[Understanding complex processes better-A case study on increasing patient safety and efficiency in a central operating room]. / Komplexe Prozesse besser verstehen ­ eine alltagsbezogene Fallstudie zur Erhöhung der Patientensicherheit und Effektivität in einem Zentral-OP.

BACKGROUND: Various professional groups are involved in the daily work of the central operating room with the aim of providing the best possible treatment for each individual using modern medical technology (sociotechnical system) in a cost-effective manner. Ensuring perioperative patient safety is of particular importance. At the same time, the efficient use of the central operating room is essential for the economic success of a hospital. Preoperative preparation is a complex process with many substeps that are often difficult to manage. Historically, the focus has been on retrospective learning from errors and incidents. More recent approaches take a systemic view. A central idea is to consider the mostly positive course of treatment and the adjustments to daily work that are currently required by the people involved (Safety-II). By taking greater account of how the many components of the system interact, processes can be better understood and specific measures derived. This strengthens the system's ability to adapt to changes and disturbances, thus ensuring that goals are achieved. The functional resonance analysis method (FRAM) is an internationally recognized method for modelling work as done compared to work as imagined. This paper presents the application of FRAM to preoperative preparation in a major regional hospital. OBJECTIVE: Is FRAM suitable for improving process understanding in preoperative preparation? MATERIAL AND METHODS: An interdisciplinary project team identified relevant functions of preoperative preparation through document analysis and walkthroughs. Based on this, more than 30 guided interviews were conducted with functionaries. The results were presented graphically and specific information, such as safety-related statements or reasons for the variability of functions, were also presented textually. In the next phase, statements were evaluated and compared with the target model and the job descriptions. RESULTS: The FRAM revealed the process as a complex network of relationships. During the modelling process, a varying degree of centrality and variability of certain functions became apparent. From the observations, the project team selected those with high relevance for patient safety and for the efficiency of the overall process in order to prioritize starting points for deriving measures to increase resilience. These starting points relate either to single functions, such as surgical site marking or to multiple functions that are variable in their execution, such as delays due to nonsynchronized duty times. CONCLUSION: The FRAM conducted provides valuable new insights into the functioning of complex sociotechnical systems that go far beyond classical linear methods. The awareness of operational processes gained and the resulting dynamic view of interactions within the system enable specific measures to be derived that promote resilient behavior and reduce critical variability, thus contributing to increased patient safety and efficiency.

[Safety­II: a systemic approach for an effective clinical risk management]. / Safety­II: ein systemischer Ansatz für ein effektives klinisches Risikomanagement.

The healthcare system is an example of a complex sociotechnical system where the goal is the best possible individual treatment together with the cost-effective use of modern technology. Working in anesthesia requires medical knowledge as well as manual skills and the use of specialized technical equipment in an interdisciplinary and interprofessional setting. The susceptibility to errors and adverse events, especially in the care of critically ill patients, is high.In order to avoid unintentional hospital-induced patient harm, the healthcare system has recently taken the path of prescribing the best possible care for a large number of patients with the help of evidence-based guidelines and specific algorithms or instructions for action. Patient safety is defined accordingly as a state in which adverse events occur as rarely as possible (Safety­I).Following this approach clinical risk management is defined as the purposeful planning, coordination, execution and control of all measures that serve to avoid unintended hospital-induced patient harm or to limit its effects. For this purpose, the focus has recently been placed on instruments such as Critical Incident Reporting Systems (CIRS) or Morbidity and Mortality Conferences (M&MC); however, it is increasingly recognized that adverse events in complex sociotechnical systems such as the healthcare system arise situationally from the interaction of numerous components of the system. The effectiveness of CIRS and M&MC is limited because they do not comprehensively take situational effects into account. Thus, only selective changes are possible which, however, do not imply a sustainable improvement of the system. Newer approaches to strengthening safety in complex sociotechnical systems understand positive as well as negative events as being equally caused by the variable adaptation of behavior to daily practice. They therefore focus on the majority of positive courses of treatment and the necessary adaptations of the health professionals involved in daily practice (Safety­II). In this way, the adaptability of the system under unexpected conditions should be increased (Resilience Engineering). Taking this systemic approach into account, the Functional Resonance Analysis Method (FRAM) offers a variety of possibilities for the prospective analysis of a complex sociotechnical system or for retrospective incident analysis through modelling of actual everyday actions (work as done). Through interviews with the health professionals involved, document analyses and work inspections, processes and their functions as well as the associated variability are assessed and graphically presented. The FRAM models the collected information of the process as complexes of interconnected functions represented by hexagonal symbols. Each corner of the hexagon represents a given aspect, which together form the properties of the function (input, output, precondition, resource, time, control). Through this visualization and evaluation of the interview results, the actual everyday actions (work as done) can be compared with the predefined ones (work as imagined). The evaluation of the variability found in this way enables the strengths and weaknesses of processes to be uncovered. As a result, specific measures can be derived to strengthen the system. Increased consideration of the Safety­II approach within clinical risk management can be a valuable addition to existing clinical risk management methods.

