Clinical importance of distinguishing true anemia from dilutional pseudo-anemia: Consequences of a 3-year follow-up volume assessment in a heart failure patient.

In chronic heart failure, dilutional anemia and hypervolemia may occur due to plasma volume expansion, the latter sometimes exacerbated by an increase in red cell volume. Diagnosis and a therapeutic strategy require determination of vascular volumes.

Neural network for autonomous segmentation and volumetric assessment of clot and edema in acute and subacute intracerebral hemorrhages.

INTRODUCTION: Minimally-invasive surgical techniques for intracerebral hemorrhage (ICH) evacuation use imaging to guide the suction, lysing and/or drainage from the hemorrhage site via various designs. A previous international surgical study has shown that reduction of hematoma volume below 15 ml is indicative of improved long term patient outcomes. The study noted a need for tools to periodically visualize remaining clot during intervention to increase the likelihood of evacuating sufficient clot volumes without endangering rebleeds. Robust segmentation of MRI could guide surgeons and radiologists regarding remaining regions and approaches for prudent evacuation. We thus propose a Convolutional Neural Network (CNN) to identify and autonomously segment clot and peripheral edema in MR images of the brain and generate an estimate of the remaining clot volume. MATERIALS AND METHODS: We used a retrospective, locally-acquired dataset of ICH patient scans taken on 3 T MRI scanners. Three sets of ground truth manual segmentations were independently generated by two imaging scientists and one radiology fellow. Evaluation of clot age was determined based on relative contrast of hemorrhage components and reviewed by a neurosurgeon. Model accuracy was determined by pixel-wise Dice coefficient (DC) calculations between each ground truth manual segmentation and the machine-derived autonomous segmentations. RESULTS: The model produced autonomous segmentations of clot core with an average DC of 0.75 ± 0.21 relative to manual segmentations of the same scans. For edema, it produced segmentations with an average DC of 0.68 ± 0.16 relative to manual. From these pixel-wise segmentations, clot volume can be calculated. Model-produced segmentations underestimated clot volumes by an average of 17% relative to ground-truth. CONCLUSION: The machine learning models were able to identify and segment volumes of ICH components swiftly and accurately.

Toward the optimized assessment of clinical outcomes in studies of novel treatments for Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is characterized by a progressive decline in cognition and daily function, leading to a greater need for caregiver support. Clinical disease is segmented into a preclinical stage, mild cognitive impairment, and mild, moderate, and severe stages of Alzheimer's dementia. Although AD trials enroll participants at various stages of illness, treatment efficacy is often assessed using endpoints based on measures of outcomes that are held fixed across disease stages. We hypothesize that matching the primary outcomes measured in the endpoint hierarchy to the stage of disease targeted by the trial will increase the likelihood of detecting true treatment benefits. AREAS COVERED: We discuss current approaches to assessing clinical outcomes in AD trials, followed by a consideration of how effect detection can be improved by linking the stage of AD to the endpoints that most likely reflect stage-specific disease progression. EXPERT OPINION: Failing to account for stage-specific relevance and sensitivity of clinical outcomes may be one factor that contributes to trial failures in AD. Given the history of failure, experts have begun to scrutinize the relevance and sensitivity of outcomes as a potentially modifiable barrier to successful trials. To this end, we present a framework for refining trial endpoint selection and evaluation.

Daratumumab Interferes with Allogeneic Crossmatch Impacting Immunological Assessment in Solid Organ Transplantation.

We report the first case of Daratumumab interference of allogeneic crossmatch tests repeatedly causing aberrant false-positive results, which inadvertently delayed transplant for a waitlisted renal patient with multiple myeloma. Daratumumab is an IgG1κ human monoclonal antibody commonly used to treat multiple myeloma, characterized by cancerous plasma cells and often leads to renal failure requiring kidney transplant, by depleting CD38-expressing plasma cells. In this case study, the patient had end-stage renal disease secondary to multiple myeloma and was continuously receiving Daratumumab infusions. The patient did not have any detectable antibodies to human leukocyte antigens but repeatedly had unexpected positive crossmatch by the flow cytometry-based method with 26 of the 27 potential deceased organ donors, implying donor-recipient immunological incompatibility. However, further review and analysis suggested that the positive crossmatches were likely false-positive as a result of interference from Daratumumab binding to donor cell surface CD38 as opposed to the presence of donor-specific antibodies. The observed intensity of the false-positive crossmatches was also highly variable, potentially due to donor- and/or cell-dependent expression of CD38. The variability of CD38 expression was, therefore, for the first time, characterized on the T and B cells isolated from various tissues and peripheral blood of 78 individuals. Overall, T cells were found to have a lower CD38 expression profile than the B cells, and no significant difference was observed between deceased and living individuals. Finally, we show that a simple cell treatment by dithiothreitol can effectively mitigate Daratumumab interference thus preserving the utility of pre-transplant crossmatch in multiple myeloma patients awaiting kidney transplant.

