[Utilization rates and turnover times as indicators of OR workflow efficiency]

[Influencing factors on operating times for metal bar removal after Nuss repair]

BACKGROUND

Metal bar removal after the Nuss repair procedure is prone to be cancelled in cases of operating time shortages due it being suitable to be postponed without harming patients. Consequently, planning operation times as exactly as possible could be one solution.

OBJECTIVE

Statistical modelling of operation times of metal bar removal after Nuss repair using the prespecified independent predictors of age, sex, intraoperative complications, and number of implanted metal bars.

MATERIAL AND METHODS

We included all patients whose operation notes included an operation time, which was modelled via linear regression and subject to internal validation via bootstrap. Exploratory analyses also consisted of the surgeon's experience, the number of stabilizers, the body mass index, and preceding re-do surgery for bar dislocation.

RESULTS

We included 265 patients (14% ♀) with a median age of 19 years (interquartile range 17-20 years), of whom 81% had 1 and 17% had 2 metal bars removed. The prespecified regression model was statistically significant (likelihood ratio 56; df = 5; P < 0.001) and had a bias corrected R2 of 0.148. Patient age influenced operation times by 2.1min per year of life (95% confidence interval 1.3-2.9min; P < 0.001) and 16min per explanted metal bar (95% confidence interval: 10-22min; P < 0.001).

CONCLUSION

The patient-specific factors of age and the number of explanted metal bars influenced the operation times and can be included into scheduling operation times.

[Modern OR management in tertiary care hospitals]

Institutional operating room (OR) management is an established and indispensable component of organizational structures in most hospitals, independent of the level of care. In this role, OR management needs to consider both the high fixed costs of operation areas and the increased relevancy of generated revenue. Therefore, in the day to day operations, OR management strives to ensure reliable and safe patient care amidst efficient use of resources and high patient and employee satisfaction. Given these aims, proven strategies, such as constituting processes and responsibilities by OR statutes or tracking key figures and indicators of the OR, must be continuously supplemented and improved upon by OR management. In the future, OR management can derive innovative methods from adaptable capacity management, agile forms of collaboration, integrating upstream and downstream segments into OR management general process organization, and harnessing the potentials of artificial intelligence. Innovation in the face of these and other challenges contributes to improving long-term interdisciplinary and interprofessional collaboration in the OR and with the numerous adjacent teams.

[Does hospital volume correlate with surgical process time? : Retrospective analysis of the five most common procedures for visceral surgery, trauma and orthopedic surgery and gynecology/obstetrics from the benchmarking program of the Berufsverband Deutscher Anästhesisten (BDA), Berufsverband Deutscher Chirurgen (BDC) and Verband für OP-Management (VOPM)]

BACKGROUND

Minimum volume thresholds for specific surgical procedures in German hospitals were established in 2004 but remain controversial. For the first time, this study investigated the relationship between hospital performance volume and surgical procedure duration in a multicenter approach. The question here was whether a concentration on frequently performed procedures leads to a reduction in surgical process times.

METHODS

In a retrospective analysis, the 5 most common procedures from visceral, trauma/orthopedic and gynecological/obstetrics surgery were examined in hospitals participating in a benchmarking program. For each procedure performed between 2013 and 2015, hospitals were divided into 4 groups depending on the hospital volume provided. The average surgical duration of incision to suture time was calculated between the group with "very low" hospital volume and the other three groups ("low", "high" and "very high").

RESULTS

OR cases from 75 hospitals were analyzed. The number of included cases per procedure ranged from 31,940 to 2705. The average number of operations performed in a specific procedure was 3-4 times higher in high-volume hospitals compared to very low-volume hospitals. A linear relationship between hospital volume and surgical process time only appeared to be clearly seen in laparoscopic cholecystectomy, appendectomy and arthroscopic meniscus surgery: a higher case load led to a reduction in incision to suture time. For the other procedures, the surgical process times were inconsistent between the hospital groups.

CONCLUSION

The case volume only appeared to have a direct but limited influence on incision to suture times in laparoscopic and arthroscopic procedures. Overall, the hospital performance volume appeared to be of subordinate importance in terms of OR-economics.

[How many patient transfer rooms are necessary for my OR suite? : Effect of the number of OR transfer rooms on waiting times and patient throughput in the OR - analysis by simulation]

INTRODUCTION

In most hospitals the operating rooms (OR) are separated from the rest of the hospital by transfer rooms where patients have to pass through for reasons of hygiene. In the OR transfer room patients are placed on the OR table before surgery and returned to the hospital bed after surgery. It could happen that the number of patients who need to pass through a transfer room at a certain point in time exceed the number of available transfer rooms. As a result the transfer rooms become a bottleneck where patients have to wait and which, in turn, may lead to delays in the OR suite. In this study the ability of a discrete event simulation to analyze the effect of the duration of surgery and the number of ORs on the number of OR transfer rooms needed was investigated.

METHODS

This study was based on a discrete event simulation model developed with the simulation software AnyLogic®. The model studied the effects of the number of OR transfer rooms on the processes in an OR suite of a community hospital by varying the number of ORs from one to eight and using different surgical portfolios. Probability distributions for the process duration of induction, surgery and recovery and transfer room processes were calculated on the basis of real data from the community hospital studied. Furthermore, using a generic simulation model the effect of the average duration of surgery on the number of OR transfer rooms needed was examined.

