The Application of Blockchain Technology in Stroke Rehabilitation

A blockchain-based healthcare data marketplace: prototype and demonstration

Objectives

This study aimed to develop healthcare data marketplace using blockchain-based B2C model that ensures the transaction of healthcare data among individuals, companies, and marketplaces.

Materials and methods

We designed an architecture for the healthcare data marketplace using blockchain. A healthcare data marketplace was developed using Panacea, MySQL 8.0, JavaScript library, and Node.js. We evaluated the performance of the data marketplace system in 3 scenarios.

Results

We developed mobile and web applications for healthcare data marketplace. The transaction data queries were executed fully within about 1-2 s, and approximately 9.5 healthcare data queries were processed per minute in each demonstration scenario.

Discussion

Blockchain-based healthcare data marketplaces have shown compliance performance in the process of data collection and will provide a meaningful role in analyzing healthcare data.

Conclusion

The healthcare data marketplace developed in this project can iron out time and place limitations and create a framework for gathering and analyzing fragmented healthcare data.

Storing information of stroke rehabilitation patients using blockchain technology

Background

Stroke patients usually experience damage to multiple functions and a long rehabilitation period. Hence, there is a large volume of patient clinical information. It thus takes a long time for clinicians to identify the patient’s information and essential pieces of information may be overlooked. To solve this, we stored the essential clinical information of stroke patients in a blockchain and implemented the blockchain technology using the Java programming language.

Methods

We created a mini blockchain to store the medical information of patients using the Java programming language.

Results

After generating a unique pair of public/private keys for identity verification, a patient’s identity is verified by applying the Elliptic Curve Digital Signature Algorithm based on the generated keys. When the identity verification is complete, new medical data are stored in the transaction list and the generated transaction is verified. When verification is completed normally, the block hash value is derived using the transaction value and the hash value of the previous block. The hash value of the previous block is then stored in the generated block to interconnect the blocks.

Conclusion

We demonstrated that blockchain can be used to store and deliver the patient information of stroke patients. It may be difficult to directly implement the code that we developed in the medical field, but it can serve as a starting point for the creation of a blockchain system to be used in the field.

Essential Medical Information for Stroke Patients Undergoing Interhospital Transfer: A Delphi Study

OBJECTIVE

For improving the efficiency of transferring medical records for stroke patients undergoing interhospital transfer, we evaluated what is the essential medical information for physicians using the Delphi method.

DESIGN

We set up an expert panel of 31 physiatrists, who participated in this study. The 32 preliminary items of the transferred medical information were listed by a physiatrist for the first round of the Delphi method, and degree of necessity for these 32 items was evaluated using a 3-point scale ("very necessary," "necessary," and "not necessary"). We considered "very necessary" and "necessary" as "agreed to its necessity." According to the Delphi method, a satisfactory level of consensus can be achieved with the agreement of significant majority (≥80%) in the expert panel.

RESULTS

Based on the experts' feedback, some items were added as the preliminary items. After the second round of the Delphi method, the items confirmed to be necessary information during interhospital transfer were motor and sensory evoked potentials, Barthel Index, Mini-Mental State Examination/Global Deterioration Scale, Motor-Free Visual Perception Test, Manual Function Test, Purdue Pegboard Test, hand grip power, monofilament, 2-point discrimination test, Manual Muscle Test, Nottingham Scale, modified Brunnstrom Classification, functional ambulation category, Glasgow Coma Scale, language function test, imaging study, videofluoroscopic swallowing study, rehabilitation goal, previous medical history, comorbidity, and medication information.

CONCLUSIONS

If the previously mentioned necessary items are presented together at once during interhospital transfer, physicians who receive new stroke patients can evaluate patients' medical information more easily and completely.

Attitudes Toward Blockchain Technology in Managing Medical Information: Survey Study

BACKGROUND

The recently developed blockchain technology uses a peer-to-peer network to distribute data to all participants for storage. This method enhances data safety, reliability, integrity, and transparency. To successfully introduce blockchain technology to medical data management, it is essential to obtain consent from medical doctors and patients.

OBJECTIVE

The aim of this study was to examine medical doctors' and patients' attitudes toward the use of blockchain technology and interpret the findings within the framework of expectancy theory.

METHODS

In this questionnaire survey, we examined medical doctors' (n=90) and patients' (n=90) attitudes toward the use of blockchain technology in the management and distribution of medical information. The questionnaire comprised 8 questions that assessed attitudes toward new means of managing and distributing medical information using blockchain technology. Responses were rated on a scale that ranged from 1 (very negative) to 7 (very positive).

RESULTS

Medical doctors (mean 3.7-5.0) reported significantly more negative attitudes than patients (mean 6.3-6.8). Furthermore, self-employed doctors reported more negative attitudes than employed doctors and university professors.

CONCLUSIONS

To successfully introduce blockchain technology to medical data management, it is necessary to promote positive attitudes toward this technology among medical doctors, especially self-employed doctors.