In 1998, the "Evidence Cart" was introduced to provide decision-support tools at the point of care. A recent study showed that a majority of doctors who previously stated that evidence was not needed sought it nevertheless when it was easily available. In this study, invited clinicians were asked to rate the usefulness of evidence provided as abstracts and "the bottom-line summaries" (TBL) using a modified version of a Web app for searching PubMed and then specify reasons how it might affect their clinical decision-making. The responses were captured in the server's log. One hundred and one reviews were submitted with 22 reviews for abstracts and 79 for TBLs. The overall usefulness Likert score (1=least useful, 7=most useful) was 5.02±1.96 (4.77±2.11 for abstracts and 5.09±1.92 for TBL). The basis for scores was specified in only about half (53/101) of reviews. The most frequent single reason (32%) was that it led to a new skill, diagnostic test, or treatment plan. Two or more reasons were given in 16 responses (30.2%). Two-thirds more responders used TBL summaries than abstracts confirming further that clinicians prefer convenient easy-to-read evidence at the point of care. This study seems to show similar results as the Evidence Cart study on the usefulness of evidence in clinical decision-making. Keywordsevidence-based medicine; clinical decision-making; Web app; journal abstract; "the bottom line"

Objective During the COVID-19 pandemic, federally qualified health centers rapidly mobilized to provide SARS-CoV-2 testing, COVID-19 care, and vaccination to populations at increased risk for COVID-19 morbidity and mortality. We describe the development of a reusable public health data analytics system for reuse of clinical data to evaluate the health burden, disparities, and impact of COVID-19 on populations served by health centers. Materials and Methods The Multi-State Data Strategy engaged project partners to assess public health readiness and COVID-19 data challenges. An infrastructure for data capture and sharing procedures between health centers and public health agencies was developed to support existing capabilities and data capacities to respond to the pandemic. Results Between August 2020 - March 2021, project partners evaluated their data capture and sharing capabilities and reported challenges and preliminary data. Major interoperability challenges included poorly aligned federal, state, and local reporting requirements, lack of unique patient identifiers, lack of access to pharmacy, claims and laboratory data, missing data, and proprietary data standards and extraction methods. Discussion Efforts to access and align project partners’ existing health systems data infrastructure in the context of the pandemic highlighted complex interoperability challenges. These challenges remain significant barriers to real-time data analytics and efforts to improve health outcomes and mitigate inequities through data-driven responses. Conclusion The reusable public health data analytics system created in the Multi-State Data Strategy can be adapted and scaled for other health center networks to facilitate data aggregation and dashboards for public health, organizational planning and quality improvement and can inform local, state, and national COVID-19 response efforts.

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