Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals

Mistry, Sejal; Gouripeddi, Ramkiran; Raman, Vandana; Facelli, Julio C.

doi:10.1016/j.jbi.2023.104385

Cited by 3 publications

(2 citation statements)

References 60 publications

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“…Using concepts derived from our previous work in studying temporal biomedical data patterns, we formulated a data structure that can describe nurse EHR interactions, nurse intrinsic and situational characteristics, and nurse outcomes of interest in a scalable and extensible manner. [19,20] We believe the selected features will allow for metadata aggregation into EHR use measures that can be used for a variety of nurse centric outcomes. Then, we conceptually instantiated the model with an analysis plan for quantitative study of the characteristics and expected outcomes associated with nurse EHR interactions using AI and temporal machine learning (ML) methods.…”

Section: Resultsmentioning

confidence: 99%

“…Using concepts derived from our previous work in studying temporal biomedical data patterns [ 18 , 19 ], we formulated a data structure that can describe nurse-EHR interactions, nurse-intrinsic and situational characteristics, and nurse outcomes of interest in a scalable and extensible manner. We believe the selected features will allow for metadata aggregation into EHR use measures that can be used for a variety of nurse-centric outcomes.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

Tiase,

Sward,

Facelli

2024

JMIR Nursing

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Background: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout, and adversely affects patient safety and nurse satisfaction. Although nurse burnout has been studied for decades, little has changed in the organization of clinical care. The measurement of nursing workload is not well understood. Traditional methods for workload analysis are either administrative measures (such as nurse-patient ratio) that do not represent actual nursing care, or are subjective and limited to snapshots of care (e.g., time-motion studies). Observing care, and testing workflow changes in real-time, can be obstructive to clinical care. An examination of EHR interactions through the use of EHR audit logs could provide a scalable, unobtrusive way to quantify nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex, however, and simple analytical methods can't discover complex temporal patterns, therefore requiring the use of state-of-the-art temporal data mining approaches. To effectively use these approaches it is necessary to structure the raw audit logs into a consistent and scalable data model that can be consumed by machine learning (ML) algorithms. Objective:We aimed to conceptualize a data model for nurse-EHR interactions that would support the development of temporal ML models based on EHR audit log data. Methods:We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from literature and our previous experience studying temporal patterns in biomedical data, we formulated a data model that can describe the nurse-EHR interactions, nurse intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to nursing workload in a scalable and extensible manner. Results:We described the data structure and concepts from EHR audit log data associated with nursing workload as a data model, that we name as RNteract. We conceptually demonstrated how using this data model could support temporal unsupervised machine learning and state-of-the-art artificial intelligence methods for predictive modeling. Conclusions:The RNteract data model appears capable of supporting a variety AI-based systems (computational models), and should be generalizable to any type of EHR system or healthcare setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support nursing documentation workload and address nurse burnout. Clinical

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

Tiase,

Sward,

Facelli

2024

JMIR Nursing

View full text Add to dashboard Cite

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Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology

Mistry,

Gouripeddi,

Facelli

2023

Journal of Autoimmunity

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A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation (Preprint)

Tiase,

Sward,

Facelli

2023

Preprint

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BACKGROUND Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout, and adversely affects patient safety and nurse satisfaction. Although nurse burnout has been studied for decades, little has changed in the organization of clinical care. The measurement of nursing workload is not well understood. Traditional methods for workload analysis are either administrative measures (such as nurse-patient ratio) that do not represent actual nursing care, or are subjective and limited to snapshots of care (e.g., time-motion studies). Observing care, and testing workflow changes in real-time, can be obstructive to clinical care. An examination of EHR interactions through the use of EHR audit logs could provide a scalable, unobtrusive way to quantify nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex, however, and simple analytical methods can’t discover complex temporal patterns, therefore requiring the use of state-of-the-art temporal data mining approaches. To effectively use these approaches it is necessary to structure the raw audit logs into a consistent and scalable data model that can be consumed by machine learning (ML) algorithms. OBJECTIVE We aimed to conceptualize a data model for nurse-EHR interactions that would support the development of temporal ML models based on EHR audit log data. METHODS We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from literature and our previous experience studying temporal patterns in biomedical data, we formulated a data model that can describe the nurse-EHR interactions, nurse intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to nursing workload in a scalable and extensible manner. RESULTS We described the data structure and concepts from EHR audit log data associated with nursing workload as a data model, that we name as RNteract. We conceptually demonstrated how using this data model could support temporal unsupervised machine learning and state-of-the-art artificial intelligence methods for predictive modeling. CONCLUSIONS The RNteract data model appears capable of supporting a variety AI-based systems (computational models), and should be generalizable to any type of EHR system or healthcare setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support nursing documentation workload and address nurse burnout. CLINICALTRIAL Not Applicable

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Sequential data mining of infection patterns as predictors for onset of type 1 diabetes in genetically at-risk individuals

Cited by 3 publications

References 60 publications

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation

Prioritization of infectious epitopes for translational investigation in type 1 diabetes etiology

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation (Preprint)

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