A Method of Estimating Time-to-Recovery for a Disease Caused by a Contagious Pathogen Such as SARS-CoV-2 Using a Time Series of Aggregated Case Reports.

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak.

Measuring the impact of anonymization on real-world consolidated health datasets engineered for secondary research use: Experiments in the context of MODELHealth project.

Introduction: Electronic Health Records (EHRs) are essential data structures, enabling the sharing of valuable medical care information for a diverse patient population and being reused as input to predictive models for clinical research. However, issues such as the heterogeneity of EHR data and the potential compromisation of patient privacy inhibit the secondary use of EHR data in clinical research. Objectives: This study aims to present the main elements of the MODELHealth project implementation and the evaluation method that was followed to assess the efficiency of its mechanism. Methods: The MODELHealth project was implemented as an Extract-Transform-Load system that collects data from the hospital databases, performs harmonization to the HL7 FHIR standard and anonymization using the k-anonymity method, before loading the transformed data to a central repository. The integrity of the anonymization process was validated by developing a database query tool. The information loss occurring due to the anonymization was estimated with the metrics of generalized information loss, discernibility and average equivalence class size for various values of k. Results: The average values of generalized information loss, discernibility and average equivalence class size obtained across all tested datasets and k values were 0.008473 ± 0.006216252886, 115,145,464.3 ± 79,724,196.11 and 12.1346 ± 6.76096647, correspondingly. The values of those metrics appear correlated with factors such as the k value and the dataset characteristics, as expected. Conclusion: The experimental results of the study demonstrate that it is feasible to perform effective harmonization and anonymization on EHR data while preserving essential patient information.

Data Quality Challenges in a Learning Health System.

This paper discusses the topic of data quality, which concerns the global research and business community and constitutes a challenging task. The data quality prerequisite becomes even more critical when it pertains to critical and sensitive data, such as the healthcare domain data. To begin with, the paper outlines the basic definitions and concepts of data quality and its dimensions. The related research work on data quality assessment is presented and our approach for data quality assurance is introduced. This approach is implemented in our designed cloud platform, called MODELHealth, which is intended for supporting clinical work and administrative decision-making process.

MODELHealth: Facilitating Machine Learning on Big Health Data Networks.

MODELHealth is a platform that aims to facilitate the implementation of Machine Learning (ML) techniques on medical data in order to upgrade the delivery of healthcare services. MODELHealth platform is a "holistic" approach to the implementation of processes for the development and utilization of ML algorithms in many forms, including Neural Networks, and can be used to assist clinical work and administrative decision-making. It covers the entire lifecycle of these processes, from pumping, homogenization, anonymization, and enrichment of the initial data, to the final disposal of efficient algorithms through Application Program Interfaces for consumption by any authorized Information System.

Utilizing Key Item Method to Manage Musculoskeletal Disorders in a Hospital Workplace.

Management of musculoskeletal disorders (MSDs) is a necessity for the modern work environment. In hospitals, these disorders have a particularly high frequency among health care workers whose work entails lifting and transporting patients as well as washing, dressing and feeding them. This paper, presents an electronic application which is based on the method of basic items (KIM - Key Item Method) in order to reduce incidents of MSDs resulting from manual transport of loads in healthcare facilities. The sample consisted of 15 female hospital meal servers from Metaxa Hospital (Piraeus, Greece) in order to assess the activities of lifting, carrying, transporting, pushing and pulling of loads which are part of their daily work duties. The key requirement for the application was not only helping the risk assessment but also leading to targeted, easily applicable and low cost corrective measures. The results of this electronic tool application showed increased usability and benefits which were associated with the used database and the detailed information relatively to the corrective measures, such as training of the employees to change body posture, replacement of wheels on trolleys and redesigning of serving aisles which proved beneficial.