Virtual learning experiences in population health nursing course during the COVID-19 pandemic.

AIM: To discuss the virtual learning strategies used in population health nursing course during the coronavirus disease 2019 (COVID-19) pandemic. BACKGROUND: The School of Nursing faculty in a South Central University in the United States quickly combined innovation with digital resources and transitioned a course in population health during the COVID-19 pandemic. Nursing faculty were challenged to develop student nursing objectives in assessment, planning, intervention and evaluation of vulnerable populations in the community through a virtual environment. REFLECTIONS OF POPULATION HEALTH NURSING CLINICAL EDUCATION: The experiences of five clinical groups are described, covering adults with disabilities, older people, patients with COVID-19 and youth populations. DISCUSSION: The course objectives were met through use of a digital environment. Collaborative interventions were designed and implemented with community stakeholders while maintaining social distancing policies. Successes included increased frequency of communication and learning opportunities for students and the community, and student satisfaction. Barriers to student learning were not related to the digital learning environment, although the older adults required modifications to use electronic devices. CONCLUSION: Virtual classrooms are a viable platform to teach population health nursing and to benefit vulnerable populations. IMPLICATIONS FOR NURSING PRACTICE: Virtual learning offers benefits within academia and the community. Technology offers the possibility to improve mental health among older people and enhance knowledge among the general population. Students are better able to connect with clinical faculty and stakeholders through digital platforms. IMPLICATIONS FOR NURSING POLICY: Nurses play a vital role in improving population health and can collaborate with community stakeholders to implement innovative and sustainable solutions to nursing education, practices and policy. Digital platforms can enhance the involvement of students through these collaborations during and after the pandemic.

How High-Risk Comorbidities Co-Occur in Readmitted Patients With Hip Fracture: Big Data Visual Analytical Approach.

BACKGROUND: When older adult patients with hip fracture (HFx) have unplanned hospital readmissions within 30 days of discharge, it doubles their 1-year mortality, resulting in substantial personal and financial burdens. Although such unplanned readmissions are predominantly caused by reasons not related to HFx surgery, few studies have focused on how pre-existing high-risk comorbidities co-occur within and across subgroups of patients with HFx. OBJECTIVE: This study aims to use a combination of supervised and unsupervised visual analytical methods to (1) obtain an integrated understanding of comorbidity risk, comorbidity co-occurrence, and patient subgroups, and (2) enable a team of clinical and methodological stakeholders to infer the processes that precipitate unplanned hospital readmission, with the goal of designing targeted interventions. METHODS: We extracted a training data set consisting of 16,886 patients (8443 readmitted patients with HFx and 8443 matched controls) and a replication data set consisting of 16,222 patients (8111 readmitted patients with HFx and 8111 matched controls) from the 2010 and 2009 Medicare database, respectively. The analyses consisted of a supervised combinatorial analysis to identify and replicate combinations of comorbidities that conferred significant risk for readmission, an unsupervised bipartite network analysis to identify and replicate how high-risk comorbidity combinations co-occur across readmitted patients with HFx, and an integrated visualization and analysis of comorbidity risk, comorbidity co-occurrence, and patient subgroups to enable clinician stakeholders to infer the processes that precipitate readmission in patient subgroups and to propose targeted interventions. RESULTS: The analyses helped to identify (1) 11 comorbidity combinations that conferred significantly higher risk (ranging from P<.001 to P=.01) for a 30-day readmission, (2) 7 biclusters of patients and comorbidities with a significant bicluster modularity (P<.001; Medicare=0.440; random mean 0.383 [0.002]), indicating strong heterogeneity in the comorbidity profiles of readmitted patients, and (3) inter- and intracluster risk associations, which enabled clinician stakeholders to infer the processes involved in the exacerbation of specific combinations of comorbidities leading to readmission in patient subgroups. CONCLUSIONS: The integrated analysis of risk, co-occurrence, and patient subgroups enabled the inference of processes that precipitate readmission, leading to a comorbidity exacerbation risk model for readmission after HFx. These results have direct implications for (1) the management of comorbidities targeted at high-risk subgroups of patients with the goal of pre-emptively reducing their risk of readmission and (2) the development of more accurate risk prediction models that incorporate information about patient subgroups.