A data-sharing scheme that supports multi-keyword search for electronic medical records.
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ABSTRACT:
SUBMITTER: Niu S
PROVIDER: S-EPMC7790426 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
Publications
A data-sharing scheme that supports multi-keyword search for electronic medical records.
Niu Shufen S Liu Wenke W Han Song S Fang Lizhi L
PloS one 20210107 1
As cloud storage technology develops, data sharing of cloud-based electronic medical records (EMRs) has become a hot topic in the academia and healthcare sectors. To solve the problem of secure search and sharing of EMR in cloud platforms, an EMR data-sharing scheme supporting multi-keyword search is proposed. The proposed scheme combines searchable encryption and proxy re-encryption technologies to perform keyword search and achieve secure sharing of encrypted EMR. At the same time, the scheme ...[more]
Similar Datasets
Project description:electronic Medical Records & Genomics (eMERGE)
| PRJNA158491 | ENA
Project description:We propose an attribute-based encryption scheme with multi-keyword search and supporting attribute revocation in cloud storage environment, in which binary attributes and AND-gate access policy are used. Our proposal enjoys several advantages. Firstly, multi-keyword search is available, and only when a data user's attribute set satisfies access policy in keyword index, and keyword token generated by data user matches index successfully, then data user can obtain ciphertext containing keywords. In this way, more accurate keyword search is achievable. Secondly, the search privacy of data user is protected owing to cloud servers cannot obtain any knowledge of keywords which data user is interested in. Meanwhile, the ciphertext is able to be decrypted when data user's attribute set satisfies access policy specified in the ciphertext, which can both improve security of encryption and achieve secure fine-grained access control. Thirdly, the proposed scheme supports attribute revocation, in our scheme when a data user's attribute is revoked, the version number of attribute, non-revoked data users' secret keys and related ciphertexts will be updated, such that data user whose attribute is revoked does not decrypt updated ciphertext anymore. In addition, based on the assumption of decisional linear (DL) and decisional Diffie-Hellman (DDH), our scheme is proved to be secure against selectively chosen-keyword attacks and selectively chosen-plaintext attacks respectively, and it also ensures token privacy security.
| S-EPMC6185864 | biostudies-literature
Project description:ObjectiveTo develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities.Patients and methodsThe automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.ResultsIn the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS.ConclusionOur results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.
| S-EPMC3538495 | biostudies-literature
Project description: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.
| S-EPMC7537906 | biostudies-literature
Project description:ObjectiveA full-text search engine can be a useful tool for augmenting the reuse value of unstructured narrative data stored in electronic health records (EHR). A prominent barrier to the effective utilization of such tools originates from users' lack of search expertise and/or medical-domain knowledge. To mitigate the issue, the authors experimented with a 'collaborative search' feature through a homegrown EHR search engine that allows users to preserve their search knowledge and share it with others. This feature was inspired by the success of many social information-foraging techniques used on the web that leverage users' collective wisdom to improve the quality and efficiency of information retrieval.DesignThe authors conducted an empirical evaluation study over a 4-year period. The user sample consisted of 451 academic researchers, medical practitioners, and hospital administrators. The data were analyzed using a social-network analysis to delineate the structure of the user collaboration networks that mediated the diffusion of knowledge of search.ResultsThe users embraced the concept with considerable enthusiasm. About half of the EHR searches processed by the system (0.44 million) were based on stored search knowledge; 0.16 million utilized shared knowledge made available by other users. The social-network analysis results also suggest that the user-collaboration networks engendered by the collaborative search feature played an instrumental role in enabling the transfer of search knowledge across people and domains.ConclusionApplying collaborative search, a social information-foraging technique popularly used on the web, may provide the potential to improve the quality and efficiency of information retrieval in healthcare.
| S-EPMC3078661 | biostudies-literature
Project description:BackgroundImprovements in the quality of health care services are often measured using data present in medical records. Electronic Medical Records (EMRs) contain potentially valuable new sources of health data. However, data quality in EMRs may not be optimal and should be assessed. Data reliability (are the same data elements being measured over time?) is a prerequisite for data validity (are the data accurate?). Our objective was to measure the reliability of data for preventive services in primary care EMRs during the transition to EMR.MethodsOur data sources were randomly selected eligible patients' medical records and data obtained from provincial administrative datasets. Eighteen community-based family physicians in Toronto, Ontario that implemented EMRs starting in 2006 participated in this study. We measured the proportion of patients eligible for a service (Pap smear, screening mammogram or influenza vaccination) that received the service. We compared the change in rates of selected preventive services calculated from the medical record audits with the change in administrative datasets.ResultsIn the first year of EMR use (2006) services decreased by 8.7% more (95% CI -11.0%- - 6.4%, p < 0.0001) when measured through medical record audits as compared with administrative datasets. Services increased by 2.4% more (95% CI 0%-4.9%, p = 0.05) in the medical record audits during the second year of EMR use (2007).ConclusionThere were differences between the change measured through medical record audits and administrative datasets. Problems could include difficulties with organizing new data entry processes as well as continued use of both paper and EMRs. Data extracted from EMRs had limited reliability during the initial phase of EMR implementation. Unreliable data interferes with the ability to measure and improve health care quality.
| S-EPMC3442990 | biostudies-literature
Project description:Data outsourcing services have emerged with the increasing use of digital information. They can be used to store data from various devices via networks that are easy to access. Unlike existing removable storage systems, storage outsourcing is available to many users because it has no storage limit and does not require a local storage medium. However, the reliability of storage outsourcing has become an important topic because many users employ it to store large volumes of data. To protect against unethical administrators and attackers, a variety of cryptography systems are used, such as searchable encryption and proxy reencryption. However, existing searchable encryption technology is inconvenient for use in storage outsourcing environments where users upload their data to be shared with others as necessary. In addition, some existing schemes are vulnerable to collusion attacks and have computing cost inefficiencies. In this paper, we analyze existing proxy re-encryption with keyword search.
| S-EPMC3944203 | biostudies-other
Project description:BackgroundPassive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.MethodsZip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.ResultsPotential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.ConclusionsGI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.
| S-EPMC3377951 | biostudies-literature
Project description:BackgroundElectronic medical records (EMR) can be a cost-effective source for hypertension surveillance. However, diagnosis of hypertension in EMR is commonly under-coded and warrants the needs to review blood pressure and antihypertensive drugs for hypertension case identification.MethodsWe included all the patients actively registered in The Health Improvement Network (THIN) database, UK, on 31 December 2011. Three case definitions using diagnosis code, antihypertensive drug prescriptions and abnormal blood pressure, respectively, were used to identify hypertension patients. We compared the prevalence and treatment rate of hypertension in THIN with results from Health Survey for England (HSE) in 2011.ResultsCompared with prevalence reported by HSE (29.7%), the use of diagnosis code alone (14.0%) underestimated hypertension prevalence. The use of any of the definitions (38.4%) or combination of antihypertensive drug prescriptions and abnormal blood pressure (38.4%) had higher prevalence than HSE. The use of diagnosis code or two abnormal blood pressure records with a 2-year period (31.1%) had similar prevalence and treatment rate of hypertension with HSE.ConclusionsDifferent definitions should be used for different study purposes. The definition of 'diagnosis code or two abnormal blood pressure records with a 2-year period' could be used for hypertension surveillance in THIN.
| S-EPMC5072168 | biostudies-literature
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.
| S-EPMC6761113 | biostudies-literature