Machine Learning Methods for Identifying Critical Data Elements in Nursing Documentation.

BACKGROUND: Public health nurses (PHNs) engage in home visiting services and documentation of care services for at-risk clients. To increase efficiency and decrease documentation burden, it would be useful for PHNs to identify critical data elements most associated with patient care priorities and outcomes. Machine learning techniques can aid in retrospective identification of critical data elements. OBJECTIVE: We used two different machine learning feature selection techniques of minimum redundancy-maximum relevance (mRMR) and LASSO (least absolute shrinkage and selection operator) and elastic net regularized generalized linear model (glmnet in R). METHODS: We demonstrated application of these techniques on the Omaha System database of 205 data elements (features) with a cohort of 756 family home visiting clients who received at least one visit from PHNs in a local Midwest public health agency. A dichotomous maternal risk index served as the outcome for feature selection. APPLICATION: Using mRMR as a feature selection technique, out of 206 features, 50 features were selected with scores greater than zero, and generalized linear model applied on the 50 features achieved highest accuracy of 86.2% on a held-out test set. Using glmnet as a feature selection technique and obtaining feature importance, 63 features had importance scores greater than zero, and generalized linear model applied on them achieved the highest accuracy of 95.5% on a held-out test set. DISCUSSION: Feature selection techniques show promise toward reducing public health nursing documentation burden by identifying the most critical data elements needed to predict risk status. Further studies to refine the process of feature selection can aid in informing PHNs' focus on client-specific and targeted interventions in the delivery of care.

Creating and sustaining an academic-practice Partnership Engagement Model.

Public health clinical educators and practicing public health nurses (PHNs) are experiencing challenges in creating meaningful clinical learning experiences for nursing students due to an increase in nursing programs and greater workload responsibilities for both nursing faculty and PHNs. The Henry Street Consortium (HSC), a collaborative group of PHNs and nursing faculty, conducted a project to identify best practices for public health nursing student clinical learning experiences. Project leaders surveyed HSC members about preferences for teaching-learning strategies, facilitated development of resources and tools to guide learning, organized faculty/PHN pilot teams to test resources and tools with students, and evaluated the pilot team experiences through two focus groups. The analysis of the outcomes of the partnership engagement project led to the development of the Partnership Engagement Model (PEM), which may be used by nursing faculty and their public health practice partners to guide building relationships and sustainable partnerships for educating nursing students.

Social Determinants and Health Disparities Associated With Outcomes of Women of Childbearing Age Who Receive Public Health Nurse Home Visiting Services.

OBJECTIVE: To examine the associations between social and behavioral determinants of health (SBDH), health disparities, and the outcomes of women who received public health nurse home visits for pregnancy and parenting support. DESIGN: Observational exploratory data analysis and comparative outcome evaluation. SETTING: An extant dataset from women served in a Midwestern U.S. state, including demographics and Omaha System problems, signs/symptoms, interventions, and outcome assessments. PARTICIPANTS: Women (N = 4,263) with an average age of 23.6 years (SD = 6.1); 21.4% were married, and 39.1% were White. METHODS: An evaluation dataset was constructed that included all women of childbearing age, their demographics, and outcome assessments. A summative SBDH Index based on Institute of Medicine-recommended instruments was computed based on sign/symptom data. Visualizations were developed using Microsoft Excel, and outcome significance statistics were computed using SPSS version 22 and SAS version 9.4. RESULTS: Outcome evaluation showed positive, significant changes from baseline after public health nurse intervention. Visualization showed variable concentrations of problem-specific signs/symptoms by SBDH Index subgroups. There were between-group differences in overall outcome attainment across SBDH Index subgroups. Compared with White women, minority women had greater improvement; however, despite these gains overall minority final ratings were lower. CONCLUSION: An informatics approach showed that SBDH are important factors for understanding a comprehensive and holistic view of health and health care outcomes. There is potential to use large datasets to further explore intervention effectiveness and progress toward health equity related to SBDH.