze complex datasets, healthcare providers can benefit from more accurate predictions and personalized recommendations tailored to individual patient needs. This advancement could lead to improved patient outcomes, more efficient use of resources, and optimized treatment plans. However, the incorporation of AI into healthcare also introduces a set of regulatory, ethical, and procedural challenges. Issues such as data bias, discrimination, and the need for transparency have emerged in other industries utilizing AI and must be meticulously addressed within the healthcare sector.

By reducing cognitive burden and screen time, the introduction of AI into CDSS has the paradoxical potential of returning humanity to medicine; allowing clinician teams more time dedicated to patient interaction and less to administrative documentation. Much as the initial deployment of CDSS required adherence to the principles of the “5 Rights”, the intersection of AI with CDSS will require an expansion of these ideologies to account for the growing role that AI will play. New “Rights” such as the “Right Transparency,” “Right Ethical Use,” “Right Autonomy,” “Right Feedback & Learning,” “Right Integration,” and “Right Security” must be adopted to ensure that CDSS with AI capabilities meets the needs of clinical staff and patients without unexpectedly increasing the overall burden of efficacious care delivery. 

Transparency and responsible AI use are paramount in building trust among clinical staff and addressing regulatory and ethical concerns. CDSS recommendations must be accurate, relevant, and presented in a user-friendly manner. Clinicians need to understand how the system generates its recommendations and feel confident in the evidence supporting them. Healthcare organizations must develop transparent and explainable AI systems that clinicians can trust, while also addressing the broader ethical implications of AI in healthcare. Efforts to promote accountability and shared decision-making between clinical staff and CDSS are essential to ensure the safe and effective use of these tools while ensuring any ethical implications for bias in the decision-making can further bolster or degrade trust.

Efforts to encourage CDSS adoption must prioritize meeting clinical staff needs by involving them in development and implementation, along with comprehensive training for system familiarity. Adoption and adherence are optimized by smoothly integrating into existing workflows. Clinical informatics professionals, such as trained and experienced physicians and nurses, should lead in creating specialty-specific CDSS. Successful healthcare organizations will focus on workflow integration to ease clinician burnout, decrease cognitive load, and enhance patient interaction. Additionally, leveraging AI to analyze clinical staff usage patterns of CDSS can offer feedback for system improvement and create new value streams. To promote a culture of innovation and improvement, organizations should motivate CDS usage with policies and incentives that support and reward experimentation and ongoing enhancement.

As healthcare organizations implement more advanced CDSS with AI, the security imperative to safeguard patient data and ensure compliance with regulatory standards will increase. How AI tools are used and the implications on both the security and ethical use of patient data will have huge implications on the successful implementation of any CDSS system using AI. Staff training and understanding of data use and security protocols will become essential to mitigate risks associated with data breaches or unauthorized access. By prioritizing security, healthcare organizations can instill confidence among clinicians and patients in using CDSS while upholding the highest standards of privacy and confidentiality.

Integrating AI into clinical decision support heralds a new era of personalized, data-driven healthcare. While AI offers immense potential to enhance diagnostic accuracy, optimize treatment outcomes, and improve efficiency within healthcare systems; adoption also presents challenges such as bias, interoperability, and clinician resistance. Successful adoption of CDSS hinges on building trust among clinical users, addressing barriers to adoption, and leveraging AI responsibly. By prioritizing clinician engagement and education, designing user-friendly systems, and fostering a culture of innovation and iteration, healthcare organizations can harness the full potential of CDSS to improve patient outcomes and reduce clinician burnout. As the healthcare landscape continues to evolve, it is imperative that we refine these systems to meet the evolving needs of healthcare providers and patients alike.

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In the dynamic landscape of healthcare, Geisinger’s Steele Institute for Health Innovation is pioneering initiatives that harness automation to reshape patient care and streamline clinical workflows. Through cutting-edge technology and innovative approaches, Geisinger is ushering in an era of efficiency and excellence.

