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Recent progress in treatment interventions has resulted in increased survival rates and longevity for diagnosed heart failure patients. However, heart failure still remains one of the leading causes of rehospitalization worldwide, where emergency readmissions continue to be a common occurrence. The multifactorial complexity of heart failure makes clinical judgment difficult and may lead to erroneous discharge prognoses and estimates in recovery trajectories. Recognizing emergency readmissions among heart failure patients who have been discharged is crucial within the critical six-month post-discharge period to proactively address additional support needs. To address the research question, “To what extent can machine learning models predict emergency readmissions in Chinese heart failure patients within six months post-discharge?”, this paper uses electronic health records obtained from a single healthcare center in China, containing 2,008 validated heart failure patients. This study adopts an experimental research methodology, where four machine learning models are developed to explore the research question. To ensure robustness, 10-fold cross-validation with stratified sampling and a two-step feature selection process is performed in addition to evaluation through metrics such as the area under the receiving operating curve and F1 Score. The findings indicate only modest predictive capability among the classifiers in the validation cohort. The best-achieved area under the receiving operating curve and F1 Score are obtained from separate classifiers with scores of 0.682 and 0.577, respectively. The findings provide valuable insights into future research on the effectiveness of ML-based prediction models for emergency readmission in Chinese heart failure patients.

Frailty is a multifaceted, geriatric syndrome that is associated with age-related declines in functional reserves resulting in increased risks of in-hospital death, readmissions and discharge to nursing homes. The risks associated with frailty highlights the need for providers to be able to quickly, and accurately, assess someone’s frailty level. Previous studies have shown that bedside clinician assessment is not a reliable or valid way to determine frailty, meaning that a more reliable, valid and concise method is needed. We developed a prediction model using discharge ICD-9/ICD-10 diagnostic codes and other demographic variables to predict Reported Edmonton Frail Scale scores. Participants were from the Baystate Frailty Study, a prospective cohort design study among elderly patients greater than 65 years old who were admitted to a single academic medical center between 2014 and 2016. Three different predictive models were completed utilizing the LASSO approach. The adjusted r-square increased across the three models indicating an increase in the predictive ability of the models. In this study of 762 hospitalized patients over the age of 65 years old, we found that a frailty prediction model that included ICD codes only had a poor prediction ability (adjusted r-square=0.10). The prediction ability improved 2-fold after adding demographic information, a comorbidity score and interaction terms (adjusted r-square=0.26). This study provided additional insights into the development of an automatic frailty assessment, something which is currently missing from clinical care.

Medical record systems are used whenever caregiving is practiced. The medical records serve an important role in establishing patient safety. It is not possible to prevent honest-but-curious doctors from accessing records since it is legally required to allow doctors to access health records for emergency cases. However, it is possible to log accesses to records and mitigate malicious behaviour through rate limiting. Nevertheless, many of the records systems today are lacking good authentication, logging and auditing and existing proposals for securing medical records systems focus on the context of multiple different healthcare providers. In this thesis, an architecture for an electronic health records system for a telemedicine provider is designed. The architecture is based on several requirements from both the legal perspective and general security conventions, but also from a doctor’s perspective. Unlike the legal and general security conventions perspective, doctor requirements are more functionality and usability concerns rather than security concerns. The architecture is evaluated based on two main threat models and one secondary threat model, i.e. insider adversaries. Almost all requirements are satisfied by the solution design, but the two main threat models can not be entirely mitigated. It is found that confidentiality can be violated by the two main threat models, but the impact is heavily limited through audit logging and rate limiting. / Journalsystem är en central del inom vården och patientjournaler har en stor roll i att uppnå bra patientsäkerhet. Det är inte möjligt att förhindra läkare från att läsa särskilda journaler eftersom läkare behöver tillgång till journaler vid nödsituationer. Däremot går det att logga läkarnas handlingar och begränsa ondsint beteende. Trots det saknar många av dagens journalsystem bra metoder för autentisering, loggning och granskning. Befintliga förslag på att säkra journalsystemen fokuserar på sammanhang där flera olika vårdgivare är involverade. I den här rapporten presenteras en arkitektur för ett patientjournalsystem till en telemedicinsk leverantör. Arkitekturen utgår från flertalet krav baserade på både ett legalt perspektiv och generella säkerhetskonventioner, men även läkares perspektiv. Arkitekturen är evaluerad baserat på två huvudsakliga hotmodeller och en sekundär hotmodell. Arkitekturen uppfyller så gott som alla krav, men de två huvudsakliga hotmodellerna kan inte mitigeras helt och hållet. De två huvudsakliga hotmodellerna kan bryta sekretessen, men genom flödesbegränsning och granskning av loggar begränsas påverkan.

