Assuring Reliable and Secure IT Services Security, backup, recovery etc. No Very detailed discussion of the technology (e.g. security management), but not much on how technology enables innovation

M itigating preventable readmissions, where patients are readmitted for the same primary diagnosis within 30 days, poses a significant challenge to the delivery of high-quality healthcare. Toward this end, we develop a novel, predictive analytics model, termed as the beta geometric Erlang-2 (BG/EG) hurdle model, which predicts the propensity, frequency, and timing of readmissions of patients diagnosed with congestive heart failure (CHF). This unified model enables us to answer three key questions related to the use of predictive analytics methods for patient readmissions: whether a readmission will occur, how often readmissions will occur, and when a readmission will occur. We test our model using a unique data set that tracks patient demographic, clinical, and administrative data across 67 hospitals in North Texas over a four-year period. We show that our model provides superior predictive performance compared to extant models such as the logit, BG/NBD hurdle, and EG hurdle models. Our model also allows us to study the association between hospital usage of health information technologies (IT) and readmission risk. We find that health IT usage, patient demographics, visit characteristics, payer type, and hospital characteristics, are significantly associated with patient readmission risk. We also observe that implementation of cardiology information systems is associated with a reduction in the propensity and frequency of future readmissions, whereas administrative IT systems are correlated with a lower frequency of future readmissions. Our results indicate that patient profiles derived from our model can serve as building blocks for a predictive analytics system to identify CHF patients with high readmission risk.

Data sharing between upstream and downstream entities is vital for the success of a supply chain. However, distrust, privacy concerns, data misuse, and the asymmetric valuation of shared data between entities often hinder data sharing. This problem calls for a secure, efficient, fair, and trustworthy data‐sharing mechanism. The key to such a successful system hinges on how to trace the data usage, determine the value of the seller’s data to the buyer and then compensate the seller accordingly. To this end, we design and implement a blockchain‐enabled data‐sharing marketplace for a stylized supply chain. We demonstrate how a blockchain can be used to overcome these impediments in supply‐chain data sharing and provide a detailed tutorial with a step‐by‐step implementation for how to set up such a data exchange prototype using Hashgraph.

No abstract

Positive Review Volume Cumulative count of positive reviews posted to the focal book up to time t-1 Negative Review Volume Cumulative count of negative reviews posted to the focal book up to time t-1 Positive Helpful Vote Ratio of Helpful vote to Total vote for positive reviews posted to the focal book up to time t-1 Negative Helpful Vote Ratio of Helpful vote to Total vote for negative reviews posted to the focal book up to time t-1 Review Rating Average Average review rating posted to the focal book up to t-1 Review Rating StdDev Standard deviation of review ratings posted to the focal book up to t-1 StdDevofRating Agreement StdDev of the ratings of positive reviews made by all focal reviewers to other books up to t-1 Ratio Reviewer Agreement Ratio of the count of reviewer agreement for the focal book divided by the count of reviewer agreement for the competing book up to t-1 MIS Quarterly Vol. 38 No. 3-Appendices/September 2014 A1