abstract: In modern healthcare environments, there is a strong need to create an infrastructure that reduces time-consuming efforts and costly operations to obtain a patient's complete medical record and uniformly integrates this heterogeneous collection of medical data to deliver it to the healthcare professionals. As a result, healthcare providers are more willing to shift their electronic medical record (EMR) systems to clouds that can remove the geographical distance barriers among providers and patient. Even though cloud-based EMRs have received considerable attention since it would help achieve lower operational cost and better interoperability with other healthcare providers, the adoption of security-aware cloud systems has become an extremely important prerequisite for bringing interoperability and efficient management to the healthcare industry. Since a shared electronic health record (EHR) essentially represents a virtualized aggregation of distributed clinical records from multiple healthcare providers, sharing of such integrated EHRs may comply with various authorization policies from these data providers. In this work, we focus on the authorized and selective sharing of EHRs among several parties with different duties and objectives that satisfies access control and compliance issues in healthcare cloud computing environments. We present a secure medical data sharing framework to support selective sharing of composite EHRs aggregated from various healthcare providers and compliance of HIPAA regulations. Our approach also ensures that privacy concerns need to be accommodated for processing access requests to patients' healthcare information. To realize our proposed approach, we design and implement a cloud-based EHRs sharing system. In addition, we describe case studies and evaluation results to demonstrate the effectiveness and efficiency of our approach. / Dissertation/Thesis / M.S. Computer Science 2012

abstract: The technological revolution has caused the entire world to migrate to a digital environment and health care is no exception to this. Electronic Health Records (EHR) or Electronic Medical Records (EMR) are the digital repository for health data of patients. Nation wide efforts have been made by the federal government to promote the usage of EHRs as they have been found to improve quality of health service. Although EHR systems have been implemented almost everywhere, active use of EHR applications have not replaced paper documentation. Rather, they are often used to store transcribed data from paper documentation after each clinical procedure. This process is found to be prone to errors such as data omission, incomplete data documentation and is also time consuming. This research aims to help improve adoption of real-time EHRs usage while documenting data by improving the usability of an iPad based EHR application that is used during resuscitation process in the intensive care unit. Using Cognitive theories and HCI frameworks, this research identified areas of improvement and customizations in the application that were required to exclusively match the work flow of the resuscitation team at the Mayo Clinic. In addition to this, a Handwriting Recognition Engine (HRE) was integrated into the application to support a stylus based information input into EHR, which resembles our target users’ traditional pen and paper based documentation process. The EHR application was updated and then evaluated with end users at the Mayo clinic. The users found the application to be efficient, usable and they showed preference in using this application over the paper-based documentation. / Dissertation/Thesis / Masters Thesis Computer Science 2018

The Centers for Medicare and Medicaid Services issued over 144,000 payments totaling $7.1 billion to medical facilities that have adopted and successfully demonstrated meaningful use of certified electronic health record (EHR). Hospital organizations can increase cost savings by using the electronic components of EHRs to improve medical coding and reduce medical errors and transcription costs. Despite the incentives, some rural health care facilities are failing to progress. The purpose of this multiple case study was to explore the strategies rural hospital leaders used to implement an EHR. The target population consisted of rural hospital leaders who were involved in the successful implementation of an EHR in South Texas. The conceptual framework chosen for this study was the sociotechnical systems theory. Data were collected through telephone interviews using open-ended semistructured interviews with 5 participants from 4 rural hospitals who were involved in the EHR implementation. Data analysis occurred using Yin's 5-step process which includes compiling, disassembling, reassembling, interpreting, and concluding. Data analysis included collecting information from government websites, company documents, and open-ended information to develop recurring themes. Several themes emerged including ongoing training, provider buy-in, constant communication, use of super users, and workflow maintenance. The findings could influence social change by making the delivery of health care more efficient and improving quality, safety, and access to health care services for patients.

No description available.

<p dir="ltr">The electronic health record (EHR) documents the patient’s medical history, with information such as demographics, diagnostic history, procedures, laboratory tests, and observations made by healthcare providers. This source of information can help support preventive health care and management. The present thesis explores the potential for EHR-driven models to predict acute care utilization (ACU) which is defined as visits to an emergency department (ED) or inpatient hospitalization (IH). ACU care is often associated with significant costs compared to outpatient visits. Identifying patients at risk can improve the quality of care for patients and can reduce the need for these services making healthcare organizations more cost-effective. This is important for vulnerable patients including those suffering from schizophrenia and bipolar disorders. This study compares the ability of the MedBERT architecture, the MedBERT+ architecture and standard machine learning models to identify at risk patients. MedBERT is a deep learning language model which was trained on diagnosis codes to predict the patient’s at risk for certain disease conditions. MedBERT+, the architecture introduced in this study is also trained on diagnosis codes. However, it adds socio-demographic embeddings and targets a different outcome, namely ACU. MedBERT+ outperformed the original architecture, MedBERT, as well as XGB achieving an AUC of 0.71 for both bipolar and schizophrenia patients when predicting ED visits and an AUC of 0.72 for bipolar patients when predicting IH visits. For schizophrenia patients, the IH predictive model had an AUC of 0.66 requiring further improvements. One potential direction for future improvement is the encoding of the demographic variables. Preliminary results indicate that an appropriate encoding of the age of the patient increased the AUC of Bipolar ED models to up to 0.78.</p>

