Adoption rates of electronic health records in Turkish Hospitals and the relation with hospital sizes

Pharmacy practice and policy research in Türkiye: a systematic review of literature

Background

In recent decades, there has been an interest in clinical pharmacy practice in Türkiye with emerging studies in this area. Despite the recent emergence of diverse pharmacy practice studies in Türkiye, a comprehensive assessment of overall typology of studies and impact has not been conducted thus far.

Objectives

This systematic review aims to document and assess pharmaceutical policy and practice literature published within the last 5 years in Türkiye. The other aim is to summarise the expected impact of published studies on policy and practice research.

Methods

The systematic review was conducted according to the guidelines described in the PRISMA Statement. A comprehensive search approach, incorporating Medical Subject Headings (MeSH) queries and free-text terms was employed to locate pertinent literature related to pharmacy practice and policy in Türkiye. The search covered the period from January 1, 2019, to January 1, 2024, and involved electronic databases including PubMed, Medline Ovid, Scopus, ScienceDirect, Springer Link, PlosOne, and BMC.

Results

In the final grouping, 73 articles met the inclusion criteria and were selected for this review. Among the quantitative studies, majority studies were cross-sectional survey studies. Through the rigorous thematic content analysis seven research domains were developed from the selected literature: drug utilisation and rational drug use, the emerging role of pharmacist, access to medicines and generic medicines, community pharmacy practice, pharmacovigilance/adverse drug reactions, and pharmacoeconomic studies.

Conclusions

The pharmacist role is evolving; however, several challenges remain in fully realising the potential of pharmacists. These include regulatory barriers, limited public awareness of pharmacists' expanded roles, workforce capacity issues, and the need for ongoing professional development and training. Research studies are needed in the areas of generic prescribing, medicine adherence, intervention studies in community and hospital pharmacy practice, and on pharmacoeconomics and pharmacovigilance.

Adaptation of the Workflow Integration Survey to Turkey: A Validity and Reliability Study

Background and Purpose: Electronic health record systems (EHRSs) are widely used to record patients' data and should be compatible with nurses' workflow. The purpose of this study was to adapt the Workflow Integration Survey (WIS) to the Turkish language and examine the reliability and validity measures of the Turkish version of the scale. Methods: In this methodological study, data were collected between December 2019 and February 2020 from 120 nurses. This study included the following phases: translation and evaluation of the content validity; explanatory factor analysis and confirmatory factor analysis (CFA) and reliability analysis. The intraclass correlation coefficient (ICC) was used for the test-retest reliability with 30 nurses. Results: The results of CFA revealed a two factors' structure, and these two factors explained 50.57% of the total variance. This was confirmed (χ2/df = 1.673, goodness-of-fit index = 0.948, incremental fit index = 0.923, comparative fit index = 0.918, root mean square error of approximation = 0.075, and standardized root mean square residual = 0.0604) using structural equation modeling. The total Cronbach's alpha value was found to be .702, .636, and .649 for the subscales. The ICC was calculated for test-retest reliability and was found to be 0.871. Conclusions: The validity and reliability of the WIS have been found to be sufficient. It is recommended that the validity and reliability studies on the WIS be conducted in different hospitals with a larger number of participants. Furthermore, the use of the scale in cross-cultural studies to evaluate the compatibility of EHRSs with nurses' workflow in different cultures is also suggested.

Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems

PURPOSE

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.

DESIGN/METHODOLOGY/APPROACH

A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.

FINDINGS

The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.

ORIGINALITY/VALUE

This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

The Economic Impact of Obesity in Turkey: A Micro-Costing Analysis

Background

Turkey currently has the highest obesity prevalence among its European counterparts. 32% and 61% of the population live with obesity and overweight, respectively. Overweight and obesity are linked to non-communicable diseases that incur incremental health and economic costs. The significant public health concern warrants an assessment of the cost of obesity.

Methods

A micro-costing approach from the public payer perspective was conducted to estimate direct healthcare costs associated with ten obesity-related comorbidities (ORCs) in Turkey. Clinical practice guidelines and a systematic literature review informed ORCs and the respective cost categories. This was subsequently validated by a steering committee comprising seven experts. Seventy public sector physicians were surveyed to estimate healthcare resource use. Unit costs were derived from Social Security Institute's Healthcare Implementation Communique. Cost items were summed to determine the annual cost per patient per ORC, which was validated by the steering committee. Medical inflation was considered in a scenario analysis that varied resource unit costs.

