The impact of electronic health records on people with diabetes in three different emergency departments

Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial

BACKGROUND

Two characteristics of commonly used outcomes in medical research are zero inflation and non-negative integers; examples include the number of hospital admissions or emergency department visits, where the majority of patients will have zero counts. Zero-inflated regression models were devised to analyze this type of data. However, the performance of zero-inflated regression models or the properties of data best suited for these analyses have not been thoroughly investigated.

METHODS

We conducted a simulation study to evaluate the performance of two generalized linear models, negative binomial and zero-inflated negative binomial, for analyzing zero-inflated count data. Simulation scenarios assumed a randomized controlled trial design and varied the true underlying distribution, sample size, and rate of zero inflation. We compared the models in terms of bias, mean squared error, and coverage. Additionally, we used logistic regression to determine which data properties are most important for predicting the best-fitting model.

RESULTS

We first found that, regardless of the rate of zero inflation, there was little difference between the conventional negative binomial and its zero-inflated counterpart in terms of bias of the marginal treatment group coefficient. Second, even when the outcome was simulated from a zero-inflated distribution, a negative binomial model was favored above its ZI counterpart in terms of the Akaike Information Criterion. Third, the mean and skewness of the non-zero part of the data were stronger predictors of model preference than the percentage of zero counts. These results were not affected by the sample size, which ranged from 60 to 800.

CONCLUSIONS

We recommend that the rate of zero inflation and overdispersion in the outcome should not be the sole and main justification for choosing zero-inflated regression models. Investigators should also consider other data characteristics when choosing a model for count data. In addition, if the performance of the NB and ZINB regression models is reasonably comparable even with ZI outcomes, we advocate the use of the NB regression model due to its clear and straightforward interpretation of the results.

Count data models for outpatient health services utilisation

BACKGROUND

Count data from the national survey captures healthcare utilisation within a specific reference period, resulting in excess zeros and skewed positive tails. Often, it is modelled using count data models. This study aims to identify the best-fitting model for outpatient healthcare utilisation using data from the Malaysian National Health and Morbidity Survey 2019 (NHMS 2019) and utilisation factors among adults in Malaysia.

METHODS

The frequency of outpatient visits is the dependent variable, and instrumental variable selection is based on Andersen's model. Six different models were used: ordinary least squares (OLS), Poisson regression, negative binomial regression (NB), inflated models: zero-inflated Poisson, marginalized-zero-inflated negative binomial (MZINB), and hurdle model. Identification of the best-fitting model was based on model selection criteria, goodness-of-fit and statistical test of the factors associated with outpatient visits.

RESULTS

The frequency of zero was 90%. Of the sample, 8.35% of adults utilized healthcare services only once, and 1.04% utilized them twice. The mean-variance value varied between 0.14 and 0.39. Across six models, the zero-inflated model (ZIM) possesses the smallest log-likelihood, Akaike information criterion, Bayesian information criterion, and a positive Vuong corrected value. Fourteen instrumental variables, five predisposing factors, six enablers, and three need factors were identified. Data overdispersion is characterized by excess zeros, a large mean to variance value, and skewed positive tails. We assumed frequency and true zeros throughout the study reference period. ZIM is the best-fitting model based on the model selection criteria, smallest Root Mean Square Error (RMSE) and higher R2. Both Vuong corrected and uncorrected values with different Stata commands yielded positive values with small differences.

CONCLUSION

State as a place of residence, ethnicity, household income quintile, and health needs were significantly associated with healthcare utilisation. Our findings suggest using ZIM over traditional OLS. This study encourages the use of this count data model as it has a better fit, is easy to interpret, and has appropriate assumptions based on the survey methodology.

[Information on medical history in the emergency department : Influence on therapy and diagnostic decisions]

Hintergrund

Die Einführung einer elektronischen Patientenakte (ePA) bzw. eines Notfalldatensatzes (NFD) ist ebenso wie die Reform der Notfallversorgung in Deutschland derzeit immer wieder Teil politischer Diskussionen. Derzeit existieren in Deutschland keine Daten zum Nutzen einer solchen ePA bzw. NFD für die Notaufnahmen. Ziel dieser Studie war es herauszufinden, ob mitgebrachte Vorbefunde Einfluss auf Diagnostik- und Therapieentscheidungen in der Notaufnahme haben.

