Extraction of Unstructured Electronic Health Records to Evaluate Glioblastoma Treatment Patterns.

PURPOSE: Data on lines of therapy (LOTs) for cancer treatment are important for clinical oncology research, but LOTs are not explicitly recorded in electronic health records (EHRs). We present an efficient approach for clinical data abstraction and a flexible algorithm to derive LOTs from EHR-based medication data on patients with glioblastoma multiforme (GBM). METHODS: Nonclinicians were trained to abstract the diagnosis of GBM from EHRs, and their accuracy was compared with abstraction performed by clinicians. The resulting data were used to build a cohort of patients with confirmed GBM diagnosis. An algorithm was developed to derive LOTs using structured medication data, accounting for the addition and discontinuation of therapies and drug class. Descriptive statistics were calculated and time-to-next-treatment (TTNT) analysis was performed using the Kaplan-Meier method. RESULTS: Treating clinicians as the gold standard, nonclinicians abstracted GBM diagnosis with a sensitivity of 0.98, specificity 1.00, positive predictive value 1.00, and negative predictive value 0.90, suggesting that nonclinician abstraction of GBM diagnosis was comparable with clinician abstraction. Of 693 patients with a confirmed diagnosis of GBM, 246 patients contained structured information about the types of medications received. Of them, 165 (67.1%) received a first-line therapy (1L) of temozolomide, and the median TTNT from the start of 1L was 179 days. CONCLUSION: We described a workflow for extracting diagnosis of GBM and LOT from EHR data that combines nonclinician abstraction with algorithmic processing, demonstrating comparable accuracy with clinician abstraction and highlighting the potential for scalable and efficient EHR-based oncology research.

Learnings in Digital Health Design: Insights From a Pilot Web App for Structured Note-Taking for Patients With Rheumatoid Arthritis.

BACKGROUND: Patients fail to accurately remember 40% to 80% of medical information relayed during doctor appointments, and most standard after-visit summaries fail to effectively help patients comply with behaviors to manage their health conditions. The value of technology to empower and engage patients in their health management has been shown, and here we apply technology to help patients remember and act upon information communicated during their medical appointments. OBJECTIVE: We describe the development of WellNote, a digital notebook designed for patients to create a customized plan to manage their condition, plan for their appointments, track important actions (eg, medications and labs), and receive reminders for appointments and labs. METHODS: For this pilot, we chose to focus on rheumatoid arthritis, a chronic condition that relies on many of these features. The development of WellNote followed a structured method based on design thinking and co-design principles, with the app built in close collaboration with patients and a physician partner to ensure clinical relevance. Our design process consisted of 3 rounds: patient and physician interviews, visual prototypes, and a functional pilot app. RESULTS: Over the course of the design process, WellNote's features were refined, with the final version being a digital notebook designed for patients with rheumatoid arthritis to manage their health by helping them track medications and labs and plan for appointments. It features several pages, like a dashboard, patient profile, appointment notes, preplanning, medication management, lab tracking, appointment archives, reminders, and a pillbox for medication visualization. CONCLUSIONS: WellNote's active and structured note-taking features allow patients to clearly document the information from their physician without detracting from the conversation, helping the patient to become more empowered and engaged in their health management. The co-design process empowered these stakeholders to share their needs and participate in the development of a solution that truly solves pain points for these groups. This viewpoint highlights the role of digital health tools and the co-design of new health care innovations to empower patients and support clinicians.

Selective prediction for extracting unstructured clinical data.

OBJECTIVE: While there are currently approaches to handle unstructured clinical data, such as manual abstraction and structured proxy variables, these methods may be time-consuming, not scalable, and imprecise. This article aims to determine whether selective prediction, which gives a model the option to abstain from generating a prediction, can improve the accuracy and efficiency of unstructured clinical data abstraction. MATERIALS AND METHODS: We trained selective classifiers (logistic regression, random forest, support vector machine) to extract 5 variables from clinical notes: depression (n = 1563), glioblastoma (GBM, n = 659), rectal adenocarcinoma (DRA, n = 601), and abdominoperineal resection (APR, n = 601) and low anterior resection (LAR, n = 601) of adenocarcinoma. We varied the cost of false positives (FP), false negatives (FN), and abstained notes and measured total misclassification cost. RESULTS: The depression selective classifiers abstained on anywhere from 0% to 97% of notes, and the change in total misclassification cost ranged from -58% to 9%. Selective classifiers abstained on 5%-43% of notes across the GBM and colorectal cancer models. The GBM selective classifier abstained on 43% of notes, which led to improvements in sensitivity (0.94 to 0.96), specificity (0.79 to 0.96), PPV (0.89 to 0.98), and NPV (0.88 to 0.91) when compared to a non-selective classifier and when compared to structured proxy variables. DISCUSSION: We showed that selective classifiers outperformed both non-selective classifiers and structured proxy variables for extracting data from unstructured clinical notes. CONCLUSION: Selective prediction should be considered when abstaining is preferable to making an incorrect prediction.

