Background: Electronic Health Records (EHR) are digital versions of patients' charts (HealthIT.gov, 2013). The government has incentivized current use to allow all healthcare organizations to progress from paper charting. Goals of EHR adoption include improving workflow, documentation, and to improve the quality of care being provided (Weiner, Fowles, & Chan, 2012). Objective: The purpose of this DNP project was to conduct a readiness assessment of the asthma and allergy specialty organization's staff members to identify perceived barriers and advantages of adopting an EHR. Design: This project was guided by the Institute for Healthcare Improvement (IHI) Model for Improvement (Institute for Healthcare Improvement [IHI], 2016). This model was incorporated with the PDSA cycle and DOQ-IT EHR Implementation Roadmap. Descriptive statistics were used for data analysis. Setting: Allergy and asthma specialty practice consisting of 12 clinics within the Denver Metro and Northern Colorado area. Participants: 155 members of the organization including physicians, nurse practitioners, physician assistants, nurses, medical assistants, front office and administrative staff. Measurements: 60 out of 155 staff members completed the readiness assessment survey from HealthInsight (HealthInsight, n.d.).Results: A response rate of 38.7% (n=60) of participants completed the readiness assessment survey. The top two barriers were medical records being unavailable (n= 48, 80%) and the inability to read what is written in the medical record (n= 51, 85%). The top barrier for adopting EHR is having the system freeze or crash (n=36, 65%), followed by, 22 participants or 40% stating EHR is depersonalizing in an exam room. The highest advantage identified was the reduction in paper-based medical charting and filing (n=56, 93%). The second highest advantage was more timely access to patient records (n=55, 92%).Conclusion: Perceived barriers and advantages for EHR adoption within the organization are similar to what literature has currently identified. The information gained from this study will provide a better understanding of the decision and adoption process. The information will help the organization decide whether or not to adopt EHR and how to successfully move through the DOQ-IT EHR Implementation Roadmap, IHI Model for Improvement and PDSA cycle.

Government initiatives called for electronic health records for each individual healthcare consumer by 2014. the purpose of the initiatives is to provide for the common exchange of clinical information between healthcare consumers, healthcare providers, third-party payers and public healthcare officials.This exchange of healthcare information will impact the healthcare industry and enable more effective and efficient application of healthcare so that there may be a decrease in medical errors, increase in access to quality of care tools, and enhancement of decision making abilities by healthcare consumers, healthcare providers and government health agencies. an electronic personal health record (ePHR) created, managed and accessed by healthcare consumers may be the answer to fulfilling the national initiative. However, since healthcare consumers potentially are in control of their own ePHR, the healthcare consumer’s concern for privacy may be a barrier for the effective implementation of a nationwide network of ePHR. a technology acceptance model, an information boundary theory model and a trust model were integrated to analyze usage intentions of healthcare consumers of ePHR. Results indicate that healthcare consumers feel there is a perceived usefulness of ePHR; however they may not see ePHR as easy to use. Results also indicate that the perceived usefulness of utilizing ePHR does not overcome the low perceived ease of use to the extent that healthcare consumers intend to utilize ePHR. in addition, healthcare consumers may not understand the different components of usage: access, management, sharing and facilitating third-party ePHR. Also, demographics, computer self-efficacy, personal innovativeness, healthcare need and healthcare literacy impact a healthcare consumer’s privacy concerns and trusting intentions in the context of ePHR and intent to utilize ePHR. Finally, this research indicates that healthcare consumers may need a better understanding of the Health Insurance and Portability and Accountability Act of 1996 (HIPAA) regulations of ePHR as well as a better understanding of the impact HIPAA has on websites that may facilitate ePHR.

Alzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of machine learning techniques to augment diagnostic procedures has been previously examined in limited capacity but to date no research examines real-world medical applications of predictive analytics for health records and cognitive exam scores. This dissertation seeks to examine the efficacy of detecting cognitive impairment due to Alzheimer’s disease using machine learning, including multi-modal neural network architectures, with a real-world clinical dataset used to determine the accuracy and applicability of the generated models. An in-depth analysis of each type of data (e.g. cognitive exams, questionnaires, demographics) as well as the cognitive domains examined (e.g. memory, attention, language) is performed to identify the most useful targets, with cognitive exams and questionnaires being found to be the most useful features and short-term memory, attention, and language found to be the most important cognitive domains. In an effort to reduce medical costs and streamline procedures, optimally predictive and efficient groups of features were identified and selected, with the best performing and economical group containing only three questions and one cognitive exam component, producing an accuracy of 85%. The most effective diagnostic scoring procedure was examined, with simple threshold counting based on medical documentation being identified as the most useful. Overall predictive analysis found that Alzheimer’s disease can be detected most accurately using a bimodal multi-input neural network model using separated cognitive domains and questionnaires, with a detection accuracy of 88% using the real-world testing set, and that the technique of analyzing domains separately serves to significantly improve model efficacy compared to models that combine them. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Rationale: The Cancer Agency Information System (CAIS) is the primary patient record for the British Columbia Cancer Agency (BCCA) but is only accessible on fixed computer workstations. The BCCA clinics have significant space limitations resulting in multiple healthcare providers sharing each workstation. Furthermore, workstations are not available in the patient examination rooms leading to multiple visit interruptions. Given that timely and efficient access to patient electronic records is fundamental in providing optimal patient care, the iPad Mobility Project was launched to introduce and evaluate the effect of mobile technologies and applications in improving access to CAIS and supporting clinicians’ workflow. Methods The project evaluation framework was created in collaboration with the project stakeholders including BCCA clinicians. The framework included pre- and post-implementation questionnaires, pre- and post-implementation observational sessions, and post-implementation semi-structured interviews. Survey questionnaires mainly included standardized scales used to measure user expectations and perceptions before and after information systems implementation. Also, based on Canada Infoway System and Use Survey, the post-implementation questionnaire included questions that measure the mobile system success in terms of information quality, system quality, service quality, user satisfaction, and use measures. The response rate was 84% (n=44) for the baseline survey and 76% (n=52) for the post-implementation survey. Also, baseline and post-implementation observational sessions (n=5, n=6 respectively) were conducted to provide real-time data about the use of the available record keeping systems before and after the mobile system implementation. Post-implementation semi-structured interviews (n=11) were conducted to allow clinicians to reflect on their use of the iPad and VitalHub Chart application. Results: The results showed an overwhelmingly positive attitude to the use of the iPad and the VitalHub Chart application to support clinicians’ mobile workflow through enhanced access to CAIS. Perceived benefits were related to three major categories: information accessibility and inter-professional communication; workflow efficiency and provider productivity, and patient care quality and safety. Conversely, perceived challenges were related to three major categories: software related challenges, hardware related challenges, and network infrastructure-related issues. Furthermore, the results showed that the success of mobile computing technology depends on its ability to support access to patients’ electronic records and other central clinical information systems, on mobile devices and their applications’ ergonomic features, and on end-user participation in mobile computing projects. Implications Mobile computing technologies have the potential to improve data accessibility, communication mechanisms, patient care quality, and workflow efficiency. However, realizing the full potential benefits of mobile computing technologies rely on several factors. Healthcare organizations need to have clear understanding of end users’ needs, expectations, clinical tasks, and workflow. Engaging end-users in mobile computing technologies projects from the early stages of the project is essential to identify the various complex human, organizational, and contextual factors that affect the success of enterprise-wide mobile computing technology projects. Due to their inherent limitations, mobile computing technologies should be considered as complementary to and not as replacement to fixed computer workstations. Also, evaluating mobile technologies and applications usability is essential for both the success and safety of such innovative solutions. / Graduate / 0723 / 0566 / banimelh@uvic.ca

