Background: Nowadays, a number of mechanisms and tools are being used by health care organizations and physicians to electronically exchange the personal health information of patients. The main objectives of different methods of health information exchange (HIE) are to reduce health care costs, minimize medical errors, and improve the coordination of interorganizational information exchange across health care entities. The main challenges associated with the common HIE systems are privacy concerns, security risks, low visibility of system transparency, and lack of patient control. Blockchain technology is likely to disrupt the current information exchange models utilized in the health care industry.
Objective: Little is known about patients’ perceptions and attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the main HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, this study aimed at exploring the core value of blockchain technology in the health care industry from health care consumers’ views.
Results: The findings show that there are significant differences in patients’ perceptions of various exchange mechanisms with regard to patient privacy concern, trust in competency and integrity, opt-in intention, and willingness to share information. Interestingly, participants hold a favorable attitude toward the implementation of blockchain-based exchange mechanisms for privacy protection, coordination, and information exchange purposes. This study proposed the potentials and limitations of a blockchain-based attempt in the HIE context.
Conclusions: The results of this research should be of interest to both academics and practitioners. The findings propose potential limitations of a blockchain-based HIE that should be addressed by health care organizations to exchange personal health information in a secure and private manner. This study can contribute to the research in the blockchain area and enrich the literature on the use of blockchain in HIE efforts. Practitioners can also identify how to leverage the benefit of blockchain to promote HIE initiatives nationwide.
Health Information Exchange Models
Individuals usually seek health care services from several providers who may practice in either affiliated or unaffiliated institutions. Accordingly, without a systematic connection among providers, patients’ medical information can become fragmented, outdated, and incomplete in health care organizations . Health information exchange (HIE) is a data exchange mechanism that was introduced and prompted by the Health Information Technology for Economic and Clinical Health Act in 2009 to improve care coordination among health care providers and reduce medical errors [ ]. HIE refers to the process of electronic transfer of patient health information and medical data among health care providers and institutions [ ]. Interoperability associated with HIE initiatives requires electronic communication among organizations to ensure that patient medical records in one health care organization are seamlessly incorporated into another.
Different sharing mechanisms are being used by public and private health care organizations to facilitate information exchange initiatives . Existing studies in HIE indicate that the following 3 exchange models are mainly applied by health care entities to electronically transmit patient health information: (1) direct, (2) query-based, and (3) patient-centered exchange [ ]. In the direct model, a provider can share encrypted patient medical records with a known recipient [ ]. This exchange model facilitates point-to-point data exchange in which the sender is aware of the recipient’s identity and patients’ medical records can be exchanged directly from one health care organization to another via widely adopted email protocols. Direct exchange initiatives, which are principally based on trust between providers, incorporate medical records into the recipient’s electronic health record (EHR) system or clinical inbox in a secure network governed by health care entities. The direct model is able to improve communication and coordination among health care organizations involved in providing treatments by securely exchanging identifiable information of patients.
The query-based models (lookup systems) grant health care providers the ability to find and request information on a patient from other providers. In this exchange mechanism, a central repository plays a critical role where electronic medical records are aggregated from multiple health care organizations’ EHR systems and will be stored in a hub . Thus, the requesting health care organizations are able to use a lookup process to pull required information from the data storage pool [ ]. The query-based model is mainly designed to create a mechanism to efficiently provide relevant, aggregated, and cross-organizational health records for care quality measurement and disease registries development.
The last model refers to a patient-centered exchange mechanism in which medical records related to episodes of care are transmitted from providers to patients. For instance, patients are able to view the laboratory results, radiology reports, progress notes, and medications that are uploaded on patient portals after each visit and share such records with other health care entities as required . This exchange architecture is developed to enable patients to engage in their care process, manage their health information, and become a component of data-sharing efforts by considering a mediating role for them. Patients can leverage the patient-centered HIE models, which are designed and controlled by health care institutions, to reinforce their access and control over their own health records.
Role of Patients in Health Information Exchange
Given the huge amount of information exchanged among health care organizations, patients would rely on HIEs to improve treatment process, enhance care coordination, and increase the quality of care before they actually experience the possible effects . In this setting, risk can also arise because patients may be concerned that too much personal information is shared or erroneous health information is exchanged among health care providers through HIEs [ ]. In the HIE context, patients may not directly share their health information through exchange mechanisms, and they are distant from care providers who actually use these systems. However, patients are recognized as an important beneficiary of HIE projects because their consent is required for sharing their health information [ ]. Patients are also considered as a significant producer of health information and their attitudes toward HIE models may refrain them from sharing their personal information with HIE networks. If patients are not willing to share their personal health information, incomplete, outdated, or inaccurate patient information will be stored in shared records of HIEs [ ]. Accordingly, HIE efforts will fail in providing health care providers with reliable, useful, and integrated health information. Previous studies highlight that to maximize the full value of HIEs, it is important to evaluate patients’ beliefs and perceptions about the widespread implementation of HIE networks [ ]. Thus, public support is necessary for the long-term success and sustainability of HIE initiatives [ ].
Different HIE models have attempted to clarify the process of electronic data sharing among health care entities. However, previous studies report that the general public is not completely aware of how health information is shared and used through the mainstream exchange mechanisms . A number of studies highlight the importance of patient privacy and security concerns in the context of HIE implementation [ ]. Patient concerns in medical practices include the volume of medical records collected and stored in health care organizations’ databases, the possibility of privacy violations (eg, unauthorized access or hacked personal data), secondary use of medical records (eg, datamining purposes), lack of control over data collection practices, lack of transparency associated with sharing efforts, and lack of visibility about how such information will be used [ ]. Patients will hold a positive attitude toward HIE networks when their health records are collected, stored, and exchanged confidentially [ ]. According to Wright et al [ ], if a patient’s privacy and security needs are not met, he or she will become more likely to hide further health information from health care providers. Previous research indicates that patient decision to support HIE projects is a function of multiple factors such as type of information exchanged, privacy and security protections, and purpose behind the exchange [ ]. Favorable attitude toward a HIE system is a result of a solid match between the HIE mechanisms and transparency, security, as well as privacy requirements [ ].
Blockchain in Health Care
Recent studies propose that blockchain is able to disrupt trusted business models mainly used in health care systems for information exchange purposes . Considering the number of transactions (eg, information sharing) among health care entities and the expenses that hospitals experience in maintaining the HIE systems, the underlying blockchain technology of democratically sustained public ledgers of the records opens new and challenging opportunities for the health care industry. Blockchain can create an electronic context in which business transactions (such as information-sharing initiatives) between parties are conducted via a distributed community rather than a central authority or a single entity. This might essentially affect the transparency of the system and the role each entity plays [ ]. Blockchain can also facilitate information exchange and coordination among health care entities and help patients become independent in the sharing of their medical records with providers. The mainstream HIE servers, depending on scale, are principally controlled by large corporations or health care institutions. This centralized control may raise privacy and security concerns because of abuses of power, which may result in secondary use of medical data, unauthorized access, and hacker attacks. Alternatively, the blockchain technology may promote a number of capabilities such as decentralization, security, privacy, breach resistance, and speed of certain features of the internet’s infrastructure.