Results

Chronic kidney disease, heart failure and type 2 diabetes are the costliest ORCs, incurring an annual cost of 28,600 TRY, 16,639 TRY and 11,993 TRY, respectively. Individuals in Turkey with any ORC triggered direct healthcare costs ranging 1857-28,600 TRY annually. Costs were driven by tertiary care resources arising from treatment-related adverse events, disease complications and inpatient procedures. In the scenario analysis, medical resource unit costs were inflated by 18.7% and 39.4%, triggering an average increase in cost across all ORCs of 1998 TRY and 4210 TRY, respectively.

Conclusion

Our findings confirm that obesity and its complications result in significant financial burden to the public healthcare system. By quantifying the burden of obesity across a comprehensive spectrum of ORCs, our study aims to support the economic case for investing in appropriate obesity interventions.

Big 5 Personality Traits and Individual- and Practice-Related Characteristics as Influencing Factors of Digital Maturity in General Practices: Quantitative Web-Based Survey Study

BACKGROUND

Various studies propose the significance of digital maturity in ensuring effective patient care and enabling improved health outcomes, a successful digital transformation, and optimized service delivery. Although previous research has centered around inpatient health care settings, research on digital maturity in general practices is still in its infancy.

OBJECTIVE

As general practitioners (GPs) are the first point of contact for most patients, we aimed to shed light on the pivotal role of GPs' inherent characteristics, especially their personality, in the digital maturity of general practices.

METHODS

In the first step, we applied a sequential mixed methods approach involving a literature review and expert interviews with GPs to construct the digital maturity scale used in this study. Next, we designed a web-based survey to assess digital maturity on a 5-point Likert-type scale and analyze the relationship with relevant inherent characteristics using ANOVAs and regression analysis.

RESULTS

Our web-based survey with 219 GPs revealed that digital maturity was overall moderate (mean 3.31, SD 0.64) and substantially associated with several characteristics inherent to the GP. We found differences in overall digital maturity based on GPs' gender, the expected future use of digital health solutions, the perceived digital affinity of medical assistants, GPs' level of digital affinity, and GPs' level of extraversion and neuroticism. In a regression model, a higher expected future use, a higher perceived digital affinity of medical assistants, a higher digital affinity of GPs, and lower neuroticism were substantial predictors of overall digital maturity.

CONCLUSIONS

Our study highlights the impact of GPs' inherent characteristics, especially their personality, on the digital maturity of general practices. By identifying these inherent influencing factors, our findings support targeted approaches to drive digital maturity in general practice settings.

Basic electronic health record (EHR) adoption in **Türkiye is nearly complete but challenges persist

BACKGROUND

The digitalization studies in public hospitals in Türkiye started with the Health Transformation Program in 2003. As digitalization was accomplished, the policymakers needed to measure hospitals' electronic health record (EHR) usage and adoptions. The ministry of health has been measuring the dissemination of meaningful usage and adoption of EHR since 2013 using Electronic Medical Record Adoption Model (EMRAM). The first published study about this analysis covered the surveys applied between 2013 and 2017. The results showed that 63.1% of all hospitals in Türkiye had at least basic EHR functions, and 36% had comprehensive EHR functions. Measuring the countrywide EHR adoption level is becoming popular in the world. This study aims to measure adoption levels of EHR in public hospitals in Türkiye, indicate the change to the previous study, and make a benchmark with other countries measuring national EHR adoption levels. The research question of this study is to reveal whether there has been a change in the adoption level of EHR in the three years since 2018 in Türkiye. Also, make a benchmark with other countries such as the US, Japan, and China in country-wide EHR adoption in 2021.

METHODS

In 2021, 717 public hospitals actively operating in Türkiye completed the EMRAM survey. The survey results, deals with five topics (General Stage Status, Information Technology Security, Electronic Health Record/Clinical Data Repository, Clinical Documentation, Closed-Loop Management), was reviewed by the authors. Survey data were compared according to hospital type (Specialty Hospitals, General Hospitals, Teaching and Research Hospitals) in terms of general stage status. The data obtained from the survey results were analyzed with QlikView Personal Edition. The availability and prevalence of medical information systems and EHR functions and their use were measured.

RESULTS

We found that 33.7% of public hospitals in Türkiye have only basic EHR functions, and 66.3% have extensive EHR functions, which yields that all hospitals (100%) have at least basic EHR functions. That means remarkable progress from the previous study covering 2013 and 2017. This level also indicates that Türkiye has slightly better adoption from the US (96%) and much better than China (85.3%) and Korea (58.1%).