A pioneering initiative at Geisinger involves automating compliance audits for patient charts, targeting critical areas. This initiative aimed to revolutionize auditing, ensuring timely completion while upholding stringent quality standards. Digital algorithms are assigned to navigate electronic health records (EHRs) almost in real-time to identify patients under specific conditions, such as those requiring non-violent restraints or those on suicide watch. These algorithms then check for necessary orders and flowsheet completions per policy requirements to determine compliance.

Automations streamline the review of admitted patients, identifying and addressing missing information, and ensuring it’s promptly accessible online as needed by policy. This enables real-time access for nursing leadership. Additionally, compliance dashboards offer valuable insights into audit compliance rates across various units, hospitals, and regions, empowering nurse leaders to support documentation compliance within the EHR, thus enhancing patient safety. It’s crucial to emphasize the importance of documentation and our partnership with native systems, which helps us give time back to clinicians by managing tasks efficiently. This avoids inconsistencies and leads to quicker turnaround times for audit requirements and a better understanding of compliance rates.

Geisinger’s automation journey represents a paradigm shift in CDS. By harnessing the power of technology, they have revolutionized internal processes and set a new standard for excellence in patient care.

Through meticulous automation driven by Robotic Process Automation (RPA) technology, Geisinger ensures that individual chart audits are completed close to real-time, enabling the organization to achieve a nearly 100% completion rate in quality audits. This achievement represents a significant milestone in Geisinger’s journey toward excellence in patient care. The RPA technology employed by Geisinger plays a pivotal role in eliminating delays and errors inherent in human manual processes.

By creating a standardized method for completing each chart audit for all patients in all hospitals at Geisinger, the organization ensures consistency and adherence to the same high-quality standards across the board. This standardization enhances efficiency and facilitates seamless auditing processes, further contributing to the organization’s ability to achieve real-time completion of chart audits.

Leveraging cutting-edge technology to extract and analyze data from electronic medical records (EMRs), Geisinger maximizes the effectiveness of its automation initiatives. By automating routine tasks and streamlining workflows, Geisinger enables nursing managers and leaders to focus their time and expertise on initiatives to improve patient outcomes.

With RPA technology leading the way, Geisinger empowers its nursing managers and leaders to focus on driving positive change and innovation in patient care delivery, maximizing their potential. This strategic approach boosts operational efficiency and underscores Geisinger’s dedication to providing the highest quality of care to every patient.

Geisinger’s automation initiatives have far-reaching effects, yielding concrete improvements in patient safety, nursing compliance, and job satisfaction. By automating routine tasks, nurses and nurse managers gain valuable time to dedicate to direct patient care, elevating the standard of healthcare delivery. Geisinger places a premium on the well-being of its nursing and clinical staff, understanding that relieving them of mundane duties can significantly enhance job satisfaction and promote a healthier work-life balance. Through proactive efforts to streamline workflows and automate administrative processes, Geisinger reaffirms its commitment to fostering the professional growth and overall well-being of its healthcare professionals.

Geisinger’s proactive approach to addressing compliance challenges sets a precedent for other healthcare organizations and serves as a model for best practices in the industry. By leveraging automation to ensure adherence to regulatory standards, Geisinger enhances operational efficiency while promoting transparency and accountability across the organization. Geisinger’s commitment to utilizing cutting-edge technology to streamline processes benefits its operations and contributes to raising the standard of care and compliance across the healthcare sector.

Geisinger’s automation journey represents a paradigm shift in CDS. By harnessing the power of technology, they have revolutionized internal processes and set a new standard for excellence in patient care. As healthcare organizations worldwide embrace automation, Geisinger’s experience is a guiding light, illuminating the path toward a future where automation and innovation converge to create a healthier world for all.

