Search

Routinely collecting and using electronic patient-reported outcome (ePRO) data in clinical practice can improve patients' experience and outcomes, but implementing this at scale has proved challenging. As part of the Optimising routine collection of electronic patient-reported outcomes (OPT-ePRO) study, we therefore developed an intervention that aimed to facilitate the implementation of ePROs. We are conducting OPT-ePRO in the context of secondary care for people with chronic kidney disease in the UK, with three renal units participating as our study sites. Intervention design was guided by Normalisation Process Theory, and informed by published literature and qualitative research. The intervention consisted of a national infrastructure to securely collect, transfer and display ePRO data, complemented with materials and procedures to support kidney patients and renal unit staff with embedding ePROs in usual care pathways. The next step will be to bring the OPT-ePRO intervention into practice and iteratively refine it.

Painful conditions are prevalent and substantially contribute to disability worldwide. Digital manikins are body-shaped drawings to facilitate self-reporting of pain. Some of them have been validated, but without allowing for recording of location-specific pain intensity and for use on a smartphone. This paper describes the initial development of a digital pain manikin to support self-reporting of pain location and location-specific intensity using people's own mobile device. Subsequently, we conducted reliability and usability tests with eight researchers and seven patient representatives. Test-retest reliability depended on the manikin's level of detail, but was generally high with most intraclass correlation coefficients âĽě0.70 and all similarity coefficients âĽě0.50. Participants found the manikin easy to use, but suggested clearer orientation (front/back, certain body locations) and would value additional feedback and diary functions. We will address these issues in the next version of the manikin before conducting a validation study.

Smartphones offer new opportunities to monitor health-related behaviours in the real world. This allows researchers to go beyond traditional data collection methods, such as interviews and questionnaires that suffer from recall bias and low spatio-temporal resolution. In this study, we present an experiment that uses advanced analytical methods to identify daily activities relevant to assess social functioning, from geolocation data. Twenty-one healthy volunteers used a smartphone to continuously record their GPS location for up to 10 days. Participants also completed a diary to record their daily activities that was used as ground truth. Using clustering algorithms and semantic enrichment methods we were able to predict these activities from the GPS data with a precision of 0.75 (standard deviation [SD] 0.13) and a recall of 0.60 (SD 0.11). Although performed on a limited sample, our study shows potential for continuous, and passive geolocation-based monitoring of patient behaviour in mental health.

Despite the increasing availability of online patient portals that provide access to electronic health records, little is known about their adoption by patients. We systematically reviewed the literature to investigate adoption of patient portals across studies. We searched MEDLINE and Scopus to identify relevant papers. We included 40 studies: 24 were controlled experiments, with prospective data collection in an actively recruited population; 16 were real-world experiments, with adoption being evaluated retrospectively after system deployment in clinical practice. Our meta-analysis showed an overall mean adoption rate of 52% (95% Confidence Interval [CI], 42 to 62%). Rates differed markedly between study types: controlled experiments yielded a mean adoption rate of 71% (95% CI 64 to 79%), compared to 23% (95% CI, 13 to 33%) in real-world experiments. This difference was confirmed in a meta-regression analysis of the influence of study characteristics on adoption rates. Our findings suggest that adoption rates reported in controlled studies do not reflect those in everyday clinical practice. Until we understand how to effectively increase adoption, patient portals are unlikely to consistently lead to improvements in care processes and health outcomes.

Audit and feedback (A&F) is a common strategy to improve quality of care. Meta-analyses have indicated that A&F may be more effective in realizing desired change when baseline performance is low, it is delivered by a supervisor or colleague, it is provided frequently and in a timely manner, it is delivered in both verbal and written formats, and it includes specific targets and an action plan. However, there is little information to guide operationalization of these factors. Researchers have consequently called for A&F interventions featuring well-described and carefully justified components, with their theoretical rationale made explicit. This paper describes the rationale and development of a quality dashboard including an improvement toolbox for four previous developed pain indicators, guided by Control Theory.

Health informatics interventions such as clinical decision support (CDS) and audit and feedback (A&F) are variably effective at improving care because the underlying mechanisms through which these interventions bring about change are poorly understood. This limits our possibilities to design better interventions. Process evaluations can be used to improve this understanding by assessing fidelity and quality of implementation, clarifying causal mechanisms, and identifying contextual factors associated with variation in outcomes. Coiera describes the intervention process as a series of stages extending from interactions to outcomes: the "information value chain". However, past process evaluations often did not assess the relationships between those stages. In this paper we argue that the chain can be measured quantitatively and unobtrusively in digital interventions thanks to the availability of electronic data that are a by-product of their use. This provides novel possibilities to study the mechanisms of informatics interventions in detail and inform essential design choices to optimize their efficacy.

Audit and feedback (A&F) is widely used to aid healthcare professionals in improving clinical performance, but there is little understanding of the underlying mechanism that determines its effectiveness. The aim of this paper is to investigate the process by which healthcare professionals select indicators as improvement targets based on A&F. We performed a laboratory study among 41 healthcare professionals in the context of a web-based A&F intervention designed to improve the quality of cardiac rehabilitation care in the Netherlands. Feedback was provided on eighteen quality indicators, including a score and a colour (representing a recommendation for selection (red and yellow) or non-selection (green)). Indicators with more room for improvement were more likely to be selected, although this varied substantially between participants. In more than a quarter of the cases, participants did not select indicators with obvious room for improvement (yellow or red colour), or selected indicators without apparent room for improvement (green colour). We conclude that personal preferences and beliefs concerning quality and performance targets may dilute the efficiency of A&F.

Continuous monitoring and systematic improvement of quality have become increasingly common in healthcare. To support multidisciplinary care teams in improving their clinical performance using feedback on quality indicators, we developed the CARDSS Online system. This system supports (i) monitoring of indicator-based performance, (ii) selecting aspects of care that need improvement, (iii) developing a quality improvement (QI) plan, and (iv) periodically adjusting the QI plan. During educational outreach visits, the system actively involves the team in the improvement effort, and guides them through the process of systematic QI without needing extensive knowledge of the underlying concepts. During the implementation of the system in the field of cardiac rehabilitation (CR) in the Netherlands, we have conducted the first outreach visits to four CR teams. During the visits, the teams formulated QI plans consisting of 4 to 7 improvement goals, each goal accompanied by 1 to 5 QI actions. Currently, we are evaluating the effect of CARDSS Online on the quality of CR in the Netherlands in a cluster randomized trial.