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/v4

Cited by 4 publications

(4 citation statements)

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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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“…Moreover, automated versions of such track-and-trigger systems cannot be effectively implemented in hospitals with basic EMRs (i.e., staged as 0, 1, or 2 according to the Healthcare Information and Management Systems Society Electronic Medical Record Adoption Model adoption model classification 10 ), because they do not include an electronic nursing flowsheet documentation module. It is to be noted that the proportion of hospital with such basic EMRs is significant worldwide, especially in third-world countries 11 …”

mentioning

confidence: 99%

“…It is to be noted that the proportion of hospital with such basic EMRs is significant worldwide, especially in third-world countries. 11 A new and promising approach described in recent studies [12][13][14][15][16] involves the addition of physiological biomarkers measurements to the traditionally measured vital signs and demographic patient data routinely available in the EMR. Biomarkers are defined as biological characteristics (such as, e.g., the C-reactive protein (CRP), procalcitonin, serum creatinine, etc) that are objectively measured and used as indicators of certain physiopathological processes.…”

mentioning

confidence: 99%

Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations

et al. 2022

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ObjectiveThe aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients.DesignThis is a retrospective single-institution study. All consecutive adult patients’ cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient’s electronic medical record (EMR).SettingThe setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards.PatientsThe study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 “nonevent” cases to build the training and validation data set.InterventionsNoneMeasurements and Main ResultsThe best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0–6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0–42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon.ConclusionsIn hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.

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

Cited by 4 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

Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations

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