ObjectiveTo evaluate clinical healthcare performance in Aboriginal Medical Services in Queensland and to consider future directions in supporting improvement through measurement, target setting and standards development.DesignLongitudinal study assessing baseline performance and improvements in service delivery, clinical care and selected outcomes against key performance indicators 2009–2010.Setting27 Aboriginal and Islander Community Controlled Health Services (AICCHSs) in Queensland, who are members of the Queensland Aboriginal and Islander Health Council (QAIHC).Participants22 AICCHS with medical clinics.InterventionImplementation and use of an electronic clinical information system that integrates with electronic health records supported by the QAIHC quality improvement programme—the Close the Gap Collaborative.Main outcome measuresProportion of patients with current recording of key healthcare activities and the prevalence of risk factors and chronic disease.ResultsAggregated performance was high on a number of key risk factors and healthcare activities including assessment of tobacco use and management of hypertension but low for others. Performance between services showed greatest variation for care planning and health check activity.ConclusionsData collected by the QAIHC health information system highlight the risk factor workload facing the AICCHS in Queensland, demonstrating the need for ongoing support and workforce planning. Development of targets and weighting models is necessary to enable robust between-service comparisons of performance, which has implications for health reform initiatives in Australia. The limited information available suggests that although performance on key activities in the AICCHS sector has potential for improvement in some areas, it is nonetheless at a higher level than for mainstream providers.ImplicationsThe work demonstrates the role that the Community Controlled sector can play in closing the gap in Aboriginal and Torres Strait Islander health outcomes by leading the use of clinical data to record and assess the quality of services and health outcome.

Background Evidence suggests that eHealth tools adoption is associated with better health outcomes among various populations. The patterns and factors influencing eHealth adoption among the US Medicaid population remain obscure. Objective The objective of this study is to explore patterns of eHealth tools adoption among the Medicaid population and examine factors associated with eHealth adoption. Methods Data from the Health Information National Trends Survey from 2017 to 2019 were used to estimate the patterns of eHealth tools adoption among Medicaid and non-Medicaid populations. The effects of Medicaid insurance status and other influencing factors were assessed with logistic regression models. Results Compared with the non-Medicaid population, the Medicaid beneficiaries had significantly lower eHealth tools adoption rates for health information management (11.2% to 17.5% less) and mobile health for self-regulation (0.8% to 9.7% less). Conversely, the Medicaid population had significantly higher adoption rates for using social media for health information than their counterpart (8% higher in 2018, P=.01; 10.1% higher in 2019, P=.01). Internet access diversity, education, and cardiovascular diseases were positively associated with health information management and mobile health for self-regulation among the Medicaid population. Internet access diversity is the only factor significantly associated with social media adoption for acquisition of health information (OR 1.98, 95% CI 1.26-3.11). Conclusions Our results suggest digital disparities in eHealth tools adoption between the Medicaid and non-Medicaid populations. Future research should investigate behavioral correlates and develop interventions to improve eHealth adoption and use among underserved communities.

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Background Patients’ withholding information from doctors can undermine medical treatment, create barriers for appropriate diagnoses, and increase systemic cost in health care systems. To date, there is limited literature detailing the association between trends of patients withholding information behavior (WIB) and the patient-physician relationship (PPR). Objective The aim of this study was to explore the prevalence trend of WIB after 2011 and examine the effects of PPR on WIB and its time trend. Methods A total of 5 iterations of data from the Health Information National Trends Survey (years: 2011-2018; n=11,954) were used to explore curvilinear trends of WIB among the US population. Multiple logistic regression models were used to examine curvilinear time trends of WIB, effects of PPR on WIB, and moderation effects of PPR on the WIB time trend. Results The WIB prevalence has an increasing trend before 2014, which has the highest rate of 13.57%, and then it decreases after 2014 to 8.65%. The trend of WIB is curvilinear as the quadratic term in logistic regression model was statistically significant (P=.04; beta=−.022; SE=0.011; odds ratio [OR] 0.978, 95% CI 0.957-0.999). PPR is reversely associated with WIB (P<.001; beta=−.462; SE=0.097; OR 0.630, 95% CI 0.518-0.766) and has a significant moderation effect on time trends (P=.02; beta=−.06; SE=0.025; OR 0.941, 95% CI 0.896-0.989). In general, poor quality of PPR not only significantly increased the WIB probability but also postponed the change of point for WIB curvilinear trend. Conclusions Findings suggest that the time trend of WIB between 2011 and 2018 is curvilinear and moderated by the quality of the PPR. Given these results, providers may reduce WIB by improving PPR. More research is needed to confirm these findings.