A hospital environment is fast-paced and stressful, with many opportunities for minor setbacks to become major issues. Over the past few years, there has been an increasing number of technological advancements in the form of digital tools designed to assist teams in managing their workload, reducing errors, and identifying potential risk factors earlier in the process. One of the largest impacts on this transition is AI in Hospitals. AI in Hospitals may assist doctors during the diagnostic process, support nursing professionals as they manage patient needs, ensure that supplies flow throughout the hospital, and do not impede the flow of patient care.
The article provides an easy-to-understand explanation of these systems. The reader will also learn where these systems provide the greatest benefit, where they may fall short, and what actions hospitals need to take to use them safely and responsibly.
AI in Hospitals refers to the application of technology, using computer systems that can analyze data and support hospital staff in making quicker, more accurate decisions. AI in Hospitals does not replace the functions of doctors and nurses. Rather, AI in Hospitals supports doctors and nurses in identifying patterns that can be difficult to detect in a busy hospital environment.
Beginning with medical applications, one of the most well-known uses of AI tools is in reviewing medical images, such as X-rays, MRIs, and CT scans, for potential abnormalities and alerting radiologists to areas warranting greater scrutiny. As a result, radiologists can quickly identify potentially serious issues (e.g., emergencies) and prioritize their workload accordingly, helping minimize the likelihood that serious issues will be missed early in the treatment process. In addition, using AI to analyze laboratory test results and/or other clinical information related to the patient’s vital signs, AI can help identify patients whose condition may be deteriorating earlier than would otherwise be apparent.
Additionally, healthcare organizations have been using AI to streamline operational tasks in hospital environments. For example, AI can assist in forecasting how many patients will present to the Emergency Department over the course of an hour; assist in determining necessary personnel assignments; and improve the efficiency of the scheduling process for imaging studies and/or other hospital-based treatments. While improvements in hospital operations may seem minor, they can directly affect patient satisfaction and overall experience (e.g., shorter wait times, lower cancellation rates, smoother transitions between units).
Finally, hospitals are beginning to leverage automation and robotics to enhance patient care and optimize workflows. For example, delivery robots are being used to transport medications, lab samples, and food throughout the hospital, thereby reducing the time caregivers must walk to and from locations and allowing them to focus on direct patient care. Additionally, in some hospitals, robots and automated tracking systems are helping promote infection prevention by minimizing caregiver travel and improving supply management.
Even though there are many benefits to using AI in a hospital setting, AI systems must be used thoughtfully. This is because if an AI system has been trained on limited data or on inherently biased data, it will likely have a greater impact on certain patient populations than others. Additionally, when alerts are issued too frequently, it can result in “alarm fatigue.” Alarm fatigue results from healthcare professionals receiving so many alarms (alerts) during their shift that they become desensitized to them and begin to ignore the alarm(s).
Another area of concern is the privacy and security of patient health information. Patient health information is very personal; therefore, it is critical to ensure proper precautions are taken to protect it.
When hospitals use AI technology in a practical manner, they should follow the same process as for all clinical resources. Clinical resources include testing the resource before rolling it out to the clinical team. The clinical resource should also be continually monitored by the clinical team to evaluate its effectiveness and identify any issues. In addition, the clinical team should remain accountable for making the ultimate decision regarding the patient’s care. When AI technology is used in this manner, it can help reduce the workload associated with routine tasks, improve communication among care team members, and enable care teams to provide timely responses to patient needs, while maintaining the human judgment and compassion that patients require most.
AI in Hospitals: A Simple Way to Think About It
In order to comprehend AI in hospitals, we should consider AI to be software that looks at patterns within the data given to it and makes a prediction based on this information. AI doesn’t “think” like humans; instead, it learns by seeing many examples of things, then provides an idea of what will occur next.
In health care, those “examples” can include medical images, lab test results, patient vital signs, and the physician’s and nurse’s notes from each patient visit. The goal is generally straightforward: help health professionals identify and respond to important signals earlier and better manage their workload.
The term Healthcare Artificial Intelligence describes a broader set of tools used in clinical settings, laboratories, and across all operational aspects of hospitals. Some systems are designed to support clinical care, while others may focus on activities such as scheduling, supply, and logistics.
