Search

ObjectiveDashboards can support person-centered care by helping people partner with their clinicians to coproduce care based on preferences, shared decision-making, and evidence-based treatments. We engaged caregivers of children with juvenile idiopathic arthritis (JIA), adults with rheumatoid arthritis (RA), and clinicians in a pilot study to assess their experiences and the utility and impact of an electronic previsit questionnaire and point-of-care dashboard to support coproduction of rheumatology care.MethodsWe employed a mixed-methods design to assess users' perceptions of a customized electronic health record rheumatology module at four pediatric rheumatology practices and two adult rheumatology practices. We surveyed a convenience sample of caregivers of children with JIA (n = 113), adults with RA (n = 116), and clinicians (n = 12). We conducted semistructured interviews with 13 caregivers and patients and six care teams. Experiences were evaluated using descriptive statistics and thematic analyses.ResultsCaregivers of children with JIA and adults with RA reported the dashboards were useful during discussions (88%) and helped them talk about what mattered most (82%), make health care decisions (83%), and create a treatment plan (77%). Clinicians provided similar feedback. Two-thirds (67%) of caregivers and adults and 55% of clinicians would recommend the dashboard to peers. System usability scores (77.1 ± 15.6) were above average. Dashboards helped users make sense of health information, communicate more effectively, and make decisions. Improvements to the dashboards and workflows could enhance patient self-management and clinician efficiency.ConclusionVisual point-of-care dashboards can support caregivers, patients, and clinicians to coproduce rheumatology care. Findings demonstrate a need to spread and scale for broader benefit and impact.

Ninety percent of health care systems now offer patient portals to access electronic health records (EHRs) in the United States, but only 15% to 30% of patients use these platforms. Using PubMed, the authors identified 53 studies published from September 2013 to June 2019 that informed best practices and priorities for future research on patient engagement with EHR data through patient portals, These studies mostly involved outpatient settings and fell into 3 major categories: interventions to increase use of patient portals, usability testing of portal interfaces, and documentation of patient and clinician barriers to portal use. Interventions that used one-on-one patient training were associated with the highest portal use. Patients with limited health or digital literacy faced challenges to portal use. Clinicians reported a lack of workflows to support patient use of portals in routine practice. These studies suggest that achieving higher rates of patient engagement through EHR portals will require paying more attention to the needs of diverse patients and systematically measuring usability as well as scope of content. Future work should incorporate implementation science approaches and directly address the key role of clinicians and staff in promoting portal use.

Background: Shared decision making (SDM) is the process by which patients and clinicians exchange information and preferences to come to joint healthcare decisions. Clinical dashboards can support SDM by collecting, distilling, and presenting critical information, such as patient-reported outcomes (PROs), to be shared at points of care and in between appointments. We describe the implementation strategies and outcomes of a multistakeholder collaborative process known as "co-design" to develop a PRO-informed clinical dashboard to support SDM for patients with advanced cancer or chronic kidney disease (CKD). Methods: Across 14 sessions, two multidisciplinary teams comprising patients, care partners, clinicians, and other stakeholders iteratively co-designed an SDM dashboard for either advanced cancer (N = 25) or CKD (N = 24). Eligible patients, care partners, and frontline clinicians were identified by six physician champions. The co-design process included four key steps: (1) define "the problem", (2) establish context of use, (3) build a consensus on design, and (4) define and test specifications. We also evaluated our success in implementing the co-design strategy using measures of fidelity, acceptability, adoption, feasibility, and effectiveness which were collected throughout the process. Results: Mean (M) scores across implementation measures of the co-design process were high, including observer-rated fidelity and adoption of co-design practices (M = 19.1 on a 7–21 scale, N = 36 ratings across 9 sessions), as well as acceptability based on the perceived degree of SDM that occurred during the co-design process (M = 10.4 on a 0 to 12 adapted collaboRATE scale). Capturing the feasibility and adoption of convening multistakeholder co-design teams, min–max normalized scores (ranging from 0 to 1) of stakeholder representation demonstrated that, on average, 95% of stakeholder types were represented for cancer sessions (M = 0.95) and 85% for CKD sessions (M = 0.85). The co-design process was rated as either "fully" or "partially" effective by 100% of respondents, in creating a dashboard that met its intended objective. Conclusions: A co-design process was successfully implemented to develop SDM clinical dashboards for advanced cancer and CKD care. We discuss key strategies and learnings from this process that may aid others in the development and uptake of patient-centered healthcare innovations. [ABSTRACT FROM AUTHOR]

