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PurposeRoutinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and methodsWe conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.ResultsWe aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.ConclusionWe constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

Our goal is to summarize the collective experience of 15 organizations in dealing with uncoordinated efforts that result in unnecessary delays in understanding, predicting, preparing for, containing, and mitigating the COVID-19 pandemic in the US. Response efforts involve the collection and analysis of data corresponding to healthcare organizations, public health departments, socioeconomic indicators, as well as additional signals collected directly from individuals and communities. We focused on electronic health record (EHR) data, since EHRs can be leveraged and scaled to improve clinical care, research, and to inform public health decision-making. We outline the current challenges in the data ecosystem and the technology infrastructure that are relevant to COVID-19, as witnessed in our 15 institutions. The infrastructure includes registries and clinical data networks to support population-level analyses. We propose a specific set of strategic next steps to increase interoperability, overall organization, and efficiencies.

As artificial intelligence grows in informing and guiding clinical care, the need to develop strategic workforce and implementation planning is paramount. In this commentary, the authors lay out opportunities and ways in which to meet this challenge. [ABSTRACT FROM AUTHOR]

Failed diffusion of innovations may be linked to an inability to use and apply data, information, and knowledge to change perceptions of current practice and motivate change. Using qualitative and quantitative data from three large-scale health care delivery innovations-accountable care organizations, advanced primary care practice, and EvidenceNOW-we assessed where data-driven innovation is occurring and where challenges lie. We found that implementation of some technological components of innovation (for example, electronic health records) has occurred among health care organizations, but core functions needed to use data to drive innovation are lacking. Deficits include the inability to extract and aggregate data from the records; gaps in sharing data; and challenges in adopting advanced data functions, particularly those related to timely reporting of performance data. The unexpectedly high costs and burden incurred during implementation of the innovations have limited organizations' ability to address these and other deficits. Solutions that could help speed progress in data-driven innovation include facilitating peer-to-peer technical assistance, providing tailored feedback reports to providers from data aggregators, and using practice facilitators skilled in using data technology for quality improvement to help practices transform. Policy efforts that promote these solutions may enable more rapid uptake of and successful participation in innovative delivery system reforms.

PURPOSE We undertook a study to assess whether implementing 7 evidence-based strategies to build improvement capacity within smaller primary care practices was associated with changes in performance on clinical quality measures (CQMs) for cardiovascular disease. METHODS A total of 209 practices across Washington, Oregon, and Idaho participated in a pragmatic clinical trial that focused on building quality improvement capacity as measured by a validated questionnaire, the 12-point Quality Improvement Capacity Assessment (QICA). Clinics reported performance on 3 cardiovascular CQMs--appropriate aspirin use, blood pressure (BP) control ( RESULTS Practices improved QICA scores by 1.44 points (95% CI, 1.20-1.68; P CONCLUSIONS Improvements in clinic-level performance on BP control may be attributed to implementation of 7 evidence-based strategies to build quality improvement capacity. These strategies were feasible to implement in small practices over 15 months. Key words: practice facilitation; high-leverage change; quality improvement; cardiovascular disease; risk factors; preventive medicine; evidence-based practice; clinical quality measure; Healthy Hearts Northwest study; EvidenceNOW; AHRQ; organizational innovation, INTRODUCTION Primary care practices in the United States are in a period of transition, with considerable pressure from payers and policy makers to improve quality of care and reduce costs. [...]

Context: High blood pressure (HBP) affects nearly half of adults in the United States and is a major factor in heart attacks, strokes, kidney disease, and other morbidities. Many primary care guidelines exist for reducing risk, including several related to patient behaviors such as home monitoring and lifestyle changes. Patient-facing, primary care-based clinical decision support (CDS) tools may help patients adhere to evidence-based recommendations, but a deeper understanding of their motivations and preferences will be necessary to succeed. Objective: Understand how to best engage patients in controlling their HBP, in order to design effective patient facing CDS tools. Population Studied: Adults between the ages of 18 and 85 who had been diagnosed with hypertension. Study Design: Mixed methods study with two phases: (1) 18 semi-structured interviews with a limited cohort; and (2) a nationwide online survey of 519 people. Instrument: Both the interviews and the survey included questions assessing comorbid conditions, medication load, treatment priorities, satisfaction with BP management goal-setting, and attitudes towards CDS. The survey also included a set of dichotomous (A/B) preference questions using paired information displays to assess perceived trustworthiness of potential CDS user interface options and likelihood of initiating patient action. Analysis: Interview responses were summarized using descriptive statistics with content analysis on the open-ended questions. Survey questions using a 7 point Likert scale were summarized by percent responding in the top 3 categories. A/B preference displays were analyzed with a sign test and categorical responses about likelihood of taking action were compared using a chi-squared test at the p=0.05 level. Results: Patients knew their BP goals, and identified that behavioral and lifestyle changes were important and underutilized. Social-relational and higher information displays were perceived as more trustworthy (55-60% vs. 16-26%) but did not change reported actions (75%). Conclusions: Our survey results suggest that patients perceive they would act on information and recommendations displayed by a CDS tool; however, significant previous work has shown that people tend to over-estimate their own actions. A first version of the CDS tool has been developed, and the next steps include testing the visualizations, reminders, and tailored messages in the real world through a pragmatic trial., Presenters Michelle Bobo, BA, MS, David Dorr, MD, MS, Anthony Cheng, MD, Christopher d'Autremont, MBI, Christopher Terndrup, MJ Dunne, MA, Robert Rope, MD, Joshua Richardson, PhD, MLIS, [...]

