Supporting Collaborative Health Tracking in the Hospital: Patients' Perspectives

The hospital setting creates a high-stakes environment where patients' lives depend on accurate tracking of health data. Despite recent work emphasizing the importance of patients' engagement in their own health care, less is known about how patients track their health and care in the hospital. Through interviews and design probes, we investigated hospitalized patients' tracking activity and analyzed our results using the stage-based personal informatics model. We used this model to understand how to support the tracking needs of hospitalized patients at each stage. In this paper, we discuss hospitalized patients' needs for collaboratively tracking their health with their care team. We suggest future extensions of the stage-based model to accommodate collaborative tracking situations, such as hospitals, where data is collected, analyzed, and acted on by multiple people. Our findings uncover new directions for HCI research and highlight ways to support patients in tracking their care and improving patient safety

Evaluating the Landscape of Personal Health Records in Korea: Results of the National Health Informatization Survey

Objectives This study examined the adoption and utilization of personal health records (PHR) across Korean medical institutions using data from the 2020 National Health and Medical Informatization Survey. Methods Spearheaded by the Ministry of Health and Welfare and prominent academic societies, this study surveyed PHR utilization in 574 medical institutions. Results Among these institutions, 84.9% (487 hospitals) maintained medical portals. However, just 14.1% (81 hospitals) had web-based or mobile PHRs, with 66.7% (28 of 42) of tertiary care hospitals adopting them. Tertiary hospitals led in PHR services: 87.8% offered certification issuance, 51.2% provided educational information, 63.4% supported online payment, and 95.1% managed appointment reservations. In contrast, general and smaller hospitals had lower rates. Online medical information viewing was prominent in tertiary hospitals (64.3%). Most patients accessed test results via PHRs, but other data types were less frequent, and only a few allowed downloads. Despite the widespread access to medical data through PHRs, integration with wearables and biometric data transfers to electronic medical records remained low, with limited plans for expansion in the coming three years. Conclusions Approximately two-thirds of the surveyed medical institutions provided PHRs, but hospitals and clinics in charge of community care had very limited PHR implementation. Government-led leadership is required to invigorate the use of PHRs in medical institutions

Design and Evaluation of User-Centered Explanations for Machine Learning Model Predictions in Healthcare

Challenges in interpreting some high-performing models present complications in applying machine learning (ML) techniques to healthcare problems. Recently, there has been rapid growth in research on model interpretability; however, approaches to explaining complex ML models are rarely informed by end-user needs and user evaluations of model interpretability are lacking, especially in healthcare. This makes it challenging to determine what explanation approaches might enable providers to understand model predictions in a comprehensible and useful way. Therefore, I aimed to utilize clinician perspectives to inform the design of explanations for ML-based prediction tools and improve the adoption of these systems in practice. In this dissertation, I proposed a new theoretical framework for designing user-centered explanations for ML-based systems. I then utilized the framework to propose explanation designs for predictions from a pediatric in-hospital mortality risk model. I conducted focus groups with healthcare providers to obtain feedback on the proposed designs, which was used to inform the design of a user-centered explanation. The user-centered explanation was evaluated in a laboratory study to assess its effect on healthcare provider perceptions of the model and decision-making processes. The results demonstrated that the user-centered explanation design improved provider perceptions of utilizing the predictive model in practice, but exhibited no significant effect on provider accuracy, confidence, or efficiency in making decisions. Limitations of the evaluation study design, including a small sample size, may have affected the ability to detect an impact on decision-making. Nonetheless, the predictive model with the user-centered explanation was positively received by healthcare providers, and demonstrated a viable approach to explaining ML model predictions in healthcare. Future work is required to address the limitations of this study and further explore the potential benefits of user-centered explanation designs for predictive models in healthcare. This work contributes a new theoretical framework for user-centered explanation design for ML-based systems that is generalizable outside the domain of healthcare. Moreover, the work provides meaningful insights into the role of model interpretability and explanation in healthcare while advancing the discussion on how to effectively communicate ML model information to healthcare providers

A Patient-Facing Dashboard to Promote Shingrix™ Vaccination in a Continuing Care Retirement Community: A Quality Improvement Project

BACKGROUND: Shingles is considered one of the most significant vaccine-preventable diseases of older adults based on its morbidity and public health burden, which increase drastically with age. Adult vaccine awareness and promotion programs are undervalued in the U.S.; in particular, educational programs targeting older adults are needed. Older adults have increasing rates of adoption of health information technology (HIT) to seek guidance and support for their medical needs. Leveraging HIT in the form of clinical dashboards is an option for providing reliable, safe and cost-effective vaccine education to older adults at high risk of vaccine-preventable disease. METHODS: The specific aims of this quality improvement project were to increase knowledge and uptake of recombinant zoster vaccine (Shingrix™) in older adults of a continuing retirement community (CCRC) through creation of a patient-facing clinical dashboard. The Four Pillars™ practice transformation program was used to guide implementation of the project including utilization of self-report surveys to determine baseline vaccination rates, perceptions of the dashboard and behavioral intention to receive future vaccination. The Patient Portal Acceptance Model (PPAM) was used as a theoretical framework to evaluate respondents’ perceptions of the dashboard across four domains: ease of use, usefulness, self-efficacy, and privacy/security. RESULTS: Respondents reported high levels of education and computer literacy. The majority reported using the internet for over 20 years and over 10 hours per week and 77.8% had used the internet to search for healthcare information within the past year. Baseline Shingrix™ vaccination levels in the CCRC were higher than national average but not at goal rates, and the majority of respondents eligible for vaccination did not plan to receive it. Respondents rated the dashboard moderately high on perceived ease of use, low on concerns about privacy/security, high on ability to use independently (self-efficacy), and low on perceived usefulness. DISCUSSION: The information provided by CCRC residents during development of this dashboard was valuable for elucidating motivators and barriers to HIT use in older adults, who largely view HIT as an adjunct to in-person interaction with a trusted provider. Improving older adults’ perceptions of HIT will be critical in the era of Covid-19, when many high-risk older adults are seeking alternatives to traditional provider visits. Respondents were willing and able to access and navigate the dashboard; however, shingles knowledge did not improve in this small sample. Improvements in the presentation of the material on the dashboard may improve perceptions of usefulness and comprehension of specialized clinical information. CONCLUSION: CCRC residents were receptive to receiving vaccine information via electronic dashboard and expressed interest in using this format as a source of other healthcare information. There is ample opportunity to expand patient-facing dashboards in the CCRC setting to provide a wide array of healthcare education for this population