Intelligent Automation Hub: Enhancing Efficiency and Patient Care

At Geisinger Health System, the Intelligent Automation Hub stands as a transformative force, revolutionizing internal processes to drive efficiency and, ultimately, elevate patient care. Collaborating closely with internal teams, the Automation Hub pioneers automated solutions for various processes and tasks, significantly enhancing operational efficiency. By enabling clinicians and care team members to concentrate on delivering high-quality patient care, we drive efficiencies and give valuable time back to the clinical staff, alleviating them from unnecessary tasks and fostering a more effective healthcare environment.

The Automation Hub comprises a dedicated team of business analysts and programmers who partner with data scientists to integrate various levels of technology. This collaborative effort enables the research and implementation of leading-edge automation tools throughout the organization.

To support its mission, the Automation Hub focuses on delivering business processes with tangible benefits, continuously improving the automation development process, and creating a robust platform for process automation. The goal is to add layers of intelligence to processes through enhanced automation, freeing up employees to engage in higher-value work.

The bot creation process spans six stages, from discovery to production, ensuring seamless integration and efficiency. Geisinger’s automation initiatives have yielded remarkable outcomes, notably emphasizing time savings and efficiency enhancements. For instance, the implementation of chart audit automation has resulted in approximately $6.5 million in cost avoidance measures and a staggering 100K hours saved, with a total transaction count of 300K. This underscores the avoidance of inconsistencies and translates into a swift turnaround for audit requirements and a deeper understanding of compliance rates.

In summary, Geisinger’s Intelligent Automation Hub represents a beacon of innovation in healthcare. By harnessing the power of automation, Geisinger continues to drive efficiency, improve patient care, and set new standards of excellence in the industry. As healthcare organizations worldwide embrace automation, Geisinger’s journey serves as a testament to the transformative potential of technology in shaping the future of healthcare delivery.

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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 today’s dynamic health care environment, particularly in the post-pandemic era, clinicians are continuously striving for excellence in patient care, whether it’s delivered virtually or on premise. However, amidst organizational and economic hurdles, they face mounting pressure due to the escalating demands of an expanding patient population. This, coupled with the challenges of maintaining a healthy work-life balance and sustaining peak performance, often leads to burnout, depersonalization, and stress among dedicated health care professionals. This situation is neither sustainable nor conducive to providing optimal care.

To address these growing challenges and support the well-being of health care providers, forward-thinking, technology-focused organizations such as Providence’s Virtual Care and Digital Health, have partnered with health care professionals to develop innovative solutions, setting the bar for care excellence. Among these solutions are clinical decision support systems (CDSS) which are designed to streamline and enhance the delivery of care virtually and in person.

Despite the challenges and initial reluctance, integrating CDSS into clinical workflows holds the promise of optimizing patient outcomes and improving overall health care quality.

Developed to assist providers in delivering the best care for their patients, a CDSS is an electronic health information tool that provides clinicians with recommendations and treatment pathways. Access to this tool enables providers to make informed decisions about patient care without needing to refer the patient to another provider, which can help minimize patient dissatisfaction. Unlike traditional care diagnostic methods, which often require clinicians to specialize in a particular ailment or immediately refer patients at the onset of an illness, CDSS empowers providers to assess real-time options and choose the most appropriate course of action.

Recognizing the critical need and immense benefits of CDSS in revolutionizing health care delivery across the entire continuum of care, Dr. Eve Cunningham, a visionary leader and esteemed clinician, has spearheaded a groundbreaking initiative. With her innovative approach, she not only envisioned but also implemented a cutting-edge CDSS platform designed by clinicians to support providers in their clinical decision-making processes. “This cutting-edge CDSS platform offers specialist-level clinical expertise and just-in-time support to front-line clinicians while they are in the context of a virtual or in-person visit,” says Dr. Cunningham, “[which] translates to happier providers and improved patient access.”

By leveraging this cutting-edge CDSS platform, Dr. Cunningham seeks to optimize patient care outcomes, enhance efficiency and streamline workflows through leveraging artificial intelligence (AI), thus ushering in a new age of transformative health care delivery.

