Using natural language processing to classify social work interventions

Social Work in Outpatient Neurology at a Safety-Net Hospital: A 200-Hour Profile

Social work plays a critical role in preventive health and mitigation of healthcare disparities, but few studies focus on its role in multi-specialty clinics serving marginalized populations. We aimed to characterize the role of outpatient neurology social work at an urban, safety-net hospital. In December 2021, we introduced a dedicated social worker to a neurology clinic primarily caring for an underserved patient population. We logged and characterized the first 200 consecutive hours of patient encounters, classifying interventions based on a recently popularized 10-category scheme in social work literature derived from natural language processing and machine learning algorithms. We characterized 125 encounters with neurology patients referred to social work. The neurology social worker spent the greatest amount of time on care coordination (40%), followed by housing insecurity (14%) and applications and reporting (11%). Interventions that required the most time per case included housing (129 min), applications and reporting (120 min), care coordination (96 min). The majority of interventions were directly related to the patient's underlying neurologic disorder, highlighting the importance of a neurology-specific social worker. Embedding a social worker in a multi-specialty neurology clinic may address many of the root causes of neurologic health disparities.

Finding social need-les in a haystack: ascertaining social needs of Medicare patients recorded in the notes of care managers

BACKGROUND

Unmet social needs may impair health and access to health care, and intervening on these holds particular promise in high-risk patient populations, such as those with multiple chronic conditions. Our objective was to identify social needs in a patient population at significant risk-Medicare enrollees with multiple chronic illnesses enrolled in care management services-and measure their prevalence prior to any systematic screening.

METHODS

We partnered with Renova Health, an independent Medicare Chronic Care Management (CCM) provider with patients in 10 states during our study period (January 2017 through August 2020). Our data included over 3,000 Medicare CCM patients, representing nearly 20,000 encounters. We used a dictionary-based natural language processing approach to ascertain the prevalence of six domains of barriers to care (food insecurity, housing instability, utility hardship) and unmet social needs (health care affordability, need for supportive services, transportation) in notes taken during telephonic Medicare CCM patient encounters.

RESULTS

Barriers to care, specifically need for supportive services (2.4%) and health care affordability (0.8%), were the most prevalent domains identified. Transportation as a barrier to care came up relatively less frequently in CCM encounters (0.1%). Unmet social needs were identified at a comparatively lower rate, with potential housing instability (0.3%) flagged most followed by potential utility hardship (0.2%) and food insecurity (0.1%).

CONCLUSIONS

There is substantial untapped opportunity to systematically screen for social determinants of health and unmet social needs in care management.

Derivation of a Unique, Algorithm-Based Approach to Cancer Patient Navigator Workload Management

PURPOSE

Cancer patient navigators (CPNs) can decrease the time from diagnosis to treatment, but workloads vary widely, which may lead to burnout and less optimal navigation. Current practice for patient distribution among CPNs at our institution approximates random distribution. A literature search did not uncover previous reports of an automated algorithm to distribute patients to CPNs. We sought to develop an automated algorithm to fairly distribute new patients among CPNs specializing in the same cancer type(s) and assess its performance through simulation on a retrospective data set.

METHODS

Using a 3-year data set, a proxy for CPN work was identified and multiple models were developed to predict the upcoming week's workload for each patient. An XGBoost-based predictor was retained on the basis of its superior performance. A distribution model was developed to fairly distribute new patients among CPNs within a specialty on the basis of predicted work needed. The predicted work included the week's predicted workload from a CPN's existing patients plus that of newly distributed patients to the CPN. Resulting workload unfairness was compared between predictor-informed and random distribution.

RESULTS

Predictor-informed distribution significantly outperformed random distribution for equalizing weekly workloads across CPNs within a specialty.

CONCLUSION

This derivation work demonstrates the feasibility of an automated model to distribute new patients more fairly than random assignment (with unfairness assessed using a workload proxy). Improved workload management may help reduce CPN burnout and improve navigation assistance for patients with cancer.

Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions

Smart cities provide an efficient infrastructure for the enhancement of the quality of life of the people by aiding in fast urbanization and resource management through sustainable and scalable innovative solutions. The penetration of Information and Communication Technology (ICT) in smart cities has been a major contributor to keeping up with the agility and pace of their development. In this paper, we have explored Natural Language Processing (NLP) which is one such technical discipline that has great potential in optimizing ICT processes and has so far been kept away from the limelight. Through this study, we have established the various roles that NLP plays in building smart cities after thoroughly analyzing its architecture, background, and scope. Subsequently, we present a detailed description of NLP's recent applications in the domain of smart healthcare, smart business, and industry, smart community, smart media, smart research, and development as well as smart education accompanied by NLP's open challenges at the very end. This work aims to throw light on the potential of NLP as one of the pillars in assisting the technical advancement and realization of smart cities.