A systematic review of patient safety in mental health: a protocol based on the inpatient setting

BACKGROUND: Despite the growing international interest in patient safety as a discipline, there has been a lack of exploration of its application to mental health. It cannot be assumed that findings based upon physical health in acute care hospitals can be applied to mental health patients, disorders and settings. To the authors' knowledge, there has only been one review of the literature that focuses on patient safety research in mental health settings, conducted in Canada in 2008. We have identified a need to update this review and develop the methodology in order to strengthen the findings and disseminate internationally for advancement in the field. This systematic review will explore the existing research base on patient safety in mental health within the inpatient setting. METHODS: To conduct this systematic review, a thorough search across multiple databases will be undertaken, based upon four search facets ("mental health", "patient safety", "research" and "inpatient setting"). The search strategy has been developed based upon the Canadian review accompanied with input from the National Reporting and Learning System (NRLS) taxonomy of patient safety incidents and the Diagnostic and Statistical Manual of Mental Disorders (fifth edition). The screening process will involve perspectives from at least two researchers at all stages with a third researcher invited to review when discrepancies require resolution. Initial inclusion and exclusion criteria have been developed and will be refined iteratively throughout the process. Quality assessment and data extraction of included articles will be conducted by at least two researchers. A data extraction form will be developed, piloted and iterated as necessary in accordance with the research question. Extracted information will be analysed thematically. DISCUSSION: We believe that this systematic review will make a significant contribution to the advancement of patient safety in mental health inpatient settings. The findings will enable the development and implementation of interventions to improve the quality of care experienced by patients and support the identification of future research priorities. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016034057

A systematic review of Patient Safety in Mental Health: a protocol based on the Inpatient Setting

Background Despite the growing international interest in patient safety as a discipline, there has been a lack of exploration of its application to mental health. It cannot be assumed that findings based upon physical health in acute care hospitals can be applied to mental health patients, disorders and settings. To the authors’ knowledge, there has only been one review of the literature that focuses on patient safety research in mental health settings, conducted in Canada in 2008. We have identified a need to update this review and develop the methodology in order to strengthen the findings and disseminate internationally for advancement in the field. This systematic review will explore the existing research base on patient safety in mental health within the inpatient setting. Methods To conduct this systematic review, a thorough search across multiple databases will be undertaken, based upon four search facets (“mental health”, “patient safety”, “research” and “inpatient setting”). The search strategy has been developed based upon the Canadian review accompanied with input from the National Reporting and Learning System (NRLS) taxonomy of patient safety incidents and the Diagnostic and Statistical Manual of Mental Disorders (5th edition). The screening process will involve perspectives from at least two researchers at all stages with a third researcher invited to review when discrepancies require resolution. Initial inclusion and exclusion criteria have been developed and will be refined iteratively throughout the process. Quality assessment and data extraction of included articles will be conducted by at least two researchers. A data extraction form will be developed, piloted and iterated as necessary in accordance with the research question. Extracted information will be analysed thematically. Discussion We believe that this systematic review will make a significant contribution to the advancement of patient safety in mental health inpatient settings. The findings will enable the development and implementation of interventions to improve the quality of care experienced by patients and support the identification of future research priorities. Registration This systematic review was registered to the PROSPERO database on the 15th February 2016 (registration number CRD42016034057) and updated on 26th April 2016

Barriers and facilitators to incident reporting in mental healthcare settings: a qualitative study

Introduction: Barriers and facilitators to incident reporting have been widely researched in general healthcare. However, it is unclear if the findings are applicable to mental healthcare where care is increasingly complex. // Aim: To investigate if barriers and facilitators affecting incident reporting in mental healthcare are consistent with factors identified in other healthcare settings. // Method: Data were collected from focus groups (n=8) with 52 members of staff from across West London Trust and analysed with thematic analysis. Results: Five themes were identified during the analysis. Three themes (i)learning and improvement, (ii)time, and (iii)fear were consistent with the existing wider literature on barriers and facilitators to incident reporting. Two further themes (iv)interaction between patient diagnosis and incidents and (v)aftermath of an incident – prosecution specifically linked to the provision of mental healthcare. // Conclusions: Whilst some barriers and facilitators to incident reporting identified in other settings are also prevalent in the mental healthcare setting, the increased incidence of violent and aggressive behaviour within mental healthcare presents a unique challenge for incident reporting. // Clinical Implications: Although Interventions to improve incident reporting may be adapted/adopted from other settings, there is a need to develop specific interventions to improve reporting of violent and aggressive incidents

