دانلود مقاله ISI انگلیسی شماره 757
عنوان فارسی مقاله

یک روش سیستمیک برای مدیریت ریسک در بخش بهداشت و درمان

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
757 2011 14 صفحه PDF سفارش دهید 9000 کلمه
خرید مقاله
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عنوان انگلیسی
A systemic methodology for risk management in healthcare sector
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Safety Science, Volume 49, Issue 5, June 2011, Pages 695–708

کلمات کلیدی
بهداشت و درمان - مدیریت ریسک - خطاهای درمانی - ایمنی بیمار -
پیش نمایش مقاله
پیش نمایش مقاله یک روش سیستمیک برای مدیریت ریسک در بخش بهداشت و درمان

چکیده انگلیسی

The recent biomedical, technological, and normative changes have led healthcare organizations to the implementation of clinical governance as a way to ensure the best quality of care in an increasingly complex environment. Risk management is one of the most relevant aspects of clinical governance and approaches put forward in literature highlight the necessity to perform comprehensive analyses intended to uncover root causes of adverse events. Contributing to this field, the present paper applies Reason’s theory of failures to work out a systemic methodology to study risks impacting not only directly but also indirectly on patients. Also, the steps of such approach are organized around Human Reliability Assessment phases, in order to take into account the human component of healthcare systems. This framework is able to foster effective decision making about reducing failures and waste and to improve healthcare organizations’ maturity towards risk management. The developed methodology is applied to the pharmacy department of a large Italian hospital. An extensive validation in different healthcare settings is required to fully prove benefits and limitations.

مقدمه انگلیسی

In recent years healthcare systems have been involved in a number of different changes, ranging from technological to normative ones, all asking for increased efficiency. In addition, the biomedical progress in the last decades has contributed to raise the level of organizational complexity in hospitals, which is given by many different factors, such as multiple professional experiences, non-uniform management models, patient specificity, surgery complexity, reduced inpatient days, and a growing number of healthcare service users due to an increase in average lifetime. As a result, medicine complexity, driven by innovations in both science and technology, stresses the need for new managerial models (Bridges, 2006). Thus, this context highlights the necessity to develop systemic approaches able to detect waste and errors and to suggest organizational and/or technological solutions for continuous improvement. To this end, following the success of the application of Kaizen principles to the manufacturing sector (Liker, 2004 and Liker and Hoseus, 2008), international healthcare organizations, such as The Joint Commission on Accreditation of Healthcare Organizations and The World Health Organization, have developed and adopted the concept of clinical governance. Clinical governance aims to ensure that patients receive the best quality of care. It includes systems and processes for monitoring and improving services, risk management, clinical audit, clinical effectiveness programs, staff management, education training and continuous personal development, and the use of information to support healthcare delivery (Sale, 2005). Among the different aspects of clinical governance, risk management is crucial since it addresses the clinical risk impacting on patients. Literature shows that clinical risk management does not always take a systemic perspective. Moreover, it does often not rely on the understanding of people acting in the investigated processes, nor gives it a valuable support to decision making. This paper operationalizes Reason’s theory of failures by developing a methodology to investigate healthcare processes and related risks impacting either directly or indirectly on patients. The work provides a systemic approach based on expert knowledge and able to sustain continuous improvement. With the purpose of explaining how it works, the methodology is applied to the pharmacy department of a large hospital. However, more case studies are needed to completely assess the relevance of the framework to the healthcare sector. The paper is organized as follows: Section 2 highlights the need for a systemic perspective on healthcare risk and presents Reason’s theory of latent failures. Section 3 discusses the importance of errors to clinical risk, as well as the features characterizing a successful methodology for managing it. The proposed methodology and its application are presented in Section 4 and Section 5 respectively. Benefits and limitations of the approach, together with future research lines, are discussed in Section 6.

