Journal of General Internal Medicine最新文献

{"title":"The Master Adaptive Clinician Educator: A Framework for Future Educational Leaders in Academic Medicine.","authors":"Laura K Snydman, Milad Memari, Aditi Puri, Elisa M Sottile, Katherine Killian, David Callender, Anna K Donovan, Meghan Kiefer, Tanya Nikiforova, Mamta Singh, Wei Wei Lee, Danielle Jones, Craig Noronha","doi":"10.1007/s11606-024-09199-3","DOIUrl":"10.1007/s11606-024-09199-3","url":null,"abstract":"<p><p>Medical education continues to evolve with technologic advances, cultural changes, and progress in the application of learning theories. One example is Cutrer's concept of the Master Adaptive Learner (MAL), where the trainee self-directs learning and innovates to maintain a level of expertise in a domain. We suggest that this concept can be applied alongside ideas of adaptive and teacher expertise to represent the medical educator of the future-the Master Adaptive Clinician Educator (MACE). The MACE is a clinician-educator leader who has specific medical education training, actively engages in ongoing education-focused skills development, and creates innovative approaches to teaching. The MACE reflects and continuously develops an educational toolbox, applies lessons from learning theories, and appropriately adapts to various learning environments. In this manuscript, we build upon recent publications outlining roles and competencies of clinician-educators by applying the MAL model; we propose a dynamic, adaptable, and well-trained expert educator who is best poised to lead future generations of medical trainees. We challenge institutional leaders and national societies to take the lead in creating structures to support the growth and promotion of MACEs with the goal of positively reshaping medical education and the clinician educator.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"927-932"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Response to Letter to the Editor: ACP in Medicare Beneficiaries with HF.","authors":"Seuli Bose Brill, Sean R Riley, J Madison Hyer","doi":"10.1007/s11606-024-09238-z","DOIUrl":"10.1007/s11606-024-09238-z","url":null,"abstract":"","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"952-953"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142801106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing Single-Hospital and National Models to Predict 30-Day Inpatient Mortality.","authors":"Steven Cogill, Kent Heberer, Amit Kaushal, Daniel Fang, Jennifer Lee","doi":"10.1007/s11606-024-09315-3","DOIUrl":"10.1007/s11606-024-09315-3","url":null,"abstract":"<p><strong>Background: </strong>Advances in artificial intelligence and machine learning have facilitated the creation of mortality prediction models which are increasingly used to assess quality of care and inform clinical practice. One open question is whether a hospital should utilize a mortality model trained from a diverse nationwide dataset or use a model developed primarily from their local hospital data.</p><p><strong>Objective: </strong>To compare performance of a single-hospital, 30-day all-cause mortality model against an established national benchmark on the task of mortality prediction.</p><p><strong>Design/participants: </strong>We developed a single-hospital mortality prediction model using 9975 consecutive inpatient admissions at the Department of Veterans Affairs Palo Alto Healthcare System from July 26, 2018, to September 30, 2021, and compared performance against an established national model with similar features.</p><p><strong>Main measures: </strong>Both the single-hospital model and the national model placed each patient in one of five prediction bins: < 2.5%, 2.5-5%, 5-10%, 10-30%, and ≥ 30% risks of 30-day mortality. Evaluation metrics included receiver operator characteristic area under the curve (ROC AUC), sensitivity, specificity, and balanced accuracy. Final comparisons were made between the single-hospital model trained on the full training set and the national model for both metrics and prediction overlap.</p><p><strong>Key results: </strong>With sufficiently large training sets of 2720 or greater inpatient admissions, there was no statistically significant difference between the performances of the national model (ROC AUC 0.89, 95%CI [0.858, 0.919]) and single-hospital model (ROC AUC 0.878, 95%CI [0.84, 0.912]). For the 89 mortality events in the test set, the single-hospital model agreed with the national model risk assessment or an adjacent risk assessment in 92.1% of the encounters.</p><p><strong>Conclusions: </strong>A single-hospital inpatient mortality prediction model can achieve performance comparable to a national model when evaluated on a single-hospital population, given sufficient sample size.