Week 5 Appendix
Stephen Synchronicity
2018-10-07
rpdfclown::extractPDF("C:\\Users\\Administrator\\Documents\\Northeastern\\Fall 2018\\HINF5102 - Data Management in Healthcare\\Week 5\\An overview of patient acceptance of Health Information Technology.pdf",
c("Highlight"))[1] %>% as.character %>% gsub("\\:(?!\\s)", "\\: ", ., perl = T) %>%
gsub("\\<[U]\\+[A-Z0-9]{4}\\>", "", .) %>% gsub("\\n", " ", .) %>% gsub("\\s(?=Page\\.\\d)",
"\\\n", ., perl = T) %>% str_split(pattern = "\\n")## [[1]]
## [1] "Page.1: An overview of patient acceptance of Health Information Technology in developing countries: a review and conceptual model "
## [2] "Page.2: HIT is a term that describes the management and exchange of health information between healthcare consumers and providers using both computers and mobile devices for decision making. HIT when implemented and used properly has the potential to improve healthcare quality, efficiency, effectiveness, reduce or prevent medical errors, reduce healthcare costs, provide up-to-date information to both providers and consumers, early detection of management of disease, and reduce storage cost. "
## [3] "Page.2: CDSS is a system that assists medical practitioners with decision making tasks like diagnosis, analysis of patient data, medication, prediction, reminder, etc. This improves the physician's performance and patient outcomes, increases efficiency, and reduces healthcare costs "
## [4] "Page.3: TAM focuses on factors that determine the users\220 behavioral intentions towards accepting a new technology. The model shows that certain factors influence the decision of users when they are presented with a new technology on how and why they will use it. The factors are: perceived usefulness and perceived ease of use. Perceived usefulness is defined by Davis [17] as \215the degree to which a person believes that using a particular system would enhance his or her job performance<U+008E>; while perceived ease of use is defined as \215the degree to which a person believes that using a particular system would be free of effort<U+008E> "
## [5] "Page.3: Attitude toward using is defined as \215the degree of evaluative effect that an individual associates with using the target system in his or her job<U+008E> "
## [6] "Page.3: Perceived Usefulness, Perceived Ease of Use and User Acceptance of Information Technology conducted by Davis [17]. The research developed and validated new scales for perceived usefulness and perceived ease of use, which were hypothesized to be fundamental determinants of user acceptance. "
## [7] "Page.3: It was found based on both studies that usefulness had a significantly greater correlation with usage behavior than did ease of use. Additionally, regression analyses suggest that perceived ease of use may be causal antecedent to perceived usefulness, as opposed to a parallel and direct determinant of system use. "
## [8] "Page.5: On the other hand, the leading researchers in TAM related studies developed TAM3 by considering interventions such as user participation, management support, training, etc., as a possible candidate that can influence the acceptance and use of IT through the determinants of perceived usefulness and perceived ease of use. The interventions are grouped into pre-implementation and post-implementation interventions. The pre-implementation intervention include design characteristics, user participation, management support and incentive alignment that lead to the realization of the system, while the post-implementation intervention include phases that come after putting the system into use these are: training; organizational support; and peer support "
## [9] "Page.6: Generally, the common features of the above projects include using sensor technology, sending alert to caregiver or medical personnel. They also have distinctive features like CodeBlue has GPS integrated into the system for tracking the actual location of patients as well as doctors [30] "
## [10] "Page.6: The patient will have a bandage wrap around the area affected with ulcer, the bandage has built-in sensors that continuously monitor biomedical data concerning the ulcer like: bacteria flora, skin temperature, moisture level, and blood pressure. "
## [11] "Page.6: Jog Falls [39], is a diabetes management system using sensor devices (for collecting physiological and activity data) that monitors patient\220s physical activities, food intake, sets some goals and monitor progress towards these goals. "
## [12] "Page.6: Clinical Decision Support Systems (CDSS). Some of the works related to CDSS include a CDSS which uses data mining techniques to build cooperative knowledge bases from domain experts\220 knowledge bases, clinical databases, and "
## [13] "Page.7: The complete architecture of the system consists of wearable medical sensor module, data gathering module, PDA, remote server with CDSS and EMR capability, and web enabled remote terminal for accessing services provided by web server. The remote server after processing the data then call CDSS for analysis of the data and finally the EMR will record the data against the patient\220s profile. After analyzing the data by the CDSS a feedback is sent to the doctor for approval, and then sent to the PDA after approval. The CDSS software analyses the patient\220s physiological data like ECG, blood pressure, temperature, etc. for possible sign of abnormality. The software can forecast the health status based on the received data and also can make decision based on the health situation. A combination of model-driven and knowledge driven decision support systems were used. The model-driven makes decision based on the statistical model of the patient\220s data, while the knowledge driven use facts, rules, procedures, etc. to make decision. "
## [14] "Page.8: Another group of researchers conducted an online survey of 1,421 respondents of the