Identification of pancreatic cancer risk factors from clinical notes using natural language processing.

OBJECTIVES: Screening for pancreatic ductal adenocarcinoma (PDAC) is considered in high-risk individuals (HRIs) with established PDAC risk factors, such as family history and germline mutations in PDAC susceptibility genes. Accurate assessment of risk factor status is provider knowledge-dependent and requires extensive manual chart review by experts. Natural Language Processing (NLP) has shown promise in automated data extraction from the electronic health record (EHR). We aimed to use NLP for automated extraction of PDAC risk factors from unstructured clinical notes in the EHR. METHODS: We first developed rule-based NLP algorithms to extract PDAC risk factors at the document-level, using an annotated corpus of 2091 clinical notes. Next, we further improved the NLP algorithms using a cohort of 1138 patients through patient-level training, validation, and testing, with comparison against a pre-specified reference standard. To minimize false-negative results we prioritized algorithm recall. RESULTS: In the test set (n = 807), the NLP algorithms achieved a recall of 0.933, precision of 0.790, and F1-score of 0.856 for family history of PDAC. For germline genetic mutations, the algorithm had a high recall of 0.851, while precision and F1-score were lower at 0.350 and 0.496 respectively. Most false positives for germline mutations resulted from erroneous recognition of tissue mutations. CONCLUSIONS: Rule-based NLP algorithms applied to unstructured clinical notes are highly sensitive for automated identification of PDAC risk factors. Further validation in a large primary-care patient population is warranted to assess real-world utility in identifying HRIs for pancreatic cancer screening.

Continuity of Care: A Primer for Family Medicine Residencies.

Continuity of care has been an identifying characteristic of family medicine since its inception and is an essential ingredient for high-functioning health care teams. Many benefits, including the quadruple aim of enhancing patient experience, improving population health, reducing costs, and improving care team well-being, are ascribed to continuity of care. In 2023, the Accreditation Council for Graduate Medical Education (ACGME) added two new continuity requirements-annual patient-sided continuity and annual resident-sided continuity-in family medicine training programs. This article reviews continuity of care as it applies to family medicine training programs. We discuss the various types of continuity and issues surrounding the measurement of continuity. A generally agreed upon definition of patient-sided and resident-sided continuity is presented to allow programs to begin to collect the necessary data. Especially within resident training programs, intricacies associated with maintaining continuity of care, such as empanelment, resident turnover, and scheduling, are discussed. The importance of right-sizing resident panels is highlighted, and a mechanism for accomplishing this is presented. The recent ACGME requirements represent a cultural shift from measuring resident experience based on volume to measuring resident continuity. This cultural shift forces family medicine training programs to adapt their various systems, policies, and procedures to emphasize continuity. We hope this manuscript's review of several facets of contuinuity, some unique to training programs, helps programs ensure compliance with the ACGME requirements.

Detection and quantification of Babesia species intraerythrocytic parasites by flow cytometry.

OBJECTIVES: Recent work has demonstrated that automated fluorescence flow cytometry (FLC) is a potential alternative for the detection and quantification of Plasmodium parasites. The objective of this study was to apply this novel FLC method to detect and quantify Babesia parasites in venous blood and compare results to light microscopy and polymerase chain reaction methods. METHODS: An automated hematology/malaria analyzer (XN-31; Sysmex) was used to detect and quantify B microti-infected red blood cells from residual venous blood samples (n = 250: Babesia positive, n = 170; Babesia negative, n = 80). As no instrument software currently exists for Babesia, qualitative and quantitative machine learning (ML) algorithms were developed to facilitate analysis. RESULTS: Performance of the ML models was verified against the XN-31 software using P falciparum-infected samples. When applied to Babesia-infected samples, the qualitative ML model demonstrated an area under the curve (AUC) of 0.956 (sensitivity, 95.9%; specificity, 83.3%) relative to polymerase chain reaction. For valid scattergrams, the qualitive model achieved an AUC of 1.0 (sensitivity and specificity, 100%), while the quantitative model demonstrated an AUC of 0.986 (sensitivity, 94.4%; specificity, 100%). CONCLUSIONS: This investigation demonstrates that Babesia parasites can be detected and quantified directly from venous blood using FLC. Although promising, opportunities remain to improve the general applicability of the method.

The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review.

BACKGROUND: Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice. OBJECTIVE: This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows. METHODS: A scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria. RESULTS: Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS. CONCLUSIONS: Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.

Skin Biopsy Techniques.

Because many skin lesions and disorders can appear similar, primary care clinicians often struggle to diagnose them definitively without histopathologic information obtained from a biopsy. This review article explains how to decide whether a lesion should be biopsied and what type of biopsy technique to use and then outlines the stepwise approach to each of the most common skin biopsy techniques: shave, saucerization, punch, fusiform, and subcutaneous nodule biopsies. Finally, potential pitfalls and complications are discussed so the clinician can avoid those and can provide a cosmetically acceptable result from these common outpatient procedures.

Patient on Immunomodulatory Therapy Experiencing Joint Pain and Skin Lesions: A Case Report.

A woman in her late fifties was admitted to the Family Medicine Inpatient Service directly from Rheumatology clinic for polyarticular pain and erythema with concern for infection. She was taking immunosuppressant medications for a history of multiple autoimmune diseases. Examination showed increasing erythema and tenderness on the upper and lower extremity joints. Histologic evaluation, surgical evaluation, and cultures were consistent with mycobacterium haemophilum infection. Mycobacterium haemophilum is an uncommon opportunistic infection that usually affects immunocompromised patients. The patient was treated with a multi-drug antibiotic regimen for several months due to drug resistance. Although this opportunistic infection is not common it should be considered in the differential of immunocompromised patients with skin and articular symptoms. Treatment outcomes are usually favorable if it caught earlier in the course.