Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from these data.

Purpose Extracting information from Electronic Medical Record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. MethodsWe collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital (MGH), Brigham and Womens Hospital (BWH), and Newton-Wellesley Hospital (NWH), covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6,295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably.Results The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports.Conclusions Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from this data.

A B S T R A C T BACKGROUND AND OBJECTIVES:Although the incidence of neonatal abstinence syndrome (NAS) in the United States quintupled between 2000 and 2012, little is known about the family perspective of the hospital stay. We interviewed families to understand their experiences during the newborn hospitalization for NAS and to improve family-centered care. CONCLUSIONS: Families face many challenges during newborn hospitalization for NAS. Addressing parental needs through improved perinatal education, increased involvement in the care team, consistent care and communication, and minimized transitions in care could improve the NAS hospital experience. The results of this qualitative study may allow for improvements in family-centered care of infants with NAS. METHODS:

BACKGROUND AND OBJECTIVES Although the incidence of neonatal abstinence syndrome (NAS) in the United States quintupled between 2000 and 2012, little is known about the family perspective of the hospital stay. We interviewed families to understand their experiences during the newborn hospitalization for NAS and to improve family-centered care. METHODS A multidisciplinary team from 3 hospital units composed open-ended interview questions based on a literature review, clinical experience, and an internal iterative process. Trained investigators conducted semi-structured interviews with 20 families of newborns with NAS at hospital discharge. Interviews were recorded and transcribed verbatim. Two investigators independently analyzed each transcript, identified themes via an inductive qualitative approach, and reached a consensus on each code. The research team sorted the themes into broader domains through an iterative process that required consensus of 4 team members. RESULTS Five domains of family experience were identified: parents’ desire for education about the course and treatment of NAS; parents valuing their role in the care team; quality of interactions with staff (supportive versus judgmental) and communication regarding clinical course; transfers between units and inconsistencies among providers; and external factors such as addiction recovery and economic limitations. CONCLUSIONS Families face many challenges during newborn hospitalization for NAS. Addressing parental needs through improved perinatal education, increased involvement in the care team, consistent care and communication, and minimized transitions in care could improve the NAS hospital experience. The results of this qualitative study may allow for improvements in family-centered care of infants with NAS.

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