Background The rapidly growing field of multimorbidity research demonstrates that changes in multimorbidity in mid- and late-life have far reaching effects on important person-centered outcomes, such as health-related quality of life. However, there are few organizing frameworks and comparatively little work weighing the merits and limitations of various quantitative methods applied to the longitudinal study of multimorbidity. Methods We identify and discuss methods aligned to specific research objectives with the goals of (i) establishing a common language for assessing longitudinal changes in multimorbidity, (ii) illuminating gaps in our knowledge regarding multimorbidity progression and critical periods of change, and (iii) informing research to identify groups that experience different rates and divergent etiological pathways of disease progression linked to deterioration in important health-related outcomes. Results We review practical issues in the measurement of multimorbidity, longitudinal analysis of health-related data, operationalizing change over time, and discuss methods that align with 4 general typologies for research objectives in the longitudinal study of multimorbidity: (i) examine individual change in multimorbidity, (ii) identify subgroups that follow similar trajectories of multimorbidity progression, (iii) understand when, how, and why individuals or groups shift to more advanced stages of multimorbidity, and (iv) examine the coprogression of multimorbidity with key health domains. Conclusions This work encourages a systematic approach to the quantitative study of change in multimorbidity and provides a valuable resource for researchers working to measure and minimize the deleterious effects of multimorbidity on aging populations. [ABSTRACT FROM AUTHOR]

We conducted a meta-synthesis of five different studies that developed, tested, and implemented new technologies for the purpose of collecting Observations of Daily Living (ODL). From this synthesis, we developed a model to explain user motivation as it relates to ODL collection. We describe this model that includes six factors that motivate patients' collection of ODL data: usability, illness experience, relevance of ODLs, information technology infrastructure, degree of burden, and emotional activation. We show how these factors can act as barriers or facilitators to the collection of ODL data and how interacting with care professionals and sharing ODL data may also influence ODL collection, health-related awareness, and behavior change. The model we developed and used to explain ODL collection can be helpful to researchers and designers who study and develop new, personal health technologies to empower people to improve their health.

ObjectiveExamine how the Electronic Health Record (EHR) and its related systems support or inhibit provider collaboration.BackgroundHealth care systems in the US are simultaneously implementing EHRs and transitioning to more collaborative delivery systems; this study examines the interaction between these two changes.MethodsThis qualitative study of five US EHR implementations included 49 interviews and over 60 hours of provider observation. We examined the role of the EHR in building relationships, communicating, coordinating, and collaborative decision-making.ResultsThe EHR plays four roles in collaboration: a repository, a messenger, an orchestrator, and a monitor. While EHR performance varied, common themes were decreased trust due to poor quality documentation, incomplete communication, potential for increased effectiveness through better coordination, and the emerging role of the EHR in identifying performance gaps.ConclusionBoth organizational and technical innovations are needed if the EHR is to truly support collaborative behaviors.

PURPOSE We conducted a randomized controlled trial to compare the effectiveness of adding various forms of enhanced external support to practice facilitation on primary care practices' clinical quality measure (CQM) performance. METHODS Primary care practices across Washington, Oregon, and Idaho were eligible if they had fewer than 10 full-time clinicians. Practices were randomized to practice facilitation only, practice facilitation and shared learning, practice facilitation and educational outreach visits, or practice facilitation and both shared learning and educational outreach visits. All practices received up to 15 months of support. The primary outcome was the CQM for blood pressure control. Secondary outcomes were CQMs for appropriate aspirin therapy and smoking screening and cessation. Analyses followed an intention-to-treat approach. RESULTS Of 259 practices recruited, 209 agreed to be randomized. Only 42% of those offered educational outreach visits and 27% offered shared learning participated in these enhanced supports. CQM performance improved within each study arm for all 3 cardiovascular disease CQMs. After adjusting for differences between study arms, CQM improvements in the 3 enhanced practice support arms of the study did not differ significantly from those seen in practices that received practice facilitation alone (omnibus P = .40 for blood pressure CQM). Practices randomized to receive both educational outreach visits and shared learning, however, were more likely to achieve a blood pressure performance goal in 70% of patients compared with those randomized to practice facilitation alone (relative risk = 2.09; 95% CI, 1.16-3.76). CONCLUSIONS Although we found no significant differences in CQM performance across study arms, the ability of a practice to reach a target level of performance may be enhanced by adding both educational outreach visits and shared learning to practice facilitation. Key words: primary health care; cardiovascular disease; quality improvement; chronic illness; prevention; health promotion; quantitative methods; health services; organizational change; practice-based research, INTRODUCTION Past efforts to transform primary care have included practice redesign based on medical home principles and adoption of electronic health records. (1-4) More recently, primary care practices face increasing [...]

BackgroundIn populations with chronic illness, outcomes improve with the use of care models that integrate clinical information, evidence-based treatments, and proactive management of care. Health information technology is believed to be critical for efficient implementation of these chronic care models. Health care organizations have implemented information technologies, such as electronic medical records, to varying degrees. However, considerable uncertainty remains regarding the relative impact of specific informatics technologies on chronic illness care.ObjectiveTo summarize knowledge and increase expert consensus regarding informatics components that support improvement in chronic illness care.DesignA systematic review of the literature was performed. "Use case" models were then developed, based on the literature review, and guidance from clinicians and national quality improvement projects. A national expert panel process was conducted to increase consensus regarding information system components that can be used to improve chronic illness care.ResultsThe expert panel agreed that informatics should be patient-centered, focused on improving outcomes, and provide support for illness self-management. They concurred that outcomes should be routinely assessed, provided to clinicians during the clinical encounter, and used for population-based care management. It was recommended that interactive, sequential, disorder-specific treatment pathways be implemented to quickly provide clinicians with patient clinical status, treatment history, and decision support.ConclusionsSpecific informatics strategies have the potential to improve care for chronic illness. Software to implement these strategies should be developed, and rigorously evaluated within the context of organizational efforts to improve care.