Improving waiting times in the Emergency Department

Waiting times in the Emergency Department cause considerable delays in care and in patient satisfaction. There are many moving parts to the ED visit with multiple providers delivering care for a single patient. Factors that have been shown to delay care in the ED have been broken down into input factors such as triaging, throughput factors during the visit, and output factors, which include discharge planning and available inpatient beds for admitted patients. Research has shown that throughput factors are an area of interest to decrease time spent in the ED that will lead to decrease waiting room times. In this Quality Improvement project, we will develop a systematic check in system with ED providers that will allow providers to identify any outstanding issues that may be delaying care or discharge. We hypothesize that this system will increase throughput in the ED by resolving any lab, radiology, or treatments that were overlooked. Reviewing the results of this QI project will allow us to see if we were effective in our timing of scheduled check-ins. Ultimately, this will reduce time spent in the waiting room by allowing more patients to be seen. In the era of the Affordable Care Act, more patients have access to affordable healthcare and will increase volume in the ED. This check-in system will allow more patients to be seen smoothly and in a timely manner that will improve and increase patient care and satisfaction in the ED

Mother-child histocompatibility and risk of rheumatoid arthritis and systemic lupus erythematosus among mothers.

The study objective was to test the hypothesis that having histocompatible children increases the risk of rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE), possibly by contributing to the persistence of fetal cells acquired during pregnancy. We conducted a case control study using data from the UC San Francisco Mother Child Immunogenetic Study and studies at the Inova Translational Medicine Institute. We imputed human leukocyte antigen (HLA) alleles and minor histocompatibility antigens (mHags). We created a variable of exposure to histocompatible children. We estimated an average sequence similarity matching (SSM) score for each mother based on discordant mother-child alleles as a measure of histocompatibility. We used logistic regression models to estimate odds ratios (ORs) and 95% confidence intervals. A total of 138 RA, 117 SLE, and 913 control mothers were analyzed. Increased risk of RA was associated with having any child compatible at HLA-B (OR 1.9; 1.2-3.1), DPB1 (OR 1.8; 1.2-2.6) or DQB1 (OR 1.8; 1.2-2.7). Compatibility at mHag ZAPHIR was associated with reduced risk of SLE among mothers carrying the HLA-restriction allele B*07:02 (n = 262; OR 0.4; 0.2-0.8). Our findings support the hypothesis that mother-child histocompatibility is associated with risk of RA and SLE

A kernel-based framework for medical big-data analytics

The recent trend towards standardization of Electronic Health Records (EHRs) represents a significant opportunity and challenge for medical big-data analytics. The challenge typically arises from the nature of the data which may be heterogeneous, sparse, very high-dimensional, incomplete and inaccurate. Of these, standard pattern recognition methods can typically address issues of high-dimensionality, sparsity and inaccuracy. The remaining issues of incompleteness and heterogeneity however are problematic; data can be as diverse as handwritten notes, blood-pressure readings and MR scans, and typically very little of this data will be co-present for each patient at any given time interval. We therefore advocate a kernel-based framework as being most appropriate for handling these issues, using the neutral point substitution method to accommodate missing inter-modal data. For pre-processing of image-based MR data we advocate a Deep Learning solution for contextual areal segmentation, with edit-distance based kernel measurement then used to characterize relevant morphology

Deployment of radio frequency identification technology in healthcare organizations

Helena Carvalho was supported by a PhD fellowship from Fundação para a Ciência e Tecnologia (SFRH/BD/43984/2008).The Radio Frequency Identification (RFID) technology is a wireless technology that uses transmitted radio signals to tag, recognize, track and trace the movement of an item automatically. The study of this technology is actually considered a hot topic in all scientific areas and has been described as a major enabling technology for the automation of many processes. Although it is not a new technology it has only recently come to the awareness of the public and widely used in many sectors and particularly in the Healthcare.This paper aims to illustrate the deployment of RFID technology in Healthcare, more precisely in infant security systems. A case study about the experience of three hospitals and one RFID technology provider is presented to highlight the main architectural characteristics, functionality, and advantages associated to its deployment.After the case studies analysis it is possible to state that the infant security systems, using the RFID technology, are not so different among research case studies: they involve RFID tagging patients, they are easy to use not requiring an extensive training and also they are installed with an interface with others security systems.publishersversionpublishe

Immigrant caregivers' adherence to Child Primary Care Health recommendations: Towards a psychosocial approach

