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No. 18 page 343
If one observes a small P value, there is a good chance that the next study will produce a P value at least as small for the same hypothesis.
This is wrong because P value depend on the study size and assumption of the study. The studies are independent then P value will not be related.
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I work at Bueng Kan Hospital in the deputy director position. In the interesting of my work is about the surveillance system of antimicrobial resistance (AMR) as in the other position, head of infection control. I use statistical analysis in my previous study about descriptive study of AMR microbes epidemiology in the hospital. The other study is a historical control study about intervention (thrombolytic) in acute myocardial infarction patients.
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From the quantitative survey was founded that most of responder not use the bednets to preventing mosquitos. The explore study by qualitative research will be the answer of the question. The method is random the non-using bednets subject to interview the cause of not using bednets. The common method is the focus group. The answer will collect and report in the result part of paper. This paper can be used to plan the next solution for preventing mosquitos other than bednets.
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The usefulness of new technology can be compared to the old technology by scaling. The measurement may be use survey research to outcome of each technology. The measurement tools must test the reliability. In the obvious different of new and old technology may be compared only parameter in each technology.
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External variables that may influence to perceived ease of use or perceived usefulness for new technology are
1. User interface and user experience, this is the external factors that users can touch them and these are the factors that technology will be selected.
2. Processing speed in the application, if apps are very complex this may result in slowly response. User may not select the slower response application.
3. Output of the technology that should be responded to the requirement of users.
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The outcome of the intervention can be divided in 3 types:
1. Efficacy is the outcome of drugs.
2. Effectiveness is the outcomes of intervention.
3. Efficiency is the effectiveness that compare with intervention cost.
Efficiency = Effectiveness/Cost
In some definition Efficiency = Effectiveness/Cost x Patient experience
This definition is concerned about patient feelings of hospital services. -
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Maternal mortality rate
Definition
The maternal mortality definition by WHO is maternal death as the death of a pregnant person due to complications related to pregnancy, underlying conditions worsened by the pregnancy or management of these conditions. This can occur either while the person is pregnant or within six weeks of resolution of the pregnancy.
The maternal mortality in MOPH of Thialand is defined by all cause pregnancy mortallity that include both direct and indirect cause of death.
The maternal mortality rate is the maternal mortality in the period time usuallly used per year.
How to calculate?
The numerator is sum of cases of maternal death, The dominator is sum of pregnancy case in period time.
Main usefulness.
The main usefulness of this vital statistics is to compare the incidence of maternal mortality to show the healthcare services. When the incidence was increased, the cause of problem must be explored and the intervention must be provided to reduce the maternal mortality rate.
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The antimicrobial resistance (AMR) strain of bacteria is the major problem in the nosocomial infection, this cause the adverse outcome of the hospitalization patients such as prolonged LOS or increase mortality. The one of the invervention of the AMR nosocomial infection is the effective AMR surveillance in the hospital. The AMR surveillance can stop the spreading of the AMR bacteria in the hospital and this will reduce the NI rate in the hospital.
The features of the effective AMR surveillance system
1. real time monitoring
2. can be identified the location that occur AMR culture positive, ward and bed.
3. multi-level infection control to eliminated the AMR prevalence.The model is proper to evaluated the cost-utility of the AMR surveillance system.
Costs
1. Antibiotics cost (AMR bacteria must use high cost antibiotic for treatment)
2. ICU admission cost (Longer admission)
3. Ventilator time cost (Longer ventilator used time)Perspectives
1. Societal: Time spent by family members
2. Patients: Can not work in the admission period
3. Payers: Medical cost includes labor, equipment, capital
4. Healthcare providerReference
1. Ferri M, Ranucci E, Romagnoli P, Giaccone V. Antimicrobial resistance: A global emerging threat to public health systems. Crit Rev Food Sci Nutr. 2017 Sep 2;57(13):2857-2876. doi: 10.1080/10408398.2015.1077192. PMID: 26464037.
