Large Language Models to Identify Social Determinants of Health in Electronic Health Records

Social determinants of health (SDoH) have an important impact on patient outcomes but are incompletely collected from the electronic health records (EHR). This study researched the ability of large language models to extract SDoH from free text in EHRs, where they are most commonly documented, and explored the role of synthetic clinical text for improving the extraction of these scarcely documented, yet extremely valuable, clinical data. 800 patient notes were annotated for SDoH categories, and several transformer-based models were evaluated. The study also experimented with synthetic data generation and assessed for algorithmic bias. Our best-performing models were fine-tuned Flan-T5 XL (macro-F1 0.71) for any SDoH, and Flan-T5 XXL (macro-F1 0.70). The benefit of augmenting fine-tuning with synthetic data varied across model architecture and size, with smaller Flan-T5 models (base and large) showing the greatest improvements in performance (delta F1 +0.12 to +0.23). Model performance was similar on the in-hospital system dataset but worse on the MIMIC-III dataset. Our best-performing fine-tuned models outperformed zero- and few-shot performance of ChatGPT-family models for both tasks. These fine-tuned models were less likely than ChatGPT to change their prediction when race/ethnicity and gender descriptors were added to the text, suggesting less algorithmic bias (p<0.05). At the patient-level, our models identified 93.8% of patients with adverse SDoH, while ICD-10 codes captured 2.0%. Our method can effectively extracted SDoH information from clinic notes, performing better compare to GPT zero- and few-shot settings. These models could enhance real-world evidence on SDoH and aid in identifying patients needing social support.Comment: 38 pages, 5 figures, 5 tables in main, submitted for revie

The impact of responding to patient messages with large language model assistance

Documentation burden is a major contributor to clinician burnout, which is rising nationally and is an urgent threat to our ability to care for patients. Artificial intelligence (AI) chatbots, such as ChatGPT, could reduce clinician burden by assisting with documentation. Although many hospitals are actively integrating such systems into electronic medical record systems, AI chatbots utility and impact on clinical decision-making have not been studied for this intended use. We are the first to examine the utility of large language models in assisting clinicians draft responses to patient questions. In our two-stage cross-sectional study, 6 oncologists responded to 100 realistic synthetic cancer patient scenarios and portal messages developed to reflect common medical situations, first manually, then with AI assistance. We find AI-assisted responses were longer, less readable, but provided acceptable drafts without edits 58% of time. AI assistance improved efficiency 77% of time, with low harm risk (82% safe). However, 7.7% unedited AI responses could severely harm. In 31% cases, physicians thought AI drafts were human-written. AI assistance led to more patient education recommendations, fewer clinical actions than manual responses. Results show promise for AI to improve clinician efficiency and patient care through assisting documentation, if used judiciously. Monitoring model outputs and human-AI interaction remains crucial for safe implementation.Comment: 4 figures and tables in main, submitted for revie

Optimizing Outpatient Radiation Oncology Consult Workflow by Using Time-Driven Activity-Based Costing: Efficiency and Financial Impacts

PURPOSE: Clinical efficiency is a key component of value-based health care. Our objective here was to identify workflow inefficiencies by using time-driven activity-based costing (TDABC) and evaluate the implementation of a new clinical workflow in high-volume outpatient radiation oncology clinics. METHODS: Our quality improvement study was conducted with the Departments of GI, Genitourinary (GU), and Thoracic Radiation Oncology at a large academic cancer center and four community network sites. TDABC was used to create process maps and optimize workflow for outpatient consults. Patient encounter metrics were captured with a real-time status function in the electronic medical record. Time metrics were compared using Mann-Whitney U tests. RESULTS: Individual patient encounter data for 1,328 consults before the intervention and 1,234 afterward across all sections were included. The median overall cycle time was reduced by 21% in GI (19 minutes), 18% in GU (16 minutes), and 12% at the community sites (9 minutes). The median financial savings per consult were $52 in US dollars (USD) for the GI,$33 USD for GU, $30 USD for thoracic, and$42 USD for the community sites. Patient satisfaction surveys (from 127 of 228 patients) showed that 99% of patients reported that their providers spent adequate time with them and 91% reported being seen by a care provider in a timely manner. CONCLUSION: TDABC can effectively identify opportunities to improve clinical efficiency. Implementing workflow changes on the basis of our findings led to substantial reductions in overall encounter cycle times across several departments, as well as high patient satisfaction and significant financial savings

Pain in patients with pancreatic cancer: prevalence, mechanisms, management and future developments

Pain affects approximately 80% of patients with pancreatic cancer, with half requiring strong opioid analgesia, namely: morphine-based drugs on step three of the WHO analgesic ladder (as opposed to the weak opioids: codeine and tramadol). The presence of pain is associated with reduced survival. This article reviews the literature regarding pain: prevalence, mechanisms, pharmacological, and endoscopic treatments and identifies areas for research to develop individualized patient pain management pathways. The online literature review was conducted through: PubMed, Clinical Key, Uptodate, and NICE Evidence. There are two principal mechanisms for pain: pancreatic duct obstruction and pancreatic neuropathy which, respectively, activate mechanical and chemical nociceptors. In pancreatic neuropathy, several histological, molecular, and immunological changes occur which correlate with pain including: transient receptor potential cation channel activation and mast cell infiltration. Current pain management is empirical rather etiology-based and is informed by the WHO analgesic ladder for first-line therapies, and then endoscopic ultrasound-guided celiac plexus neurolysis (EUS-CPN) in patients with resistant pain. For EUS-CPN, there is only one clinical trial reporting a benefit, which has limited generalizability. Case series report pancreatic duct stenting gives effective analgesia, but there are no clinical trials. Progress in understanding the mechanisms for pain and when this occurs in the natural history, together with assessing new therapies both pharmacological and endoscopic, will enable individualized care and may improve patients’ quality of life and survival

Stereotactic Body Radiation Therapy for Locally Advanced Pancreatic Cancer

Shane S Neibart,1 Shalini Moningi,1 Krishan R Jethwa2 1Department of Radiation Oncology, Brigham and Women’s Hospital/Dana-Farber Cancer Institute, Boston, MA, USA; 2Department of Radiation Oncology, Mayo Clinic, Rochester, MN, USACorrespondence: Shalini Moningi, Brigham and Women’s Hospital, 75 Francis Street, Boston, MA, 02115, USA, Tel +01 (781) 624-4700, Email [email protected]: For patients with locally advanced pancreatic cancer (LAPC), who are candidates for radiation therapy, dose-escalated radiation therapy (RT) offers unique benefits over traditional radiation techniques. In this review, we present a historical perspective of dose-escalated RT for LAPC. We also outline advances in SBRT delivery, one form of dose escalation and a framework for selecting patients for treatment with SBRT.Results: Techniques for delivering SBRT to patients with LAPC have evolved considerably, now allowing for dose-escalation and superior respiratory motion management. At the same time, advancements in systemic therapy, particularly the use of induction multiagent chemotherapy, have called into question which patients would benefit most from radiation therapy. Multidisciplinary assessment of patients with LAPC is critical to guide management and select patients for local therapy. Results from ongoing trials will establish if there is a role of dose-escalated SBRT after induction chemotherapy for carefully selected patients.Conclusion: Patients with LAPC have more therapeutic options than ever before. Careful selection for SBRT may enhance patient outcomes, pending the maturation of pivotal clinical trials.Keywords: pancreatic cancer, stereotactic body radiation therapy, locally advanced pancreatic cancer, ablative radiation therap