Immigrant caregivers’ adherence to Child Primary Care (CPC) health recommendations may help minimizing health-related problems prevalent among their children (e.g. obesity, dental problems). Previous research focused on the determinants of immigrants’ access to health services, but less is known about the determinants of their adherence to health professionals’ recommendations, especially in a primary health care context. Accordingly, three main aims were outlined for this thesis: (1) investigate the psychosocial determinants of immigrant caregivers’ adherence to CPC recommendations; (2) develop and validate a measure of caregivers’ adherence to CPC recommendations; (3) explore immigrant caregivers’ social representations about the Portuguese CPC services and activities. Four studies were conducted to achieve these aims. Studies 1 and 2 were qualitative and included 35 immigrant (Cape Verdean and Brazilian) and Portuguese caregivers. Study 3 (n = 662) and Study 4 (n = 123) were quantitative cross-sectional, including immigrant (mainly Brazilian) and Portuguese parents of children aged between 2 and 6 years. Findings show that caregivers’ adherence to CPC recommendations is a two-dimensional concept – including adherence to a safe psychomotor development and nutritional counselling – reliably and validly assessed by the new CPC-Adherence Scale. Several psychosocial determinants, at different levels of analysis (structural, individual, interpersonal and intergroup), are relevant predictors of immigrant caregivers’ adherence to CPC recommendations. Caregivers’ representations of CPC and its activities to some extent differ according to caregivers' social and cultural backgrounds. These findings contribute to better understand the role of immigrants’ “non-intentional” adherence behaviors and, hence, to better inform interventions to increase CPC health professionals’ multicultural sensitivity/competence.A adesão dos cuidadores imigrantes às recomendações de Vigilância de Saúde Infantil (VSI) pode minimizar problemas de saúde prevalentes nos seus filhos (e.g. obesidade, problemas dentários). Estudos anteriores centraram-se nos determinantes de acesso dos imigrantes aos serviços de saúde, mas pouco se sabe sobre os determinantes da sua adesão às recomendações dos profissionais de saúde, especialmente num contexto preventivo de saúde. Assim, delinearam-se três objetivos principais para esta tese: (1) investigar os determinantes psicossociais da adesão dos cuidadores imigrantes às recomendações de VSI; (2) desenvolver e validar uma medida da adesão dos cuidadores às recomendações de VSI; (3) explorar as representações sociais dos cuidadores imigrantes sobre os serviços de VSI Portugueses e suas atividades. Para atingir esses objetivos, realizaram-se quatro estudos. Os Estudos 1 e 2, qualitativos, incluíram 35 cuidadoras imigrantes (Cabo-Verdianas e Brasileiras) e Portuguesas. Os Estudos 3 (n = 662) e 4 (n = 123), quantitativos e transversais, incluíram pais imigrantes (principalmente Brasileiros) e Portugueses de crianças com 2 a 6 anos de idade. Os resultados mostram que a adesão dos cuidadores às recomendações de VSI é um conceito bidimensional – inclui a adesão ao desenvolvimento psicomotor seguro e aconselhamento nutricional – medido pela desenvolvida CPC-Adherence Scale. Vários determinantes psicossociais, de diferentes níveis de análise (estrutural, individual, interpessoal e intergrupal), são preditores relevantes da adesão dos cuidadores imigrantes às recomendações de VSI. As representações sobre a VSI e suas atividades diferem, de certa forma, em função das origens sociais e culturais das cuidadoras. Estes resultados contribuem para melhorar a compreensão sobre os comportamentos de adesão “não intencionais” dos imigrantes e, consequentemente, para informar intervenções que aumentem a sensibilidade/competência multicultural dos profissionais de VSI

Modeling of a Patient Positioning System for use in MRI Machines

Since its commercial introduction in the early 1980s, Magnetic Resonance Imaging (MRI) has become an important medical diagnostic tool for radiologists. Researchers and manufacturers have refined the imaging hardware and expanded the intended uses for MRI devices over time. However, MRI manufacturers have not improved the design of the mechanisms and control schemes used to move the patient.Patient positioning systems are required to handle unknown weights up to 225 kilograms, accommodate friction disturbances, move long distances at high speed, move small distances in less than 1 second, and attain sub millimeter bidirectional precision. Little research is available on model based design of patient positioning mechanisms.A representative model of an MRI patient positioning device is proposed. The novelty of patient positioning system design in a strong magnetic field is examined in detail. Methods for creating an appropriate fidelity high level system friction, single degree of freedom Simulink model of the system are discussed. A physical facsimile of the system is constructed. The output of the Simulink model is compared to the physical system performance with respect to robustness against friction coefficient sensitivity, load mismatches and noise disturbances. Potential model simplifications and future work opportunities are highlighted

COMBINING HUMAN FACTORS AND DATA SCIENCE METHODS TO EVALUATE THE USE OF FREE TEXT COMMUNICATION ORDERS IN ELECTRONIC HEALTH RECORDS

Medication errors are a leading cause of death in the United States. Electronic Health Records (EHR) along with Computerized Provider Order Entry (CPOE) are considered promising ways to reduce these errors. However, EHR systems have not eliminated medication errors. Moreover, in some cases they have facilitated errors due to issues such as poor usability and negative effects on clinical workflows. The use of unexpected free text within a CPOE system can serve as a marker that the system does not adequately support clinical workflow. Prior studies have looked at the use of free text within medication orders, but the inclusion of medication related information in communication for non-medication orders (CNMOs), a type of free text order, has not been adequately studied. This mixed-methods study identified the prevalence, nature and reasons for the inclusion of medication related information in CNMOs using a large sample of CNMOs placed at a mid-Atlantic hospital system in 2017, and via interviews with physicians. The study found that more than 42% of CNMOs contain medication related information. Moreover, the use of CNMOs varied significantly across provider types, hospital locations, patient settings and other factors. The study found 10 themes that might cause providers to adopt such workarounds, including missing functionality and poor usability. The viii study also identified several general challenges in communicating medication information in the EHR, and potential solutions to mitigate these challenges. This dissertation also demonstrates how natural language processing could be used to identify medication related CNMOs