The factors influencing community nurses' usage of electronic health records: findings from focus groups

Electronic health records (EHRs) are integral to community nursing, and mobile access aids seamless, responsive care, prevents repetition and reduces hospital admissions. This saves time and money, enabling smoother workflows and increased productivity. Common practice among community nurses is to return to workbases to access EHRs. This research was conducted to explore what leads to inconsistency in EHR use. Focus groups were held with community nurses, and reflexive thematic analysis of the data was undertaken. Nurses who used EHRs during consultations described the practice as integrative and informed, promoting collaborative care. Those who did not described EHRs as time-consuming, template-laden and a barrier to nurse-patient communication. One barrier to mobile working is the threat to collegiate teamworking and the social and clinical supports it provides. This study suggests specific strategies could increase mobile EHR engagement: role-specific training for effective EHR use; clear organisational directives; innovative team communication; and peer-to-peer coaching.

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Development and validation of techniques for phenotyping ST-elevation myocardial infarction encounters from electronic health records

JAMIA Open ◽

10.1093/jamiaopen/ooab068 ◽

2021 ◽

Vol 4 (3) ◽

Author(s):

Sulaiman Somani ◽

Stephen Yoffie ◽

Shelly Teng ◽

Shreyas Havaldar ◽

Girish N Nadkarni ◽

...

Keyword(s):

Myocardial Infarction ◽

Electronic Health Records ◽

Hospital Admissions ◽

Coronary Intervention ◽

St Elevation Myocardial Infarction ◽

Health Records ◽

St Elevation ◽

Electronic Health ◽

Cardiac Catheterizations ◽

Discharge Summaries

Abstract Objectives Classifying hospital admissions into various acute myocardial infarction phenotypes in electronic health records (EHRs) is a challenging task with strong research implications that remains unsolved. To our knowledge, this study is the first study to design and validate phenotyping algorithms using cardiac catheterizations to identify not only patients with a ST-elevation myocardial infarction (STEMI), but the specific encounter when it occurred. Materials and Methods We design and validate multi-modal algorithms to phenotype STEMI on a multicenter EHR containing 5.1 million patients and 115 million patient encounters by using discharge summaries, diagnosis codes, electrocardiography readings, and the presence of cardiac catheterizations on the encounter. Results We demonstrate that robustly phenotyping STEMIs by selecting discharge summaries containing “STEM” has the potential to capture the most number of STEMIs (positive predictive value [PPV] = 0.36, N = 2110), but that addition of a STEMI-related International Classification of Disease (ICD) code and cardiac catheterizations to these summaries yields the highest precision (PPV = 0.94, N = 952). Discussion and Conclusion In this study, we demonstrate that the incorporation of percutaneous coronary intervention increases the PPV for detecting STEMI-related patient encounters from the EHR.

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