Physicians’ electronic inbox work patterns and factors associated with high inbox work duration

ObjectivesElectronic health record systems are increasingly used to send messages to physicians, but research on physicians’ inbox use patterns is limited. This study’s aims were to (1) quantify the time primary care physicians (PCPs) spend managing inboxes; (2) describe daily patterns of inbox use; (3) investigate which types of messages consume the most time; and (4) identify factors associated with inbox work duration.Materials and MethodsWe analyzed 1 month of electronic inbox data for 1275 PCPs in a large medical group and linked these data with physicians’ demographic data.ResultsPCPs spent an average of 52 minutes on inbox management on workdays, including 19 minutes (37%) outside work hours. Temporal patterns of electronic inbox use differed from other EHR functions such as charting. Patient-initiated messages (28%) and results (29%) accounted for the most inbox work time. PCPs with higher inbox work duration were more likely to be female (P < .001), have more patient encounters (P < .001), have older patients (P < .001), spend proportionally more time on patient messages (P < .001), and spend more time per message (P < .001). Compared with PCPs with the lowest duration of time on inbox work, PCPs with the highest duration had more message views per workday (200 vs 109; P < .001) and spent more time on the inbox outside work hours (30 minutes vs 9.7 minutes; P < .001).ConclusionsElectronic inbox work by PCPs requires roughly an hour per workday, much of which occurs outside scheduled work hours. Interventions to assist PCPs in handling patient-initiated messages and results may help alleviate inbox workload.     

Reducing electronic health record-related burnout in providers through a personalized efficiency improvement program

ObjectiveTo give providers a better understanding of how to use the electronic health record (EHR), improve efficiency, and reduce burnout.Materials and MethodsAll ambulatory providers were offered at least 1 one-on-one session with an “optimizer” focusing on filling gaps in EHR knowledge and lack of customization. Success was measured using pre- and post-surveys that consisted of validated tools and homegrown questions. Only participants who returned both surveys were included in our calculations.ResultsOut of 1155 eligible providers, 1010 participated in optimization sessions. Pre-survey return rate was 90% (1034/1155) and post-survey was 54% (541/1010). 451 participants completed both surveys. After completing their optimization sessions, respondents reported a 26% improvement in mean knowledge of EHR functionality (P < .01), a 19% increase in the mean efficiency in the EHR (P < .01), and a 17% decrease in mean after-hours EHR usage (P < .01). Of the 401 providers asked to rate their burnout, 32% reported feelings of burnout in the pre-survey compared to 23% in the post-survey (P < .01). Providers were also likely to recommend colleagues participate in the program, with a Net Promoter Score of 41.DiscussionIt is possible to improve provider efficiency and feelings of burnout with a personalized optimization program. We ascribe these improvements to the one-on-one nature of our program which provides both training as well as addressing the feeling of isolation many providers feel after implementation.ConclusionIt is possible to reduce burnout in ambulatory providers with personalized retraining designed to improve efficiency and knowledge of the EHR.     

Impact of a problem-oriented view on clinical data retrieval

ObjectiveThe electronic health record (EHR) data deluge makes data retrieval more difficult, escalating cognitive load and exacerbating clinician burnout. New auto-summarization techniques are needed. The study goal was to determine if problem-oriented view (POV) auto-summaries improve data retrieval workflows. We hypothesized that POV users would perform tasks faster, make fewer errors, be more satisfied with EHR use, and experience less cognitive load as compared with users of the standard view (SV).MethodsSimple data retrieval tasks were performed in an EHR simulation environment. A randomized block design was used. In the control group (SV), subjects retrieved lab results and medications by navigating to corresponding sections of the electronic record. In the intervention group (POV), subjects clicked on the name of the problem and immediately saw lab results and medications relevant to that problem.ResultsWith POV, mean completion time was faster (173 seconds for POV vs 205 seconds for SV; P < .0001), the error rate was lower (3.4% for POV vs 7.7% for SV; P = .0010), user satisfaction was greater (System Usability Scale score 58.5 for POV vs 41.3 for SV; P < .0001), and cognitive task load was less (NASA Task Load Index score 0.72 for POV vs 0.99 for SV; P < .0001).DiscussionThe study demonstrates that using a problem-based auto-summary has a positive impact on 4 aspects of EHR data retrieval, including cognitive load.ConclusionEHRs have brought on a data deluge, with increased cognitive load and physician burnout. To mitigate these increases, further development and implementation of auto-summarization functionality and the requisite knowledge base are needed.     