An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan

Background: Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective: We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death. Methods: After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets to assess both its performance in different patient groups, and applicability for different methods of data collection. Results: As of 5th of May 2021 we have recruited 2280 patients for the main dataset for model development, as well as a further 1741 patients for the validation dataset. Final analysis will commence as soon as data for 2880 are collected. Conclusions: We believe that the methodology for the development of the RECAP V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritise Covid-19 patients. Clinical Trial: Trial registration number: NCT0443504

Barriers and facilitators to incident reporting in mental healthcare settings: a qualitative study

INTRODUCTION: Barriers and facilitators to incident reporting have been widely researched in general healthcare. However, it is unclear if the findings are applicable to mental healthcare where care is increasingly complex. AIM: To investigate if barriers and facilitators affecting incident reporting in mental healthcare are consistent with factors identified in other healthcare settings. METHOD: Data were collected from focus groups (n=8) with 52 members of staff from across [a large Mental Health] Trust and analysed with thematic analysis. RESULTS: Five themes were identified during the analysis. Three themes (i)learning and improvement, (ii)time, and (iii)fear were consistent with the existing wider literature on barriers and facilitators to incident reporting. Two further themes (iv)interaction between patient diagnosis and incidents and (v)aftermath of an incident - prosecution specifically linked to the provision of mental healthcare. CONCLUSIONS: Whilst some barriers and facilitators to incident reporting identified in other settings are also prevalent in the mental healthcare setting, the increased incidence of violent and aggressive behaviour within mental healthcare presents a unique challenge for incident reporting. CLINICAL IMPLICATIONS: Although Interventions to improve incident reporting may be adapted/adopted from other settings, there is a need to develop specific interventions to improve reporting of violent and aggressive incidents

Identifying research priorities for patient safety in mental health: an international expert Delphi study

Objective Physical healthcare has dominated the patient safety field; research in mental healthcare is not as extensive but findings from physical healthcare cannot be applied to mental healthcare because it delivers specialised care that faces unique challenges. Therefore, a clearer focus and recognition of patient safety in mental health as a distinct research area is still needed. The study aim is to identify future research priorities in the field of patient safety in mental health. Design Semistructured interviews were conducted with the experts to ascertain their views on research priorities in patient safety in mental health. A three-round online Delphi study was used to ascertain consensus on 117 research priority statements. Setting and participants Academic and service user experts from the USA, UK, Switzerland, Netherlands, Ireland, Denmark, Finland, Germany, Sweden, Australia, New Zealand and Singapore were included. Main outcome measures Agreement in research priorities on a five-point scale. Results Seventy-nine statements achieved consensus (>70%). Three out of the top six research priorities were patient driven; experts agreed that understanding the patient perspective on safety planning, on self-harm and on medication was important. Conclusions This is the first international Delphi study to identify research priorities in safety in the mental field as determined by expert academic and service user perspectives. A reasonable consensus was obtained from international perspectives on future research priorities in patient safety in mental health; however, the patient perspective on their mental healthcare is a priority. The research agenda for patient safety in mental health identified here should be informed by patient safety science more broadly and used to further establish this area as a priority in its own right. The safety of mental health patients must have parity with that of physical health patients to achieve this

Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies

BACKGROUND: Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO2]) and RECAP-oxygen saturation score (RECAP-O2; with SpO2). METHODS: RECAP was a prospective cohort study that used multivariable logistic regression. Data on signs and symptoms (predictors) of disease were collected from community-based patients with suspected COVID-19 via primary care electronic health records and linked with secondary data on hospital admission (outcome) within 28 days of symptom onset. Data sources for RECAP-GP were Oxford-Royal College of General Practitioners Research and Surveillance Centre (RCGP-RSC) primary care practices (development set), northwest London primary care practices (validation set), and the NHS COVID-19 Clinical Assessment Service (CCAS; validation set). The data source for RECAP-O2 was the Doctaly Assist platform (development set and validation set in subsequent sample). The two probabilistic risk prediction models were built by backwards elimination using the development sets and validated by application to the validation datasets. Estimated sample size per model, including the development and validation sets was 2880 people. FINDINGS: Data were available from 8311 individuals. Observations, such as SpO2, were mostly missing in the northwest London, RCGP-RSC, and CCAS data; however, SpO2 was available for 1364 (70·0%) of 1948 patients who used Doctaly. In the final predictive models, RECAP-GP (n=1863) included sex (male and female), age (years), degree of breathlessness (three point scale), temperature symptoms (two point scale), and presence of hypertension (yes or no); the area under the curve was 0·80 (95% CI 0·76-0·85) and on validation the negative predictive value of a low risk designation was 99% (95% CI 98·1-99·2; 1435 of 1453). RECAP-O2 included age (years), degree of breathlessness (two point scale), fatigue (two point scale), and SpO2 at rest (as a percentage); the area under the curve was 0·84 (0·78-0·90) and on validation the negative predictive value of low risk designation was 99% (95% CI 98·9-99·7; 1176 of 1183). INTERPRETATION: Both RECAP models are valid tools to assess COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored and SpO2 is available, RECAP-O2 is useful to assess the need for treatment escalation. FUNDING: Community Jameel and the Imperial College President's Excellence Fund, the Economic and Social Research Council, UK Research and Innovation, and Health Data Research UK

