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Infants frequently receive metronidazole at variable doses and duration for surgical site infection prophylaxis and treatment of intra-abdominal infections. Seizures are a rare (but potentially devastating) side effect of metronidazole, yet the prevalence of seizures in infants, as well as the relationship with metronidazole dose and exposure, are unknown.We examined the Pediatrix Clinical Data Warehouse for infants in neonatal intensive care units from 1997 to 2018 who received at least 1 dose of metronidazole during their first 120 days of life. We used an existing population pharmacokinetic model to simulate exposure parameters, estimating multivariable associations between metronidazole dosing and exposure parameters, and the occurrence of seizure.There were 19,367 intravenous doses of metronidazole given to 1546 infants, and 31 experienced a seizure. Infants with a seizure had a longer median (interquartile values) duration of metronidazole exposure than those without (11 days [6, 15] vs. 7 [4, 11], P = 0.01). Each added day of metronidazole (OR = 1.06, 95% CI: 1.02-1.10), and each standard deviation increase in cumulative area under the plasma concentration-time curve (OR = 1.27, 95% CI: 1.11-1.45) were associated with increased odds of seizure. Higher simulated maximum plasma concentration was associated with lower odds of seizure (OR = 0.88, 95% CI: 0.81-0.96).Longer metronidazole exposure and higher cumulative exposure could be associated with increased odds of infant seizures. Using a large observational dataset allowed us to identify a rare adverse event, but prospective studies are needed to validate this finding and further characterize metronidazole dose- and exposure-safety relationships.

Background: Guideline adherence is important to ensure optimal and safe use of methylphenidate for children and adolescents with attention-deficit/hyperactivity disorder (ADHD). We investigated adherence to Dutch guidelines regarding dosing and monitoring of methylphenidate in child and adolescent mental health care and pediatric treatment settings.Methods: Five hundred six medical records of children and adolescents were investigated in 2015 and 2016. We assessed adherence to the following guideline recommendations: (1) at least four visits during the dose-finding phase; (2) monitoring thereafter at least every 6 months; (3) measuring height and weight at least annually; and (4) the use of validated questionnaires to assess treatment response. Pearson's chi-squared test statistics were used to examine differences between settings.Results: Only a small portion of patients had at least four visits during the dose-finding phase (5.1% in the first 4 weeks to 12.4% in the first 6 weeks). Also, less than half of the patients (48.4%) were seen at least every 6 months. Height was recorded at least annually in 42.0% of patients, weight in 44.9%, and both recorded in a growth chart in 19.5%. Questionnaires to assess treatment response were only used in 2.3% of all visits. When comparing both settings, more patients in the pediatric settings were seen every 6 months, although height and weight were recorded more often in the mental health care setting. Conclusion: Overall, guideline adherence was low. Training of clinicians and adding guideline recommendations to electronic medical records templates may improve adherence. Additionally, we should aim to close the gap between guidelines and clinical practice by looking critically at the feasibility of guidelines.

ObjectiveConvert the Medical Information Mart for Intensive Care (MIMIC)-IV database into Health Level 7 Fast Healthcare Interoperability Resources (FHIR). Additionally, generate and publish an openly available demo of the resources, and create a FHIR Implementation Guide to support and clarify the usage of MIMIC-IV on FHIR.Materials and MethodsFHIR profiles and terminology system of MIMIC-IV were modeled from the base FHIR R4 resources. Data and terminology were reorganized from the relational structure into FHIR according to the profiles. Resources generated were validated for conformance with the FHIR profiles. Finally, FHIR resources were published as newline delimited JSON files and the profiles were packaged into an implementation guide.ResultsThe modeling of MIMIC-IV in FHIR resulted in 25 profiles, 2 extensions, 35 ValueSets, and 34 CodeSystems. An implementation guide encompassing the FHIR modeling can be accessed at mimic.mit.edu/fhir/mimic. The generated demo dataset contained 100 patients and over 915 000 resources. The full dataset contained 315 000 patients covering approximately 5 840 000 resources. The final datasets in NDJSON format are accessible on PhysioNet.DiscussionOur work highlights the challenges and benefits of generating a real-world FHIR store. The challenges arise from terminology mapping and profiling modeling decisions. The benefits come from the extensively validated openly accessible data created as a result of the modeling work.ConclusionThe newly created MIMIC-IV on FHIR provides one of the first accessible deidentified critical care FHIR datasets. The extensive real-world data found in MIMIC-IV on FHIR will be invaluable for research and the development of healthcare applications.

The aim of this study was to evaluate the activity within the primary health care (PHC) in Greenland by identifying the patterns of all registered contacts made by patients in 2021, and to compare the most frequently used types of contacts and diagnostic codes in Nuuk to the rest of Greenland. The study was designed as a cross-sectional register study using data from the national electronic medical records (EMR) and diagnostic codes from the ICPC-2-system. In 2021, 83.7% (46,522) of the Greenlandic population were in contact with the PHC, resulting in 335,494 registered contacts. The majority of the contacts with PHC was made by females (61.3%). On average, females were in contact with PHC 8.4 times per patient per year, while males were in contact with PHC 5.9 times per patient per year. The most frequently used diagnostic group was "General and unspecified", followed by "Musculoskeletal" and "Skin". The results are in line with studies from other northern countries and indicate an easily accessible PHC system, with a predominance of female contacts.

Objective The authors describe a novel solution to the challenges of lengthy notes and poor note readability by creating an unobtrusive clinical decision support tool named “disappearing help text.” Methods We designed this tool in Pediatric Hospital Medicine (PHM) note templates to provide in-line decision support on best documentation practices, note bloat reduction, billing compliance, and provider workflow enhancement. Results After template changes that utilized disappearing help text, we reduced the percent of notes utilizing any laboratory SmartLink from 90.2 to 15.3% for admission notes (p Discussion Note template changes that utilized disappearing help text significantly reduced the length of both progress and admission notes, a proxy for note bloat reduction, without negatively impacting coding query frequency or internal billing audit performance. One factor that likely contributed to this reduction in note length is the reduced usage of laboratory SmartLinks prompted by disappearing help text. Conclusion We present the use of in-line disappearing help text embedded into note templates as a clinical decision support tool to improve note readability, educate trainees on note documentation, and protect confidential teen information. Help text implementation correlated with a reduction in the automatic insertion of laboratories into notes and a decrease in note character count.

Each year, thousands of older adults develop delirium, a serious, preventable condition. At present, there is no well-validated method to identify patients with delirium when using Medicare claims data or other large datasets. We developed and assessed the performance of classification algorithms based on longitudinal Medicare administrative data that included International Classification of Diseases, 10th Edition diagnostic codes.Using a linked electronic health record (EHR)-Medicare claims dataset, 2 neurologists and 2 psychiatrists performed a standardized review of EHR records between 2016 and 2018 for a stratified random sample of 1002 patients among 40,690 eligible subjects. Reviewers adjudicated delirium status (reference standard) during this 3-year window using a structured protocol. We calculated the probability that each patient had delirium as a function of classification algorithms based on longitudinal Medicare claims data. We compared the performance of various algorithms against the reference standard, computing calibration-in-the-large, calibration slope, and the area-under-receiver-operating-curve using 10-fold cross-validation (CV).Beneficiaries had a mean age of 75 years, were predominately female (59%), and non-Hispanic Whites (93%); a review of the EHR indicated that 6% of patients had delirium during the 3 years. Although several classification algorithms performed well, a relatively simple model containing counts of delirium-related diagnoses combined with patient age, dementia status, and receipt of antipsychotic medications had the best overall performance [CV- calibration-in-the-largelt;0.001, CV-slope 0.94, and CV-area under the receiver operating characteristic curve (0.88 95% confidence interval: 0.84-0.91)].A delirium classification model using Medicare administrative data and International Classification of Diseases, 10th Edition diagnosis codes can identify beneficiaries with delirium in large datasets.