The association between perceived electronic health record usability and professional burnout among US nurses

ObjectivesTo measure nurse-perceived electronic health records (EHR) usability with a standardized metric of technology usability and evaluate its association with professional burnout.MethodsA cross-sectional survey of a random sample of US nurses was conducted in November 2017. EHR usability was measured with the System Usability Scale (SUS; range 0–100) and burnout with the Maslach Burnout Inventory.ResultsAmong the 86 858 nurses who were invited, 8638 (9.9%) completed the survey. The mean nurse-rated EHR SUS score was 57.6 (SD 16.3). A score of 57.6 is in the bottom 24% of scores across previous studies and categorized with a grade of “F.” On multivariable analysis adjusting for age, gender, race, ethnicity, relationship status, children, highest nursing-related degree, mean hours worked per week, years of nursing experience, advanced certification, and practice setting, nurse-rated EHR usability was associated with burnout with each 1 point more favorable SUS score and associated with a 2% lower odds of burnout (OR 0.98; 95% CI, 0.97–0.99; P < .001).ConclusionsNurses rated the usability of their current EHR in the low marginal range of acceptability using a standardized metric of technology usability. EHR usability and the odds of burnout were strongly associated with a dose-response relationship.     

Measuring time clinicians spend using EHRs in the inpatient setting: a national,mixed-methods study

ObjectiveTo understand hospitals’ use of EHR audit-log-based measures to address burden associated with inpatient EHR use.Materials and MethodsUsing mixed methods, we analyzed 2018 American Hospital Association Information Technology Supplement Survey data (n = 2864 hospitals; 64% response rate) to characterize measures used and provided by EHR vendors to track clinician time spent documenting. We interviewed staff from the top 3 EHR vendors that provided these measures. Multivariable analyses identified variation in use of the measures among hospitals with these 3 vendors.Results53% of hospitals reported using EHR data to track clinician time documenting, compared to 68% of the hospitals using the EHR from the top 3 vendors. Among hospitals with EHRs from these vendors, usage was significantly lower among rural hospitals and independent hospitals (P < .05). Two of these vendors provided measures of time spent doing specific tasks while the third measured an aggregate of auditable activities. Vendors varied in the underlying data used to create measures, measure specification, and data displays.DiscussionTools to track clinicians’ documentation time are becoming more available. The measures provided differ across vendors and disparities in use exist across hospitals. Increasing the specificity of standards underlying the data would support a common set of core measures making these measures more widely available.ConclusionAlthough half of US hospitals use measures of time spent in the EHR derived from EHR generated data, work remains to make such measures and analyses more broadly available to all hospitals and to increase its utility for national burden measurement.     

Characterizing physician EHR use with vendor derived data: a feasibility study and cross-sectional analysis

ObjectiveTo derive 7 proposed core electronic health record (EHR) use metrics across 2 healthcare systems with different EHR vendor product installations and examine factors associated with EHR time.Materials and MethodsA cross-sectional analysis of ambulatory physicians EHR use across the Yale-New Haven and MedStar Health systems was performed for August 2019 using 7 proposed core EHR use metrics normalized to 8 hours of patient scheduled time.ResultsFive out of 7 proposed metrics could be measured in a population of nonteaching, exclusively ambulatory physicians. Among 573 physicians (Yale-New Haven N = 290, MedStar N = 283) in the analysis, median EHR-Time₈ was 5.23 hours. Gender, additional clinical hours scheduled, and certain medical specialties were associated with EHR-Time₈ after adjusting for age and health system on multivariable analysis. For every 8 hours of scheduled patient time, the model predicted these differences in EHR time (P < .001, unless otherwise indicated): female physicians +0.58 hours; each additional clinical hour scheduled per month −0.01 hours; practicing cardiology −1.30 hours; medical subspecialties −0.89 hours (except gastroenterology, P = .002); neurology/psychiatry −2.60 hours; obstetrics/gynecology −1.88 hours; pediatrics −1.05 hours (P = .001); sports/physical medicine and rehabilitation −3.25 hours; and surgical specialties −3.65 hours.ConclusionsFor every 8 hours of scheduled patient time, ambulatory physicians spend more than 5 hours on the EHR. Physician gender, specialty, and number of clinical hours practicing are associated with differences in EHR time. While audit logs remain a powerful tool for understanding physician EHR use, additional transparency, granularity, and standardization of vendor-derived EHR use data definitions are still necessary to standardize EHR use measurement.     

