Use of Failure Mode and Effects Analysis to Improve Emergency Department Handoff Processes

Using Failure Mode and Effect Analysis to Identify Potential Failures in a Psychiatric Hospital Emergency Department

OBJECTIVES

Failure mode and effect analysis (FMEA) is a powerful tool for accessing potential failures, but the participants are limited. It has not been used in psychiatric hospitals. Objectives were to implement FMEA in a psychiatric hospital and determine whether the FMEA process can be expanded by including participants who are familiar with the emergency department (ED) admission process and those who are not.

METHODS

In this prospective, questionnaire-based study, a multidisciplinary team experienced in ED admissions was trained in FMEA and determined potential failures in the process. They developed a questionnaire regarding the failures, which were ranked by 17 ED and 28 non-ED healthcare providers. Risk priority numbers were calculated for each.

RESULTS

By applying FMEA, we found 6 steps of the ED admission process, with 32 potential failures. Risk priority numbers ranged from 91 to 225. The most notable potential failure identified was during a patient's initial telephone call to the ED, before arrival. Emergency department and non-ED workers ranked 94% of the potential failures similarly.

CONCLUSIONS

Failure mode and effect analysis can be implemented in psychiatric hospitals and can be a useful tool for anticipating potential failures. The number of participants in an FMEA can be increased to include those who are not directly involved in the process and should involve several specialists from diverse fields. Increasing the number of participants allows more detailed analyses. A checklist detailing the actions to take when processing a patient's initial phone call should be implemented to decrease hazards related to ED admissions.

Evaluating Patient Identification Practices During Intrahospital Transfers: A Human Factors Approach

INTRODUCTION

Reliable patient identification is essential for safe care, and failures may cause patient harm. Identification can be interfered with by system factors, including working conditions, technology, organizational barriers, and inadequate communications protocols. The study aims to explore systems factors contributing to patient identification errors during intrahospital transfers.

METHODS

We conducted a qualitative study through direct observation and interviews with porters during intrahospital patient transfers. Data were analyzed using the Systems Engineering Initiative for Patient Safety human factors model. The patient transfer process was mapped and compared with the institutional Positive Patient Identification policy. Potential system failures were identified using a Failure Modes and Effects Analysis.

RESULTS

A total of 60 patient transfer handovers were observed. In none of the evaluable cases observed, patient identification was conducted correctly according to the hospital policy at every step of the process. The principal system factor responsible was organizational failure, followed by technology and team culture issues. The Failure Modes and Effects Analysis methodology revealed that miscommunication between staff and lack of key patient information put patient safety at risk.

CONCLUSIONS

Patient identification during intrahospital patient transfer is a high-risk event because several factors and many people interact. In this study, the disconnect between the policy and the reality of the workplace left staff and patients vulnerable to the consequences of misidentification. Where a policy is known to be substantially different from work as done, urgent revision is required to eliminate the serious risks associated with the unguided evolution of working practice.

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

Process mapping in healthcare: a systematic review

INTRODUCTION

Process mapping (PM) supports better understanding of complex systems and adaptation of improvement interventions to their local context. However, there is little research on its use in healthcare. This study (i) proposes a conceptual framework outlining quality criteria to guide the effective implementation, evaluation and reporting of PM in healthcare; (ii) reviews published PM cases to identify context and quality of PM application, and the reported benefits of using PM in healthcare.

METHODS

We developed the conceptual framework by reviewing methodological guidance on PM and empirical literature on its use in healthcare improvement interventions. We conducted a systematic review of empirical literature using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. Inclusion criteria were: full text empirical study; describing the process through which PM has been applied in a healthcare setting; published in English. Databases searched are: Medline, Embase, HMIC-Health Management Information Consortium, CINAHL-Cumulative Index to Nursing and Allied Health Literature, Scopus. Two independent reviewers extracted and analysed data. Each manuscript underwent line by line coding. The conceptual framework was used to evaluate adherence of empirical studies to the identified PM quality criteria. Context in which PM is used and benefits of using PM were coded using an inductive thematic analysis approach.

RESULTS

The framework outlines quality criteria for each PM phase: (i) preparation, planning and process identification, (ii) data and information gathering, (iii) process map generation, (iv) analysis, (v) taking it forward. PM is used in a variety of settings and approaches to improvement. None of the reviewed studies (N = 105) met all ten quality criteria; 7% were compliant with 8/10 or 9/10 criteria. 45% of studies reported that PM was generated through multi-professional meetings and 15% reported patient involvement. Studies highlighted the value of PM in navigating the complexity characterising healthcare improvement interventions.

CONCLUSION

The full potential of PM is inhibited by variance in reporting and poor adherence to underpinning principles. Greater rigour in the application of the method is required. We encourage the use and further development of the proposed framework to support training, application and reporting of PM.

TRIAL REGISTRATION

Prospero ID: CRD42017082140.

