Vesta Teleradiology

RSNA 2024 Highlights: AI, Imaging Advancements, and Industry Recognition

The Radiological Society of North America’s (RSNA) 2024 annual meeting showcased significant advancements in medical imaging, including artificial intelligence (AI), innovations in computed tomography (CT) and magnetic resonance imaging (MRI), and strategies to address the ongoing radiology staffing shortage. The event also honored leaders in the field for their exceptional contributions.

Advancements in Radiology Technology

AI Integration

Artificial intelligence was a major focus, with over 200 exhibitors showcasing AI-related innovations. The FDA has approved nearly 1,000 clinical AI algorithms, 80% of which pertain to medical imaging. AI’s expanding role includes rapid stroke detection, workflow orchestration, and FFR-CT assessments, now included in national guidelines. However, discussions emphasized the need for rigorous monitoring to prevent bias and performance degradation in these algorithms.

CT and MRI Innovations

The conference highlighted advancements in imaging technologies, unveiling three new AI-enhanced CT systems aimed at improving diagnostics and patient comfort. These innovations promise faster, more accurate imaging, enhancing both clinical outcomes and workflow efficiency.

Addressing the Staffing Shortage

The radiology workforce shortage remains a critical challenge. Proposed solutions include expanding residency programs, adopting AI to reduce workloads, and ensuring fair compensation. While AI shows promise in easing demands, experts caution it is not a standalone solution to the staffing crisis. Teleradiology companies like Vesta can play a vital role in addressing these shortages by providing access to qualified radiologists, ensuring timely interpretations, and supporting healthcare facilities in maintaining efficient workflows.

Industry Recognition

RSNA 2024 also celebrated the accomplishments of industry leaders:

Gold Medal Awards: Recognizing excellence in leadership and innovation, the awards were presented to James P. Borgstede, MD, Elizabeth S. Burnside, MD, MPH, and Beverly G. Coleman, MD, for their groundbreaking contributions to radiology and healthcare.
Alexander R. Margulis Award for Scientific Excellence: This award honored the authors of a 20-year study on low-dose CT screening for lung cancer, emphasizing its life-saving potential through early detection.
Outstanding Researcher Award: Jeffrey G. Jarvik, MD, MPH, was recognized for his impactful work in spine imaging and back pain research.

Conclusion

RSNA 2024 reinforced radiology’s pivotal role in healthcare, spotlighting transformative technologies, addressing workforce challenges, and honoring outstanding achievements. The integration of AI, combined with ongoing innovation in imaging and efforts to bolster the radiology workforce, ensures the field continues to thrive and evolve.

Sources:

Radiologbusiness.com
rsna.org
openai.com

Update on ChatGPT and Radiology Readings

ChatGPT-4 Vision and Radiology Exam Questions

ChatGPT-4 Vision is an enhanced version of OpenAI’s GPT-4 that can interpret both text and images. This multimodal capability allows it to analyze visual content, such as photos, diagrams, and medical images, in addition to understanding text.

Applications of ChatGPT-4 Vision include assisting with medical imaging analysis, enhancing accessibility by describing images, extracting data from visual documents, and supporting creative tasks. However, it has limitations, such as occasional inaccuracies when interpreting complex images, especially in specialized fields like radiology.

A study published in Radiology evaluated the performance of ChatGPT-4 Vision on radiology exam questions, revealing that while the model performed well on text-based questions, it struggled with image-related questions. ChatGPT-4 Vision is the first version of the language model capable of interpreting both text and images.

The study, led by Dr. Chad Klochko, used 377 retired questions from the American College of Radiology’s Diagnostic Radiology In-Training Examinations. The model answered 65.3% of all questions correctly, achieving 81.5% accuracy on text-only questions but only 47.8% on questions with images. The model performed best on image-based questions in chest and genitourinary radiology, and worst in nuclear medicine.

The study explored different prompting techniques and found that the model declined to answer 120 questions, primarily image-based, and showed hallucinatory responses when interpreting images, suggesting incorrect interpretations leading to correct diagnoses. Dr. Klochko emphasized the need for more specialized evaluation methods, as the model’s current limitations in accurately interpreting radiologic images restrict its applicability in clinical settings.