Objectives To improve clinical decision support (CDS) by allowing users to provide real-time feedback when they interact with CDS tools and by creating processes for responding to and acting on this feedback. Methods Two organizations implemented similar real-time feedback tools and processes in their electronic health record and gathered data over a 30-month period. At both sites, users could provide feedback by using Likert feedback links embedded in all end-user facing alerts, with results stored outside the electronic health record, and provide feedback as a comment when they overrode an alert. Both systems are monitored daily by clinical informatics teams. Results The two sites received 2,639 Likert feedback comments and 623,270 override comments over a 30-month period. Through four case studies, we describe our use of end-user feedback to rapidly respond to build errors, as well as identifying inaccurate knowledge management, user-interface issues, and unique workflows. Conclusion Feedback on CDS tools can be solicited in multiple ways, and it contains valuable and actionable suggestions to improve CDS alerts. Additionally, end users appreciate knowing their feedback is being received and may also make other suggestions to improve the electronic health record. Incorporation of end-user feedback into CDS monitoring, evaluation, and remediation is a way to improve CDS.

ImportanceHidradenitis suppurativa (HS) is associated with considerable diagnostic delay. Although most patients report adolescent onset, existing HS diagnostic criteria may not adequately capture disease in pediatric populations.ObjectivesTo determine the proportion of physician-diagnosed pediatric patients with HS who met diagnostic criteria, and describe demographics, disease characteristics, and diagnostic patterns among pediatric patients with HS.Design, Setting, and ParticipantsIn this retrospective, cross-sectional study, electronic medical records from 2 sites of a single academic tertiary care center were included. Eligible patients were those born after January 1, 1993, and assigned International Classification of Diseases, Ninth and Tenth Revisions (ICD-9/10) codes for HS (ICD-9 705.83/ICD-10 L73.2) between January 1, 2012, and July 1, 2021. Patients were excluded if they were older than 18 years at diagnosis, had inaccessible diagnostic visit notes, or were unintentionally assigned an HS ICD code.ExposuresPediatric patients with HS.Main Outcomes and MeasuresFulfillment of diagnostic criteria in pediatric patients with HS.ResultsA total of 297 adolescents with HS were included in the study; 123 patients were female (78.1%), 78 self-identified as Black (26.3%), and 116 self-identified as Hispanic (39.1%). The median (IQR) age at diagnosis was 14.0 (13.0-16.0) years. Documentation from the diagnostic visit demonstrated that 127 (42.8%) patients did not meet all 3 major HS diagnostic criteria. Of these patients, 122 (96.1%) did not meet the recurrence interval criterion (≥2 lesions within 6 months). Overall, 96 patients who did not meet the recurrence interval criterion had documentation from additional visits in the health system; 59 (61.5%) had documentation of 1 or more additional lesions consistent with HS. Review of these additional records demonstrated that 26 of these 59 (44.1%) patients met the recurrence interval criterion after diagnosis, and 44 (74.6%) had recurrent lesions within a 1-year interval (median, 6.5 months; interquartile range, 3.5-12.2 months). Medical chart review was conducted from November 22, 2021, to January 12, 2022. Analysis was conducted from January 12, 2022, to January 15, 2022.Conclusions and RelevanceOverall, 118 (40%) of 297 pediatric patients with HS in this retrospective cross-sectional study did not meet all major diagnostic criteria at the time of diagnosis, largely due to failure to fulfill the 6-month recurrence interval criterion. Future studies are needed to determine the appropriate recurrence interval to facilitate timely diagnosis and promote clinical trial eligibility for pediatric patients with HS.

Objectives The purpose of this study is to understand the relationship between documentation burden and clinician burnout syndrome in nurses working in direct patient care. The Office of the National Coordinator considers documentation burden a high priority problem. However, the presence of documentation burden in nurses working in direct patient care is not well known. Furthermore, the presence of documentation burden has not been linked to the development of clinician burnout syndrome. Methods This paper reports that the results of a cross-sectional survey study comprised of three tools: (1) The burden of documentation for nurses and mid-wives survey, (2) the system usability scale, and (3) Maslach's burnout inventory for medical professionals. Results Documentation burden has a weak to moderate correlation to clinician burnout syndrome. Furthermore, poor usability of the electronic health record (EHR) is also associated with documentation burden and clinician burnout syndrome. Conclusion This study suggests that there is a relationship between documentation burden and clinician burnout syndrome. The correlation of poor usability and domains of clinician burnout syndrome implies the need for more work on improving the usability of EHR for nursing documentation. Further study regarding the presence of documentation burden and its correlation to clinician burnout syndrome should focus on specific areas of nursing to understand the drivers of documentation burden variation within and across specialty domains.

Background In response to surges in demand for intensive care unit (ICU) care related to the COVID-19 pandemic, health care systems have had to increase hospital capacity. One institution redeployed certified registered nurse anesthetists (CRNAs) as ICU clinicians, which necessitated training in ICU-specific electronic health record (EHR) workflows prior to redeployment. Under time- and resource-constrained settings, clinical informatics (CI) fellows could effectively be lead instructors for such training. Objective This study aimed to deploy CI fellows as lead EHR instructional trainers for clinician redeployment as part of an organization's response to disaster management. Methods CI fellows led a multidisciplinary team alongside subject matter experts to develop and deploy a tailored EHR curriculum comprising in-person classes and online video modules, leveraging high-fidelity simulated patient cases. The participants completed surveys immediately after the in-person training session and after deployment. Results Eighteen CRNAs participated, with 15 completing the postactivity survey (83%). All felt the training was useful and improved their EHR skills with a Net Promoter score of +87. Most (93%) respondents indicated the pace of the session was “just right,” and 100% felt the clarity of instruction was “just right” or “extremely easy” to understand. Twelve participants (67%) completed the postdeployment survey. The training increased comfort in the ICU for all respondents, and 91% felt the training prepared them to work in the ICU with minimal guidance. All stated that the concepts learned would be useful in their anesthesia role. Fifty-eight percent viewed the online video library. Conclusion This case report demonstrates that CI fellows with dual domain expertise in their clinical specialty and informatics are uniquely poised to deliver clinician redeployment EHR training in response to operational crises. Such opportunities can achieve fellowship educational goals while conserving physician resources which can be a strategic option as organizations plan for disaster management.

