Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Community-level data are siloed and challenging to integrate with patient care.Vast potential exists to tailor community-level and geographic data for patients. propose an automated technique to present these data at the point-of-care. BackgroundCommunity-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health. PurposeThis study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University xner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as ll as to assess the utility of this EHR integration methodology. Materials and methodsCVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients re linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses re conducted in 2014. ResultsUsing the linked data, ekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) re associated with CVH at the p<0.10 level. ConclusionsThis work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.
['Caryn Roth', 'Philip R. O. Payne', 'Rory C. Weier', 'Abigail B. Shoben', 'Erica N. Fletcher', 'Albert M. Lai', 'Marjorie M. Kelley', 'Jesse J. Plascak', 'Randi E. Foraker']
I am writing a research paper on a idea The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study
[{"from": "human", "value": "I am writing a research paper on a idea The geographic distribution of cardiovascular health in the stroke prevention in healthcare delivery environments (SPHERE) study"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Display Omitted Community-level data are siloed and challenging to integrate with patient care.Vast potential exists to tailor community-level and geographic data for patients. propose an automated technique to present these data at the point-of-care. BackgroundCommunity-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health. PurposeThis study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University xner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as ll as to assess the utility of this EHR integration methodology. Materials and methodsCVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients re linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses re conducted in 2014. ResultsUsing the linked data, ekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) re associated with CVH at the p<0.10 level. ConclusionsThis work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein."}]