[Delayed Takotsubo syndrome - A critical perioperative incident]. / Das verspätete Tako-Tsubo-Syndrom ­ Ein kritischer perioperativer Zwischenfall.

INTRODUCTION: The term Takotsubo syndrome (TTS) describes a transient ventricular dysfunction. Symptoms and complication rate are similar to those of a myocardial infarction. MEDICAL HISTORY: An 81-year-old female patient was admitted for thrombendarterectomy of the left femoral artery. Prior to a recent biological aortic valve replacement, coronary heart disease had been ruled out. ANESTHETIC INDUCTION AND CLINICAL FINDINGS: After induction of anesthesia, relevant arterial hypotension and sinus bradycardia occurred. After catecholamine administration, transient ST segment elevations were observed, which postoperatively developed a myocardial infarction-like dynamic. Echocardiography and values of cardiac enzymes initially revealed no abnormalities. THERAPY, COURSE AND DIAGNOSIS: After a symptom-free interval the patient developed severe cardiac decompensation on the third postoperative day. At this point, the clinical picture of TTS was visible. Stabilisation of the clinical condition was achieved with levosimendan therapy. CONCLUSION: Dynamic ECG changes in the perioperative situation always require differentiated diagnosis and possibly longer monitoring. TTS is a relevant differential diagnosis because it is subject to severe complications.

Mechanical Ventilation During Resuscitation: How Manual Chest Compressions Affect a Ventilator's Function.

INTRODUCTION: Guidelines for resuscitation recommend positive-pressure ventilation with a fixed ventilation rate as provided by an automated transport ventilator during cardiopulmonary resuscitation (CPR) with a secured airway. We investigated the influence of manual chest compressions (CC) on the accuracy of ventilator presets and the quality of CC with intermittent positive-pressure ventilation (IPPV), bilevel ventilation (BiLevel), and the novel ventilation mode chest compression synchronized ventilation (CCSV) in a simulation model. METHODS: Ninety paramedics performed continuous CC for 2 min on a modified advanced life support mannequin with a realistic lung model. IPPV, BiLevel, and CCSV were applied in a randomized order. CCSV is a novel type of pressure-controlled ventilation with short insufflations synchronized with CC, which are stopped before decompression begins. The ventilator presets (tolerance range) were IPPV Vt = 450 (400-500) ml, PEEP = 0 hPa, f = 10/min; BiLevel Pinsp = 19 (17.1-20.9) hPa, PEEP = 5 hPa, f = 10/min; CCSV Pinsp = 60 (54-66) hPa, PEEP = 0 hPa, Tinsp = 205 ms, f = CC rate. Preset values were compared with the measured results. Values were defined as correct within a tolerance range. Quality of CC was evaluated using ERC guidelines (depth >50 mm, CC rate 100-120/min). RESULTS: Median (25th/75th percentiles) IPPV V t = 399 (386/411) ml, BiLevel Pinsp = 22.0 (19.7/25.6) hPa, and CCSV Pinsp = 55.2 (52.6/56.7) hPa. Relative frequency of delivering correct ventilation parameters according to ventilation mode: IPPV = 40 (0/100)% vs. BiLevel = 20 (0/100)%, p = 0.37 and vs. CCSV = 71 (50/83)%, p < 0.02. Pinsp was too high in BiLevel = 80 (0/100)% vs. CCSV = 0(0/0)%, p < 0.001. CC depth: IPPV 56 (48/63) mm, BiLevel 57 (48/63) mm, CCSV 60 (52/67) mm; CC rate: IPPV 117 (105/124)/min, BiLevel 116 (107/123)/min, CCSV 117 (107/125)/min. CONCLUSION: When compared to IPPV and BiLevel, CCSV works best with preset values, without exceeding the upper pressure preset during simulated CPR. Quality of CC is not negatively affected by any of the ventilation patterns. FUNDING: Parts of this study were supported by Weinmann Emergency Medical Technology GmbH + Co.KG.