Dykes, Patricia C.

A Randomized Trial of a Multifactorial Strategy to Prevent Serious Fall Injuries

Bhasin, S., Gill, T. M., Reuben, D. B., Latham, N. K., Ganz, D. A., Greene, E. J., Dziura, J., Basaria, S., Gurwitz, J. H., Dykes, P. C., et al. (2020) · New England Journal of Medicine

Balance, Gait, and Falls PreventionPharmaceutical Practices and Patient Outcomes

Cited by 228

Medication-related clinical decision support alert overrides in inpatients

Nanji, K. C., Seger, D. L., Slight, S. P., Amato, M. G., Beeler, P. E., Her, Q. L., Dalleur, O., Eguale, T., Wong, A., Silvers, E. R., et al. (2018) · Journal of the American Medical Informatics Association

Electronic Health Records SystemsPharmaceutical Practices and Patient Outcomes

Cited by 179

Documentation burden in nursing and its role in clinician burnout syndrome

Gesner, E., Dykes, P. C., Zhang, L., & Gazarian, P. (2022) · Applied Clinical Informatics

Electronic Health Records SystemsNursing Diagnosis and Documentation

Cited by 118

Development and validation of early warning score system: A systematic literature review

Fu, L.-H., Schwartz, J., Moy, A., Knaplund, C., Kang, M.-J., Schnock, K. O., Garcia, J. P., Jia, H., Dykes, P. C., Cato, K., et al. (2020) · Journal of Biomedical Informatics

Healthcare Technology and Patient MonitoringSepsis Diagnosis and Treatment

Cited by 102

Factors influencing clinician trust in predictive clinical decision support systems for in-hospital deterioration: qualitative descriptive study

Schwartz, J. M., George, M., Rossetti, S. C., Dykes, P. C., Minshall, S. R., Lucas, E., & Cato, K. D. (2022) · JMIR Human Factors

Artificial Intelligence in Healthcare and EducationElectronic Health Records Systems

Cited by 88

Are We Heeding the Warning Signs? Examining Providers’ Overrides of Computerized Drug-Drug Interaction Alerts in Primary Care

Slight, S. P., Seger, D. L., Nanji, K. C., Cho, I., Maniam, N., Dykes, P. C., & Bates, D. W. (2013) · PLoS ONE

Electronic Health Records SystemsPharmaceutical Practices and Patient Outcomes

Cited by 86

Predicting pressure injury using nursing assessment phenotypes and machine learning methods

Song, W., Kang, M.-J., Zhang, L., Jung, W., Song, J., Bates, D. W., & Dykes, P. C. (2021) · Journal of the American Medical Informatics Association

Pressure Ulcer Prevention and ManagementMachine Learning in Healthcare

Cited by 67

Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial

Rossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., Albers, D., Lee, R. Y., Jia, H., Bakken, S., et al. (2025) · Nature Medicine

Healthcare Technology and Patient MonitoringMachine Learning in Healthcare

Cited by 54

Reducing anxiety and improving engagement in health care providers through an Auricular Acupuncture intervention

Buchanan, T. M., Reilly, P. M., Vafides, C., & Dykes, P. (2018) · Dimensions of Critical Care Nursing

Acupuncture Treatment Research StudiesHealthcare professionals’ stress and burnout

Cited by 47

Healthcare Process Modeling to Phenotype Clinician Behaviors for Exploiting the Signal Gain of Clinical Expertise (HPM-ExpertSignals): Development and evaluation of a conceptual framework

Rossetti, S. C., Knaplund, C., Albers, D., Dykes, P. C., Kang, M. J., Korach, T. Z., Zhou, L., Schnock, K., Garcia, J., Schwartz, J., et al. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 42

Unsupervised Machine Learning of Topics Documented by Nurses about Hospitalized Patients Prior to a Rapid-Response Event

Korach, Z. T., Cato, K. D., Collins, S. A., Kang, M. J., Knaplund, C., Dykes, P. C., Wang, L., Schnock, K. O., Garcia, J. P., Jia, H., et al. (2019) · Applied Clinical Informatics

Machine Learning in HealthcareNursing Diagnosis and Documentation

Cited by 32

Identifying nurses’ concern concepts about patient deterioration using a standard nursing terminology

Kang, M.-J., Dykes, P. C., Korach, T. Z., Zhou, L., Schnock, K. O., Thate, J., Whalen, K., Jia, H., Schwartz, J., Garcia, J. P., et al. (2020) · International Journal of Medical Informatics

Nursing Diagnosis and DocumentationMachine Learning in Healthcare

Cited by 24

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

Fu, L.-H., Knaplund, C., Cato, K., Perotte, A., Kang, M.-J., Dykes, P. C., Albers, D., & Collins Rossetti, S. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareHealthcare Technology and Patient Monitoring

Cited by 14

Nursing Documentation Variation Across Different Medical Facilities Within an Integrated Healthcare System

Kang, M.-J., Rossetti, S. C., Knaplund, C., Chang, F. Y., Schnock, K. O., Whalen, K., Gesner, E. J., Garcia, J. P., Cato, K. D., & Dykes, P. C. (2021) · CIN: Computers, Informatics, Nursing

Nursing Diagnosis and DocumentationElectronic Health Records Systems

Cited by 13

Identifying Barriers to The Implementation of Communicating Narrative Concerns Entered by Registered Nurses, An Early Warning System SmartApp

Hobensack, M., Withall, J., Douthit, B., Cato, K., Dykes, P., Cho, S., Lowenthal, G., Ivory, C., Yen, P.-Y., & Rossetti, S. (2024) · Applied Clinical Informatics

Health Policy Implementation ScienceElectronic Health Records Systems

Cited by 3