Browsing by Author "Seme, Assefa( PhD)"

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    Assessment of the Current Paper Based Medical Record System At Multi-Drug Resistance Tuberculosis Department in Saint Peter Hospital for Introducing Electronic Medical Record System.
    (Addis Ababa University, 2014-05) Melaku, Zenebech; Yifiru, Martha (PhD); Seme, Assefa( PhD)
    Introduction: Health care is one of the critical components of basic social services that have a direct linkage to the growth and development of a country as well as to the wellbeing of society. In response to this, the Federal Ministry of Health, supported by its technical partners, is involved in a number of ICT projects and services. And one of these projects is electronic medical record (EMR) which is computerized medical information systems that collect, store and display patient information. It is a means to create legible and organized recordings and to access clinical information about individual patients. As the medical recorded system of Ethiopia had been entangled with a number of problems, this study tried to assess the problem of paper based medical record for introducing EMR at MDR TB department in St. Peter hospital. Objective: the general objective of this study is assessment of the current paper based medical record for introducing EMR at MDR TB department in St. Peter Hospital. Methodology: Across-section study design with quantitative and qualitative methods of collecting data was conducted at MDR TB department in St. Peter TB Hospital from April/2014 to May/2014. All the 27 health professionals (physicians ,nurses and health officers) staff working in the MDR department including head of laboratory, pharmacy, imaging, MDRTB department and head nurse of the department were selected purposive sampling technique was utilized for the selection. Moreover, all the 182 MDR TB patients were included in the study. Self administered questionnaires, Interviews and observation techniques were utilized SPSS version 20 was utilized to analyze quantitative data, frequency and percentages were used to describe the study population. . Findings: gaps or problems identified in the paper based medical record system were illegibility, incompleteness, redundancy of data, difficulty in accessing data, inefficient communication system with different departments about patient issues, and shortage of storage space, digital x-ray machine shortage of computer and misplacing of patient card. Conclusion and Recommendations: Most of the problems identified with the paper based medical system at MDR-TB are typical of those problems faced in any paper based system. Therefore, Introduction of EMR would help to reduce problems associated with legibility, completeness, redundancy and other problems and foster good communication of patient data among the departments. Moreover, it would relieve storage space problems. The Hospital plans to adopt Smart care developed by Tulane University. But it would be important to analyze the actual situation in the department and study the software so as to make it adaptable.
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    Developing A Predictive Model For Pre- Diabetes Screening By Using Data Mining Technology
    (Addis Ababa University, 2017-06) Zerihun, Bezehagn; Meshesh, Million (PhD); Seme, Assefa( PhD)
    Introduction - Diabetes is one of the most common non-communicable diseases. That has a significant contribution of increased morbidity, mortality and admission rate of patients in both developed and developing country. The burden is also very enormous in Ethiopia with estimated 1.4 million in World Health Organization country profile report (2014); even this doesn’t included pre-diabetes and undiagnosed cases. International Diabetes Federation report an estimated 83.8% of all cases of undiagnosed diabetes mellitus are in low- and middle-income countries. Therefore early screening, diagnosis and prompt treatment are needed to prevent comorbidity and mortality, delay the onset of disease, and reduce serious complication and permanent damage. Objective: The aim of this study was to develop a predictive model for screening of pre-diabetes patient using data mining technology. Method: This study conducted in Adare general hospital in Hawassa city, south Ethiopia. The methods used for mining, Cross-Industry Standard Process of Data Mining which contains six phases such as problem understanding, data understand, data preparation, model building, evaluation and deployment was used. In general, 4529 of age > 20 years visiting diabetic unit for general medical examination and follow up were included from January to March 2017. Designed template was used for data collection. For data pre- processing was used Microsoft Excel and WEKA open source software for mining. Results and discussion: - The study has revealed that the model constructed PART with all attributes registers the highest accuracy of 96.78% as compared to J48 decision tree which was 93.66%. The finding of the study clearly presents that screening of diabetes and pre diabetes patient. Based on result of prediction designed project prototype model that predict whether the positive risk of diabetes or not based on this result patients should link further investigation or provide council for future the way to prevent or delay on set of diabetes. Conclusion: - Generally, the prototype system serves as a guideline, diabetic screening to support early detection of patient. The initial feedback from health works has been extremely positive. Hence the developed prototype system achieves a good performance and meets the objectives of the project. Recommendation: - based on finding of project forwarded recommendation for respective stockholders.Develop diabetes screening model by using data mining technology
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    Exploring the Prevalence of Diarrheal Disease Using Data Mining Technology (A Case of Tikur Anbessa Hospital)
    (Addis Ababa University, 2011-06) Endalew, Muluneh; Abebe, Ermias (PhD); Seme, Assefa( PhD)