Background: Inherited primary arrhythmia syndromes and arrhythmogenic cardiomyopathies can lead to sudden cardiac arrest in otherwise healthy individuals. The burden and expression of these diseases in a real-world, well-phenotyped cardiovascular population is not well understood. Methods: Whole exome sequencing was performed on 8574 individuals from the CATHGEN cohort (Catheterization Genetics). Variants in 55 arrhythmia-related genes (associated with 8 disorders) were identified and assessed for pathogenicity based on American College of Genetics and Genomics/Association for Molecular Pathology criteria. Individuals carrying pathogenic/likely pathogenic (P/LP) variants were grouped by arrhythmogenic disorder and matched 1:5 to noncarrier controls based on age, sex, and genetic ancestry. Long-term phenotypic data were annotated through deep electronic health record review. Results: Fifty-eight P/LP variants were found in 79 individuals in 12 genes associated with 5 arrhythmogenic disorders (arrhythmogenic right ventricular cardiomyopathy, Brugada syndrome, hypertrophic cardiomyopathy, LMNA -related cardiomyopathy, and long QT syndrome). The penetrance of these P/LP variants in this cardiovascular cohort was 33%, 0%, 28%, 83%, and 4%, respectively. Carriers of P/LP variants associated with arrhythmogenic disorders showed significant differences in ECG, imaging, and clinical phenotypes compared with noncarriers, but displayed no difference in survival. Carriers of novel truncating variants in FLNC, MYBPC3 , and MYH7 also developed relevant arrhythmogenic cardiomyopathy phenotypes. Conclusions: In a real-world cardiovascular cohort, P/LP variants in arrhythmia-related genes were relatively common (1:108 prevalence) and most penetrant in LMNA . While hypertrophic cardiomyopathy P/LP variant carriers showed significant differences in clinical outcomes compared with noncarriers, carriers of P/LP variants associated with other arrhythmogenic disorders displayed only ECG differences.

Objective We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. Materials and Methods We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Results Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. Conclusions Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.

Continuous prediction surveillance modeling is an emerging tool giving dynamic insight into conditions with potential mitigation of adverse events (AEs) and failure to rescue. The Epic electronic medical record contains a Deterioration Index (DI) algorithm that generates a prediction score every 15 min using objective data. Previous validation studies show rapid increases in DI score (≥14) predict a worse prognosis. The aim of this study was to demonstrate the utility of DI scores in the trauma intensive care unit (ICU) population.A prospective, single-center study of trauma ICU patients in a Level 1 trauma center was conducted during a 3-mo period. Charts were reviewed every 24 h for minimum and maximum DI score, largest score change (Δ), and AE. Patients were grouped as low risk (ΔDI14) or high risk (ΔDI ≥14).A total of 224 patients were evaluated. High-risk patients were more likely to experience AEs (69.0% versus 47.6%, P = 0.002). No patients with DI scores30 were readmitted to the ICU after being stepped down to the floor. Patients that were readmitted and subsequently died all had DI scores of ≥60 when first stepped down from the ICU.This study demonstrates DI scores predict decompensation risk in the surgical ICU population, which may otherwise go unnoticed in real time. This can identify patients at risk of AE when transferred to the floor. Using the DI model could alert providers to increase surveillance in high-risk patients to mitigate unplanned returns to the ICU and failure to rescue.

PURPOSE:As the role of clinical ethics consultation in health care advances, there are calls to standardize the process of consultation. The Ethics Committee at Memorial Sloan Kettering Cancer Center (MSK) hypothesized that the process of requesting an ethics consultation could be improved by instituting an electronic health record (EHR) order for consultation requests. This report summarizes the impact of adopting an EHR order for ethics consultation requests at MSK.METHODS:This retrospective review of all clinical ethics consultations requested at a tertiary cancer center from May 2017 to February 2020 spans 17 months before and after implementation of an electronic order for consultation requests. Summary statistics are presented using Pearson chi-square analyses with a significance level of 0.05.RESULTS:There was a significant increase in the total number of consultation requests placed after implementation of the EHR order (n = 165, 0.08% of total patients) compared with before (n = 108, 0.05% of total patients; P = .007). The number of consults requested by providers from inpatient ( P = .02) and outpatient ( P = .04) settings significantly increased. The proportion of consults placed by medical versus nonmedical providers remained unchanged ( P = .32).CONCLUSION:In this large single-institution retrospective study, implementation of an EHR order for ethics consultation requests was associated with a significant increase in the number of consultation requests. Implementation of an electronic order may decrease barriers to ethics consultation in diverse practice settings. Further longitudinal, multicenter studies are needed to assess strategies to improve access to clinical ethics consultation for oncology patients.

Background:Approximately 10% of patients report allergies to penicillin, yet >90% of these allergies are not clinically significant. Patients reporting penicillin allergies are often treated with second-line, non–β-lactam antibiotics that are typically broader spectrum and more toxic. Orders for β-lactam antibiotics for these patients trigger interruptive alerts, even when there is electronic health record (EHR) data indicating prior β-lactam exposure.Objective:To describe the rate that interruptive penicillin allergy alerts display for patients who have previously had a β-lactam exposure.Design:Retrospective EHR review from January 2013 through June 2018.Setting:A nonprofit health system including 1 large tertiary-care medical center, a smaller associated hospital, 2 emergency departments, and ˜250 outpatient clinics.Participants:All patients with EHR-documented of penicillin allergies.Methods:We examined interruptive penicillin allergy alerts and identified the number and percentage of alerts that display for patients with a prior administration of a penicillin class or other β-lactam antibiotic.Results:Of 115,081 allergy alerts that displayed during the study period, 8% were displayed for patients who had an inpatient administration of a penicillin antibiotic after the allergy was noted, and 49% were displayed for patients with a prior inpatient administration of any β-lactam.Conclusions:Many interruptive penicillin allergy alerts display for patients who would likely tolerate a penicillin, and half of all alerts display for patients who would likely tolerate another β-lactam.

Background There is a common belief that seniority and gender are associated with clinicians' perceptions of the value of electronic health record (EHR) technology and the propensity for burnout. Insufficient evidence exists on the relationship between these variables. Objective The aim of this study was to investigate how seniority/years of practice, gender, and screened burnout status are associated with opinions of EHR use on quality, cost, and efficiency of care. Methods We surveyed ambulatory primary care and subspecialty clinicians at three different institutions to screen for burnout status and to measure their opinions (positive, none, negative, don't know) on how EHR technology has impacted three important attributes of health care: quality, cost, and efficiency of care. We used chi-square tests to analyze association between years of practice (≤10 years or 11+ years), gender, and screened burnout status and the reported attributes. We used a Bonferroni-corrected α = 0.0167 for significance to protect against type I error among multiple comparisons. Results Overall, 281 clinicians responded from 640 that were surveyed with 44% overall response rate. There were no significant associations of years in practice (≤10 years or 11+ years) or gender (p > 0.0167 for both) with any of the health care attributes. Clinicians who screened burnout negative (n = 154, 55%) were more likely to indicate that EHR technology has a positive impact on both the quality (p = 0.0025) and efficiency (p = 0.0003) health care attributes compared with those who screened burnout positive (n = 127, 45%). Conclusion Burnout status is significantly associated with clinicians' perceived value of EHR technologies, while years of practice and gender are not. This contests the popular notion that junior clinicians view EHR technology more favorably than their more senior counterparts. Hence, burnout status may be an important factor associated with the overall value clinicians ascribe to EHR technologies.

