Operationalizing Healthcare Big Data in the Electronic Health Records using a Heatmap Visualization Technique

Background: The majority of the electronic health record (EHR) contains a wealth of information, including unstructured notes. Healthcare professionals may be missing substantial portions of essential diagnostic and treatment information by not focusing on unstructured texts. The objective of this study is to present progress notes data using heatmap visualization. Methods: In this study, the research team used the unstructured text from the progress notes of deidentified patient data. The research team conducted qualitative content-coding based on the clinical complexity model and developed a heatmap based on the processed frequency data. Result: The researchers developed a color-coded heatmap focusing on the severity and acuity of patients’ status accumulated through multiple previous patient’s visits. Conclusions: Future research into creating an automated process to generate the heatmap from an unstructured dataset can open up opportunities to operationalize big data in healthcare

Determinants of Idiosyncratic Volatility for Biotech IPO Firms

In this paper we examine the cross-sectional determinants of idiosyncratic volatility of biotech IPO firms. We extend current research in two directions. First, we test whether CEO stock options impact on idiosyncratic volatility. Second, we test new hypotheses that relate some easily identifiable managerial characteristics to idiosyncratic volatility. We find that the CEO stock options, resource dependence capabilities, and the age of board members help predict idiosyncratic volatility. Our results are robust for the various measures of idiosyncratic volatility and model specifications.National Natural Science Foundation of China (No. 70632001) Nanyang Business Schoo

The Inclusion of Health Data Standards in the Implementation of Pharmacogenomics Systems: A Scoping Review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffusewidely. In this study,we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods:We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation

Artificial Intelligence–Powered Smartphone App to Facilitate Medication Adherence: Protocol for a Human Factors Design Study

Background: Medication Guides consisting of crucial interactions and side effects are extensive and complex. Due to the exhaustive information, patients do not retain the necessary medication information, which can result in hospitalizations and medication nonadherence. A gap exists in understanding patients’ cognition of managing complex medication information. However, advancements in technology and artificial intelligence (AI) allow us to understand patient cognitive processes to design an app to better provide important medication information to patients. Objective: Our objective is to improve the design of an innovative AI- and human factor–based interface that supports patients’ medication information comprehension that could potentially improve medication adherence. Methods: This study has three aims. Aim 1 has three phases: (1) an observational study to understand patient perception of fear and biases regarding medication information, (2) an eye-tracking study to understand the attention locus for medication information, and (3) a psychological refractory period (PRP) paradigm study to understand functionalities. Observational data will be collected, such as audio and video recordings, gaze mapping, and time from PRP. A total of 50 patients, aged 18-65 years, who started at least one new medication, for which we developed visualization information, and who have a cognitive status of 34 during cognitive screening using the TICS-M test and health literacy level will be included in this aim of the study. In Aim 2, we will iteratively design and evaluate an AI-powered medication information visualization interface as a smartphone app with the knowledge gained from each component of Aim 1. The interface will be assessed through two usability surveys. A total of 300 patients, aged 18-65 years, with diabetes, cardiovascular diseases, or mental health disorders, will be recruited for the surveys. Data from the surveys will be analyzed through exploratory factor analysis. In Aim 3, in order to test the prototype, there will be a two-arm study design. This aim will include 900 patients, aged 18-65 years, with internet access, without any cognitive impairment, and with at least two medications. Patients will be sequentially randomized. Three surveys will be used to assess the primary outcome of medication information comprehension and the secondary outcome of medication adherence at 12 weeks. Results: Preliminary data collection will be conducted in 2021, and results are expected to be published in 2022. Conclusions: This study will lead the future of AI-based, innovative, digital interface design and aid in improving medication comprehension, which may improve medication adherence. The results from this study will also open up future research opportunities in understanding how patients manage complex medication information and will inform the format and design for innovative, AI-powered digital interfaces for Medication Guides

Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design

Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications. Objective: The objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype. Methods: Two pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted. Results: A mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations. Conclusions: Using HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype

Raising Capital with Uncertainty: Overpricing Initial Public Offering for Science-Based Firms with Multiple Ties to the Food and Drug Administration

Atlanta Conference on Science and Innovation Policy 2009This presentation was part of the session : Organizations of Science and InnovationThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. ©2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.I study the effect of indirect ties between the firm's scientific advisory board members and the Food and Drug Administration advisory committees on underwriter prestige, underwriting fee, underpricing and the initial offering price range for firms pursuing highly uncertain opportunities. Prestigious underwriters compete to underwrite securities offered by firms connected to the regulator. This result in overpricing even though the underwriter seeks to enforce underpricing. The findings contribute to the Coase-Knight debate about the role of uncertainty for firm boundaries

Regulatory dependence and Scientific Advisory Boards

The Food and Drug Administration (FDA) uses scientific procedures to evaluate regulated firms' new product applications. Much of its basic intellectual resources, in the form of scientific advisory committee members, come from research institutions. Regulated firms may seek connections to the FDA advisory committee members to affect the regulatory approval process. However, individual linkages may fail. The use of Scientific Advisory Boards (SAB) with multiple members provides redundant ties to the regulator, which means that the failure of each tie becomes less material. This paper is principally concerned with the firm's motivation to rebalance power imbalances rather than any actual regulatory outcomes. Controlling for alternative explanations, I find that dependence on the regulator is positively associated with the probability of having a SAB. Selected network diagrams add credibility to the hypothesis.Scientific Advisory Boards Food and drug administration Regulatory dependence

Comparable Stocks, Boundedly Rational Stock Markets and IPO Entry Rates

<div><p>In this study, we examine how initial public offerings (IPO) entry rates are affected when stock markets are boundedly rational and IPO firms infer information from their counterparts in the market. We hypothesize a curvilinear relationship between the number of comparable stocks and initial public offerings (IPO) entry rates into the NASDAQ Stock Exchange. Furthermore, we argue that trading volume and changes in stock returns partially mediates the relationship between the number of comparable stocks and IPO entry rates. The statistical evidence provides strong support for the hypotheses.</p></div

Sensitivity Analysis.

<p>Firm age is computed as the difference between the founding date and IPO date. JD/Ph.D/MD refers to the number of executives in the firm with a Juris Doctorate, Medical doctorate or Ph.D. Venture capital invested refers to the amount of money invested in the firm by venture capitalists. VCs refers to venture capitalists. Spin off refers to the IPO of a subsidiary. Nasdaq refers to a IPO that is listed on the Nadaq stock exchange. Foreign issuer refers to a foreign company listing in the United States.</p>*<p>Statistical significance at 10%,</p>**<p>Statistical significance at 5%,</p>***<p>Statistical significance at 1%.</p

Relational embeddedness and disruptive innovations: The mediating role of absorptive capacity

What are the driving factors of disruptive innovations in emerging economies from a network perspective? We argue that three forms of absorptive capacity (i.e. exploratory, exploitative and transformative learning) mediate the relationship between a firm’s network relations and disruptive innovations. We tested the hypotheses with a survey of 251 firms in China and iterated the conceptual model to reflect the crucial insight that disrupters in emerging economies can leap from exploratory learning to disruptive innovation. We surmise exploratory learning help disrupters outsource transformative and exploitative learning via their supply chains. Suggestions for future research and limitations are also discussed