نتیجه گیری انگلیسی

Based on the results of its application to the drug management process at a hospital pharmacy, strengths and weaknesses of the developed approach to clinical risk management are here discussed. First of all, the systemic feature of the suggested methodology is assured by the adoption of the RBM. The RBM frames all risk sources into the specific activities characterizing the process at issue. Furthermore, it gives a global view of criticalities, making it easy to define correlations among different failure modes in order to trace at the root all the determinants of adverse events. This is crucial in healthcare since the occurring of an adverse event that may hurt hospitalized patients is often linked to multiple interrelated failure modes giving rise to a failure mode chain. For example, in the drug management process, the administration of a wrong medicine may be due to a picking error by the pharmacy operator that has not been detected before the drug arrives at the patient’s bed (Hollnagel, 2004). The systemic perspective of the RBM enhances the effectiveness of FMEA because it supports a more comprehensive understanding of the relationships between causes and effects of failure modes. Furthermore, the RBM provides not only a systemic but also a schematic representation of criticalities, thus making the proposed methodology a valid communication tool for organizational members. In addition, the methodology revealed to be extremely flexible since it is able to work at different levels of detail according to the specific case and the information available. In the developed clinical risk management approach, first process criticalities are identified by means of a reactive analysis based on past adverse events. Usually, such events have not been recorded, thus expert knowledge elicitation is used to encourage the emergence of process actors’ experience about inefficiencies and ineffectiveness. As a further step, thanks to the mapping of the discrepancies in the system barriers (failure modes and kinds of waste), the RBM methodology, integrated with FMEA and waste analysis, is able to make operators aware of both risks and waste existing in a healthcare process. Therefore, in a sense, the proposed methodology also constitutes a valid tool for stimulating a structured analysis of criticalities, which is absolutely important in a highly human-based context like the healthcare one. Moreover, the present framework could support decision makers in setting correct priority areas for intervention and may be a part of Health Technology Assessment programs. This is guaranteed by the identification of improvement actions in the last step of the method. Finally, the developed clinical risk management method may be applied overtime to review the effectiveness of the implementation of corrective actions to limit risks and waste. To this end, the RBM and FMEA and Waste tables will be updated, and, if necessary, new corrective actions will be developed and adopted. As a consequence, the RBM and FMEA and Waste tables also prove to be useful means of communication among people involved in the improvement process. The implementation of the methodology in the case hospital revealed great difficulty in gathering all the pieces of information necessary to fully apply the four steps, due to a scarce aptitude for risk management and, as a consequence, for supporting such a comprehensive organizational analysis by both personnel and informational systems. As a matter of fact, this first application to the logistics process of a pharmacy department was limited to risk identification, without performing any quantitative evaluations. To this end, it stimulated an increase in the level of maturity towards risk of the studied organization, thus enabling future deeper analyses. Overall, the application of the proposed methodology may serve as a first step towards a deeper understanding of risk and waste in healthcare processes and the definition of the most appropriate measures to reduce them. It may be the foundation of a quantitative risk evaluation by numerically determining the probabilities of occurrence of risks as well as their impacts on process activities. However, in order to prove the full benefits and limitations of the suggested approach and understand if it requires further conceptual refinements, an extensive application to a variety of healthcare settings is needed. The flexibility of our methodology potentially allows the integration with risk management approaches already established in the healthcare sector, such as for instance Fault Tree Analysis (FTA), Hazard and Operability Study (HAZOP), and Incident Reporting (Armitage et al., 2007 and Lyons, 2009). These techniques may work at the level of single RBM cells by performing either qualitative (e.g. HAZOP, Incident Reporting) or quantitative analyses (e.g. Monte Carlo simulation), according to the availability of data and the degree of organizational maturity towards risk management. Also, multiple RBM cells may be considered in order to understand the root causes of a failure mode or of a kind of waste. FTA could be applied for this purpose, since it is not limited to the investigation of a single system but usually crosses system boundaries. To be more precise, FTA would break down the top event to find out the parallel and sequential combinations of basic faults responsible for it. To this end, the use of logical operators to link failure modes in FMEA tables is a first attempt to correlate different risky events. Moreover, the role that Key Performance Indicators (KPIs) could have in the approach as pre-warning signals anticipating the occurrence of adverse events should be investigated. In particular, RBM cells could be associated with proper metrics able to capture the impact of the symptoms of a risk source manifestation on the performance of a given activity. Although combing the mentioned approaches with our methodology increases the knowledge about the origins of patients’ exposure to risks and allows a better planning of proper countermeasures, it may require healthcare organizations additional efforts to develop new skills about the management of risk and safety. Nevertheless, we believe that this stream of research deserves future attention because it contributes to enhance the suitability of the methodology discussed in the paper for a variety of settings.

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