In this research, we detail the development of a novel, easy-to-use system to facilitate electronic patient falls reporting within a long-term residential care facility (LTRCF) using off-the-shelf technology that can be inexpensively implemented in a wide variety of settings. We report the results of four complimentary system evaluation measures that take into consideration varied organizational stakeholders’ perspectives: 1) System-level benefits and costs, 2) System usability, via scenario-based use cases, 3) A holistic assessment of users’ physical, cognitive, and marcoergonomic (work system) challenges in using the system, and 4) User technology acceptance. We report the viability of collecting and analyzing data specific to each evaluation measure and detail the relative merits of each measure in judging whether the system is acceptable to each stakeholder. The electronic falls reporting system was successfully implemented, with 100% electronic submission rate at 3-months post-implementation period. The system-level benefits and costs approach showed that the electronic system required no initial investment costs aside from personnel costs and significant benefits accrued from user time savings. The usability analysis revealed several fixable design flaws and demonstrated the importance of scenario-based user training. The technology acceptance model showed that users perceived the reporting system to be useful and easy to use, even more so after implementation. Finally, the holistic human factors evaluation identified challenges encountered when nurses used the system as a part of their daily work, guiding further system redesign. The four-pronged evaluation framework accounted for varied stakeholder perspectives and goals and is a highly scalable framework that can be easily applied to Health IT (Information Technology) implementations in other LTRCFs.

Regular physical activity, timely medication, controlled diet, and blood glucose monitoring is crucial for any diabetic patient. Laxity on following these treatment regimens can cause severe health complexity. Moreover, A physician’s surveillance on a patient, based on the patient’s real-time progress is difficult with the existing health care system. This research aims to provide a more accurate objective data in real-time to the physicians to help both patients and providers. The data being generated is mined later to investigate interesting questions regarding diabetic care. The resultant system is a mobile healthcare monitoring system for type – 2 diabetic patients that traces patients daily progress. Although many mobile apps provide self-monitoring tools for the patient, an interactive platform for monitoring all relevant parameters of diabetes where patients and physicians both are end users is unique. The Android app is designed with 3 major modules and two submodules: 1. Carb Intake Tracker (CIT), 2. Blood Glucose Tracker (BGT), 3. Physical Activity Tracker (PAT), 4. Medicine and 5. Blood Glucose (BG) reading reminder. Since Carb is an important factor for a diabetic patient’s meals, the CIT provides a platform to record daily meals from which the patient can see the total carb intake. Through BGT, patients can record their fasting or non-fasting blood glucose reading. The PAT collects a patient’s movement data via Bluetooth from a pair of wearable insole devices, and processing the data identifies and records the current activity. The PAT can detect if the patient is active in sedentary, as well as the type of exercise done by the patient. Using BG reminder and medicine reminder, the patient can set reminders which supports the apps self-monitoring aspect. All the data collected by CIT, BGT, and PAT are stored in Microsoft Azure cloud database, an authorized physician can access the database and see graphical statistics of a patient’s diet, physical activity, and glycemic index level. The app portrays statistics of carbs taken over a period, calories burned, and Glucose level trends through graphical representation. This has two advantages: 1. Patients can improve lifestyle observing records and following reminders, 2. Physicians can prescribe actions perceiving a patient’s trends over time. This research presents unique collaborative interaction between diabetic patients and physicians to create a real time patient portal based on android APIs and wearable devices.

