Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.

Real world performance of the 21st Century Cures Act population-level application programming interface.

OBJECTIVE: To evaluate the real-world performance of the SMART/HL7 Bulk Fast Health Interoperability Resources (FHIR) Access Application Programming Interface (API), developed to enable push button access to electronic health record data on large populations, and required under the 21st Century Cures Act Rule. MATERIALS AND METHODS: We used an open-source Bulk FHIR Testing Suite at 5 healthcare sites from April to September 2023, including 4 hospitals using electronic health records (EHRs) certified for interoperability, and 1 Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across 6 types of FHIR. RESULTS: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1555-2500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12 000 resources/min. DISCUSSION: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. CONCLUSION: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Cumulus: A federated EHR-based learning system powered by FHIR and AI.

Objective: To address challenges in large-scale electronic health record (EHR) data exchange, we sought to develop, deploy, and test an open source, cloud-hosted app 'listener' that accesses standardized data across the SMART/HL7 Bulk FHIR Access application programming interface (API). Methods: We advance a model for scalable, federated, data sharing and learning. Cumulus software is designed to address key technology and policy desiderata including local utility, control, and administrative simplicity as well as privacy preservation during robust data sharing, and AI for processing unstructured text. Results: Cumulus relies on containerized, cloud-hosted software, installed within a healthcare organization's security envelope. Cumulus accesses EHR data via the Bulk FHIR interface and streamlines automated processing and sharing. The modular design enables use of the latest AI and natural language processing tools and supports provider autonomy and administrative simplicity. In an initial test, Cumulus was deployed across five healthcare systems each partnered with public health. Cumulus output is patient counts which were aggregated into a table stratifying variables of interest to enable population health studies. All code is available open source. A policy stipulating that only aggregate data leave the institution greatly facilitated data sharing agreements. Discussion and Conclusion: Cumulus addresses barriers to data sharing based on (1) federally required support for standard APIs (2), increasing use of cloud computing, and (3) advances in AI. There is potential for scalability to support learning across myriad network configurations and use cases.

Real World Performance of the 21st Century Cures Act Population Level Application Programming Interface.

Objective: To evaluate the real-world performance in delivering patient data on populations, of the SMART/HL7 Bulk FHIR Access API, required in Electronic Health Records (EHRs) under the 21st Century Cures Act Rule. Materials and Methods: We used an open-source Bulk FHIR Testing Suite at five healthcare sites from April to September 2023, including four hospitals using EHRs certified for interoperability, and one Health Information Exchange (HIE) using a custom, standards-compliant API build. We measured export speeds, data sizes, and completeness across six types of FHIR resources. Results: Among the certified platforms, Oracle Cerner led in speed, managing 5-16 million resources at over 8,000 resources/min. Three Epic sites exported a FHIR data subset, achieving 1-12 million resources at 1,555-2,500 resources/min. Notably, the HIE's custom API outperformed, generating over 141 million resources at 12,000 resources/min. Discussion: The HIE's custom API showcased superior performance, endorsing the effectiveness of SMART/HL7 Bulk FHIR in enabling large-scale data exchange while underlining the need for optimization in existing EHR platforms. Agility and scalability are essential for diverse health, research, and public health use cases. Conclusion: To fully realize the interoperability goals of the 21st Century Cures Act, addressing the performance limitations of Bulk FHIR API is critical. It would be beneficial to include performance metrics in both certification and reporting processes.

Explaining vaccine hesitancy: A COVID-19 study of the United States.

Using recent data on the unvaccinated population across US states, this paper focuses on the determinants of vaccine hesitancy related to the COVID-19 pandemic. Findings show that more prosperous states and states with more elderly residents and more physicians have lower vaccine hesitancy. There was some evidence of the significance of race, but internet access and history of other contagious diseases failed to make a difference. States with centralized health systems and those with mask mandates generally had a lower percentage of unvaccinated populations. Finally, the presence of Democrats in state legislatures tended to lower vaccination hesitancies, ceteris paribus.

Managing the risk of COVID-19 via vaccine passports: Modeling economic and policy implications.

The increasing use of vaccine passports (VPs) to certify immunity from the prevailing coronavirus has created positive and negative aspects that have shaken the workings of markets. The VPs are, however, not universally used and not required by all businesses and governments at this point. Given the newness associated with VPs and the ongoing uncertainty of the pandemic, full implications of VPs have not been considered. This paper provides some formal insights into the implications of the use of VPs, borrowing from the established economic theory. Recommendations for public policy are provided.

Quality Improvement Tools in Total Joint Arthroplasty: A Systematic Review.

Systems review and quality improvement (QI) is a significant need within orthopaedic surgery. The focus of this paper is to systematically review QI principles utilized in total joint arthroplasty to determine most successful QI tools. A systematic search on MEDLINE/Pubmed, Embase, Cochrane Library and other sources was conducted from September 1991 through October 2018. The three primary improved outcomes from each article were recorded along with the date, author and subspecialty. Thirty-four eligible studies related to joint arthroplasty were identified for inclusion in the systematic review. The most common outcomes that were improved in these publications were: length of stay (LOS), cost, medication management, and patient education. Lean, clinical care pathways (CCP), plan-do-check-act (PDCA), and shared decision-making improved those metrics. Four metrics were found that were consistently improved by certain quality improvement tools: LOS, cost, medication management, and patient education. Further research is warranted to continue to build a framework for quality improvement in orthopaedic surgery. (Journal of Surgical Orthopaedic Advances 30(3):125-130, 2021).

Do Geographic Region, Medicaid Status, and Academic Affiliation Affect Access to Care Among Medicaid and Privately Insured Total Hip Arthroplasty Patients?

BACKGROUND: Medicaid provides health coverage to those beneath the federal poverty line. The literature shows that patients with Medicaid experience barriers to scheduling initial and follow-up visits, although this has not been studied in patients undergoing total hip arthroplasty (THA). The purpose of this study is to assess whether insurance type, geographic location, Medicaid expansion, or academic affiliation affect access to evaluation for THA. METHODS: The American Academy of Orthopedic Surgeons directory was used to call a total of 100 practices. Five random private and 5 random academic medical facilities were called from each of 5 Medicaid-expanded and 5 non-expanded states representing different US geographic regions. Calls were made by an investigator requesting the earliest available appointment for their fictitious parent to be evaluated for a THA. Half of the calls were made with the investigator reporting private insurance of Blue Cross Blue Shield (BCBS), and half reporting Medicaid. Appointment success rate and average time to appointment were compared. Further comparisons were drawn among Medicaid-expanded vs non-expanded states, geographic regions, and private vs academic affiliation. RESULTS: Appointments were successful for 99 of 100 (99%) calls made with BCBS, and 72 of 100 (72%) with Medicaid (P < .001). Success rates were significantly higher for BCBS, regardless of academic vs private affiliation. In all geographic regions, appointment success rate was significantly lower with Medicaid than with BCBS (P ≤ .01). Average time to appointment was also significantly longer for Medicaid (26 days) than private (13 days) insurance (P = .020). In the Medicaid group, appointment success rate was significantly greater for academically affiliated practices compared to private practices (84.0% vs 60.0%, respectively; P = .008). CONCLUSION: Patients with Medicaid seeking consultation for THA experience limits in access to evaluation for THA when compared to patients with private insurance, regardless of geographic region or affiliation of the practice.