ObjectivesPatients in inpatient mental health settings face similar risks (eg, medication errors) to those in other areas of healthcare. In addition, some unsafe behaviours associated with serious mental health problems (eg, self-harm), and the measures taken to address these (eg, restraint), may result in further risks to patient safety. The objective of this review is to identify and synthesise the literature on patient safety within inpatient mental health settings using robust systematic methodology.DesignSystematic review and meta-synthesis. Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, MEDLINE, PsycINFO and Web of Science were systematically searched from 1999 to 2019. Search terms were related to ‘mental health’, ‘patient safety’, ‘inpatient setting’ and ‘research’. Study quality was assessed using the Hawker checklist. Data were extracted and grouped based on study focus and outcome. Safety incidents were meta-analysed where possible using a random-effects model.ResultsOf the 57 637 article titles and abstracts, 364 met inclusion criteria. Included publications came from 31 countries and included data from over 150 000 participants. Study quality varied and statistical heterogeneity was high. Ten research categories were identified: interpersonal violence, coercive interventions, safety culture, harm to self, safety of the physical environment, medication safety, unauthorised leave, clinical decision making, falls and infection prevention and control.ConclusionsPatient safety in inpatient mental health settings is under-researched in comparison to other non-mental health inpatient settings. Findings demonstrate that inpatient mental health settings pose unique challenges for patient safety, which require investment in research, policy development, and translation into clinical practice.PROSPERO registration numberCRD42016034057.

ObjectivePhysical 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.DesignSemistructured 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 participantsAcademic and service user experts from the USA, UK, Switzerland, Netherlands, Ireland, Denmark, Finland, Germany, Sweden, Australia, New Zealand and Singapore were included.Main outcome measuresAgreement in research priorities on a five-point scale.ResultsSeventy-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.ConclusionsThis 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.

Background During the pandemic, remote consultations have become the norm for assessing patients with signs and symptoms of COVID-19 to decrease the risk of transmission. This has intensified the clinical uncertainty already experienced by primary care clinicians when assessing patients with suspected COVID-19 and has prompted the use of risk prediction scores, such as the National Early Warning Score (NEWS2), to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and are not designed to capture the idiosyncrasies of COVID-19 infection. Objective The objective of this study is to produce a multivariate risk prediction tool, RECAP-V1 (Remote COVID-19 Assessment in Primary Care), to support primary care clinicians in the identification of those patients with COVID-19 that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes. Methods The study follows a prospective cohort observational design, whereby patients presenting in primary care with signs and symptoms suggestive of COVID-19 will be followed and their data linked to hospital outcomes (hospital admission and death). Data collection will be carried out by primary care clinicians in four arms: North West London Clinical Commissioning Groups (NWL CCGs), Oxford-Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC), Covid Clinical Assessment Service (CCAS), and South East London CCGs (Doctaly platform). The study involves the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with disease outcome according to previous qualitative work. Data collected will be linked to patient outcomes in highly secure environments. We will then use multivariate logistic regression analyses for model development and validation. Results Recruitment of participants started in October 2020. Initially, only the NWL CCGs and RCGP RSC arms were active. As of March 24, 2021, we have recruited a combined sample of 3827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting the recruitment process on March 15, 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCGs and RCGP RSC combined data set. Posteriorly, the model will be validated with the rest of the NWL CCGs and RCGP RSC data as well as the CCAS and Doctaly data sets. The study was approved by the Research Ethics Committee on May 27, 2020 (Integrated Research Application System number: 283024, Research Ethics Committee reference number: 20/NW/0266) and badged as National Institute of Health Research Urgent Public Health Study on October 14, 2020. Conclusions We believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of severity in patients with suspected COVID-19 in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes. Trial Registration ISRCTN registry ISRCTN13953727; https://www.isrctn.com/ISRCTN13953727 International Registered Report Identifier (IRRID) DERR1-10.2196/29072

BackgroundDespite 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.MethodsTo 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.DiscussionWe 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 registrationPROSPERO CRD42016034057 Electronic supplementary materialThe online version of this article (doi:10.1186/s13643-016-0365-7) contains supplementary material, which is available to authorized users.

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 hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This 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 the risk of deterioration and hospitalization. Methods After the data have 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 regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection. Results As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform. Conclusions 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 prioritize COVID-19 patients. Trial Registration ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041 International Registered Report Identifier (IRRID) DERR1-10.2196/30083

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. CLINICALTRIAL Trial registration number: NCT04435041

BACKGROUND During the pandemic, remote consultations have become the norm for the assessment of patients with signs and symptoms of COVID-19 in order to decrease the risk of transmission. This has added to the already existing challenges experienced by primary care clinicians when assessing suspected COVID-19 patients due to the uncertainty around disease progression (e.g., risk of deterioration around the 8th day of disease) and has prompted the use of risk prediction scores, such as NEWS2, to assess severity and guide treatment. However, the risk prediction tools available have not been validated in a community setting and have not been designed to capture the idiosyncrasy of COVID-19 infection. OBJECTIVE The objective of this study is to produce a multivariate risk prediction tool (RECAP–V1) to support primary care clinicians in the identification of those COVID-19 patients that are at higher risk of deterioration and facilitate the early escalation of their treatment with the aim of improving patient outcomes. METHODS The study follows a prospective cohort observational design, whereby patients presenting in primary or community care with signs and symptoms suggestive of COVID-19 will be followed and their data linked with hospital outcomes (hospital admission, intensive care unit admission and death). The collection of the primary data for the model will be carried out by primary care clinicians in four arms, i.e., North West London Clinical Commissioning Groups (NWL CCG), Royal College of General Practitioners Research and Surveillance Centre (RCGP RSC), Covid Clinical Assessment Service (CCAS) and South East London CCGs (Doctaly platform), and will involve the use of an electronic template that incorporates a list of items (known as RECAP-V0) thought to be associated with worse disease outcome according to previous qualitative work.. This data will be linked to patient outcomes in highly secure environments (iCARE and ORCHID secure environments). We will then use multivariate logistic regression analyses for model development and validation. RESULTS Recruitment of participants started in October 2021. Initially, only NWL CCGs and RCGP RSC arms were active. As of 24th of March 2021, we have recruited a combined sample of 3,827 participants in these two arms. CCAS and Doctaly joined the study in February 2021, with CCAS starting recruitment process on the 15th of March 2021. The first part of the analysis (RECAP-V1 model development) is planned to start in April 2021 using the first half of the NWL CCG and RCGP RSC combined datasets. Posteriorly, the model will be validated with the rest of NWL CCG and RCGP RSC data as well as CCAS and Doctaly datasets. The study was approved by the Research Ethics Committee on the 27th of May 2020 (IRAS number 283024, REC reference number: 20/NW/0266) and badged as NIHR Urgent Public Health Study on 14th of October 2020. CONCLUSIONS We believe the validated RECAP-V1 early warning score will be a valuable tool for the assessment of suspected COVID-19 patients’ severity in the community, either in face-to-face or remote consultations, and will facilitate the timely escalation of treatment with the potential to improve patient outcomes. CLINICALTRIAL ISRCTN registry (ISRCTN13953727)