ChatGPT-4 For Summarizing Radiology Reports for Pancreatic Ductal Adenocarinoma

A study published in Radiology found that ChatGPT-4 outperforms GPT-3.5 in creating structured, summarized radiology reports for pancreatic ductal adenocarcinoma (PDAC), potentially improving surgical decision-making. Led by Dr. Rajesh Bhayana from the University of Toronto, the study demonstrated that GPT-4 generated near-perfect PDAC synoptic reports and achieved high accuracy in categorizing resectability using a chain-of-thought prompting strategy, resulting in more accurate and efficient surgical decision-making.

The study included 180 PDAC staging CT reports from Princess Margaret Cancer Centre in 2018. Two radiologists set a reference standard for 14 key features and the National Comprehensive Cancer Network (NCCN) resectability category. ChatGPT-4 was found to have equal or higher F1 scores than GPT-3.5 for all features and outperformed GPT-3.5 in categorizing resectability. Surgeons using AI-generated reports reduced their review time by 58%.

The findings suggest that ChatGPT-4 can improve standardization, communication, and efficiency in pancreatic cancer care. However, Paul Chang, MD, from the University of Chicago, emphasized the need to integrate these AI capabilities into scalable and comprehensive workflows, acknowledging the gap between feasibility and operational solutions.

Sources:

Auntminnie.com
medicalexpress.com
openai.com

AI in Radiology: Biden’s New Executive Order and Latest News

The Biden administration’s recent executive order on artificial intelligence (AI) has significant implications for radiology, as discussed in a review published in JACR. The order aims to ensure responsible use of AI in healthcare and establish a federal program to address unsafe practices. While immediate changes to radiology practice may not be expected, the order signals forthcoming regulatory shifts, particularly in oversight and enforcement by government agencies. This includes scrutiny of computer-aided detection systems and AI for noninterpretative tasks. The FDA premarket review for medical devices like CAD programs is likely to be augmented with additional quality and equity requirements. Health and Human Services will oversee data input into AI algorithms, possibly mandating disclosure of training datasets. Radiologists seeking Medicare reimbursement for AI products will need to prioritize security and compliance with nondiscrimination laws. The order also emphasizes data sharing with the National AI Research Resource and encourages radiologists to engage in policy creation and provide input on regulatory frameworks. However, specific parameters and regulatory details are yet to be defined. Overall, the order serves as a call for federal agencies to mobilize efforts in AI oversight, with radiologists urged to actively participate in shaping policies and best practices.

Predicting Lymph Node Metastasis

A recent study published in Radiology: Imaging Cancer compared the effectiveness of a four-dimensional (4D) convolutional neural network (CNN) model, incorporating clinical and breast MRI findings, with a machine learning model based solely on clinicopathologic features in predicting axillary node status in women with breast cancer. The 4D CNN model achieved a significantly higher area under the curve (AUC) of 87% compared to 63% for the clinical model. It also demonstrated higher sensitivity (89% vs. 75%) and specificity (76% vs. 52%), with a lower false-negative rate (11% vs. 25%). The study suggests that the 4D hybrid model could serve as a valuable tool in selecting patients who may avoid invasive procedures like sentinel lymph node biopsy and aid in treatment decisions for breast cancer patients. However, further external validation of the model is needed, and limitations such as reliance on manual tumor bounding boxes and specific MRI device imaging need to be addressed for broader clinical adoption.

FDA Clearance for Cardiac and Lung AI

Exo, a medical imaging software and device company, has announced FDA clearance for its cardiac and lung artificial intelligence (AI) applications on Exo Iris, their handheld ultrasound device. This expands Exo’s cleared applications to include cardiac, lung, bladder, hip, and thyroid assessments. Iris, powered by AI, facilitates point-of-care ultrasound, particularly benefiting rural or underserved communities, enabling faster diagnosis and treatment. Sandeep Akkaraju, Exo’s CEO, emphasizes the aim of democratizing AI-empowered medical imaging for all caregivers. The AI applications were trained on a diverse dataset and validated across various patient populations and scan types. They enable reliable assessment of pulmonary edema and cardiac function, with additional doppler capabilities for cardiac, abdominal, and vascular applications. Clinicians, including those with limited experience, welcome the efficiency and reliability of Exo’s AI applications, which enhance patient care and healthcare system efficiency.