Background: The increasing use of electronic health records (EHR) provides a novel opportunity to evaluate hospital-based nutritional outcomes, such as malnutrition. There is no universally accepted screening tool for the detection of malnutrition. However, assessment for malnutrition should be made early, be simple, based on scientific evidence, and include data on age, gender, and disease severity. The malnutrition screening tool (MST) used in this study is a two question tool that assesses two parameters commonly seen when diagnosing malnutrition (weight loss and loss of appetite). Objective: The purpose of this study is to determine the ability of the MST used at a tertiary or quaternary hospital to accurately identify patients with malnutrition by comparing it against the Academy of Nutrition and Dietetics and American Society for Parenteral and Enteral Nutrition criteria for malnutrition. Participants/setting: A descriptive cohort study was conducted that included 167 patients admitted to Emory University Hospital between October 1 - 14, 2014. MST score, malnutrition diagnostic criteria, and demographic and anthropometric characteristics were obtained to describe and assess the study population. Statistical Analysis: Frequency statistics were used to describe the demographic and anthropometric characteristics and MST score results. Normality statistics were used to determine the distribution of continuous variables. A Chi Square table was used to determine the significance of the association between the MST score and diagnosis of malnutrition made by the Registered Dietitian (RD) as well as the sensitivity and specificity of the MST. Results: A total of 167 patients (48.5% male, 51.5% Caucasian, non-Hispanic) were admitted during the study period. The vast majority of the patient population with malnutrition (79%), as diagnosed by the RD, was identified as such by the MST (p < 0.01). The sensitivity and specificity of the MST was 79% and 62%, respectively. Conclusion: The MST is a useful screening tool for malnutrition in adults admitted to a large urban university hospital. There is a lack of research validating the MST in the adult outpatient population. Therefore, future studies are necessary to evaluate the effectiveness of the MST in this population.

Magister Commercii - MCom / This study aimed to understand the readiness and perceptions of Electronic Health Record (EHR) adoption among dentists in the public service of the Western Cape. A qualitative study design was chosen due to a lack of understanding of the phenomena. Additionally, the research sought to identify the factors that would potentially influence readiness and perceptions in order to identify how these factors could potentially influence EHR adoption among dentists.

Indiana University-Purdue University Indianapolis (IUPUI) / Health IT has recently seen a significant progress with the nationwide migration of several hospitals from legacy patient records to standardized Electronic Health Record (EHR) and the establishment of various Health Information Exchanges that facilitate access to patient health data across multiple networks. While this progress is a major enabler of improved health care services, it is unable to deliver the continuum of the patient's current and historical health data needed by emerging trends in medicine. Fields such as precision and preventive medicine require longitudinal health data in addition to complementary data such as social, demographic and family history. This thesis introduces a person health record (PHR) which overcomes the above gap through a personalized framework that organizes health data according to the patient’s disease condition. The proposed personalized person health record (P2HR) represents a departure from the standardized one-size-fits-all model of currently available PHRs. It also relies on a hybrid peer-to-peer model to facilitate patient provider communication. One of the core challenges of the proposed framework is the mapping between the event-based data model used by current EHRs and PHRs and the proposed condition-based data model. Effectively mapping symptoms and measurements to disease conditions is challenging given that each symptom or measurement may be associated with multiple disease conditions. To alleviate these problems the proposed framework allows users and their health care providers to establish the relationships between events and disease conditions on a case-by-case basis. This organization provides both the patient and the provider with a better view of each disease condition and its progression.

