Assessing hospitals' clinical risk management: Development of a monitoring instrument

Briner, Matthias; Kessler, Oliver; Pfeiffer, Yvonne; Wehner, Theo; Manser, Tanja

doi:10.1186/1472-6963-10-337

Cited by 40 publications

(41 citation statements)

References 38 publications

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“…The interviewees were selected following a national study on CRM in Switzerland in 2007/08 [see [4,17]. The sampling technique was purposive: all 11 experts were responsible for the coordination of CRM in their psychiatric hospital and had considerable knowledge and experience in the field of patient safety in mental health care.…”

Section: Methodsmentioning

confidence: 99%

“…It was based upon results from a literature review on CRM and was critically examined by an expert panel consisting of 11 patient safety experts (comprised of the persons in charge of patient safety and/or quality of five main Swiss healthcare institutions, the president of the Swiss Society for Quality Management in Health Care, the head of quality of a major reinsurance company, and four clinical experts with a proven record of accomplishment in patient safety. For details see [17]). The manual included exploratory questions on tasks, content and organization of CRM (e.g.…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, a systematic clinical risk management (CRM) can play a crucial role in enabling health care organizations to assess, manage, and contain risks related to patient safety and aims at reducing or eliminating harm to patients [8,17]. The more complex an organization, the greater the need for CRM.…”

Section: Introductionmentioning

confidence: 99%

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Clinical risk management in mental health: a qualitative study of main risks and related organizational management practices

Briner

Manser

2013

BMC Health Serv Res

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BackgroundA scientific understanding of clinical risk management (CRM) in mental health care is essential for building safer health systems and for improving patient safety. While evidence on patient safety and CRM in physical health care has increased, there is limited research on these issues in mental health care. This qualitative study provides an overview of the most important clinical risks in mental health and related organizational management practices.MethodsWe conducted in-depth expert interviews with professionals responsible for CRM in psychiatric hospitals. Interviews were transcribed and analyzed applying qualitative content analysis to thematically sort the identified risks.ResultsThe main concerns for CRM in mental health are a) violence and self-destructive behavior (i.e. protecting patients and staff from other patients, and patients from themselves), b) treatment errors, especially in the process of therapy, and c) risks associated with mental illnesses (e.g. psychosis or depression). This study identified critical differences to CRM in hospitals for physical disorder and challenges specific to CRM in mental health. Firstly, many psychiatric patients do not believe that they are ill and are therefore in hospital against their will. Secondly, staff safety is a much more prominent theme for CRM in mental health care as it is directly related to the specifics of mental illnesses.ConclusionsThe current study contributes to the understanding of patient safety and raises awareness for CRM in mental health. The mental health specific overview of central risks and related organizational management practices offers a valuable basis for CRM development in mental health and an addition to CRM in general.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Clinical risk management in mental health: a qualitative study of main risks and related organizational management practices

Briner

Manser

2013

BMC Health Serv Res

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“…In turn, this will facilitate multisite learning and allow the formation of strategic changes. 157 This is of similar aim to the clinical risk management monitoring instrument developed by Briner et al, 158 aiming to allow for continuous monitoring of current and planned developments within hospitals, enabling repeated assessment and allowing differentiation between various service qualities.…”

Section: Theoretical Basis Of the Surveymentioning

confidence: 99%

Evaluation of a continuous monitoring and feedback initiative to improve quality of anaesthetic care: a mixed-methods quasi-experimental study