Towards clinical data-driven eligibility criteria optimization for interventional COVID-19 clinical trials

ObjectiveThis research aims to evaluate the impact of eligibility criteria on recruitment and observable clinical outcomes of COVID-19 clinical trials using electronic health record (EHR) data.Materials and MethodsOn June 18, 2020, we identified frequently used eligibility criteria from all the interventional COVID-19 trials in ClinicalTrials.gov (n = 288), including age, pregnancy, oxygen saturation, alanine/aspartate aminotransferase, platelets, and estimated glomerular filtration rate. We applied the frequently used criteria to the EHR data of COVID-19 patients in Columbia University Irving Medical Center (CUIMC) (March 2020–June 2020) and evaluated their impact on patient accrual and the occurrence of a composite endpoint of mechanical ventilation, tracheostomy, and in-hospital death.ResultsThere were 3251 patients diagnosed with COVID-19 from the CUIMC EHR included in the analysis. The median follow-up period was 10 days (interquartile range 4–28 days). The composite events occurred in 18.1% (n = 587) of the COVID-19 cohort during the follow-up. In a hypothetical trial with common eligibility criteria, 33.6% (690/2051) were eligible among patients with evaluable data and 22.2% (153/690) had the composite event.DiscussionBy adjusting the thresholds of common eligibility criteria based on the characteristics of COVID-19 patients, we could observe more composite events from fewer patients.ConclusionsThis research demonstrated the potential of using the EHR data of COVID-19 patients to inform the selection of eligibility criteria and their thresholds, supporting data-driven optimization of participant selection towards improved statistical power of COVID-19 trials.     

Multi-PheWAS intersection approach to identify sex differences across comorbidities in 59 140 pediatric patients with autism spectrum disorder

ObjectiveTo identify differences related to sex and define autism spectrum disorder (ASD) comorbidities female-enriched through a comprehensive multi-PheWAS intersection approach on big, real-world data. Although sex difference is a consistent and recognized feature of ASD, additional clinical correlates could help to identify potential disease subgroups, based on sex and age.Materials and MethodsWe performed a systematic comorbidity analysis on 1860 groups of comorbidities exploring all spectrum of known disease, in 59 140 individuals (11 440 females) with ASD from 4 age groups. We explored ASD sex differences in 2 independent real-world datasets, across all potential comorbidities by comparing (1) females with ASD vs males with ASD and (2) females with ASD vs females without ASD.ResultsWe identified 27 different comorbidities that appeared significantly more frequently in females with ASD. The comorbidities were mostly neurological (eg, epilepsy, odds ratio [OR] > 1.8, 3-18 years of age), congenital (eg, chromosomal anomalies, OR > 2, 3-18 years of age), and mental disorders (eg, intellectual disability, OR > 1.7, 6-18 years of age). Novel comorbidities included endocrine metabolic diseases (eg, failure to thrive, OR = 2.5, ages 0-2), digestive disorders (gastroesophageal reflux disease: OR = 1.7, 6-11 years of age; and constipation: OR > 1.6, 3-11 years of age), and sense organs (strabismus: OR > 1.8, 3-18 years of age).DiscussionA multi-PheWAS intersection approach on real-world data as presented in this study uniquely contributes to the growing body of research regarding sex-based comorbidity analysis in ASD population.ConclusionsOur findings provide insights into female-enriched ASD comorbidities that are potentially important in diagnosis, as well as the identification of distinct comorbidity patterns influencing anticipatory treatment or referrals. The code is publicly available (https://github.com/hms-dbmi/sexDifferenceInASD).     