Methodik

Zur Beantwortung der Frage wurde eine deskriptive Beobachtungsstudie in einer interdisziplinären Notaufnahme durchgeführt mit einer Studienpopulation von n = 96.

Ergebnisse

Hinsichtlich vorhandener Vorbefunde konnten bei 55 Patienten (59 %) weder ein Arztbrief noch eine Medikamentenliste gefunden werden. Jedoch konnten bei 48 % der Patienten, die über die Notaufnahme stationär aufgenommen wurden, Ergänzungen der Anamnese nachgewiesen werden.

Bei 8 (9 %) Patienten zeigte sich, dass Therapie- und/oder Diagnostikentscheidungen hätten diskutiert bzw. geändert werden müssen, falls die ergänzten anamnestischen Informationen in der Notaufnahme vorgelegen hätten. Die Dauer der Anamnese zeigte sich ebenfalls verlängert bei fehlenden Vorbefunden seitens des Patienten (Mittelwert: 10–15 min; Standardabweichung: ±<5 min) im Gegensatz zu den Patienten mit Vorbefunden (Mittelwert: 5–10 min; Standardabweichung: ±<5 min).

Diskussion

Mithilfe von ePA und NFD könnten Therapie- und Diagnostikentscheidungen sicherer gestellt werden. Beim Fehlen von Vorbefunden ist die Anamnesedauer in Notaufnahmen deutlich verlängert, was sich durch die Einführung einer ePA bzw. eines NFD reduzieren ließe.

Physician Use of Electronic Health Records: Survey Study Assessing Factors Associated With Provider Reported Satisfaction and Perceived Patient Impact

BACKGROUND

The effect electronic health record (EHR) implementation has on physician satisfaction and patient care remains unclear. A better understanding of physician perceptions of EHRs and factors that influence those perceptions is needed to improve the physician and patient experience when using EHRs.

OBJECTIVE

The objective of this study was to determine provider and clinical practice factors associated with physician EHR satisfaction and perception of patient impact.

METHODS

We surveyed a random sample of physicians, including residents and fellows, at a US quaternary care academic hospital from February to March 2016. The survey assessed provider demographics, clinical practice factors (ie, attending, fellow, or resident), and overall EHR experience. The primary outcomes assessed were provider satisfaction and provider perceptions of impact to patient care. Responses on the satisfaction and patient impact questions were recorded on a continuous scale initially anchored at neutral (scale range 0 to 100: 0 defined as "extremely negatively" and 100 as "extremely positively"). Independent variables assessed included demographic and clinical practice factors, including perceived efficiency in using the EHR. One-way analysis of variance or the Kruskal-Wallis test was used for bivariate comparisons, and linear regression was used for multivariable modeling.

RESULTS

Of 157 physicians, 111 (70.7%) completed the survey; 51.4% (57/111) of the respondents were attending physicians, and of those, 71.9% (41/57) reported a >50% clinical full-time-equivalency and half reported supervising residents >50% of the time. A total of 50.5% (56/111) of the respondents were primary care practitioners, previous EHR experience was evenly distributed, and 12.6% (14/111) of the total sample were EHR super-users. Responses to how our current EHR affects satisfaction were rated above the neutral survey anchor point (mean 58 [SD 22]), as were their perceptions as to how the EHR impacts the patient (mean 61 [SD 18]). In bivariate comparisons, only physician age, clinical role (resident, fellow, or attending), and perceived efficiency were associated with EHR satisfaction. In the linear regression models, physicians with higher reported perceived efficiency reported higher overall satisfaction and patient impact after controlling for other variables in the model.

CONCLUSIONS

Physician satisfaction with EHRs and their perception of its impact on clinical care were generally positive, but physician characteristics, greater age, and attending level were associated with worse EHR satisfaction. Perceived efficiency is the factor most associated with physician satisfaction with EHRs when controlling for other factors. Understanding physician perceptions of EHRs may allow targeting of technology resources to ensure efficiency and satisfaction with EHR system use during clinical care.

Electronic health record phenotyping improves detection and screening of type 2 diabetes in the general United States population: A cross-sectional, unselected, retrospective study

OBJECTIVES

An estimated 25% of type two diabetes mellitus (DM2) patients in the United States are undiagnosed due to inadequate screening, because it is prohibitive to administer laboratory tests to everyone. We assess whether electronic health record (EHR) phenotyping could improve DM2 screening compared to conventional models, even when records are incomplete and not recorded systematically across patients and practice locations, as is typically seen in practice.