Introduction: The Cystic Fibrosis (CF) Foundation sponsored the design, pilot testing, and implementation of the CF Learning Network (CFLN) to explore how the Foundation's Care Center Network (CCN) could become a learning health system. Six years after the design, the Foundation commissioned a formative mixed methods evaluation of the CFLN to assess: CFLN participants' understanding of program goals, attributes, and perceptions of current and future impact. Methods: We performed semi‐structured interviews with CFLN participants to identify perceived goals, attributes, and impact of the network. Following thematic analyses, we developed and distributed a survey to CFLN members and a matched sample of CCN programs to understand whether the themes were unique to the CFLN. Results: Interviews with 24 CFLN participants were conducted. Interviewees identified the primary CFLN goal as improving outcomes for people living with CF, with secondary goals of providing training in quality improvement (QI), creating a learning community, engaging all stakeholders in improvement, and spreading best practices to the CCN. Project management, use of data, common QI methods, and the learning community were seen as critical to success. Survey responses were collected from 103 CFLN members and 25 CCN members. The data revealed that CFLN respondents were more likely than CCN respondents to connect with other CF programs, routinely use data for QI, and engage patient and family partners in QI. Conclusions: Our study suggests that the CFLN provides value beyond that achieved by the CCN. Key questions remain about whether spread of the CFLN could improve outcomes for more people living with CF. [ABSTRACT FROM AUTHOR]

Background Despite progress in developing learning health systems (LHS) and associated metrics of success, a gap remains in identifying measures to guide the implementation and assessment of the impact of an oncology LHS. Our aim was to identify a balanced set of measures to guide a person-centered oncology LHS. Methods A modified Delphi process and clinical value compass framework were used to prioritize measures for tracking LHS performance. A multidisciplinary group of 77 stakeholders, including people with cancer and family members, participated in 3 rounds of online voting followed by 50-minute discussions. Participants rated metrics on perceived importance to the LHS and discussed priorities. Results Voting was completed by 94% of participants and prioritized 22 measures within 8 domains. Patient and caregiver factors included clinical health (Eastern Cooperative Oncology Group Performance Status, survival by cancer type and stage), functional health and quality of life (Patient Reported Outcomes Measurement Information System [PROMIS] Global-10, Distress Thermometer, Modified Caregiver Strain Index), experience of care (advance care planning, collaboRATE, PROMIS Self-Efficacy Scale, access to care, experience of care, end-of-life quality measures), and cost and resource use (avoidance and delay in accessing care and medications, financial hardship, total cost of care). Contextual factors included team well-being (Well-being Index; voluntary staff turnover); learning culture (Improvement Readiness, compliance with Commission on Cancer quality of care measures); scholarly engagement and productivity (institutional commitment and support for research, academic productivity index); and diversity, equity, inclusion, and belonging (screening and follow-up for social determinants of health, inclusivity of staff and patients). Conclusions The person-centered LHS value compass provides a balanced set of measures that oncology practices can use to monitor and evaluate improvement across multiple domains. [ABSTRACT FROM AUTHOR]

Background: Modifiable risks account for a large fraction of disease and death, but clinicians and patients lack tools to identify high risk populations or compare the possible benefit of different interventions. Methods: We used data on the distribution of exposure to 12 major behavioral and biometric risk factors in the US population, mortality rates by cause, and estimates of the proportional hazards of risk factor exposure from published systematic reviews to develop a risk prediction model that estimates an adult's 10 year mortality risk compared to a population with optimum risk factors. We compared predicted risk to observed mortality in 8,241 respondents in NHANES 1988-1994 and NHANES 1999-2004 with linked mortality data up to the end of 2006. Results: Predicted risk showed good discrimination with an area under the receiver operating characteristic (ROC) curve of 0.84 (standard error 0.01) for women and 0.84 (SE 0.01) for men. Across deciles of predicted risk, mortality was accurately predicted in men (X² statistic = 12.3 for men, p=0.196) but slightly overpredicted in the highest decile among women (X² statistic = 22.8, p=0.002). Mortality risk was highly concentrated; for example, among those age 30-44 years, 5.1 % (95 % CI 4.1 % - 6.0 %) of the male and 5.9 % (95 % CI 4.8 % - 6.9 %) of the female population accounted for 25 % of the risk of death. Conclusion: The risk model accurately predicted mortality in a representative sample of the US population and could be used to help inform patient and provider decision-making, identify high risk groups, and monitor the impact of efforts to improve population health. [ABSTRACT FROM AUTHOR]

Abstract: We conducted a trial to evaluate the impact of medical self-care education on 330 elders whose average age was 71. The test group participated in a 13-session educational intervention with training in clinical medicine, life-style, and use of health services. The comparison group received a two-hour lecture-demonstration. Both groups were assessed pre-intervention, post-intervention, and one year after entry. The rest. Its indicate medical self-care instruction: produces substantial improvements, that were sustained for one year, in health knowledge, skills performance, and skills confidence; stimulates many attempts to improve life-style, and generates improvements in life quality. The program had little influence on utilization of medical care or hearth status. [ABSTRACT FROM AUTHOR]