Good risk assessment practice in hospitals

Risk assessment is essential to ensure safety in hospitals. However, hospitals have paid little attention to risk assessment. Several problems have already been identified in the literature about current risk assessment practice, such as inadequate risk assessment guidance and bias in risk scoring. This research aimed to improve current risk assessment practice in hospitals in the National Health Service (NHS) in England. To address this aim, the research investigated current risk assessment practice and designed a new risk assessment approach by the use of mixed methods. One hundred hospitals’ risk assessment documents were reviewed to examine the current recommended risk assessment practice. Seventeen interviews and sixty-one questionnaires were conducted, a risk management system from a single hospital was reviewed, and strategic risks from thirty-four hospitals were reviewed, in order to examine how risks are assessed in actual practice. Following that, the proposed approach was designed by conducting requirements analysis and then evaluated by interviews and questionnaires with ten healthcare staff. The findings of this research reveal that hospitals conduct risk assessments in different ways (i.e. with a focus on individual patient-based, operational and strategic risks). There are also many problems involved in current risk assessment practice regarding both the foundations and use of risk assessment. For example, organisation-wide risk assessments predominantly rely on risk matrices which might lead to wrong risk prioritisation and resource allocation; and risks tend to reflect existing or past problems rather than being proactive. All these reveal a need to improve current risk assessment practice. This research makes an important contribution to the current understanding of risk assessment practice in hospitals by providing extensive evidence on both recommended and actual practice, and proposes a new risk assessment framework. The framework guides healthcare staff on how to conduct risk assessment in a more comprehensive way by encouraging its potential users to consider good risk assessment practice.The Ministry of National Education, Republic of Turke

Exploring the impact of safety culture on incident reporting: lessons learned from machine learning analysis of NHS England staff survey and incident data

Safety culture is one of the key factors contributing to safety, even though limited evidence supports its impact on safety outcomes. This study uses supervised machine learning algorithms to explore the association between safety culture and incident reporting. The study used National Health Service (NHS) England annual staff survey data as a proxy of safety culture to predict eighteen incident reporting variables. The study did not achieve high accuracy rates in the prediction models. The highest association was found between safety culture and the number of incidents reported in class low, medium and high. LightGBM was the best-performed algorithm. SHAP plots were used to explain the model. Findings suggest that compassionate culture, violence and harassment and work pressure are critical in predicting the number of incidents reported. More specifically, the violence and harassment had a more significant impact on predicting the number of incidents reported in class high than in class medium and low. The involvement had more effect on predicting class low. The results demonstrated different behaviours in predicting different incident reporting classes. The findings facilitate lessons learned from staff surveys and incident reporting data in NHS England. Consequently, the findings can contribute to improving the safety culture in hospitals

Evaluating inputs of failure modes and effects analysis in identifying patient safety risks.

PURPOSE: There is a growing awareness on the use of systems approaches to improve patient safety and quality. While earlier studies evaluated the validity of such approaches to identify and mitigate patient safety risks, so far only little attention has been given to their inputs, such as structured brainstorming and use of system mapping approaches (SMAs), to understand their impact in the risk identification process. To address this gap, the purpose of this paper is to evaluate the inputs of a well-known systems approach, failure modes and effects analysis (FMEA), in identifying patient safety risks in a real healthcare setting. DESIGN/METHODOLOGY/APPROACH: This study was conducted in a newly established adult attention deficit hyperactivity disorder service at Cambridge and Peterborough Foundation Trust in the UK. Three stakeholders of the chosen service together with the facilitators conducted an FMEA exercise along with a particular system diagram that was initially found as the most useful SMA by eight stakeholders of the service. FINDINGS: In this study, it was found that the formal structure of FMEA adds value to the risk identification process through comprehensive system coverage with the help of the system diagram. However, results also indicates that the structured brainstorming refrains FMEA participants from identifying and imagining new risks since they follow the process predefined in the given system diagram. ORIGINALITY/VALUE: While this study shows the potential contribution of FMEA inputs, it also suggests that healthcare organisations should not depend solely on FMEA results when identifying patient safety risks; and therefore prioritising their safety concerns