CONCLUSIONS

Although there has been outstanding (50%) progress since 2017 in Turkish public hospitals, it seems there is still a long way to disseminate comprehensive EHR functions, such as closed-loop medication administration, clinical decision support systems, patient engagement, etc. Measuring the stage of EHR adoption at regular intervals and on analytical scales is an effective management tool for policymakers. The bottom-up adoption approach established for adopting and managing EHR functions in the US has also yielded successful results in Türkiye.

The effect of digitalization of nursing forms in ICUs on time and cost

OBJECTIVE

Intensive Care Units are one of the areas with the lowest digitization rate. This study aims to measure the effect of digitizing medical records kept in paper forms in ICUs on time-saving and paper consumption. In our study, care forms in ICUs were transferred to digital media. In our research, care forms in ICUs were transferred to digital media.

METHODS

The time required to fill out the nursing care forms on paper and digital media was measured, the change in paper and printer costs was determined, and the results were compared. Two volunteer nurses working in the ICU of a university hospital in Istanbul measured the time it took to fill out the forms of patients on paper. Then, a future projection was made using digital form data of 5,420 care days of 428 patients hospitalized between October 2017 and September 2018. Only anonymous data of patients hospitalized in the general ICU were used, and other untempered were not included in the study.

RESULTS

When the forms were filled in digitally by the nurses, one nurse per patient per day saved 56.82 min (3.95% per day).

DISCUSSION

Health care services are provided in hospitals in Turkey with 28,353 adult intensive care beds and an occupancy rate of 68%. Based on the occupancy rate of 68%, the number of full beds is 19,280. When 56.82 min are saved per bed from the forms filled by the nurses, 760.71 care days are dedicated. Considering the salary of 1,428.67 US dollars per nurse, the savings to be achieved are estimated to be 13,040,804.8 US dollars per year.

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

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 PPV of 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.

SurvMaximin: Robust federated approach to transporting survival risk prediction models

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 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.

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

OBJECTIVE

The 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.

DESIGN

This 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).

SETTING

The setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards.

PATIENTS

The 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.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The 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.

CONCLUSIONS

In 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.

Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review

BACKGROUND

Digital health in hospital settings is viewed as a panacea for achieving the "quadruple aim" of health care, yet the outcomes have been largely inconclusive. To optimize digital health outcomes, a strategic approach is necessary, requiring digital maturity assessments. However, current approaches to assessing digital maturity have been largely insufficient, with uncertainty surrounding the dimensions to assess.

OBJECTIVE

The aim of this study was to identify the current dimensions used to assess the digital maturity of hospitals.

METHODS

A systematic literature review was conducted of peer-reviewed literature (published before December 2020) investigating maturity models used to assess the digital maturity of hospitals. A total of 29 relevant articles were retrieved, representing 27 distinct maturity models. The articles were inductively analyzed, and the maturity model dimensions were extracted and consolidated into a maturity model framework.

RESULTS

The consolidated maturity model framework consisted of 7 dimensions: strategy; information technology capability; interoperability; governance and management; patient-centered care; people, skills, and behavior; and data analytics. These 7 dimensions can be evaluated based on 24 respective indicators.

CONCLUSIONS

The maturity model framework developed for this study can be used to assess digital maturity and identify areas for improvement.

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

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.

Understanding How to Improve the Use of Clinical Coordination Mechanisms between Primary and Secondary Care Doctors: Clues from Catalonia

Clinical coordination between primary (PC) and secondary care (SC) is a challenge for health systems, and clinical coordination mechanisms (CCM) play an important role in the interface between care levels. It is therefore essential to understand the elements that may hinder their use. This study aims to analyze the level of use of CCM, the difficulties and factors associated with their use, and suggestions for improving clinical coordination. A cross-sectional online survey-based study using the questionnaire COORDENA-CAT was conducted with 3308 PC and SC doctors in the Catalan national health system. Descriptive bivariate analysis and logistic regression models were used. Shared Electronic Medical Records were the most frequently used CCM, especially by PC doctors, and the one that presented most difficulties in use, mostly related to technical problems. Some factors positively associated with frequent use of various CCM were: working full-time in integrated areas, or with local hospitals. Interactional and organizational factors contributed to a greater extent among SC doctors. Suggestions for improving clinical coordination were similar between care levels and related mainly to the improvement of CCM. In an era where management tools are shifting towards technology-based CCM, this study can help to design strategies to improve their effectiveness.