A great deal of interest has been reflected by recent studies to analyze the effects of blockchain-distributed ledger technologies on health care practices, and most of them are conceptual research . However, little quantitative work has been conducted to investigate the exposure of HIE to blockchain technology. Little is also known about patients’ attitudes toward the implementation of blockchain-enabled HIE networks, and it is still not clear if patients (as one of the key HIE stakeholders) are likely to opt in to the applications of this technology in HIE initiatives. Thus, more research is required to explore the core value of blockchain technology in the health care industry from health care consumers. Our work is among the first attempts to study the possible use of blockchain-based models in HIE from patients’ perspectives. The results of this research can extend the current understanding of blockchain technology by helping health care organizations, health care communities, and policy makers identify the potential benefits and risks of using this technology in health care practices. From a practical standpoint, this study can be useful for HIE policy makers to better examine the patients’ attitude toward the use of blockchain in HIEs, how it should be leveraged, and how patients can be impacted.
In this section, first the shortcomings and problems with traditional health exchanges are explained to better clarify the research gap. Then, we investigate blockchain-based HIE as a potential solution to the problems.
Trust Issues in the Health Information Exchange Context
Trust plays a significant role in situations where there is a distance between consumers and vendors, such as in internet-dependent contexts . HIE networks share individuals’ health information electronically with other care providers to improve care coordination and enhance patient safety. HIE initiatives utilize sharing mechanisms with which health information is mostly transmitted without a patient’s close supervision and control. Thus, patient’s trust in the HIE is the core in this setting where a great deal of security concerns and privacy risks may entail [ ]. Trust in HIE can predict patients’ reactions to the implementation of HIE models because patients need to feel assured that the HIE networks will not compromise personal health information or misuse sensitive medical records [ ]. Therefore, patients should trust HIE systems before they make an opt-in decision or disclose their personal health information.
Individual trust in HIE models can be a function of reliance on competence and integrity of sharing mechanisms . Trust in HIE competence specifies the extent to which patients rely on technologically competent performance of the HIE to effectively disseminate health information between a wide variety of health organizations. Moreover, trust in HIE integrity refers to the belief that the agreement between the patients and HIE is reliable and honest. The lack of trust in HIE is mainly because of the distance imposed between patients and the actual users (health care organizations), lack of direct interactions between patients and HIE models, centralized control exerted by health care organizations, and the unfamiliar mechanisms used in the HIE system to share medical records electronically [ ]. These characteristics create a setting that is more intangible than the traditional sharing methods (such as fax or mail). The mentioned reasons may make patient trust more critical in the settings where the 3 exchange models (ie, direct, query-based, and patient-mediated exchange) are mainly used.
HIE initiatives are developed to provide interorganizational networks in which patients’ medical records are shared with a number of health care entities that are geographically scattered. When a networked-based technology (eg, HIE systems) deals with sharing sensitive information (such as health records), it is very likely that it exacerbates privacy concerns. Information privacy concerns may influence the validity and completeness of HIEs’ patient databases, which may result in wasteful investment, inaccurate treatments, erroneous care planning, and higher mortality rates . To avoid such issues, HIE networks should assure patients that their medical records would be well protected during exchange transactions. Thus, privacy policies should be clearly presented by health care organizations to highlight how sensitive health information will be used inside/outside the organizations and what security means will be utilized to protect such data from unauthorized access and secondary use [ ]. The risks of violated privacy, information misuse, or unauthorized disclosure highlight the importance of developing a transparent privacy statement before patient medical records are disclosed and shared.
Previous studies emphasize that patients are highly concerned about losing control over how the mainstream HIE systems handle their health information . The concern is mostly because of a lack of transparency on the HIEs’ information practices and privacy policies. Privacy policies should be comprehensive and transparent enough to address all principles mentioned in the Health Insurance Portability and Accountability Act (HIPAA) [ ]. The notice principle articulates what health information is collected and exchanged, what the purpose of data exchange is, how such information will be used internally, and whether patient data will be disclosed to third parties. The choice principle delineates the consent process and permission requirements. This dimension provides the choice to patients to put limits on providers for the exchange of health data. It also provides patients with the options to disclose such records to other third-party entities (eg, voluntary data disclosure for research purposes). The access principle entails granting the right to patients to obtain, review, and amend their personal information to ensure data accuracy and completeness. The security principle implies the adoption of reasonable measures and technical security steps to protect health information from unauthorized access, improper use, loss, unapproved alteration, or unanticipated disclosure during data exchange processes. The retention principle clarifies the acceptable duration of keeping and analysis of shared health information by health care providers for health care purposes. This dimension articulates the reasonable steps to permanently delete shared personal data if it is no longer required for the consented purpose. Finally, the enforcement principle highlights self-regulation such as privacy seals to protect information privacy by informing the public whether the exchange procedures correspond to the legal requirements [ ]. Thus, highly transparent principles of privacy policies are able to demonstrate how safe, reliable, and dependent HIE networks are to reduce patients’ concerns for information privacy.
Blockchain-Based Health Information Exchange as an Alternative
New ways of conducting business and operating economic activities are emerging through blockchain technology. Using dynamic shared ledgers, blockchain is able to facilitate recording business transactions between parties involved. Moreover, based on a peer-to-peer network of nodes, blockchain can also remove the need for intermediaries’ interactions and direct control by third parties in running a business. According to Crosby et al , the underlying features of blockchain make it to be considered as a disruptive technology that has potentials to fundamentally change current business models. Most studies in the blockchain domain have investigated cryptocurrency for its technical properties [ ]. However, blockchain technology has broader and deeper applications beyond cybercurrencies and can be used for other purposes than financial transactions. As the interest in this technology has been rising, blockchain is attracting a great deal of attention and investment from numerous projects in different sections [ ]. Blockchain technology is transforming several industries, such as banking, electronic governance, electronic commerce (e-commerce), legal contracts, automation, logistics, and health care [ ]. Owing to its underpinning technology, one of the most conceivable applications of blockchain is in establishing coordination and managing communication between networked companies (such as hospitals). In a networked business model, all the involved companies are required to uninterruptedly communicate and constantly update their supply chain components to track the latest status of orders, processes, and transactions.