Human-centered development of an electronic health record-embedded,interactive information visualization in the emergency department using fast healthcare interoperability resources

ObjectiveDevelop and evaluate an interactive information visualization embedded within the electronic health record (EHR) by following human-centered design (HCD) processes and leveraging modern health information exchange standards.Materials and MethodsWe applied an HCD process to develop a Fast Healthcare Interoperability Resources (FHIR) application that displays a patient’s asthma history to clinicians in a pediatric emergency department. We performed a preimplementation comparative system evaluation to measure time on task, number of screens, information retrieval accuracy, cognitive load, user satisfaction, and perceived utility and usefulness. Application usage and system functionality were assessed using application logs and a postimplementation survey of end users.ResultsUsability testing of the Asthma Timeline Application demonstrated a statistically significant reduction in time on task (P < .001), number of screens (P < .001), and cognitive load (P < .001) for clinicians when compared to base EHR functionality. Postimplementation evaluation demonstrated reliable functionality and high user satisfaction.DiscussionFollowing HCD processes to develop an application in the context of clinical operations/quality improvement is feasible. Our work also highlights the potential benefits and challenges associated with using internationally recognized data exchange standards as currently implemented.ConclusionCompared to standard EHR functionality, our visualization increased clinician efficiency when reviewing the charts of pediatric asthma patients. Application development efforts in an operational context should leverage existing health information exchange standards, such as FHIR, and evidence-based mixed methods approaches.     

Using event logs to observe interactions with electronic health records: an updated scoping review shows increasing use of vendor-derived measures

ObjectiveThe aim of this article is to compare the aims, measures, methods, limitations, and scope of studies that employ vendor-derived and investigator-derived measures of electronic health record (EHR) use, and to assess measure consistency across studies.Materials and MethodsWe searched PubMed for articles published between July 2019 and December 2021 that employed measures of EHR use derived from EHR event logs. We coded the aims, measures, methods, limitations, and scope of each article and compared articles employing vendor-derived and investigator-derived measures.ResultsOne hundred and two articles met inclusion criteria; 40 employed vendor-derived measures, 61 employed investigator-derived measures, and 1 employed both. Studies employing vendor-derived measures were more likely than those employing investigator-derived measures to observe EHR use only in ambulatory settings (83% vs 48%, P = .002) and only by physicians or advanced practice providers (100% vs 54% of studies, P < .001). Studies employing vendor-derived measures were also more likely to measure durations of EHR use (P < .001 for 6 different activities), but definitions of measures such as time outside scheduled hours varied widely. Eight articles reported measure validation. The reported limitations of vendor-derived measures included measure transparency and availability for certain clinical settings and roles.DiscussionVendor-derived measures are increasingly used to study EHR use, but only by certain clinical roles. Although poorly validated and variously defined, both vendor- and investigator-derived measures of EHR time are widely reported.ConclusionThe number of studies using event logs to observe EHR use continues to grow, but with inconsistent measure definitions and significant differences between studies that employ vendor-derived and investigator-derived measures.     