While a CDSS holds immense potential to enhance the efficiency of care delivery, particularly in resource-constrained clinical environments, its widespread adoption has historically been hindered by various challenges. These obstacles include excessive documentation burden on providers, clinician reluctance stemming from a lack of confidence in the system and negative patient perceptions.

Many clinicians, for example, have found themselves overwhelmed by the sheer volume of alerts generated by certain CDSS platforms, which can impede their ability to focus on patient care. Some clinicians also prefer relying on their own judgment and experience rather than incorporating CDSS recommendations into their practice. Additionally, there is a notable preference among some patients for providers who employ traditional diagnostic methods, viewing them as more trustworthy and competent compared to those who rely on clinical decision support tools. Regrettably, this small subset of individuals may overlook the advantages offered by CDSS, such as mitigating clinician burnout and errors and promoting improved health outcomes for patients.

Yet, as the world of health care continues to evolve, it becomes increasingly important for both providers and patients to embrace practices and technologies that facilitate the most effective and efficient delivery of care. Despite the challenges and initial reluctance, integrating CDSS into clinical workflows holds the promise of optimizing patient outcomes and improving overall health care quality. Therefore, fostering greater acceptance and utilization of these tools is essential for advancing health care practices and ensuring the delivery of high-quality care to all patients, especially in an era where there’s a strong drive to reduce costs, minimize risks to patients, prioritize clinician well-being and exceed the satisfaction of both clinicians and patients.  

Every clinician aims to deliver exceptional care to their patients while also seeking the most efficient methods. As health care leaders navigate the complexities of traditional practices, they pave the way for technological advancements. Organizations offering innovative solutions, such as a CDSS, are at the forefront of redefining care delivery by clinicians. Through these advancements, trust and engagement from patients can be enhanced, marking a significant step forward in improving health care outcomes and experiences for all.

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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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What is CDSS?

Clinical Decision Support Systems (CDSS) are sophisticated tools that utilize data within electronic health records (EHRs) to help healthcare workers make more reasonable clinical decisions. These systems provide timely, relevant information and patient-specific recommendations to enhance healthcare delivery and, thus, health. By integrating a wealth of medical knowledge, patient data, and other pertinent information, CDSS tools assist in diagnosing, prescribing, and managing patient care. They function as a second set of eyes, offering critical insights and alerts to ensure patients receive optimal, evidence-based care. CDSS ranges from simple notifications to comprehensive care plans, aiding decision-making processes and improving overall healthcare outcomes.

Advantages of CDSS

CDSS boasts numerous benefits that streamline healthcare delivery. They improve efficiency by quickly processing patient data and generating pertinent health insights, enabling faster decision-making. CDS tools facilitate earlier detection and screening of diseases, leading to timely interventions. They standardize diagnosis and treatment protocols, ensuring consistent and accurate care delivery. Importantly, CDS systems help prevent adverse outcomes by identifying potential risks and contraindications, thus enhancing patient safety. Follow-up management is bolstered through reminders and alerts, promoting adherence to care plans. Economically, CDS contributes to cost reductions and increases convenience by minimizing unnecessary tests and procedures, optimizing healthcare resource utilization, and fostering a more streamlined, effective healthcare environment.

CDS can only enhance right actions and block wrong ones if powered by quality input and diligent effort.

Level of Restrictiveness

The level of restrictiveness is the redundancy built by CDSS, which slows the clinician to rethink. The most restrictive CDS makes specific actions impossible without a significant cognitive effort, ensuring adherence to critical pathways. Less restrictive systems provide guidance based on the patient’s data, acting as a nudge toward best practices without stringent enforcement. These systems suggest actions but leave room for professional judgment. The least restrictive CDS integrates consensus-driven protocols into the workflow, so certain best practices become automatic and unobtrusive, seamlessly guiding healthcare workers without imposing decision points. This gradation ensures that CDSS can be tailored to the needs of the healthcare setting, balancing between directive and supportive roles.

