Objective Patients can provide a unique perspective on the safety of care in hospitals. Understanding that the extent to which the way hospitals are organized for quality and safety is associated with patient perceptions of care is becoming increasingly valued and necessary for the direction of targeted interventions across healthcare systems. The UK-developed patient measure of safety (PMOS) assesses eight domains of ward safety from the patient point of view and has recently been adapted and piloted in Australia. The aim of this study is to test the psychometric properties of PMOS-Australia (PMOS-A) amongst a large cohort of hospitalized patients. Design Cross-sectional questionnaire validation assessment. Setting and participants As part of the DUQuA project, the PMOS-A survey was distributed within acute myocardial infarction, hip fracture and stroke departments across 32 large public hospitals in Australia. Patients could complete the PMOS-A independently, or request the assistance of a family member/guardian, or staff on the wards—space was included to record mode of completion. Main outcome measures Confirmatory factor analysis (CFA) was undertaken on a calibration sample to generate the model, and a validation sample was used to cross-validate the model. A subset of only those participants who received assistance for PMOS-A completion was also tested using CFA on a calibration and validation sample. Model fit indices (chi-square to degrees of freedom ratio [Chi-square:DF], root mean square error of approximation [RMSEA], comparative fit indices [CFI], standardized root mean squared residual [SRMR]), Cronbach’s α, average inter-item correlations, construct reliability and cross-loadings were examined with reference to recommended thresholds to establish the extent of convergent validity and discriminant validity. A marker of criterion validity was assessed through testing associations between the PMOS-A and adherence to clinical guidelines. Results Across the calibration and validation samples of the full (N = 911) and assisted completers only subset (N = 490), three (Chi-square:DF, SRMR, RMSEA) of the four indices consistently or almost always met thresholds for acceptable model fit. CFI indices did not meet the recommended limits (0.72–0.78, against a target > 0.9). Positive relationships were found for all tests between PMOS-A and adherence to clinical guidelines, and these were significant when assessed in the calibration datasets for the full and assisted completion samples. Conclusion A sufficiently reliable and valid measure of patient perceptions of safety has been developed. These findings should provide adequate support to justify the use of this measure to assess patient perceptions of safety in Australian hospitals and can be modified for use elsewhere.

BackgroundThis retrospective process evaluation reports on the application of a 1-year implementation program to increase identification and management of patients at high risk of a hereditary cancer syndrome. The project used the Theoretical Domains Framework Implementation (TDFI) approach, a promising implementation methodology, used successfully in the United Kingdom to address patient safety issues. This Australian project run at two large public hospitals aimed to increase referrals of patients flagged as being at risk of Lynch syndrome on the basis of a screening test to genetic services. At the end of the project, the pathologists’ processes had changed, but the referral rate remained inconsistent and low.MethodsSemi-structured interviews explored participants’ perceptions of the TDFI approach and Health services researchers wrote structured reflections. Interview transcripts and reflections were coded initially against implementation outcomes for the various TDFI approach activities: acceptability, appropriateness, feasibility, value for time cost, and adoption. On a second pass, themes were coded around challenges to the approach.ResultsInterviews were held with nine key project participants including pathologists, oncologists, surgeons, genetic counsellors and an administrative officer. Two health services researchers wrote structured reflections. The first of two major themes was ‘Theory-related challenges’, with subthemes of accessibility of theory underpinning the TDFI, commitment to that theory-based approach, and the problem of complexity. The second theme was ‘Practical challenges’ with subthemes of stakeholder management, navigating the system, and perceptions of the problem.Health services researchers reflected on the benefits of bridging professional divides and facilitating collective learning and problem solving, but noted frustrations around clinicians’ time constraints that led to sparse interactions with the team, and lack of authority to effect change themselves.ConclusionsMixed success of adoption as an outcome was attributed to the complexity and highly nuanced nature of the setting. This made identifying the target behaviour, a key step in the TDFI approach, challenging. Introduced changes in the screening process led to new, unexpected issues yet to be addressed. Strategies to address challenges are presented, including using an internal facilitator with a focus on applying a theory-based implementation approach.Electronic supplementary materialThe online version of this article (10.1186/s12913-019-3985-5) contains supplementary material, which is available to authorized users.