Use of Failure Mode and Effect Analysis to Reduce Admission of Asymptomatic COVID-19 Patients to the Adult Emergency Department: An Institutional Experience

BACKGROUND

Failure mode and effect analysis is an important tool to identify failures in a system with its possible cause, effect, and set actions to be implemented proactively before the occurrence of problems. This study tries to identify common failure modes with its possible causes and effect to the health service and to plot actions to be implemented to reduce COVID-19 transmission to clients, staff, and subsequent service compromise from asymptomatic COVID-19 patients visiting the adult emergency department of SPHMMC (non-COVID-19 setup).

METHOD AND STUDY DESIGN

A multidisciplinary team, representing different divisions of the adult emergency department at St. Paul's Hospital Millennium Medical College (SPHMMC), was chosen. This team was trained on failure mode and effect analysis and basics of COVID-19, to identify possible causes of failures and their potential effects, to calculate a risk priority number (RPN) for each failure, and plan changes in practice.

RESULTS

A total of 22 failure modes and 89 associated causes and effects were identified. Many of these failure modes (12 out of 22) were found in all steps of patient flow and were associated with either due to lack of or failure to apply standard and transmission-based precautions. This suggests the presence of common targets for improvement, particularly in enhancing the safety of staff and clients. As a result of this FMEA, 23 general improvement actions were proposed.

CONCLUSION

FMEA can be used as a useful tool for anticipating potential failures in the process and proposing improvement actions that could help in reducing secondary transmissions during the pandemic.

Ferramentas para a organização do processo de trabalho na segurança do paciente

RESUMO Objetivo discutir acerca da utilização das ferramentas de Análise de Modo e Efeitos de Falha e sua aplicação na assistência à saúde. Método trata-se de um artigo de reflexão visando à apresentação do formato próprio de aplicação de ambas as ferramentas seguida das suas diferenças de execução nos processos de trabalho. Resultados ambos os modelos possuem a mesma finalidade, sendo direcionados para a detecção de falhas antes mesmo da sua manifestação, auxiliando diretamente na promoção da segurança. A análise do erro, com a participação das equipes e a geração de índices de falhas, repercute no planejamento e na implementação de ações práticas voltadas à segurança do paciente. Conclusão e implicações para a prática embora semelhantes, existem, entre eles, distinções quanto à priorização das falhas para elencar ações práticas corretivas, principalmente no cálculo do Índice de Prioridade de Risco relacionado à gravidade, na probabilidade de ocorrência e na detecção das falhas. Ambas as ferramentas se mostram como importantes aliadas dos gestores de saúde para a detecção de falhas graves que colocam em risco a assistência livre de eventos adversos.

Healthcare Failure Mode and Effect Analysis (HFMEA) as an Effective Mechanism in Preventing Infection Caused by Accompanying Caregivers During COVID-19-Experience of a City Medical Center in Taiwan

BACKGROUND AND OBJECTIVES

In response to the COVID-19 pandemic outbreak and to ensure the safety of epidemic prevention in the hospital, the hospital has established mitigation strategies in advance including risk assessment and effect analysis to control hospital visitors and accompanying persons. The study aims to assess the effectiveness of mitigation strategies implemented to effectively prevent the invasion and spread of the virus.

METHOD

Conduct a status analysis in accordance with the Healthcare Failure Mode and Effect Analysis (HFMEA) 4-step model, construct a response workflow, confirm the failure mode and potential causes, perform hazard matrix analysis and decision tree analysis, and formulate risk control management measures.

RESULTS

For the 4 main processes and 9 subprocesses of the accompanying carers and contract caregivers entering the hospital, 26 potential failure modes and 42 potential causes of failure were analyzed. Following implementing improvement measures including strategies targeting the accompanying person, mitigation workflow failure rates decreased from 42 to 13 items, the pass rate for the maximum body temperature cutoff increased from 53.1% to 90.8%, and the compliance rate of hand washing increased from 89.5% to 100%.

CONCLUSION

The HFMEA model can effectively implement preventive risk assessment and workflow management of high-risk medical procedures. The model can adjudicate the health of hospital visitors during the epidemic/pandemic, provide epidemic/pandemic education training and preventive measure health education guidance for hospital visits, and improve their epidemic prevention cognition. When combined, these strategies can prevent nosocomial infection to achieve the best anti-epidemic effect.

Failure mode and effects analysis for proactive healthcare risk evaluation: A systematic literature review

RATIONALE, AIMS, AND OBJECTIVES

Failure mode and effects analysis (FMEA) is a valuable reliability management tool that can preemptively identify the potential failures of a system and assess their causes and effects, thereby preventing them from occurring. The use of FMEA in the healthcare setting has become increasingly popular over the last decade, being applied to a multitude of different areas. The objective of this study is to review comprehensively the literature regarding the application of FMEA for healthcare risk analysis.

METHODS

An extensive search was carried out in the scholarly databases of Scopus and PubMed, and we only chose the academic articles which used the FMEA technique to solve healthcare risk analysis problems. Furthermore, a bibliometric analysis was performed based on the number of citations, publication year, appeared journals, authors, and country of origin.