Picture Archiving and Communication Systems (PACS) have been traditionally constrained to the premises of the healthcare provider. This has limited the availability of these systems in many parts of the world and mandated major costs in infrastructure for those who employ them. Public cloud services could be a solution that eases the cost of ownership and provides greater flexibility for PACS implementations. This could make it possible to bring medical imaging services to places where it was previously unavailable and reduce the costs associated with these services for those who utilize them. Moving these systems to public cloud infrastructure requires that an authentication and encryption policy for communications is established within the PACS environment to mitigate the risks incurred by using the Internet for the communication of medical data. This thesis proposes a framework which can be used to create an authenticated and encrypted channel to secure the communications with a cloud-based PACS. This framework uses the Transport Layer Security (TLS) protocol and X.509 certificates to create a secured channel. An enterprise style PKI is used to provide a trust model to authorize endpoints to access the system. The validity of this framework was tested by creating a prototype cloud-based PACS with secured communications. Using this framework will provide a system based on trusted industry standards which will protect the confidentiality and integrity of medical data in transit when using a cloud-based PACS service.

Background Due to the COVID-19 pandemic, there was a significant reduction in in-person healthcare visits as a precautionary measure to minimize the risk of infection for both patients and healthcare provides. Consequently, there was a remarkable surge in the adoption of telehealth services, although mental healthcare services were already using it more frequently than primary and specialty care services before the pandemic. Despite the continued increase in telehealth services, there are differing views among healthcare providers on the efficacy of providing healthcare services remotely, leading to an opportunity to investigate this matter further. Objective The primary goal of this study was to evaluate differences in perceptions and attitudes, experience with patient interactions and overall telehealth experiences between mental health providers and non-mental health providers. Methods The proposed study collected primary data through surveying providers across the United States from November 2022-March 2023. The surveys were conducted through REDCap, and disseminated through research staff outreach and recruitment. Providers were asked to complete a 33-item survey, which took them about 30 minutes to finish and queried them about their satisfaction, utilization, and experiences with telehealth. The provider survey was disseminated to both mental health providers and non-mental health providers who use telehealth technology. Descriptive analysis was conducted to determine the characteristics of the population surveyed. Data was then dichotomized by mental health providers vs. non-mental health providers. Differences in mean responses for all variables between mental health and non-mental health providers was assessed using Mann-Whitney U (MW) tests. Results A Mann-Whitney Wilcoxon test indicated that the satisfaction with the telemedicine platform was greater for non-mental healthcare providers than mental healthcare providers (p < .05). A Mann-Whitney Wilcoxon test indicated that the reliability of the telemedicine app for facilitating health care services, that the ability to trust the telemedicine application to work, the lack of physical contact during a video visit being a problem, the video visits being a convenient form of healthcare delivery, the visits on the telehealth system are the same as in-person visits, and that continued use of telehealth services in the future was greater for mental healthcare providers than non-mental healthcare providers (p < .05). Conclusions Looking forward, we expect to see more extensive studies involving providers from various regions and the implementation of additional approaches to enhance their experiences.

No description available.