Geisinger Health System, to valuate patients\220 values and perceptions regarding web-based access to their record. One-on-one interview with 10 clinicians and focus groups with 25 patients were also used to supplement the survey. The result of the study shows a positive patient\220s attitudes towards the use of Web messaging and online access to their EHR as dominant. Also patients described their medical information as complete and accurate when using the system. Some patients expressed their concern about the confidentiality and privacy of their information. On the other hand, clinicians prefer other types of communication like letters than electronic communication [ "
## [15] "Page.10: A total of 450 physicians were randomly selected and given questionnaire out of which 335 were returned and 309 were used. The hypotheses were tested using SEM and the result shows that Physicians\220 perceived threat to professional autonomy lowers the intention to use CDSS; Physicians involvement in the planning, design and implementation increases their intention to use CDSS; Physicians belief that the new CDSS will improve his/her job performance increases their intention to use CDSS [9]. "
## [16] "Page.13: Therefore we combine TAM variables, perceived output quality fromTAM2 and two additional constructs, perceived cost-effectiveness and trust, to form the new model. The new relationships postulated are trust influences perceived usefulness, perceived output quality influences perceived cost-effectiveness, perceived cost-effectiveness influences attitude toward using, and perceived cost-effectiveness influences intention to use the system. Trust to perceived usefulness: we envisage that when the system is perceived as trustworthy and the patients have confidence in the system then they will consider it as useful. Perceived output quality to perceived cost-effectiveness: when the quality of the system is high, it is expected that the cost will reduce, in other words the system will be cost-effective. For instance, when the hospitalization rate is reduced, the cost related to the care will also be reduced. Perceived cost-effectiveness to attitude toward using: if the users believe that the system reduces cost of their care, their attitude toward using it will be positive. Perceived cost-effectiveness to intention to use: the intention of the patients to use the system will be high if they realize that using it will reduce the cost related to their care. "
## [17] "Page.14: When the factors that lead to low adoption of HIT are known, they can be tackled before implementation which will enhance the rate of user adoption. Therefore, we proposed an extended TAM for assessing factors that contribute to HIT acceptance by patients in developing countries. "library(tabulizer)
pdftable3 <- tabulizer::extract_areas(file = "C:\\Users\\Administrator\\Documents\\Northeastern\\Fall 2018\\HINF5102 - Data Management in Healthcare\\Week 5\\An overview of patient acceptance of Health Information Technology.pdf",
pages = 12)
mvec <- pdftable[[1]][, 1] %>% sapply(nchar) %>% {
. < 4
} %>% which
pdftable[[1]][{
mvec - 1
}, 2] <- mvec %>% sapply(function(x) {
paste(pdftable[[1]][{
x - 1
}, 2], pdftable[[1]][x, 2], collapse = "\\s")
})
pdftable[[1]][-mvec, ]
pdftable2[[1]] %>% knit_print.data.frame()
tables <- list(pdftable[[1]][-mvec, ], rbind(pdftable2[[1]], pdftable3[[1]]))
tables <- lapply(tables, as.data.frame)
names(tables[[1]]) <- tables[[1]][1, , drop = T] %>% unlist
tables[[1]] <- tables[[1]][-1, ]
names(tables[[2]]) <- tables[[2]][1:3, ] %>% lapply(function(l) paste0(l, collapse = ""))
tables[[2]] <- tables[[2]][-c(1:3), ]
row.names(tables[[2]]) <- 1:nrow(tables[[2]])
ldf <- lapply(list(1:5, 6:10, 11:13, 14:17, 18:21, 22:25, 26:29, 30:34, 35:37, 38:43,
47:53, 54:56, 57:59, 60:62, 63:68), tdata = tables[[2]], function(rows, tdata) {
tdata[rows, ] %>% lapply(paste0, collapse = " ")
})
ldf <- data.frame(matrix(unlist(ldf), ncol = 7, byrow = T))
names(ldf) <- names(tables[[2]])
tables[[2]] <- ldf
save(tables, file = "Tables.Rdata")pdftable3 <- tabulizer::extract_tables(file = "C:\\Users\\Administrator\\Documents\\Northeastern\\Fall 2018\\HINF5102 - Data Management in Healthcare\\Week 5\\EHR-CIA-Blueprint-Report.pdf",
pages = 13:17)
CIMtable <- do.call("rbind", pdftable3) %>% as.data.frame()
names(CIMtable) <- CIMtable[1, , drop = T] %>% unlist
CIMtable <- CIMtable[-1, ]
entries <- CIMtable[, 1] %>% as.character %>% sapply(function(x) {
str_detect(x, "\\w{2,}")
}) %>% rle
items <- inverse.rle(entries) %>% which
lengths <- diff(items) - 1
CIM2 <- data.frame(matrix(rep(NA, 55 * 2), ncol = 2))
for (i in seq_along(items)) {
CIM2[i, 1] <- CIMtable$`Entity Name`[items[i]] %>% as.character()
CIM2[i, 2] <- CIMtable[seq(items[i], {
items[i] + (lengths[i])
}, 1), 2, drop = T] %>% paste0(collapse = " ")
}
CIM2[nrow(CIM2), 2] <- CIMtable[nrow(CIMtable), 2] %>% as.character()
names(CIM2) <- names(CIMtable)
save(CIM2, file = "CIM.RData")- Ahlan, Abd Rahman & Ahmad, Barroon. (2015). An Overview of Patient Acceptance of Health Information Technology in Developing Countries: a Review and Conceptual Model. International Journal of Information Systems and Project Management. 3. 29-48. 10.12821/ijispm030102.
- eHealth Ontario. EHR Conceptual Information Architecture Blueprint Report. (2013). Retrieved 2018-10-07 from https://www.ehealthontario.on.ca/architecture/education/courses/information-architecture/downloads/EHR-CIA-Blueprint-Report.pdf
load("Tables.Rdata")
load("CIM.RData")
tags$h5("TAM Constructs & Explanations", HTML("<sup>[1]</sup>"))TAM Constructs & Explanations [1]
tables[[1]]tags$h5("Studies Considered and their Characteristics", HTML("<sup>[1]</sup>"))Studies Considered and their Characteristics [1]
tables[[2]]tags$h5("Conceptual Information Model Entity Table", HTML("<sup>[2]</sup>"))Conceptual Information Model Entity Table [2]
CIM2
CIM Diagram