Blockchain may also contribute to other organizational initiatives such as information exchange across affiliated/unaffiliated health care entities (ie, all parties involved in the health care process, such as physicians, hospitals, and clinics). It has been proposed that blockchain-based sharing models, which use immutability and built-in autonomy features of the blockchain, are able to efficiently track records of access to sensitive medical data stored in the cloud . According to Xia et al [ ], health care organizations can take advantage of the access control framework that is based on blockchain to facilitate and expedite medical data sharing with other institutions. This technology provides secure cryptographic techniques to strongly control the access to patient medical records stored and processed on cloud platforms. Relying on the robust security platform, the system can detect and validate users that have access to sensitive medical records and keep track of all sharing activities.
The technology behind blockchain enables anonymous/pseudonymous actors in sharing initiatives, especially in a cross-border setting such as HIE. Blockchain can also resolve technical issues such as security and scalability as it operates based on a peer-to-peer network with no central authority, administrator, or a firm controlling the transactions. This decentralized network prevents a single point of failure and a security breach . Moreover, cryptographic protocol used by blockchain technology provides communications security over a computer network. Using smart contracts embedded in blockchain technology, health care institutions can tap into automated execution of business interactions to notably decrease the need for majority of office operations in the sharing process.
|Direct exchange||Point-to-point information exchange in which a physician is able to share medical information with a known recipient over a secure network||Williams et al |
|Query-based exchange||A single data repository that enables health care providers to share patient medical data with a centralized data warehouse. It also allows health care organizations to search for the required health information||Campion et al |
|Patient-mediated exchange||This HIE model gives patients the ability to aggregate and manage their health information on the internet. Thus, patients can help share information between providers to track and monitor their own health||Rudin et al |
|Blockchain-enabled exchange||A decentralized and trustless HIE model in which each block contains an episode of care and each node operates independently while following the sharing protocols. This model synthesizes medical data from patient-centered management tools and the EHRb systems to provide access only to authorized stakeholders through secure transactions||Jiang et al |
aHIE: health information exchange.
bEHR: electronic health record.
Blockchain technology is considered as a trustless distributed ledger to collect, store, share, analyze, and validate medical data exchange among different stakeholders (such as health care organizations, providers, and patients) . Therefore, one of the most promising applications of blockchain in the health care domain is in health data transmissions between patients, providers, hospitals, and relevant entities [ ]. Blockchain technology has been suggested as an underpinning infrastructure for HIE to improve medical data storage, information exchange, and medical record management [ ]. Recent studies also propose adoption of blockchain-based data-sharing networks to analyze secondary medical data for biomedical research purposes [ ]. Another stream of research focuses on the use of blockchain to store patient-centered outcomes [ ] and patient consent data [ ]. Several companies, such as Deloitte [ ], Accenture [ ], and Guardtime [ ], have initiated adoption of blockchain-based systems to store, manage, and exchange patient care. Therefore, consistent with previous research, blockchain technology is able to contribute to the health care industry and HIE efforts. In summary, the main characteristics of the 4 HIE models examined in this study are described in .
Each scenario pertains to a separate experiment. Therefore, we conducted 16 separate experiments. As a between-subject experiment is a better choice than a within-subject experiment for attitude formation , in this study, we used between-subject experiments in which participants are randomly exposed to only 1 experiment. The total minimum sample required is 100 per experiment considering alpha=.05 and power beta=.95. As there are 6 main outcome variables in this study with 30 measures, we used minimum 120 respondents per experiment to reduce possible sampling errors. shows the experimental design used in this study.
|HIEa model||HIE architecture|
|Strong policy/sensitive information||Strong policy/nonsensitive information||Weak policy/sensitive information||Weak policy/nonsensitive information|
|Experiment #||n||Experiment #||n||Experiment #||n||Experiment #||n|
|Direct exchange model||1||128||5||123||9||128||13||132|
|Query-based exchange model||2||128||6||131||10||124||14||126|
|Patient-centered exchange model||3||122||7||126||11||127||15||125|
|Blockchain-based exchange model||4||120||8||125||12||126||16||122|
aHIE: health information exchange.
We used the expert judgment approach to improve the content validity and completeness of our study. We sent the scenarios and questions to 5 professional health informatics practitioners and 3 blockchain experts. Then, the scenarios and questions were modified based on the experts’ suggestions to ensure that they were clear and easy to understand for the public. Before conducting the main study, we also conducted a pilot test with 86 students at a large Southeastern university in the United States. We provided an open-ended essay box at the end of the survey for the students to comment on the clarity of the scenarios and the questions. Furthermore, we followed up on the comments by conducting interviews with the students to understand any ambiguity in the scenario and the surveys. We revised the scenario and the surveys based on the comments from the students before final data collection. To ensure the reliability and validity of the instrument, the Cronbach alpha was computed for each construct (privacy concern alpha=.85, trust in competency alpha=.76, trust in integrity alpha=.91, opt-in intention alpha=.88, willingness to share information alpha=.90, and perceived benefit alpha=.93). All the Cronbach alpha values were above the cutoff point of .7, which indicated that the instrument was internally consistent .
Recruitment and Participants
Data were collected in October 2018 using Amazon’s Mechanical Turk (MTurk) to obtain a representative group of subjects. MTurk is used by a number of studies as an acceptable means to collect individual-level data [, ]. Research in different domains (especially psychological and social behavior) recruits respondents through MTurk to analyze the perceptions of samples that are more representative of the general workforce, including a wide range of ages, ethnicities, and work experiences [ ]. We defined a location filter to collect data from the United States. The 16 experiments were posed to MTurk at the same time. We used a randomizer function to assign respondents randomly to the 16 scenarios to minimize the likelihood that 1 respondent could participate in more than 1 experiment. Moreover, a microcode was activated in the survey to keep individuals from taking each experiment more than once. Finally, all experiments were also double-checked using generated respondent identification and internet protocol address to ensure that the respondents were unique between experiments. The incentive for participation was a monetary reward. The range of average completion time for the 16 experimental groups was between 21:49 and 32:36 min that implied acceptable responses in terms of timing.
The 16 experiments obtained data from 2013 respondents, ranging between 120 and 132 participants each. We matched the respondents across the 16 groups to avoid any potential problem of individual differences between groups. Results of chi-square tests show that there were no significant differences among participants in all 16 groups, and they are very similar in terms of the demographic variables (seefor results of chi-square tests). For instance, the distribution of data related to gender (χ215=12.1; P=.66), age (χ275=92.8; P=.08), health status (χ260=49.9; P=.91), household income (χ260=59.1; P=.51), race (χ260=81.5; P=.06), education level (χ275=76.1; P=.44), employment status (χ260=69.1; P=.19), and computer experience (χ260=51.7; P=.77) was notably similar across the 16 scenarios. Thus, we had enough evidence to assume that matched groups were used in this study (see for respondent characteristics across the 16 experiments).