It is worth noting that AI is typically used to assist in decision-making, not to replace it. Ultimately, the decision-maker is the clinician.
What is AI in hospitals, really? A Simple Guide to the “Smart” Tools Behind the Scenes
Artificial Intelligence (AI) in hospitals is not a “thinking” robot — it is an algorithm designed by humans. Most algorithms follow a series of instructions. For example, if a patient’s fever exceeds 101 degrees F and he reports a cough, send his file to a lab for a flu test. It is a computer following a list of very specific, human-made instructions.
The strength of AI in hospitals comes from how it “learns” to create much longer lists of rules. That process is called training. Think of training as giving a computer 1 million X-rays — half with small cracks in bones and half with no cracks in bones. The computer will study every X-ray and learn to recognize subtle visual clues associated with a bone crack — and may be able to identify cracks that a human cannot see. The computer does not know what a bone is; however, it can quickly match patterns.
So, ultimately, the AI in hospitals is much like a powerful new tool for your doctors, rather than a new doctor. The AI is a supercomputer capable of sifting through vast amounts of data to provide your doctors with clues to diagnose your medical condition; however, the doctor is still responsible for making decisions based on those clues. Through repetition, the AI has already begun helping alleviate some of the major problems in healthcare, including long waits.
How AI Can Cut Your Wait Time Before You Even Leave Home
The most uncertain part of going to the hospital is always going to be the wait, but what if a hospital had the ability to predict “rush hour” (just as your traffic app can predict when there will be a traffic jam)? This is an area where AI in hospitals has already made a difference using predictive analytics. think of predictive analytics for hospitals like an extremely detailed weather report on the number of patients coming into the hospital; by reviewing years’ worth of admissions data, local flu trends, and even the time of day, AI systems can tell hospital administrators how busy the emergency room will be. This allows hospital administrators to plan ahead, have additional staff ready, and open rooms before a large number of patients arrive, helping reduce wait times for all patients.
This exact type of forecasting is also helping to streamline the process of making routine doctor visits. We’ve all sat around, frustrated by a 10:00 a.m. appointment that doesn’t start until 11:00 a.m.; AI-powered scheduling systems can create a much more flexible, intelligent calendar that can make real-time adjustments to accommodate schedule delays. If one appointment runs longer than expected, the system can make subtle adjustments to the rest of the schedule to help prevent a cascade of delays. Some systems can even alert you to a potential delay before you leave for your appointment, so you don’t have to sit in the waiting room. The end result is a hospital that operates much like a modern logistics operation, rather than a chaotic line. The next time you use a hospital application that shows you how long the wait is in the emergency department, you are most likely seeing examples of the applications of AI solutions for hospital administration. However, improving operational efficiencies at the front entrance is only the first step. AI is now being used in the examination room to provide medical professionals with another valuable perspective (not to replace them).
Where Hospitals Use AI Day to Day
Artificial Intelligence (AI) is currently used in many hospital settings as an unobtrusive support system for routine tasks, so you may not even know that it is being used by the hospital staff.
Some of the most common applications of Automation in Hospitals include:
- Automating the sorting and prioritization of messages, lab results, etc.
- Finding potential gaps in patient care plans
- Predicting which departments will be over capacity in the near future
- Aiding hospital staff in matching the correct bed for each patient to their assigned department.
The purpose of using Automation in Hospitals is to assist hospital staff in saving valuable time; however, it only does so when used within the context of a realistic workflow. When too many alerts are generated by a piece of automation, staff may simply disregard them. Likewise, if a piece of automation is difficult to utilize, then the staff will find a way to circumvent it.
When hospitals succeed in using artificial intelligence, they generally begin by focusing on the basics: high-quality data, clearly defined roles and job responsibilities, and training that accounts for how staff currently perform their jobs.
AI Medical Diagnosis: Helping Clinicians See Risks Earlier
AI Medical Diagnosis is one of the most talked-about applications of AI in healthcare. Often, these systems will analyze medical images or patient information and highlight possible areas of concern (risk factors) that may have been missed by a physician during a busy workday.