Target Domain

We must consider which level the CDSS will impact: the population, encounter, or precision level. They influence long-term patient population outcomes by utilizing data from patient registries and focusing on care transitions and outcomes-based care. As a typical application, CDSS offers real-time assistance to physicians and nurses at the individual encounter level. It includes reminders or care suggestions during clinical interactions, addressing potential drug-disease, drug-allergy, and drug-drug interactions. CDSS incorporates individual characteristics like demographics, clinical history, and genetic/genomic information at a precision level, tailoring interventions to each patient’s unique profile. Examples include adjustments in care based on specific metrics like BMI, enhancing the personalization and effectiveness of healthcare delivery.

Five Rights

The efficacy of CDSS hinges on fulfilling the ‘five rights’: delivering the right information to the right people in the right format, through the right channel, and at the right time. We must consider these criteria for effective decision-making in healthcare. The right information means pertinent, evidence-based data tailored to the clinical scenario. The right people refer to ensuring that those in a position to act upon the information receive it. The right format denotes presenting information in a user-friendly manner that facilitates comprehension and action. The right channel uses the most effective means to deliver the information through alerts, reminders, or reports. Lastly, timing is critical; we should provide information at the right time in the care process to positively influence outcomes.

Reducing Noise

The primary role of Clinical Decision Support (CDS) is to reduce noise in clinical settings, thereby enhancing decision-making. Human decision-making is fraught with complexities, including cognitive biases and susceptibility to behavioral economic factors. CDSS helps to streamline the influx of information, filter out irrelevant data, and present the most pertinent facts. This support is crucial in high-stakes environments where healthcare professionals make numerous critical decisions daily. By providing clear, evidence-based recommendations, CDS tools help mitigate biases and improve the consistency and quality of patient care, ensuring decisions are informed and data-driven.

Challenges

Despite their benefits, CDSS face challenges like alert fatigue, where too many alerts can desensitize users, and redundancy, leading to potential oversight of critical information. Burnout among healthcare workers, exacerbated by information overload and system complexities, further complicates CDSS implementation. CDSS makes healthcare professionals more prone to automation bias and an inclination to trust and excessively rely on automated systems. Additionally, the effectiveness of CDS can be undermined by insufficient or inaccurate data, highlighting the need for robust, up-to-date information to inform decisions adequately.

Governance and Evaluation

Effective governance and ongoing evaluation are paramount to ensure CDSS fulfill their potential. Regular assessment of CDS functionality, user satisfaction, and clinical outcome impact is necessary to refine and optimize these systems. This process includes analyzing how CDS tools affect clinical practice, patient outcomes, and healthcare efficiency. Governance mechanisms must be in place to oversee CDS implementation, ensuring alignment with evidence-based practices and addressing ethical, legal, privacy, risk, and value of alert concerns. Continuous feedback loops between end-users and developers are crucial to adapt CDS systems to evolving healthcare needs and technological advancements.

Conclusion

Reflecting on the evolution of Clinical Decision Support (CDS), it’s notable that two decades ago, Bates et al. penned a seminal paper outlining the Ten Commandments for Effective Clinical Decision Support to actualize evidence-based medicine. Their insights have shaped the development and integration of CDS systems in healthcare, emphasizing the importance of user-centered design, evidence integration, and measurable outcomes. As CDS continues to evolve, its potential to transform healthcare remains immense, promising a future where data-driven, patient-centered care is the norm.

The post Enhancing Health and Care Through the Power of Clinical Decision Support Systems appeared first on HealthTech Magazines.