Artificial intelligence and machine learning algorithms for early detection of skin cancer in community and primary care settings: a systematic review.

Skin cancers occur commonly worldwide. The prognosis and disease burden are highly dependent on the cancer type and disease stage at diagnosis. We systematically reviewed studies on artificial intelligence and machine learning (AI/ML) algorithms that aim to facilitate the early diagnosis of skin cancers, focusing on their application in primary and community care settings. We searched MEDLINE, Embase, Scopus, and Web of Science (from Jan 1, 2000, to Aug 9, 2021) for all studies providing evidence on applying AI/ML algorithms to the early diagnosis of skin cancer, including all study designs and languages. The primary outcome was diagnostic accuracy of the algorithms for skin cancers. The secondary outcomes included an overview of AI/ML methods, evaluation approaches, cost-effectiveness, and acceptability to patients and clinicians. We identified 14 224 studies. Only two studies used data from clinical settings with a low prevalence of skin cancers. We reported data from all 272 studies that could be relevant in primary care. The primary outcomes showed reasonable mean diagnostic accuracy for melanoma (89·5% [range 59·7-100%]), squamous cell carcinoma (85·3% [71·0-97·8%]), and basal cell carcinoma (87·6% [70·0-99·7%]). The secondary outcomes showed a heterogeneity of AI/ML methods and study designs, with high amounts of incomplete reporting (eg, patient demographics and methods of data collection). Few studies used data on populations with a low prevalence of skin cancers to train and test their algorithms; therefore, the widespread adoption into community and primary care practice cannot currently be recommended until efficacy in these populations is shown. We did not identify any health economic, patient, or clinician acceptability data for any of the included studies. We propose a methodological checklist for use in the development of new AI/ML algorithms to detect skin cancer, to facilitate their design, evaluation, and implementation.

Acute Effects of Esports on the Cardiovascular System and Energy Expenditure in Amateur Esports Players.

Introduction: Esports is practiced by millions of people worldwide every day. On a professional level, esports has been proven to have a high stress potential and is sometimes considered equivalent to traditional sporting activities. While traditional sports have health-promoting effects through muscle activity and increased energy expenditure, amateur esports could represent a purely sedentary activity, which would carry potentially harmful effects when practiced regularly. Therefore, this study aims to investigate the acute effects of esports on the cardiovascular system and energy expenditure in amateur esports players to show whether esports can be considered as physical strain or mental stress or whether amateur esports has to be seen as purely sedentary behavior. Methods: Thirty male subjects participated in a 30-min gaming session, playing the soccer simulation game FIFA 20 or the tactical, first-person multiplayer shooter Counter-Strike: Global Offensive. Respiratory and cardiovascular parameters, as well as energy expenditure, blood glucose, lactate, and cortisol, were determined pre-, during, and post-gaming. Results: There were no significant changes in oxygen uptake, carbon dioxide output, energy expenditure, stroke volume, or lactate levels. Heart rate, blood glucose and cortisol decreased through the intervention until reaching their minimum levels 10 min post-gaming (Cortisolpre: 3.1 ± 2.9 ng/ml, Cortisolpost: 2.2 ± 2.3 ng/ml, p < 0.01; HRmin0.5: 82 ± 11 bpm, HRpost: 74 ± 13 bpm, p < 0.01). Conclusion: A 30-min esports intervention does not positively affect energy expenditure or metabolism in amateur esports players. Therefore, it cannot provide the same health-promoting effects as traditional sports participation, but could in the long-term rather cause the same potentially health-damaging effects as purely sedentary behavior. However, it does not trigger a negative stress response in the players. Deliberate physical activity and exercise routines adapted to these demands should therefore be part of the daily life of amateur esports players.

Imaging AI in Practice: A Demonstration of Future Workflow Using Integration Standards.

