Acupressure to Reduce Treatment-Related Symptoms for Children With Cancer and Recipients of Hematopoietic Stem Cell Transplant: Protocol for a Randomized Controlled Trial.

BackgroundWe describe the study design and protocol of a pragmatic randomized controlled trial (RCT) Acupressure for Children in Treatment for a Childhood Cancer (ACT-CC).ObjectiveTo describe the feasibility and effectiveness of an acupressure intervention to decrease treatment-related symptoms in children in treatment for cancer or recipients of a chemotherapy-based hematopoietic stem cell transplant (HSCT).DesignTwo-armed RCTs with enrollment of 5 to 30 study days.SettingTwo pediatric teaching hospitals.PatientsEighty-five children receiving cancer treatment or a chemotherapy-based HSCT each with 1 parent or caregiver.InterventionPatients are randomized 1:1 to receive either usual care plus daily professional acupressure and caregiver delivered acupressure versus usual care alone for symptom management. Participants receive up to 20 professional treatments.Main outcomeA composite nausea/vomiting measure for the child.Secondary outcomesChild's nausea, vomiting, pain, fatigue, depression, anxiety, and positive affect.Parent outcomesDepression, anxiety, posttraumatic stress symptoms, caregiver self-efficacy, and positive affect. Feasibility of delivering the semistandardized intervention will be described. Linear mixed models will be used to compare outcomes between arms in children and parents, allowing for variability in diagnosis, treatment, and age.DiscussionTrial results could help childhood cancer and HSCT treatment centers decide about the regular inclusion of trained acupressure providers to support symptom management

Integrating heterogeneous data into electronic medical record analysis

Electronic medical records (EMRs) are the digital equivalent of paper records at a clinician's office. They contain patient information such as treatment and medical history, and have been shown to have a wide variety of benefits. However, EMRs typically contain a multitude of diverse data, including images, doctor notes, medical test results, and genomic data. This heterogeneity generates high dimensionality and data sparsity, which are two of the most prevalent culprits that exacerbate already difficult computational problems. Additionally, domain-specific characteristics, such as the existence of synonyms in the medical vocabulary, introduce ambiguity. This can further reduce the data mining potential of EMRs. This thesis is a systematic study that addresses these issues associated with EMRs. In particular, I utilized heterogeneous data sources that are typically incompatible, and then developed frameworks in which these data sources complement one another. As a result, these methods have the potential for direct clinical translation, paving the way for improving healthcare from a data-driven perspective. To improve a variety of downstream healthcare applications, such as patient subcategorization, survival analysis, and visualization, I used external networks of domain knowledge consisting of drug-symptom relationships, protein-protein interactions, and genetic information to enhance patient records. I found that this enhancement process increased the data mining capabilities as well as the interpretability of the EMRs. To improve EMR retrieval systems, I developed a query expansion method that frames symptoms and treatments as two different languages. I found that a topic modeling method that follows this dual-language framework yielded the highest performance. Lastly, I showed that due to pathological similarities, jointly studying Alzheimer's disease and Parkinson's disease resulted in higher computational power by effectively increasing the size of the training datasets. This allowed for the accurate prediction of the onset of dementia in both diseases. Each of these results can lay the groundwork for applications that have the potential to be implemented directly in clinical practice, improving the safety and quality of patient care

Pharmacogenomics

This Special Issue focuses on the current state of pharmacogenomics (PGx) and the extensive translational process, including the identification of functionally important PGx variation; the characterization of PGx haplotypes and metabolizer statuses, their clinical interpretation, clinical decision support, and the incorporation of PGx into clinical care

Identifying key influences on antibiotic use in China: a systematic scoping review and narrative synthesis