Sources:
Radiologybusiness.com
mddionline.com
Openai.com

March AI News in Diagnostic Imaging

New Research by Harvard Medical School, MIT and Stanford on AI and Clinician Performance

The potential of medical artificial intelligence (AI) tools to enhance clinicians’ performance in interpreting medical images varies among individual clinicians, as highlighted by recent research led by Harvard Medical School, MIT, and Stanford. Published in Nature Medicine, the study underscores the intricate nature of human-AI interaction, which remains incompletely understood. While some radiologists benefit from AI assistance, others experience interference, affecting diagnostic accuracy.

The findings stress the necessity for personalized AI systems tailored to individual clinicians, emphasizing careful implementation to maximize benefits and minimize harm. Despite variations in AI’s impact, the results shouldn’t deter AI adoption but rather prompt a deeper understanding of human-AI dynamics to design approaches that enhance human performance.

To ensure effective integration of AI in clinical practice, collaboration between AI developers and clinicians is essential, alongside rigorous testing in real-world scenarios. Furthermore, efforts should focus on improving AI accuracy and training radiologists to discern AI inaccuracies, facilitating informed decision-making. Ultimately, understanding the complexities of machine-human interaction is pivotal for optimizing patient care through AI integration in radiology.

AI and Workflows

New research highlights a novel reporting workflow that automatically incorporates artificial intelligence (AI) findings into structured radiology reports, streamlining physicians’ tasks and saving valuable time. German experts shared their experience with the “AI to SR pipeline,” which integrates a commercially available AI tool for chest X-ray pathology detection and localization into structured report templates.

In evaluations conducted at University Medical Center Mainz, expert radiologists found that reports generated using the AI to SR pipeline were faster compared to free-text reporting and conventional structured reporting. Additionally, subjective quality assessments indicated higher ratings for reports created with the pipeline.

In the hospital’s clinical routine, chest X-ray images are sent to the picture archiving and communication system, then automatically forwarded to the AI tool for analysis. The results are output in a DICOM structured reporting format, taking approximately five minutes from image acquisition to final reporting. Radiologists were able to create chest X-ray reports significantly faster with the pipeline compared to free-text and conventional structured reporting, while also rating the AI-generated reports more favorably.

The authors suggest that this AI-driven reporting pipeline offers standardized, time-efficient, and high-quality reporting for chest X-rays, potentially enhancing AI integration into daily clinical practice and maximizing its benefits.

Sources:

Medicalxpress.com
Radiologybusiness.com
Openai.com

The Latest in Brain Imaging News

In recent years, awareness surrounding brain injuries has steadily risen, prompting significant strides in diagnostic technologies and treatment modalities. As we delve into the latest developments in this critical area of healthcare, it becomes increasingly apparent that advancements in medical imaging, particularly in the realm of neurological disorders, are poised to revolutionize the landscape of brain injury diagnosis and management.

AI-based Quantitative Brain Imaging System

Philips and Synthetic MR have joined forces to advance the diagnosis of neurological disorders through cutting-edge quantitative brain imaging tools. Their collaboration introduces the Smart Quant Neuro 3D MRI software suite, combining Philips’ SmartSpeed image-reconstruction technology, the 3D SyntAc clinical application, and SyntheticMR’s SyMRI NEURO 3D software. This innovation employs AI to analyze brain tissues, enhancing the detection and analysis of conditions like multiple sclerosis, traumatic brain injuries, and dementia.

The rise of AI in diagnostic imaging, projected to reach $1.2bn by 2027, signifies a transformative shift in improving accuracy and patient outcomes. With the diagnostic imaging market expected to grow to $9.1bn by 2030, fueled by demand for early disease diagnosis and personalized medicine, this partnership underscores the crucial role of AI in enhancing medical imaging.