Despite a Presidential Order in 2004 that launched national incentives for the use of health information technology, specifically the Electronic Health Record (EHR), adoption of the EHR has been slow. This study attempts to quantify factors associated with adoption of the EHR and Computerized Provider Order Entry (CPOE) by combining multiple organizational theories and empirical studies. The study is conducted in two phases. The primary phase of this study identifies and evaluates the effects of external environmental and internal organizational factors on healthcare organizations to adopt the EHR. From secondary data, twelve IVs (df=19) are chosen based on existing models and literature. Logistic regression is used to determine the association between the environmental factors and EHR adoption. The secondary phase of this study examines the adoption of five variations of CPOE using the same IVs from phase one. This EHR component of CPOE is chosen due to its promotion as a solution to help cross the quality chasm (IOM, 2001). Secondary data are analyzed and logistic regression is used to quantify the association between the factors of EHR adoption and CPOE adoption. Eleven of the twelve IVs are significant between the two phases (p<.1). This study uses data from 2009 because the HITECH Act was passed that year and significant government incentives were offered for those health care organizations (HCOs) that meet the qualifications of meaningful use. This study serves as a baseline for future studies, extends the work of other empirical studies, and fills a gap in the literature concerning factors associated with the adoption of the EHR and specific dimensions of CPOE. The Kruse Theory developed is strongly based in literature and reflects complexity commensurate with the health care industry.

Electronic Health Record (EHR) is an umbrella term encompassing demographics and health information of a patient from many different sources in a digital format. Deep learning has been used on EHRs in many successful studies and there is great potential in future implementations. In this study, diagnosis classification of EHRs with Multi-layer Perceptron models are studied. Two MLPs with different architectures are constructed and run on both a modified version of the EHR dataset and the raw data. A Random Forest is used as baseline for comparison. The MLPs are not successful in beating the baseline, with the best-performing MLP having a classification accuracy of 48.1%, which is 13.7 percentage points lower than that of the baseline. The results indicate that when the dataset is small, this approach should not be chosen. However, the dataset is growing over time and thus there is potential for continued research in the future. / Elektronisk patientjournal (EHR) är ett paraplybegrepp som används för att beskriva en digital samling av demografisk och medicinsk data från olika källor för en patient. Det finns stor potential i användandet av djupinlärning på dessa journaler och många framgångsrika studier har redan gjorts på området. I denna studie undersöks diagnosklassificering av elektroniska patientjournaler med Multi-layer perceptronmodeller. Två MLP-modeller av olika arkitekturer presenteras. Dessa körs på både en anpassad version av EHR-datamängden och på den råa EHR-datan. En Random Forest-modell används som baslinje för jämförelse. MLP-modellerna lyckas inte överträffa baslinjen, då den bästa MLP-modellen ger en klassifikationsnoggrannhet på 48,1%, vilket är 13,7 procentenheter mindre än baslinjens. Resultaten visar att en liten datamängd indikerar att djupinlärning bör väljas bort för denna typ av problem. Datamängden växer dock över tid, vilket gör områdetattraktivt för framtida studier.

The use of Electronic Health Records (EHR) for reporting patient data has been widely adopted by healthcare providers. This data can encompass many forms of medical information such as disease symptoms, results from laboratory tests, ICD-10 classes and other information from patients. Structured EHR data is often high-dimensional and contain many missing values, which impose a complication to many computing problems. Detecting meaningful structures in EHR data could provide meaningful insights in diagnose detection and in development of medical decision support systems. In this work, a subset of EHR data from patient questionnaires is explored through two well-known clustering algorithms: K-Means and Agglomerative Hierarchical. The algorithms were tested on different types of data, primarily raw data and data where missing values have been imputed using different imputation techniques. The primary evaluation index for the clustering algorithms was the silhouette value using euclidean and cosine distance measures. The result showed that natural groupings most likely exist in the data set. Hierarchical clustering created higher quality clusters than k-means, and the cosine measure yielded a good interpretation of distance. The data imputation imposed large effects to the data and likewise to the clustering results, and other or more sophisticated techniques are needed for handling missing values in the data set. / Användandet av digitala journaler för att rapportera patientdata har ökat i takt med digitaliseringen av vården. Dessa data kan innehålla många typer av medicinsk information så som sjukdomssymptom, labbresultat, ICD-10 diagnoskoder och annan patientinformation. EHR data är vanligtvis högdimensionell och innehåller saknade värden, vilket kan leda till beräkningssvårigheter i ett digitalt format. Att upptäcka grupperingar i sådana patientdata kan ge värdefulla insikter inom diagnosprediktion och i utveckling av medicinska beslutsstöd. I detta arbete så undersöker vi en delmängd av digital patientdata som innehåller patientsvar på sjukdomsfrågor. Detta dataset undersöks genom att applicera två populära klustringsalgoritmer: k-means och agglomerativ hierarkisk klustring. Algoritmerna är ställda mot varandra och på olika typer av dataset, primärt rådata och två dataset där saknade värden har ersatts genom imputationstekniker. Det primära utvärderingsmåttet för klustringsalgoritmerna var silhuettvärdet tillsammans med beräknandet av ett euklidiskt distansmått och ett cosinusmått. Resultatet visar att naturliga grupperingar med stor sannolikhet finns att hitta i datasetet. Hierarkisk klustring visade på en högre klusterkvalitet än k-means, och cosinusmåttet var att föredra för detta dataset. Imputation av saknade data ledde till stora förändringar på datastrukturen och således på resultatet av klustringsexperimenten, vilket tyder på att andra och mer avancerade dataspecifika imputationstekniker är att föredra.