PURPOSE Electronic medical records are a valuable source of information about patients' clinical status but are often free-text documents that require laborious manual review to be exploited. Techniques from computer science have been investigated, but the literature has marginally focused on non-English language texts. We developed RUBY, a tool designed in collaboration with IBM—France to automatically structure clinical information from French medical records of patients with breast cancer. MATERIALS AND METHODS RUBY, which exploits state-of-the-art Named Entity Recognition models combined with keyword extraction and postprocessing rules, was applied on clinical texts. We investigated the precision of RUBY in extracting the target information. RESULTS RUBY has an average precision of 92.8% for the Surgery report, 92.7% for the Pathology report, 98.1% for the Biopsy report, and 81.8% for the Consultation report. CONCLUSION These results show that the automatic approach has the potential to effectively extract clinical knowledge from an extensive set of electronic medical records, reducing the manual effort required and saving a significant amount of time. A deeper semantic analysis and further understanding of the context in the text, as well as training on a larger and more recent set of reports, including those containing highly variable entities and the use of ontologies, could further improve the results.

Background Health care institutions have their own “picklist” for clinicians to document adverse drug reactions (ADRs) into the electronic health record (EHR) allergy list. Whether the lack of a nationally standardized picklist impacts clinician data entries is unknown. Objectives The objective of this study was to assess the impact of defined reaction picklists on clinical documentation and, therefore, downstream analytics and clinical research using these data at two institutions. Methods ADR data were obtained from the EHRs of patients who visited the emergency department or outpatient clinics at Brigham and Women's Hospital (BWH) and University of Colorado Hospital (UCH) from 2013 to 2018. Reported drug class ADR prevalences were calculated. We investigated the reactions on each picklist and compared the top 40 reactions at each institution, as well as the top 10 reactions within each drug class. Results Of 2,160,116 patients, 640,444 (30%) had 928,973 active drug allergies. The most commonly reported drug class allergens were similar between BWH and UCH. BWH's picklist had 48 reactions, and UCH's had 160 reactions; 29 reactions were shared by both picklists. While the top four reactions overall (rash, GI upset/nausea/vomiting, hives, itching) were identical between sites, reactions by drug class exhibited greater documentation diversity. For example, while the summed prevalence of swelling-related reactions to angiotensin-converting-enzyme inhibitors was comparable across sites, swelling was represented by two terms (“swelling,” “angioedema”) at BWH but 11 terms at UCH (e.g., “swelling,” “edema,” by body locality). Conclusion The availability and granularity of reaction picklists impact ADR documentation in the EHR by health care providers; picklists may partially explain variations in reported ADRs across health care systems.

Background Automated electronic result notifications can alert health care providers of important clinical results. In contrast to historical notification systems, which were predominantly focused on critical laboratory abnormalities and often not very customizable, modern electronic health records provide capabilities for subscription-based electronic notification. This capability has not been well studied. Objectives The purpose of this study was to develop an understanding of when and how a provider decides to use a subscription-based electronic notification. Better appreciation for the factors that contribute to selecting such notifications could aid in improving the functionality of these tools. Methods We performed an 8-month quantitative assessment of 3,291 notifications and a qualitative survey assessment of 73 providers who utilized an elective notification tool in our electronic health record. Results We found that most notifications were requested by attending physicians (∼60%) and from internal medicine specialty (∼25%). Most providers requested only a few notifications while a small minority (nearly 5%) requested 10 or more in the study period. The majority (nearly 30%) of requests were for chemistry laboratories. Survey respondents reported using the tool predominantly for important or time-sensitive laboratories. Overall opinions of the tool were positive (median = 7 out of 10, 95% confidence interval: 6–9), with 40% of eligible respondents reporting the tool improved quality of care. Reported examples included time to result review, monitoring of heparin drips, and reviewing pathology results. Conclusion Developing an understanding for when and how providers decide to be notified of clinical results can help aid in the design and improvement of clinical tools, such as improved elective notifications. These tools may lead to reduced time to result review which could in turn improve clinical care quality.

BackgroundMedication adherence is usually defined as the extent of the agreement between the medication regimen agreed to by patients with their healthcare provider and the real-world implementation. Proactive identification of those with poor adherence may be useful to identify those with poor disease control and offers the opportunity for ameliorative action. Adherence can be estimated from Electronic Health Records (EHRs) by comparing medication dispensing records to the prescribed regimen. Several methods have been developed in the literature to infer adherence from EHRs, however there is no clear consensus on what should be considered the gold standard in each use case.Our objectives were to critically evaluate different measures of medication adherence in a large longitudinal Scottish EHR dataset. We used asthma, a chronic condition with high prevalence and high rates of non-adherence, as a case study.MethodsOver 1.6 million asthma controllers were prescribed for our cohort of 91,334 individuals, between January 2009 and March 2017. Eight adherence measures were calculated, and different approaches to estimating the amount of medication supply available at any time were compared.ResultsEstimates from different measures of adherence varied substantially. Three of the main drivers of the differences between adherence measures were the expected duration (if taken as in accordance with the dose directions), whether there was overlapping supply between prescriptions, and whether treatment had been discontinued. However, there are also wider, study-related, factors which are crucial to consider when comparing the adherence measures.ConclusionsWe evaluated the limitations of various medication adherence measures, and highlight key considerations about the underlying data, condition, and population to guide researchers choose appropriate adherence measures. This guidance will enable researchers to make more informed decisions about the methodology they employ, ensuring that adherence is captured in the most meaningful way for their particular application needs.

The digitization of German hospitals is proceeding continuously, leading to the implementation of new digital technologies, such as electronic health records (EHRs) or other technologies, used for the purpose of medical documentation tasks. Even though the replacement of paper documentation through digitized documentation in general promises to come along with plenty of benefits, the daily utilization of technologies might also lead to stresses and strains among the medical staff, eventually possibly leading to the development of different negative work and health-related outcomes. This study, therefore, aims at identifying persisting digitization-associated stressors and resources among medical hospital staff, examining their influences on different work and health-related outcomes, and finally, identifying potential needs for preventive measures. A quantitative study in the form of an online questionnaire survey was conducted among physicians working in the medical field of neuro- and vascular surgery in German hospitals. The study was carried out between June and October 2022 utilizing an online questionnaire based on several standardized scales, such as the technology acceptance model (TAM) and the technostress model, as well as on several scales from the Copenhagen Psychosocial Questionnaire (COPSOQ). The study found medium levels of technostress among the participating physicians (n = 114), as well as low to medium levels of persisting resources. The queried physicians, on average, reported low levels of burnout symptoms, generally described their health status as good, and were mostly satisfied with their job. Despite the prevalence of technostress and the low levels of resources among the surveyed physicians, there is little awareness of the problem of digital stress, and preventive measures have not been widely implemented yet in the clinics, indicating a needs gap and the necessity for the strategic and quality-guided implementation of measures to effectively prevent digital stress from developing.

The world is on the verge of rapid technological advancements, and acceptance. Healthcare is also influenced by recent innovations such as medical imaging and electronic patient health records. Thus, to maintain the patient's record in this digital era giant healthcare providers are adopting internet-based services such as patient portals that are portraying great results for patient involvement in this mechanism. However, it has been observed that patients, physicians, and healthcare providers are facing certain challenges to synch the health information cycle among them. Therefore, the focus of this study is to examine the interleaved issues, challenges, and benefits of healthcare patient portals. We have studied existing literature to retrieve our findings that accomplished the identified objective. We have found that patient portals are beneficial for maintaining and updating patients’ health histories at any time and can be shared across the healthcare network. However, physician acceptance and patient resistance are key challenges to implementing patient portals in healthcare settings.