Analysis of Variance Test
We used IBM SPSS Statistics 24 to perform analysis of variance (ANOVA) to examine whether the 16 groups are significantly different by our main outcome variables: privacy concerns, opt-in intention, trust in competency, trust in integrity, willingness to share information, and perceived benefits. Before performing ANOVA analysis, we ran the Levene test to examine the homogeneity of variance, as this is one of the fundamental assumptions of 1-way ANOVA. The results do not show enough evidence to hold the assumption of homogeneity of variance for outcome variables. Therefore, we conduct Welch ANOVA that presents the most power and lowest type I error rate when data violate the assumption of homogeneity of variances . shows the descriptive statistics (mean score, SE, and Welch values) and the significance of each outcome variable.
|Outcome variable and scenario #||Mean||SE||Welch||P value|
|Trust in HIEa competency||5.64||<.001|
|Trust in HIE integrity||8.40||<.001|
|Willingness to share information||6.67||<.001|
aHIE: health information exchange.
|Scenario||Health information exchange mechanism||t||P value|
|Strong policy/sensitive information|
|Blockchain technology||Direct exchange model||−1.97||.03a|
|Blockchain technology||Query-based exchange model||−3.49||<.001a|
|Patient-centered exchange model||Query-based exchange model||−2.64||<.001a|
|Weak policy/sensitive information|
|Blockchain technology||Direct exchange model||−2.82||<.001a|
|Blockchain technology||Query-based exchange model||−2.06||.02a|
|Weak policy/nonsensitive information|
|Blockchain technology||Direct exchange model||−2.65||<.001a|
|Blockchain technology||Query-based exchange model||−2.05||.02a|
|Patient-centered exchange model||Query-based exchange model||−1.72||.04a|
aThe mean difference is significant at the .05 level.
Trust in Health Information Exchange Competency
Next, we compared the participants’ responses to the level of trust in the capability of HIE mechanisms described in the sixteen 16 scenarios. According to, the results indicates that respondents who are exposed to strong privacy policies used to exchange sensitive information, express significantly more trust in the patient-centered exchange model (t=1.87; P=.03) and the direct exchange model (t=−2.39; P=.01) compared with the query-based model. We could not find significant differences in terms of trust in the competency of exchange technologies in other scenarios.
Trust in Exchange Integrity
Regarding respondents’ level of trust in the integrity of the HIE mechanisms, the findings shown inreveal that in the scenarios where sensitive information is shared with the help of strong privacy policies, there is a significant difference between blockchain versus query-based models (t=1.74; P=.04). In the same scenarios, our results show that trust in the integrity of the query-based model is significantly lower than that in the direct exchange model (t=−3.04; P=.001). There are no significant differences in terms of trust in the integrity and reliability of exchange mechanisms in other scenarios.
|Scenario (strong policy/sensitive information)||Health information exchange mechanism||t||P value|
|Direct exchange model||Query-based exchange model||−2.39||.01a|
|Patient-centered exchange model||Query-based exchange model||1.87||.03a|
aThe mean difference is significant at the .05 level.
|Scenario (strong policy/sensitive information)||Health information exchange mechanism||t||P value|
|Blockchain technology||Query-based exchange model||1.74||.04a|
|Query-based exchange model||Direct exchange model||−3.04||<.001a|
aThe mean difference is significant at the .05 level.
|Scenario||Health information exchange mechanism||t||P value|
|Strong policy/sensitive information|
|Direct exchange model||Query-based exchange model||1.62||.04a|
|Blockchain Technology||Query-based exchange model||2.71||<.001a|
|Patient- centered exchange model||Query-based exchange model||1.93||.03a|
|Weak policy/sensitive information|
|Blockchain technology||Direct exchange model||2.95||.001a|
|Blockchain technology||Query-based exchange model||2.63||.004a|
|Blockchain Technology||Patient-centered exchange model||1.70||.04a|
|Weak policy/nonsensitive information|
|Blockchain technology||Direct exchange model||2.57||.005a|
|Blockchain technology||Query-based exchange model||2.22||.01a|
aThe mean difference is significant at the .05 level.
Willingness to Share Health Information
We further investigated whether respondents are willing to share their health information given the scenarios. In scenarios where sensitive information is shared under strong privacy policies, participants prefer blockchain technology significantly more than the query-based model (t=3.03; P=.001).also shows that in the same scenarios, respondents express more willingness to share their information through the patient-centered model the than query-based model (t=2.01; P=.02). In scenarios where sensitive information is exchanged based on weak privacy policies, respondents exhibit significantly more willingness to share health information through blockchain technology compared with the direct model (t=5.07; P<.001), query-based model (t=5.61; P<.001), and patient-centered model (t=4.21; P<.001). In the same scenarios, we also found that respondents prefer the patient-centered model better than the query-based model (t=1.95; P=.03). Moreover, participants show more willingness toward blockchain technology for sharing nonsensitive information under weak privacy policies compared with the direct model (t=3.89; P<.001), query-based model (t=3.27; P<.001) and patient-centered model (t=2.001; P=.02). In the same scenarios, respondents also prefer the patient-centered model versus the direct model (t=2.09; P=.02) and the query-based-model (t=2.001; P=.02).
|Scenario||Health information exchange mechanism||t||P value|
|Strong policy/sensitive information|
|Blockchain technology||Query-based exchange model||3.03||.001a|
|Patient-centered exchange model||Query-based exchange model||2.01||.02a|
|Weak policy/sensitive information|
|Blockchain technology||Direct exchange model||5.07||<.001a|
|Blockchain technology||Query-based exchange model||5.61||<.001a|
|Blockchain technology||Patient-centered exchange model||4.21||<.001a|
|Patient-centered exchange model||Query-based exchange model||1.95||.03a|
|Weak policy/nonsensitive information|
|Blockchain technology||Direct exchange model||3.89||<.001a|
|Blockchain technology||Query-based exchange model||3.27||<.001a|
|Blockchain technology||Patient-centered exchange model||2.001||.02a|
|Patient-centered exchange model||Query-based exchange model||2.04||.02a|
|Patient-centered exchange model||Direct exchange model||2.09||.02a|
aThe mean difference is significant at the .05 level.
This study has implications for researchers conducting studies in the HIE context. Our study is different from previous research by examining patients’ perspectives of 4 HIE models. This research is mainly designed to address how different models of HIE can affect patients’ attitude toward electronic data exchange between health care providers. To do so, we investigated whether levels of patients’ privacy concerns, perceived benefit of HIE, trust in HIE competency, trust in HIE integrity, willingness to share personal information, and opt-in intentions are different across multiple HIE models (ie, direct, query based, patient centered, and blockchain based). This study also contributes to the literature by providing new insights on how blockchain technology can be leveraged in the context of HIE and how patients may be affected.