Applying Process-Oriented Data Science to Dentistry

Background: Healthcare services now often follow evidence-based principles, so technologies such as process and data mining will help inform their drive towards optimal service delivery. Process mining (PM) can help the monitoring and reporting of this service delivery, measure compliance with guidelines, and assess effectiveness. In this research, PM extracts information about clinical activity recorded in dental electronic health records (EHRs) converts this into process-models providing stakeholders with unique insights to the dental treatment process. This thesis addresses a gap in prior research by demonstrating how process analytics can enhance our understanding of these processes and the effects of changes in strategy and policy over time. It also emphasises the importance of a rigorous and documented methodological approach often missing from the published literature. Aim: Apply the emerging technology of PM to an oral health dataset, illustrating the value of the data in the dental repository, and demonstrating how it can be presented in a useful and actionable manner to address public health questions. A subsidiary aim is to present the methodology used in this research in a way that provides useful guidance to future applications of dental PM. Objectives: Review dental and healthcare PM literature establishing state-of-the-art. Evaluate existing PM methods and their applicability to this research’s dataset. Extend existing PM methods achieving the aims of this research. Apply PM methods to the research dataset addressing public health questions. Document and present this research’s methodology. Apply data-mining, PM, and data-visualisation to provide insights into the variable pathways leading to different outcomes. Identify the data needed for PM of a dental EHR. Identify challenges to PM of dental EHR data. Methods: Extend existing PM methods to facilitate PM research in public health by detailing how data extracts from a dental EHR can be effectively managed, prepared, and used for PM. Use existing dental EHR and PM standards to generate a data reference model for effective PM. Develop a data-quality management framework. Results: Comparing the outputs of PM to established care-pathways showed that the dataset facilitated generation of high-level pathways but was less suitable for detailed guidelines. Used PM to identify the care pathway preceding a dental extraction under general anaesthetic and provided unique insights into this and the effects of policy decisions around school dental screenings. Conclusions: Research showed that PM and data-mining techniques can be applied to dental EHR data leading to fresh insights about dental treatment processes. This emerging technology along with established data mining techniques, should provide valuable insights to policy makers such as principal and chief dental officers to inform care pathways and policy decisions

Recommendations for the management of the haematological and onco-haematological aspects of Gaucher disease1

Current knowledge of the haematological and onco-haematological complications of type 1 Gaucher disease has been reviewed with the aim of identifying best clinical practice for treatment and disease management. It was concluded that: (i) Awareness of typical patterns of cytopenia can help clinicians distinguish haematological co-morbidities. (ii) Red blood cell studies and complete iron metabolism evaluation at baseline are recommended. (iii) Haemoglobin levels defining anaemia should be raised and used in Gaucher disease treatment and monitoring. (iv) Surgeons should be aware of potential bleeding complications during surgery in Gaucher patients. The higher incidence of multiple myeloma in Gaucher disease suggests that Gaucher patients should have their immunoglobulin profile determined at diagnosis and monitored every 2 years (patients <50 years) or every year (patients >50 years). If monoclonal gammopathy of undetermined significance (MGUS) is found, general MGUS guidelines should be followed. Future studies should focus on the utility of early treatment to prevent immunoglobulin abnormalities and multiple myeloma

HLA-A*32:01 is strongly associated with vancomycin-induced drug reaction with eosinophilia and systemic symptoms

Background Vancomycin is a prevalent cause of the severe hypersensitivity syndrome drug reaction with eosinophilia and systemic symptoms (DRESS) which leads to significant morbidity and mortality and commonly occurs in the setting of combination antibiotic therapy which impacts future treatment choices. Variations in human leukocyte antigen (HLA) class I in particular have been associated with serious T-cell mediated adverse drug reactions which has led to preventive screening strategies for some drugs. Objective To determine if variation in the HLA region is associated with vancomycin-induced DRESS. Methods Probable vancomycin DRESS cases were matched 1:2 with tolerant controls based on sex, race, and age using BioVU, Vanderbilt’s deidentified electronic health record database. Associations between DRESS and carriage of HLA class I and II alleles were assessed by conditional logistic regression. An extended sample set from BioVU was utilized to conduct a time-to-event analysis of those exposed to vancomycin with and without the identified HLA risk allele. Results Twenty-three individuals met inclusion criteria for vancomycin-associated DRESS. 19/23 (82.6%) cases carried HLA-A*32:01 compared to 0/46 (0%) of the matched vancomycin tolerant controls (p=1x10-8) and 6.3% of the BioVU population (n=54,249) (p=2x10-16). Time-to-event analysis of DRESS development during vancomycin treatment among the HLA-A*32:01 positive group indicated that 19.2% developed DRESS and did so within four weeks. Conclusions HLA-A*32:01 is strongly associated with vancomycin DRESS in a population of predominantly European ancestry. HLA-A*32:01 testing could improve antibiotic safety, help implicate vancomycin as the causal drug and preserve future treatment options with co-administered antibiotics