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- Creators: Department of Psychology
Emerging Information Technology, Storage and Evaluation within Healthcare: A Discerning IMT Analysis
This study examines patient care in the SHOW free clinic in Phoenix, Arizona, which serves adults experiencing homelessness. This study asks two questions: First, do clinicians in Phoenix’s SHOW free clinic discuss with patients how to pay for and where to access follow-up services and medications? Second, how do the backgrounds of patients, measured by scales based on the Gelberg-Anderson behavioral model for vulnerable populations, correlate with patient outcomes, including number of unmet needs in clinic, patient satisfaction with care, and patient perceived health status? To answer these questions, structured surveys were administered to SHOW clinic patients at the end of their visits. Results were analyzed using Pearson’s correlations and odds ratios. 21 patients completed the survey over four weeks in February-March 2017. We did not identify any statistically significant correlations between predisposing factors such as severity/duration of homelessness, mental health history, ethnicity, or LGBTQ status and quality of care outcomes. Twenty nine percent of surveyed patients reported having one or more unmet needs following their SHOW clinic visit suggesting an important area for future research. The results from this study indicate that measuring unmet needs is a feasible alternative to patient satisfaction surveys for assessing quality of care in student-run free clinics for homeless populations.
Through my work with the Arizona State University Blockchain Research Lab (BRL) and JennyCo, one of the first Healthcare Information (HCI) HIPAA - compliant decentralized exchanges, I have had the opportunity to explore a unique cross-section of some of the most up and coming DLTs including both DAGs and blockchains. During this research, four major technologies (including JennyCo’s own systems) presented themselves as prime candidates for the comparative analysis of two models for implementing JennyCo’s system architecture for the monetization of healthcare information exchanges (HIEs). These four identified technologies and their underlying mechanisms will be explored thoroughly throughout the course of this paper and are listed with brief definitions as follows: Polygon - “Polygon is a “layer two” or “sidechain” scaling solution that runs alongside the Ethereum blockchain. MATIC is the network’s native cryptocurrency, which is used for fees, staking, and more” [8]. Polygon is the scalable layer involved in the L2SP architecture. Ethereum - “Ethereum is a decentralized blockchain platform that establishes a peer-to-peer network that securely executes and verifies application code, called smart contracts.” [9] This foundational Layer-1 runs thousands of nodes and creates a unique decentralized ecosystem governed by turing complete automated programs. Ethereum is the foundational Layer involved in the L2SP. Constellation - A novel Layer-0 data-centric peer-to-peer network that utilizes the “Hypergraph Transfer Protocol or HGTP, a DLT known as a [DAG] protocol with a novel reputation-based consensus model called Proof of Reputable Observation (PRO). Hypergraph is a feeless decentralized network that supports the transfer of $DAG cryptocurrency.” [10] JennyCo Protocol - Acts as a HIPAA compliant decentralized HIE by allowing consumers, big businesses, and brands to access and exchange user health data on a secure, interoperable, and accessible platform via DLT. The JennyCo Protocol implements utility tokens to reward buyers and sellers for exchanging data. Its protocol nature comes from its DLT implementation which governs the functioning of on-chain actions (e.g. smart contracts). In this case, these actions consist of secure and transparent health data exchange and monetization to reconstitute data ownership to those who generate that data [11]. With the direct experience of working closely with multiple companies behind the technologies listed, I have been exposed to the benefits and deficits of each of these technologies and their corresponding approaches. In this paper, I will use my experience with these technologies and their frameworks to explore two distributed ledger architecture protocols in order to determine the more effective model for implementing JennycCo’s health data exchange. I will begin this paper with an exploration of blockchain and directed acyclic graph (DAG) technologies to better understand their innate architectures and features. I will then move to an in-depth look at layered protocols, and healthcare data in the form of EHRs. Additionally, I will address the main challenges EHRs and HIEs face to present a deeper understanding of the challenges JennyCo is attempting to address. Finally, I will demonstrate my hypothesis: the Hypergraph Transfer Protocol (HGTP) model by Constellation presents significant advantages in scalability, interoperability, and external data security over the Layer-2 Scalability Protocol (L2SP) used by Polygon and Ethereum in implementing the JennyCo protocol. This will be done through a thorough breakdown of each protocol along with an analysis of relevant criteria including but not limited to: security, interoperability, and scalability. In doing so, I hope to determine the best framework for running JennyCo’s HIE Protocol.
receptor (RAR) and vitamin D receptor (VDR). The RXR/RAR dimer is activated by ligand all
trans retinoic acid (ATRA), which culminates in gut-specific effector T cell migration. Similarly,
the VDR/RXR dimer binds 1,25(OH)2D3 to cause skin-specific effector T cell migration.
Targeted migration is a potent addition to current vaccines, as it would induce activated T cell
trafficking to appropriate areas of the immune system and ensure optimal stimulation (40).
ATRA, while in use clinically, is limited by toxicity and chemical instability. Rexinoids
are stable, synthetically developed ligands specific for the RXR. We have previously shown that
select rexinoids can enhance upregulation of gut tropic CCR9 receptors on effector T cells.
However, it is important to establish whether these cells can actually migrate, to show the
potential of rexinoids as vaccine adjuvants that can cause gut specific T cell migration.
Additionally, since the RXR is a major contributor to VDR-mediated transcription and
epidermotropism (15), it is worth investigating whether these compounds can also function as
adjuvants that promote migration by increasing expression of skin tropic CCR10 receptors on T
cells.
Prior experiments have demonstrated that select rexinoids can induce gut tropic migration
of CD8+ T cells in an in vitro assay and are comparable in effectiveness to ATRA (7). The effect
of rexinoids on CD4+ T cells is unknown however, so the aim of this project was to determine if
rexinoids can cause gut tropic migration in CD4+ T cells to a similar extent. A secondary aim
was to investigate whether varying concentrations in 1,25-Dihydroxyvitamin D3 can be linked to
increasing CCR10 upregulation on Jurkat CD4+ T cells, with the future aim to combine 1,25
Dihydroxyvitamin D3 with rexinoids.
These hypotheses were tested using murine splenocytes for the migration experiment, and
human Jurkat CD4+ T cells for the vitamin D experiment. Migration was assessed using a
Transwell chemotaxis assay. Our findings support the potential of rexinoids as compounds
capable of causing gut-tropic migration in murine CD4+ T cells in vitro, like ATRA. We did not
observe conclusive evidence that vitamin D3 causes upregulated CCR10 expression, but this
experiment must be repeated with a human primary T cell line.