The adoption of healthcare information technology (HIT) has been advocated by various groups as critical in addressing the growing crisis in the healthcare industry. Despite the plethora of evidence on the benefits of HIT, however, the healthcare industry lags behind many other economic sectors in the adoption of information technology. A significant number of healthcare providers still keep patient information on paper. With the recent trends of reimbursement reduction and rapid technological advances, therefore, it would be critical to understand differences in structural characteristics and healthcare performance between providers that do and that do not adopt HIT. This is accomplished in this research, first by identifying organizational and contextual factors associated with the adoption of HIT in US acute care hospitals and second by examining the relationships between the adoption of HIT and two important healthcare outcomes: patient safety and quality of care. After conducting literature a review, the structure-process-outcome model and diffusion of innovations theory were used to develop a conceptual framework. Hypotheses were developed and variables were selected based on the conceptual framework. Publicly available secondary data were obtained from the American Hospital Association (AHA), the Health Information and Management Systems Society (HIMSS), and the Healthcare Cost and Utilization Project (HCUP) databases. The information technologies were grouped into three clusters: clinical, administrative, and strategic decision making ITs. After the data from the three sources were cleaned and merged, regression models were built to identify organizational and contextual factors that affect HIT adoption and to determine the effects of HIT adoption on patient safety and quality of care. Most prior studies on HIT were restricted in scope as they primarily focused on a limited number of technologies, single healthcare outcomes, individual healthcare institutions, limited geographic locations, and/or small market segments. This limits the generalizability of the findings and makes it difficult to draw definitive conclusions. The new contribution of the present study lies in the fact that it uses nationally representative latest available data and it incorporates a large number of technologies and two risk adjusted healthcare outcomes. Large size and urban location were found to be the most influential hospital characteristics that positively affect information technology adoption. However, the adoption of HIT was not found to significantly affect hospitals' performance in terms of patient safety and quality of care measures. Perhaps a remarkable finding of this study is the better quality of care performance of hospitals in the Midwest, South, and West compared to hospitals in the Northeast despite the fact that the latter reported higher HIT adoption rates. In terms of theoretical implications, this study confirms that organizational and contextual factors (structure) affect adoption of information technology (process) which in turn affects healthcare outcomes (outcome), though not consistently, validating Avedis Donabedian's structure-process-outcome model. In addition, diffusion of innovations theory links factors associated with resource abundance, access to information, and prestige with adoption of information technology. The present findings also confirm that hospitals with these attributes adopted more technologies. The methodological implication of this study is that the lack of a single common variable and uniformity of data among the data sources imply the need for standardization in data collection and preparation. In terms of policy implication, the findings in this study indicate that a significant number of hospitals are still reluctant to use clinical HIT. Thus, even though the passage of the American Recovery and Reinvestment Act (ARRA) of 2009 was a good stimulus, a more aggressive policy intervention from the government is warranted in order to direct the healthcare industry towards a better adoption of clinical HIT.

Context: Computerized clinical decision support systems (CCDSSs) give practitioners patient-specific care advice and are considered an important increment to electronic clinical documentation and order entry systems. Despite decades of research on CCDSS, results from rigorous clinical evaluations remain mixed and systems vary greatly in design and implementation. Objective: To identify factors differentiating CCDSSs that improve the process of care or patient outcomes from those that do not. Data Sources: We searched major bibliographic databases and scanned reference lists for eligible articles up to January 2010. Study selection: 162 eligible comparisons from randomized controlled trials of CCDSS to non-CCDSS care. We deemed successful those systems that improved either 50% of reported process of care outcomes or 50% of patient outcomes. We extracted system characteristics hypothesized to impact patient care and tested them for association with system effectiveness in logistic models. Results: Our primary analysis showed that CCDSSs presented in electronic health records or order entry systems were less likely to be effective than their counterparts (OR, 0.37; 95% CI, 0.17 to 0.80). Systems more likely to succeed than their counterparts provided advice for patients in addition to practitioners (OR, 2.77; 95% CI, 1.07 to 7.17), required from practitioners a reason to override advice (OR, 11.23; 95% CI, 1.98 to 63.72), or were evaluated by their developers (OR, 4.35; 95% CI, 1.66 to 11.44). These findings remained consistent across different statistical methods, sensitivity analyses, and adjustment for other potentially important factors. Conclusions: We identified several factors that may partially explain why some systems succeed and others fail. Primary studies should investigate the impact and optimal implementation of advice provided to patients and practitioners and advice that requires reasons to be overridden. Researchers should also address the reasons for failure of advice presented within charting and order entry systems. / Master of Science (MSc)

