Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) patients are at risk for resource-intensive outcomes including mechanical ventilation (MV), renal replacement therapy (RRT), and readmission. Accurate outcome prognostication could facilitate hospital resource allocation. We develop and validate predictive models for each outcome using retrospective electronic health record data for COVID-19 patients treated between March 2 and May 6, 2020. MATERIALS AND METHODS: For each outcome, we trained 3 classes of prediction models using clinical data for a cohort of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2)-positive patients (n = 2256). Cross-validation was used to select the best-performing models per the areas under the receiver-operating characteristic and precision-recall curves. Models were validated using a held-out cohort (n = 855). We measured each model's calibration and evaluated feature importances to interpret model output. RESULTS: The predictive performance for our selected models on the held-out cohort was as follows: area under the receiver-operating characteristic curve-MV 0.743 (95% CI, 0.682-0.812), RRT 0.847 (95% CI, 0.772-0.936), readmission 0.871 (95% CI, 0.830-0.917); area under the precision-recall curve-MV 0.137 (95% CI, 0.047-0.175), RRT 0.325 (95% CI, 0.117-0.497), readmission 0.504 (95% CI, 0.388-0.604). Predictions were well calibrated, and the most important features within each model were consistent with clinical intuition. DISCUSSION: Our models produce performant, well-calibrated, and interpretable predictions for COVID-19 patients at risk for the target outcomes. They demonstrate the potential to accurately estimate outcome prognosis in resource-constrained care sites managing COVID-19 patients. CONCLUSIONS: We develop and validate prognostic models targeting MV, RRT, and readmission for hospitalized COVID-19 patients which produce accurate, interpretable predictions. Additional external validation studies are needed to further verify the generalizability of our results.

The advent of electronic medical records and health information exchange raise the possibility of expanding public health reporting to detect a broad range of clinical conditions and of monitoring the health of the public on a broad scale. Expanding public health reporting may require patient anonymity, matching records, re-identifying cases, and recording patient characteristics for localization. The privacy regulations under the Health Insurance Portability and Accountability Act of 1996 (HIPAA) provide several mechanisms for public health surveillance, including using laws and regulations, public health activities, de-identification, research waivers, and limited data sets, and in addition, surveillance may be distributed with aggregate reporting. The appropriateness of these approaches varies with the definition of what data may be included, the requirements of the minimum necessary standard, the accounting of disclosures, and the feasibility of the approach.

Our goal is to assess how clinical information from previous visits is used in the emergency department. We used detailed user audit logs to measure access to different data types. We found that clinician-authored notes and laboratory and radiology data were used most often (common data types were used up to 5% to 20% of the time). Data were accessed less than half the time (up to 20% to 50%) even when the user was alerted to the presence of data. Our access rate indicates that health information exchange projects should be conservative in estimating how often shared data will be used and the wide breadth of data accessed indicates that although a clinical summary is likely to be useful, an ideal solution will supply a broad variety of data.

The Columbia University Informatics for Diabetes Education and Telemedicine IDEATel) project is a four-year demonstration project funded by the Centers for Medicare and Medicaid Services with the overall goal of evaluating the feasibility, acceptability, effectiveness, and cost-effectiveness of telemedicine. The focal point of the intervention is the home telemedicine unit (HTU), which provides four functions: synchronous videoconferencing over standard telephone lines, electronic transmission for fingerstick glucose and blood pressure readings, secure Web-based messaging and clinical data review, and access to Web-based educational materials. The HTU must be usable by elderly patients with no prior computer experience. Providing these functions through the HTU requires tight integration of six components: the HTU itself, case management software, a clinical information system, Web-based educational material, data security, and networking and telecommunications. These six components were integrated through a variety of interfaces, providing a system that works well for patients and providers. With more than 400 HTUs installed, IDEATel has demonstrated the feasibility of large-scale home telemedicine.

A version control mechanism is proposed that enhances the modularity and extensibility of multiversion concurrency control algorithms. The multiversion algorithms are decoupled into two components: version control and concurrency control. This permits modular development of multiversion protocols and simplifies the task of proving the correctness of these protocols. A set of procedures for version control is described that defines the interface with the version control component. It is shown that the same interface can be used by the database actions of both two-phase locking and time-stamp concurrency control protocols to access multiversion data. An interesting feature of the framework is that the execution of read-only transactions becomes completely independent of the underlying concurrency control implementation. Unlike other multiversion algorithms, read-only transactions in this scheme do not modify any version-related information, and therefore do not interfere with the execution of read-write transactions. The extension of the multiversion algorithms to a distributed environment becomes very simple.>

The problem of managing large communication networks using statistical tests, alerts, and correlation among alerts is considered. The authors propose a model of these network management functions as data-manipulation operations. They argue that this approach can improve the flexibility of network management systems by providing a language that is declarative and set-oriented. These are properties of existing data-manipulation languages and it is shown that any data-manipulation language, augmented with several new capabilities, can serve as a language for specifying the aforementioned network management functions. The new capabilities required are specification of events, correlation among events, and change-tracking.>

In this paper we propose a version control mechanism that enhances the modularity and extensibility of multiversion concurrency control algorithms. We decouple the multiversion algorithms into two components: version control and concurrency control. This permits modular development of multiversion protocols, and simplifies the task of proving the correctness of these protocols. An interesting feature of our framework is that the execution of read-only transactions becomes completely independent of the underlying concurrency control implementation. Also, algorithms with the version control mechanism have several advantages over most other multiversion algorithms.