The content of the blockchain is information; thus, its use is adaptable in different business sectors. In line with the study by Liu and Tsyvinski , industries have different reactions to blockchain as they may benefit or become disrupted by this technology. Although there are several attempts among information systems (IS) scholars to recognize the applications of blockchain technology in different business contexts, significant theoretical contributions are still scarce, especially in the health care context. Economists, computer scientists, finance scholars, and IS scholars analyze blockchain technology and its adoption from various lenses. According to the existing blockchain literature, the majority of studies focus on its applications in financial transactions. This research domain can be divided into 5 main categories. One group of studies has focused on the potential use of cryptocurrency for illegal activities and has not examined the motives of mainstream users to adopt it for legitimate uses such as for e-commerce, information exchange, or money transfer [ ]. The second group of studies has investigated cryptocurrency for its technical properties such as design science, cryptography, proof-of-work algorithm, or exchange rates perspectives [ ]. The third category of research has called readers’ attention to analyze the differences between the technical, usability, and social characteristics of different forms of cryptocurrencies [ ]. The fourth category of studies has attempted to distinguish cryptocurrency adopters from nonadopters based on either drivers or risks associated with its underlying technology [ ]. The last category has used widely accepted adoption models (eg, technology acceptance model [TAM] and unified theory of acceptance and use of technology [UTAUT]) and mainly focused on the perceived usefulness and perceived ease of use in the context of cryptocurrency [ ].
Blockchain-based sharing frameworks to facilitate the exchange of medical information between health care and research institutions are under development. These medical blockchain applications sufficiently control access to medical data stored and processed on cloud systems. They also offer secure cryptographic techniques to identify and authenticate users who have access to medical data to keep track of all exchange transactions . Thus, sharing data for telemedicine and medical consultations in remote areas becomes more efficient. One of the main factors affecting the widespread adoption of blockchain-based HIE is social acceptance of this exchange mechanism. A patient may seek medical treatments and care planning from different health care organizations and providers. In a situation where patient-provider interactions are growing, a technology may be required to facilitate communications and medical records exchange without a centralized authority but relying on a distributed public ledger of all data exchange transactions. However, previous research on how patients would react to medical information sharing through blockchain is still in its nascent stage. In this study, we analyzed 6 outcome variables that need to be considered to measure the success and effectiveness of HIE models from patients’ views. This work is among the first studies to empirically examine the potential role of blockchain in the HIE context. The results imply the significance of blockchain-based technology for health care applications when compared with the mainstream HIE models (ie, direct, query based, and patient mediate exchange).
Trust in Health Information Exchange Models
Consistent with previous studies, in the process of forming trust in technology (as an impersonal entity), consumers’ awareness of the unknown should be resolved . Previous research indicates that the public awareness about HIE mechanisms, functions, integrity, and security safeguards needs to be raised [ , ]. For example, one area could be the differences between the open transparency of information in cryptocurrency and blockchain-based HIE. The transparency of information in cryptocurrency means that all nodes in the network have the right to access the whole information related to financial transactions. However, this feature is not desirable for transmitting highly sensitive health information. To implement blockchain exchange methods to share sensitive health data across providers, it is required to develop security features (eg, confidentiality, availability, and integrity), which is considered as one of the main aspects of blockchain technology. A blockchain-based HIE system is a decentralized framework where all medical records are confidential and the availability of such information does not rely on any third parties (eg, hospitals or providers). Furthermore, data integrity can be ensured because this form of HIE uses a distributed file system where participants in exchange activities will keep copies of all files, including the shared health information. Moreover, they agree to share, change, and update medical data by permission requests and consent processes. Therefore, the rational expectations about the HIE’s ability to fulfil its obligations (cognitive trust in competence) and the rational reasons associated with the reliability of the HIE principles (cognitive trust in integrity) can be increased through raised awareness about the use of various types of blockchain innovations such as smart contract applications and permissioned networks.
Opt-In Intentions to Health Information Exchanges and Willingness to Share Information
The results manifest that with the current blockchain technology, patients may not feel skeptical about relying on blockchain-enabled HIE to manage the exchange of their highly sensitive information among a wide range of providers. This finding also emphasizes the importance of raising patient awareness of how the consent process and permissioned HIE networks operate in practice. Moreover, more efforts are required to improve the legal image of blockchain technology in health care to enable at-scale interoperability for information exchange, patient tracking, identity assurance, as well as validation among health care institutions and between patients and their providers . Our findings also propose possible direct relationships of trust in blockchain HIE with patients’ opt-in intentions and their willingness to disclose health information. Thus, a high level of trust in blockchain competence and integrity may encourage patients to opt in to this technology and disclose their sensitive health information when visiting a physician participating in a blockchain-based HIE network.
Perceived Benefits of Health Information Exchange
Patient Awareness About Blockchain-Based Health Information Exchange
There are also a number of important practical implications derived from this study. First, the findings suggest the importance of educating consumers about the use of blockchain technology in HIE mechanisms and sharing procedures. For instance, national educational programs, health conferences, and webinars that are easily accessible to a wide range of people can be administered to clearly publicize the key goals and advantages of blockchain-based HIE efforts. Educational forums available on official health websites, Web-based tutorials accessible on patient portals or Web-based health communities, and computerized help programs can be used by health care organizations to improve the transparency of blockchain applications in HIEs, broadcast their expected benefits, and increase public awareness and patient familiarity with this exchange mechanisms.
Third, lack of public awareness about the blockchain-based HIE model as well as the components of its privacy statement may impede the progress of sharing information between providers because of the lack of patients’ support for HIE. This study suggests that both physicians and health care organizations (such as hospitals) can directly play an important role in persuading patients to give consent to sharing medical records using blockchain-enabled HIE. Physicians’ role may be more effective because they have face-to-face encounters with patients and during consultations, they can enlighten the patients about the benefits of using blockchain in HIEs, and how they could be impacted. For instance, health care professionals can explain how HIE, which is enabled by blockchain can help physicians detect diseases faster, coordinate treatments with other providers, and finally, improve patient safety. Hospitals can also influence how patients shape opt-in decisions toward blockchain-based HIE by educating them using brochures, leaflets, diagrams, and fact sheets that are comprehensible for an average person. These efforts should be able to clearly highlight why health information is shared, what types of information can be exchanged, how such information is shared from one point to another, what exchange mechanisms are used, who can access the medical data, what security safeguards will protect their records, and how often the transmission takes place.