For instance, an AI system might review a patient’s X-ray and flag an area for further examination. Alternatively, another system might examine a patient’s lab results and vital signs and alert the healthcare team that the patient’s condition is at a higher risk of deterioration soon.
AI-Powered Healthcare has potential here: it can help healthcare teams identify at-risk patients sooner and respond more quickly. However, AI Medical Diagnosis does not always give a correct answer. A tool can provide a false positive or negative result due to several reasons, such as:
• Blurry images
• Missing patient history
• A lack of accuracy from the algorithms used.
Typically, when hospitals use AI Medical Diagnosis, they create a set of safety rules for the use of this technology, including:
• Thresholds for alerting healthcare professionals of critical issues
• A secondary review of critical issues found by the AI tool
• Continuous evaluation of how accurate the tool remains over time
These safeguards are important because clinical decisions can be life-altering, and even small errors can have significant consequences for patient care.
Can an AI Read Your X-Ray? The Truth About AI as a Doctor’s Assistant
AI in hospitals is used not just for efficient front-desk services but also to support hospital staff in diagnostic processes. In order to understand how a computer can be taught to interpret a medical scan, think about a classroom where you have shown students an endless number of flashcards. An artificial intelligence (AI) is “taught” to analyze medical scans through a similar process — hundreds of thousands of images of chest x-rays or MRIs, showing either healthy individuals or patients with documented health issues such as small tumors or hairline fractures. As the AI views these images over time, it develops the ability to recognize small, almost imperceptible visual patterns associated with many medical conditions — including identifying small abnormalities that may be difficult to detect by the human eye.
As a result of this training, the AI becomes a second pair of eyes for radiologists, providing them with support throughout long work days reviewing potentially hundreds of images per day. Fatigue is a normal part of being a doctor. However, AI-assisted diagnostic tools can review every pixel of an image and highlight any areas that appear abnormal, regardless of the size of the abnormality.
In addition to identifying potential areas of concern in images that need to be reviewed by the doctor, the AI acts as a safety net, preventing the doctor from missing anything when multiple images need to be reviewed. While the AI assists the doctor in analyzing medical images, it does not provide the final diagnosis. Rather than making decisions, the AI will provide the doctor with additional information to support their decision-making. The final diagnosis will continue to be made by the doctor based on the AI’s information, the patient’s overall medical history, and all other test results. The doctor remains the primary person responsible for caring for each individual, with the AI serving as an advanced tool to aid the doctor in diagnosing illnesses and determining the best course of treatment. Together, the doctor and the AI represent a collaborative relationship that can ultimately lead to more accurate and timely diagnoses. Accurate diagnosis represents the first of two major steps toward effective healthcare. After an accurate diagnosis has been made, the AI can also assist the doctor in answering the most important question: what is the best treatment option for this patient’s specific needs?
Beyond One-Size-Fits-All: How AI Helps Create Your Personal Treatment Plan
For decades, medicine often relied on a standard playbook: if you have a certain condition, you get a certain treatment. But we all know people react differently to the same medication or therapy. Today, AI in Hospitals is helping move healthcare beyond the “one-size-fits-all” model toward a far more precise approach. Instead of just treating a disease, it helps your doctor tailor the treatment specifically to you.
Think of it like a master strategist connecting clues that no single person could track on their own. By analyzing vast amounts of information, an AI system can identify subtle patterns to help find the optimal path forward. The factors it can consider are incredibly diverse:
- Your specific genetic makeup
- Your recent lab results
- Your lifestyle, like diet and exercise habits
- Outcomes from thousands of similar, anonymous patients
By cross-referencing all these data points, the AI can help predict which treatments are most likely to work for you while also flagging those that might cause negative side effects. This doesn’t replace your doctor’s judgment; it supercharges it. Armed with these data-driven insights, your doctor is better equipped to design a treatment plan with greater confidence and a higher likelihood of success. This same ability to manage complex data isn’t just improving individual patient care; it’s also making the entire hospital run more smoothly, from the pharmacy to the operating room.