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":"40 4","pages":"803-810"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143649175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Direct-Acting Oral Anticoagulants and Potential Inconsistencies with FDA-Approved Dosing for Non-Valvular Atrial Fibrillation: A Retrospective Real-World Analysis Across Nine US Healthcare Systems.","authors":"Bonnie DeLor, Jon J Glover, Timothy J Hartman, Laura L Manzey, Mohammad Ateya, Shelby Kelsh, Katie Taylor, Wesley R Zemrak, Jaclynne R Gowen, Ann Parks, Carmen Gust, Charles Medico, Ukwen C Akpoji, Shane Naylor, Carolyn W Chou, Gregory Fakelmann, Sara Hart, Eryne E Wiethorn, Thach Trinh, William W Wilson, Rachel Bowen, Jennifer Stanton, Laura Duvall, Lynette T Davis","doi":"10.1007/s11606-024-09106-w","DOIUrl":"10.1007/s11606-024-09106-w","url":null,"abstract":"<p><strong>Background: </strong>Direct-acting oral anticoagulants (DOACs) are recommended to reduce risk of stroke and systemic embolism in patients with non-valvular atrial fibrillation (NVAF). However, DOAC dosing inconsistent with FDA-approved product labels is common and associated with poor clinical outcomes.</p><p><strong>Objectives: </strong>Identify DOAC dosing inconsistent with FDA-approved product labels in ambulatory care patients with NVAF; identify variables associated with dosing lower and higher than label.</p><p><strong>Design: </strong>Retrospective analysis using electronic health records from nine US healthcare systems.</p><p><strong>Patients: </strong>Adults with NVAF receiving DOAC therapy in 2022.</p><p><strong>Main measures: </strong>Rates of label-inconsistent dosing; multivariable regression analysis to identify demographic and clinical variables associated with dosing lower and higher than label.</p><p><strong>Key results: </strong>Among 51,128 NVAF patients (56.1% male, 94.3% White, mean [SD] age 73.5 [10.5] years), 5008 (9.8%) were prescribed label-inconsistent doses of DOACs (6.8% lower and 3.0% higher than label). Age ≥ 75 years, renal impairment, and hypertension were significantly associated with inconsistent dosing both higher and lower than label. Female sex and higher weight were significantly associated with dosing lower than label, as were heart failure, vascular or liver disease, and bleeding history. Dosing higher than label was significantly associated with male sex, race (African American/Black), weight < 60 kg, and use of drugs with potential drug-drug interactions. When prescribed by primary care physicians, DOAC doses were 37% (95% CI, 27-49%) more likely to be lower than label and 30% (95% CI, 16-46%) more likely to be higher than label than when prescribed by cardiologists or electrophysiologists. Label-inconsistent dosing varied (6.7 to 15.8%) across participating systems.</p><p><strong>Conclusions: </strong>DOAC dosing inconsistent with label varied by demographics, clinical characteristics, prescriber specialty, and healthcare system, suggesting a need to monitor and assess dosing decisions in NVAF. Identification of variables associated with dosing inconsistencies may enable targeted interventions to ensure label-consistent dosing in vulnerable populations.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"828-837"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142467335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charting Diagnostic Safety: Exploring Patient-Provider Discordance in Medical Record Documentation.","authors":"Traber D Giardina, Viral Vaghani, Divvy K Upadhyay, Taylor M Scott, Saritha Korukonda, Christiane Spitzmueller, Hardeep Singh","doi":"10.1007/s11606-024-09007-y","DOIUrl":"10.1007/s11606-024-09007-y","url":null,"abstract":"<p><strong>Background: </strong>The 21st Century Cures Act enables patients to access their medical records, thus providing a unique opportunity to engage patients in their diagnostic journey.</p><p><strong>Objective: </strong>To explore the concordance between patients' self-reported diagnostic concerns and clinician-interpreted information in their electronic health records.</p><p><strong>Design: </strong>We conducted a mixed-methods analysis of a cohort of 467 patients who completed a structured data collection instrument (the Safer Dx Patient) to identify diagnostic concerns while reviewing their clinician's notes. We conducted a qualitative content analysis of open-ended responses on both the tools and the case summaries. Two clinical chart reviewers, blinded to patient-reported diagnostic concerns, independently conducted chart reviews using a different structured instrument (the Revised Safer Dx Instrument) to identify diagnostic concerns and generate case summaries. The primary outcome variable was chart review-identified diagnostic concerns. Multivariate logistic regression tested whether the primary outcome was concordant with patient-reported diagnostic concerns.</p><p><strong>Setting: </strong>Geisinger, a large integrated healthcare organization in rural and semi-urban Pennsylvania.</p><p><strong>Participants: </strong>Cohort of adult patients actively using patient portals and identified as \"at-risk\" for diagnostic concerns using an electronic trigger algorithm based on unexpected visit patterns in a primary care setting.