Journal of Health Management and Informatics
Volume:6 Issue: 2, Apr 2019

Brain-Computer Interface (BCI) offers a non-muscle way between the human brain and the outside world to make a better life for disabled people. In BCI applications P300 signal has an effective role; therefore, distinguishing P300 and non-P300 components in EEG signal (i.e. P300 detection) becomes a vital problem in BCI applications. Recently, Convolutional Neural Networks (CNNs) have had a significant application in detection of P300 signals in the field of BCIs. The P300 signal has low Signal to Noise Ratio (SNR). On the other hand, the CNN detection rate is so sensitive to SNR; therefore, CNN detection rate drops dramatically when it is faces with P300 data. In this study, a novel structure is proposed to improve the performance of CNN in P300 signal detection by means of improving its performance against low SNR signals. In the proposed structure, Sparse Representation-based Classification (SRC) was used as the first substructure. This block is responsible for prediction of the expected P300 signal among artifacts and noise. The second substructure performed P300 classification with Adadelta algorithm. Thanks to such SNR improvement scheme; the proposed structure i able to increase the rate of accuracy in the field of P300 signal detection. To evaluate the performance of the proposed structure, we applied it on EPFL dataset for P300 detection, and then the achieved results were compared with those obtained from the basic CNN structure. The comparisons revealed the superiority of the proposed structure against its alternative, so that its True Positive Rate (TPR) was promoted about 19.66%. Such improvements for false detections and accuracy parameters were 1.93% and 10.46%, respectively, which show the effectiveness of applying the proposed structure in detecting P300 signals. The better accuracy of the proposed algorithm compared to basic CNN, in parallel with its more robustness, showed that the Sparse Representation-based Classification (SRC) had a considerable potential to be used as an improving idea in CNN-based P300 detection.

Business Process Management (BPM) is a disciplined approach that allows a business to identify, model, deploy, execute, manage, monitor, and improve its processes in a standardized manner. This research aimed to identify the prerequisites for the deployment of this approach in Iran’s Hospitals. The present research was a qualitative cross-sectional study which was conducted using the content analysis method in 2017. Sampling was performed using the purposive sampling method and continued until data saturation. The participants were 18 men and 5 women. The data were collected through semi-structured interviews. Data analysis was performed using the content analysis method. After analyzing the contents of the interviews, we classified the prerequisites for the deployment of BPM practices into six themes and 14 subthemes: Process Engineering, Flexible Treatment Guidelines and Procedures, Flexible Organizational Rules, Learning Organization, Smart Electronic Filing, and Access Control Systems. According to the experts interviewed, decision-makers have to carefully address the prerequisites such as legal and cultural requirements and the limitations such as budgetary constraints before initiating the deployment of BPM systems. Overall, it appears that the localization and deployment of this approach, as much as it is currently possible, can benefit the Iranian healthcare systems as well as Iranian patients.

The high prevalence of cardiovascular diseases has caused many health problems in countries. Cardiac Rehabilitation Programs (CRPs) is a complementary therapy for Percutaneous Coronary Intervention (PCI) patients. However, PCI patients hardly attend CRPs. This study aims to decipher the reasons why PCI patients rarely participate in CRPs after PCI. The parameters affecting the attendance of the patients at CRPs were identified by using the previous studies and opinions of experts. A questionnaire was designed based on the identified parameters and distributed among PCI patients who were referred to Tehran Heart Center Hospital. According to data mining approach, 184 samples were collected and classified with three algorithms (Decision Trees, k-Nearest Neighbor (kNN), and Naïve Bayes). The obtained results by decision trees were superior with the average accuracy of 82%, while kNN and Naïve Bayes obtained 81.2% and 78%, respectively. Results showed that lack of physician’s advice was the most significant reason for non-participation of PCI patients in CRPs (P< .0001). Other factors were family and friends’ encouragement, paying expenses by insurance, awareness of the benefits of the CRPs, and comorbidity, respectively. Results of the best model can enhance the quality of services, promote health and prevent additional costs for patients.

Hospitals are considered as the most important consumer units in the healthcare sector and are one of the main organizations providing health care services. Therefore, efficiency assessment is very important in hospital sectors. Besides, in order to be able to develop and compete, hospitals need a performance evaluation system to evaluate the efficiency and effectiveness of their programs, processes, and human resources. The aim of this paper was to assess the efficiency of hospitals by a combined model of balanced scorecard-fuzzy data envelopment analysis (BSC-fuzzy DEA). The present study was a descriptive-analytical study that was conducted to assess the efficiency of 8 hospitals in Qazvin province in 2018. The required data were collected through historical data and a questionnaire. 30 experts, including hospital managers and staff, and patients were randomly chosen to collect data in each hospital. The methods used in this study were balanced scorecard (BSC) for determining performance indicators in hospitals and fuzzy data envelopment analysis for assessing the efficiency score of hospitals. Data were analyzed by GAMS software version 23.5.1. The results of applying fuzzy DEA revealed that Amiralmomenin Hospital, Bu Ali Clinic, and 22 Bahman Hospital have the best performances among Qazvin hospitals. The technical efficiency scores of these hospitals under the uncertainty level of α=0.75 are 1.72, 1.58, and 1.53, respectively. The use of BSC measures in four perspectives of customer, financial, internal processes and growth, and innovation reflects the overall strategic objectives of the hospitals in the performance evaluation process. Furthermore, applying the BSC and fuzzy DEA methods provides a comprehensive performance assessment tool for hospitals, and helps decision makers to obtain more accurate planning to expand the capacity of health services and save the resources.

There is much less attention to the structural, processing, and functional standards in accreditation of health care organizations. The purpose of this study was to determine and prioritize the factors affecting the implementation of accreditation system in hospitals affiliated with the Social Security Organization in Tehran in 2016. This is a cross‐sectional quantitative study conducted among hospital staff recruited through census sampling. To collect the data, a researcher-made questionnaire consisting of 24 factors was designed using hierarchical analysis method. After collecting the questionnaires, studying criteria and factors were analyzed and prioritized based on Analytic Hierarchy Process model (AHP) and inconsistency ratio (ICR) using the Super Decisions Software. To determine whether there is a significant difference between the respondents’ answers, we performed one-sample t-test using SPSS software. According to the findings, 49 out of the 170 participants were male and the rest were female. In order to investigate the factors affecting the establishment of the accreditation system, we the ranking of factors showed that the output criterion with the weight of 0.443 had the highest priority, and then the criterion of the structure with a weight of 0.279 and the process criterion with a weight of 0.278 in the next priorities were placed. The findings of the present study, scientifically through the review of documents and evidence, as well as their integration with the opinions of domestic experts, resulted in achieving an effective model for establishing accreditation based on structural, processing, and output standards and considering the weight of each group of standards. The factors affecting the accreditation system take into account the constraints on the content and implementation process of the current accreditation program and complements the existing gaps by adding the dimensions and components required. Using a simple, comprehensive and efficient approach, it is possible to provide an opportunity to improve the status of accreditation and quality of services in hospitals of Tehran’s social security hospitals.