Read the press release here.

A New Way of Diagnosing Mild TBIs

Researchers have developed a novel brain imaging method to diagnose mild traumatic brain injuries (mTBIs), which are often missed by standard techniques like MRI. This method involves loading gadolinium, a common MRI contrast agent, into micropatches attached to immune cells called macrophages. These cells migrate to areas of brain inflammation caused by mTBIs, enabling MRI detection. The technique, called M-GLAMs, was successfully tested in mice and pigs, showing promise for accurately diagnosing mTBIs. It also allows imaging at lower gadolinium doses, potentially benefiting patients with kidney issues. While unable to pinpoint injury locations, M-GLAMs could aid in identifying and treating brain inflammation. The researchers aim to bring this technology to clinical trials, with support from grants and intellectual property protection.

Read the study here.

New Imaging Tech that Captures Neuronal Activity Across the Brain During Recovery

Researchers at Tufts University School of Medicine have developed a novel imaging technology to monitor neuronal activity throughout the entire brain during the initial weeks of recovery from traumatic brain injury (TBI). Their study, published in Cerebral Cortex, reveals that TBI can induce changes in brain function beyond the injury site. Using a combination of fluorescent sensors and electrodes, they observed altered connectivity patterns in mice post-injury, even in regions distant from the impact. Despite the mice’s ability to perform physical tasks normally, their brain activity during both exercise and rest differed significantly from healthy brains. This impaired ability to switch between states suggests underlying brain state dysfunction post-injury. The findings highlight the brain’s plasticity in response to injury and have potential clinical implications for understanding TBI impacts and tailoring treatments. The researchers aim to further investigate long-term neural activity changes post-recovery and explore the technology’s potential in predicting specific dysfunctions or long-term outcomes of TBI.

Read the study here.

Sources:

Medicaldevice-network.com
Otd.harvard.edu
Scitechdaily.com
Openai.com

A Look at 2023 and ChatGPT In Radiology

ChatGPT has quickly moved beyond its niche beginnings and become an integral part of everyday life. Its reach extends well past casual conversation, now penetrating various industries, notably the intricate world of radiology. As we close out 2023, we take a look at some headlines that show how far ChatGPT has advanced in the realm of diagnostic imaging.

Smart Enough to Pass Exam Questions

In two recent studies published in Radiology, researchers evaluated ChatGPT’s performance in answering radiology board exam questions. While the AI showed potential, it also demonstrated limitations affecting its reliability. ChatGPT, based on GPT-3.5, answered 69% of questions correctly, struggling more with higher-order thinking questions due to its lack of radiology-specific training.

A subsequent study with GPT-4 showcased improvement, answering 81% correctly and excelling in higher-order thinking questions. However, it still faced reliability concerns, answering some questions incorrectly and exhibiting occasional inaccuracies termed “hallucinations.”

Confident language was consistently used, even in incorrect responses, posing a risk, especially for novices who might not recognize inaccuracies.

Decision Making in Cancer Screening: Bard Vs ChatGPT

A study recently published in American Radiology compares ChatGPT-4 and Bard, two large language models, in aiding radiology decisions for breast, ovarian, colorectal, and lung cancer screenings. They tested various prompts, finding both models to perform well overall. ChatGPT-4 showed higher accuracy in certain scenarios, especially with ovarian cancer screening. However, Bard performed better with specific prompts for breast and colorectal cancer. Open-ended prompts improved both models’ performance, suggesting their potential use in unique clinical scenarios. The study acknowledged limitations in scoring subjectivity, limited scorers, and the focus on specific cancer screenings based on ACR guidelines.

bard AI — Can AI assist in diagnostic imaging?

Simplifying Readability of Reports

The study in European Radiology explores using ChatGPT and similar large language models to simplify radiology reports for easier patient comprehension. Researchers had ChatGPT translate complex reports into simpler language for patient understanding. Fifteen radiologists evaluated these simplified reports, finding them generally accurate and complete, yet also identified factual errors and potentially misleading information in a significant portion of the simplified reports. Despite these issues, the study highlights the potential for large language models to enhance patient-centered care in radiology and other medical fields, emphasizing the need for further adaptation and oversight to ensure accuracy and patient safety.

