A mixed methods study of clinical information availability in obstetric triage and prenatal offices

Meyerhoefer, Chad D.; Sherer, Susan A.; Deily, Mary E.; Peng, Lizhong; Hu, Tianyan; Nihen, Marion B; Sheinberg, Michael; Levick, Donald

doi:10.1093/jamia/ocw113

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…Access to a patient's medical and obstetric history has been linked to maternal and neonatal outcomes (Meyerhoefer et al, 2017) and informs clinical decision‐making (Semenic et al, 2015). Nurses at both sites, however, reported pervasive problems with accessing and reviewing the patient's prenatal record and history due to system interface and interoperability issues.…”

Section: Discussionmentioning

confidence: 99%

“…Nurses at both sites, however, reported pervasive problems with accessing and reviewing the patient's prenatal record and history due to system interface and interoperability issues. Clinicians working in EHRs where information transfer is automated from ambulatory or office settings to discrete fields in the inpatient record report higher use of such information (Meyerhoefer et al, 2017), which may improve safety.…”

Section: Discussionmentioning

confidence: 99%

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Managing the tension between caring and charting: Labor and delivery nurses' experiences of the electronic health record

Wisner

Chesla

Spetz

et al. 2021

Research in Nursing & Health

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Over a decade following the nationwide push to implement electronic health records (EHRs), the focus has shifted to addressing the cognitive burden associated with their use. Most research and discourse about the EHR's impact on clinicians' cognitive work has focused on physicians rather than on nursing‐specific issues. Labor and delivery nurses may encounter unique challenges when using EHRs because they also interact with an electronic fetal monitoring system, continuously managing and synthesizing both maternal and fetal data. This grounded theory study explored labor and delivery nurses' perceptions of the EHR's impact on their cognitive work. Data were individual interviews and participant observations with twenty‐one nurses from two labor and delivery units in the western U.S. and were analyzed using dimensional analysis. Nurses managed the tension between caring and charting using various strategies to integrate the EHR into their dynamic, high‐acuity, specialty practice environment while using EHRs that were not designed for perinatal patients. Use of the EHR and associated technologies disrupted nurses' ability to locate and synthesize information, maintain an overview of the patient's status, and connect with patients and families. Individual‐, group‐, and environmental‐level factors facilitated or constrained nurses' integration of the EHR. These findings represent critical safety failures requiring comprehensive changes to EHR designs and better processes for responding to end‐user experiences. More research is needed to develop EHRs that support the dynamic and relationship‐based nature of nurses' work and to align with specialty practice environments.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Managing the tension between caring and charting: Labor and delivery nurses' experiences of the electronic health record

Wisner

Chesla

Spetz

et al. 2021

Research in Nursing & Health

View full text Add to dashboard Cite

show abstract

“…EHRs were used previously to study birth-related effects 13 with machine learning algorithms showing promise 14 , 15 . Many birth-related elements are available within EHRs even if sometimes access is limited 16 . Therefore, an EHR system containing linked maternal and fetal information would be the ideal dataset for an algorithm that classifies FDA category C (i.e., drugs with unknown fetal effect) into harmful and safe bins.…”

Section: Introductionmentioning

confidence: 99%

Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect

Boland

Polubriaginof

Tatonetti

2017

Sci Rep

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Many drugs commonly prescribed during pregnancy lack a fetal safety recommendation – called FDA ‘category C’ drugs. This study aims to classify these drugs into harmful and safe categories using knowledge gained from chemoinformatics (i.e., pharmacological similarity with drugs of known fetal effect) and empirical data (i.e., derived from Electronic Health Records). Our fetal loss cohort contains 14,922 affected and 33,043 unaffected pregnancies and our congenital anomalies cohort contains 5,658 affected and 31,240 unaffected infants. We trained a random forest to classify drugs of unknown pregnancy class into harmful or safe categories, focusing on two distinct outcomes: fetal loss and congenital anomalies. Our models achieved an out-of-bag accuracy of 91% for fetal loss and 87% for congenital anomalies outperforming null models. Fifty-seven ‘category C’ medications were classified as harmful for fetal loss and eleven for congenital anomalies. This includes medications with documented harmful effects, including naproxen, ibuprofen and rubella live vaccine. We also identified several novel drugs, e.g., haloperidol, that increased the risk of fetal loss. Our approach provides important information on the harmfulness of ‘category C’ drugs. This is needed, as no FDA recommendation exists for these drugs’ fetal safety.

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Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems

Meyerhoefer

Sherer

Deily

et al. 2018

Journal of the American Medical Informatics Association

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A mixed methods study of clinical information availability in obstetric triage and prenatal offices

Cited by 4 publications

References 13 publications

Managing the tension between caring and charting: Labor and delivery nurses' experiences of the electronic health record

Managing the tension between caring and charting: Labor and delivery nurses' experiences of the electronic health record

Development of A Machine Learning Algorithm to Classify Drugs Of Unknown Fetal Effect

Provider and patient satisfaction with the integration of ambulatory and hospital EHR systems

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