Demand for patient-centered care
As the health care system has gotten more complex and costly, patients are exercising their right to focus care on their goals and expectations. This is particularly true for patients with complex medical conditions and those requiring major surgical procedures. In addition, as the American populace ages, there is increasing demand for surgical and anesthesia services, particularly for age-related conditions such as arthritis, cardiac disease, and cancer. One of the most important and patient-centered innovations in health care has been the development of the patient-centered medical home (PCMH). There are five key functions and attributes of the PCMH as defined by the Agency for Healthcare Research and Quality (AHRQ)1:
- Patient-Centered: Care provided is oriented to the whole person, partnering with families and recognizing culture, value, and preferences
- Coordinated Care: Includes specialty, primary care, and community services
- Comprehensive Care: The PCMH should be accountable for the majority of the patient’s needs, which will require a team of providers, including physicians, nurses, pharmacists, and others
- Accessible: This could include remote monitoring, telephone, or text messaging to facilitate easy, rapid communication with health care providers
- Quality and Safety: Using evidence-based medicine and clinical decision-support tools to guide shared decision-making with patients and families. Results should be shared publicly.
While the PCMH is based on primary care, the same concepts should apply to the Patient-Centered Surgical Home (PCSH). The initial concept of the Perioperative Surgical Home (PSH) has been recognized for years. The majority of associated interventions have been delivered in the hospital based on defined care pathways. But more needs to be done to bring the true meaning of “home” to the surgical patient population. This will require us to move beyond the hospital-centric model to one allowing the patient to receive treatment in other settings and allow anesthesiologists and surgeons to support recovery at home.
Hospital-based ORs of the future
ORs have changed relatively little in the last 50 years. While the procedures performed within them have become increasingly complex, with the advent of laparoscopy, robotics, and image guidance, the basic layout remains the same. In essence, OR design has trailed innovation, adapting rather than innovating as surgical techniques have changed. New OR construction is focused on larger rooms with increased technological capacity for intraoperative imaging (hybrid OR), including intraoperative MRI, but the basic design remains unchanged. That is particularly true for the anesthesia team, which often finds itself in unwieldy and constrained positions relative to the patient.
New construction of ORs should take into consideration not only technology but human factors such as light and noise in order to reduce errors and improve visibility and communication between surgical, anesthesia, and nursing teams. Staging of mock-ups with equipment taped out on the floor often shows designs that are unworkable. It is essential that anesthesiologists actively participate in the design of new hospital ORs in order to “future proof” these designs as much as possible and ensure that design fosters, rather than constrains, innovation and patient care.
Movement of surgery from inpatient to outpatient settings
The biggest change in surgery in 2030 will likely be the continued growth of procedures performed outside of the hospital, in either facility-based or free-standing ambulatory surgery centers (ASCs). Since the majority of surgical procedures are now done on an outpatient basis, identifying new ambulatory models of care to optimize both the provider and patient experience will be critical. Munnich studied the growth of Medicare-certified ASCs between 1990 and 2015 and showed an increase from 1,300 to 5,500 ASCs.4 In addition to the increase in ambulatory surgery locations, the volume of ambulatory surgery increased from about 20 million cases in 2015 to an estimated 27 million cases in 2021. Although the growth rate slowed when Medicare reduced payment for outpatient procedures in 2008, the COVID-19 pandemic has rekindled interest in bringing care, including surgery, closer to the patient’s home. Recent changes in payment proposed by CMS in 2021 will require careful thought about how to provide high-value care in these alternative environments.
With advances in both minimally invasive surgical techniques and the ability to provide both advanced pain management and remote monitoring without the need for inpatient hospitalization, procedures that used to require inpatient admission can now be performed on an ambulatory basis. No procedure demonstrates this change more clearly than outpatient arthroplasty. Advances in surgical approaches and regional anesthesia with ambulatory catheters have made this both safe and effective for selected patients. A successful program requires a team capable and experienced in performing hip and knee arthroplasty in the outpatient setting. The anesthesia team, the surgical team, and the recovery room staff should all be familiar with outpatient early recovery discharge modalities that include adequate perioperative pain control, fluid resuscitation, early patient mobilization, and medical management. The facility in which the surgical procedure and immediate recovery is performed should also have adequate and sufficient equipment, staff, and facilities to ensure patient safety and a successful total hip or knee arthroplasty procedure and a coordinated and collaborative approach to ensure that patients have access to appropriate postoperative resources.
