MediLinker: a blockchain-based decentralized health information management platform for patient-centric healthcare.

Patients' control over how their health information is stored has been an ongoing issue in health informatics. Currently, most patients' health information is stored in centralized but siloed health information systems of healthcare institutions, rarely connected to or interoperable with other institutions outside of their specific health system. This centralized approach to the storage of health information is susceptible to breaches, though it can be mitigated using technology that allows for decentralized access. One promising technology that offers the possibility of decentralization, data protection, and interoperability is blockchain. In 2019, our interdisciplinary team from the University of Texas at Austin's Dell Medical School, School of Information, Department of Electrical and Computer Engineering, and Information Technology Services developed MediLinker-a blockchain-based decentralized health information management platform for patient-centric healthcare. This paper provides an overview of MediLinker and outlines its ongoing and future development and implementation. Overall, this paper contributes insights into the opportunities and challenges in developing and implementing blockchain-based technologies in healthcare.

Pandemics are catalysts of scientific novelty: Evidence from COVID-19.

Scientific novelty drives the efforts to invent new vaccines and solutions during the pandemic. First-time collaboration and international collaboration are two pivotal channels to expand teams' search activities for a broader scope of resources required to address the global challenge, which might facilitate the generation of novel ideas. Our analysis of 98,981 coronavirus papers suggests that scientific novelty measured by the BioBERT model that is pretrained on 29 million PubMed articles, and first-time collaboration increased after the outbreak of COVID-19, and international collaboration witnessed a sudden decrease. During COVID-19, papers with more first-time collaboration were found to be more novel and international collaboration did not hamper novelty as it had done in the normal periods. The findings suggest the necessity of reaching out for distant resources and the importance of maintaining a collaborative scientific community beyond nationalism during a pandemic.

Clinical, Organizational and Regulatory, and Ethical and Social (CORES) Issues and Recommendations on Blockchain Deployment for Healthcare: Evidence from Experts.

Objective: While existing research by our team has demonstrated the feasibility of building a decentralized identity management application ("MediLinker") for health information, there are implementation issues related to testing such blockchain-based health applications in real-world clinical settings. In this study, we identified clinical, organizational and regulatory, and ethical and social (CORES) issues, including recommendations, associated with deploying MediLinker, and blockchain in general, for clinical testing. Methods: CORES issues and recommendations were identified through a focus group with 11 academic, industry, and government experts on March 26, 2021. They were grouped according to their expertise: clinical care (n = 4), organizational and regulatory concerns (n = 4), and ethical and social issues (n = 3). The focus group was conducted via Zoom in which experts were briefed about the study aims, formed into breakout groups to identify key issues based on their group's expertise, and reconvened to share identified issues with other groups and to discuss potential recommendations to address such issues. The focus group was video recorded and transcribed. The resulting transcriptions and meeting notes were imported to MAXQDA 2018 for thematic analysis. Results: Clinical experts identified issues that concern the clinical system, clinical administrators, clinicians, and patients. Organizational and regulatory experts emphasized issues on accountability, compliance, and legal safeguards. Ethics and social-context experts raised issues on trust, transparency, digital divide, and health-related digital autonomy. Accordingly, experts proposed six recommendations that could address most of the identified issues: (1) design interfaces based on patient preferences, (2) ensure testing with diverse populations, (3) ensure compliance with existing policies, (4) present potential positive outcomes to top management, (5) maintain clinical workflow, and (6) increase the public's awareness of blockchain. Conclusions: This study identified a myriad of CORES issues associated with deploying MediLinker in clinical settings. Moreover, the study also uncovered several recommendations that could address such issues. The findings raise awareness on CORES issues that should be considered when designing, developing, and deploying blockchain for healthcare. Further, the findings provide additional insights into the development of MediLinker from a prototype to a minimum viable product for clinical testing. Future studies can use CORES as a socio-technical model to identify issues and recommendations associated with deploying health information technologies in clinical settings.

Technical Design and Development of A Self-Sovereign Identity Management Platform for Patient-Centric Health Care Using Blockchain Technology.