Benn

Arnold

D’Lima

et al. 2015

Health Serv Deliv Res

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BackgroundThis study evaluated the impact of a continuous quality monitoring and feedback initiative in anaesthesia.ObjectivesTo conduct a quasi-experimental evaluation of the feedback initiative and its effect on quality of anaesthetic care and perioperative efficiency. To understand the longitudinal effects of passive and active feedback and investigate the mechanisms and interactions underpinning those effects.DesignMixed-methods evaluation with analysis and synthesis of data from longitudinal qualitative interviews, longitudinal evaluative surveys and an interrupted time series study.InterventionContinuous measurement of a range of anaesthetic quality indicators was undertaken in a London teaching hospital alongside monthly personal feedback from case summary data to a cohort of anaesthetists, with follow-up roll-out to the whole NHS trust. Basic feedback consisted of the provision of passive monthly personalised feedback reports containing summary case data. In the enhanced phase, data feedback consisted of more sophisticated statistical breakdown of data, comparative and longitudinal views, and was paired with an active programme of dissemination and professional engagement.MethodsBaseline data collection began in March 2010. Implementation of basic feedback took place in October 2010, followed by implementation of the enhanced feedback protocol in July 2012. Weekly aggregated quality indicator data, coupled with surgical site infection and mortality rates, was modelled using interrupted time series analyses. The study anaesthetist cohort comprised 50,235 cases, performed by 44 anaesthetists over the course of the study, with 22,670 cases performed at the primary site. Anaesthetist responses to the surveys were collected pre and post implementation of feedback at all three sites in parallel with qualitative investigation. Seventy anaesthetists completed the survey at one or more time points and 35 health-care professionals, including 24 anaesthetists, were interviewed across two time points.ResultsResults from the time series analysis of longitudinal variation in perioperative indicators did not support the hypothesis that implementation of basic feedback improved quality of anaesthetic care. The implementation of enhanced feedback was found to have a significant positive impact on two postoperative pain measures, nurse-recorded freedom from nausea, mean patient temperature on arrival in recovery and Quality of Recovery Scale scores. Analysis of survey data demonstrated that anaesthetists value perceived credibility of data and local relevance of quality indicators above other criteria when assessing utility of feedback. A significant improvement in the perceived value of quality indicators, feedback, data use and overall effectiveness was observed between baseline and implementation of feedback at the primary site, a finding replicated at the two secondary sites. Findings from the qualitative research elucidated processes of interaction between context, intervention and user, demonstrating a positive response by clinicians to this type of initiative and willingness to interact with a sustained and comprehensive feedback protocol to understand variations in care.ConclusionsThe results support the potential of quality monitoring and feedback interventions as quality improvement mechanisms and provide insight into the positive response of clinicians to this type of initiative, including documentation of the experiences of anaesthetists that participated as users and codesigners of the feedback. Future work in this area might usefully investigate how this type of intervention may be transferred to other areas of clinical practice and further explore interactions between local context and the successful implementation of quality monitoring and feedback systems.FundingThe National Institute for Health Research Health Services and Delivery Research programme.

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“…The risk identification process needs to be proactive, to involve multiple employees, and to create value for and protect the organization [13, 14]. In previous research that explored how ERM is conducted in healthcare organizations, it was established that the guidelines that currently exist are not practical because they only include a list of risk domains [12].…”

Section: Introductionmentioning

confidence: 99%

Development of an enterprise risk inventory for healthcare

Etges

Grenon²,

Lu³

et al. 2018

BMC Health Serv Res

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BackgroundThe first phase of an enterprise risk management (ERM) program is the identification of risks. Accurate identification is essential to a proactive and effective ERM function. The authors identified a lack of such risk identification in the literature and in practical cases when interviewing the chief risk officers from healthcare organizations. A risk inventory specific to healthcare organizations that includes detailed risk scenarios and risk impacts currently does not exist. Thus, the objective of this research is to develop an enterprise risk inventory for healthcare organizations to create a common understanding of how each type of risk impacts a healthcare organization.MethodERM guidelines and data from 15 interviews with chief risk officers were analyzed to create the risk inventory. The identified risks were confirmed through a survey of risk managers from a range of global healthcare organizations during the ASHRM conference in 2017. Descriptive statistics were developed and cluster analysis was performed using the survey results.ResultsThe risk inventory includes 28 risks and their specific risk scenarios. Cyberattack was ranked as the principal risk by the participants, followed by sentinel events and risks associated with human capital management (organizational culture, use of electronic medical records and physician wellness). The data analysis showed that the specific characteristics of the survey participants, such as the length of time working in risk management, the size of the organization, and the presence of a school of medicine, do not impact an individual’s opinion of the importance of the risks identified. A personal background in risk management (clinical or enterprise) was a characteristic that showed a small difference in the perceived importance of the risks from the proposed risk inventory.ConclusionsIn addition to defining specific risk scenarios, the enterprise risk inventory presented in this research can contribute to guiding the risk identification phase of an ERM program and thereby support the development of a risk culture. Patient data security in hospitals that operate with high levels of technology is fundamental to delivering high quality and safe care to patients. At the top of the risk ranking, the identification of cyberattacks reflects the importance that healthcare risk managers place on this risk by allocating time and other resources. Exploring opportunities to improve cyber risk management and evaluating the benefits of using the risk inventory at the beginning of the risk identification phase in an ERM program are suggestions for future studies.Electronic supplementary materialThe online version of this article (10.1186/s12913-018-3400-7) contains supplementary material, which is available to authorized users.

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Assessing hospitals' clinical risk management: Development of a monitoring instrument

Cited by 40 publications

References 38 publications

Clinical risk management in mental health: a qualitative study of main risks and related organizational management practices

Clinical risk management in mental health: a qualitative study of main risks and related organizational management practices

Evaluation of a continuous monitoring and feedback initiative to improve quality of anaesthetic care: a mixed-methods quasi-experimental study

Development of an enterprise risk inventory for healthcare

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