Designing a Distributed Ledger Technology System for Interoperable and General Data Protection Regulation-Compliant Health Data Exchange: A Use Case in Blood Glucose Data.

BACKGROUND: Distributed ledger technology (DLT) holds great potential to improve health information exchange. However, the immutable and transparent character of this technology may conflict with data privacy regulations and data processing best practices. OBJECTIVE: The aim of this paper is to develop a proof-of-concept system for immutable, interoperable, and General Data Protection Regulation (GDPR)-compliant exchange of blood glucose data. METHODS: Given that there is no ideal design for a DLT-based patient-provider data exchange solution, we proposed two different variations for our proof-of-concept system. One design was based purely on the public IOTA distributed ledger (a directed acyclic graph-based DLT) and the second used the same public IOTA ledger in combination with a private InterPlanetary File System (IPFS) cluster. Both designs were assessed according to (1) data reversal risk, (2) data linkability risks, (3) processing time, (4) file size compatibility, and (5) overall system complexity. RESULTS: The public IOTA design slightly increased the risk of personal data linkability, had an overall low processing time (requiring mean 6.1, SD 1.9 seconds to upload one blood glucose data sample into the DLT), and was relatively simple to implement. The combination of the public IOTA with a private IPFS cluster minimized both reversal and linkability risks, allowed for the exchange of large files (3 months of blood glucose data were uploaded into the DLT in mean 38.1, SD 13.4 seconds), but involved a relatively higher setup complexity. CONCLUSIONS: For the specific use case of blood glucose explored in this study, both designs presented a suitable performance in enabling the interoperable exchange of data between patients and providers. Additionally, both systems were designed considering the latest guidelines on personal data processing, thereby maximizing the alignment with recent GDPR requirements. For future works, these results suggest that the conflict between DLT and data privacy regulations can be addressed if careful considerations are made regarding the use case and the design of the data exchange system.

Missing data in medical databases: impute, delete or classify?

BACKGROUND: The multiplicity of information sources for data acquisition in modern intensive care units (ICUs) makes the resulting databases particularly susceptible to missing data. Missing data can significantly affect the performance of predictive risk modeling, an important technique for developing medical guidelines. The two most commonly used strategies for managing missing data are to impute or delete values, and the former can cause bias, while the later can cause both bias and loss of statistical power. OBJECTIVES: In this paper we present a new approach for managing missing data in ICU databases in order to improve overall modeling performance. METHODS: We use a statistical classifier followed by fuzzy modeling to more accurately determine which missing data should be imputed and which should not. We firstly develop a simulation test bed to evaluate performance, and then translate that knowledge using exactly the same database as previously published work by [13]. RESULTS: In this work, test beds resulted in datasets with missing data ranging 10-50%. Using this new approach to missing data we are able to significantly improve modeling performance parameters such as accuracy of classifications by an 11%, sensitivity by 13%, and specificity by 10%, including also area under the receiver-operator curve (AUC) improvement of up to 13%. CONCLUSIONS: In this work, we improve modeling performance in a simulated test bed, and then confirm improved performance replicating previously published work by using the proposed approach for missing data classification. We offer this new method to other researchers who wish to improve predictive risk modeling performance in the ICU through advanced missing data management.

Using discrete event simulation to compare the performance of family health unit and primary health care centre organizational models in Portugal.

BACKGROUND: Recent reforms in Portugal aimed at strengthening the role of the primary care system, in order to improve the quality of the health care system. Since 2006 new policies aiming to change the organization, incentive structures and funding of the primary health care sector were designed, promoting the evolution of traditional primary health care centres (PHCCs) into a new type of organizational unit--family health units (FHUs). This study aimed to compare performances of PHCC and FHU organizational models and to assess the potential gains from converting PHCCs into FHUs. METHODS: Stochastic discrete event simulation models for the two types of organizational models were designed and implemented using Simul8 software. These models were applied to data from nineteen primary care units in three municipalities of the Greater Lisbon area. RESULTS: The conversion of PHCCs into FHUs seems to have the potential to generate substantial improvements in productivity and accessibility, while not having a significant impact on costs. This conversion might entail a 45% reduction in the average number of days required to obtain a medical appointment and a 7% and 9% increase in the average number of medical and nursing consultations, respectively. CONCLUSIONS: Reorganization of PHCC into FHUs might increase accessibility of patients to services and efficiency in the provision of primary care services.