Objective: Clinical data in the United States are highly fragmented, stored in numerous different databases, and are defined by service providers or clinical specialties rather than by individuals or their families. As a result, linking or aggregating a complete record for a patient is a major technological, legal, and operational challenge. One of the factors that has made clinical data integration so difficult to achieve is the lack of a universal ID for everyone. This leads to other related problems of having to prove identity at each interaction with the health system and repeatedly providing basic information on demographics, insurance, payment, and medical conditions. Traditional solutions that require complex governance, expensive technology, and risks to privacy and security of the data have failed adequately to solve this interoperability problem. We describe the technical design decisions of a patient-centric decentralized health identity management system using the blockchain technology, called MediLinker, to address some of these challenges. Design: Our multidisciplinary research group developed and implemented an identity wallet, which uses the blockchain technology to manage verifiable credentials issued by healthcare clinics, banks, and insurance companies. To manage patient's self-sovereign identity, we leveraged the Hyperledger Indy blockchain framework to store patient's decentralized identifiers (DIDs) and the schemas or format for each credential type. In contrast, the credentials containing patient data are stored 'off-ledger' in each person's wallet and accessible via a computer or smartphone. We used Hyperledger Aries as a middleware layer (API: Application Programming Interface) to connect Hyperledger Indy with the front-end, which was developed using a JavaScript framework, ReactJS (Web Application) and React Native (iOS Application). Results: MediLinker allows users to store their personal data on digital wallets, which they control. It uses a decentralized trusted identity using Hyperledger Indy and Hyperledger Aries. Patients use MediLinker to register and share their information securely and in a trusted system with healthcare and other service providers. Each MediLinker wallet can have six credential types: health ID with patient demographics, insurance, medication list including COVID-19 vaccination status, credit card, medical power of attorney (MPOA) for guardians of pediatric or geriatric patients, and research consent. The system allows for in-person and remote granting and revoking of such permissions for care, research, or other purposes without repeatedly requiring physical identity documents or enrollment information. Conclusion: We successfully developed and tested a blockchain-based technical architecture, described in this article, as an identity management system that may be operationalized and scaled for future implementation to improve patient experience and control over their personal information.

Qualitative and quantitative approach to assess of the potential for automating administrative tasks in general practice.

OBJECTIVE: To identify the extent to which administrative tasks carried out by primary care staff in general practice could be automated. DESIGN: A mixed-method design including ethnographic case studies, focus groups, interviews and an online survey of automation experts. SETTING: Three urban and three rural general practice health centres in England selected for differences in list size and organisational characteristics. PARTICIPANTS: Observation and interviews with 65 primary care staff in the following job roles: administrator, manager, general practitioner, healthcare assistant, nurse practitioner, pharmacy technician, phlebotomist, practice nurse, pharmacist, prescription clerk, receptionist, scanning clerk, secretary and medical summariser; together with a survey of 156 experts in automation technologies. METHODS: 330 hours of ethnographic observation and documentation of administrative tasks carried out by staff in each of the above job roles, followed by coding and classification; semistructured interviews with 10 general practitioners and 6 staff focus groups. The online survey of machine learning, artificial intelligence and robotics experts was analysed using an ordinal Gaussian process prediction model to estimate the automatability of the observed tasks. RESULTS: The model predicted that roughly 44% of administrative tasks carried out by staff in general practice are 'mostly' or 'completely' automatable using currently available technology. Discussions with practice staff underlined the need for a cautious approach to implementation. CONCLUSIONS: There is considerable potential to extend the use of automation in primary care, but this will require careful implementation and ongoing evaluation.

The social informatics of knowledge.

In the Introduction to this special issue on the Social Informatics of Knowledge, the editors of the issue reflect on the history of the term "social informatics" and how the articles in this issue both reflect and depart from the original concept. We examine how social informatics researchers have studied knowledge, computerization, and the workplace, and how all of those have evolved over time. We describe the process by which articles were included, how they help us understand the field of social informatics scholarship today, and reflect briefly on what the future of the field holds.