Nomogram to predict pregnancy outcomes of emergency oocyte freeze-thaw cycles

Background:Existing clinical prediction models for in vitro fertilization are based on the fresh oocyte cycle, and there is no prediction model to evaluate the probability of successful thawing of cryopreserved mature oocytes. This research aims to identify and study the characteristics of pre-oocyte-retrieval patients that can affect the pregnancy outcomes of emergency oocyte freeze-thaw cycles.Methods:Data were collected from the Reproductive Center, Peking University Third Hospital of China. Multivariable logistic regression model was used to derive the nomogram. Nomogram model performance was assessed by examining the discrimination and calibration in the development and validation cohorts. Discriminatory ability was assessed using the area under the receiver operating characteristic curve (AUC), and calibration was assessed using the Hosmer–Lemeshow goodness-of-fit test and calibration plots.Results:The predictors in the model of “no transferable embryo cycles” are female age (odds ratio [OR] = 1.099, 95% confidence interval [CI] = 1.003–1.205, P = 0.0440), duration of infertility (OR = 1.140, 95% CI = 1.018–1.276, P = 0.0240), basal follicle-stimulating hormone (FSH) level (OR = 1.205, 95% CI = 1.051–1.382, P = 0.0084), basal estradiol (E2) level (OR = 1.006, 95% CI = 1.001–1.010, P = 0.0120), and sperm from microdissection testicular sperm extraction (MESA) (OR = 7.741, 95% CI = 2.905–20.632, P < 0.0010). Upon assessing predictive ability, the AUC for the “no transferable embryo cycles” model was 0.799 (95% CI: 0.722–0.875, P < 0.0010). The Hosmer–Lemeshow test (P = 0.7210) and calibration curve showed good calibration for the prediction of no transferable embryo cycles. The predictors in the cumulative live birth were the number of follicles on the day of human chorionic gonadotropin (hCG) administration (OR = 1.088, 95% CI = 1.030–1.149, P = 0.0020) and endometriosis (OR = 0.172, 95% CI = 0.035–0.853, P = 0.0310). The AUC for the “cumulative live birth” model was 0.724 (95% CI: 0.647–0.801, P < 0.0010). The Hosmer–Lemeshow test (P = 0.5620) and calibration curve showed good calibration for the prediction of cumulative live birth.Conclusions:The predictors in the final multivariate logistic regression models found to be significantly associated with poor pregnancy outcomes were increasing female age, duration of infertility, high basal FSH and E2 level, endometriosis, sperm from MESA, and low number of follicles with a diameter >10 mm on the day of hCG administration.     

Risk factors for gastric cancer: a large-scale,population-based case-control study

Background:Early detection of gastric cancer (GC) has been the topic of major efforts in China. This study aimed to explore the risk factors associated with GC and to provide evidence for the selection of a high-risk population of GC.Methods:Based on the cancer screening cohort of the National Cancer Screening Program in Urban China, GC patients diagnosed by endoscopy and pathological examinations constituted the case group, and controls were 1:3 matched by sex and age (±5 years) individually. The variables were selected by univariable analysis of factors such as body mass index (BMI), dietary habits, lifestyle, stomach disease history, and family history of GC; and multivariable logistic regression was used to analyze the influencing factors of GC and to calculate the odds ratio (OR) of related factors and its 95% confidence interval (CI).Results:A total of 215 GC cases and 645 matched healthy controls were included in the final analysis, with a median age of 61 years for the case and control groups. Overall analysis showed that high educational level (above primary school) (OR = 0.362, 95% CI = 0.219–0.599, P < 0.001), overweight/obesity (BMI ≥24 kg/m²; OR = 0.489, 95% CI = 0.329–0.726, P < 0.001), cigarette smoking (OR = 3.069, 95% CI = 1.700–5.540, P < 0.001), alcohol consumption (OR = 1.661, 95% CI = 1.028–2.683, P = 0.038), history of stomach disease (OR = 6.917, 95% CI = 4.594–10.416, P < 0.001), and family history of GC in first-degree relatives (OR = 4.291, 95% CI = 1.661–11.084, P = 0.003) were significantly correlated with the occurrence of GC. Subgroup analyses by age and gender indicated that GC risk was still increased in the presence of a history of stomach disease. A history of chronic gastritis, gastric ulcer, or gastric polyposis was positively associated with GC, with adjusted ORs of 4.155 (95% CI = 2.711–6.368), 1.839 (95% CI = 1.028–3.288), and 2.752 (95% CI = 1.197–6.326).Conclusions:Subjects who smoke, drink, with history of stomach disease and family history of GC in first-degree relatives are the high-risk populations for GC. Therefore, attention should be paid to these subjects for GC screening.     

Can decision support combat incompleteness and bias in routine primary care data?

