scholarly journals CM-SHARE: Development, Integration, and Adoption of an Electronic Health Record–Linked Digital Health Solution to Support Care for Diabetes in Primary Care

2019 ◽  
Vol 37 (4) ◽  
pp. 338-346
Author(s):  
James B. Jones ◽  
Shuting Liang ◽  
Hannah M. Husby ◽  
Jake K. Delatorre-Reimer ◽  
Cory A. Mosser ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Digital Health ◽  
Health Record ◽  
Electronic Health

Digital Health Interventions to Enhance Prevention in Primary Care: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Van C Willis ◽  
Kelly Jean Thomas Craig ◽  
Yalda Yabbarpour ◽  
Elisabeth L Scheufele ◽  
Yull E Arriaga ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Subgroup Analysis ◽  
Care Management ◽  
Digital Health ◽  
Health Interventions ◽  
Health Record ◽  
Primary Care Settings ◽  
Electronic Health

BACKGROUND Digital transformation of primary care practices, including the use digital health interventions (DHIs), has yet to be systematically evaluated. OBJECTIVE To identify and describe the scope and use of current DHIs for preventive care in primary care settings. METHODS A scoping review to identify literature published from 2014 to 2020 was conducted across multiple databases using keywords and MeSH terms covering primary care professionals AND prevention and care management AND digital health. A subgroup analysis identified relevant studies conducted in US primary care settings excluding DHIs that use the electronic health record (EHR) as a retrospective data capture tool. Technology descriptions, outcomes (e.g., healthcare performance and implementation science), and study quality as per Oxford Levels of Evidence were abstracted. RESULTS The search yielded 5,274 citations of which 1,060 full-texts were identified. Following a subgroup analysis, 241 articles met inclusion criteria. Studies primarily examined DHIs among health information technology including EHRs (69%), clinical decision support (41%), telehealth (37%), or multiple technologies (61%). DHIs were predominantly used for tertiary prevention (55%). Of the core primary care functions, comprehensiveness was addressed most frequently (87%). DHI users were providers (85%), patients (46%), or multiples (37%). Reported outcomes were primarily clinical (70%) and statistically significant improvements were common (69%). Results were summarized across five topics for the most novel/distinct DHIs: population-centered, patient-centered, care access expansion, panel-centered (dashboarding), and application-driven DHIs. Quality of the included studies was moderate-to-low. CONCLUSIONS Preventive DHIs used in primary care settings demonstrated meaningful improvements in both clinical and non-clinical outcomes across user types; however, adoption and implementation in the US was limited to primarily electronic health record-centric platforms and users were mainly clinicians receiving alerts regarding care management for their patients. Evaluation of negative results, effects on health disparities, and many other gaps remain to be explored.

scholarly journals Approaches for combining primary care electronic health record data from multiple sources: a systematic review of observational studies

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e037405
Author(s):  
Daniel Dedman ◽  
Melissa Cabecinha ◽  
Rachael Williams ◽  
Stephen J W Evans ◽  
Krishnan Bhaskaran ◽  
...  
Keyword(s):  
Systematic Review ◽  
Primary Care ◽  
Electronic Health Record ◽  
Observational Studies ◽  
Meta Analysis ◽  
Data Sources ◽  
Health Record ◽  
Two Stage ◽  
One Stage ◽  
Electronic Health

ObjectiveTo identify observational studies which used data from more than one primary care electronic health record (EHR) database, and summarise key characteristics including: objective and rationale for using multiple data sources; methods used to manage, analyse and (where applicable) combine data; and approaches used to assess and report heterogeneity between data sources.DesignA systematic review of published studies.Data sourcesPubmed and Embase databases were searched using list of named primary care EHR databases; supplementary hand searches of reference list of studies were retained after initial screening.Study selectionObservational studies published between January 2000 and May 2018 were selected, which included at least two different primary care EHR databases.Results6054 studies were identified from database and hand searches, and 109 were included in the final review, the majority published between 2014 and 2018. Included studies used 38 different primary care EHR data sources. Forty-seven studies (44%) were descriptive or methodological. Of 62 analytical studies, 22 (36%) presented separate results from each database, with no attempt to combine them; 29 (48%) combined individual patient data in a one-stage meta-analysis and 21 (34%) combined estimates from each database using two-stage meta-analysis. Discussion and exploration of heterogeneity was inconsistent across studies.ConclusionsComparing patterns and trends in different populations, or in different primary care EHR databases from the same populations, is important and a common objective for multi-database studies. When combining results from several databases using meta-analysis, provision of separate results from each database is helpful for interpretation. We found that these were often missing, particularly for studies using one-stage approaches, which also often lacked details of any statistical adjustment for heterogeneity and/or clustering. For two-stage meta-analysis, a clear rationale should be provided for choice of fixed effect and/or random effects or other models.

Identifying Factors Predicting Immunization Delay for Children Followed in an Urban Primary Care Network Using an Electronic Health Record

PEDIATRICS ◽  
2006 ◽  
Vol 118 (6) ◽  
pp. e1680-e1686 ◽  
Author(s):  
A. G. Fiks ◽  
E. A. Alessandrini ◽  
A. A. Luberti ◽  
S. Ostapenko ◽  
X. Zhang ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Health Record ◽  
Care Network ◽  
Electronic Health

scholarly journals Comparing Electronic Health Record Portals to Obtain Patient-Entered Family Health History in Primary Care

2013 ◽  
Vol 28 (12) ◽  
pp. 1558-1564 ◽  
Author(s):  
Michael F. Murray ◽  
Monica A. Giovanni ◽  
Elissa Klinger ◽  
Elise George ◽  
Lucas Marinacci ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Record ◽  
Family Health ◽  
Family Health History ◽  
Health Record ◽  
Health History ◽  
Electronic Health

scholarly journals Use of electronic health record data from diverse primary care practices to identify and characterize patients’ prescribed common medications

2018 ◽  
Vol 26 (1) ◽  
pp. 172-180 ◽  
Author(s):  
Allison M Cole ◽  
Kari A Stephens ◽  
Imara West ◽  
Gina A Keppel ◽  
Ken Thummel ◽  
...  
Keyword(s):  
Primary Care ◽  
Adverse Events ◽  
Electronic Health Record ◽  
Research Network ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Record Data ◽  
Care Practices ◽  
Electronic Health ◽  
Primary Care Practices

We use prescription of statin medications and prescription of warfarin to explore the capacity of electronic health record data to (1) describe cohorts of patients prescribed these medications and (2) identify cohorts of patients with evidence of adverse events related to prescription of these medications. This study was conducted in the WWAMI region Practice and Research Network (WPRN)., a network of primary care practices across Washington, Wyoming, Alaska, Montana and Idaho DataQUEST, an electronic data-sharing infrastructure. We used electronic health record data to describe cohorts of patients prescribed statin or warfarin medications and reported the proportions of patients with adverse events. Among the 35,445 active patients, 1745 received at least one statin prescription and 301 received at least one warfarin prescription. Only 3 percent of statin patients had evidence of myopathy; 51 patients (17% of those prescribed warfarin) had a bleeding complication. Primary-care electronic health record data can effectively be used to identify patients prescribed specific medications and patients potentially experiencing medication adverse events.

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