Sources:

Rsna.org
diagnosticimaging.com
Radiologybusiness.com
openai.com

What You Missed at RSNA 2023

The RSNA annual meeting draws tens of thousands of healthcare professionals in medical imaging, offering a comprehensive platform for unveiling groundbreaking innovations and fostering discussions among industry leaders. This year’s conference just ended, so if you didn’t get to join, we’ll be highlighting some interesting takeaways from this amazing event.

New Technology

Royal Philips introduces the BlueSeal MR Mobile, a groundbreaking mobile MRI system featuring helium-free operations, marking a significant advancement in diagnostic imaging technology. This pioneering device, equipped with the industry’s first fully sealed 1.5T magnet, provides patient-centric MRI services, offering agility and flexibility in placement, especially near hospital entrances for patient convenience. Developed initially for Akumin, the first unit to be showcased at the event, this innovation extends Philips’ BlueSeal magnet technology, having saved over 1.5 million liters of helium since 2018. The helium-free mobile unit expands access to MRI exams sustainably, catering to more patients in diverse locations, addressing resource constraints, and enhancing healthcare delivery, as highlighted by Ruud Zwerink, General Manager Magnetic Resonance at Philips. Notably, the BlueSeal MR Mobile’s reduced helium requirements improve operational efficiency and connect to Philips’ Radiology Operations Command Center (ROCC), enabling real-time remote support for imaging experts, ensuring quality care delivery.

Radpair, a pioneering platform in radiology innovation, unveiled its cutting-edge generative AI-driven technology at the conference. This groundbreaking system, described by Avez Rizvi, Radpair’s CEO, as a revolutionary advancement, promises to reshape radiology reporting and elevate patient care standards. Positioned as the first of its kind, Radpair’s web-based and user-friendly platform utilizes generative AI in clinical settings to automate radiology report generation, streamlining radiologists’ workflow and enhancing efficiency while prioritizing patient care. Vesta Teleradiology is proud to collaborate with Radpair, with Vesta CEO, Vijay Vonguru stating, “This partnership propels us to the forefront of innovation in radiology. The synergy between Radpair’s advanced generative AI technology and Vesta’s robust teleradiology platform and onsite Radiology will redefine the standards of care we provide, ensuring high-quality, swift, and more nuanced radiological interpretations.”

Addressing the People

Dr. Pedram Keshavarz from UCLA presented findings indicating widespread burnout symptoms among radiologists and trainees. Emotional exhaustion and depersonalization were prevalent, particularly among residents and trainees who exhibited the highest rates of low personal accomplishment. These symptoms are considered warning signs for potential professional dropout or retirement. The study reviewed multiple contributing factors to burnout, including sleep deprivation, heavy workloads, low salaries, and various responsibilities. Analyzing nine studies with over 15,000 participants, the research highlighted different rates of burnout across radiology subspecialties, linking factors like having a partner, child, and lower debt levels to reduced emotional exhaustion and higher personal accomplishment. The presentation emphasized the need for future research to focus on interventions to alleviate burnout symptoms, potentially exploring the impact of remote work and other aspects on radiologists’ well-being. Large cross-sectional studies were suggested to further understand and address burnout progression among radiologists.

Sources:

Auntminnie.com
itnonline.com
Phillips.com
Openai.com

Mammography: Is AI Better than Humans?

In recent years, artificial intelligence (AI) has made remarkable strides in revolutionizing the landscape of the medical field, offering unprecedented opportunities for enhanced patient care, diagnosis, and treatment. From accelerating the analysis of medical imagery to predicting disease outcomes with unparalleled accuracy, AI-powered technologies have swiftly established themselves as indispensable tools for healthcare professionals. Beyond diagnostics, AI has played a pivotal role in drug discovery, streamlining clinical trials, and personalizing patient interventions. As AI continues to evolve, its potential to transform healthcare systems globally is becoming increasingly evident, promising not only improved medical outcomes but also cost-effective solutions and optimized resource allocation. The fusion of AI’s computational prowess with medical expertise heralds a new era of medical advancements that hold the potential to alleviate the burden on healthcare systems, save lives, and redefine the standards of patient well-being.