Artificial intelligence (AI) tools are rapidly being developed for radiology and other clinical areas. These tools have the potential to dramatically change clinical practice; however, for these tools to be usable and function as intended, they must be integrated into existing radiology systems. In a collaborative effort between the Radiological Society of North America, radiologists, and imaging-focused vendors, the Imaging AI in Practice (IAIP) demonstrations were developed to show how AI tools can generate, consume, and present results throughout the radiology workflow in a simulated clinical environment. The IAIP demonstrations highlight the critical importance of semantic and interoperability standards, as well as orchestration profiles for successful clinical integration of radiology AI tools. Keywords: Computer Applications-General (Informatics), Technology Assessment © RSNA, 2021.

Predicting need for advanced illness or palliative care in a primary care population using electronic health record data.

Timely outreach to individuals in an advanced stage of illness offers opportunities to exercise decision control over health care. Predictive models built using Electronic health record (EHR) data are being explored as a way to anticipate such need with enough lead time for patient engagement. Prior studies have focused on hospitalized patients, who typically have more data available for predicting care needs. It is unclear if prediction driven outreach is feasible in the primary care setting. In this study, we apply predictive modeling to the primary care population of a large, regional health system and systematically examine the impact of technical choices, such as requiring a minimum number of health care encounters (data density requirements) and aggregating diagnosis codes using Clinical Classifications Software (CCS) groupings to reduce dimensionality, on model performance in terms of discrimination and positive predictive value. We assembled a cohort of 349,667 primary care patients between 65 and 90 years of age who sought care from Sutter Health between July 1, 2011 and June 30, 2014, of whom 2.1% died during the study period. EHR data comprising demographics, encounters, orders, and diagnoses for each patient from a 12 month observation window prior to the point when a prediction is made were extracted. L1 regularized logistic regression and gradient boosted tree models were fit to training data and tuned by cross validation. Model performance in predicting one year mortality was assessed using held-out test patients. Our experiments systematically varied three factors: model type, diagnosis coding, and data density requirements. We found substantial, consistent benefit from using gradient boosting vs logistic regression (mean AUROC over all other technical choices of 84.8% vs 80.7% respectively). There was no benefit from aggregation of ICD codes into CCS code groups (mean AUROC over all other technical choices of 82.9% vs 82.6% respectively). Likewise increasing data density requirements did not affect discrimination (mean AUROC over other technical choices ranged from 82.5% to 83%). We also examine model performance as a function of lead time, which is the interval between death and when a prediction was made. In subgroup analysis by lead time, mean AUROC over all other choices ranged from 87.9% for patients who died within 0 to 3 months to 83.6% for those who died 9 to 12 months after prediction time.

Integrating Storytelling into a Communication Skills Teaching Program for Medical Oncology Fellows.

Oncology training focuses primarily on biomedical content rather than psychosocial content, which is not surprising in light of the enormous volume of technical information that oncology fellows assimilate in a short time. Nonetheless, the human connection, and specifically communication skills, remains as important as ever in caring for highly vulnerable patients with cancer. We previously described a year-long communication skills curriculum for oncology fellows that consisted of monthly 1-hour seminars with role play as the predominant teaching method (Epner and Baile, Acad Med. 89:578-84, 2014). Over several years, we adapted the curriculum based on learner feedback and reflection by faculty and teaching assistants and consolidated sessions into quarterly 3-4-hour workshops. We now describe integrating stories into the curriculum as a way of building empathy and warming fellows to the arduous task of dealing with highly emotional content, such as conversations with young patients about transitioning off disease-directed therapy. Learners read and discussed published, medically themed stories; discussed their own patient care stories; and completed brief writing reflections and discussions. They then worked in small groups facilitated by faculty and upper level fellows who functioned as teaching assistants to work on applying specific skills and strategies to scenarios that they chose. Fellows completed anonymous surveys on which they rated the curriculum highly for relevance, value, organization, content, and teaching methods, including storytelling aspects. We conclude that sharing stories can help highly technical learners build reflective ability, mindfulness, and empathy, which are all critical ingredients of the art of medicine.

Moderating Effects of Patient Characteristics on the Impact of Financial Incentives.

