Alireza Darrudi

Bio: Alireza Darrudi is an academic researcher from Tehran University of Medical Sciences. The author has contributed to research in topics: Health care & Equity (economics). The author has an hindex of 3, co-authored 9 publications receiving 34 citations.

Electronic Health Record Implementation: A SWOT Analysis

Abstract: Electronic Health Record (EHR) is one of the most important achievements of information technology in healthcare domain, and if deployed effectively, it can yield predominant results. The aim of this study was a SWOT (strengths, weaknesses, opportunities, and threats) analysis in electronic health record implementation. This is a descriptive, analytical study conducted with the participation of a 90-member work force from Hospitals affiliated to Tehran University of Medical Sciences (TUMS). The data were collected by using a self-structured questionnaire and analyzed by SPSS software. Based on the results, the highest priority in strength analysis was related to timely and quick access to information. However, lack of hardware and infrastructures was the most important weakness. Having the potential to share information between different sectors and access to a variety of health statistics was the significant opportunity of EHR. Finally, the most substantial threats were the lack of strategic planning in the field of electronic health records together with physicians' and other clinical staff's resistance in the use of electronic health records. To facilitate successful adoption of electronic health record, some organizational, technical and resource elements contribute; moreover, the consideration of these factors is essential for HER implementation.

The impact of health transformation plan on health services fees: brief report

Abstract: * :لوئسم هدنسیون م ،نارهت ی ناد خ ،بلاقنا ی نابا ،سدق خ ی نابا سروپ ی ،ان کشزپ مولع هاگشناد ی کشناد ،نارهت هد تشادهب . نفلت : 42933006 021 E-mail: Sadaf_af2007@yahoo.com فده و هنیمز : هفرعت راذگ ي تملاس تامدخ ی ک ی مرها زا اه ي ک رـب هـک تـسا تملاـس ماظن لرتنک ی ف ـی ،ت ـسرتسد ی و زه ی هن ثأت تملاس تامدخ ی ر ا .دراد ی ن سررب فده اب شهوژپ ی ثأت ی ر لاـس تامدـخ هـفرعت رـب تملاـس لوـحت حرط تم .دش ماجنا سررب شور :ی ا ی ن صوت شهوژپ ی ف ی عطقم و ی اقم هب ی هس لاک هـفرعت باتک ساسا رب تملاس تامدخ هفرعت ی نرف ـی ا ) شیـپ ارجا زا ي حرط شزرا باتک و (تملاس لوحت اه ي بسن ی ا تملاس تبقارم و تامدخ ی نار ) سپ ارـجا زا ي حرـط لوـحت .تخادرپ (تملاس هتفای :اه لاک باتک ی نرف ی ا شزرا باتک و اـه ي بـسن ی دج ـی د رد 13 ـصصخت هورـگ ی هـب ترت ی ب لماـش 5281 و 3448 ـک د شهاک) دندوب تمدخ 7 / 34 دصرد ي لاک باتک .( ی نرف ی ا شزرا باتک و اـه ي بـسن ی دج ـی د ترت هـب ـی ب دادـعت 9 / 64985 و 6 / 125133 K حارج ی ازفا) ی ش 6 / 92 دصرد ي و ( 25976 و 22307 K ب ی شوه ی شهاک) 1 / 14 دـصرد ي ماـمت رد ( ی 13 صصخت هورگ ی هتفرگ رظن رد بسن شزرا .دندوب ی م روط هب تامدخ ی گنا ی ن 9 / 1 .دش ربارب هجیتن :يریگ ارجا ي ازفا هب رجنم تملاس لوحت حرط ی ش ربارب ود دودح ي هفرعت اه ي تبقارم و تامدخ اه ي ملاس ت لام راب هک دش ی ز ی دا ي نامزاس رب اه ي ب ی هم .تخاس دراو تملاس

Equity in distribution of hospital beds in Iran

Abstract: Background and Aim: Equitable distribution of health system resources and hospital beds is crucial for an acceptable level of health for all the people of the country. The aim of this study was to examine the equity in distribution of hospital beds in Iran. Materials and Methods: In this descriptive and cross sectional study data were collected from Ministry of Health and Medical Education and Iranian statistics Center. The study population consisted of all Iranian hospitals in 2016. The equity in the distribution of hospitals’ beds was evaluated using the Lorenz curve and Gini coefficient. Excel software was used for data analysis. Results: Iranian population was 79,926,270 and there were 930 hospitals with 118,894 beds in 2016. (1.2 hospitals per 100,000 population and 1.5 hospital beds per 1000 population). Yazd, Semnan and Tehran provinces had the highest hospital beds per 1000 population. About 17% of the total hospitals and 22% of the hospital beds were located in Tehran. The Gini coefficient for hospital bed distribution among Iranian provinces was 0.107. Distribution of the hospital beds was equitable in Alborz and Yazd provinces and inequitable in North Khorasan, Sistan & Baluchestan and Charmahal & Bakhtiary provinces. Conclusion: The distribution of hospital beds among Iranian provinces has been fair. However, hospital beds were not distributed equitably among the cities in the provinces and were concentrated in the capital cities of the provinces of the country. Healthcare policymakers should take appropriate measures to reduce inequality in the distribution of hospital beds.