Potential Benefits of Blockchain-Based Health Information Exchange
Relying on the key findings as well as characteristics and features of blockchain, the main benefits of using blockchain for improving medical record sharing among health care organizations are discussed in the following section. Decentralized management of the blockchain technology can notably contribute to HIE by providing patient-managed health care records. In these platforms, patients are considered as the owner of their medical records and are able to efficiently control access to such information . This aspect can also help patients reduce all possible barriers associated with obtaining copies of their medical information and potential risks related to sharing them with other health care organizations. In a blockchain-based HIE, each block can contain an episode of care and each node operates independently while following the sharing protocols. Blockchain has the potential to become an electronic health information pool by synthesizing medical data from patient-centered management tools and the EHR systems to provide access only to authorized stakeholders (such as patients and providers). The peer-to-peer architecture of blockchain also enables health care institutions to keep control of their own IT resources and collaborate with other organizations to enhance information sharing initiatives without ceding control [ ]. Thus, incorporating blockchain into HIE is appropriate for health care providers/organizations that seek to cooperate with each other with no centralized management intermediary. On the contrary, most of HIE mechanisms (eg, direct model or lookup networks) are centrally managed.
The immutable audit trail is another characteristic of blockchain technology that is likely to contribute to HIE. On the basis of this aspect, patient health information is not changeable in any steps of the data-sharing initiatives. Thus, the medical records that are stored in the private blockchain cloud cannot be altered, manipulated, or removed by any entities participating in HIE initiatives such as health care providers and organizations . Furthermore, patient medical records that are generated and shared with health care providers through a blockchain-based sharing platform are trackable and timestamped.
Managing patient consent records during data-sharing processes can be improved by the data provenance of blockchain technology. This aspect can help the owner of medical records to change the ownership or give permission to other entities to view, process, and share such information using the cryptographic protocols. Moreover, patients or providers can trace the source of data and verify legitimacy as well as accuracy of records to be used for exchange purposes. Thus, using blockchain-based HIE, the source of medical records is detected and any ownership transfer in each block will be transparent and available to everyone involved in the data-sharing efforts.
Blockchain is built upon distributed technology that does not suffer from a single point of failure. Relying on this feature, patient health information can be collected, stored, and shared on a decentralized network, where there is no central institution that could be hacked or compromised. This robustness feature has the potential to decrease the risk of patient recordkeeping as medical data cannot be faked or manipulated. Moreover, one of the main threats related to the mainstream HIE models is unavailability of patient data when incomplete or inaccurate patient information is stored in shared records . This issue can be resolved by blockchain technology as each node in the network has a copy of historical medical records and is able to continuously update such data. This characteristic may guarantee that the electronic medical records of patients are continuously available in real time [ ]. Real-time access to patient data is one of the main promises of HIE efforts that enable providers to advance care coordination, detect epidemics rapidly, and improve care delivery in emergency situations [ ].
All information exchange initiatives in the United States health care industry (such as HIE projects) fall under HIPAA security rules . Under HIPAA, security policies and procedures should be implemented to prevent, detect, and correct security violations [ ]. For example, a thorough analysis of the potential risks and vulnerabilities to the confidentiality, integrity, and availability of electronic health information held by the covered entity should be conducted before exchanging any information. Moreover, procedures for the authorization and supervision of members who use electronic protected health information should be clarified [ ]. For instance, security procedures should determine whether the access of a health care organization to electronic health information is appropriate or should be terminated. On the basis of HIPAA guidelines, procedures are implemented to verify that a physician or entity seeking access to electronic health information is the one claimed [ ]. For instance, technical security measures and encryption mechanisms are implemented to guard against unauthorized access to electronic health information that is being transmitted over an electronic communications network (such as HIE).
A number of studies have argued that privacy and security concerns are identified as the most pressing barriers to widespread consumer participation in the implementation of mainstream HIE models . Privacy policies of HIE efforts should be comprehensive and transparent enough to address all the principles mentioned in HIPAA [ ]. One of the main advantages of blockchain technology that can be utilized by HIE models is improving safety, integrity, and confidentiality of patient health information using cryptographic algorithms and consent recording systems. Episodes of medical care can be stored in blocks and only decrypted for exchange purposes with the patient’s private key. Even if the distributed network is breached by a malicious entity, with current technology means, it is extremely unlikely that patient data can be illegally accessed by unauthorized parties. Therefore, blockchain-enabled sharing platforms have the potential to connect a vast number of patients, health care providers, and health care organizations to exchange variety of medical records while information privacy and security are protected.
Plausible Challenges of Blockchain-Based Health Information Exchange
The objective of this study was not to propose blockchain as the most advantageous method of information exchange in the health care industry. Results of our research indicate that there are still a number of criticisms attributed to blockchain-based solutions in the health care area. In this section, the main shortcomings of blockchain are highlighted to imply that current blockchain solutions need some necessary modifications to be implemented in the HIE context.
Although blockchain is an appropriate means to facilitate interoperability, current studies have also emphasized that the open transparency of data during exchange transactions is not desirable in health care applications . In the HIE context, identifiable information of patients is highly sensitive. The key objective of HIPAA compliance is that information exchange must be protected against a confidentiality breach. The end-to-end workflow of a blockchain-based HIE (ie, entering, processing, and delivering of health data) must be HIPAA compliant. Any personal health information accessed by the blockchain-enabled HIE must be encrypted and securely managed by parties interacting with this HIE model. A blockchain-based HIE should not make all personal information publicly available so it can securely store and manage sensitive data. Blockchain-enabled HIE should ensure the anonymity of each identity and transaction using unique authentication protocol (data protection methods such as tokenization or masking) [ ]. Thus, each data exchange performed by a user should not be linked to the user and the ownership of the key should remain anonymous. Moreover, privacy policies designed for blockchain-based HIE can provide different levels of data access and, if required, time-limited access. Another way to alleviate the open transparency issue is encrypting sensitive health records on the network of blockchain-based HIE [ ]. Recent studies also propose that sensitive medical records can be stored off-blockchain network and only encrypted links and permission information should be exchanged on network [ ]. According to Ekblaw et al [ ], data exchange protocols can be automated using smart contracts to attenuate this risk.
Another potential challenge with the adoption of blockchain in HIE networks is the speed of transactions. Depending on the authentication and verification protocol used in blockchain, data exchange processes could be time consuming. This could challenge the real-time communication, coordination, and data sharing among health care providers, which is critical in many health care situations . According to Linn and Koo [ ], ongoing verified exchange transactions can only be stored in blocks instead of the complete past medical histories. Another plausible solution is to implement blockchain-based platforms that provide higher transaction speed compared with the Bitcoin network [ ].
The risk of a 51% attack has been considered as an important threat to blockchain networks . This attack, which occurs when the whole network is controlled by attackers or malicious nodes, could critically threaten the security of HIE platforms. HIE can adopt permissioned blockchain networks in which malicious nodes are not able to randomly contribute to the network, and in turn, the risk of a 51% attack could be minimized. For instance, implementation of a virtual private network in which medical records are stored and exchanged on private cloud resources complied with HIPAA can notably mitigate this risk [ ].