Yes / This study explored healthcare quality issues affecting the reporting and investigation levels of digital incident reporting systems. Methods: A total of 38 health information technology-related incident reports (free-text narratives) were collected from one of Sweden’s national incident reporting repositories. The incidents were analysed using an existing framework, i.e., the Health Information Technology Classification System, to identify the types of issues and consequences. The framework was applied in two fields, ‘event description’ by the reporters and ‘manufacturer’s measures’, to assess the quality of reporting incidents by the reporters. Additionally, the contributing factors, i.e., either human or technical factors for both fields, were identified to evaluate the quality of the reported incidents. Results: Five types of issues were identified and changes made between before-and-after investigations: Machine to software- related issues (n = 8), machine to use-related issues (n = 5), software to software-related issues (n = 5), use to software- related issues (n = 4) and use to use-related issues (n = 1). Over two-thirds (n = 15) of the incidents demonstrated a change in the contributing factors after the investigation. Only four incidents were identified as altering the consequences after the investigation. Conclusion: This study shed some light on the issues of incident reporting and the gap between the reporting and investigation levels. Facilitating sufficient staff training sessions, agreeing on common terms for health information technology systems, refining the existing classifications systems, enforcing mini-root cause analysis, and ensuring unit-based local reporting and standard national reporting may help bridge the gap between reporting and investigation levels in digital incident reporting.

Little is known regarding registered nurse (RN) information management practice in long-term care (LTC) settings. This study identifies LTC RNs’ information management practice and needs, which are important for designing and implementing health information technology (HIT) in LTC settings. Methods: This descriptive qualitative study combines direct observations and semi-structured interviews, conducted at Alberta’s LTC facilities between May 2014 and August 2015. The constant comparative method of joint coding was used for data analysis. Results: Nine RNs from six nursing homes participated in the study. Based on the RNs’ existing information management system requirements, a graphic information flow model was constructed. Conclusion: This baseline study identified key components of LTC RNs’ information management system. The information flow model may assist HIT developers with future design and development of HIT solutions for LTCs, serve as a communication tool between RNs and developers to refine requirements and support further LTC HIT research. / Graduate

This research presents a framework for enhancing and analyzing time-sequenced mammographic images for detection of cancerous tissue, specifically designed to assist radiologists and physicians with the detection of breast cancer. By using computer aided diagnosis (CAD) systems as a tool to help in the detection of breast cancer in computed tomography (CT) mammography images, previous CT mammography images will enhance the interpretation of the next series of images. The first stage of this dissertation applies image subtraction to images from the same patient over time. Image types are defined as temporal subtraction, dual-energy subtraction, and Digital Database for Screening Mammography (DDSM). Image enhancement begins by applying image registration and subtraction using Matlab 2012a registration for temporal images and dual-energy subtraction for dual-energy images. DDSM images require no registration or subtraction as they are used for baseline analysis. The image data are from three different sources and all images had been annotated by radiologists for each image type using an image mask to identify malignant and benign. The second stage involved the examination of four different thresholding techniques. The amplitude thresholding method manipulates objects and backgrounds in such a way that object and background pixels have grey levels grouped into two dominant and different modes. In these cases, it was possible to extract the objects from the background using a threshold that separates the modes. The local thresholding introduced posed no restrictions on region shape or size, because it maximized edge features by thresholding local regions separately. The overall histogram analysis showed minima and maxima of the image and provided four feature types--mean, variance, skewness, and kurtosis. K-means clustering provided sequential splitting, initially performing dynamic splits. These dynamic splits were then further split into smaller, more variant regions until the regions of interest were isolated. Regional-growing methods used recursive splitting to partition the image top-down by using the average brightness of a region. Each thresholding method was applied to each of the three image types. In the final stage, the training set and test set were derived by applying the four thresholding methods on each of the three image types. This was accomplished by running Matlab 2012a grey-level, co-occurrence matrix (GLCM) and utilizing 21 target feature types, which were obtained from the Matlab function texture features. An additional four feature types were obtained from the state of the histogram-based features types. These 25 feature types were applied to each of the two classifications malignant and benign. WEKA 3.6.10 was used along with classifier J48 and cross-validation 10 fold to find the precision, recall, and f-measure values. Best results were obtained from these two combinations: temporal subtraction with amplitude thresholding, and temporal subtraction with regional-growing thresholding. To summarize, the researcher's contribution was to assess the effectiveness of various thresholding methods in the context of a three-stage approach, to help radiologists find cancerous tissue lesions in CT and MRI mammography images.