Remote covid assessment in primary care (RECAP) risk prediction tool: derivation and real-world validation studies

AbstractBackgroundAccurate assessment of COVID-19 severity in the community is essential for best patient care and efficient use of services and requires a risk prediction score that is COVID-19 specific and adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms and risk factors, we sought to develop and validate two COVID-19-specific risk prediction scores RECAP-GP (without peripheral oxygen saturation (SpO2)) and RECAP-O2 (with SpO2).MethodsProspective cohort study using multivariable logistic regression for model development. Data on signs and symptoms (model predictors) were collected on community-based patients with suspected COVID-19 via primary care electronic health records systems and linked with secondary data on hospital admission (primary outcome) within 28 days of symptom onset. Data sources: RECAP-GP: Oxford-Royal College of General Practitioners Research and Surveillance Centre (RSC) primary care practices (development), Northwest London (NWL) primary care practices, NHS COVID-19 Clinical Assessment Service (CCAS) (validation). RECAP-O2: Doctaly Assist platform (development, and validation in subsequent sample). Estimated sample size was 2,880 per model.FindingsData were available from 8,311 individuals. Observations, such SpO2, were mostly missing in NWL, RSC, and CCAS data; however, SpO2 was available for around 70% of Doctaly patients. In the final predictive models, RECAP-GP included sex, age, degree of breathlessness, temperature symptoms, and presence of hypertension (Area Under the Curve (AUC): 0.802, Validation Negative Predictive Value (NPV) of ‘low risk’ 98.8%. RECAP-O2 included age, degree of breathlessness, fatigue, and SpO2 at rest (AUC: 0.843), Validation NPV of ‘low risk’ 99.4%.InterpretationBoth RECAP models are a valid tool in the assessment of COVID-19 patients in the community. RECAP-GP can be used initially, without need for observations, to identify patients who require monitoring. If the patient is monitored at home and SpO2 is available, RECAP-O2 is useful to assess the need for further treatment escalation.Research in context panelEvidence before the studyThis study was conceived during the first COVID-19 wave in the UK (March - April 2020), as members of the research team contributed to the development of national clinical guidelines for COVID-19 management in the community and to the Oxford COVID-19 rapid review to track signs and symptoms of COVID-19 internationally. The review was carried out according to Cochrane Collaboration standards for rapid reviews and identified systematic reviews and large-scale observational studies describing the signs and symptoms of COVID-19. Evidence gathered showed worsening of COVID-19 symptoms around the 7th day of disease and challenges in identifying patients with higher likelihood of severity to increase their monitoring. To this end, tools such NEWS2 have been used in the UK to assess COVID-19 patients in primary care, but they do not capture the characteristics of COVID-19 infection and/or are not suitable for community remote assessment. Several COVID-19 risk scores have been developed. QCOVID provides a risk of mortality considering patients’ existing risk factors but does not include acute signs and symptoms. ISARIC 4C Deterioration model has been specifically developed for hospital settings. In England, the NHS has implemented the Oximetry @home strategy to monitor patients with acute COVID-19 deemed at risk (older than 64 years old or with comorbidities) by providing pulse oximeters; however, the criteria for monitoring or for escalation of care have not been validated. There is, therefore, the need to develop a risk prediction score to establish COVID-19 patients’ risk of deterioration to be used in the community for both face to face or remote consultation.Added value of this studyWe developed and validated two COVID-19 specific risk prediction scores. One to be used in the initial remote assessment of patients with acute COVID-19 to assess need for monitoring (RECAP-GP). The second one to assess the need for further treatment escalation and includes peripheral saturation of oxygen among the model predictors (RECAP-O2). To our knowledge, this is the first COVID-19 specific risk prediction score to assess and monitor COVID-19 patients’ risk of deterioration remotely. This will be a valuable resource to complement the use of oximetry in the community clinical decision-making when assessing a patient with acute COVID-19.Implications of all available evidenceTo manage pandemic waves and their demand on healthcare, acute COVID-19 patients require close monitoring in the community and prompt escalation of their treatment. Guidance available so far relies on unvalidated tools and clinician judgement to assess deterioration. COVID-19 specific community-based risk prediction scores such as RECAP may contribute to reducing the uncertainty in the assessment and monitoring of COVID-19 patients, increase safety in clinical practice and improve outcomes by facilitating appropriate treatment escalation.</jats:sec