RESULTS

A total of 158 journal papers published over the period of 1998 to 2018 were extracted and reviewed. These publications were classified into four categories (ie, healthcare process, hospital management, hospital informatization, and medical equipment and production) according to the healthcare issues to be solved, and analyzed regarding the application fields and the utilized FMEA methods.

CONCLUSION

FMEA has high practicality for healthcare quality improvement and error reduction and has been prevalently employed to improve healthcare processes in hospitals. This research supports academics and practitioners in effectively adopting the FMEA tool to proactively reduce healthcare risks and increase patient safety, and provides an insight into its state-of-the-art.

Improving Patient Throughput With an Electronic Nursing Handoff Process in an Academic Medical Center: A Rapid Improvement Event Approach

OBJECTIVE

Rush University Medical Center nursing leadership undertook a process improvement project to revamp nursing handoff during unit transfer with the goal of improving patient throughput. The aim was to decrease assign-to-occupy time, the duration from bed assignment to bed occupancy.

BACKGROUND

There was a lengthy lag time in admitting/transferring patients, leading to delays in patient throughput and potential threats to patient safety. In fiscal year 2016, assign-to-occupy time averaged 97 minutes. The goal was to decrease that time to 60 minutes or less.

METHODS

Process improvement leaders held a rapid improvement event to determine viable solutions. A team then standardized handoff workflow; created an electronic tool, virtually eliminating verbal report; and implemented a new handoff process.

RESULTS

Assign-to-occupy time at 1 year after go-live averaged 55 minutes, and it has been staying less than 60 minutes since the implementation.

CONCLUSIONS

Key success strategies included engaging stakeholders during the rapid improvement event, imploring frontline nurses to create and promote the revised process to facilitate staff engagement, and leveraging electronic health records.

Improving Throughput for Patients Admitted From the Emergency Department: Implementation of a Standardized Report Process

BACKGROUND

Inefficient emergency department to inpatient handoff processes can contribute to delayed care.

LOCAL PROBLEM

The average emergency department length of stay for admitted patients and admission wait times at this institution were well above national averages, and a standard handoff process was lacking.

METHODS

Lean methodology was used to evaluate flow and identify opportunities for improvement.

INTERVENTIONS

Two tools were developed to standardize handoff.

RESULTS

Emergency department length of stay and admission wait times were not significantly improved following intervention implementation. However, patient transfer time decreased significantly (P < .01, F = 29.02) from 30.5 minutes (SD = 18.2) to 21.7 minutes (SD = 7.4). The length of time to give/receive report also decreased significantly (P = .04, F = 2.2) from 3.8 (SD = 1.6) minutes to 2.8 (SD = 1.2) minutes.

CONCLUSIONS

Although length of stay and admission wait times did not decline significantly, implementation of standard work and tools can potentially improve patient flow.

[Modal analysis of failures and effects in intra-hospital transfers]

OBJECTIVES

To identify gaps in patient safety during intra-hospital transfers.

MATERIAL AND METHODS

A working group was set up and patient transfers carried out in the different healthcare areas of a hospital were identified. Using the Modal Failure and Effects Analysis (FMEA), the risks of each failure mode identified were quantified using the Risk Prioritisation Index (RPI) and establishing improvement measures for all RPIs with scores greater than 100.

RESULTS

There were 31 critical points that could lead to failures / deficiencies in 20 types of transfers. A total of 35 safety improvement measures were proposed for the transfers in the different areas analysed.

CONCLUSIONS

The use of FMEA has made it possible to objectify the risks for patient safety during internal hospital transfers by providing information to prioritise improvement strategies.

A mixed-methods process evaluation of Family Navigation implementation for autism spectrum disorder

There is growing interest in Family Navigation as an approach to improving access to care for children with autism spectrum disorder, yet little data exist on the implementation of Family Navigation. The aim of this study was to identify potential failures in implementing Family Navigation for children with autism spectrum disorder, using a failure modes and effects analysis. This mixed-methods study was set within a randomized controlled trial testing the effectiveness of Family Navigation in reducing the time from screening to diagnosis and treatment for autism spectrum disorder across three states. Using standard failure modes and effects analysis methodology, experts in Family Navigation for autism spectrum disorder (n = 9) rated potential failures in implementation on a 10-point scale in three categories: likelihood of the failure occurring, likelihood of not detecting the failure, and severity of failure. Ratings were then used to create a risk priority number for each failure. The failure modes and effects analysis detected five areas for potential "high priority" failures in implementation: (1) setting up community-based services, (2) initial family meeting, (3) training, (4) fidelity monitoring, and (5) attending testing appointments. Reasons for failure included families not receptive, scheduling, and insufficient training time. The process with the highest risk profile was "setting up community-based services." Failure in "attending testing appointment" was rated as the most severe potential failure. A number of potential failures in Family Navigation implementation-along with strategies for mitigation-were identified. These data can guide those working to implement Family Navigation for children with autism spectrum disorder.