INTRODUCTION: The inappropriate use of antibiotics is a key driver of antimicrobial resistance. In China, antibiotic prescribing and consumption exceed recommended levels and are relatively high internationally. Understanding the influences on antibiotic use is essential to informing effective evidence-based interventions. We conducted a scoping review to obtain an overview of empirical research about key behavioural, cultural, economic and social influences on antibiotic use in China. METHODS: Searches were conducted in Econlit, Medline, PsycINFO, Social Science citation index and the Cochrane Database of Systematic Reviews for the period 2003 to early 2018. All study types were eligible including observational and intervention, qualitative and quantitative designs based in community and clinical settings. Two authors independently screened studies for inclusion. A data extraction form was developed incorporating details on study design, behaviour related to antibiotic use, influences on behaviour and information on effect (intervention studies only). RESULTS: Intervention studies increased markedly from 2014, and largely focused on the impact of national policy and practice directives on antibiotic use in secondary and tertiary healthcare contexts in China. Most studies used pragmatic designs, such as before and after comparisons. Influences on antibiotic use clustered under four themes: antibiotic prescribing; adherence to antibiotics; self-medicating behaviour and over-the-counter sale of antibiotics. Many studies highlighted the use of antibiotics without a prescription for common infections, which was facilitated by availability of left-over medicines and procurement from local pharmacies. CONCLUSIONS: Interventions aimed at modifying antibiotic prescribing behaviour show evidence of positive impact, but further research using more robust research designs, such as randomised trials, and incorporating process evaluations is required to better assess outcomes. The effect of national policy at the primary healthcare level needs to be evaluated and further exploration of the influences on antibiotic self-medicating is required to develop interventions that tackle this behaviour

Three Research Essays on Propensity to Disclose Medical Information Through Formal and Social Information Technologies

Abstract This dissertation, which is comprised of three essays, examined disclosure propensity of healthcare providers from the US and Thailand and disclosure of personal health problems of healthcare consumers in social media context. Essay 1: A Deterrence Approach in Medical Data Misuse among Healthcare Providers Information and communication technology (ICT) have long been available for use in health care. With the potential to improve the quality, safety, and efficiency of health care, the diffusion of these technologies has steadily increased in the health care industry. With the adoption of electronic health records, personal electronics devices, internet connections and social network connections, comes the increased risk of medical data breaches. Due to the sensitivity of the information involved, and the existence of laws governing the use of this data, the responsibilities of a healthcare provider after a data breach remain a concern. Based on previous breach reports, institutional insiders were among the leading causes of medical data breaches. The causes were related to unawareness of institutional information security policies and system misuse. Thus it has become important to understand how to reduce such behaviors. Previous studies suggested deterrence theory that relies on security countermeasures can deter individuals\u27 misuse behaviors by increasing the perceived threat of punishment. Thus our model posits that security countermeasures decrease medical data misuse through the two mediators; perceived certainty of sanctions and perceived severity of sanctions. This model was tested by 176 healthcare providers from different institutions across the US. The results suggested that perceived severity of sanctions has more effect in reducing medical data misuse than perceived certainty of sanctions. Hospital information security policies and HIPAA has stronger effect on perceived severity of sanctions than perceived certainty of sanctions whereas EHR monitoring and auditing has stronger effect on perceived certainty of sanctions than perceived severity of sanctions. Results of the study and implications for the research are discussed. Essay 2: Propensity to Misuse Medical Data in an International Context - Deterrence and Cultural Values As information abuse by healthcare providers is a problem that is faced around the globe, our study examined the effect of deterrence within two cultures; Asian and American (Thailand and the US). The reason to compare these two countries is because the foundation of the structures of the laws and the hospital policies for medical data protection of these two countries are similar. Thus others confounding factors are minimized. In terms of cultural influences, Hofstede\u27s cultural dimensions that describe the effects of society\u27s culture on the values to its members are considered as factors that can have an interaction effect with deterrence. Four Hofstede\u27s cultural values were used; individualism-collectivism (IDV); uncertainty avoidance (UAI); power distance (PD); and long-term orientation (LTO). Also, social norms and morality were included. This study employed espoused values of Hofstede\u27s cultural values, since all individuals from a country will not have identical values. In this study, we examined 1) the effect of espoused cultural values on deterrence, and 2) the effect of Hofstede\u27s national cultural values on deterrence in two different healthcare cultures. Our model was tested by 613 healthcare providers; 437 from Thailand and 176 from the US. The results suggested that technical countermeasures had stronger effect on certainty and severity perception for both Thai and US cases, whereas procedural countermeasures had uncertain effect on sanctions perception for both cultures. The young generation of Thais was found more individualized and tended to have the same perception on sanctions as the Westerners. Social norms played an important role in reducing medical data misuse for Thai providers, whereas moral beliefs were more important for the US providers. Individuals who espoused different cultural values had different responses on medical data misuse. Results of the study and implications for the research are discussed. Essay 3: Intention to self-disclose personal health information in social media context In recent years social media is quickly becoming a large part of people\u27s everyday lives. With the availability of smartphones and tablets, coupled with a slew of apps for these devices, people now have ubiquitous access to social media. Virtual social media application encourages people to meet, and share information. Health problems represent one aspect that is shared in a social media context. Benefits and risks of self-disclosure are two main factors that determine social media users\u27 intention to share their sensitive information on social network. This paper integrates social exchange theory, a theory that focuses on gains and losses of building a relationship, and the social penetration theory, a theory that explains human\u27s self-disclosure, to construct the model for investigating self-disclosure intention on personal health problems of social medial users. In addition, we included factors that affect self-disclosure intention including ease of use of social media, social influence, and nature of health problems. Through an online survey, we examined factors that determine self-posting in social media account with 374 social media users across the US. The results suggested that individual and social benefits of self- disclosure outweighed the risks and have significant effect on self-disclosure intention on personal health problems. The individual risks and social risks had little negative effect on self-posting about health problems. In addition, social influence, and social networking experiences were factors that encouraged social media users to reveal their personal health problems

