Journal of Health Management and Informatics
Volume:7 Issue: 1, Jan 2020

Strategic planning is a process that involves reviewing the organization’s needs, environmental conditions, customer, organizational capabilities and weaknesses that are designed with the goal of deciding on the organization’s mission, goals, and strategies. Each organization’s strategy is a set of goals. In this study, we decided to have a review on strategic planning in health domain.

Embase, PubMed/MEDLINE, ISI/Web of Science (WOS), Scopus, and Iranian databases, such as MagIran, SID, and Irandoc, were searched from 2000 to 2017. Also, the grey literature (via Google Scholar) was searched. Studies written in English or in Persian were searched, and keywords used included Strategic Management, Strategic Health Planning, and Health strategic management.

Based on the inclusion criteria, the search of databases resulted in 30 articles that fully covered the studies that carried out a full strategic management and planning in health. The results of the present study indicated that RBV is used for strategic health planning and RDT has a strong influence on strategic decision-making. Strategic planning is one of the most important issues in a productive health care center. The decision-making levels in the health sector can be divided into three strategic, middle, and operational categories. During the strategic planning process in the health sector, attention is drawn to the two approaches to RDT) Resource dependency theory) and RBV (Resource based view).

Health by investing and using resources and key talents of management, in the strategic planning process increases the organization’s capacity and its ability to face changing circumstances and environments. Studies show that the success of a strategic planning in a health center depends on participation of beneficiaries, namely doctors, nurses and managers.

The awareness of the current compliance of Joint commission international standards is a prerequisite for their upgrading. The purpose of this study was to provide a systematic review of the evaluation of Iranian hospitals in accordance with International Joint Commission standard.

The present systematic review was conducted in 2019. Data were gathered by searching the Google Scholar, Scopus, PubMed, and Web of science databases. Search keywords were “medical tourism”, “health tourism”, “joint commission international”, “JCI”, “Hospital”, “medical center” and “Iran”. The search protocol was limited to 2009-2019.

The findings showed that the average compliance of patient-based standards was about 68.89% and the average adherence to organization-based standards was about 69.05%. Also, the most compliance with patient-based standards was related to “Anesthesia and Surgical Care”, while the least adherence to them belonged to the area of “Patient and Family Rights”.

According to the results of the study, Iran’s medical tourism standards are not universally desirable; authorities are recommended to focus on continual improvement of their reliability and removal of their weaknesses. In this regard, strategies such as developing a comprehensive and mandatory national qualification program and evaluating its periodic performance in this field are suggested.

Recent studies on business strategy show that the competitive advantage for enterprises can be achieved through a focus on human resources as the most important strategic resource to the organization. In this regard, many of the modern organizations spend much capital on their employees in the form of recruitment, training, development and maintenance. Therefore, it is important to retain the staff and reduce turnover rates. Due to the serious need to modify and reduce the rate of turnover in different businesses, many researchers tend to examine why and how to reduce the turnover. The aim of this study was to investigate the role of employee perceptions of job characteristics and work environment and person-organizations fit elements in creating tendency toward turnover among the staff in Shiraz University of Medical Sciences.

This is a cross-sectional quantitative study. To test some proposed hypotheses, a random sample of the personnel working at Shiraz University of Medical Sciences (n=105) completed a standard survey questionnaire consisting of questions about their turnover intentions, job characteristics, job environmental characteristics, person-organization fit. For testing the hypotheses of the study, the data were analyzed through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM), using AMOS software.

The data revealed high reliability and validity (based on Cronbach’s alpha, composite reliability, convergent and discriminant validities), so they were suitable for further analyses. SEM revealed that all job characteristic variables, except for the importance of job variable, significantly affected the turnover intention.

This article contributes to the literature since to the best of our knowledge it is the first to investigate the role of employee perceptions of job characteristics, work environment, person fit elements and organizations in creating tendency toward turnover.

Knowledge management (KM) has a pivotal role in optimizing performance at organizations. In recent decades, hospitals used KM to achieve optimized performance. This study aimed to determine the knowledge management status in a non-governmental general private hospital in Tehran in 2019.

This cross-sectional study was done in 2019. We selected 171 clinical and administrative staff at the hospital by using random sampling. Data were collected through a valid and reliable questionnaire. Data analysis was done by SPSS v 22 through descriptive and analytical statistics.

The results of the study showed that KM dimensions had an inappropriate status (2.99 out of 5). Among the KM dimensions, Technology had a worse status than others (2.72 out of 5). Moreover, there was a significant relationship between People and Technology (P<0.0001, r=0.59). Also, there was a significant relationship among all of the KM Process components (P<0.01).

Due to the inappropriate situation of technology in the hospital, the managers should help to provide hardware and software requirements and make it a leading hospital for technology use. Due to the positive relationship between People and Technology, equipping the hospital with new technologies led to an increase in the person’s abilities and improvement of the health care services delivered to the patients.