While financial incentives to providers or patients are increasingly common as a quality improvement strategy, their impact on patient subgroups and health care disparities is unclear. To examine these patterns, we analyzed data from a randomized clinical trial of financial incentives to lower low-density lipoprotein (LDL) cholesterol levels in patients at risk for cardiovascular disease. Patients with higher baseline LDL experienced greater cholesterol reductions in the shared incentive arm (0.23 mg/dL per unit change in baseline LDL, 95% CI [-0.46, -0.00]) but were also less likely to have medication potency increases in the physician incentive arm ( OR = 0.98, 95% CI [0.97, 0.996]). Uninsured patients and those of race other than Black or White were less likely to have potency increases in the shared incentive arm ( OR = 0.15, 95% CI [0.03, 0.70] and OR = 0.09, 95% CI [0.01, 0.93], respectively). These findings suggest some differential response to incentives, particularly in the form of targeted medication changes.

Translation, cross-cultural adaptation and reliability of the German version of the migraine disability assessment (MIDAS) questionnaire.

BACKGROUND: The Migraine Disability Assessment (MIDAS) is a brief questionnaire and measures headache-related disability. This study aimed to translate and cross-culturally adapt the original English version of the MIDAS to German and to test its reliability. METHODS: The standardized translation process followed international guidelines. The pre-final version was tested for clarity and comprehensibility by 34 headache sufferers. Test-retest reliability of the final version was quantified by 36 headache patients completing the MIDAS twice with an interval of 48 h. Reliability was determined by intraclass correlation coefficients and internal consistency by Cronbach's α. RESULTS: All steps of the translation process were followed, documented and approved by the developer of the MIDAS. The expert committee discussed in detail the complex phrasing of the questions that refer to one to another, especially exclusion of headache-days from one item to the next. The German version contains more active verb sentences and prefers the perfect to the imperfect tense. The MIDAS scales intraclass correlation coefficients ranged from 0.884 to 0.994 and was 0.991 (95% CI: 0.982-0.995) for the MIDAS total score. Cronbach's α for the MIDAS as a whole was 0.69 at test and 0.67 at retest. CONCLUSIONS: The translation process was challenged by the comprehensibility of the questionnaire. The German version of the MIDAS is a highly reliable instrument for assessing headache related disability with moderate internal consistency. Provided validity testing of the German MIDAS is successful, it can be recommended for use in clinical practice as well as in research.

Patient-Clinician Communication: American Society of Clinical Oncology Consensus Guideline.

Purpose To provide guidance to oncology clinicians on how to use effective communication to optimize the patient-clinician relationship, patient and clinician well-being, and family well-being. Methods ASCO convened a multidisciplinary panel of medical oncology, psychiatry, nursing, hospice and palliative medicine, communication skills, health disparities, and advocacy experts to produce recommendations. Guideline development involved a systematic review of the literature and a formal consensus process. The systematic review focused on guidelines, systematic reviews and meta-analyses, and randomized controlled trials published from 2006 through October 1, 2016. Results The systematic review included 47 publications. With the exception of clinician training in communication skills, evidence for many of the clinical questions was limited. Draft recommendations underwent two rounds of consensus voting before being finalized. Recommendations In addition to providing guidance regarding core communication skills and tasks that apply across the continuum of cancer care, recommendations address specific topics, such as discussion of goals of care and prognosis, treatment selection, end-of-life care, facilitating family involvement in care, and clinician training in communication skills. Recommendations are accompanied by suggested strategies for implementation. Additional information is available at www.asco.org/supportive-care-guidelines and www.asco.org/guidelineswiki .

[Use of the Symptom Validation Test BEVA in Social Medical Assessment]. / Einsatz des Beschwerdenvalidierungstests BEVA in der sozialmedizinischen Begutachtung.

The present study investigates whether the BEVA is suitable to detect negative response bias by claimants with psychosomatic symptoms in the social medical assessment. 107 claimants for disability pension to a psychosomatic disorder fulfilled the BEVA, SIMS and a sociodemographic questionnaire. The social medical examiners assess the credibility of the complaints representation. A Known-Group-Design and a Bootstrapping-Design was used to estimate the validity of the BEVA. For the external criterion SFSS the following psychometric characteristics for the BEVA could be determined: specificity=0.86, sensitivity=0.42, positive predictive value=0.81, negative predictive value=0.51. The interrater reliability estimates with Cohen κ=0.253. If the expert's assessments are chosen as external criterion following psychometric characteristics were estimated: specificity=0.71, sensitivity=0.32, positive predictive value=0.38, negative predictive value=0.66. The rater agreement is Cohen κ=0.031. The results show that the BEVA has an excellent specificity in the detection of negative response bias. This is highly desirable in terms of an ethically moral discussion. Furthermore, the study shows that - in addition to the expert estimates - further screenings can improve the assessment of negative response bias.