Background Personal electronic health records (PEHRs) allow patients to view, generate, and manage their personal and medical data that are relevant across illness episodes, such as their medications, allergies, immunizations, and their medical, social, and family health history. Thus, patients can actively participate in the management of their health care by ensuring that their health care providers have an updated and accurate overview of the patients’ medical records. However, the uptake of PEHRs remains low, especially in terms of patients entering and managing their personal and medical data in their PEHR. Objective This scoping review aimed to explore the barriers and facilitators that patients face when deciding to review, enter, update, or modify their personal and medical data in their PEHR. This review also explores the extent to which patient-generated and -managed data affect the quality and safety of care, patient engagement, patient satisfaction, and patients’ health and health care services. Methods We searched the MEDLINE, Embase, CINAHL, PsycINFO, Cochrane Library, Web of Science, and Google Scholar web-based databases, as well as reference lists of all primary and review articles using a predefined search query. Results Of the 182 eligible papers, 37 (20%) provided sufficient information about patients’ data management activities. The results showed that patients tend to use their PEHRs passively rather than actively. Patients refrain from generating and managing their medical data in a PEHR, especially when these data are complex and sensitive. The reasons for patients’ passive data management behavior were related to their concerns about the validity, applicability, and confidentiality of patient-generated data. Our synthesis also showed that patient-generated and -managed health data ensures that the medical record is complete and up to date and is positively associated with patient engagement and patient satisfaction. Conclusions The findings of this study suggest recommendations for implementing design features within the PEHR and the construal of a dedicated policy to inform both clinical staff and patients about the added value of patient-generated data. Moreover, clinicians should be involved as important ambassadors in informing, reminding, and encouraging patients to manage the data in their PEHR.

Providing trainees with data and benchmarks on their own patient populations is an Accreditation Council for Graduate Medical Education core residency requirement. Leveraging electronic health records (EHRs) for this purpose relies on correctly attributing patients to the trainees responsible for their care. EHR activity logs are useful for attributing interns to inpatients but not for attributing supervising residents, who often have no inpatient EHR usage obligations, and therefore may generate no digital "footprints" on a given patient-day from which to ascertain attribution.The authors developed and tested a novel team-centered binary logistic regression model leveraging EHR activity logs from July 1, 2018, to June 30, 2019, for pediatric hospital medicine (PHM) supervising residents at the University of California, San Francisco. Unlike patient-centered models that determine daily attribution according to the trainee generating the greatest relative activity in individual patients' charts, the team-centered approach predicts daily attribution based on the trainee generating EHR activity across the greatest proportion of a team's patients. To assess generalizability, the authors similarly modeled supervising resident attribution in adult hospital medicine (AHM) and orthopedic surgery (OS).For PHM, AHM, and OS, 1,100, 1,399, and 803 unique patient encounters and 29, 62, and 10 unique supervising residents were included, respectively. Team-centered models outperformed patient-centered models for the 3 specialties, with respective accuracies of 85.4% versus 72.4% (PHM), 88.7% versus 75.4% (AHM), and 69.3% versus 51.6% (OS; P.001 for all). AHM and PHM models demonstrated relative generalizability to one another while OS did not.Validation at other institutions will be essential to understanding the potential for generalizability of this approach. Accurately attributed data are likely to be trusted more by trainees, enabling programs to operationalize feedback for use cases including performance measurement, case mix assessment, and postdischarge opportunities for follow-up learning.

This study aimed to evaluate whether coaching features were successfully transmitted via electronic health record (EHR) communication and to evaluate their relationship with weight change in a previously tested EHR-based coaching intervention.A secondary analysis from the Maintaining Activity and Nutrition through Technology-Assisted Innovation in Primary Care (MAINTAIN-pc) study randomized clinical trial was conducted in nine primary care practices and one specialty practice (endocrinology) affiliated with the University of Pittsburgh Medical Center. Eligibility included age 18 to 75 years, intentional 5% weight loss in the previous 2 years, access to an internet-connected computer, and receipt of care from a University of Pittsburgh Medical Center primary care provider. Survey data were collected during the randomized clinical trial.Participants content with intervention delivery via the EHR and those who felt a strong connection to their coach had significantly less weight regain (p = 0.013 and p 0.01, respectively). Participants who had needs unmet by the intervention (e.g., "in-person" support in a group setting or individual settings) regained more weight (p 0.01).The data suggest heterogeneity in the patient population regarding preference for in-person versus EHR-based coaching formats. Such heterogeneity might result in the differential success of EHR-based interventions.

Patient-reported outcome measures (PROMs) are metrics that assess physical health, mental health, pain, and satisfaction. However, PROM collection in orthopaedic clinics presents numerous logistical and financial challenges. These challenges are reduced when PROMs are completed before clinic encounters, relieving the workflow constraints of in-office PROM collection. The purpose of this study was to determine the efficacy of 3 different methods with respect to pre-visit electronic PROM completion.Consecutive adult orthopaedic patients with no previous PROM participation were enrolled. Patients who registered with the electronic medical record (EMR) patient portal (MyChart) and with active e-mail addresses were randomly assigned to 1 of 3 arms: control (no pre-visit messages), MyChart (EMR patient portal pre-visit messages), and e-mail (e-mail pre-visit messages). The primary outcome measure was pre-visit PROM completion rates in orthopaedic patients, and the secondary outcome measures were time to pre-visit PROM form completion and PROM form completion rates according to patient demographic characteristics. By default, the Patient-Reported Outcomes Measurement Information System (PROMIS) forms were available for completion through the portal by 7 days before scheduled visits. Pre-visit messages were sent 7 days prior to the scheduled visit except in the control group, with reminders sent 3 days prior if still not completed. The patients in each arm who completed all assigned forms were labeled as having total PROM completion, and those who completed at least 1 completed form were considered as having partial PROM completion. Multivariable logistic regression models were used to assess differences in PROM completion rates between study arms. Kruskal-Wallis tests were performed to compare the date of the form completion.A total of 291 patients were included. The pre-visit total completion rates for assigned PROMs were higher in the MyChart arm (49% of 97 patients; p = 0.005) and the e-mail arm (52% of 100 patients; p = 0.002) in comparison with the control arm (30% of 94 patients). Male patients were more likely than female patients to have partial pre-visit PROM completion (odds ratio [OR], 1.74; p = 0.03), and Caucasian patients were more likely to have partial pre-visit PROM completion than African American patients (OR, 2.28; p = 0.01).Orthopaedic patients receiving either e-mail or patient portal messages demonstrated higher pre-visit PROM completion rates. Pre-visit messaging appears to be a useful strategy for increasing PROM completion rates and limiting the clinical workflow strain imposed by in-clinic PROM administration.Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

Despite the increasing interest in applying machine learning (ML) methods in health economics and outcomes research (HEOR), stakeholders face uncertainties in when and how ML can be used. We reviewed the recent applications of ML in HEOR.We searched PubMed for studies published between January 2020 and March 2021 and randomly chose 20% of the identified studies for the sake of manageability. Studies that were in HEOR and applied an ML technique were included. Studies related to wearable devices were excluded. We abstracted information on the ML applications, data types, and ML methods and analyzed it using descriptive statistics.We retrieved 805 articles, of which 161 (20%) were randomly chosen. Ninety-two of the random sample met the eligibility criteria. We found that ML was primarily used for predicting future events (86%) rather than current events (14%). The most common response variables were clinical events or disease incidence (42%) and treatment outcomes (22%). ML was less used to predict economic outcomes such as health resource utilization (16%) or costs (3%). Although electronic medical records (35%) were frequently used for model development, claims data were used less frequently (9%). Tree-based methods (eg, random forests and boosting) were the most commonly used ML methods (31%).The use of ML techniques in HEOR is growing rapidly, but there remain opportunities to apply them to predict economic outcomes, especially using claims databases, which could inform the development of cost-effectiveness models.