The task of ensuring Patient Safety is, more than ever, central in Healthcare. The report “To Err is Human” [Kohn et al. 2000], was revealing alarming numbers of incidents, injuries and deaths caused by deficiencies in healthcare activities. The book initiated assessment and change of Healthcare methods and procedures. In addition, numerous reports to Swedish HSAN (Medical Responsibility Board) have shown a high rate of information and communication deficiencies in Healthcare has a direct or indirect cause of incidents, injuries and deaths. Despite numerous of new sophisticated tools for information management in recent years, e.g., tools such as Electronic Health Records (EHR) and Clinical Decision Support Systems (CDSS), the threats to Patient Safety have not been redeemed. Rather to the contrary. Underlying reasons for this paradox are twofold. Firstly, advancements in diagnosing techniques have given rise to increasing volumes of data at the same time as the number of patients has increased due to demographic changes and advancements in treatments. Secondly, the information processing systems are far from aligned to related workflow processes. In short, we do not at present have interoperability in our Healthcare systems. In this doctoral dissertation, we present an in-depth analysis of two different “HSAN-typical” cases, where Patient Safety was jeopardized by incomplete information flows and/or information breakdowns. The cases are mirroring the apprehension of Simplicity, that is, Occam´s Razor of Diagnostic Parsimony. A well-known protocol used in Healthcare and implemented in most (knowledge based) CDSS. This rule of thumb is the foundation for the well-known adage: “when you hear hoof beats, think horses, not zebras”. Hickam´s Dictum is one well known objection to the simplifications of Occam´s Razor stating "Patients can have as many diseases as they damn well please". Of course, this Dictum is harder to implement effectively! In the thesis we suggest a visualization tool Visual Incidence Anamneses (VIA) to provide middle out compromise between Ockham and Hickam but providing means to increase Patient Safety. The findings of our Study for the thesis have resulted in a number of Aspects and Principles as well as Core-principles for future CDSS design, That is, tools and methodologies that will support designing and validating Interoperability of Healthcare systems across patient-centric workflows. The VIA tool should be used as the initiating point in a patient (individual) centered workflow, quickly visualizing vital information such as symptoms, incidents and diagnoses, occurring earlier in the medical history, at different times, to ground further vital decisions on. The visualization will enable analysis of timelines and earlier diagnoses of the patient, using visually salient nodes for visualization of causalities in context. Furthermore, support for customization of the tool to the views of stakeholders, members of healthcare teams and empowerments of the patient, is crucial.

The use of electronic health records (EHR) has the potential to improve relationships between physicians and patients and significantly improve care delivery. The purpose of this study was to analyze the relationships between hospital attributes and EHR implementation. The research design for this study was the cross-sectional approach. Secondary data from the Health Information and Management Systems Society (HIMSS) Analytics Database was utilized (n = 169) in a correlational crosssectional research design. Normalization Process Theory (NPT) and implementation theory were the theoretical underpinnings used in this study. Multiple linear regressions results showed statistically significant relationships between the 4 independent variables (region, ownership status, number of staffed beds [size], and organizational control) and the outcomes for the dependent variables of EHR software application attributes (Clinical Decision Support Systems (CDSS) components), EHR software application attributes (major systems), and successful implementation of Meaningful Use (MU) (p = .001). A statistically significant relationship (p = .001) was also found between the 2 independent variables (EHR software application attributes [CDSS components] and EHR software application attributes [major systems]) and the outcome of successful implementation of MU when combined. This evidence should provide policy makers and health practitioners support for their attempts to implement EHR systems to result in positive Meaningful Use which has been shown to be more cost effective and result in better quality of care for patients.The potential social change is improved medication prescribing and administration for hospitals and, lower cost and better quality of care for patients.