Assessment of Bleeding and Thrombosis Based on Aspirin Responsiveness after Continuous-Flow Left Ventricular Assist Device Placement.

Pump thrombosis (PT) is a severe complication of left ventricular assist device (LVAD) support. This study evaluated PT and bleeding after LVAD placement in patients responsive to a standard aspirin dose of 81 mg using platelet inhibition monitoring compared with initial nonresponders who were then titrated upward to achieve therapeutic response. Patients ≥ 18 years of age with initial placement of HeartMate II LVAD at our institution and at least one VerifyNow Aspirin test performed during initial hospitalization were included. The primary endpoints were bleeding and PT compared between initial aspirin responders and nonresponders. Of 85 patients, 19 (22%) were nonresponsive to initial aspirin therapy. Responders and nonresponders showed similar survival (p = 0.082), freedom from suspected/confirmed PT (p = 0.941), confirmed PT (p = 0.273), bleeding (p = 0.401), and incidence rates in PT and bleeding. Among the initial responders (<500 vs. 500-549 aspirin reaction units), there were no significant differences in survival (p = 0.177), freedom from suspected/confirmed PT (p = 0.542), confirmed PT (p = 0.159), bleeding (p = 0.879), and incidence of PT and bleeding. Platelet function testing may detect resistance to standard aspirin regimens used in LVAD patients. Dose escalation in initially nonresponsive patients to achieve responsiveness may confer a similar PT risk to patients initially responsive to standard aspirin dosing without increased bleeding risk.

The Application of Paraphenylenediamine Staining for Assessment of Phospholipidosis.

Drug-induced phospholipidosis is characterized by intracellular accumulation of phospholipids with lamellar bodies in cells exposed to xenobiotics. Demonstration of the lamellar bodies by transmission electron microscopy (TEM) is the hallmark for a definitive diagnosis of phospholipidosis. However, the preparation of tissue samples for TEM and their ultrastructural evaluation are technically challenging and time consuming. Paraphenylenediamine (PPD) is essentially a fat stain, and the staining mechanism is based upon the osmication of unsaturated lipids. Thus, the application of PPD staining to osmicated tissue samples is considered an optimal way to identify lipids. We evaluated the potential of PPD staining to localize phospholipid accumulations on osmium-fixed semi-thin tissue sections of the lung, kidney, and liver, which were affected with phospholipidosis, under a light microscope. PPD staining revealed the presence of PPD positive dark fine granular material in the cytoplasm for all affected tissues examined, which correlated ultrastructurally with lamellar bodies as well as a light microscopic finding of cytoplasmic vacuolation. The great advantage of PPD is that it can be incorporated into the protocol for standard TEM tissue preparation and significantly improve the efficiency of TEM work. In conclusion, PPD provides a simple, sensitive, and reliable method for visualizing phospholipid accumulation on light microscopy and represents an easy tool to study drug-induced phospholipidosis.

[Assessment of Negative Response Bias - Development and Validation of the BEVA]. / Erfassung von negativen Antwortverzerrungen - Entwicklung und Validierung des Beschwerdenvalidierungstests BEVA.

AIM OF THE STUDY: A screening will be developed and validated which measure the negative response bias by inpatients with depressive symptoms. The questionnaire should be appropriate for the claimants for disability pension due to a mental illness. MATERIALS AND METHOD: The present study uses for the construction (K) and validation (V) an analog study design. Healthy participants (depression: K: N=58; V=100; pain: K: N=50; V: N=90) obtained a scenario to feign major depression or pain disorder in the BEVA. In addition, a control group (K: N=78; V: N=89), patients with major depression (K: N=58; V: N=162) and patients with pain disorder (K: N=35; V: N=46) completed the survey about their current mental complaints. RESULTS: Using the data of the construction sample the cut-off values were determined. In the validation sample could be analyzed a sensitivity of 0,75, specificity of 0,95, a positive predictive value of 0,90, and a negative predictive value of 0,86 for the study group "depression". For the pain group had a sensitivity of 0,81, specificity of 0,96, a positive predictive value of 0,97 and a negative predictive value of 0,72 resulted. CONCLUSION: The developed screening has very well values for the validity and reliability. Thus, the method seems suitable to measure negative response bias.