The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System.

BACKGROUND: Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care. OBJECTIVE: This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care-specific occupation and task observational data to provide good insights on this misunderstood topic. METHODS: This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy. RESULTS: Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019. CONCLUSIONS: We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/11232.

Evidence of association between brain-derived neurotrophic factor gene and bipolar disorder.

OBJECTIVE: Brain-derived neurotrophic factor (BDNF) plays an important role in the survival, differentiation, and outgrowth of select peripheral and central neurons throughout adulthood. Growing evidence suggests that BDNF is involved in the pathophysiology of mood disorders. METHODS: Ten single nucleotide polymorphisms (SNPs) across the BDNF gene were genotyped in a sample of 1749 Caucasian Americans from 250 multiplex bipolar families. Family-based association analysis was used with three hierarchical bipolar disorder models to test for an association between SNPs in BDNF and the risk of bipolar disorder. In addition, an exploratory analysis was performed to test for an association of the SNPs in BDNF and the phenotypes of rapid cycling and episode frequency. RESULTS: Evidence of association (P<0.05) was found with several of the SNPs using multiple models of bipolar disorder; one of these SNPs also showed evidence of association (P<0.05) with rapid cycling. CONCLUSION: These results provide further evidence that variation in BDNF affects the risk for bipolar disorder.

Patterns of regional brain activity in alcohol-dependent subjects.

BACKGROUND: Electroencephalographic (EEG) measures of hemispheric asymmetry in anterior brain activity have been related to a variety of indices of psychopathology and emotionality. However, little is known about patterns of frontal asymmetry in alcohol-dependent (AD) samples. It is also unclear whether psychiatric comorbidity in AD subjects accounts for additional variance in frontal asymmetry, beyond a diagnosis of AD alone. METHODS: We compared 193 AD subjects with 108 control subjects on resting brain activity in anterior and posterior regions, as indexed by asymmetries in alpha band power in the left and right hemispheres. Within the AD group alone, we examined whether comorbid major depressive disorder (MDD) or antisocial personality disorder (ASPD) had effects on regional asymmetry. RESULTS: Compared with control subjects, AD subjects exhibited lower left, relative to right, cortical activation in anterior regions. Evidence that comorbidity in AD subjects accounted for further variance in EEG asymmetry was mixed; AD subjects with comorbid ASPD were not significantly different from those without ASPD, while AD subjects with a lifetime history of MDD showed less asymmetry in anterior regions than those without MDD. CONCLUSIONS: Our findings indicate that AD subjects exhibit a pattern of frontal asymmetry similar to that found in other psychiatric groups. Results examining the effects of comorbidity in AD on EEG asymmetry were inconclusive. The implications of our findings for future work are described.

A family study of alcohol dependence: coaggregation of multiple disorders in relatives of alcohol-dependent probands.

BACKGROUND: Alcohol dependence tends to aggregate within families. We analyzed data from the family collection of the Collaborative Study on the Genetics of Alcoholism to quantify familial aggregation using several different criterion sets. We also assessed the aggregation of other psychiatric disorders in the same sample to identify areas of possible shared genetic vulnerability. DESIGN: Age-corrected lifetime morbid risk was estimated in adult first-degree relatives of affected probands and control subjects for selected disorders. Diagnostic data were gathered by semistructured interview (the Semi-Structured Assessment for the Genetics of Alcoholism), family history, and medical records. Rates of illness were corrected by validating interview and family history reports against senior clinicians' all sources best estimate diagnoses. Sex, ethnicity, comorbidity, cohort effects, and site of ascertainment were also taken into account. RESULTS: Including data from 8296 relatives of alcoholic probands and 1654 controls, we report lifetime risk rates of 28.8% and 14.4% for DSM-IV alcohol dependence in relatives of probands and controls, respectively; respective rates were 37.0% and 20.5% for the less stringent DSM-III-R alcohol dependence, 20.9% and 9.7% for any DSM-III-R diagnosis of nonalcohol nonnicotine substance dependence, and 8.1% and 5.2% for antisocial personality disorder. Rates of specific substance dependence were markedly increased in relatives of alcohol-dependent probands for cocaine, marijuana, opiates, sedatives, stimulants, and tobacco. Aggregation was also seen for panic disorder, obsessive-compulsive disorder, posttraumatic stress disorder, and major depression. CONCLUSIONS: The risk of alcohol dependence in relatives of probands compared with controls is increased about 2-fold. The aggregation of antisocial personality disorder, drug dependence, anxiety disorders, and mood disorders suggests common mechanisms for these disorders and alcohol dependence within some families. These data suggest new phenotypes for molecular genetic studies and alternative strategies for studying the heterogeneity of alcohol dependence.