Optimal quality within health care is no longer narrowly focused on preventing harm and has evolved to include the attainment of best outcomes through an understanding of the features attributed to effectively delivering care in complex work environments. The use of the electronic health record has contributed greatly to creating a repository of data that can be leveraged to comprehend the details associated with health care delivery. Medical knowledge alone is no longer sufficient to guarantee safe care or ensure the best outcomes. Clinicians who wish to achieve successful outcomes in the future must partner with their organizations to invest in appropriate infrastructure, anticipate increased accountability, manage an ever-increasing volume of data, and commit to learning how they must both change in order to succeed.

Purpose: To address the feasibility, reliability and internal validity of natural language processing (NLP) for automated functional assessment of hospitalised COVID-19 patients in key International Classification of Functioning, Disability and Health (ICF) categories and levels from unstructured text in electronic health records (EHR) from a large teaching hospital. Materials and methods: Eight human annotators assigned four ICF categories to relevant sentences: Emotional functions, Exercise tolerance, Walking and Moving, Work and Employment and their ICF levels (Functional Ambulation Categories for Walking and Moving, metabolic equivalents for Exercise tolerance). A linguistic neural network-based model was trained on 80% of the annotated sentences; inter-annotator agreement (IAA, Cohen’s kappa), a weighted score of precision and recall (F1) and RMSE for level detection were assessed for the remaining 20%. Results: In total 4112 sentences of non-COVID-19 and 1061 of COVID-19 patients were annotated. Average IAA was 0.81; F1 scores were 0.7 for Walking and Moving and Emotional functions; RMSE for Walking and Moving (5- level scale) was 1.17 for COVID-19 patients. Conclusion: Using a limited amount of annotated EHR sentences, a proof-of-concept was obtained for automated functional assessment of COVID-19 patients in ICF categories and levels. This allows for instantaneous assessment of the functional consequences of new diseases like COVID-19 for large numbers of patients.Key messages Hospitalised Covid-19 survivors may persistently suffer from low physical and mental functioning and a reduction in overall quality of life requiring appropriate and personalised rehabilitation strategies. For this, assessment of functioning within multiple domains and categories of the International Classification of Function is required, which is cumbersome using structured data. We show a proof-of-concept using Natural Language Processing techniques to automatically derive the aforementioned information from free-text notes within the Electronic Health Record of a large academic teaching hospital.

Postoperative bleeding complications surveillance is done primarily through manual chart review. The purpose of this study was to develop and validate a detection model for postoperative bleeding complications using structured electronic health records data.Patients who underwent operations at 1 of 5 hospitals within our local health system between 2013 and 2019 and whose complications were reported by the American College of Surgeons National Surgical Quality Improvement Program were included. Electronic health records data were linked to American College of Surgeons National Surgical Quality Improvement Program data using personal health identifiers. Electronic health records predictors included diagnosis codes mapped to PheCodes, procedure names, and medications within 30 days after surgery. We defined bleeding events as the transfusion of red blood cell components within 30 days after surgery. The knockoff filter and the lasso were used to develop a model in a training set of operations from January 2013 to March 2017. Performance of each model was tested in a held-out data set of patients who underwent operations from March 2017 to October 2019.A total of 30,639 patients were included; 1,112 patients (3.6%) had a bleeding event. Eight predictor variables were selected by the knockoff filter. When applied to the test set, specificity was 94%, sensitivity was 94%, area under the curve was 0.97, and accuracy was 93%. Calibration was consistent in lower predicted risk patients, whereas the model slightly overpredicted risk in high-risk patients.We created a parsimonious, accurate model for identifying patients with bleeding complications. This model can be used to augment manual chart review for surveillance and reporting of perioperative bleeding complications, enabling inclusion of all surgeries in quality improvement efforts.

INTRODUCTION: Electronic health record (EHR) discontinuity (missing out-of-network encounters) can lead to information bias. We sought to construct an algorithm that identifies high EHR-continuity among oncology patients. METHODS: Using a linked Medicare-EHR database and regression, we sought to 1) measure how often Medicare claims for outpatient encounters were substantiated by visits recorded in the EHR, and 2) predict continuity ratio, defined as the yearly proportion of outpatient encounters reported to Medicare that were captured by EHR data. The prediction model’s performance was evaluated with the coefficient of determination and Spearman’s correlation. We quantified variable misclassification by decile of continuity ratio using standardized difference and sensitivity. RESULTS: A total of 79,678 subjects met all eligibility criteria. Predicted and observed continuity was highly correlated (σ(Spearman) = 0.86). On average across all variables measured, MSD was reduced by a factor of 1/7(th) and sensitivity was improved 35-fold comparing subjects in the highest vs. lowest decile of CR. CONCLUSION: In the oncology population, restricting EHR-based study cohorts to subjects with high continuity may reduce misclassification without greatly impacting representativeness. Further work is needed to elucidate the best manner of implementing continuity prediction rules in cohort studies.

Prescriptions for biologic therapy for treatment of Crohn's disease (CD) and ulcerative colitis (UC) have increased during the past two decades; however, trends are less clear regarding corticosteroid prescriptions in this context. We designed a cross-sectional study using the IQVIA Ambulatory Electronic Medical Records databases. Weighted linear regressions by age group were used to estimate annual percentage change from 2011 to 2020 in prescriptions for biologics and for corticosteroids among patients with or without biologic prescriptions within the same calendar year. Using 2019 data, we compared patient demographic and lifestyle risk factors using χ2 test for biologic prescriptions and corticosteroids with or without biologics prescriptions. There was an 11% (CD) and 16% (UC) annual increase in the percentage of patients prescribed biologics during the study period. The percentage of patients with biologics prescriptions prescribed corticosteroids decreased by 2% (CD) and 3% (UC) annually after 2015, while the percentage remained unchanged for corticosteroid prescriptions among patients without biologics. In 2019, differences in medication prescriptions existed by patient's demographic and lifestyle factors for patients with CD (n=52,892) and UC (n=52,280), including a higher percentage prescribed biologics among younger patients, men, those with fewer comorbidities, and current alcohol drinkers, and a higher percentage prescribed corticosteroids without biologics among women, those with more comorbidities, and a history of smoking. While medications continue to evolve during the biologic era, it is important to continue to monitor trends and differences in prescription patterns to assess progress toward optimizing treatment for patients with CD or UC.

The University of Miami Mitchell Wolfson Sr. Department of Community Service (DOCS) is a student-run organization providing free health care to the medically underserved in South Florida. For a large organization providing care to thousands of people per year, an effective electronic medical record (EMR) is necessary to keep track of patient records.A REDCap project was configured in a way that allows it to mimic a basic EMR. This was done by assigning patients medical record numbers, creating repeating events allowing patient results to be seen over time, and incorporating extensive logic to facilitate clinical decision-making.DOCS was able to create a basic EMR using REDCap and has seen success with this approach.REDCap is capable of functioning as a robust, basic EMR which can be suitable for any purpose. It is HIPAA-compliant, comprehensive and low-cost, making it suitable for serving underserved populations.