The Effect of Iran Health System Evolution Plan on Tehran University of Medical Sciences Hospitals’ Performance Indicators: a Case Study Using the Pabon Lasso Model

Abstract: Background and Aim: All hospitals need to be monitored and continuously evaluated. Pabon Lasso graphical model assesses the efficiency of hospitals using a combination of their input data and performance indicators. The aim of this study was to determine the effects of Iran Health System Evolution Plan on Tehran University of Medical Sciences (TUMS) hospitals’ performance indicators using the Pabon Lasso model in 2016. Materials and Methods: This was a descriptive-analytical and cross-sectional study including 8 general and specialized hospitals. The hospital input data and performance indicators were collected by permission of TUMS Vice-chancellor’s Office for Therapeutic Services. Chisquare and the two-sample T2 tests were used for data analysis, and hospital performance was assessed using the Pabon Lasso Model. Results: The results showed that the number of TUMS hospitals located in zones 3 and 4 had increased after implementation of the Iran Health System Evolution Plan, which indicates improved efficiency of these hospitals, as confirmed by the chi-square test. Conclusion: Based on the Pabon Lasso graphical model, it can be concluded that the performance indicators of the hospitals show improvements in performance after implementation of the Iran Health System Evolution Plan. The successful experience in the management of the hospitals in this study can act as a practical guide for managers of other hospitals throughout the country for better management.

Workflow and Electronic Health Records in Small Medical Practices | NIST

Abstract: This paper analyzes the workflow and implementation of electronic health record (EHR) systems across different functions in small physician offices. We characterize the differences in the offices based on the levels of computerization in terms of workflow, sources of time delay, and barriers to using EHR systems to support the entire workflow. The study was based on a combination of questionnaires, interviews, in situ observations, and data collection efforts. This study was not intended to be a full-scale time-and-motion study with precise measurements but was intended to provide an overview of the potential sources of delays while performing office tasks. The study follows an interpretive model of case studies rather than a large-sample statistical survey of practices. To identify time-consuming tasks, workflow maps were created based on the aggregated data from the offices. The results from the study show that specialty physicians are more favorable toward adopting EHR systems than primary care physicians are. The barriers to adoption of EHR systems by primary care physicians can be attributed to the complex workflows that exist in primary care physician offices, leading to nonstandardized workflow structures and practices. Also, primary care physicians would benefit more from EHR systems if the systems could interact with external entities.

Can Artificial Intelligence Improve the Management of Pneumonia

Abstract: The use of artificial intelligence (AI) to support clinical medical decisions is a rather promising concept. There are two important factors that have driven these advances: the availability of data from electronic health records (EHR) and progress made in computational performance. These two concepts are interrelated with respect to complex mathematical functions such as machine learning (ML) or neural networks (NN). Indeed, some published articles have already demonstrated the potential of these approaches in medicine. When considering the diagnosis and management of pneumonia, the use of AI and chest X-ray (CXR) images primarily have been indicative of early diagnosis, prompt antimicrobial therapy, and ultimately, better prognosis. Coupled with this is the growing research involving empirical therapy and mortality prediction, too. Maximizing the power of NN, the majority of studies have reported high accuracy rates in their predictions. As AI can handle large amounts of data and execute mathematical functions such as machine learning and neural networks, AI can be revolutionary in supporting the clinical decision-making processes. In this review, we describe and discuss the most relevant studies of AI in pneumonia.

The Relative Efficiency of Jordanian Public Hospitals Using Data Envelopment Analysis and Pabon Lasso Diagram

Abstract: This study aims at investigating and measuring the relative efficiency of public hospitals performance in Jordan, during the period (2006-2008), using DEA and Pabon-Lasso Diagram. The results indicate that the average efficiency of those hospitals is varied and ranges between (73%) to (100%). The average of the relative efficiency of the sample hospitals over the study period is 94%. Of 15 hospitals, the number of efficient hospitals increased from 7 in 2006 to 9 in 2007, but decreased to 8 in 2008. The results are compared with old efficiency scores of the same hospitals reported in the literature. This comparison leads to signify that the efficiency of public sector hospitals in Jordan is varied over time, due to decreasing public expenditures on health care per capita. However, the results concerning ratio analysis in explaining efficiency are inconsistent; implying that these institutions are either having poor management or they treat long stays diseases.

Electronic Medical Records implementation in hospital: An empirical investigation of individual and organizational determinants.

Abstract: The implementation of hospital-wide Electronic Medical Records (EMRs) is still an unsolved quest for many hospital managers. EMRs have long been considered a key factor for improving healthcare quality and safety, reducing adverse events for patients, decreasing costs, optimizing processes, improving clinical research and obtaining best clinical performances. However, hospitals continue to experience resistance from professionals to accepting EMRs. This study combines institutional and individual factors to explain which determinants can trigger or inhibit the EMRs implementation in hospitals, and which variables managers can exploit to guide professionals' behaviours. Data have been collected through a survey administered to physicians and nurses in an Italian University Hospital in Rome. A total of 114 high-quality responses had been received. Results show that both, physicians and nurses, expect many benefits from the use of EMRs. In particular, it is believed that the EMRs will have a positive impact on quality, efficiency and effectiveness of care; handover communication between healthcare workers; teaching, tutoring and research activities; greater control of your own business. Moreover, data show an interplay between individual and institutional determinants: normative factors directly affect perceived usefulness (C = 0.30 **), perceived ease of use (C = 0.26 **) and intention to use EMRs (C = 0.33 **), regulative factors affect the intention to use EMRs (C = -0.21 **), and perceived usefulness directly affect the intention to use EMRs (C = 0.33 **). The analysis carried out shows that the key determinants of the intention to use EMRs are the normative ones (peer influence) and the individual ones (perceived usefulness), and that perceived usefulness works also as a mediator between normative factors and intention to use EMRs. Therefore, Management can leverage on power users to motivate, generate and manage change.