    The amount of health related data available to healthcare providing organizations for various diseases is being massive and ongoing to collect from time to time. As a result, huge amount of data is being stored in the health care organizations and facilities. Diarrheal disease is one of those which is being the causes of morbidity and mortality for many children especially under the age of five and from which large amount of data is being collected in both Rural and Urban health facilities of Ethiopia. This data represents a useful resource for making a wide variety of real-time decisions and determinations, from the quality of care delivered to trends in treatment modalities and staffing issues. The problem is to be able to handle this huge amount of data and information in such a way that they can identify what is important and be able to extract it from the accumulated data. It is too complex and voluminous to be processed and analyzed by traditional methods. Now a days, data mining technology is being used as a tool that provides the techniques to transform these mounds of data into useful information which in turn enables to derive knowledge for decision making. A number of data mining techniques and tools are available to perform this task. The researcher considered selective techniques and tools which were used to explore the prevalence of diarrheal disease and develop classification and prediction models. Thus, the purpose of this study is to investigate the potential applicability of data mining techniques in exploring the prevalence of diarrheal disease using the data collected from the diarrheal disease control and training center of African sub Region II in Tikur Anbessa Hospital. Patients’ records with age of five years (60 months) and under are included in the study. Two machine learning algorithms from WEKA software such as J48 Decision Trees(DT) and Naïve Bayes(NB) classifiers are adopted to classify diarrheal disease records on the basis of the values of attributes ‘Treatment’ and ‘Type of Diarrhea’. Initially, a total dataset of 5,572 records with 9 attributes were collected for the study. However, the size of class labels for the selected target classes was not balancedand number of records were resample using ‘SMOTE (Synthetic Minority Oversampling TEchnique) from Weka preprocess package. After this process, the number of records used for model building was increased to 13,710 and 16, 460, for ‘Treatment’ and ‘Type of diarrhea’ target classes respectively. This was done in order to decrease biasness or preconception of classifiers in model building process. Results of the experiments have shown that J48 DT classifier has better classification and accuracy performance as compared to NB classifier. Two consecutive models selected in evaluation performance of these classifiers depicted that J48 DT and NB classified ‘treatment modalities’ and ‘diarrheal types’ with the accuracy of 88.3%, 79.54%, 85.64% and 73.94% respectively. Overall, this study has proved that data mining techniques are valuable to support and scale up the efficacy of health care services provision process.
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    Factors Affecting the Adoption of Health Management Information Systems (HMIS) Among Health Workers: The case of SmartCare Software in Addis Ababa Regional Public Hospitals
    (Addis Ababa University, 2013-06) Teshager, Dereje; Teferi, Dereje (PhD); Seme, Assefa( PhD)
    Background: There has been an increasing interest in the area of Electronic Medical Records (EMR) and more and more hospitals all over the world try to keep their patients’ records electronically. The adoption of EMR has become a major concern in the healthcare industry, as it is a key factor to the healthcare quality improvement. In Ethiopia, the implementation of Electronic Medical Record (EMR) is through software called SmartCare. SmartCare software possesses numerous advantages and features such as Simultaneous, remote access to patient data, Legibility of record, Safer data, Patient data confidentiality, greater range of data output modalities and Service Integration within the facility (laboratory, pharmacy, prescription & scheduling). However, these systems are not used by the health workers in Addis Ababa Regional Public hospitals. Objective: The objective of this study was to identify and measure the factors affecting the behavioral intention and usage behavior of health workers EMR-SmartCare Software adoption in public Hospitals of Addis Ababa City Administration. Methodology: To identify the factors affecting the utilization of EMR-SmartCare software, a cross-sectional descriptive study which was quantitative were employed and a total of 303 study participants were randomly selected from health workers based on their population size proportionally in selected 5 regional hospitals of city administration using Selfadministered questionnaires. Results: The findings provide strong empirical support for all of the main constructs mentioned in the research model, which posits five direct determinants of intention to use EMR-SmartCare software and another two direct determinants of actual Use Behavior as follow: Performance Expectancy(PE), Effort Expectancy(EE), Social Influence (SI), Computer Attitude(CA), Personal Innovativeness in IT(PIIT) as determinant of Behavioral Intention and; Facilitating Conditions(FC) and Behavioral Intention(BI) as determinants of Actual Usage Behavior(AUB). These results maintain enough explanatory power R2 =.702 (Adjusted R squared=.333) to help explain the intentions and actual use behavior of health workers in adopting EMR- SmartCare software. Conclusion: These research findings indicate that the variables in the proposed research model significantly and positively impact the behavioral intention and actual use behavior to adopt EMR-SmartCare software. Among these, attitude towards computers has the most significant positive impact on adoption intentions. Therefore this study suggests that in order to enhance the intention to adopt and use EMR-SmartCare software, hospitals should strengthen independent impact variables, including Attitude towards Computers, Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions and Personal Innovativeness in IT. In view of the fact that the achieved conceptual framework considers the particular characteristics of the health workers, contributions and implications of this study are significant both at the theoretical level as well as the practical level. This study not only provided some interesting findings and suggestions for practice but also produced a paradigm for scholars who are interested in the behavior of technology adoption for health care sectors.