Coproduction, Coeducation, and Patient Involvement: Everyone Included Framework for Medical Education Across Age Groups and Cultures.

Medical education, research, and health care practice continue to grow with minimal coproduction guidance. We suggest the Commons Principle approach to medical education as modeled by Ostrom and Williamson, where we share how adapting these models to multiple settings can enhance empathy, increase psychological safety, and provide robust just-in-time learning tools for practice. We here describe patient and public coproduction in diverse areas within health care using the commons philosophy across populations, cultures, and generations with learning examples across age groups and cultures. We further explore descriptive, mixed methods participatory action in medical and research education. We adopt an "Everyone Included" perspective and sought to identify its use in continuing medical education, citizen science, marginalized groups, publishing, and student internships. Overall, we outline coproduction at the point of need, as we report on strategies that improved engagement. This work demonstrates coproduction with the public across multiple settings and cultures, showing that even with minimal resources and experience, this partnership can improve medical education and care.

Effects of a 2-Week Remote Learning Program on Empathy and Clinical and Communication Skills in Premedical Students: Mixed Methods Evaluation Study.

BACKGROUND: Expressing empathy builds trust with patients, increases patient satisfaction, and is associated with better health outcomes. Research shows that expressing empathy to patients improves patient adherence to medications and decreases patient anxiety and the number of malpractice lawsuits. However, there is a dearth of research on teaching empathy to premedical students. The Clinical Science, Technology, and Medicine Summer Internship of Stanford Medicine (also called the Stanford Anesthesia Summer Institute) is a 2-week collaborative medical internship for high school and undergraduate students to inspire learners to be compassionate health care providers. The summer 2020 program was adapted to accomplish these objectives in a fully remote environment because of the COVID-19 global pandemic. OBJECTIVE: This study aims to measure the change in empathy and competencies of participants in clinical and communication skills before and after program participation. METHODS: A total of 41 participants completed only the core track of this program, and 39 participants completed the core + research track of this program. Participants in both tracks received instructions in selected clinical skills and interacted directly with patients to improve their interviewing skills. Research track participants received additional instructions in research methodology. All participants completed web-based pre- and postsurveys containing Knowledge and Skills Assessment (KSA) questions. Participant empathy was assessed using the validated Consultation and Relational Empathy measure. A subset of participants completed optional focus groups to discuss empathy. The pre- and post-KSA and Consultation and Relational Empathy measure scores were compared using paired 2-tailed t tests and a linear regression model. Open-ended focus group answers were then analyzed thematically. RESULTS: Participants in both tracks demonstrated significant improvement in empathy after the 2-week remote learning course (P=.007 in core track; P<.001 in research track). These results remained significant when controlling for gender and age. A lower pretest score was associated with a greater change in empathy. Participants in both tracks demonstrated significant improvement in KSA questions related to surgical skills (P<.001 in core track; P<.001 in research track), epinephrine pen use (P<.001 in core track; P<.001 in research track), x-ray image interpretation (P<.001 in core track; P<.001 in research track), and synthesizing information to solve problems (P<.001 in core track; P=.05 in research track). The core track participants also showed significant improvements in health communication skills (P=.001). Qualitative analysis yielded 3 themes: empathy as action, empathy as a mindset, and empathy in designing health care systems. CONCLUSIONS: Summer internships that introduce high school and undergraduate students to the field of health care through hands-on interaction and patient involvement may be an effective way to develop measurable empathy skills when combined with clinical skills training and mentorship. Notably, increases in empathy were observed in a program administered via a remote learning environment.

Critical patient insights from the same-day feedback programme at Stanford Health Care.

PATIENT-CENTERED ORGANISATIONS: Healthcare organisations now integrate patient feedback into value-based compensation formulas. This research considered Stanford Healthcare's same-day feedback, a programme designed to evaluate the patient experience. Specifically, how did patients with cancer interviewed in the programme assess their physicians? Furthermore, how did assessments differ across emotional, physical, practical and informational needs when interviewed by volunteer patient and family partners (PAFPs) versus hospital staff? PATIENT-PHYSICIAN COMMUNICATION BARRIERS: Integral to this research was Communication Accommodation Theory (CAT), which suggests individuals adjust interactions based on conversational roles, needs and understanding. Previous influential research was conducted by Frosch et al (2012) and Di Bartolo et al (2017), who revealed barriers to patient-physician communication, and Baker et al (2011) who associated CAT with these interactions. However, we still did not know if patients alter physician assessments between interviewers. VOLUNTEERS COLLECT PATIENT NEEDS: This mixed methods study worked with 190 oncology unit patient interviews from 2009 to 2017. Open-ended interview responses underwent thematic analysis. When compared with hospital staff, PAFPs collected more practical and informational needs from patients. PAFPs also collected more verbose responses that resembled detailed narratives of the patients' hospital experiences. This study contributed insightful patient perspectives of physician care in a novel hospital programme.