Patients go to multiple healthcare providers for treatment, and their health data is generally distributed among providers. The distributed health data and the decentralized health care system structure make it ideal for blockchain-based health information systems. The authors consider the referral use case; for instance, a patient goes to his primary health Centre (PHC) for treatment and is referred to a hospital. Authentication is usually done using certificates or key cryptography, which could become cumbersome when multiple parties are involved in a healthcare interaction. The security requirements were defined, and a novel multi-party, mutual patient identity authentication scheme called "Distributed Dynamic Mutual Identity Authentication (DDMIA)" was proposed for the referral use case in a blockchainbased e-health network. The DDMIA enables the PHC to authenticate the patient to the referred hospital. The DDMIA scheme was designed using Elliptic Curve Cryptography. It was proven to be secure by assuming the hardness of the elliptic curve discrete log problem (ECDLP) and Elliptic curve computational Diffie-Hellman problem (ECDH) using CK-Model. The formal security analysis using BAN logic proved that the sessions are secure after authentication. The DDMIA scheme was simulated in the AVISPA tool and proven safe against all active attacks. DDMIA scheme was simulated in the AVISPA tool and proven safe against all active attacks. The scheme allows a patient to be authenticated by multiple parties without registering with all parties. It eliminates the need for multiple registration centers as well as digital certificates. Hence, the DDMIA scheme can be implemented for similar multiparty authentication requirements in blockchain-based networks.
data collection is used for the same resource items. Methods: Articles citing 38 trial-based resource use instruments catalogued in the MRC-funded Database of Instruments for Resource Use Measurement (www.DIRUM.org) were identified using Google Scholar, ISI Web of Science and Scopus, and screened according to resource use measure usage. Data were extracted on: the method of administration, resources measured, rates of return and the nature of the other methods of resource use measurement. Results: A total of 146/1503 citations met the screening criteria. Nearly all (143/146) used resource use instruments derived from Beecham and Knapp's Client Service Receipt Inventory. Most instruments relied on patient-or proxy-recall (126/146) generally administered during researcher interviews. Primary and secondary care usage were the most widely asked items (136/146) with 75 using no supplementary supporting data such as from hospital notes. Twelve studies compared one or more method of data collection for the same resource items with 8 indicating good agreement between medical records and patient/carer recall, 1 indicating the greater reliability of case notes and 3 were not evaluable. ConClusions: Resource use instruments based on patient recall are valuable complements to other methods and essential for certain items (e.g. out of pocket costs, non-medical costs). However, there remains inappropriate use in circumstances where more objective measures are available.
found no manuscripts published that presented analyses using CHIRA data. It uses ICD-10 codes and collects cross-sectional data annually from inpatient claims records from sample cities. Advantages include the availability of demographics, institution, diagnoses, medications, service use, hospital stay, insurance type and service cost information for a large, diverse local population. Limitations include the lack of longitudinal patient records, incomplete data, the lack of outpatient data and standardized billing codes, and limited access for research purposes. ConClusions: At present claims data in China are relatively difficult to access and to use. However the use of claims data for health services research is expected to increase in line with planned enhancements to data availability and quality, and the increasing needs for RWE by decision makers.
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