2. Fernando SA, Gray TJ, Gottlieb T. Healthcare-acquired infections: prevention strategies. Intern Med J. 2017 Dec;47(12):1341-1351. doi: 10.1111/imj.13642. PMID: 29224205. -
Acinetobacter baumannii is the gram negative bacteria that is the major serious MDR bacterial infection in ICU patients. By the SIR model I find the R0 from one study in Austraria (Doan TN, Kong DCM, Marshall C, Kirkpatrick CMJ, McBryde ES (2015) Characterising the Transmission Dynamics of Acinetobacter baumannii in Intensive Care Units Using Hidden Markov Models. PLOS ONE 10(7): e0132037. https://doi.org/10.1371/journal.pone.0132037) that A. baumannii has R0 equal to 1.5 when transmitted by cross transmission. And the every time of treatment in the overall course of antibiotics is 14 days. then I can created the model from R0 (1.5) , gramma (1/14) , beta (1.5/14) then I create the R code for plot SIR model graph of A. baumannii infection in ICU.
SIR.dyn <- function(t,var,par) {
S <- var[1]
I <- var[2]
R <- var[3]
N <- S+I+R
beta <- par[1]
gamma <- par[2]
dS <- -beta*S*I/N
dI <- beta*S*I/N – gamma*I
dR <- gamma*I
list(c(dS,dI,dR))}beta <- 1.5/14
gamma <- 1/14
SIR.par <- c(beta,gamma)
SIR.init <- c(14,1,0)
SIR.t <- seq(0,60,by=1)SIR.sol <- lsoda(SIR.init,SIR.t,SIR.dyn,SIR.par)
TIME <- SIR.sol[,1]
S <- SIR.sol[,2]
I <- SIR.sol[,3]
R <- SIR.sol[,4]
N <- S + I + Rplot(0, 0, type=’n’, xlim=c(0,60),ylim=c(0,20), xlab=’time’, ylab=’infecteds’)
points(TIME,S,type=’l’,col=’blue’,lwd=3)
points(TIME,I,type=’l’,col=’red’,lwd=3)
points(TIME,R,type=’l’,col=’green’,lwd=3)
legend(10,20,legend=c(“Susceptible”,”Infected”,”Recovered”),col=c(‘blue’,’red’,’green’),lty=rep(1,3))In the study that I citation, this paper use Hidden Markov models (HMMs) that can solve the problem of the undetected colonization patients by the imputation. The nosocomial infection is the complex problem and there are many risk factor the can influenced the infection such as host (underlying disease), LOS, previously antibiotic used, Colonization of A. baumannii in the environment. The result of this study is like to my observation epidemiology of A. baumannii in my hospital. the susceptible cases are dynamic due to the new case that was admitted to the hospital and some infected patient was died due to A. baumannii infection (the mortality rate is 30-60%). The SIR model of R-code may use loop for achieve the true model with the dynamic parameters.
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Topic : Evaluating the effectiveness of the MDR surveillance system in Buengkan hospital
Rationale : Multidrug-resistant bacteria (MDR) are major causes of nosocomial infections and are associated with high morbidity, and mortality. The nosocomial infection surveillance system is the intervention to reduce the MDR problem. In Buengkan hospital has the real time MDR surveillance system and I want to determine the effectiveness of this surveillance system by using mathematical modeling.
Research question: How much of an effect of the MDR surveillance system in the reduction of the incidence rate of MDR bacteria in hospital?
Reference
Karl G. Kristinsson, Mathematical Models as Tools for Evaluating the Effectiveness of Interventions: A Comment on Levin, Clinical Infectious Diseases, Volume 33, Issue Supplement_3, September 2001, Pages S174–S179, https://doi.org/10.1086/321845
Tahir H, López-Cortés LE, Kola A, Yahav D, Karch A, Xia H, et al. (2021) Relevance of intra-hospital patient movements for the spread of healthcare-associated infections within hospitals – a mathematical modeling study. PLoS Comput Biol 17(2): e1008600. https://doi.org/10.1371/journal.pcbi.1008600
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1. The suicidal rate was increasing in that period, but suicidal rate in men was about 3 times compare to women. The author want to know what is the factors that involve to suicidal rate.
2. Alcohol consumption is the potential factor for suicide. In this paper 20 year alcohol consumption has significant more than 15 year. Alcohol may be cause direct and indirect effect to suicide and men population have higher rate of alcohol consumption than female that can explain why male suicidal rate was higher than the other.