Smart Hospitals Technology and the Flow of Care
Hospitals function based on time constraints. Delayed laboratory results can delay treatment; delayed beds can delay transfers; and supply shortages can slow procedures. Smart Hospital Technology aims to eliminate this friction and simplify the entire tracking process.
In many areas, AI in hospitals supports “Patient Flow,” helping patients move from one step to the next as quickly as possible without unnecessary delays. This could include estimating when a patient will be discharged, estimating how long various tests will take, or suggesting staff levels for the next shift.
Additionally, this is part of Healthcare Artificial Intelligence (AI) because it relates to operational needs (hospital processes) rather than clinical needs (treating patients). Although this technology does not treat patients directly, it can help to reduce the waiting and uncertainty that many patients experience.
However, hospitals need to be careful. If an application relies solely on efficiency, it may neglect aspects of patient comfort and individual needs. Therefore, the most effective applications will have to balance efficiency with patient-centered care.
Medical Robots in Hospitals: More Than a Futuristic Idea
Medical Robots in hospitals are not all surgeons. Many medical robots have simpler jobs but still perform them very well.
Linens, meals, medications, lab samples, and even blood products can be delivered by delivery robots in hospital halls. These robots will ride elevators, avoid other objects, and determine their own routes to reduce congestion in high-volume areas. As a result, staff can focus on caring for their patients rather than delivering items.
Automation in healthcare (e.g., inventory tracking) and Medical Robots in hospitals are typically used together. The automation system sends a request to replenish an item when its quantity falls below a certain threshold, and the robot delivers it as soon as possible, eliminating multiple phone calls and trips to find the requested item.
Therefore, Medical Robots in hospitals require a thoughtful process. A hospital must consider patient safety, infection control, and how the robots will interact with wheelchairs and stretchers in the areas where they will be placed.
Meet the New Hospital Staff: How Robots and AI Keep Things Running Smoothly
While a robotic surgeon is a fascinating vision for the future, many of the most influential robots in hospitals today are more likely to go unnoticed because they do not perform surgical procedures. Their function is much more mundane yet equally vital, as they serve as reliable assistants in handling the logistical details of hospital operations.
Think of how much time is spent “running around” inside a hospital. A nurse goes to get a patient’s medication from the pharmacy, a laboratory technician picks up lab samples, or the housekeeping department is restocked with linen. Many of these delivery and pickup functions in hospitals today are handled by robots that use artificial intelligence (AI) to navigate the hospital, such as self-driving carts.
In addition to the robots’ transportation role, smart hospital administrative AI also works behind the scenes as a high-efficiency inventory manager for the pharmacy, identifying and ordering medications at the right time based on demand.
In terms of value, this type of automation is more than just an increase in speed; it is the increase in value of human time. Each trip a robot takes is one less trip a nurse, a technician, etc., has to take. The amount of time nurses, technicians, etc., have available each day is significantly increased, allowing them to focus on the core of their job: patient care. As the integration of these types of smart systems continues throughout the entire hospital, from the logistics to the patient’s medical record, numerous questions will be asked.
AI-Powered Healthcare at the Bedside
There are many types of AI in hospitals, but most are focused on monitoring and communication.
Examples include systems that monitor a stream of data related to a patient’s vital signs (e.g., heart rate, respiratory rate, oxygen saturation) and flag patterns indicative of early decline in a patient’s condition. The earlier these flags are identified, the sooner a nurse can visit a patient who may need attention, something that would not have been possible without AI-powered healthcare.
Another type of AI-Powered Healthcare used by hospital staff is tools that help nurses draft summaries, organize notes, and translate the language of complex medical instructions into simpler language to promote better understanding of what is being told to patients.
However, there are still many concerns with using AI at the bedside. Nurses must continue to provide compassionate care to their patients while leveraging AI to enhance the quality of that care. Additionally, AI tools must respect patient confidentiality, avoid bias in decision-making, and leave room for human judgment and compassion.
Risks, Limits, and Why Oversight Matters
Because of this reliance on data, AI in Hospitals has a risk of inheriting the same issues as its data; i.e., if there are certain groups that are represented less frequently in the training data (e.g., women, minorities), the tool will be less effective for those populations, if documentation habits vary across departments, etc.
