Dataset Information

A blockchain-based framework for electronic medical records sharing with fine-grained access control.

ABSTRACT: In the medical system, the verification, preservation and synchronization of electronic medical records has always been a difficult problem, and the random dissemination of patient records will bring various risks to patient privacy. Therefore, how to achieve secure data sharing on the basis of ensuring users' personal privacy becomes the key. In recent years, blockchain has been proposed to be a promising solution to achieve data sharing with security and privacy preservation due to its advantages of immutability. So, a distributed electronic medical records searchable scheme was proposed by leveraging blockchain and smart contract technology. Firstly, we perform a hash calculation on the electronic medical data and store the corresponding value on the blockchain to ensure its integrity and authenticity. Then, we encrypt the electronic medical data and store it in the interplanetary file system which is a distributed storage protocol. These operations not only can solve centralized data store of servers of several medical institutions, but also be good at lowering stress from data store and high-frequency access to blockchain. Secondly, the encrypted keyword index information of electronic medical records was stored on the Ethereum blockchain, meanwhile a smart contract deployed in the Ethereum blockchain is used to realize keyword search instead of depending on a centralized third party. Furthermore, we use attribute-based encryption scheme to ensure that only the attributes meeting the access policy can decrypt the encrypted electronic medical records. Finally, our performance analysis and security analysis show that the scheme is secure and efficient.

SUBMITTER: Sun J

PROVIDER: S-EPMC7537906 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A blockchain-based framework for electronic medical records sharing with fine-grained access control.

Sun Jin J Ren Lili L Wang Shangping S Yao Xiaomin X

PloS one 20201006 10

In the medical system, the verification, preservation and synchronization of electronic medical records has always been a difficult problem, and the random dissemination of patient records will bring various risks to patient privacy. Therefore, how to achieve secure data sharing on the basis of ensuring users' personal privacy becomes the key. In recent years, blockchain has been proposed to be a promising solution to achieve data sharing with security and privacy preservation due to its advanta ...[more]

PMID: 33022027

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Project description:BackgroundAlthough researchers are giving increased attention to blockchain-based personal health records (PHRs) and data sharing, the majority of research focuses on technical design. Very little is known about health care consumers' intentions to adopt the applications.ObjectiveThis study aims to explore the intentions and concerns of health care consumers regarding the adoption of blockchain-based personal health records and data sharing.MethodsThree focus groups were conducted, in which 26 participants were shown a prototype of a user interface for a self-sovereign blockchain-based PHR system (ie, a system in which the individual owns, has custody of, and controls access to their personal health information) to be used for privacy and secure health data sharing. A microinterlocutor analysis of focus group transcriptions was performed to show a descriptive overview of participant responses. NVivo 12.0 was used to code the categories of the responses.ResultsParticipants did not exhibit a substantial increase in their willingness to become owners of health data and share the data with third parties after the blockchain solution was introduced. Participants were concerned about the risks of losing private keys, the resulting difficulty in accessing care, and the irrevocability of data access on blockchain. They did, however, favor a blockchain-based PHR that incorporates a private key recovery system and offers a health wallet hosted by government or other positively perceived organizations. They were more inclined to share data via blockchain if the third party used the data for collective good and offered participants nonmonetary forms of compensation and if the access could be revoked from the third party.ConclusionsHealth care consumers were not strongly inclined to adopt blockchain-based PHRs and health data sharing. However, their intentions may increase when the concerns and recommendations demonstrated in this study are considered in application design.

Project description:Post-market medical device surveillance is a challenge facing manufacturers, regulatory agencies, and health care providers. Electronic health records are valuable sources of real-world evidence for assessing device safety and tracking device-related patient outcomes over time. However, distilling this evidence remains challenging, as information is fractured across clinical notes and structured records. Modern machine learning methods for machine reading promise to unlock increasingly complex information from text, but face barriers due to their reliance on large and expensive hand-labeled training sets. To address these challenges, we developed and validated state-of-the-art deep learning methods that identify patient outcomes from clinical notes without requiring hand-labeled training data. Using hip replacements-one of the most common implantable devices-as a test case, our methods accurately extracted implant details and reports of complications and pain from electronic health records with up to 96.3% precision, 98.5% recall, and 97.4% F1, improved classification performance by 12.8-53.9% over rule-based methods, and detected over six times as many complication events compared to using structured data alone. Using these additional events to assess complication-free survivorship of different implant systems, we found significant variation between implants, including for risk of revision surgery, which could not be detected using coded data alone. Patients with revision surgeries had more hip pain mentions in the post-hip replacement, pre-revision period compared to patients with no evidence of revision surgery (mean hip pain mentions 4.97 vs. 3.23; t?=?5.14; p?<?0.001). Some implant models were associated with higher or lower rates of hip pain mentions. Our methods complement existing surveillance mechanisms by requiring orders of magnitude less hand-labeled training data, offering a scalable solution for national medical device surveillance using electronic health records.

Project description:BACKGROUND:Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. OBJECTIVE:The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input-unstructured nursing records obtained from Japanese electronic medical records (EMRs)-using a natural language processing (NLP) algorithm and machine learning. METHODS:The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. RESULTS:The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. CONCLUSIONS:We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased.

Dataset Information

A blockchain-based framework for electronic medical records sharing with fine-grained access control.

Publications

A blockchain-based framework for electronic medical records sharing with fine-grained access control.

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