Introduction

An important aspect of the work of any modern clinic is improvement of treatment quality and safety.

Ensuring health care safety is one of the priorities of health systems. In 2000, a report “To err is human: building a safer health system” was published in the United States [1]. According to this report, 44–98 thousand patients die annually from preventable medical errors in the US hospitals.

Incidence of adverse events in medical care is still unacceptably high despite advanced attention to this problem. Adverse event may be determined as “unintentional damage to physical health as a result of medical care requiring additional monitoring, treatment or hospitalization or leading to patient death" [2].

According to the report of Yasuhiro Suzuki at the “Global Ministerial Summit on Patient Safety” in 2018, there are 29 cases of persistent harm to health for every fatal outcome. Moreover, 300 patients are near miss to death due to safety problems in medical organization [3].

Systematic approach to identifying, planning, monitoring, measuring and eliminating of adverse events is required in order to effectively manage the risks of medical care and improve patient safety in medical institutions.

Accreditation (certification) should implies the presence of these technologies in the clinic. Accreditation per se itself encourages staff to improve treatment quality through the introduction of modern protocols and international safety standards.

The characteristics essential for modern clinic are detailed in the accreditation manuals. Joint Commission International (JCI) standards are the best-known in the world. Most experts consider that these standards are the most reliable and confirm impeccable management and the highest quality of medical care.

In Russia, certification is currently carried out in accordance with the recommendations of the National Institute of Quality. These standards include 11 sections (Fig. 1).

The main purpose of work ensuring patient safety improvement in a hospital is decrease of the number of preventable adverse events. In this regard, identification and analysis of these events are extremely relevant.

The strategy for improving patient safety is based on a systematic approach. In 1990, psychologist James Reason proposed a model of the occurrence of adverse events ("Swiss cheese" model) (Fig. 2).

According to this model, global error (adverse event) in an actively functioning system (clinic) occurs when the "holes" in the cheese (individual errors) coincide and appear on the same line. Moreover, 85-90% of errors are associated with system defects and only 10-15% - with individual actions (active personnel errors). That is, most errors is the results of system malfunction although they look like human errors at the first glance. Therefore, these errors may be prevented [2].

In 2013, James Reason [4] analyzed 4 reports devoted to nosocomial adverse events for 2002—2008 and extrapolated these results to the entire in-hospital care in the USA. He concluded that number of deaths from potentially preventable complications of treatment is 210—440 thousand per year. These data put medical errors on the third place among the causes of death. James Reason analyzed possible reasons of significant discrepancy between his data and results of the Institute of Medicine. The author noted that the so-called “Global Trigger Tool (GTT)” was used to identify adverse events in all analyzed studies.

Hershel Jick first proposed the concept of “trigger” or “key” (a clue factor influencing the development of clinical event) for identifying undesirable adverse drug reactions. Later, D. Glassen [5] developed this idea and used it for automatic searching for the triggers of adverse drug reactions in hospital and pharmacy information systems. The presence of trigger (for example, appointment of antidote or abnormal laboratory parameters) justified further analysis of medical records for complications of drug therapy.

At the turn of the XXI century, the technique for identifying treatment complications via retrospective analysis of a random sample of medical records was developed at the Institute for Healthcare Improvement (IHI). This technique was based on Glassen’s researches and other trigger approaches. GTT method is a relatively simple and inexpensive approach to identify triggers of possible adverse events and assess the nature and severity of damage to the patient’s health.

It should be emphasized that GTT approach focuses on comprehensive identification of nosocomial complications rather medical errors per se. These results may be subsequently used to develop certain in-hospital systemic measures for patient safety improvement. In addition, GTT method is aimed mainly at identifying adverse events caused by therapeutic and diagnostic measures (i.e. active actions) and, to a lesser extent, the absence of timely actions (inaction). GTT is usually insensitive for other types of errors (interruptions in information exchange, diagnostic errors).

Triggers of adverse events are divided into the following groups in the original method:

• Triggers of complications and care

• Triggers of postoperative complications

• Triggers of nosocomial infections

• Triggers of adverse drug reactions

Analysis of triggers comprised of several stages. The first one is sampling medical records of discharged patients with length of hospital-stay of at least 24 hours (10 records per month is recommended). Information is analyzed by at least 2 specialists who must have basic clinical knowledge. The specialists should know about the content and structure of medical records and be able to review medical records. Certain parts of medical records containing the information about triggers are reviewed: discharge epicrisis, data on infections, complications or certain diagnoses, lists of appointments, laboratory survey data, surgical protocols, nursing procedures).

Identification of positive trigger is followed by analysis of certain part of medical record in order to identify documentary evidence that damage to patient's health was the result of medical care.