To address this, most hospitals have established processes for reviewing the performance of Healthcare Artificial Intelligence systems. This process includes tracking and testing performance, tracking errors, and creating guidelines as to when the staff must disregard recommendations made by the system.
Hospitals must also be mindful of privacy. Health information is private. Therefore, hospital systems must include robust security measures, strict access controls, and written policies governing the sharing of patient information. Additionally, patients should be informed about how their health information is being used to provide their care.
Lastly, hospitals must develop plans for the event of an AI system failure due to power outages, software updates, etc. The teams must have available safe manual workflows for all critical tasks.
Implementing AI Without Disrupting Care
Hospital managers don’t simply install Apps on Phones when it comes to installing technology into a hospital. There are higher stakes and a far more complex environment than in the average App installation.
Generally speaking, the most effective implementation of AI in hospitals occurs through small, incremental steps after thorough testing.
There are several practical strategies that can be employed during the rollout process:
- Identify a limited scope of problems that have specific, measurable metrics
- Frontline staff should be included at all stages of development, including at least the initial phases; it is generally insufficient to involve only Information Technology (IT) staff.
- Test using actual patient records and monitor for false positives.
- Update employee training as workflows evolve.
- Monitor the actual effectiveness of the application, rather than only tracking how many employees have adopted the new technology
Effective change management will also alleviate fears of being replaced by the technology and the potential for blame if the technology makes a mistake. By establishing clear roles and shared responsibility for the technology, both fears will be alleviated.
In addition to effective change management, hospitals that implement Smart Hospital Technology will need to develop a maintenance plan for their systems. Although a system is performing effectively today, its performance can deteriorate over time due to changes in the patient population it serves or to equipment updates that alter the data on which it is based.
The Big Questions: Is My Health Data Safe and Is AI Fair?
As hospitals and healthcare organizations continue to integrate smarter systems into patient care, many questions remain about how those systems use patient data. Most importantly, do smart systems protect patients’ private health data? That is determined through the data anonymization process. Before an AI is developed and trained, all identifiable patient information — including names, addresses, etc. — is removed from the patient’s medical record, much like a chart with the name redacted. Data anonymization is regulated under federal law (HIPAA) and India’s DPDP Act, DISHA (proposed), and the Information Technology Act, and allows AI to develop a learning model based on the medical facts contained in the record without identifying the patient.
A separate issue, and potentially a more complex issue, is whether or not the AI is unbiased. A system can be no better than the data used to train it. Therefore, if the data used to train an AI system consists of a large number of cases from a single demographic group, then that AI may provide less accurate results when applied to other demographics. This is referred to as “algorithmic bias.” For instance, a chef attempting to create a global cookbook using ingredients from only a single country would have difficulty creating recipes useful in every country.
The potential implications of “algorithmic bias” are particularly serious in a hospital setting. For example, an AI system designed to predict the likelihood of a patient experiencing a heart attack has been trained solely with data obtained from male patients. Because the predictive models were created utilizing only male data, the system may fail to recognize the various warning signs that occur more frequently in female patients. Consequently, a delay in providing appropriate treatment to the patient may result in lower-quality care.
One of the biggest challenges facing developers of medical AI is ensuring that the data used to train the AI system is both vast and representative of all types of individuals (e.g., age, gender, ethnicity).
Therefore, while the development and implementation of medical AI has tremendous potential to improve the quality of patient care, the most important element of any hospital’s AI strategy is not the computer itself, but rather the individual(s) working behind the scenes. Doctors, data scientists, and ethicists must continually monitor and evaluate the performance of medical AI systems to detect and rectify any potential biases. The AI system serves as a strong second opinion for doctors; however, the doctor remains responsible for making the final decision on a patient’s course of treatment. The long-term success of medical AI depends on continued collaboration between human experts and intelligent systems.
Your Partner in Health: What AI Means for the Future of Your Care
What was once the realm of science-fiction robots that came to mind with the phrase “AI in Hospitals” is now a real, practical tool assisting those who work behind the scenes. You know how Artificial Intelligence can help improve patient care, not by replacing the expertise of a human, but by enhancing it in very tangible and life-saving ways.