As the population ages, the imbalance between the demand for care and providers of care will continue to fuel the need for innovative solutions in our healthcare system. Digital technologies continue to be developed and applying these solutions across the continuum of care is increasingly popular. Surgeons, such as myself, who are directly involved in patient care have witnessed firsthand the evolution of technology from an electronic health record (EHR) to today’s AI-based clinical decision support (CDS) tools. However, their success is not solely determined by technological advancements and unique problem-solving; rather, effective change management plays a pivotal role in ensuring seamless integration into clinical practice. I will discuss the significance of change management in driving the adoption and success of digital health solutions.

The Promise of Digital Health

Digital health technologies impact patient care, offering unprecedented opportunities for enhanced diagnosis, treatment, and patient engagement. From EHRs to telemedicine platforms and CDS systems, these innovations have the potential to streamline workflows, expand access and improve outcomes.

Nevertheless, none of the current digital health and CDS tools we use today would be successful in health care practice if they had not been solutions to pressing challenges facing clinicians and hospital teams. At Houston Methodist, as with other healthcare systems, we had been using telemedicine programs prior to the COVID-19 pandemic.  Nonetheless, when COVID cases first hit our hospital system, our ability to scale our virtual platform and learn its most effective role was our first encounter using many digitally based innovations in direct patient care. This would not have been successful without aligning and synchronously engaging our formal change management strategy and the telemedicine integration.

The successful implementation of digital health tools requires more than just technological prowess; they demand a strategic approach to change management.

Change Management

The successful implementation of digital health tools requires more than just technological prowess; they demand a strategic approach to change management. Digital technologies are not commodities; they look to enhance real-time decision-making based on historical data. In other words, the digital tool tells you something, but it is up to the user to execute the information. The successful adoption of technologies tends to follow these three critical steps:

1) How big is the problem that the technology attempts to solve,

2) Ease in the first step of the change process, and

3) Positive feedback after the first step. 

If any of these steps faces significant challenges, then the likelihood of adoption tends to be less successful. This strategy encompasses a series of structured processes aimed at training and supporting clinical teams, administrative staff, and hospital operations personnel to navigate change successfully. Effective change management ensures that these individuals are capable of using new technologies and embracing them as integral components of their practice.

Resistance to change, workflow disruptions, and concerns about data security are just a few of the hurdles that must be addressed. Moreover, the fast-paced nature of health care delivery leaves little room for extensive training or experimentation with unfamiliar technologies. An analogy for implementing change in health care is like trying to change a tire on a moving car.  Thus, it becomes imperative to implement change management strategies that minimize disruptions while maximizing user acceptance and proficiency.

At Houston Methodist, we begin with a small pilot program to determine quickly whether an innovation can expand throughout our hospital system, building upon our “succeed fast, fail fast” agile model. With a recent pilot project, we co-developed an innovation to use ambient technology in the operating room (OR) to improve accuracy in our OR data analytics and reduce the reliance on nurse-driven data entry into the EHR. The objective was to focus on maximizing case duration accuracy to improve scheduling and staffing and reduce the data entry demands of the clinical staff. Educating clinical teams about the new ambient intelligence cameras in their ORs was and continues to be a delicate process for change management within our organization. Strategies around its application and engagement are iteratively evolving and are integral to evaluating the success of an executed technology.   

Disruptive Innovation

Startups that challenge the status quo and introduce groundbreaking solutions often find success in reshaping the health care landscape, but the iterative process is essential for new technologies to develop and integrate in an impactful manner in health care. Without this collaboration, the technology would still be unrealized because just creating great technology is not enough. Startups which are amenable and flexible to co-develop their products with industry partners will be more successful in the long run.

The concept of disruptive innovation is no stranger to the health care sector. It is typically touted as a key component to innovation. However, at this institution, we recognize all change; therefore, all change management is disruptive. Therefore, our goal is to create strategies for adopting these digital innovations that are as small a disruption as possible to the end user in regards to their daily activities. Disruption is not our goal; technological adoption, execution of new workflows, and improved alignment with digital technologies are the focus. Developing an effective process in this regard is as critical as our innovation.