Problem statement: As a result of the aging population, the number of people living with chronic disease has increased to almost 50% (CDC, 2004). Two of the main goals in treating patients with chronic diseases are to provide seamless care from setting to setting and prevent disability in the older adult. Many have proposed the use of electronic personal health record systems (PHRs) in the self-management process, but adoption remains low. The purpose of this research was to validate and test an explanatory model of the barriers and facilitators to older adults' adoption of personal health records for self-managing chronic illnesses. The long range goal of the research is to use the explanatory model to develop interventions that will maximize the facilitators and minimize the barriers to adoption. Methods: A preliminary attempt to capture the essential barriers and facilitators that predict adoption of PHRs among older adults with chronic illness was synthesized from the literature. In Phase One of the study, the model was integrated from existing literature and validated using a Delphi method. In Phase Two of the study, the model was pilot tested and refined for future investigations. Findings: The results of this study validated the Personal Health Records Adoption Model (PHRAM) and a preliminary instrument that measured barriers and facilitators to the adoption of PHRs in older adults who are self managing chronic illness. Additional findings indicate that while seniors are seeking options to manage their health and have expressed an interest in using Internet-based PHRs, they may require assistance to gain access to PHRs. Implications: The potential for PHRs to increase patient autonomy and reduce for disability and the resulting negative health consequences needs further investigation as we move into the next era of healthcare delivery. The results of this study provided the foundation for continued theoretically-based research in this area.

Melanoma is one of the fastest growing cancers in the world, and can affect patients earlier in life than most other cancers. Therefore, it is imperative to be able to identify patients at high risk for melanoma and enroll them in screening programs to detect the cancer early. Electronic health records collect an enormous amount of data about real-world patient encounters, treatments, and outcomes. This data can be mined to increase our understanding of melanoma as well as build personalized models to predict risk of developing the cancer. Cancer risk models built from structured clinical data are limited in current research, with most studies involving just a few variables from institutional databases or registries. This dissertation presents data processing and machine learning approaches to build melanoma risk models from a large database of de-identified electronic health records. The database contains consistently captured structured data, enabling the extraction of hundreds of thousands of data points each from millions of patient records. Several experiments are performed to build effective models, particularly to predict sentinel lymph node metastasis in known melanoma patients and to predict individual risk of developing melanoma. Data for these models suffer from high dimensionality and class imbalance. Thus, classifiers such as logistic regression, support vector machines, random forest, and XGBoost are combined with advanced modeling techniques such as feature selection and data sampling. Risk factors are evaluated using regression model weights and decision trees, while personalized predictions are provided through random forest decomposition and Shapley additive explanations. Random undersampling on the melanoma risk dataset shows that many majority samples can be removed without a decrease in model performance. To determine how much data is truly needed, we explore learning curve approximation methods on the melanoma data and three publicly-available large-scale biomedical datasets. We apply an inverse power law model as well as introduce a novel semi-supervised curve creation method that utilizes a small amount of labeled data. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

By the end of 2015, Medicare-eligible physicians at primary care practices (PCP) who do not use an electronic health record (EHR) system will incur stiff penalties if they fail to meet the deadline for using EHRs. Yet, less than 30% of rural primary clinics have fully functional EHR systems. The purpose of this phenomenology study was to explore rural primary care physicians and physician assistants' experiences regarding overcoming barriers to implementing EHRs. Complex adaptive systems formed the conceptual framework for this study. Data were collected through face-to-face interviews with a purposeful sample of 21 physicians and physician assistants across 2 rural PCPs in the southeastern region of Missouri. Participant perceptions were elicited regarding overcoming barriers to implementing EHRs under the American Recovery and Reinvestment Act, Health Information Technology for Economic and Clinical Health, and the Patient Protection and Affordable Care Act legislation. Interview questions were transcribed and processed through qualitative software to discern themes of how rural PCP physicians and physician assistants might overcome barriers to implementing electronic health records. Through the exploration of the narrative segments, 4 emergent themes were common among the participants: (a) limited finances to support EHRs, (b) health information exchange issues, (c) lack of business education, and (d) lack of transformation at rural medical practices. The implications for positive social change include the potential implementation of EHRs particularly in physician practices in rural communities, which could provide cost-efficient health care services for those communities and a more sustainable future at primary care practices.