Genome-wide scan and conditional analysis in bipolar disorder: evidence for genomic interaction in the National Institute of Mental Health genetics initiative bipolar pedigrees.

BACKGROUND: In 1989 the National Institute of Mental Health began a collaborative effort to identify genes for bipolar disorder. The first 97 pedigrees showed evidence of linkage to chromosomes 1, 6, 7, 10, 16, and 22 (Nurnberger et al 1997). An additional 56 bipolar families have been genotyped, and the combined sample of 153 pedigrees studied. METHODS: Three hierarchical affection status models were analyzed with 513 simple sequence repeat markers; 298 were common across all pedigrees. The primary analysis was a nonparametric genome-wide scan. We performed conditional analyses based on epistasis or heterogeneity for five regions. RESULTS: One region, on 16p13, was significant at the genome-wide p <.05 level. Four additional chromosomal regions (20p12, 11p15, 6q24, and 10p12) showed nominally significant linkage findings (p

Genomewide linkage analyses of bipolar disorder: a new sample of 250 pedigrees from the National Institute of Mental Health Genetics Initiative.

We conducted genomewide linkage analyses on 1,152 individuals from 250 families segregating for bipolar disorder and related affective illnesses. These pedigrees were ascertained at 10 sites in the United States, through a proband with bipolar I affective disorder and a sibling with bipolar I or schizoaffective disorder, bipolar type. Uniform methods of ascertainment and assessment were used at all sites. A 9-cM screen was performed by use of 391 markers, with an average heterozygosity of 0.76. Multipoint, nonparametric linkage analyses were conducted in affected relative pairs. Additionally, simulation analyses were performed to determine genomewide significance levels for this study. Three hierarchical models of affection were analyzed. Significant evidence for linkage (genomewide P<.05) was found on chromosome 17q, with a peak maximum LOD score of 3.63, at the marker D17S928, and on chromosome 6q, with a peak maximum LOD score of 3.61, near the marker D6S1021. These loci met both standard and simulation-based criteria for genomewide significance. Suggestive evidence of linkage was observed in three other regions (genomewide P<.10), on chromosomes 2p, 3q, and 8q. This study, which is based on the largest linkage sample for bipolar disorder analyzed to date, indicates that several genes contribute to bipolar disorder.

Is there a genetic relationship between alcoholism and depression?

The Collaborative Study on the Genetics of Alcoholism (COGA) seeks to identify genes contributing to alcoholism and related traits (i.e., phenotypes), including depression. Among alcoholic subjects the COGA study found an increased prevalence of depressive syndrome (i.e., depression that may or may not occur in conjunction with increased drinking). This combination of alcoholism and depression tends to run in families. Comorbid alcoholism and depression occurred substantially more often in first-degree relatives of COGA participants with alcoholism than in relatives of control participants. Based on these data, COGA investigators defined three phenotypes--"alcoholism," "alcoholism and depression," and "alcoholism or depression"--and analyzed whether these phenotypes were linked to specific chromosomal regions. These analyses found that the "alcoholism or depression" phenotype showed significant evidence for genetic linkage to an area on chromosome 1. This suggests that a gene or genes on chromosome 1 may predispose some people to alcoholism and others to depression (which may be alcohol induced).