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I remember when clinical decision support (CDS) tools would tell me to do a mammogram on a male patient and a pap smear on an 85-year-old woman. A lot has changed since that time, and we need to embrace and understand how these incredible tools can help us in population health management (PHM) by reducing unnecessary illness and reigning healthcare spending.

CDS has gone a long way since we started using electronic health records (EHRs). At the fundamental level, a well-oiled CDS program should provide real-time guidance to all healthcare providers. The entire healthcare team should use it to ensure patients receive the best quality of care with up-to-date guidance. They need immunizations and screenings to receive medications that are on their pharmacy benefit manager’s (PBM) plan. They adhere to the current standards of care. This should have happened, but unfortunately, in the current state, this is not working in the best interest of the patient.

Using the latest artificial intelligence (AI) tools would allow decisions to be made based on the latest clinical evidence, which is not only cost-effective but results in better outcomes when used appropriately. Currently, there remains a disconnect between the designers and developers. CDS must not interfere with users’ clinical workflows, whether physicians or medical assistants. Alert fatigue has forced users to press the “X” and close the window. We have the opportunity to fill this wide gap with AI. Electronic medical records (EMRs) are now easily integrated with CDS to provide the much-needed guidance to achieve better outcomes.

CDS has traditionally relied on current practice guidelines. Now, using AI and large language models (LLMs), we can provide the most up-to-date guidance for preventive care, follow up and medication adherence. Guidelines change continuously; leaning on AI tools would realize the best outcomes. Because clinical guidelines change rapidly as new studies and treatments are developed, it becomes exceedingly difficult and time-consuming to digest and apply this information. However, clinicians can know about these changes that occur so rapidly; therefore, support trees would be fluid and clinical pathways would change continuously using AI and alert clinicians to new issues or problems that they would otherwise not have known.

The idea of CDS was a great leap forward; now we have the opportunity to fine-tune this software. We still need to consider the ethical and moral risks of using such software.

As genetic testing becomes more mainstream in the country, it is imperative that current clinicians learn how genomic testing can assist them in providing better outcomes. CDS systems should incorporate such information to change treatment plans and tailor their treatment based on what is hidden in their genetic code.

Variance in the standard of care is a particularly important phenomenon due to geographic differences of availability of clinicians, and technology. Patients living in Area A may not have the same access to the technology or trained providers as those in Area B. This is where properly and intelligently deployed AI tools with CDS systems will provide information to clinicians on the best course of action and treatment of their patients.

Alert fatigue sometimes makes it very difficult for clinicians to look at their screens in a manner that would allow them to digest and act on what is best for their patients. What is very important in this context is to provide clinicians with relevant, timely, and useful information. The new clinical support systems need to ensure that this alert fatigue is minimized by learning what their user is doing with their patient and only displaying relevant information to the current encounter. This type of iterative learning will maximize the use of clinical support systems.

Interoperability has always become a problem with EMRs and the other programs they are attached to. With the widespread adoption of FHIR (Fast Healthcare Interoperability Resources), it will become easier to design and implement CDS systems that can be used and shared amongst various EMR systems easily and translate to a common unified output.

As the American population ages, it becomes even more critical that we leverage the benefits of CDS and AI to predictive analytics to provide clinicians with a predictive score based on medical history, medications, family history and baseline condition of the patient. By utilizing this tool, we can dispatch the correct treatment to the right patient when needed. Not only will this save resources, but quality of life and outcomes will also be improved.

In order to facilitate the adoption and widespread use of these new robust CDS systems, we will need to develop comprehensive academic programs that will allow students to focus on learning how the intersection of medicine and IT (information technology) can save lives.

The idea of CDS was a great leap forward; now we have the opportunity to fine-tune this software. We still need to consider the ethical and moral risks of using such software. It becomes incumbent on the provider and the patient to discuss what is good and bad and what course of action the patient should take based on this discussion. This great tool in our toolbox will guide providers to make better decisions, save resources and provide better care.

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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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