A learning health systems approach to integrating electronic patient-reported outcomes across the health care organization

Introduction: Foundational to a learning health system (LHS) is the presence of a data infrastructure that can support continuous learning and improve patient outcomes. To advance their capacity to drive patient-centered care, health systems are increasingly looking to expand the electronic capture of patient data, such as electronic patient-reported outcome (ePRO) measures. Yet ePROs bring unique considerations around workflow, measurement, and technology that health systems may not be poised to navigate. We report on our effort to develop generalizable learnings that can support the integration of ePROs into clinical practice within an LHS framework. Methods: Guided by action research methodology, we engaged in iterative cycles of planning, acting, observing, and reflecting around ePRO use with two primary goals: (1) mobilize an ePRO community of practice to facilitate knowledge sharing, and (2) establish guidelines for ePRO use in the context of LHS practice. Multiple, emergent data collection activities generated generalizable guidelines that document the tangible best practices for ePRO use in clinical care. We organized guidelines around thematic areas that reflect LHS structures and stakeholders. Results: Three core thematic areas (and 24 guidelines) emerged. The theme of governance reflects the importance of leadership, knowledge management, and facilitating organizational learning around best practice models for ePRO use. The theme of integration considers the intersection of workflow, technology, and human factors for ePROs across areas of care delivery. Lastly, the theme of reporting reflects critical considerations for curating data and information, designing system functions and interactions, and presentation of ePRO data to support the translation of knowledge to action. Conclusions: The guidelines produced from this work highlight the complex, multidisciplinary nature of implementing change within LHS contexts, and the value of action research approaches to enable rapid, iterative learning that leverages the knowledge and experience of communities of practice

The electronic self report assessment and intervention for cancer: promoting patient verbal reporting of symptom and quality of life issues in a randomized controlled trial

Background: The electronic self report assessment - cancer (ESRA-C), has been shown to reduce symptom distress during cancer therapy The purpose of this analysis was to evaluate aspects of how the ESRA-C intervention may have resulted in lower symptom distress (SD). Methods: Patients at two cancer centers were randomized to ESRA-C assessment only (control) or the Web-based ESRA-C intervention delivered to patients’ homes or to a tablet in clinic. The intervention allowed patients to self-monitor symptom and quality of life (SxQOL) between visits, receive self-care education and coaching to report SxQOL to clinicians. Summaries of assessments were delivered to clinicians in both groups. Audio-recordings of clinic visits made 6 weeks after treatment initiation were coded for discussions of 26 SxQOL issues, focusing on patients’/caregivers’ coached verbal reports of SxQOL severity, pattern, alleviating/aggravating factors and requests for help. Among issues identified as problematic, two measures were defined for each patient: the percent SxQOL reported that included a coached statement, and an index of verbalized coached statements per SxQOL. The Wilcoxon rank test was used to compare measures between groups. Clinician responses to problematic SxQOL were compared. A mediation analysis was conducted, exploring the effect of verbal reports on SD outcomes. Results: 517 (256 intervention) clinic visits were audio-recorded. General discussion of problematic SxQOL was similar in both groups. Control group patients reported a median 75% of problematic SxQOL using any specific coached statement compared to a median 85% in the intervention group (p = .0009). The median report index of coached statements was 0.25 for the control group and 0.31 for the intervention group (p = 0.008). Fatigue, pain and physical function issues were reported significantly more often in the intervention group (all p < .05). Clinicians' verbalized responses did not differ between groups. Patients' verbal reports did not mediate final SD outcomes (p = .41). Conclusions: Adding electronically-delivered, self-care instructions and communication coaching to ESRA-C promoted specific patient descriptions of problematic SxQOL issues compared with ESRA-C assessment alone. However, clinician verbal responses were no different and subsequent symptom distress group differences were not mediated by the patients' reports. Trial registration NCT00852852; 26 Feb 200

Perceived usefulness of a distributed community-based syndromic surveillance system: a pilot qualitative evaluation study

<p>Abstract</p> <p>Background</p> <p>We conducted a pilot utility evaluation and information needs assessment of the Distribute Project at the 2010 Washington State Public Health Association (WSPHA) Joint Conference. Distribute is a distributed community-based syndromic surveillance system and network for detection of influenza-like illness (ILI). Using qualitative methods, we assessed the perceived usefulness of the Distribute system and explored areas for improvement. Nine state and local public health professionals participated in a focus group (<it>n = 6</it>) and in semi-structured interviews (<it>n = 3</it>). Field notes were taken, summarized and analyzed.</p> <p>Findings</p> <p>Several emergent themes that contribute to the perceived usefulness of system data and the Distribute system were identified: 1) <it>Standardization: </it>a common ILI syndrome definition; 2) <it>Regional Comparability: </it>views that support county-by-county comparisons of syndromic surveillance data; 3) <it>Completeness: </it>complete data for all expected data at a given time; <it>4) Coverage: </it>data coverage of all jurisdictions in WA state; 5) <it>Context: </it>metadata incorporated into the views to provide context for graphed data; 6) <it>Trusted Data</it>: verification that information is valid and timely; and 7) <it>Customization: </it>the ability to customize views as necessary. As a result of the focus group, a new county level health jurisdiction expressed interest in contributing data to the Distribute system.</p> <p>Conclusion</p> <p>The resulting themes from this study can be used to guide future information design efforts for the Distribute system and other syndromic surveillance systems. In addition, this study demonstrates the benefits of conducting a low cost, qualitative evaluation at a professional conference.</p

A Personal Annotated Image Server

(not yet available

A Portable Image Annotation Tool for Web-based Anatomy Atlases

(no abstract

Visualizing the quality of partially accruing data for use in decision making

Secondary use of clinical health data for near real-time public health surveillance presents challenges surrounding its utility due to data quality issues. Data used for real-time surveillance must be timely, accurate and complete if it is to be useful; if incomplete data are used for surveillance, understanding the structure of the incompleteness is necessary. Such data are commonly aggregated due to privacy concerns. The Distribute project was a near real-time influenza-like-illness (ILI) surveillance system that relied on aggregated secondary clinical health data. The goal of this work is to disseminate the data quality tools developed to gain insight into the data quality problems associated with these data. These tools apply in general to any system where aggregate data are accrued over time and were created through the end-user-as-developer paradigm. Each tool was developed during the exploratory analysis to gain insight into structural aspects of data quality. Our key finding is that data quality of partially accruing data must be studied in the context of accrual lag—the difference between the time an event occurs and the time data for that event are received, i.e. the time at which data become available to the surveillance system. Our visualization methods therefore revolve around visualizing dimensions of data quality affected by accrual lag, in particular the tradeoff between timeliness and completion, and the effects of accrual lag on accuracy. Accounting for accrual lag in partially accruing data is necessary to avoid misleading or biased conclusions about trends in indicator values and data quality