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This chapter more clearly identifies the distinction between Electronic Health Record (EHR) and Electronic Medical Record (EMR), and states their value in obtaining individual-level data. Synthetic medical records may be used as a surrogate for EHR data in order to ensure digital data privacy is maintained during the development of the LHS. Synthea is an open-source simulation tool available through GitHub.¹ Extensive descriptive analysis of synthesized data is provided as a foundation for the analysis in Chapter 7.

Synthetic patient data produced by Synthea was described in Chapter 6. That data is used to create a baseline for all patients, palliative patients, and deceased palliative patients. Distributions of comorbidities across the patient groups are examined and demographic characteristics. The factors used in palliative care groupings are presented with the synthesized data fields used. The size of the palliative population is again estimated to establish validity.

Data mapping from synthesized data to palliative care characteristics was the final step before the final analysis of survival. Background and foundation for Kaplan-Meier curves are provided before generating curves for the three Palliative Care Groups. Interpretations of the Kaplan-Meier curves are presented along with interpretation of the associated Hazard Curves. Three statistical hypothesis tests, completed on a pairwise basis, are used to verify that the survival curves differ by group. Patients mapped to specific groups may be further supported through advice, counseling, and other services to assist them in moving to a more advantageous care group.

Theories of platform strategy and adoption have been largely derived from studies of their application in the information and communications technology (ICT) sector. These platforms vary in openness, with the model of open source software providing the best-known exemplar for open platforms.

This exploratory field study examines the degree to which nine attributes of ICT platforms are applicable to open platforms in biotechnology. Using a combination of interview and secondary data, it identifies three patterns of such biotechnology platforms – IP commons, hackerspaces, and crowdsourced patient registries – and the degree to which these nine attributes apply. It shows the impact of ICT platforms and open source software on open source approaches to biotechnology, and how the latter are affected by the technical, legal, and institutional differences between information technology and biotechnology.

Instead of open source software platforms organized around modular interfaces, complements, ecosystems, and two-sided markets, this study instead suggests a model of open source knowledge platforms which benefits from economies of scale but not indirect network effects. From this, it discusses the generalizability of the ICT-derived models of open source platforms and offers suggestions for future research.

Predicting a patient's length of stay (LOS) in a hospital setting has been widely researched. Accurately predicting an individual's LOS can have a significant impact on a healthcare provider's ability to care for individuals by allowing them to properly prepare and manage resources. A hospital's productivity requires a delicate balance of maintaining enough staffing and resources without being overly equipped or wasteful. This has become even more important in light of the current COVID-19 pandemic, during which emergency departments around the globe have been inundated with patients and are struggling to manage their resources.

In this study, the authors focus on the prediction of LOS at the time of admission in emergency departments at Rhode Island hospitals through discharge data obtained from the Rhode Island Department of Health over the time period of 2012 and 2013. This work also explores the distribution of discharge dispositions in an effort to better characterize the resources patients require upon leaving the emergency department.