Infections in patients with chronic kidney disease : patterns, outcomes and the role of vitamin D for future prevention

Background: Chronic kidney disease (CKD) is increasingly recognized as a global public health problem. Patients with CKD are at high risk of infections. Frequent episodes of infections with greater use of antibiotics might put this population at risk of infections caused by resistant organisms. Thus, infection issues in patients with CKD could be related to another public health problem - antibiotic resistance. Aim: To investigate the antibiotic resistant patterns of pathogens responsible for infections, ascertain short-term and long-term patient outcomes during and after hospitalizations with infections and explore the role of vitamin D for infection prevention in patients with CKD. Methods: The thesis consists of two observational studies (Paper I & II), one cohort study (Paper III) and one systematic review and meta-analysis (Paper IV). Paper I, II & III explored the association between kidney function (defined as estimated glomerular filtration rate, eGFR) and various outcomes. These outcomes included microbial pattern (Paper I), prevalence of infections with multi-drug resistant organisms (MDROs) in the first positive microbial cultures (Paper I), intensive care unit admission (Paper II), length of hospital stay (Paper II), medical expense (Paper II), and mortality (Paper II & III). These were assessed in patients hospitalized with infections, using electronic medical records from four hospitals from 2012 to 2015 in China. Paper IV obtained data from existing literature to explore the association of infections with vitamin D status or use of vitamin D in patients treated with long-term dialysis. Results: In adult patients hospitalized with infections, the proportion of Gram-negative bacteria decreased while the proportion of Gram-positive bacteria increased across eGFR strata. Compared with the reference eGFR, lower eGFR was associated with: higher odds of infections by MDROs (19% and 41% higher in those with eGFR between 30-59 ml/min/1.73 m2 and eGFR <30 ml/min/1.73 m2, respectively) (Paper I); more than twofold higher adjusted odds of ICU admission, longer median length of hospital stay (P< 0.001), inferred 20.0% higher costs in those with eGFR< 60 ml/min/1.73 m2 (P< 0.001) (Paper II); progressively increased risks of cardiovascular mortality (subdistribution hazard ratio [SHR] 2.15 for eGFR 30-59 mL/min/1.73m2; SHR 3.19 for eGFR<30 mL/min/1.73m2) (Paper III). In the systematic review of vitamin D and infections in patients treated with long-term dialysis, the risk of composite infections was 39% lower in those with high/normal levels of 25-hydroxy vitamin D than that in those with low levels. Compared to those who did not use vitamin D, the pooled adjusted risk of composite infection was 41% lower in those who used vitamin D (Paper IV). Conclusions: CKD patients hospitalized with infections have a higher risk of infections by MDROs, poorer in-hospital outcomes resulting in higher medical costs and increased risk of cardiovascular mortality in the long-run. Use of vitamin D to achieve high/normal serum levels of 25(OH)-vitamin D might help lowering the risk of infections in maintenance dialysis patients. Further research is needed to investigate the potential role of vitamin D therapy in infection prevention among non-dialysis dependent CKD patients

