OBJECTIVE: To describe the characteristics of patients who received outpatient therapy services through an infant bridge program using telehealth mode of service delivery and to identify if attendance rates vary by mode of service delivery. We hypothesized that telehealth visits will increase attendance rates. DESIGN: Retrospective, cross-sectional study. SETTING: UCSF Benioff Childrens Hospital outpatient infant bridge program. PARTICIPANTS: Eighty infants with a history of NICU admission and scheduled for a therapy appointment between June 1, 2019 and December 31, 2020 were included in the study. Participants had an average(SD) gestational age of 34.63(4.41) weeks and length of stay was 43.55(56.03) weeks. The majority were English-speaking (96.3%), White (37.5%), and had commercial insurance (72.5%). MAIN OUTCOME MEASURE: Descriptive analyses were conducted across the entire group along with service delivery model subgroup analysis. Logistic regression was performed to assess patient characteristics associated with attendance and if service delivery model influences attendance. RESULTS: In the analysis of 596 scheduled visits, there were more completed telehealth sessions than for in-person sessions (90.0% versus 84.1%, p = .011). For in-person sessions, infants (N = 40) with lower birth gestational ages (p = .009), longer length of stay (p = .041), and Medi-Cal insurance (p = .006) were more likely to have ≥2 missed appointments. For the telehealth sessions, infants (N = 40) who had longer length of stay (p = .040) were more likely to have ≥2 missed appointments. There is a higher likelihood of ≥2 missed appointments for patients with a longer length of stay (OR = 1.02, 95% CI [1.01, 1.03]) and for in-person service delivery when compared to telehealth (OR = 6.25, 95% CI [1.37, 28.57]). CONCLUSIONS: Telehealth was associated with higher likelihood of attendance, revealing that telehealth has the potential to increase access to early therapy services for certain populations. Future studies with larger sample sizes to determine which populations benefit from telehealth is recommended.

BACKGROUND: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. METHODS: This was a retrospective cohort study of patients access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. RESULTS: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p < .05). The hotspot regions had higher deprivation scores; higher proportions of residents who were older, privately insured, female, lived further from clinics; and a higher proportion of Black patients with orthopaedic diagnoses compared to the coldspot regions. CONCLUSIONS: Our study identified and described local areas with higher densities of patients that experienced longer access times to outpatient physical therapy services. Integration of EHR and GIS data is a more robust method to identify health disparities in access to care. With this approach, we can better understand the intricate interplay between social, economic, and environmental factors contributing to health disparities in access to care.

Background

Although evidence-based guidelines for physical therapy for patients with chronic low back pain (cLBP) are available, selecting patient-reported outcome measures to capture complexity of health status and quality of life remains a challenge. PROMIS-10 Global Health (GH) may be used to screen for impactful health risks and enable patient-centered care. The purpose of this study was to investigate the interrelationships between PROMIS-10 GH scores and patient demographics, health status, and healthcare utilization in patients with cLBP who received physical therapy.

Methods

A retrospective review of de-identified electronic health records of patients with cLBP was performed. Data were collected for 328 patients seen from 2017 to 2020 in three physical therapy clinics. Patients were grouped into HIGH and LOW initial assessment scores on the PROMIS-10 Global Physical Health (PH) and Global Mental Health (MH) measures. Outcomes of interest were patient demographics, health status, and healthcare utilization. Mann-Whitney U and chi-square tests were used to determine differences between groups, and binary logistic regression was used to calculate odds ratios (OR) to determine predictors of PH-LOW and MH-LOW group assignments.

Results

The PH-LOW and MH-LOW groups contained larger proportions of patients who were African American, non-Hispanic, and non-commercially insured compared to PH-HIGH and MH-HIGH groups (p < .05). The PH-LOW and MH-LOW groups also had a higher Charlson comorbidity index (CCI), higher rates of diabetes and depression, and more appointment cancellations or no-shows (p < .05). African American race (OR 2.54), other race (2.01), having Medi-Cal insurance (OR 3.37), and higher CCI scores (OR 1.55) increased the likelihood of being in the PH-LOW group. African American race (OR 3.54), having Medi-Cal insurance (OR 2.19), depression (OR 3.15), kidney disease (OR 2.66), and chronic obstructive pulmonary disease (OR 1.92) all increased the likeihood of being in the MH-LOW group.

Conclusions

Our study identified groups of patients with cLBP who are more likely to have lower PH and MH scores. PROMIS-10 GH provides an opportunity to capture and identify quality of life and global health risks in patients with cLBP. Using PROMIS-10 in physical therapy practice could help identify psychosocial factors and quality of life in the population with cLBP.

OBJECTIVE: We applied natural language processing and inference methods to extract social determinants of health (SDoH) information from clinical notes of patients with chronic low back pain (cLBP) to enhance future analyses of the associations between SDoH disparities and cLBP outcomes. MATERIALS AND METHODS: Clinical notes for patients with cLBP were annotated for 7 SDoH domains, as well as depression, anxiety, and pain scores, resulting in 626 notes with at least one annotated entity for 364 patients. We used a 2-tier taxonomy with these 10 first-level classes (domains) and 52 second-level classes. We developed and validated named entity recognition (NER) systems based on both rule-based and machine learning approaches and validated an entailment model. RESULTS: Annotators achieved a high interrater agreement (Cohens kappa of 95.3% at document level). A rule-based system (cTAKES), RoBERTa NER, and a hybrid model (combining rules and logistic regression) achieved performance of F1 = 47.1%, 84.4%, and 80.3%, respectively, for first-level classes. DISCUSSION: While the hybrid model had a lower F1 performance, it matched or outperformed RoBERTa NER model in terms of recall and had lower computational requirements. Applying an untuned RoBERTa entailment model, we detected many challenging wordings missed by NER systems. Still, the entailment model may be sensitive to hypothesis wording. CONCLUSION: This study developed a corpus of annotated clinical notes covering a broad spectrum of SDoH classes. This corpus provides a basis for training machine learning models and serves as a benchmark for predictive models for NER for SDoH and knowledge extraction from clinical texts.

Introduction

Care and outcomes for patients with chronic low back pain (cLBP) are influenced by the social risk factors that they experience. Social risk factors such as food insecurity and housing instability have detrimental effects on patient health and wellness, healthcare outcomes, and health disparities.

Objectives

This retrospective cross-sectional study examined how social risk factors identified in unstructured and structured electronic health record (EHR) data for 1,295 patients with cLBP were associated with health care utilization. We also studied the impact of social risk factors, controlling for back pain-related disability on health care utilization.

Methods

Included patients who received outpatient spine and/or physical therapy services at an urban academic medical center between 2018 and 2020. Five identified social risks were financial insecurity, housing instability, food insecurity, transportation barriers, and social isolation. Outcomes included 4 categories of health care utilization: emergency department (ED) visits/hospitalizations, imaging, outpatient specialty visits related to spine care, and physical therapy (PT) visits. Poisson regression models tested associations between the presence of identified social risks and each outcome measure.

Results

Identified social risks in 12.8% of the study population (N = 166/1,295). In multivariate models, social isolation was positively associated with imaging, specialty visits, and PT visits; housing instability was positively associated with ED visits/hospitalizations and imaging; food insecurity was positively associated with ED visits/hospitalizations and specialty visits but negatively associated with PT visits; and financial strain was positively associated with PT visits but negatively associated with ED visits/hospitalization.

Conclusion

These associations were seen above and beyond other factors used as markers of socioeconomic marginalization, including neighborhood-level social determinants of health, race/ethnicity, and insurance type. Identifying and intervening on social risk factors that patients with cLBP experience may improve outcomes and be cost-saving.