Finally, it should be mentioned that the spread of the blockchain-based framework in health care practices might be challenging, particularly in developing countries that do not have adequate technical infrastructure and social support. Moreover, the long-term success of blockchain-based HIE needs favorable attitude and active participation of all stakeholders (such as physicians, health care organizations, and patients). According to Dixon et al , HIE projects may become ineffective and disabled because of a lack of participation and support from HIE stakeholders. With respect to patients, there is a need to increase public awareness about blockchain technology. For instance, national educational programs such as educational videos or webinars can be used by health care providers to convey key information about blockchain-based HIE and how it facilitates the sharing of medical data securely with and between health care providers. Patients should be educated on the aspects of blockchain to realize how the technology is able to exchange sensitive medical data securely, improve confidentiality of all sharing activities, enable patients to track who can access episode-of-care data, and increase patient control over their medical records.
Limitations and Future Research
Consistent with the results of this study, further research can also develop and empirically test a causal model using the outcome variables proposed by this study to predict the success of blockchain in HIE initiatives from consumers, health care professionals, and hospital managers’ perspectives. Health care industry is considered as a highly regulated environment. Future studies can extend this work by identifying approaches to address governance conflicts arising from the technology being used in the health care context. It can also be of interest for future research to investigate the role of regulatory bodies in keeping control, on the one hand, and having systems that run on their own, on the other. In this study, we discussed the key risks involved along with several plausible solutions related to the adoption of blockchain technology in the HIE context. Future research is required to shed more light on the design and implementation of blockchain-enabled HIE applications. Finally, this study provides a footstone for further theoretical development and practical investigation. For instance, future work can study the return on investment and cost impact to health care delivery as a result of a blockchain-enabled HIE implementation. Moreover, the legal and policy implications/requirements can be addressed by further research.
Blockchain is considered as one of the most important technologies that can be applied in many sectors in the future. One of the most interesting cases of blockchain technology application is in health care domains. Research on the use of blockchain technology in the health care context is still in its early stages, and its widespread adoption needs further efforts. This work uses an experimental approach to better articulate the prospective application of blockchain technology in creating an infrastructure for sharing medical records. The findings indicate that blockchain technology has a great potential to be integrated in existing HIE architectures to improve system transparency, patient consent tracking, and privacy protection of information exchange initiatives. Blockchain-based HIE is able to provide a platform for data exchange that does not need a centralized authority to operate. This aspect promotes a protocol supporting a network-based communication between patients and physicians and a well-organized coordination among health care organizations to accurately diagnose diseases, provide timely treatments, and improve patient safety. According to the results of this study, patients perceive that blockchain technology can be a reliable replacement for current exchange models, which are mainly managed by mainstream bureaucratic systems or large institutions with centralized control (such as hospitals). Consistent with results, we also discuss the key benefits and possible risks of adopting blockchain technology in HIE efforts. This research can serve as a foundation for future studies in the domain of blockchain-based HIE.
Conflicts of Interest
Multimedia Appendix 1
Experiments: scenarios and questions.DOCX File, 26KB
Multimedia Appendix 2
Respondent characteristics across the 16 experiments.DOCX File, 30KB
- Cherry JC, Dryden K, Kobb R, Hilsen P, Nedd N. Opening a window of opportunity through technology and coordination: a multisite case study. Telemed J E Health 2003;9(3):265-271. [CrossRef] [Medline]
- Edaibat EA, Dever J, Stuban SM. System dynamics simulation modeling of health information exchange (HIE) adoption and policy intervention: a case study in the State of Maryland. Oper Res Health Care 2017 Mar;12:60-70. [CrossRef]
- Vest JR, Gamm LD. Health information exchange: persistent challenges and new strategies. J Am Med Inform Assoc 2010;17(3):288-294 [FREE Full text] [CrossRef] [Medline]
- Lenert L, Sundwall D, Lenert ME. Shifts in the architecture of the Nationwide Health Information Network. J Am Med Inform Assoc 2012;19(4):498-502 [FREE Full text] [CrossRef] [Medline]
- Campion TR, Edwards AM, Johnson SB, Kaushal R, HITEC Investigators. Health information exchange system usage patterns in three communities: practice sites, users, patients, and data. Int J Med Inform 2013 Sep;82(9):810-820. [CrossRef] [Medline]
- Williams C, Mostashari F, Mertz K, Hogin E, Atwal P. From the Office of the National Coordinator: the strategy for advancing the exchange of health information. Health Aff (Millwood) 2012 Mar;31(3):527-536. [CrossRef] [Medline]
- Walker J, Pan E, Johnston D, Adler-Milstein J, Bates DW, Middleton B. The value of health care information exchange and interoperability. Health Aff (Millwood) 2005;Suppl Web Exclusives:W5-10. [CrossRef] [Medline]
- Ancker JS, Edwards AM, Miller MC, Kaushal R. Consumer perceptions of electronic health information exchange. Am J Prev Med 2012 Jul;43(1):76-80. [CrossRef] [Medline]
- Rudin R, Volk L, Simon S, Bates D. What affects clinicians' usage of health information exchange? Appl Clin Inform 2011 Jan 1;2(3):250-262 [FREE Full text] [CrossRef] [Medline]
- Patel VN, Abramson E, Edwards AM, Cheung MA, Dhopeshwarkar RV, Kaushal R. Consumer attitudes toward personal health records in a beacon community. Am J Manag Care 2011 Apr;17(4):e104-e120 [FREE Full text] [Medline]
