Adoption Rates of Electronic Health Records in Turkish Hospitals and the Relation with Hospital Sizes

Kose, Ilker; Rayner, John; Birinci, Şuayıp; Ülgü, Mustafa Mahir; Yılmaz, İsmayil; Güner, Şeyma

doi:10.21203/rs.2.17869/v3

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…Furthermore, it has been found that at least 60% of hospitals between the United States, Europe, and Asia still have a basic EMR system, 62 where a majority are classified as stages 0 to 2 according to the Healthcare Information and Management Systems Society’s Electronic Medical Record Adoption Model. The fact that our system stemmed from a basic HIS itself (stage 1) renders its results promising for hospitals with basic HIS and limited resources.…”

Section: Discussionmentioning

confidence: 99%

Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources

et al. 2022

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Objectives: This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm.Methods: Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel. The positive predictive value (PPV) of each trigger was evaluated, and the detection sensitivity of the surveillance system was estimated relative to incidence ranges in the literature. Results:The system identified 394 AEs among 946 triggered cases, associated with 291 patients, yielding an overall PPVof 42%. Variability was observed among the trigger PPVs and among the estimated detection sensitivities across the harm categories, the highest being for the healthcare-associated infections. The median length of stay of patients with an AE showed to be significantly higher than the median for the overall patient population. Conclusions:This application was able to identify AEs across a broad spectrum of harm categories, in a real-time manner, while reducing the use of resources required by other harm detection methods. Such a system could serve as a promising patient safety tool for AE surveillance, allowing for timely, targeted, and resource-efficient interventions, even for hospitals with limited resources.

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Section: Discussionmentioning

confidence: 99%

Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources

et al. 2022

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“…Then, we measured patient rating of the overall quality of care by using a single question using a 5-point Likert scale (1 = excellent, 2 = very good, 3 = good, 4 = fair, 5 = poor). Finally, we included patient demographics such as age group, gender, and race, and hospital characteristics such as hospital location (based on United States Census Bureau's urban and rural definitions of urban or rural) (25), size (small: 0–99 beds, medium: 100–399 beds, large: 400+ beds) (26,27), and teaching status (yes/no). A teaching hospital, also known as an academic medical center, partners with medical and/or nursing schools, education programs, and research centers to improve health care delivery through learning and research (28).…”

Section: Methodsmentioning

confidence: 99%

Factors Impacting Patients’ Willingness to Recommend: A Structural Equation Modeling Approach

Park

et al. 2022

Journal of Patient Experience

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Patient ratings of inpatient stay have been the focus of prior research since better patient satisfaction results in a financial benefit to hospitals and are associated with better patient health care outcomes. However, studies that simultaneously account for within- and between-hospital effects are uncommon. We constructed a multilevel structural equation model to identify predictors of patients’ willingness to recommend a hospital at both within-hospital and between-hospital levels. We used data from 60 U.S. general medical and surgical hospitals and 12,115 patients. Multilevel structural equation modeling reported that patient ratings on the overall quality of care significantly affect the willingness to recommend within hospitals. Also, patients’ perspectives on the hospital environment and nursing are the significant factors that predict the patient ratings on the overall quality of care. Overall patient satisfaction significantly predicts the willingness to recommend at the between-hospital level, whereas hospital size and location have marginal impacts.

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“…We first obtain as the maximizer of the penalized partial likelihood where is the log partial likelihood associated with and is the elastic net penalty function, which is frequently used to overcome high dimensionality and collinearity of features with α = 1 corresponding to the standard LASSO and α = 0 corresponding to the ridge penalty [28]. The non-negative penalty parameter can be selected via standard tuning criteria including the AIC, BIC or cross-validation.…”

Section: Step I: Training L Local Risk Prediction Modelsmentioning

confidence: 99%

SurvMaximin: Robust Federated Approach to Transporting Survival Risk Prediction Models

Wang

Zhang

Xiong

et al. 2022

Preprint

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Objective: For multi-center heterogeneous Real-World Data (RWD) with time-to-event outcomes and high-dimensional features, we propose the SurvMaximin algorithm to estimate Cox model feature coefficients for a target population by borrowing summary information from a set of health care centers without sharing patient-level information. Materials and Methods: For each of the centers from which we want to borrow information to improve the prediction performance for the target population, a penalized Cox model is fitted to estimate feature coefficients for the center. Using estimated feature coefficients and the covariance matrix of the target population, we then obtain a SurvMaximin estimated set of feature coefficients for the target population. The target population can be an entire cohort comprised of all centers, corresponding to federated learning, or can be a single center, corresponding to transfer learning. Results: Simulation studies and a real-world international electronic health records application study, with 15 participating health care centers across three countries (France, Germany, and the U.S.), show that the proposed SurvMaximin algorithm achieves comparable or higher accuracy compared with the estimator using only the information of the target site and other existing methods. The SurvMaximin estimator is robust to variations in sample sizes and estimated feature coefficients between centers, which amounts to significantly improved estimates for target sites with fewer observations. Conclusions: The SurvMaximin method is well suited for both federated and transfer learning in the high-dimensional survival analysis setting. SurvMaximin only requires a one-time summary information exchange from participating centers. Estimated regression vectors can be very heterogeneous. SurvMaximin provides robust Cox feature coefficient estimates without outcome information in the target population and is privacy-preserving.

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Adoption Rates of Electronic Health Records in Turkish Hospitals and the Relation with Hospital Sizes

Cited by 7 publications

References 14 publications

Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources

Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources

Factors Impacting Patients’ Willingness to Recommend: A Structural Equation Modeling Approach

SurvMaximin: Robust Federated Approach to Transporting Survival Risk Prediction Models

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