We hypothesized that a pain management prescribing tool embedded in the electronic health record system of a multihospital health care system would decrease prescription opioids for postoperative pain by hand, orthopedic, plastic, and spine surgeons.A prescribing tool for postoperative pain was designed for hand, orthopedic, plastic, and spine surgeons and implemented into electronic discharge order sets in a 10-hospital health care system. Stakeholders were educated on tool use in person and/or by email on 2 occasions. A dashboard was created to monitor opioid pill quantities and morphine milligram equivalents (MMEs) prescribed. Overall compliance with the suggested opioid amounts was assessed for 20 months after tool implementation. A subgroup of 6 hand surgeons, one of whom was instrumental in designing the tool, were evaluated for MMEs prescribed, opioid refills, patient emergency room visits, and patient readmissions within 30 days after discharge. Comparisons in this subgroup were made from 12 months before to 15 months after tool implementation.The mean system-wide compliance with the suggested opioid pill quantities and MMEs prescribed in all 4 specialties improved by less than 5%. In the subgroup of hand surgeons, 5 of whom championed tool use, prescribed MMEs decreased by 10% during each of the 4 quarters before launching the tool and contracted an additional 26% in the first quarter after tool implementation. Opioid refills held steady at 5%, and there were no emergency room visits or readmissions within 30 days after discharge in this patient subgroup.The prescribing tool had a negligible impact on system-wide compliance with suggested prescription opioid pill quantities and MMEs. In a small group of surgeons who championed the use of the tool, there was a significant and sustained decline in MMEs prescribed without adversely impacting patient refills, emergency room visits, or readmissions.An electronic prescribing tool to assist surgeons in lowering opioid prescription pill quantities and MMEs may have a negligible impact on prescribing behavior in a multihospital health care system.

Lung cancer screening (LCS) eligibility is largely determined by tobacco consumption. Primary care smoking data could guide LCS invitation and eligibility assessment. We present observational data from the SUMMIT Study, where individual self-reported smoking status was concordant with primary care records in 75.3%. However, 10.3% demonstrated inconsistencies between historic and most recent smoking status documentation. Quantified tobacco consumption was frequently missing, precluding direct LCS eligibility assessment. Primary care recorded "ever-smoker" status, encompassing both recent and historic documentation, can be used to target LCS invitation. Identifying those with missing or erroneous "never-smoker" smoking status is crucial for equitable invitation to LCS.

Despite increasingly stringent requirements from regulatory agencies, clinical trials often fail to recruit study populations representative of real-world demographics and disease prevalence and are often skewed away from racial/ethnic minorities. Consequently, data produced by such trials can result in treatment guidelines and outcome expectations that do not apply to racial/ethnic minorities, further widening health disparities. In this study, we describe a new tool, the TriNetX Diversity Lens ("Diversity Lens"), which augments the existing electronic health record querying functionality of TriNetX and allows clinical trial sponsors to rapidly evaluate the potential impact of inclusion and exclusion criteria on the eligibility rates of different racial and ethnic groups. We describe the development of Diversity Lens in collaboration with public and private stakeholders. Additionally, we feature examples of how Diversity Lens can bring to the surface insights into existing health disparities and prospectively explore the impact of study criteria on the eligibility of racial/ethnic minorities.

Handoffs and documentation are a potentially modifiable source of medical error. However, little attention has been given toenhancementof these within the neurosurgical field. We aim to increase efficiency and accuracy of neurosurgical handoffs, including the neurological exam, thus decreasing medical documentation time within current duty-hour restrictions.The existing Epic electronic medical record system was modified to include the neurological exam in the handoff: a tool used to generate lists including relevant patient clinical details and plans. The handoff tool was also converted into a subjective, objective, assessment, and plan (SOAP) format, which was leveraged to efficiently generate daily progress notes. A four-question survey was developed to assess the effectiveness of this new format. Mean note times were compared before and after the EPIC update using an independent samples t-test.All of the surveyed neurosurgery residents at our institution reported a decrease in documentation time per progress note, felt the notes were more accurate, and found it easier to recall the neurological exams of patients. 8/9 residents felt that the new handoff made in-house call less stressful. There was a significant difference in mean note time, with the mean note time of 37.9 s after the EPIC upgrade compared to 120 s prior the upgrade. We project that over 241 h of documentation will be saved annually at our institution.This QI project demonstrates how a low-effort initiative improved resident recall of patients' neurological exams while saving time spent documenting daily progress notes.

Background: Patients with sickle cell disease (SCD) are exposed to numerous drugs over their lifespan, and many of these drugs have Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines for personalized dosing. The authors' aim was to ascertain the number of drugs with CPIC guidelines prescribed to SCD patients. Materials & methods: A search of Indiana University Health affiliated hospitals' electronic medical record identified 957 patients with a diagnosis of SCD. Drugs or drug classes with CPIC actionable guidelines ordered as inpatient and outpatient prescriptions were collected from SCD patients. Results: During the 16-year period, 892 (93%) patients received at least one drug that could have been dosed according to CPIC guidelines. Conclusion: Preemptive pharmacogenetics testing should be considered in SCD patients in order to utilize these data throughout the patient's life.

Daily progress notes are the backbone of all inpatient hospitalizations. Progress notes serve as a lasting record of a patient's diagnoses, condition, and planned interventions and are essential communication tools. We designed a study to identify patterns in progress note filing and use on general surgical services.The electronic health record (EHR) data warehouse was queried for general surgical progress notes signed between July 1, 2020, and July 1, 2021. Only notes authored by resident physicians or advanced practice providers (APPs) were included, and those filed on the day of a surgery were excluded. 10 am was identified as a target for note completion as it coincided with multidisciplinary discharge rounds. Physician, case managers pharmacist, physical therapist, dietician, nurse (and collaborating disciplines) progress note views were measured using EHR access log data.A total of 8384 progress notes were analyzed; 4146 notes (49%) were authored by 81 trainees. A total of 4433 (53%) progress notes were filed before 10 am, 3673 (44%) were filed between 10 am and 6 pm, and 278 (3%) were filed after 6 pm. Variation in progress note file time was observed and associated with individual habits, residents vs APPs, day-of-week, and service structure. Surgery progress notes are viewed by collaborating disciplines throughout the workday, with high-volume viewership occurring by mid-morning. Each individual progress note received an average of 17.6 lifetime views with a range of 1 to 76. An average of 10.2 of those views occurred on the same day that the note was written. Notes that were filed after 10 am received a significantly lower number of same-day views compared to notes filed before 10am (8.4 vs 11.8, p0.0001).Progress notes are identified as a significant burden by trainees and even considered to contribute to duty hour violations, yet they are used regularly as a source of information for collaborating disciplines. Progress notes filed earlier are viewed more frequently. Efforts to identify barriers to timeliness may help communication and efficiency of inpatient surgical care.

Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine.The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery.The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P.001) and managing (1 vs 5 minutes, P.001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success.The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.