Neurophysiological assessment of sympathetic cardiovascular activity after loss of postganglionic neurons in the anesthetized rat.

The goal of this study was to determine the degree of sympathetic postganglionic neuronal loss required to impair cardiovascular-related sympathetic activity. To produce neuronal loss separate groups of rats were treated daily with guanethidine for either 5days or 11days, followed by a recovery period. Sympathetic activity was measured by renal sympathetic nerve activity (RSNA). Stereology of thoracic (T13) ganglia was performed to determine neuronal loss. Despite loss of more than two thirds of neurons in T13 ganglia in both treated groups no effect on resting blood pressure (BP) or heart rate (HR) was detected. Basal RSNA in rats treated for 5days (0.61±0.10µV∗s) and 11days (0.37±0.08µV∗s) was significantly less than vehicle-treated rats (0.99±0.13µV∗s, p<0.05). Increases in RSNA by baroreceptor unloading were significantly lower in 5-day (1.09±0.19µV∗s) and 11-day treated rats (0.59±0.11µV∗s) compared with vehicle-treated rats (1.82±0.19µV∗s, p<0.05). Increases in RSNA to chemoreceptor stimulation were significantly lower in 5-day treated rats (1.54±0.25µV∗s) compared with vehicle-treated rats (2.69±0.23µV∗s, p<0.05). Increases in RSNA in 11-day treated rats were significantly lower (0.75±0.15µV∗s, p<0.05) compared with both vehicle-treated and 5-day treated rats. A positive correlation of neurons to sympathetic responsiveness but not basal activity was detected. These data suggest that diminished capacity for reflex sympathetic responsiveness rather than basal activity alone must be assessed for complete detection of neurophysiological cardiovascular impairment.

Assessment of ultra high performance supercritical fluid chromatography as a separation technique for the analysis of seized drugs: Applicability to synthetic cannabinoids.

The recent development of modern methods for ultra high performance supercritical fluid chromatography (UHPSFC) has great potential for impacting the analysis of seized drugs. In the separation of synthetic cannabinoids the technique has the potential to produce superior resolution of positional isomers and diastereomers. To demonstrate this potential we have examined the capability of UHPSFC for the analysis of two different groups of synthetic cannabinoids. The first group was a mixture of 22 controlled synthetic cannabinoids, and the second group included JWH018 and nine of its non-controlled positional isomers The clear superiority of UHPSFC over other separation techniques was demonstrated, in that it was capable of near baseline separation of all 10 positional isomers using a chiral column. In total we examined four achiral columns, including Acquity UPC(2) Torus 2-PIC, Acquity UPC(2) Torus Diol, Acquity UPC(2) Torus DEA and Acquity UPC(2) Torus 1-AA (1.7µm 3.0×100mm), and three chiral columns, including Acquity UPC(2) Trefoil AMY1, Acquity UPC(2) Trefoil CEL1 and Acquity UPC(2) Trefoil CEL2 (2.5µm 3.0×150mm), using mobile phase compositions that combined carbon dioxide with methanol, acetonitrile, ethanol or isopropanol modifier gradients. Detection was performed using simultaneous PDA UV detection and quadrupole mass spectrometry. The orthogonality of UHPSFC, GC and UHPLC for the analysis of these compounds was demonstrated using principal component analysis. Overall we feel that this new technique should prove useful in the analysis and detection of seized drug samples, and will be a useful addition to the compendium of methods for drug analysis.

Using Electronic Health Records for Population Health Research: A Review of Methods and Applications.

The use and functionality of electronic health records (EHRs) have increased rapidly in the past decade. Although the primary purpose of EHRs is clinical, researchers have used them to conduct epidemiologic investigations, ranging from cross-sectional studies within a given hospital to longitudinal studies on geographically distributed patients. Herein, we describe EHRs, examine their use in population health research, and compare them with traditional epidemiologic methods. We describe diverse research applications that benefit from the large sample sizes and generalizable patient populations afforded by EHRs. These have included reevaluation of prior findings, a range of diseases and subgroups, environmental and social epidemiology, stigmatized conditions, predictive modeling, and evaluation of natural experiments. Although studies using primary data collection methods may have more reliable data and better population retention, EHR-based studies are less expensive and require less time to complete. Future EHR epidemiology with enhanced collection of social/behavior measures, linkage with vital records, and integration of emerging technologies such as personal sensing could improve clinical care and population health.