Phenomenological Assessment of Integrative Medicine Decision-making and the Utility of Predictive and Prescriptive Analytics Tools

The U.S. Healthcare system is struggling to manage the burden of chronic disease, racial and socio-economic disparities, and the debilitating impact of the current global pandemic caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). More patients need alternatives to allopathic or “Western” medicine focused on fighting disease with mechanism, pharmaceuticals, and invasive measures. They are seeking Integrative Medicine which focuses on health and healing, emphasizing the centrality of the patient-physician relationship. In addition to providing the best conventional care, IM focuses on preventive maintenance, wellness, improved behaviors, and a holistic care plan. This qualitative research assessed whether predictive and prescriptive analytics (artificial intelligence tools that predict patient outcomes and recommend treatments, interventions, and medications) supports the decision-making processes of IM practitioners who treat patients suffering from chronic pain. PPA was used in a few U.S. hospitals but was not widely available for IM practitioners at the time of this research. Phenomenological interviews showed doctors benefit from technology that aggregates data, providing a clear patient snapshot. PPA exposed historical information that doctors often miss. However, current systems lacked the design to manage individualized, holistic care focused on the mind, body, and spirit. Using the Future-Focused Task-Technology Fit theory, the research suggested PPA could actually do more harm than good in its current state. Future technology must be patient-focused and designed with a better understanding of the IM task and group characteristics (e.g., the unique way providers practice medicine) to reduce algorithm aversion and increase adoption. In the ideal future state, PPA will surface healthcare Big Data from multiple sources, support communication and collaboration across the patient’s support system and community of care, and track the various objective and subjective factors contributing to the path to wellness

Predictive Learning from Real-World Medical Data: Overcoming Quality Challenges

Randomized controlled trials (RCTs) are pivotal in medical research, notably as the gold standard, but face challenges, especially with specific groups like pregnant women and newborns. Real-world data (RWD), from sources like electronic medical records and insurance claims, complements RCTs in areas like disease risk prediction and diagnosis. However, RWD's retrospective nature leads to issues such as missing values and data imbalance, requiring intensive data preprocessing. To enhance RWD's quality for predictive modeling, this thesis introduces a suite of algorithms developed to automatically resolve RWD's low-quality issues for predictive modeling. In this study, the AMI-Net method is first introduced, innovatively treating samples as bags with various feature-value pairs and unifying them in an embedding space using a multi-instance neural network. It excels in handling incomplete datasets, a frequent issue in real-world scenarios, and shows resilience to noise and class imbalances. AMI-Net's capability to discern informative instances minimizes the effects of low-quality data. The enhanced version, AMI-Net+, improves instance selection, boosting performance and generalization. However, AMI-Net series initially only processes binary input features, a constraint overcome by AMI-Net3, which supports binary, nominal, ordinal, and continuous features. Despite advancements, challenges like missing values, data inconsistencies, and labeling errors persist in real-world data. The AMI-Net series also shows promise for regression and multi-task learning, potentially mitigating low-quality data issues. Tested on various hospital datasets, these methods prove effective, though risks of overfitting and bias remain, necessitating further research. Overall, while promising for clinical studies and other applications, ensuring data quality and reliability is crucial for these methods' success