A comparison of machine learning algorithms in predicting COVID-19 prognostics

ML algorithms are used to develop prognostic and diagnostic models and so to support clinical decision-making. This study uses eight supervised ML algorithms to predict the need for intensive care, intubation, and mortality risk for COVID-19 patients. The study uses two datasets: (1) patient demographics and clinical data (n = 11,712), and (2) patient demographics, clinical data, and blood test results (n = 602) for developing the prediction models, understanding the most significant features, and comparing the performances of eight different ML algorithms. Experimental findings showed that all prognostic prediction models reported an AUROC value of over 0.92, in which extra tree and CatBoost classifiers were often outperformed (AUROC over 0.94). The findings revealed that the features of C-reactive protein, the ratio of lymphocytes, lactic acid, and serum calcium have a substantial impact on COVID-19 prognostic predictions. This study provides evidence of the value of tree-based supervised ML algorithms for predicting prognosis in health care

What kinds of insights do Safety-I and Safety-II approaches provide? a critical reflection on the use of SHERPA and FRAM in healthcare

Over the past decade, the field of healthcare has seen a significant shift in its approach to patient safety. Traditionally, safety efforts focused on understanding past harm and preventing errors, primarily through the use of standardisation and the introduction of barriers and safeguards, such as standardised communication protocols (e.g., SBAR (Haig et al., 2006)), checklists (e.g., WHO surgical safety checklist (Haynes et al., 2009)) and technology with safety features (e.g., smart infusion pumps (Taxis and Franklin, 2011)). This type of thinking about patient safety in terms of past harm and errors is also referred to as Safety-I (Hollnagel, 2014), even though this terminology has been criticised as it does not reflect adequately the diversity in safety science thinking (Leveson, 2020). However, the evidence for whether interventions based on this (Safety-I) thinking lead to improvements in patient safety is mixed at best (Kellogg et al., 2017, Wears and Sutcliffe, 2019), and critics have argued that the additional “safety clutter” produced as a result of such interventions might be counterproductive (Rae et al., 2018, Halligan et al., 2023).This work was funded by the National Institute for Health Research (NIHR) [Programme Grant for Applied Research NIHR200868

Systems approach to health service design, delivery and improvement: a systematic review and meta-analysis.

OBJECTIVES: To systematically review the evidence base for a systems approach to healthcare design, delivery or improvement. DESIGN: Systematic review with meta-analyses. METHODS: Included were studies in any patients, in any healthcare setting where a systems approach was compared with usual care which reported quantitative results for any outcomes for both groups. We searched Medline, Embase, HMIC, Health Business Elite, Web of Science, Scopus, PsycINFO and CINAHL from inception to 28 May 2019 for relevant studies. These were screened, and data extracted independently and in duplicate. Study outcomes were stratified by study design and whether they reported patient and/or service outcomes. Meta-analysis was conducted with Revman software V.5.3 using ORs-heterogeneity was assessed using I2 statistics. RESULTS: Of 11 405 records 35 studies were included, of which 28 (80%) were before-and-after design only, five were both before-and-after and concurrent design, and two were randomised controlled trials (RCTs). There was heterogeneity of interventions and wide variation in reported outcome types. Almost all results showed health improvement where systems approaches were used. Study quality varied widely. Exploratory meta-analysis of these suggested favourable effects on both patient outcomes (n=14, OR=0.52 (95% CI 0.38 to 0.71) I2=91%), and service outcomes (n=18, OR=0.40 (95% CI 0.31 to 0.52) I2=97%). CONCLUSIONS: This study suggests that a systems approaches to healthcare design and delivery results in a statistically significant improvement to both patient and service outcomes. However, better quality studies, particularly RCTs are needed.PROSPERO registration numberCRD42017065920.Health Foundation, NIH