AI Health Care

Artificial Intelligence and Ambulatory Surgical Centers

Artificial Intelligence and Ambulatory Surgical Centers


Artificial intelligence and ambulatory surgical centers have the potential to revolutionize healthcare & improve patient care exponentially. Across the United States, ambulatory surgery centers (ASC) have offered various surgeries in an outpatient setting since 1970. Their efficiency, affordability, and success rate have seen these health care organizations grow, and they occupy a significant market share today.

Now, artificial intelligence (AI) technology looks set to improve the performance of ambulatory surgery centers (ASC) even further. Here is what patients and surgeons can look forward to.

Artificial Intelligence (AI) has begun to revolutionize the landscape of ambulatory surgical centers (ASCs), bringing forth significant improvements in efficiency, safety, and patient outcomes. By integrating AI-driven tools and systems, ASCs can enhance preoperative assessment, streamline scheduling, and optimize resource allocation. Advanced machine learning algorithms enable real-time analysis of patient data, identifying potential complications, and recommending tailored treatment options.

AI-powered surgical robots assist surgeons in performing precise and minimally invasive procedures, reducing patient recovery time and overall healthcare costs. As AI technology continues to advance, its integration within ASCs will undoubtedly yield increasingly sophisticated solutions, bolstering the quality of care and reshaping the future of outpatient surgery.

Also Read: Robotic Surgery

What are Ambulatory Surgery Centers (ASC)?

Ambulatory surgery centers are health care facilities with fully equipped operating rooms to perform surgeries without admitting patients to a hospital setting. They provide high-quality care more cost-effectively and conveniently. The majority of ambulatory surgery centers (ASC) offer surgeries in several disciplines.

Patients attending an ambulatory surgery centers (ASC) have already visited their primary care practices and been evaluated and diagnosed by a primary health care provider. They are only attending the facility for their surgery and post-anesthesia care and recovery under the supervision of qualified medical personnel.

Key Terms in Ambulatory Surgery Centers

Here are four key terms that are associated with ambulatory surgery centers (ASC).

  1. Surgicenter or outpatient surgery center are alternative terms for ambulatory surgical centers.
  2. Ambulatory denotes a condition that can be treated with hospital admission or a procedure that is suitable for selection for outpatient surgery
  3. Dedicated operating rooms (minimum one) are a prerequisite for accreditation as an ambulatory surgery center (ASC).
  4. Elective surgeries are those that will benefit a patient but are not urgently required.

Accreditation and Ownership

The Joint Commission on Accreditation of Healthcare Organizations (JCAHO) is responsible for accrediting ambulatory surgery centers. At the moment, the commission recognizes facilities in nine different categories:

  • Multi-specialty surgery centers
  • Endoscopy centers
  • Plastic surgery centers
  • Ophthalmology practices
  • Laser eye surgery centers
  • Centers for oral and maxillofacial surgery
  • Orthopedic surgery centers
  • Cosmetic and facial surgery centers
  • Podiatry clinics

Ambulatory Surgery Centers During pandemic and Future Pandemics

The coronavirus pandemic put temporary restrictions on elective surgeries. As a result, numerous ambulatory surgery centers had to close. As they are reopening, the demand for surgeries carried out in an outpatient setting will likely grow because patients recognize the facilities as safe and efficient. Another reason for their growing popularity is the fact that ambulatory surgery centers (ASC) have been approved by the Centers for Medicare & Medicaid Services (CMS), making it easier to secure reimbursement.

While it is difficult to predict if future pandemics would shut ambulatory surgery centers down once again, managers expect an increased caseload for the coming years. Not only are ambulatory surgery centers becoming more popular with patients, but they are also dealing with clearing the previous pandemic’s case backlog. Block time utilization will be critical to achieving that.

Also Read: When Was the First Robotic Surgery?

How is AI revolutionizing Ambulatory Surgery

Across different industries and subject matters, artificial intelligence and machine learning approaches have proven their unparalleled capacity to handle large amounts of data. In short, technology beats humans at analyzing more data more accurately and in less time than humans can. Observational studies also show that AI and ML excel at recognizing patterns and potential areas for efficiency.

Recent research by scientists at the University of California San Diego considered specifically how these capabilities could be used to optimize time and space use in ambulatory surgery centers (ASC) without compromising patient care. Using retrospective data, a group of researchers set out to develop machine learning models that would predict how often two outcomes coincided: (1) surgery finished by the end of the allocated operating room block time, and (2) the patient was discharged from the post-anesthesia care unit by the end of the relevant nursing shift.

If those two criteria coincided in a so-called composite outcome, it could free up previously unused surgical block time which could then be filled. As a result, ambulatory surgery centers (ASC) could clear the backlogs faster, and more patients would benefit. The researchers hypothesized that the model performance of ensemble learning models would beat that of logistic regressions and their decision functions. Ensemble learning models combine the algorithms and predictive variables of several separate models to make more accurate predictions than individual models can.

To test their theory, they used several different approaches and classifiers. These included regression, random forest classifiers using decision trees, balanced random forest, balanced bagging models, neural network classifiers, and support vector classifiers.

Their analysis also covered the following features: type of surgery, surgeon, service line, and American Society of Anesthesiologists score but not anesthesiologist identification. Other independent features included demographic variables like age, sex, patient weight, and scheduled case duration. The impact of these features was ranked on a feature importance graph. The model performance was evaluated using the synthetic minority oversampling technique (SMOTE).