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No description available.

An empirical investigation of the security flaws and features of an in-use modern electronic health record system is performed. The investigation was carried out using dynamic analysis, manual testing and interviews with developers. The results indicate that in-use electronic health record systems suffer from serious authentication flaws, arising from the interaction of many different proprietary systems. The authentication problems are so severe that gaining access to any user’s computer on the hospital intranet would compromise a large database of patient medical records, including radiological data regarding the patients. Common web vulnerabilities were also present, such as injections and incorrectly configured HTTP security headers. These vulnerabilities were heavily mitigated by the use of libraries for constructing web interfaces. / En empirisk undersökning av säkerheten inom ett modernt elektroniskt patientjournal-system har utförts. Undersökningen genomfördes med hjälp av dynamisk analys, manuell testning och intervjuer med utvecklarna. Resultatet indikerar att system för elektroniska patientjournaler har stora brister inom autentisering, vilka uppstår p.g.a. att flera olika kommersiella system måste samarbeta. Problemen är så allvarliga att med tillgång till en enda dator på intranätet kan en stor databas med patientdata äventyras, inklusive radiologisk data gällande patienterna. Vanliga websårbarheter fanns också, så som injektioner av skript och inkorrekt konfigurerade HTTP säkerhetsheaders. Dessa sårbarheter mitigerades starkt genom användandet av bibliotek för webinterface.

Electronic Health Records (EHR) data contain comprehensive longitudinal patient information, which is usually stored in databases in the form of either multi-relational structured tables or unstructured texts, e.g., clinical notes. EHR provides a useful resource to assist doctors' decision making, however, they also present many unique challenges that limit the efficient use of the valuable information, such as large data volume, heterogeneous and dynamic information, medical term abbreviations, and noisy nature caused by misspelled words. This dissertation focuses on the development and evaluation of advanced machine learning algorithms to solve the following research questions: (1) How to seek answers from EHR for clinical activity related questions posed in human language without the assistance of database and natural language processing (NLP) domain experts, (2) How to discover underlying relationships of different events and entities in structured tabular EHRs, and (3) How to predict when a medical event will occur and estimate its probability based on previous medical information of patients. First, to automatically retrieve answers for natural language questions from the structured tables in EHR, we study the question-to-SQL generation task by generating the corresponding SQL query of the input question. We propose a translation-edit model driven by a language generation module and an editing module for the SQL query generation task. This model helps automatically translate clinical activity related questions to SQL queries, so that the doctors only need to provide their questions in natural language to get the answers they need. We also create a large-scale dataset for question answering on tabular EHR to simulate a more realistic setting. Our performance evaluation shows that the proposed model is effective in handling the unique challenges about clinical terminologies, such as abbreviations and misspelled words. Second, to automatically identify answers for natural language questions from unstructured clinical notes in EHR, we propose to achieve this goal by querying a knowledge base constructed based on fine-grained document-level expert annotations of clinical records for various NLP tasks. We first create a dataset for clinical knowledge base question answering with two sets: clinical knowledge base and question-answer pairs. An attention-based aspect-level reasoning model is developed and evaluated on the new dataset. Our experimental analysis shows that it is effective in identifying answers and also allows us to analyze the impact of different answer aspects in predicting correct answers. Third, we focus on discovering underlying relationships of different entities (e.g., patient, disease, medication, and treatment) in tabular EHR, which can be formulated as a link prediction problem in graph domain. We develop a self-supervised learning framework for better representation learning of entities across a large corpus and also consider local contextual information for the down-stream link prediction task. We demonstrate the effectiveness, interpretability, and scalability of the proposed model on the healthcare network built from tabular EHR. It is also successfully applied to solve link prediction problems in a variety of domains, such as e-commerce, social networks, and academic networks. Finally, to dynamically predict the occurrence of multiple correlated medical events, we formulate the problem as a temporal (multiple time-points) and multi-task learning problem using tensor representation. We propose an algorithm to jointly and dynamically predict several survival problems at each time point and optimize it with the Alternating Direction Methods of Multipliers (ADMM) algorithm. The model allows us to consider both the dependencies between different tasks and the correlations of each task at different time points. We evaluate the proposed model on two real-world applications and demonstrate its effectiveness and interpretability. / Doctor of Philosophy / Healthcare is an important part of our lives. Due to the recent advances of data collection and storing techniques, a large amount of medical information is generated and stored in Electronic Health Records (EHR). By comprehensively documenting the longitudinal medical history information about a large patient cohort, this EHR data forms a fundamental resource in assisting doctors' decision making including optimization of treatments for patients and selection of patients for clinical trials. However, EHR data also presents a number of unique challenges, such as (i) large-scale and dynamic data, (ii) heterogeneity of medical information, and (iii) medical term abbreviation. It is difficult for doctors to effectively utilize such complex data collected in a typical clinical practice. Therefore, it is imperative to develop advanced methods that are helpful for efficient use of EHR and further benefit doctors in their clinical decision making. This dissertation focuses on automatically retrieving useful medical information, analyzing complex relationships of medical entities, and detecting future medical outcomes from EHR data. In order to retrieve information from EHR efficiently, we develop deep learning based algorithms that can automatically answer various clinical questions on structured and unstructured EHR data. These algorithms can help us understand more about the challenges in retrieving information from different data types in EHR. We also build a clinical knowledge graph based on EHR and link the distributed medical information and further perform the link prediction task, which allows us to analyze the complex underlying relationships of various medical entities. In addition, we propose a temporal multi-task survival analysis method to dynamically predict multiple medical events at the same time and identify the most important factors leading to the future medical events. By handling these unique challenges in EHR and developing suitable approaches, we hope to improve the efficiency of information retrieval and predictive modeling in healthcare.