</p><p><strong>Results: </strong>In 467 cohort patients, chart review identified 31 (6.4%) diagnostic concerns, of which only 11 (21.5%) overlapped with 51 patient-reported diagnostic concerns. Content analysis revealed several areas of discordant understanding of the diagnostic process between clinicians and patients. Multivariate logistic regression analysis showed that clinician-identified diagnostic concerns were associated with patients who self-reported \"I feel I was incorrectly diagnosed during my visit\" (odds ratio 1.65, 95% CI 1.17-2.3, p < 0.05).</p><p><strong>Conclusion: </strong>Patients and clinicians appear to have certain differences in their mental models of what is considered a diagnostic concern. Efforts to integrate patient perspectives and experiences with the diagnostic process can lead to better measurement of diagnostic safety.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"773-781"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142140235","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Approach to Estimating Adherence to Heart Failure Medications Using Linked Electronic Health Record and Pharmacy Data.","authors":"Saul Blecker, Yunan Zhao, Xiyue Li, Ian M Kronish, Amrita Mukhopadhyay, Tyrel Stokes, Samrachana Adhikari","doi":"10.1007/s11606-024-09216-5","DOIUrl":"10.1007/s11606-024-09216-5","url":null,"abstract":"<p><strong>Background: </strong>Medication non-adherence, which is common in chronic diseases such as heart failure, is often estimated using proportion of days covered (PDC). PDC is typically calculated using medication fill information from pharmacy or insurance claims data, which lack information on when medications are prescribed. Many electronic health records (EHRs) have prescription and pharmacy fill data available, enabling enhanced PDC assessment that can be utilized in routine clinical care.</p><p><strong>Objective: </strong>To describe our approach to calculating PDC using linked EHR-pharmacy data and to compare to PDC calculated using pharmacy-only data for patients with heart failure.</p><p><strong>Methods: </strong>We performed a retrospective cohort study of adult patients with heart failure who were prescribed guideline-directed medical therapy (GDMT) and seen in a large health system. Using linked EHR-pharmacy data, we estimated medication adherence by PDC as the percent of days in which a patient possessed GDMT based on medication pharmacy fills over the number of days the prescription order was active. We also calculated PDC using pharmacy-only data, calculated as medications possessed over days with continued medication fills. We compared these two approaches for days observed and PDC using a paired t-test.</p><p><strong>Results: </strong>Among 33,212 patients with heart failure who were prescribed GDMT, 2226 (6.7%) never filled their medications, making them unavailable in the assessment of PDC using pharmacy-only data (n = 30,995). Linked EHR-pharmacy data had slightly longer days observed for PDC assessment (164.7 vs. 163.4 days; p < 0.001) and lower PDC (78.5 vs. 90.6, p < 0.001) as compared to assessment using pharmacy-only data.</p><p><strong>Conclusions: </strong>Linked EHR-pharmacy data can be used to identify patients who never fill their prescriptions. Estimating adherence using linked EHR-pharmacy data resulted in a lower mean PDC as compared to estimates using pharmacy-only data.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"811-817"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing a Model of Augmented Postpartum Primary Care to Usual Care in an Urban Medical Center.","authors":"Sam Wainwright, Anne Elizabeth Glassgow, Abigail Holicky, Eric Kim, Melissa Wagner-Schuman, Kavya Anjur, Shreya Bellur, Rachel Caskey","doi":"10.1007/s11606-024-09165-z","DOIUrl":"10.1007/s11606-024-09165-z","url":null,"abstract":"<p><strong>Background: </strong>The US faces a maternal health crisis and struggles to deliver recommended postpartum care. In some populations, less than half of mothers attend a postpartum visit.</p><p><strong>Objective: </strong>To determine if a two-generation (Two-Gen) model of interdisciplinary, postpartum primary care was associated with increased visit attendance for postpartum care, primary care, and behavioral health.</p><p><strong>Design: </strong>Retrospective study of care delivered at a single, urban, academic, safety-net medical center between 2020 and 2023.</p><p><strong>Participants: </strong>Mothers who received postpartum care in Two-Gen and a comparison group who received usual postpartum care.</p><p><strong>Main measures: </strong>Adjusted logistic regression to estimate the effect of Two-Gen participation on the odds of attending an early (birth-to-3 weeks) postpartum visit, later (4-to-12 weeks) postpartum visit, OB/GYN visit, and primary care visit.