During their analysis, the scientists looked at more than 13,000 procedures, comparing how the allocated procedure duration days before surgery compared to the actual surgery time and actual room time needed. Actual time was measured in preoperative time, combining the actual case duration and the stay in the post-anesthetic care unit. These retrospective studies showed that the balanced bagging classifier performed best at predicting correct outcomes. Random forest, balanced bagging, and balanced random forest classifiers all delivered more accurate predictions with the SMOTE technique as compared to without.

Source: YouTube

What it Means for Patient Care

Despite being major contributors to the financial success of the healthcare industry, ambulatory surgery centers (ASC) run on lean business models. For that reason, these centers and their healthcare leaders must improve staff scheduling, including flexibility in scheduling. A more efficient approach to scheduling improves patient care and institutional profits at the same time.

Current studies often focus on actual case duration or length of PACU stay, making comparison difficult. But to truly maximize efficiencies, ambulatory surgery centers (ASC) need access to models predicting a combination of both criteria. Exceeding hard deadlines such as surgical blocks or nursing staff hours quickly leads to overages and threatens profitability. At the same time, schedulers must prioritize patient safety in their clinical predictions and achieve a balance.

Managing Short-Term and Long-Term Scheduling

For outpatient surgeries, operating room managers often need to balance the long-term scheduling of staff with the short-term scheduling of surgeries. Gaps between surgical procedures often only become obvious a few days before planned surgeries. Filling those gaps without Filling unused time without overfilling it has been something of a dark art to date.

ML algorithms have the potential of removing the guesswork and improving case duration accuracy, cancelation prevention, and recovery room management. Using logistic regressions, machine learning models can only work in a linear way. More advanced models and techniques have the potential to deliver more accurate and actionable outcomes for hospital leadership.

A Closer Look at Performance Metrics and Limitations

The primary outcome measurement used by the researchers was the so-called F1 score, a combination of the surgery being completed on time and an on-time, same-day discharge. Both classifier precision and recall scores were assigned the same weight. Combining these parameters into a single metric made it easier to analyze a classification task. In addition, the results also report the Matthews correlation coefficient.

To increase the robustness of their evaluation metrics, the team also implemented stratified k-folds cross-validation. They found that the overall accuracy of prediction models is improved when using advanced machine learning techniques. One of the scheduling dilemmas of ambulatory outpatient surgery practices is that although surgical services are booked according to blocks, they do not always fill the entire block because of asymmetrical service times.

Deep learning and machine learning offer pathways toward more effective methods for scheduling. With the type of analysis this particular University of California study used, occupancy can be analyzed further in advance, allowing for better planning and a more efficacious approach.

Even though ensemble learning approaches were found to be superior in this study, they have limitations. One of those limitations is their reliance on electronic health monitoring data which can suffer from class imbalances. Replicating data from the original set would not lead to better class balance, but SMOTE helps with that. However, the SMOTE method creates its own problems for high-dimensional data.

Because the study used that was extracted from actual surgical patients’ cases retrospectively, the informed consent requirement was waived. Statistical analyses were completed using Python. SMOTE was used as a statistical technique to enhance balanced class distribution in the generation of a minority class data set with no more than standard deviation. The algorithm used in this study also takes samples of the feature space for each class and five of its nearest neighbors. Applying this method improves downstream analysis.

The entire study evaluated six classification models that may be useful in clinical settings, ranging from linear regression analysis to neural network classifiers. One of those models, multivariable logistic regression, asserts a binary outcome based on class-weight parameters. The model the scientists initially tested was a base model without specified individual class weights. Cross-validation then allowed the researchers to find optimal parameter values for maximum depth of their predictive models.

The researchers used support vector classifiers to find a hyperplane decision boundary that split the data into classes. They modified the support vector machines so that they would be cost-sensitive support vector classifiers.

Bagging methods were used as an alternative to ensemble models that is more immune to overfitting, a common issue for other ensemble learning models. In addition, they build a multilayer perceptron neural network with its activation function set to the rectified linear unit function.

Ensuring correct classification of the data used to train the individual models is as critical to delivering meaningful, actionable outcomes as the selection of independent variables to be tested.

AI in Obstetrical Care and Other Areas of Medicine

Artificial intelligence is not only making its mark on surgical disciplines, but female patients stand to benefit from technological advances in obstetrical care. In this context, machine learning and deep learning have so far shown the most promising results. Obstetrical services providers are starting to utilize artificial intelligence in breast imaging, for example. The goal is to detect cancerous cells sooner, giving female patients a better chance of full recovery.

Also Read: Artificial Intelligence in Healthcare.


Ambulatory, value-based care is soon developing into one of the healthcare areas with the largest market share. Its popularity is increasing at a rapid pace. That pace is putting pressure on those working in clinical settings to deliver improved performance across all disciplines of surgical care considering the patient population to doctor ratio.

Artificial intelligence approaches to the opportunity of predicting operative time and actual discharge charge does not only serve to keep an ambulatory surgery centers (ASC) day on schedule, but correct predictions also contribute directly to a healthcare facility’s ability to serve more patients. As a result, patient care and profitability improve.

Ambulatory surgery is not the only field that stands to benefit from the increased use of AI technology. The growing neurology operating room market is also increasingly looking out for accurate predictions to increase efficiencies in the use of surgical time and space.


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“Machine Learning-Based Models Predicting Outpatient Surgery … : Anesthesia & Analgesia.” LWW, Accessed 30 Mar. 2023.

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