ObjectiveRoutine primary care data may be used for the derivation of clinical prediction rules and risk scores. We sought to measure the impact of a decision support system (DSS) on data completeness and freedom from bias.Materials and MethodsWe used the clinical documentation of 34 UK general practitioners who took part in a previous study evaluating the DSS. They consulted with 12 standardized patients. In addition to suggesting diagnoses, the DSS facilitates data coding. We compared the documentation from consultations with the electronic health record (EHR) (baseline consultations) vs consultations with the EHR-integrated DSS (supported consultations). We measured the proportion of EHR data items related to the physician’s final diagnosis. We expected that in baseline consultations, physicians would document only or predominantly observations related to their diagnosis, while in supported consultations, they would also document other observations as a result of exploring more diagnoses and/or ease of coding.ResultsSupported documentation contained significantly more codes (incidence rate ratio [IRR] = 5.76 [4.31, 7.70] P < .001) and less free text (IRR = 0.32 [0.27, 0.40] P < .001) than baseline documentation. As expected, the proportion of diagnosis-related data was significantly lower (b = −0.08 [−0.11, −0.05] P < .001) in the supported consultations, and this was the case for both codes and free text.ConclusionsWe provide evidence that data entry in the EHR is incomplete and reflects physicians’ cognitive biases. This has serious implications for epidemiological research that uses routine data. A DSS that facilitates and motivates data entry during the consultation can improve routine documentation.     

Optimizing the electronic health record: An inpatient sprint addresses provider burnout and improves electronic health record satisfaction

ObjectiveWe sought reduce electronic health record (EHR) burden on inpatient clinicians with a 2-week EHR optimization sprint.Materials and MethodsA team led by physician informaticists worked with 19 advanced practice providers (APPs) in 1 specialty unit. Over 2 weeks, the team delivered 21 EHR changes, and provided 39 one-on-one training sessions to APPs, with an average of 2.8 hours per provider. We measured Net Promoter Score, thriving metrics, and time spent in the EHR based on user log data.ResultsOf the 19 APPs, 18 completed 2 or more sessions. The EHR Net Promoter Score increased from 6 to 60 postsprint (1.0; 95% confidence interval, 0.3-1.8; P = .01). The NPS for the Sprint itself was 93, a very high rating. The 3-axis emotional thriving, emotional recovery, and emotional exhaustion metrics did not show a significant change. By user log data, time spent in the EHR did not show a significant decrease; however, 40% of the APPs responded that they spent less time in the EHR.ConclusionsThis inpatient sprint improved satisfaction with the EHR.     

Is rs759853 polymorphism in promoter of aldose reductase gene a risk factor for diabetic nephropathy? A meta-analysis

Background

So far, a number of case-control or cohort studies have been carried out to investigate the relationship between rs759853 polymorphism in the promoter of aldose reductase (AR) gene and the risk of diabetic nephropathy (DN). However, the results have generated considerable controversy. We performed this study to clarify the linkage between this gene mutation and the risk of DN.

Methods

A comprehensive literature search of electronic databases and a well-organized meta-analysis were conducted.

Results

Twelve comparisons and 4,735 individuals from nine published case-control or cohort studies were included finally. From none to large heterogeneity was observed, therefore, both fixed and random models were used. Significant differences were found between AR rs759853 polymorphism and susceptibility of DN from both type 1 and type 2 diabetes in all genetic models (allele contrast, OR = 1.37, CI (1.18, 1.59), P < 0.0001; additive model, OR = 1.78, CI (1.25, 2.53), P = 0.01; recessive model OR = 1.33 CI (1.08, 1.63), P = 0.008; dominant model, OR = 1.52, CI (1.26, 1.84), P < 0.0001; codominance model OR = 1.30 (1.15, 1.47), P < 0.0001). In stratified meta-analyses for type 2 diabetes by ethnicity, the significant relationship was found in allele contrast and dominant model in Caucasians, and in allele contrast and codominance model in Asians. However, data do not support the linkage between this gene mutation and the progression of DN. There was no significant publication bias.

Conclusions

The evidence currently available shows that the AR rs759853 polymorphism may correlate with the susceptibility of DN. However, data do not support the association between this DNA variation and the progression of DN.     