In the United States alone, it is estimated that around 40 million mammograms were performed each year. Mammograms are crucial as they are the primary method for early detection of breast cancer, enabling timely intervention and improving survival rates. By detecting small abnormalities and tumors that may not be palpable, mammograms help identify potential breast cancer cases in their earliest stages, allowing for more effective and less invasive treatment options.

Radiologists often find themselves overwhelmed due to the increasing volume of medical images requiring analysis, coupled with a shortage of radiology specialists. The demand for accurate and timely diagnoses, especially in fields like mammography, can lead to extended work hours and heightened stress levels among radiologists. Introducing AI technologies can alleviate this burden by assisting in image analysis, enabling radiologists to focus on complex cases and ensuring more efficient patient care.

How AI Helps in Mammography

A recent study published in The Lancet Oncology suggests that artificial intelligence (AI) may outperform trained doctors in detecting breast cancer from mammogram images. Mammograms face challenges due to factors like breast density, leading to missed cancer cases. The study analyzed 80,000 mammograms from Swedish women, finding that AI-assisted readings detected 20% more cancers compared to human radiologists. While not a standalone solution, AI could alleviate doctors’ workloads, enhancing accuracy without increasing false negatives. Despite FDA-approved AI technologies, integration with conventional methods is likely, aiding radiologists in managing a growing workload. The balance between AI and human expertise remains essential, ensuring optimal patient care and early cancer detection.

Healthcare experts, including the NHS and the Royal College of Radiologists, acknowledge AI’s promise in enhancing efficiency, decision-making, and prioritizing critical cases.

Vesta Teleradiology

AI applied to diagnostic imaging holds the potential to significantly enhance the level of patient care. We eagerly anticipate further progress in this field. However, we maintain the viewpoint that presently, no machine can effectively substitute for the expertise of a skilled human observer for interpretations. At Vesta, we offer the services of radiologists who are US Board Certified, dedicated to delivering precise preliminary and final analyses. Discover how we can bolster your radiology department by reaching out to us today.

Sources:

Criver.com
health.com
theguardian.com
openai.com

How is Teleradiology and AI Impacting the Medical Industry Today?

Artificial Intelligence (AI) is revolutionizing the medical industry, transforming the way healthcare is delivered, diagnosed, and managed. With its ability to analyze vast amounts of data quickly and accurately, AI is reshaping various aspects of healthcare. From aiding in disease diagnosis to personalized treatment recommendations, AI is enhancing the precision and efficiency of medical practices. Moreover, AI-powered technologies are streamlining administrative tasks, optimizing resource allocation, and improving patient outcomes. As AI continues to advance, it holds immense potential to revolutionize healthcare delivery, foster medical innovations, and ultimately improve the quality of patient care on a global scale.

Teleradiology has had a profound impact on healthcare by enabling remote access to radiology expertise, bridging geographical barriers, and ensuring timely diagnoses. It has improved patient care by providing faster turnaround times, facilitating collaboration among radiologists, and increasing access to specialized interpretations, ultimately enhancing diagnostic accuracy and treatment outcomes. Going even further, a latest white paper from One Call describes how teleradiology and AI are helping reduce the strain of the radiology shortage.

Teleradiology and AI in Action

Medical imaging vendor, Nanox, is looking to address heath disparities and lack of access care with a new x-ray system which would be offered to countries in Africa, Asian and South American using a pay-per-scan model. The potential of combining cold cathode X-ray technology with teleradiology and artificial intelligence (AI) to enhance diagnostic capabilities and improve healthcare economics. Cold cathode X-ray systems offer advantages such as reduced energy consumption and improved image quality. When integrated with teleradiology, these systems can enable remote interpretation of X-rays, leading to faster diagnoses and improved patient care. Additionally, the use of AI algorithms in conjunction with cold cathode X-ray technology has the potential to enhance image analysis, automate certain tasks, and optimize resource allocation, offering cost-saving opportunities in healthcare settings.