The transition from inpatient to ambulatory surgical care has other implications that we must consider. The COVID-19 pandemic has shined a bright light on health disparities that might be exacerbated by changes in how and where anesthesia and surgery will be provided. With this lens, it is essential that we take into account how disparities and social determinants of health impact our decision making and planning. New perioperative support options may be needed for patients without the resources or options to manage preoperative and postoperative needs when outside of the hospital environment. In addition, with changes in payment for ambulatory surgery care, there is risk that underserved populations may not have equal access to this modality of care. It will require intentionality to prevent exacerbating, as opposed to than addressing, health disparities.6
Remote monitoring of patients after surgery
With growth in ambulatory surgery and the desire to shorten inpatient hospital length of stay, the ability to monitor patients using remote sensing has increased exponentially. Familiar devices such as the Apple Watch® and Fit Bit® are widely available, and their data can be transmitted to a provider via a smart phone. More comprehensive data such as heart rate, ECG, blood pressure, respiratory rate, and temperature are also available on advanced devices. These devices have the ability to detect early deterioration so that a provider can be alerted and contact can be made with the patient or caregivers. In patients undergoing laparoscopic Roux-en-Y gastric bypass surgery, Nijland et al. demonstrated an 88% success rate in same-day discharge by utilizing these monitoring techniques. Same-day discharge patients were remotely monitored for 48 hours after surgery using a medical device measuring vital signs three times a day. Video consultations were performed by a doctor twice a day for two postoperative days. Notably, the patient satisfaction scores were high.
Artificial intelligence and deep learning in perioperative care
We typically think of intraoperative anesthetic care as being too complex to be automated, and anesthesia care must be highly reliable to prevent potentially fatal complications. While we aren’t at risk of being replaced by computers yet, it is increasingly clear that these devices can assist and improve decision-making. Indeed, devices like depth of anesthesia monitors use computer algorithms to analyze and signal process electroencephalogram data to help guide intraoperative care, especially for total intravenous anesthesia.
Deep learning is a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher-level features from data. By comparing patient-specific data to an archive of known patient data, a machine can make recommendations for interventions such as early treatment of blood pressure or changing the depth of anesthesia. Online deep learning is a type of machine learning in which data becomes available in a sequential order and is used to update the prediction model for future data. In other words, online deep learning is able to customize predictions based on actual events. Connor provides a comprehensive review of machine learning in anesthesiology. Joosten et al. used computer-assisted individualized hemodynamic management to reduce the amount of time patients spent with intraoperative hypotension, proving that computer guidance may be able to improve care.10 The figure describes the technique used.
“With advances in both minimally invasive surgical techniques and the ability to provide both advanced pain management and remote monitoring without the need for inpatient hospitalization, procedures that used to require inpatient admission can now be performed on an ambulatory basis.”
One can imagine the future anesthesia machine and electronic record, informed by an individual patient’s co-morbidities, body mass index, and even genomics, programmed to optimize anesthetic care. Another example of deep learning can be found in the work of Cherifa et al., who studied 1,151 patients in the ICU to predict hypotensive episodes up to 10 minutes before they occurred. Hill et al. were able to use deep learning to impute the continuous arterial blood pressure waveform non-invasively by using electrocardiogram and photoplethysmography. The prediction model was highly accurate and suggests that this technique could greatly reduce the need for invasive blood pressure measurement.
It is likely that this same technology will be used to select the appropriate setting for providing care to individual patients based on underlying conditions and other criteria. For example, machine learning could be used for risk stratification of patients suitable for invasive outpatient surgery. However, as London rightfully points out, we are unlikely to reach the level of sophistication needed for successful prediction until such models can take into account a patient’s pre-existing comorbidities and physiology.
In summary, the OR of the future is likely to be very different from current models. In the hospital, they will be large, complex, and capable of advanced modalities such as robotic surgery, biplanar fluoroscopy, and intraoperative MRI for image-guided surgery. However, the larger change is likely to be the movement of surgeries currently performed in the inpatient setting to the ambulatory surgery setting. It is incumbent upon us to lead this transition and ensure equitable, patient-focused care in every setting.