The present study aimed to compare the effectiveness of dialectical behavior therapy and mindfulness training in improving the sleep quality and reducing distress tolerance in drug-addicted treatment seekers.

The research method was quasi-experimental with pretest-posttest design and control group. Using a convenience sampling method, 120 men who were admitted in the mid-term residential addiction treatment center in Baghmalek city were first selected, and then 80 individuals were randomly assigned to three experimental and one control groups (n=20 per group). Experimental groups received twelve 45-minute sessions of mindfulness training, dialectical behavior therapy, and a combination of mindfulness and dialectical behavior therapy. The control group did not receive any intervention training program. All participants responded to the Pittsburgh Sleep Quality Index (PSQI) and Distress Tolerance Scale (DTS) at the beginning of the study, at the end of intervention and one month after the treatment (one-month follow-up). The multivariate analysis of covariance and one-way analysis of variance were utilized to analyze the data. SPSS software was used for data analysis. A significance level of 0.05 was considered statistically significant.

Dialectical behavior therapy and mindfulness training were effective in reducing distress tolerance (F=124.33, P=0.0001) and improving sleep quality (F=37.03, P=0.001). The combination of dialectical behavior therapy and mindfulness training had no significant effect on distress tolerance (P=0.071) and sleep quality (P=0.090).

Both methods had similar effects on the participants. The follow-up results indicated lasting effects of dialectical behavior therapy and mindfulness training on sleep quality and distress tolerance.

P300 speller is a kind of Brain-Computer Interface (BCI) system in which the user may type words by using the responses obtained from human focus on different characters. The high sensitivity of brain signals against noise in parallel with the similarity of responses obtained from the user focus on different characters makes it difficult to classify the characters based on their respective P300 wave. On the other hand, all areas of the brain does not carry useful P300 information.

In this study, a new method is proposed to improve the performance of speller system which is based on selecting optimal P300 channels. In the proposed method, recursive elimination algorithm is presented for channel optimization, which utilizes deep learning concept (e.g. Convolutional Neural Network) as its cost function. The proposed method is examined on a data set from EEG signals recorded in a P300 speller system, including 64 different channels of responses to 29 characters. Then, its performance is compared with some existing methods.

The obtained results showed the ability of the proposed method in recognizing the characters in such way that it could accurately (i.e. 97.34%) detect 29 characters by using only 24 out of all 64 electrodes.

Applying the proposed method in speller systems led to considerable improvement in classification of characters compared to its alternatives. Several experiments proved that utilizing the proposed scheme may increases the accuracy almost 12.9 percent compared to non-optimized case in parallel with reduction of the number of involved channels by approximately 1/3. Based on these results, the proposed method may be considered as an effective choice for application in P300 speller systems, thanks to reduction of the complexity of the system which is caused by the reduced number of channels and, on the other hand, due to its potential in increasing the accuracy of character recognition.

Selecting the best set of input and output indicators and allocation of correct weights to them is a sensitive step in any efficiency evaluation study. Therefore, the present study aims to determine and rank the efficiency indicators of hospitals.

This mixed-method study was carried out in three steps: comprehensive literature review, application of the Delphi method to determine the best indicators for efficiency evaluation of the hospitals, and utilization of a fuzzy analytic hierarchical process (FAHP) for weighting of final indicators and ranking them.

8 input and 9 output indicators were selected for efficiency evaluation of the hospitals which were weighted by FAHP. Among the input indicators, the number of physicians and active beds and among the output indicators, length of stay and number of surgeries were identified to be the most important indicators.

According to the proposed indicators and their accurate weights, efficiency evaluation of hospitals can be done more accurately, reliably, and comprehensively.

Hospitals are regarded as the largest and most expensive operational units of the health system. Also, bed is one of the most valuable hospital resources. Due to the limited resources of the health system, paying attention to efficient resource allocation is necessary. This study aimed to allocate common beds to hospitals to optimize their overall efficiency.

This is a cross-sectional and applied study. 70 Iranian hospitals affiliated to social security organizations were examined. The required data were collected from the statistical yearbook of this organization (2016). A centralized data envelopment analysis model with an input-orientation approach was used to assess the performance and bed reallocation to hospitals. The input data consist of the number of active beds, and the output data consist of bed occupancy rate, average patient stay, and survival of patients in thousand. Hospitals were clustered by k‐means clustering method for analysis. The data were analyzed by GAMS and SPSS softwares.

Hospitals were clustered into 4 groups of homogenous units. Based on this clustering, the number of hospitals in clusters 1 to 4 was 23, 2, 13, 32, and the overall efficiencies of them were 0.703, 1, 0.827, and 0.732, respectively. Before bed reallocation, 13 (18.6%) hospitals were efficient. After bed reallocation by Centralized data envelopment analysis, 31 (44.3%) hospitals became efficient.

It seems that the active bed factor can be one of the inputs influencing the overall efficiency of hospitals. However, in this regard, inclusive attention to other resources such as physicians and nurses will be necessary to achieve desirable hospital efficiency.