Mining Patterns Associated With Mobility Outcomes in Home Healthcare

Content Validity of the Omaha System Target Terms for Integrative Healthcare Interventions

Background and Purpose: The objective of this study was to examine the content validity of the Omaha System to represent integrative healthcare (IH) interventions.Methods: A two-step classification procedure was used to validate Omaha System target terms that can represent IH interventions. Target terms were initially sorted based on evidence of use in IH interventions, including systematic reviews published in scientific journals and the Omaha System Guidelines website. Three Omaha System and integrative nursing content experts reviewed and validated target terms based on their definitions. Expert comments were reviewed and addressed, and final decisions were reached by consensus.Results: The content validity of Omaha System target terms was established for 49 of 75 (65.3%) target terms for IH interventions. These 49 targets were employed in 1145 of 1639 (69.9%) interventions in all Omaha System guidelines available online.Implications for Practice: A majority of Omaha System target terms may be used to represent IH interventions. Use of the Omaha System may facilitate efficient, structured, and thorough IH data collection to leverage informatics technology for supporting IH intervention clinical decisions, delivery, evaluation, and knowledge discovery.

Human-centered explainability for life sciences, healthcare, and medical informatics

Rapid advances in artificial intelligence (AI) and availability of biological, medical, and healthcare data have enabled the development of a wide variety of models. Significant success has been achieved in a wide range of fields, such as genomics, protein folding, disease diagnosis, imaging, and clinical tasks. Although widely used, the inherent opacity of deep AI models has brought criticism from the research field and little adoption in clinical practice. Concurrently, there has been a significant amount of research focused on making such methods more interpretable, reviewed here, but inherent critiques of such explainability in AI (XAI), its requirements, and concerns with fairness/robustness have hampered their real-world adoption. We here discuss how user-driven XAI can be made more useful for different healthcare stakeholders through the definition of three key personas-data scientists, clinical researchers, and clinicians-and present an overview of how different XAI approaches can address their needs. For illustration, we also walk through several research and clinical examples that take advantage of XAI open-source tools, including those that help enhance the explanation of the results through visualization. This perspective thus aims to provide a guidance tool for developing explainability solutions for healthcare by empowering both subject matter experts, providing them with a survey of available tools, and explainability developers, by providing examples of how such methods can influence in practice adoption of solutions.

A Holistic Clustering Methodology for Liver Transplantation Survival

BACKGROUND

Liver transplants account for a high number of procedures with major investments from all stakeholders involved; however, limited studies address liver transplant population heterogeneity pretransplant predictive of posttransplant survival.

OBJECTIVE

The aim of the study was to identify novel and meaningful patient clusters predictive of mortality that explains the heterogeneity of liver transplant population, taking a holistic approach.

METHODS

A retrospective cohort study of 344 adult patients who underwent liver transplantation between 2008 through 2014. Predictors were summarized severity scores for comorbidities and other suboptimal health states grouped into 11 body systems, the primary reason for transplantation, demographics/environmental factors, and Model for End Liver Disease score. Logistic regression was used to compute the severity scores, hierarchical clustering with weighted Euclidean distance for clustering, Lasso-penalized regression for characterizing the clusters, and Kaplan-Meier analysis to compare survival across the clusters.

RESULTS

Cluster 1 included patients with more severe circulatory problems. Cluster 2 represented older patients with more severe primary disease, whereas Cluster 3 contained healthiest patients. Clusters 4 and 5 represented patients with musculoskeletal (e.g., pain) and endocrine problems (e.g., malnutrition), respectively. There was a statistically significant difference for mortality between clusters (p < .001).

CONCLUSIONS

This study developed a novel methodology to address heterogeneous and high-dimensional liver transplant population characteristics in a single study predictive of survival. A holistic approach for data modeling and additional psychosocial risk factors has the potential to address holistically nursing challenges on liver transplant care and research.

Clustering of elderly patient subgroups to identify medication-related readmission risks

INTRODUCTION

High Risk Medication Regimen (HRMR) scores are weakly predictive of hospital readmissions for elderly home health care patients. HRMR is composed of three elements related to drug risks: polypharmacy (number of medications); Potentially Inappropriate Medications (PIM) known to be harmful to the elderly; and the Medication Regimen Complexity Index (MRCI) that weighs drugs by the complexity of their dosing and instructions. In this paper, we hypothesized that HRMR scores are more predictive for demographic subgroups of elderly patients. The study used Outcome and Assessment Information Set (OASIS) variables to identify subgroups of patients for whom the HRMR measures appeared more predictive for hospital readmissions.

METHODS

OASIS and medication data were reused from a study of 911 patients (355 males, 556 females; mean age 78.9) from 15 Medicare-certified home health care agencies that established the relationship between HRMR and hospital readmissions. Hierarchical agglomerative clustering using the Jaccard distance measure and average-link method identified patient subgroups based on the OASIS data. Receiver operating curve (ROC) analyses evaluated the predictive strength of the HRMR variables for each subgroup. Additional False Discovery Rate (FDR) analyses assessed whether the clustered relationships were chance.

RESULTS

Clustering of OASIS data for 911 patients identified six subgroups: patients with Good Functional Status (n=382); Females with Moderate to Severe Pain (n=354); patients with poor prognosis needing functional status assistance (n=419); patients with Poor Functional Status (n=287); Males with Adult Children as Caregiver (n=198); adults living alone with spouses as primary caregiver (n=127). ROC results relating these subgroups to HRMR risks were strongest for Males with Adult Children as Caregivers (AUC: polypharmacy, 0.73; PIM, 0.64; MRCI, 0.77). The findings for this subgroup also met the FDR analysis threshold (<=0.20).

CONCLUSIONS

A risk of medication-related readmissions in elderly men with adult children as caregivers is consistent with research showing problems in medication adherence when seniors are supported by informal caregivers. The results from clustering analysis present a hypothesis for research on HRMR and on the relationship between adult caregivers and their fathers.