3. The north and northeast regions had highest prevalence of suicide that accordingly to some factor like a alcohol consumption and this is the relation between spatial data and potential factor to suicidal rate.
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1. The difficulty to collect location data is the possible cause of why epidemiological research not use the geographical data. If the collection of geographical data is easily than in the past, the epidemiological data may be one of the factor that include to analysis in the research papers.
2. Some infectious disease occur in some place such as melioidosis that frequently occur in north-east region in Thailand. If patients in this region came with the problem such as arthritis, liver or splenic abscess, melioidosis is the considerable differential diagnosis of that patients.
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Thank you for your presentation
1. The barriers of the tracking system by smartphone
-Absent of smartphone in rural area
-Internet data access cost
-Unfamiliar of the application in rural peoples
-Data privacy concern2. I think data tracking system was useful in the easily spreading infectious diseases such as new emerging, re-emerging disease because this will help for the disease control.
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Thank you for your presentation
1. I think the previous positive outcome in the other place of the health program is the important factor (external evaluation).
2. In my work place has moderate use of information. I think the information not easy to understand especially for decision supportive information system or for strategic designing then the training to use information is important for use the available information.
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Thank you for your presentation.
1. In my opinion, the different between animal and human surveillance system are the reporting system, in human disease surveillance have many established system but the other has few surveillance system.
2. Animal and human disease was in the one ecosystem then the animal surveillance system that can pick up the emerging disease in the animal, this system can prevent disease in human such as avian Flu.
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Thank you for your presentation.
1. In my opinion, In current situation the EMR in Thailand may not be use Blockchain technology because this technology must have more space to collect data and strong CPU for processing and EMR collect many data then the cost of this technology may be higher than the hospital can pay for them.
2. Subtype of healthcare IT system that this paper not mention are
-Surveillance system
-PACS
-LIS -
Thank you for your presentation.
1. In my opinion, In current situation the EMR in Thailand may not be use Blockchain technology because this technology must have more space to collect data and strong CPU for processing and EMR was collect many data then the cost of this technology may be higher than the hospital can pay for them.
2. Subtype of healthcare IT system that this paper not mention are
-Surveillance system
-PACS
-LIS -
Thank you for your presentation.
1. In my opinion, WHO was the key success factor of EWARS because WHO is the organization that have strong power for any operation.
2. The report both event-based or indicator-based have many process that required the verification by the officer, this process take varying of time then the timeliness may be affected. If these process can automation by machine or AI, the timeliness can be improved.
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My wrap-up https://prnt.sc/13zjn9b
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Summary link https://prnt.sc/130ug3l
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I think the health informatician was less than information technologist or programmer. In overall information technologist or programmer take all position in system development then the product may not proper for the users. In GEEKS program is the good program for develop the health informaticians in Thailand. The health informatician can see in overall of the system and have the creativity to develop better or new information technology system for health system. Health informatics workforce is the two domain of the six building box of the WHO health system model. In Thailand which low medical resource compare to workload or maldistribution of resource, the health informatics workforce is the key success for better health outcome. The problem not only the number of health informaticians but the training programs are limited.
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I think data sharing is the new normal for today. Now we use many type of data sharing in COVID control such as Thaichana application. The benefit of data sharing in public health are
1. Medical history of patient in anywhere that they visit for more accuracy of diagnosis and treatment.
2. Decrease same investigation in the other health care, then investigation cost was decreased.
3. If data sharing is real time the great of surveillance system will develop in data sharing system.
4. The rare diseases can be collected to study and many new knowledge that limited by sample size will occur.
5. Strategic plan in health will go to right direction from information from big data.But the privacy, confidentiality must be concerned then policy and security was established before data sharing.
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This problem of overcharging medical service in US is due to capitalism health system. The capitalism health system will encourage for over-investigation and over-treatment then overall price is very highest. Some country use welfare health system such as Germany in this health system use higher tax rate to funding the system, health purchaser will set standard price. The universal coverage system use tax for funding, the purchaser have many logic to pay for healthcare provider such as per capita, central reimbursement, fee schedule, UC-AE. The overall medical charge in UC will low than the above two system because of all citizens that not have other health insurance are cover by UC then in the private health sector can not set price much as capitalism health system.