</p><p><strong>Key results: </strong>A total of 247 mothers (98 Two-Gen and 149 usual care) were included for analysis. Most identified as Non-Hispanic Black (55%) or Hispanic (34%) and had Medicaid insurance (74%). On average, Two-Gen mothers were younger and more likely to be primiparous. Compared to usual care, Two-Gen mothers had similar rates of early postpartum visits (79% vs 64%; adjusted odds ratio (aOR) 1.70; 95% confidence interval (CI) 0.92-3.14) and were significantly more likely to have a later postpartum visit (92% vs 79%; aOR 2.46; 95%CI 1.06-5.74) in adjusted analyses. Almost all Two-Gen mothers (97%) had a visit with a primary care doctor in the first postpartum year, compared to 19% of mothers receiving usual care (aOR 12.95; 95%CI 6.80-24.68). Of those with behavioral health diagnoses, Two-Gen mothers had higher rates of psychiatrist visits than usual care mothers (49% vs 13%; p = 0.001).</p><p><strong>Conclusions: </strong>Two-Gen clinic participation was associated with high rates of timely postpartum care in a group of predominantly young, publicly insured, racial, and ethnic minority mothers and compared favorably to usual care across multiple metrics, notably utilization of primary and behavioral health care.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"854-861"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federally Illicit Lifetime Drug Use in the Health Professions.","authors":"Ellen T Kurtzman","doi":"10.1007/s11606-024-09052-7","DOIUrl":"10.1007/s11606-024-09052-7","url":null,"abstract":"","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"955-957"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trends in Methamphetamine-Related Admissions among People Experiencing Homelessness, 2006-2021.","authors":"Andrea E Tyrer, Robert A Kleinman","doi":"10.1007/s11606-024-09000-5","DOIUrl":"10.1007/s11606-024-09000-5","url":null,"abstract":"","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"958-961"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142348087","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pulmonary and Cardiac Smoking-Related History Improves Abstinence Rates in an Urban, Socioeconomically Disadvantaged Patient Population.","authors":"Zain Khera, Nicholas Illenberger, Scott E Sherman","doi":"10.1007/s11606-024-09071-4","DOIUrl":"10.1007/s11606-024-09071-4","url":null,"abstract":"<p><strong>Background: </strong>Tobacco use continues to take the lives of many, and targeted interventions can counter this health burden. One possible target population is patients who have had a smoking-related diagnosis, as they may have a greater drive to quit.</p><p><strong>Objective: </strong>To assess whether patients with previous cardiac or pulmonary conditions directly attributable to smoking have greater rates of abstinence post-discharge from hospitalization in the CHART-NY trial.</p><p><strong>Design: </strong>CHART-NY was a randomized comparative effectiveness trial comparing a more intensive versus a less intensive smoking cessation intervention after hospital discharge. We divided the 1618 CHART-NY participants into a smoking-related history group of 597 and a nonsmoking-related history group of 1021 based on cardiac or pulmonary conditions in a retrospective chart review. We conducted chi-squared analyses on baseline characteristics. Using follow-up survey data, we conducted chi-squared analyses on abstinence outcomes and made logistic regression models for the predictive value of smoking-related conditions on abstinence.</p><p><strong>Participants: </strong>A total of 1059 and 1084 participants in CHART-NY who completed both 2- and 6-month follow-up surveys respectively.</p><p><strong>Main measures: </strong>Self-reported 30-day abstinence at 2- and 6-month follow-up and survey data for baseline characteristics.</p><p><strong>Key results: </strong>Those abstinent at 6-month follow-up were more likely to have a smoking-attributable history (OR = 1.40, 95% CI 1.09-1.81). When stratified based on intervention, only the intensive counseling group was significant (OR = 1.53, 95% CI 1.08-2.17). The regression model using a smoking-related comorbidity score was significant at 6 months (OR = 1.29, p = 0.03), and the multivariate logistic regression model analyzing each smoking-related condition separately demonstrated significance for myocardial infarction at 6 months (OR = 1.66, p = 0.03).</p><p><strong>Conclusions: </strong>People who smoke who have experienced smoking-related conditions may be more likely to benefit from smoking cessation interventions, especially intensive telephone-based counseling. Multiple conditions had an additive effect in predicting long-term abstinence after intervention, and myocardial infarction had the greatest predictive value.</p>","PeriodicalId":15860,"journal":{"name":"Journal of General Internal Medicine","volume":" ","pages":"749-755"},"PeriodicalIF":4.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142365470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}