You can think of this as giving your doctor a set of superpowers. For example, AI can provide “super vision” to detect an anomaly on a scan, no matter how small, and also give it “super memory” by instantaneously recalling all relevant medical information. This assistance will allow medical teams to make quicker and more accurate decisions based on the large amount of data available to them.
The ultimate outcome of using artificial intelligence in medicine is that technology handles the numbers, allowing humans to focus on what is truly important to them: You. When you hear about AI being used in health care, you can be assured that the end result will not be lessening the human touch, but providing more time and space for it to flourish.
The Near Future: What to Expect Next
Over the coming years, AI in Hospitals is likely to become much less obvious and much more embedded into all aspects of the hospital. Instead of a single “AI Screen,” there will be numerous small AI components integrated across various Hospital Systems, such as Imaging, Labs, Scheduling, and Patient Communication.
There will probably be more capable medical robots in Hospitals, particularly in logistical areas. These robots may be able to work with Supply Chain Systems, help avoid delays, and assist with Infection Control by reducing non-essential hospital traffic.
However, hospitals will also need to develop strategies to demonstrate that the aforementioned tools are both safe and fair. This means that Hospitals will need to implement more robust Monitoring capabilities, Clearer Reporting Requirements, and better methods of explaining how a model arrived at a particular output.
When used appropriately, automation in healthcare can take some of the repetitive, mundane tasks from staff, allowing them to focus on care that requires a human element.
Conclusion
The idea that “AI in Hospitals” is either pure “magic” or “will replace trained clinicians” is just that…an idea. What AI really does is provide a set of tools that help Hospital Teams identify potential risks earlier, improve internal communications, and move critical supplies around a hospital’s complex layout as quickly and safely as possible. Everything from AI medical diagnosis to robots that assist in a variety of ways are systems that address specific problems that add clarity, not confusion, to how care is provided.
To gain the greatest benefit from these tools, those who use them will evaluate them, continually monitor & adjust as needed, with patient safety always their top priority.
Q&A
- Question: What does “AI in Hospitals” actually mean in everyday care?
Answer: AI in hospitals refers to using computer systems to analyze hospital data (e.g., medical information), assist hospital staff with their decision-making processes. This could be seen as an example of how a system could use software to quickly identify unusual lab results, prioritize radiology studies, or identify patients at risk of needing closer monitoring. The goal of AI in this context is to support staff, not replace them. While AI can rapidly perform many routine analyses and indicate potential problems that require further attention from clinical staff, clinical staff will ultimately have to make the final determination of what action to take in response to those alerts. Ultimately, when done correctly, it can help eliminate delays and allow clinical staff to spend more time focused on providing direct patient care. - Question: How does AI Medical Diagnosis help doctors without “replacing” them?
Answer: AI Medical Diagnosis tools take in information from sources such as images (e.g., x-ray, CT scan, pathology slide), vital sign trends, lab trend data, etc., and provide potential areas of concern to a clinician for follow-up. The tool may help a clinician identify an area of interest that might otherwise have been missed due to time constraints. However, the clinician should verify the relevance of the identified areas of interest with their own experience and judgment, as the tool will only identify based on what has been provided; therefore, incomplete or irregular data can lead to false positives. Therefore, diagnosis is still a task for clinicians. Clinicians provide the “second set of eyes” when using these tools; they are responsible for confirming the relevance of findings and selecting treatment options. - Question: What is Healthcare Artificial Intelligence, and how is it different from regular hospital software?
Answer: Healthcare artificial intelligence refers to all of the applications of artificial intelligence in health care environments. Traditional software follows established algorithms based on “if-then” statements (e.g., if x occurs, then perform y). However, artificial intelligence applications learn by using patterns in data to predict future events, such as a patient’s likelihood of deterioration or possible diagnoses. While this ability to adapt can be beneficial, there is also an increased need for oversight, testing, and ongoing evaluation to ensure the application remains accurate and unbiased as actual-world conditions continue to evolve. - Question: What are Medical Robots in Hospitals used for today (beyond surgery)? Answer: Medical robots in hospitals can be used for many non-surgical tasks, such as delivering medication, collecting lab samples, transporting linens to patients, and feeding patients. Some medical robots clean patient rooms by spraying a mist (using liquid) or by using controlled lighting systems. Some of these medical robots will perform simple manual labor tasks, like moving equipment and supplies, thereby reducing the workload on hospital staff and saving them time. Robots are most beneficial to a hospital when they fix an obvious workflow problem; for example, reduce walking distances, and create no new hazards in a busy hallway.