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I agree with the article. The internet bandwidth not true bandwidth but it’s the shared bandwidth then in the busy period the internet speed will slower than 30/10 Mbps. I think about users will not satisfy with this speed. The distribution of hotspot is one barrier to access the internet. Then the goal of this project may not success form many problems.
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I use EMR for 16 years ago by HOSXP. EMR is very useful in many aspect. I write my first study by EMR (MySQL query result and put data to SPSS for analysis) and I use the result of that study to compare outcome of patients in 10 years later. This is the my benefit experience of EMR.
The advantages of EMR
– Easy to review medical history.
– Laboratory data compare.
– Drug alert
Drug interaction
Drug allergy
Dosage
Disease specific alert
– Laboratory critical value alert
– Availability for 24 hours
– Data interoperability for transfer referral dataThe disadvantage
– Wrong data input correct by machine data input such as blood pressure data input, LIS.
– Can not access if server down both master or replication server. Correct by IT emergency plan and server maintenance.
– Network speed correct by intranet network design by layer3 core switch and use only gigabit switch, gigabit media converter.
– Heavy query may slow down speed to access EMR corrected by query in replication server instate of master server
– Cyber security.
– Workload to input in IPD situation. I think this can correct by new HIS that support tablet for input data. -
Big health data challenges
1. Missing data
1.1 Assign must input data in HIS (don’t blank)
1.2 Mapping to correct data field in many HIS have different filed data name that is same data filed.
1.3 Data type correction, in some type of data such as date time is very difficult to use together especially in Thai date.
1.4 Statistical correction such as data imputation.2. Selection bias
2.1 Use as much as data from multiple site to analysis.
2.2 Data pre-analysis for evaluate quality of data and distribution of data.3. Data analysis and training
Machine learning method– multiple time for training and compare the result.
– try different multiple data source to compare result if result very different may due to missing data, selection bias
– compare to statistical methodStatistical method
– clean data before analysis
– blind in all process as possible.4. Interpretation and applicability of result
Post analysis study for the result from big data analysis to evaluate result.5. Privacy
Inform consent at point of service that include data analysis. -
Corruption is the major problem in many organization both public and private sector. The definition of corruption is unclear in some situation. I think the corruption can not be eradicated but can be taper. My opinion in the four recommendation in the article.
1.Convene key stakeholders in the health system to seek agreement on the scale and nature of corruption in each health system. : This recommend can not success in corruption society because they not accept in the agreement that break their benefit.
2. Prioritize action. I think this process vary by experience from stakeholders, some view was the health system, some view was only in some process or unit. Key of this process is what is the true priority.
3. Take a holistic view. Same as the second recommendation, It’s difficulty to find the best holistic view.
4. Sets out what it can offer. I think this recommend not useful for inhibit corruption. Now I think society know that corruption was in every organization from small to large organization then the publication may not useful.
I think the corruption can be tapering by surveillance system. The data that record in many database can be analyze to pick up the corruption such as in the buying system of the government, the specific buying method can be used under 500,000 baht if the buying statement nearly 500,000 baht this is the clue of corruption. By this condition in database system can create corruption surveillance system in health system. The condition of clue of the corruption is the new solution to detection and can inhibit the corruption.
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Now the policy of 30 baht treatment in anywhere of the MOPH was established. In my regional of health will be enabled this policy in 1st March 2021. This policy must have three major components.
1. EMR of every HIS data from hospital in regional health.
2. The new claim system for extra condition such as walk in from other provincial patient that not emergency (in term not UC-AE in E-claim system of NHSO).
3. The new referral system that can improvement by pooling of HIS data.The above topics look difficulty and must take a long time to create them but it’s the policy from regional health director that must finish in time. In the last 4 years ago our IT team in regional health was study the essential tools and method for this policy then our team can finish this work. Now I can watch the visiting history patient, drug, lab in hospital in regional health from anywhere and anytime. The method was divided in many part and integrated together. The backend was create by python script that collect data to cloud system (GDCC). The data set was generated to one common dataset. The HIS that can use this system are HOSXP, HOSXP PCU, JHCIS, and HOMC. The database was collected by document database system not relational database system. The API that connect to the DB is Loopback API (Node.js). The frontend was created by React.js that the one of best language for PWA. The login system was identified by Line UDID.