Through significant federal investment and incentives, Electronic Health Records have become ubiquitous in modern hospitals. Over the past decade, these computer support systems have provided healthcare operations with new safety nets, and efficiency increases, but also introduce new problems when they suddenly go offline. These downtime events are chaotic and dangerous for patients. With the safety systems clinicians have become accustomed to offline, patients are at risk from errors and delays. This work applies the Macroergonomic methodology to facilitate an exploratory study into the issues related to patient care during downtime events. This work uses data from existing sources within the hospital, such as the electronic health record itself. Data collection mechanisms included interviews, downtime paper reviews, and workplace observations. The triangulation of data collection mechanisms facilitated a thorough exploration of the issues of downtime. The Macroergonomic Analysis and Design (MEAD) methodology was used to guide the analysis of the data, and identify variances and shifts in responsibility due to downtime. The analysis of the data supports and informs developing potential intervention strategies to enable hospitals to better cope with downtime events. Within MEAD, the assembled data is used to inform the creation of a simulation model which was used to test the efficacy of the intervention strategies. The results of the simulation testing are used to determine the specific parameters of the intervention suggestions as they relate to the target hospitals. The primary contributions of this work are an exploratory study of electronic health record downtime and impacts to patient safety, and an adaptation of the Macroergonomic Analysis and Design methodology, employing multiple data collection methods and a high-fidelity simulation model. The methodology is intended to guide future research into the downtime issue, and the direct findings can inform the creation of better downtime contingency strategies for the target hospitals, and possibly to offer some generalizability for all hospitals. / Ph. D. / Hospitals experience periodic outages of their computerized work support systems from a variety of causes. These outages can range from partial communication and or access restrictions to total shutdown of all computer systems. Hospitals operating during a computerized outage or downtime are potentially unable to access computerized records, procedures and conduct patient care activities which are facilitated by computerized systems. Hospitals are in need of a means to cope with the complications of downtime and the loss of computerized support systems without risking patient care. This dissertation assesses downtime preparedness and planning through the application of Macroergonomics which has incorporated discrete event simulation. The results provide a further understanding of downtime risks and deficiencies in current planning approaches. The study enhances the application of Macroergonomics and demonstrates the value of discrete event simulation as a tool to aid in Macroergonomic evaluations. Based on the Macroergonomic Analysis and Design method, downtime improvement strategies are developed and tested, demonstrating their potential efficacy over baseline. Through this dissertation, the deficiencies in current contingency plans are examined and exposed and further the application of Macroergonomics in healthcare.