RESULTS

The discrete event simulation model enabled the analysis of both quantitative as well as qualitative changes in the OR process and setting. Key performance indicators of the simulation model were patient throughput per day, the probability of waiting and duration of waiting time in front of OR transfer rooms. In the case of a community hospital with 1 transfer room the average proportion of patients waiting before entering the OR was 17.9 % ± 9.7 % with 3 ORs, 37.6 % ± 9.7 % with 5 ORs and 62.9 % ± 9.1 % with 8 ORs. The average waiting time of patients in the setting with 3 ORs was 3.1 ± 2.7 min, with 5 ORs 5.0 ± 5.8 min and with 8 ORs 11.5 ± 12.5 min. Based on this study the community hospital needs a second transfer room starting from 4 ORs so that there is no bottleneck for the subsequent OR processes. The average patient throughput in a setting with 4 ORs increased significantly by 0.3 patients per day when a second transfer room is available. The generic model showed a strong effect of the average duration of surgery on the number of transfer rooms needed.

CONCLUSION

There was no linear correlation between the number of transfer rooms and the number of ORs. The shorter the average duration of surgery, the earlier an additional transfer room is required. Thus, hospitals with shorter duration of surgery and fewer ORs may need the same or more transfer rooms than a hospital with longer duration of surgery and more ORs. However, with respect to an economic analysis, the costs and benefits of installing additional OR transfer rooms need to be calculated using the profit margins of the specific hospital.

An observation support system with an adaptive ontology-driven user interface for the modeling of complex behaviors during surgical interventions

The field of surgical interventions emphasizes knowledge and experience; explicit and detailed models of surgical processes are hard to obtain by observation or measurement. However, in medical engineering and related developments, such models are highly valuable. Surgical process modeling deals with the generation of complex process descriptions by observation. This places high demands on the observers, who have to use a sizable terminology to denominate surgical actions, instruments, and patient anatomies, and to describe processes unambiguously. Here, we present a novel method, employing an ontology-based user interface that adapts to the actual situation and describe the principles of the system. A validation study showed that this method enables observers with little recording experience to reach a recording accuracy of >90%. Furthermore, this method can be used for live and video observation. We conclude that the method of ontology-supported recording for complex behaviors can be advantageously employed when surgical processes are modeled.

Status quo and current trends of operating room management in Germany

PURPOSE OF REVIEW

Ongoing healthcare reforms in Germany have required strenuous efforts to adapt hospital and operating room organizations to the needs of patients, new technological developments, and social and economic demands. This review addresses the major developments in German operating room management research and current practice.

RECENT FINDINGS

The introduction of the diagnosis-related group system in 2003 has changed the incentive structure of German hospitals to redesign their operating room units. The role of operating room managers has been gradually changing in hospitals in response to the change in the reimbursement system. Operating room managers are today specifically qualified and increasingly externally hired staff. They are more and more empowered with authority to plan and control operating rooms as profit centers. For measuring performance, common perioperative performance indicators are still scarcely implemented in German hospitals. In 2008, a concerted time glossary was established to enable consistent monitoring of operating room performance with generally accepted process indicators. These key performance indicators are a consistent way to make a procedure or case - and also the effectiveness of the operating room management - more transparent.

SUMMARY

In the presence of increasing financial pressure, a hospital's executives need to empower an independent operating room management function to achieve the hospital's economic goals. Operating room managers need to adopt evidence-based methods also from other scientific fields, for example management science and information technology, to further sustain operating room performance.

Analysis of surgical intervention populations using generic surgical process models

PURPOSE

According to differences in patient characteristics, surgical performance, or used surgical technological resources, surgical interventions have high variability. No methods for the generation and comparison of statistical 'mean' surgical procedures are available. The convenience of these models is to provide increased evidence for clinical, technical, and administrative decision-making.

METHODS

Based on several measurements of patient individual surgical treatments, we present a method of how to calculate a statistical 'mean' intervention model, called generic Surgical Process Model (gSPM), from a number of interventions. In a proof-of-concept study, we show how statistical 'mean' procedure courses can be computed and how differences between several of these models can be quantified. Patient individual surgical treatments of 102 cataract interventions from eye surgery were allocated to an ambulatory or inpatient sample, and the gSPMs for each of the samples were computed. Both treatment strategies are exemplary compared for the interventional phase Capsulorhexis.

RESULTS

Statistical differences between the gSPMs of ambulatory and inpatient procedures of performance times for surgical activities and activity sequences were identified. Furthermore, the work flow that corresponds to the general recommended clinical treatment was recovered out of the individual Surgical Process Models.

CONCLUSION

The computation of gSPMs is a new approach in medical engineering and medical informatics. It supports increased evidence, e.g. for the application of alternative surgical strategies, investments for surgical technology, optimization protocols, or surgical education. Furthermore, this may be applicable in more technical research fields, as well, such as the development of surgical workflow management systems for the operating room of the future.