Classen D.C. et al. [6] reported that GTT results identification of at least 10 times more serious adverse events compared with other methods. Moreover, this approach shows that in-hospital adverse events occur in approximately one third of patients. In other studies, this value is 36 per 100 hospitalizations (28% of patients) [7], 25 per 100 hospitalizations (18% of patients) [8]. According to various experts, 44—63% of these adverse events could be prevented. However, some authors note that true sensitivity of GTT is unknown since there is no “gold standard” for detection of adverse events [9].

GTT is far from the only way to detect nosocomial adverse events. Other methods, such as patient safety characteristics of the U.S. Agency for Healthcare Research and Quality (AHRQ) or Utah/Missouri adverse event classification (extended set of patient safety characteristics) were designed for simple application and automatic data extraction from administrative and financial databases. Both of these tools are based on automatic reviewing of codes assigned at discharge of patient to identify adverse event [10, 11].

The following groups of adverse event triggers are used in original GTT technique developed for a multi-field hospital (Table 1).

Each adverse event is evaluated for its potential prevention using the 4-point Likert scale (Fig. 3).

The authors of GTT method used the classification of National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP) to assess severity of harm to the patient’s health. They excluded the categories of errors, which did not affect patient’s health (Table 2).

Trigger event is a reason for preliminary analysis and selection of medical record for clinical audit. Monitoring of adverse event triggers is a part of clinical audit with consideration of all events regardless of possibility of their prevention.

GTT analysis allows us to identify those fragments, which are relevant for neurosurgical clinic (Table 3).

Ensuring patient safety is especially important in surgical clinic with a great number of operations. Data on the number of operations performed in our Center for the period 2014—2018 are shown in Fig. 4.

Obviously, the number of operations has been increasing over the past few years. In this regard, registration and analysis of postoperative complications as a surrogate marker of the quality of neurosurgical care are extremely urgent. The difficulty is determined by the absence of unified approaches and classification scheme approved by the professional community. Nevertheless, this work is necessary. Registration of complications is performed at the Burdenko Neurosurgery Center. Moreover, a model and technology for continuous recording and analysis of postoperative complications based on modern information technologies are being developed in our center. Today, we use Clinical Data Management System as traditionally applied information technology in clinical research industry.

To date, the following approaches to terminology exist in the literature (Clavien et al. [12]). Postoperative complications are conditionally divided into two types: “surgical” - deviations from the ideal course of postoperative period associated with surgery and surgical equipment; “non-surgical” - adverse events not directly related to surgery and surgical technique. Types of adverse events proposed for monitoring by the authors of this report are shown in Table 4.

This model include any undesirable phenomena (not included in this list) which are significant in experts’ opinion. It is assumed that frequent complications described in an arbitrary style in the medical record will eventually be added to the formalized list of complications for their subsequent monitoring.

Information obtained during monitoring is presented in reports. The number of complications is calculated per 1000 operations (Fig. 5).

We describe an example of analysis of cases of pulmonary embolism to illustrate the application of the described approach. A risk-based approach in prevention of venous thromboembolic complications is used in the Center. Risk assessment is carried out using the Caprini scale. Prevention of these events is based on national, American and European guidelines for the prevention of pulmonary embolism in neurosurgical patients. Analysis of each case is presented as a diagram (Fig. 6).

There were 5 cases of pulmonary embolism for the period May—October 2019. The following data of analysis were obtained. Correct risk assessment and prevention with suboptimal dosages and multiplicity of administration of the drugs were observed in 3 cases. Incorrect risk assessment and no prevention were emphasized in 2 cases.

In accordance with the Likert scale, probability of prevention of more than 50% were recognized in 5 cases. These cases were analyzed by the subcommittee of medical commission for quality and safety control of medical care using the results of audit.

In our opinion, the most effective analysis of adverse events is achieved by using of a complex of indicators of medical care quality and safety. On the one hand, this approach is valuable for objective analysis of current in-hospital processes and on the other hand for comparison of own results with those of leading world clinics (external benchmarking).

Benchmarking should be understood as a complex of measures for collection and analysis of information about the effective methods of work in leading world clinics for subsequent implementation of the most successful strategies.

In our opinion, management of the quality and patient safety in neurosurgical clinic is based on systematic approach. Additional automation of the processes is required for implementation of systematic approach [13]. Development and improvement of these processes is an important and perspective objective.

Obvious advantage of our center is a large amount of data on the treatment of neurosurgical patients (electronic medical record was created more than 18 years ago). Therefore, we are able to obtain necessary information and knowledge for subsequent extrapolation of the most successful experience to other hospitals. Considering development of information technology and new opportunities in this area, the immediate prospect is the use of artificial intelligence for analysis of data and development of intelligent systems.