We evaluated the prevalence of high weight status in children ages 0 to 24 months (m) using data from electronic health records (EHR) and NHANES. We also examined relationships between weight status during infancy and obesity at 24 months of age.EHR data from 4 institutions in North and South Carolina included patients born January 1, 2013-October 10, 2017 (N = 147,290). NHANES data included study waves from 1999 to 2018 (unweighted N = 5121). We calculated weight-for-length (WFL), weight-for-age (WFA), and body mass index (BMI), excluding implausible values, and categorized weight status (85th, 85th to95th, or ≥95th percentile), assessing prevalence at birth, 6, 12, 18, and 24 months. Utilizing individual, longitudinal EHR data, we used separate regression models to assess obesity risk at 24 months based on anthropometrics at birth, 6, 12, and 18 months, adjusting for sex, race/ethnicity, insurance, and health system.Prevalence of BMI ≥95th percentile in EHR data at 6, 12, 18, and 24 months were 9.7%, 15.7%, 19.6%, and 20.5%, respectively. With NHANES the prevalence was 11.6%, 15.0%, 16.0%, and 8.4%. For both, the prevalence of high weight status was higher in Hispanic children. In EHR data, high weight status at 6, 12, and 18 months was associated with obesity at 24 months, with stronger associations as BMI category increased and as age increased.High weight status is common in infants and young children, although lower at 24 months in NHANES than EHR data. In EHR data, high BMI at 6, 12, and 18 months was associated with increased risk of obesity at 24 months.

Acute pancreatitis (AP) is a frequently encountered adverse drug reaction. However, the validity of diagnostic codes for AP is unknown. We aimed to determine the positive predictive value (PPV) of a diagnostic code-based algorithm for identifying patients with AP within the US Veterans Health Administration and evaluate the value of adding readily available structured laboratory information.We identified patients with possible AP events first based on the presence of a single hospital discharge ICD-9 or ICD-10 diagnosis of AP (Algorithm 1). We then expanded Algorithm 1 by including relevant laboratory test results (Algorithm 2). Specifically, we considered amylase or lipase serum values obtained between 2 days before admission and the end of the hospitalization. Medical records of a random sample of patients identified by the respective algorithms were reviewed by two separate gastroenterologists to adjudicate AP events. The PPV (95% confidence interval [CI]) for the algorithms were calculated.Algorithm 2, consisting of one ICD-9 or ICD-10 hospital discharge diagnosis of AP and the addition of lipase serum value ≥200 U/L, had a PPV 89.1% (95% CI 83.0%-95.2%), improving from the PPV of algorithm 1 (57.9% [95% CI 46.8-69.0]).An algorithm consisting of an ICD-9 or ICD-10 diagnosis of AP with a lipase value ≥200 U/L achieved high PPV. This simple algorithm can be readily implemented in any electronic health records (EHR) systems and could be useful for future pharmacoepidemiologic studies on AP.

Objective: Though psychiatric illnesses have been associated with increased COVID-19 infection risk, limited information exists about the relationship between ADHD and COVID-19. Methods: Using the TriNetX COVID-19 Research Network, we examined the impact of ADHD diagnosis and treatment on COVID-19 infection rates and outcomes. Results: ADHD patients had greater risk of COVID-19 (risk ratio (RR) 1.11, 95% CI [1.09, 1.12]). Increased risk was higher in females than males, and highest among Asian and Black patients. Within 60 days after COVID-19 diagnosis, ADHD patients had lower rates of hospitalization (RR 0.91, 95% CI [0.86, 0.96]) and mechanical ventilation (RR 0.69, 95% CI [0.58, 0.83]), and a nonsignificant reduced death rate (RR 0.65, 95% CI [0.42, 1.02]). Patients who recently received ADHD medication had higher rates of COVID-19 (RR 1.13; 95% CI [1.10, 1.15]). Conclusion: ADHD poses increased risk for COVID-19, but may reduce risk of severe outcomes. ADHD medications modestly impacted COVID-19 risk.

Administrative burden is one of many potential root causes of physician burnout. Scribe documentation assistance can reduce this burden. However, traditional in-person scribe services are challenged by consistent staffing because the model requires the physical presence of a scribe and limits the team to a single individual. In addition, in-person scribes cannot provide the flexible support required for virtual care encounters, which can now pivot geographically and temporally. To respond to these challenges, our health network implemented an asynchronous virtual scribe model and evaluated the program's impact on clinician perceptions of burnout across multiple outpatient specialties.Using a mixed-methods, pre-/postdesign, this evaluation measured the impact of an asynchronous virtual scribe program on physician burnout. Physicians were given the Professional Fulfillment Index tool (to self-assess their mental state) and free-text comment surveys before virtual scribe initiation and again at 3-, 6-, and 12-month intervals after program implementation. Descriptive statistics of survey results and qualitative review of free-text entries were analyzed for themes of facilitation and barriers to virtual scribe use.Of 50 physician participants in this study, 42 (84%) completed the preintervention survey and 15 (36%) completed all 4 surveys; 25 participants (50%) discontinued scribe use after 12 months. Burnout levels-as defined by dread, exhaustion, lack of enthusiasm, decrease in empathy, and decrease in colleague connection-all trended toward improvement during this study. Importantly, quality, time savings, burnout, and productivity moved in positive directions as well.The cost burden to physicians and the COVID-19 pandemic inhibited the continued use of asynchronous virtual medical scribes. Nevertheless, those who continued in the program have reported positive outcomes, which indicates that the service can be a viable and effective tool to reduce physician burnout.

"Big data" refers to a growing field of large database research. Administrative data, a subset of big data, includes information from insurance claims, electronic medical records, and registries that can be useful for investigating novel research questions. While its use provides salient advantages, potential researchers relying on big data would benefit from knowing about how these databases are coded, common errors they may encounter, and how to best use large data to address various research questions. In the first section of this paper, Dr. Nicholas A. Bedard addresses the four major pitfalls to avoid with diagnosis and procedure codes in administrative data. In the next section, Dr. Jeffrey N. Katz et al. focus on the strengths and limitations of administrative data, suggesting methods to mitigate these limitations. Lastly, Dr. Elena Losina et al. review the uses and misuses of large databases for cost-effectiveness research, detailing methods for careful economic evaluations.

Electronic health records (EHRs) have created great opportunities to collect various information from clinical patient encounters. However, most EHR data are stored in unstructured form (e.g., clinical notes, surgical notes, and medication instructions), and researchers need data to be in computable form (structured) to extract meaningful relationships involving variables that can influence patient outcomes. Clinical natural language processing (NLP) is the field of extracting structured data from unstructured text documents in EHRs. Clinical text has several characteristics that mandate the use of special techniques to extract structured information from them compared with generic NLP methods. In this article, we define clinical NLP models, introduce different methods of information extraction from unstructured data using NLP, and describe the basic technical aspects of how deep learning-based NLP models work. We conclude by noting the challenges of working with clinical NLP models and summarizing the general steps needed to launch an NLP project.

PURPOSE OF REVIEW: Recent advancements in big data analytical tools and large patient databases have expanded tremendously the opportunities to track patient and safety outcomes. We discuss the strengths and limitations of large databases and implementation in practice with a focus on the current opportunities to use technological advancements to improve patient safety. RECENT FINDINGS: The most used sources of data for large patient safety observational studies are administrative databases, clinical registries, and electronic health records. These data sources have enabled research on patient safety topics ranging from rare adverse outcomes to large cohort studies of the modalities for pain control and safety of medications. Implementing the insights from big perioperative data research is augmented by automating data collection and tracking the safety outcomes on a provider, institutional, national, and global level. In the near future, big data from wearable devices, physiological waveforms, and genomics may lead to the development of personalized outcome measures. SUMMARY: Patient safety research using large databases can provide actionable insights to improve outcomes in the perioperative setting. As datasets and methods to gain insights from those continue to grow, adopting novel technologies to implement personalized quality assurance initiatives can significantly improve patient care.