Background The COVID-19 pandemic and the associated economic recession has increased parental psychosocial stress and mental health challenges. This has adversely impacted child development and wellbeing, particularly for children from priority populations (culturally and linguistically diverse (CALD) and rural/regional communities) who are at an already increased risk of health inequality. The increased mental health and psychosocial needs were compounded by the closure of in-person preventive and health promotion programs resulting in health organisations embracing technology and online services. Watch Me Grow- Electronic (WMG-E) – developmental surveillance platform- exemplifies one such service. WMG-E was developed to monitor child development and guide parents towards more detailed assessments when risk is identified. This Randomised Controlled Trial (RCT) aims to expand WMG-E as a digital navigation tool by also incorporating parents’ mental health and psychosocial needs. Children and families needing additional assessments and supports will be electronically directed to relevant resources in the ‘care-as-usual’ group. In contrast, the intervention group will receive continuity of care, with additional in-person assessment and ‘warm hand over’ by a ‘service navigator’ to ensure their needs are met. Methods Using an RCT we will determine: (1) parental engagement with developmental surveillance; (2) access to services for those with mental health and social care needs; and (3) uptake of service recommendations. Three hundred parents/carers of children aged 6 months to 3 years (recruited from a culturally diverse, or rural/regional site) will be randomly allocated to the ‘care-as-usual’ or ‘intervention’ group. A mixed methods implementation evaluation will be completed, with semi-structured interviews to ascertain the acceptability, feasibility and impact of the WMG-E platform and service navigator. Conclusions Using WMG-E is expected to: normalise and de-stigmatise mental health and psychosocial screening; increase parental engagement and service use; and result in the early identification and management of child developmental needs, parental mental health, and family psychosocial needs. If effective, digital solutions such as WMG-E to engage and empower parents alongside a service navigator for vulnerable families needing additional support, will have significant practice and policy implications in the pandemic/post pandemic period. Trial registration The trial (Protocol No. 1.0, Version 3.1) was registered with ANZCTR (registration number: ACTRN12621000766819) on July 21st, 2021 and reporting of the trial results will be according to recommendations in the CONSORT Statement.

This paper examines the principles of benchmarking in healthcare and how benchmarking can contribute to practice improvement and improved health outcomes for patients. It uses the Deepening our Understanding of Quality in Australia (DUQuA) study published in this Supplement and DUQuA’s predecessor in Europe, the Deepening our Understanding of Quality improvement in Europe (DUQuE) study, as models. Benchmarking is where the performances of institutions or individuals are compared using agreed indicators or standards. The rationale for benchmarking is that institutions will respond positively to being identified as a low outlier or desire to be or stay as a high performer, or both, and patients will be empowered to make choices to seek care at institutions that are high performers. Benchmarking often begins with a conceptual framework that is based on a logic model. Such a framework can drive the selection of indicators to measure performance, rather than their selection being based on what is easy to measure. A Donabedian range of indicators can be chosen, including structure, process and outcomes, created around multiple domains or specialties. Indicators based on continuous variables allow organizations to understand where their performance is within a population, and their interdependencies and associations can be understood. Benchmarking should optimally target providers, in order to drive them towards improvement. The DUQuA and DUQuE studies both incorporated some of these principles into their design, thereby creating a model of how to incorporate robust benchmarking into large-scale health services research.

Objective This study aimed to refine and validate a tool to measure safety culture and leadership in Australian hospitals. Design The clinician safety culture and leadership questionnaire was constructed by combining and refining the following two previously validated scales: Safety Attitudes Questionnaire and the Leadership Effectiveness Survey. Statistical processes were used to explore the factor structure, reliability, validity and descriptive statistics of the new instrument. Setting Thirty-two large Australian public hospitals. Participants 1382 clinicians (doctors, nurses and allied health professionals). Main Outcome Measure(s) Descriptive statistics, structure and validity of clinician safety culture and leadership scale. Results We received 1334 valid responses from participants. The distribution of ratings was left-skewed, with a small ceiling effect, meaning that scores were clustered toward the high end of the scale. Using confirmatory factor analysis, we confirmed the structure of the three scales as a combined measure of safety culture and leadership. The data were divided into equal calibration and validation datasets. For the calibration dataset, the Chi-square: df ratio was 4.4, the root mean square error of approximation RMSEA (a measure of spread of the data) was 0.071, the standardized root mean square residual SRMR (an absolute measure of the fit of the data) was 0.058 and the Confirmatory Fit Index (CFI) (another test confirming the fit of the data) was 0.82; while none of the indices suggested good fit, all but CFI fell within acceptable thresholds. All factors demonstrated adequate internal consistency and construct reliability, as desired. All three domains achieved discriminant validity through cross-loadings, meaning that the three domains were determined to be independent constructs. Results for the validation dataset were effectively identical to those found in the calibration dataset. Conclusions While the model may benefit from additional refinement, we have validated the tool for measuring clinician safety culture and leadership in our Australian sample. The DUQuA safety culture and leadership scale can be used by Australian hospitals to assess clinician safety culture and leadership, and is readily modifiable for other health systems depending on their needs.