Optimizing the perioperative care spectrum is essential for improving clinical outcomes as well as patient satisfaction. The patient’s progression before, during, and after surgery has significant impacts on patient outcomes, as well as to the hospital at-large. Within each of these stages, opportunities exist to enhance the patient’s experience, such as minimizing the patient’s wait-time to ameliorating patient-physician communication. Such improvements carry clinical, as well as operational effects to the hospital. In this manner, operational analytics are increasingly utilized as an effective tool for measuring, addressing, and impacting patient experience throughout the perioperative care spectrum.
For reference, operational analytics is inclusive of quantitative variables such as census, site of admission, and average length of stay, as well as qualitative factors, such as the measurement of patient satisfaction at each stage of the perioperative cycle. At the hospital level, operational analytics can be applied to identify trends that elucidate relationships between operational features and patient satisfaction. More specifically, operational analytics are a lever for generating data on the patient’s journey throughout the perioperative care cycle, allowing for areas of intervention. Although specific operational factors are subject to change by the patient segments and region each healthcare system serves, there are several operational analytics that can be applied to the majority surgical settings.
Hospital census, or the number of patients admitted to a hospital at a given time, is a factor of interest for practice management. For anesthesiologists and surgeons, census is most useful when measured at the site of entry for surgical services, such as when the patient undergoes pre-operative preparation or is transferred into the OR. By understanding exactly how many patients are present at each stage of the perioperative cycle, physicians and trainees can be allocated to service during high-volume times, and otherwise triaged to other services. Furthermore, evaluating census also has a benefit to the administrative side of the healthcare system, as it enhances the surgical practice manager’s ability to efficiently admit patients to hospital beds. Without analytics, patients may be admitted in a non-effective manner and beds in the surgical wing may be under- or over-utilized. Yet, by employing operational analytics to measure census, beds can be strategically distributed to accommodate the most emergent cases soonest, thus contributing to intelligent anesthesia care delivery at the patient population level.
Operations analytics research has greatly improved the capacity of health systems to measure and address concerns within analytics sectors such as census and bed management. Recently, researchers from Hong Kong conducted a study across government hospitals that applied an intervention for hospital-wide master surgery schedules, optimizing for patient flow, capacity management, and resource allocation through the broad variable of measuring bed occupancy. The researchers produced a simulation tool that integrated real-time information from multiple hospitals in order to predict each variable at different times of the day and year. In addition, the collected analytics were then applied to a decision-making tool for hospital practice managers, allowing for stakeholders to observe how the master surgery schedule changes over time and with patient flow. Such an intervention has the potential to significant effect clinical outcomes as well as economic outcomes over time. Further technological interventions that prioritize high-volume data collection for integration into analytics will continue to progress the movement on hospital operations management.
Operational analytics are essential for optimizing the efficacy of the perioperative care spectrum. By increasing the data available on patient experience, while also identifying areas of improvement, physicians and practice managers can continue to develop highly effective systems for delivering anesthesia and surgical care.
 Institute for Healthcare Management. “Real-Time Demand/Capacity Management to Improve Flow.” 2019. http://www.ihi.org/resources/Pages/Changes/RealTimeDemandCapacityManagement.aspx
 The American Health Information Management Association. “AHIMA’s Long-Term Care Health Information Practice and Documentation Guidelines: Practice Guidelines for LTC Health Information and Record Systems.” 2014.
 Yip et al. “Levelling bed occupancy: reconfiguring surgery schedules via simulation.” Int J Health Care Qual Assur. 2018;31(7):864-876. doi: 10.1108/IJHCQA-12-2017-0237.