The claim system have new condition for walk in patient not emergency that have extra funding from NHSO. From this system was used three component of six building box, service delivery, IT, and Financing and I this that this system can improve health outcome, patient satisfaction, and equity of health service.
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I design replication server for disaster recovery plan. My hospital has two opposite sites that has about 800 meters distance, My hospital database server was run on mysql on linux server. I have main server and replication server. The data backup transfer by 1 Gb fiber optic. I can recovery server when disaster was occurred. And I plan to backup database to cloud server in the next year.
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Benefit for patient
1. No loss of benefit in available medical history data for treatment
2. Medical reconciliation can continue.
3. No delayed treatment due to unavailable data.
Benefit for hospital
1. Available medical history data for decision making on treatment
2. No repeated record data to server again when system was available
3. No loss of benefit for medical expense claim. -
In my experience I have problem in my official hospital website in confidentiality. Website was hacked because old php framework that have low security. Some data in website was destroyed but I website was backup regularly then I restore data and replace new php framework to prevent from attacker. I replaced firewall and update antivirus in firewall to prevent from data hacking.
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Thank you for your nice presentation. The information of stakeholder that you show is very completely. AMR surveillance system is my interest project. The problem of AMR surveillance system in my experience not only RDU of antibiotic but the major problem is the timeliness to control AMR spreading. If you can improve timeliness of notify ICN or ICWN in hospital to control infection spreading you can stop AMR in the hospital by your surveillance system.
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Thank you for your nice presentation. I think the major problem of scrub typhus surveillance system is how to correct and fast diagnosis. The clinical symptom of scrub typhus is like other tropical infectious disease like a dengue infection. The laboratory diagnosis for confirmed diagnosis such as PCR of IFA is expensive for investigation and not available for all region in country. If you can correct these problems you can improve your surveillance system.
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Thank you for your nice presentation. I think the difficult problem of Dengue surveillance system is control of HI, CI in time. One of the reasons of delay control is the confirmed diagnosis process if you can shortening this process by your surveillance system it will improve overall of the system.
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Thank you for the presentation. I interest in your project until go to the budget slide. Natural history of COPD patient will decline in lung function (FEV1) in every year but the slow of FEV1, endurance (6 minute walk test) and quality of life (QoL) is the target of COPD management. The only one factor that can improve FEV1 is stop smoking. No medicine that can improve FEV1 they can decrease COPD exacerbation. If wearable monitoring can change FEV1 or decrease COPD exacerbation this is very useful. In my opinion If the vest can send GIS to cloud then I will know the place that can introduce COPD exacerbation and the physician will use this data to suggest the patient to avoid that place.
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In my knowledge on internal medicine training VAD is the device use in end stage congestive heart failure patient that failure medication or who want to cardiac transplantation then this device is critically use like cardiac pacemaker and ICD. This device work by synchronous bi-ventricular trigger for good cardiac output from heart to systemic circulation. In overall current medical devices not allow to control device via remote control. I think the SensorART is one way communication for collect data from sensor to the physician for adjust proper setting to VAD. This technique is very useful because the physician can setting before patient visit and can call patient for setting device before appointment if there are cardiac problem in patient.
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Thank you very much for your comment when I carefully read this paper again, I agree with your comment that the model of this study is SIS model. The major cause of A. baumannii is the colonization but the colonization divided into in patients and in the environment. In previous surveillance study of the environment in ICU I found that A. baumannii can colonization in bed, floor, and the medical equipment and with the conventional cleansing can not eliminate A baummannii from the environment.
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In my working hospital, The MDR bacteria frequently transfer from the ICU patients to the other ward because the ICU is the high prevalence of MDR bacteria and the Enterobacteriacae such as E. coli can colonization into the GI tract of the patients. If the destination not working properly in the infection control protocol, the medical staff will transfer the MDR bacteria to the other patients.
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