- Question: How does AI-Powered Healthcare improve patient experience in a simple, visible way? Answer: AI-Powered Health Care can improve patients’ experience through improving the wait times, clarity of information, and unnecessary question asking. For example, AI can assist with scheduling testing, send alerts to hospital staff when patients’ discharge paperwork is delayed, and convert highly technical instructions into clear, simple language. In addition, AI can enable care teams to respond more quickly to patients who exhibit early warning signs in their vital signs. While there may be several options, the most beneficial for the patient is likely to be time: less delay, more responsive answers from hospital staff, and greater focus on what is important.
- Question: What makes Smart Hospitals Technology “smart,” and what does a patient notice? Answer: A smart hospital is a place where technology connects all the pieces (e.g., beds, staff, supplies, and clinical information) so the hospital can respond to changes quickly. The patient may experience less disruption during the transition from one department to another, fewer test cancellations, quicker turnaround time for rooms, or simply have the necessary items to support their care as needed. In addition, many of the “smart” functions will likely help identify potential surges in patient volume and/or indicate areas of a lab or imaging department that could create bottlenecks. “Smart” does not necessarily need to be an additional layer of complexity, but rather an added layer of efficiency, reducing friction and not adding more screens or cumbersome processes.
- Question: What is Automation in Healthcare, and where is it most helpful (and safest)? Answer: Automation in healthcare refers to the use of technology to automate routine, rules-based processes. Automation is typically best suited for administrative and operational processes, such as sending patient reminders, managing supplies/inventory, verifying insurance/billing, routing laboratory specimens, and supporting clerical functions such as documentation. In addition, automation can aid certain clinical workflows (such as alerting when a test is due); however, it is critical that these systems be fine-tuned to prevent alarm fatigue. The ultimate goal of automation is to eliminate non-value-added time and retain human decision-making authority over high-risk, high-consequence clinical decisions.
- Question: What are the biggest risks of AI in Hospitals, and how do hospitals reduce them? Answer: Key risk areas of this type of model will be the potential for incorrect or biased prediction, as well as privacy/security concerns that could arise from using a tool on patients, such as alert fatigue due to an overwhelming number of alerts for medical personnel; and how hospitals can mitigate those risks through validation before deployment, ongoing monitoring of the performance of the system after it has been deployed, to ensure the systems are being used appropriately and to limit its application to areas where there is the greatest benefit and require a human decision maker in high-risk situations. Hospitals will need to have robust data protection policies and procedures in place to safeguard their patients’ information, as hospital data is typically highly sensitive.
- Question: How do hospitals check whether AI Medical Diagnosis tools are reliable for real patients? Answer: Local data, with an actual clinical workflow prior to a full implementation, is where hospitals test their AI Medical Diagnosis tools. Hospital personnel then review and evaluate (AI) suggestions relative to those of clinicians and the confirmed outcome; record all false positive/false negative results; determine if the use of this tool has improved speed and/or accuracy while not being harmful; monitor performance over time as factors such as new equipment, new protocols, or different patient populations may impact the accuracy of the tool. Auditing the AI tool’s performance is just as important as the original evaluation.
- Question: Will Medical Robots in Hospitals and Smart Hospitals Technology change healthcare jobs? Answer: They are going to have employees working on different jobs; delivery robots, etc., can save time performing errands, and smart systems can help to decrease paper work, and chaotic scheduling so that they can better allocate their time to do what is most important, such as being directly involved with patients and their safety checks, and complex coordination of care. These technologies create new responsibilities, including maintenance, monitoring, workflow redesign, and training. The best way to maximize the positive effects of this technology and provide quality care is for the hospital to use the reduced lower-value workload to support its staff and clearly define decision-making authority for all employees.