diagnostic imaging — A teleradiologist examines a chest x-ray

There are plans to roll out AI-powered teleradiology by the “Screen for Life” program at the Primary Health Care Corporation in Qatar, aimed at early detection and prevention of cancer in the United Arab Emirates. The program plans to utilize AI algorithms to analyze radiology images, enhancing the accuracy and efficiency of cancer screening. The integration of AI in teleradiology will help automate image interpretation, expedite diagnoses, and reduce the workload on radiologists. The implementation of AI teleradiology in the “Screen for Life” program is expected to improve cancer detection rates, streamline healthcare processes, and ultimately save lives by identifying cancers at earlier stages.

Vesta Teleradiology

Looking to outsource your radiology interpretations using an expert Teleradiology company that is at the forefront of technology including AI? Please reach out to Vesta to learn more. Vesta Teleradiology can accommodate any type of volume, large, medium and small.

Sources:

Radiologybusiness.com
menafn.com
openai.com
cdc.gov

What is a Smart Hospital?

Today’s smart hospital is not just a paperless organization with digitized charting. Although being digitized is an excellent start to a smart hospital, the process is much more advanced.

The goal of a smart hospital is better patient care while streamlining operational efficiency and costs. Three essential layers need to be addressed by a hospital to achieve the classification of a “smart” facility.

Step 1–Operations

Even though all hospitals gather information about their patients, operations, and management, a smart hospital takes gathering data to a different level.

In a smart hospital, analytic systems and software integrate all the information utilized by doctors, nurses, facility personnel, and administrators. A desktop, smartphone, or handheld device can access this information, resulting in faster, more efficient decision-making by key people.

With automated systems, the management of staff, pharmaceuticals, supplies, consumables inventory, assets, equipment, patients, and even visitors is controllable by the appropriate hospital teams. The process is cost-saving and efficient for the hospital and the patient.

To achieve these network-based services, hospitals with a large traffic volume and frequent environmental changes best utilize 5G or Wi-Fi 6 technology which is a step up for most hospital internet access.

Step 2–Clinical Tasks

The doctors’ and nurses’ efficiency depends on communication with departments like critical care, surgical, and technical (lab and X-ray) services.

A smart hospital improves patient outcomes by utilizing remote monitoring tools. Medical professionals can immediately monitor a patient’s vital signs, steps, heart rates, allergies, and lab results. The added communication also allows more patient input about likes, dislikes, and comfort zones. Smart hospitals will also utilize teleradiology services like Vesta in order to process more imaging interpretations remotely and efficiently.

INSIDE CANADA’S FIRST SMART HOSPITAL

Step 3-Patient Care

A patient’s room is very different within a smart hospital. A patient can access help through voice-based interactive devices to dim the lights, call a nurse, request pain medication, or make phone calls to loved ones—no more worries about a dropped call button on the floor.

The smart hospital design focuses on enhancing the healing process for faster recoveries by featuring open spaces and gardens. Children can also have specially designed areas for their comfort.

Caring robots in hospitals are providing added support in the facilities. Smart hospitals have programmed robots to perform surgeries; provide dementia care for the elderly; provide biofeedback for patient anxiety; transport supplies, blood, medication, meals, and garbage.

automation — Robots are reshaping hospitals

Hospitals have also programmed robots to provide care in quarantine isolation booths or entertain hospitalized children for a more positive emotional experience.

The Future

The future of smart hospital strategies is endless. The hospital environment and opportunities will continue to expand for the cost and convenience benefits of the hospital operations and its patients. The hospital will be able to extend most of the “smart” gifts to the patient’s home.

Patients will be able to take home smartwatches and other monitoring equipment for continued hospital care. More utilization of mobile monitoring equipment will allow the hospital to operate as intended–for emergencies, surgeries, and intensive care units–and will enable the patient to recover safely in the comfort of their home. And even more exciting is the future use of Artificial Intelligence to further enhance the benefits of the smart hospital.