Inaccuracies in electronic health records smoking data and a potential approach to address resulting underestimation in determining lung cancer screening eligibility

ObjectiveThe US Preventive Services Task Force (USPSTF) requires the estimation of lifetime pack-years to determine lung cancer screening eligibility. Leading electronic health record (EHR) vendors calculate pack-years using only the most recently recorded smoking data. The objective was to characterize EHR smoking data issues and to propose an approach to addressing these issues using longitudinal smoking data.Materials and MethodsIn this cross-sectional study, we evaluated 16 874 current or former smokers who met USPSTF age criteria for screening (50–80 years old), had no prior lung cancer diagnosis, and were seen in 2020 at an academic health system using the Epic^® EHR. We described and quantified issues in the smoking data. We then estimated how many additional potentially eligible patients could be identified using longitudinal data. The approach was verified through manual review of records from 100 subjects.ResultsOver 80% of evaluated records had inaccuracies, including missing packs-per-day or years-smoked (42.7%), outdated data (25.1%), missing years-quit (17.4%), and a recent change in packs-per-day resulting in inaccurate lifetime pack-years estimation (16.9%). Addressing these issues by using longitudinal data enabled the identification of 49.4% more patients potentially eligible for lung cancer screening (P < .001).DiscussionMissing, outdated, and inaccurate smoking data in the EHR are important barriers to effective lung cancer screening. Data collection and analysis strategies that reflect changes in smoking habits over time could improve the identification of patients eligible for screening.ConclusionThe use of longitudinal EHR smoking data could improve lung cancer screening.     

Genetic polymorphisms of glutathione S-transferase M1 and T1, and evaluation of oxidative stress in patients with non-small cell lung cancer

Background

Our objective is to investigate the genetic polymorphisms of the glutathione S-transferase M1 and T1 genes (GSTM1 and GSTT1) and evaluate oxidative damage in patients with non-small lung cancer (N-SCLC).

Methods

One hundred and ten patients with N-SCLC and 100 controls are included in this case-control study. Multiplex polymerase chain reaction (PCR) analyses were used to identify the genotypes. The activities of malondialdehyde (MDA) and nitric oxide (NO) and total antioxidant capacity (T-AOC) were detected by spectroscopic analysis.

Results

The frequencies of the GSTM1, T1, and GSTM1/T1 null genotypes in the patient group were significantly higher than that in the control group (OR = 2.071, P = 0.009; OR = 1.900, P = 0.024; OR = 3.258, P = 0.003). The activities of MDA and NO were significantly higher in the patient group than that in the control group (P <0.001), and T-AOC was significantly lower in patient group than that in control group (P <0.001). The activities of MDA, and NO were higher but the T-AOC was lower in patients with the GSTM1, T1 and M1/T1 null genotypes than those in patients with GSTM1, T1 and M1/T1 present genotypes (P <0.001).

Conclusions

Our results suggest that oxidative damage may be play a important role in patients with N-SCLC, and that GSTM1 and GSTT1 null genotypes may predispose the cells of patients with N-SCLC to increased oxidative damage.     

An interview study with medical scribes on how their work may alleviate clinician burnout through delegated health IT tasks

ObjectivesTo understand how medical scribes’ work may contribute to alleviating clinician burnout attributable directly or indirectly to the use of health IT.Materials and MethodsQualitative analysis of semistructured interviews with 32 participants who had scribing experience in a variety of clinical settings.ResultsWe identified 7 categories of clinical tasks that clinicians commonly choose to offload to medical scribes, many of which involve delegated use of health IT. These range from notes-taking and computerized data entry to foraging, assembling, and tracking information scattered across multiple clinical information systems. Some common characteristics shared among these tasks include: (1) time-consuming to perform; (2) difficult to remember or keep track of; (3) disruptive to clinical workflow, clinicians’ cognitive processes, or patient–provider interactions; (4) perceived to be low-skill “clerical” work; and (5) deemed as adding no value to direct patient care.DiscussionThe fact that clinicians opt to “outsource” certain clinical tasks to medical scribes is a strong indication that performing these tasks is not perceived to be the best use of their time. Given that a vast majority of healthcare practices in the US do not have the luxury of affording medical scribes, the burden would inevitably fall onto clinicians’ shoulders, which could be a major source for clinician burnout.ConclusionsMedical scribes help to offload a substantial amount of burden from clinicians—particularly with tasks that involve onerous interactions with health IT. Developing a better understanding of medical scribes’ work provides useful insights into the sources of clinician burnout and potential solutions to it.