METHODS

In this cross-sectional, retrospective study, EHR data from 9948 US patients were used to develop a pre-screening tool to predict current DM2, using multivariate logistic regression and a random-forests probabilistic model for out-of-sample validation. We compared (1) a full EHR model containing commonly prescribed medications, diagnoses (as ICD9 categories), and conventional predictors, (2) a restricted EHR DX model which excluded medications, and (3) a conventional model containing basic predictors and their interactions (BMI, age, sex, smoking status, hypertension).

RESULTS

Using a patient's full EHR or restricted EHR was superior to using basic covariates alone for detecting individuals with diabetes (hierarchical X(2) test, p<0.001). Migraines, depot medroxyprogesterone acetate, and cardiac dysrhythmias were associated negatively with DM2, while sexual and gender identity disorder diagnosis, viral and chlamydial infections, and herpes zoster were associated positively. Adding EHR phenotypes improved classification; the AUC for the full EHR Model, EHR DX model, and conventional model using logistic regression, were 84.9%, 83.2%, and 75.0% respectively. For random forest machine learning out-of-sample prediction, accuracy also was improved when using EHR phenotypes; the AUC values were 81.3%, 79.6%, and 74.8%, respectively. Improved AUCs reflect better performance for most thresholds that balance sensitivity and specificity.

CONCLUSIONS

EHR phenotyping resulted in markedly superior detection of DM2, even in the face of missing and unsystematically recorded data, based on the ROC curves. EHR phenotypes could more efficiently identify which patients do require, and don't require, further laboratory screening. When applied to the current number of undiagnosed individuals in the United States, we predict that incorporating EHR phenotype screening would identify an additional 400,000 patients with active, untreated diabetes compared to the conventional pre-screening models.

Electronic health records, adoption, quality of care, legal and privacy issues and their implementation in emergency departments

Recently, the healthcare sector has shown a growing interest in information technologies. Two popular health IT (HIT) products are the electronic health record (EHR) and health information exchange (HIE) networks. The introduction of these tools is believed to improve care, but has also raised some important questions and legal and privacy issues. The implementation of these systems has not gone smoothly, and still faces some considerable barriers. This article reviews EHR and HIE to address these obstacles, and analyzes the current state of development and adoption in various countries around the world. Moreover, legal and ethical concerns that may be encountered by EHR users and purchasers are reviewed. Finally, links and interrelations between EHR and HIE and several quality of care issues in today's healthcare domain are examined with a focus on EHR and HIE in the emergency department (ED), whose unique characteristics makes it an environment in which the implementation of such technology may be a major contributor to health, but also faces substantial challenges. The paper ends with a discussion of specific policy implications and recommendations based on an examination of the current limitations of these systems.

How is the electronic health record being used? Use of EHR data to assess physician-level variability in technology use

BACKGROUND

Studies of the effects of electronic health records (EHRs) have had mixed findings, which may be attributable to unmeasured confounders such as individual variability in use of EHR features.

OBJECTIVE

To capture physician-level variations in use of EHR features, associations with other predictors, and usage intensity over time.

METHODS

Retrospective cohort study of primary care providers eligible for meaningful use at a network of federally qualified health centers, using commercial EHR data from January 2010 through June 2013, a period during which the organization was preparing for and in the early stages of meaningful use.

RESULTS

Data were analyzed for 112 physicians and nurse practitioners, consisting of 430,803 encounters with 99,649 patients. EHR usage metrics were developed to capture how providers accessed and added to patient data (eg, problem list updates), used clinical decision support (eg, responses to alerts), communicated (eg, printing after-visit summaries), and used panel management options (eg, viewed panel reports). Provider-level variability was high: for example, the annual average proportion of encounters with problem lists updated ranged from 5% to 60% per provider. Some metrics were associated with provider, patient, or encounter characteristics. For example, problem list updates were more likely for new patients than established ones, and alert acceptance was negatively correlated with alert frequency.

CONCLUSIONS

Providers using the same EHR developed personalized patterns of use of EHR features. We conclude that physician-level usage of EHR features may be a valuable additional predictor in research on the effects of EHRs on healthcare quality and costs.