Trends and the Burden on Hospital Services from Diseases associated with Outpatient Clinic Visits in Thai Southern Elderly Population visiting Songklanagarind Hospital
Thailand (IFMSA-Thailand) - Faculty of Medicine, Songkla University, Songkhla
Public Health
Family and preventive medicine
Assoc. Prof. Dr. Pitchaya Phakthongsuk
Dr. Thammasin Ingviya and Dr. Orapan Fumaneeshoat
English or Thai
4 weeks
Cities/Months Jan Feb Mar Apr May Jun Jul Augt Sep Oct Nov Dec
No Yes Yes No Yes No No No No No No No
Type of Research Project
- Clinical Project without Laboratory work
What is the background of the project?
Like other countries, Thailand is also facing the ageing population. Mentioned in WHO report in 2015, ageing should not be thought as the sign of frailling or illnesses. Healthy ageing defined as live long and qualitatively is possible with enough health and environmental policies, preparing for elderly. Many studies in Thailand look at the changes in ageing social indicators such as the active ageing index (AAI), liver perspectives of the elderly and household environment readiness for elderly. Few studies touched upon the prevalence of diseases by ageing, however, none of them investigated on the burden of diseases related to the hospital visits of the elderly on the health and hospital services. In addition, as the biggest tertiary hospital in southern Thailand with the longest history of electronic medical records, it is a great chance to use the existing data for an estimation of burden of the ageing related diseases on hospital services and to prioritize our hospital in specific field.
What is the aim of the project?
The final goal of the research is to construct a foundation research to prioritize and measure burden of diseases-related to ageing on hospital services from the electronic medical records of patients visiting outpatient clinic in our tertiary hospital. The final goal is to use the data to prioritize the possible research and/or innovations to encourage the healthy ageing programs. Main objective: To quantify the burden of hospital services from elderly population visiting out-patient clinic in Songklanagarind Hospital Specific objective(s): - To prioritize and estimate the trend and seasonal variations and identify the top 10 diseases for pre-elderly (45-60), Elderly (60-75) and Extreme Elderly (More than 75) from year 2005 to year 2017 - To quantify the burden of the systems using (Prevalence, Visits)/providers ratio (Doctors, Nurses) from year 2005 to year 2017 - To forecast the prevalence of the top 10 diseases in the next 10 years
What techniques and methods are used?
Study design Time Series study design Target population The target population is the Thai population in middle age or pre-elderly stage (aged 45-65 years old), elderly (aged 65-75 years old) and extreme elderly (aged 75 years old and above). Study population and source population The Thai southern population in middle age or pre-elderly stage (aged 45-65 years old), elderly (aged 65-75 years old) and extreme elderly (aged 75 years old and above), visiting outpatient clinic of Songklanagarind Hospital during year 2005-2017. Study procedure(s)/stage(s) This study will use only the secondary data, recorded in HIS program from Songklanagarind Hospital. Procedures 1. Data extraction from the HIS and NHSO will be extracted using filter by years of birth and the year of outpatients clinic visit. Using the filter equations below to include only patients aged more than 45 years in the year of outpatients clinic visit. Year of visit – Years of birth > 45 2. Needed Data from HIS program will be extracted. From the HIS Program, the needed data will be extracted in the form of text format: - Date of the outpatient clinic - Hospital Number of the patients to check for duplicated recorded and dealing with multiple visits - The Doctor Note - Patient Data Vital Sign Record: Blood Pressure, Temperature, Pulse rate, Respiratory Rate, Height, Weight - The information on prescribed medication to calculate cost - The ICD10 Diagnosis - The laboratory data including UA, CBC, BUN, Cr, Lipid Profile, Liver Function Test. - The total charge for each visits. 3. Data from the text-based format will be convert to excel format using grep and text mining format using program R version 3.5.1. 4. Cleansing and Exploring Data: First, both data will be check for patterns, missing data, abnormal patterns and the inclusion criteria by using the equation mentioned in 1 5. The cleansed data will be then be used for further analysis 6. For the analysis of diseases prevalence rate or count per years, the patients coming for multiple visits will be count as one. All other visits will be removed to avoid overestimating of the diseases. Study instrument(s) and outcome measurement(s) The diseases of patients will be first explored using ICD10 for the diagnosis. Then, it will be rechecked with the data from text and laboratory data. For example, the data on lipid profile will be use to recheck the patients without the diagnosis of hyperlipidemia. The text recorded of the doctor implication will be recheck with ICD10. Data collection Methodology All of the data will be cleansed and record in the server of Songklanagarind Hospital using port 8787. Statistical analysis Descriptive Analysis: The continuous data such as age and ratios will be explored graphically using boxplot and/or histogram or the cumulative plot (Dr.Virasak). The data will be presented numerically using mean (SD) or median (IQR) where it is applicable. The exploratory comparison will be done using t-test or Wilcoxon’s test. For the count data, the time-series plot will be used to explore the trend and seasonality of the plot. Inferential analysis: The data on the diseases will be explored using ICD10 code to rank the top 10 diseases with the maximum number of visits throughout the 12 years periods (2005-2017). Then the top 10 diseases will be further analyzed with the time serie analysis. The main analysis of the trend in the numbers of the OPD visits and disease counts will be done using the time-series based on Poisson or negative binomial, followed the generalized linear method. The association between independent factors and the outcomes will be assessed the same way.The trend in ratios and the association with independent variables will be analyzed using autoregressive with moving average method.
What is the role of the student?
- The student will mainly observe
- The student will observe the practical experiments but will be highly involved in the analysis of the results
- The tasks will be done under supervision
What are the tasks expected to be accomplished by the student?
Students will have experience in cleansing and examining clinical big data and also will be learning about basic R programming and time series analysis. Student will have a chance to study clinical term and diagnosis of diseases related to aging. Basic knowledge on diseases classification (ICD10) will be taught. The process will be coached by Dr. Thammasin and his team. Students’ assignments: 1.Assisting in constructing and completing data dictionary for OPD database 2.Cleansing and Exploratory Analysis of the OPD database 3.Assisting in creating model for forecasting the trend in age-related diseases using R program.
Will there be any theoretical teaching provided (preliminary readings, lectures, courses, seminars etc)
Sessions of hand-on workshop on clinical data analysis will be provided by the Dr. Thammasin and his team.
What is expected from the student at the end of the research exchange? What will be the general outcome of the student?
- The student will prepare a presentation
- No specific outcome is expected
What skills are required of the student? Is there any special knowledge or a certain level of studies needed?
Basic knowledge in computer programming, especially R-programming, and background knowledge in biomedical science.
Are there any legal limitations in the student’s involvement
Type of students accepted
This project accepts:
- Medical students
- Pre-Medical students from the American-British system
- Students in biomedical fields
- Effects of intraoperative leak testing on postoperative leak-related outcomes after primary bariatric surgery: An analysis of the MBSAQIP database. - PubMed—NCBI. (n.d.). Retrieved October 31; 2019; from
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