- Esmaeilzadeh P. The effects of public concern for information privacy on the adoption of Health Information Exchanges (HIEs) by healthcare entities. Health Commun 2018 May 8:1-10. [CrossRef] [Medline]
- Whiddett R, Hunter I, Engelbrecht J, Handy J. Patients' attitudes towards sharing their health information. Int J Med Inform 2006 Jul;75(7):530-541. [CrossRef] [Medline]
- Dimitropoulos L, Patel V, Scheffler SA, Posnack S. Public attitudes toward health information exchange: perceived benefits and concerns. Am J Manag Care 2011 Dec;17(12 Spec No):SP111-SP116 [FREE Full text] [Medline]
- Tripathi M, Delano D, Lund B, Rudolph L. Engaging patients for health information exchange. Health Aff (Millwood) 2009;28(2):435-443. [CrossRef] [Medline]
- Wang JY, Ho HY, Chen JD, Chai S, Tai CJ, Chen YF. Attitudes toward inter-hospital electronic patient record exchange: discrepancies among physicians, medical record staff, and patients. BMC Health Serv Res 2015 Jul 12;15:264 [FREE Full text] [CrossRef] [Medline]
- Dimitropoulos L, Rizk S. A state-based approach to privacy and security for interoperable health information exchange. Health Aff (Millwood) 2009;28(2):428-434. [CrossRef] [Medline]
- Agaku IT, Adisa AO, Ayo-Yusuf OA, Connolly GN. Concern about security and privacy, and perceived control over collection and use of health information are related to withholding of health information from healthcare providers. J Am Med Inform Assoc 2014;21(2):374-378 [FREE Full text] [CrossRef] [Medline]
- Abdulnabi M, Al-Haiqi A, Kiah ML, Zaidan AA, Zaidan BB, Hussain M. A distributed framework for health information exchange using smartphone technologies. J Biomed Inform 2017 Dec;69:230-250 [FREE Full text] [CrossRef] [Medline]
- Wright A, Soran C, Jenter CA, Volk LA, Bates DW, Simon SR. Physician attitudes toward health information exchange: results of a statewide survey. J Am Med Inform Assoc 2010;17(1):66-70 [FREE Full text] [CrossRef] [Medline]
- Willison DJ, Schwartz L, Abelson J, Charles C, Swinton M, Northrup D, et al. Alternatives to project-specific consent for access to personal information for health research: what is the opinion of the Canadian public? J Am Med Inform Assoc 2007;14(6):706-712 [FREE Full text] [CrossRef] [Medline]
- Peterson K, Deeduvanu R, Kanjamala P, Boles K. HealthIT.gov. 2016. A Blockchain-Based Approach to Health Information Exchange Networks URL: https://www.healthit.gov/sites/default/files/12-55-blockchain-based-approach-final.pdf [accessed 2019-06-03] [WebCite Cache]
- Chen Y. Blockchain tokens and the potential democratization of entrepreneurship and innovation. Bus Horiz 2018 Jul;61(4):567-575. [CrossRef]
- Kuo TT, Kim HE, Ohno-Machado L. Blockchain distributed ledger technologies for biomedical and health care applications. J Am Med Inform Assoc 2017 Nov 1;24(6):1211-1220 [FREE Full text] [CrossRef] [Medline]
- Baptista G, Oliveira T. Understanding mobile banking: the unified theory of acceptance and use of technology combined with cultural moderators. Comput Hum Behav 2015 Sep;50:418-430. [CrossRef]
- McGraw D, Dempsey JX, Harris L, Goldman J. Privacy as an enabler, not an impediment: building trust into health information exchange. Health Aff (Millwood) 2009;28(2):416-427. [CrossRef] [Medline]
- Eden KB, Totten AM, Kassakian SZ, Gorman PN, McDonagh MS, Devine B, et al. Barriers and facilitators to exchanging health information: a systematic review. Int J Med Inform 2016 Apr;88:44-51 [FREE Full text] [CrossRef] [Medline]
- Kim KK, Joseph JG, Ohno-Machado L. Comparison of consumers' views on electronic data sharing for healthcare and research. J Am Med Inform Assoc 2015 Jul;22(4):821-830 [FREE Full text] [CrossRef] [Medline]
- Crosby M, Pradan P, Sanjeev V, Kalyanaraman V. Blockchain technology: beyond bitcoin. Appl Innov Rev 2016 Jun(2):6-19 [FREE Full text]
- Li X, Wang C. The technology and economic determinants of cryptocurrency exchange rates: the case of bitcoin. Deci Supp Syst 2017 Mar;95:49-60. [CrossRef]
- Nofer M, Gomber P, Hinz O, Schiereck D. Blockchain. Bus Inf Syst Eng 2017 Mar 20;59(3):183-187. [CrossRef]
- Underwood S. Blockchain beyond bitcoin. Commun ACM 2016 Oct 28;59(11):15-17. [CrossRef]
- Giungato P, Rana R, Tarabella A, Tricase C. Current trends in sustainability of bitcoins and related blockchain technology. Sustainability 2017 Nov 30;9(12):2214. [CrossRef]
- Xia Q, Sifah E, Smahi A, Amofa S, Zhang X. BBDS: blockchain-based data sharing for electronic medical records in cloud environments. Information 2017 Apr 17;8(2):44. [CrossRef]
- Fromknecht C, Velicanu D, Yakoubov S. A decentralized public key infrastructure with identity retention. Cryptol ePrint Arch 2014 Nov:1-16 [FREE Full text]
- Yue X, Wang H, Jin D, Li M, Jiang W. Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. J Med Syst 2016 Oct;40(10):218. [CrossRef] [Medline]
- Azaria A, Ekblaw A, Vieira T, Lippman A. MedRec: Using Blockchain for Medical Data Access and Permission Management. 2016 Aug Presented at: 2016 2nd International Conference on Open and Big Data (OBD); August, 22-24, 2016; Vienna, Austria p. 25-30 URL: https://ieeexplore.ieee.org/abstract/document/7573685 [CrossRef]
- Mettler M. Blockchain technology in healthcare: The revolution starts here. : IEEE; 2016 Presented at: 2016 IEEE 18th International Conference on e-Health Networking, Applications and Services (Healthcom); September 14-16, 2016; Munich, Germany p. 1-3. [CrossRef]
- Cichosz SL, Stausholm MN, Kronborg T, Vestergaard P, Hejlesen O. How to use blockchain for diabetes health care data and access management: an operational concept. J Diabetes Sci Technol 2019 Mar;13(2):248-253. [CrossRef] [Medline]
- Brodersen C, Kalis B, Leong C, Mitchell E, Pupo E, Truscott A. Semantic Scholar. 2016. Blockchain: Securing a New Health Interoperability Experience URL: https://pdfs.semanticscholar.org/8b24/dc9cffeca8cc276d3102f8ae17467c7343b0.pdf [accessed 2019-06-03] [WebCite Cache]
- Krawiec RJ, Barr D, Killmeyer J, Filipova M, Nesbitt A, Israel A, et al. Semantic Scholar. UK: Deloitte Development LLC; 2016. Blockchain: Opportunities for Health Care URL: https://pdfs.semanticscholar.org/b992/77c3eecfe6d3dd784fe572a45780ffd040e2.pdf [accessed 2019-06-03] [WebCite Cache]
- Rudin R, Volk L, Simon S, Bates D. What affects clinicians' usage of health information exchange. Appl Clin Inform 2011 Jan 1;2(3):250-262 [FREE Full text] [CrossRef] [Medline]
- Jiang S, Cao J, Wu H, Yang Y, Ma M, He J. Blochie: a blockchain-based platform for healthcare information exchange. : IEEE; 2018 Presented at: 2018 IEEE International Conference on Smart Computing (SMARTCOMP); June 18-20, 2018; Taormina, Italy p. 49-56 URL: https://ieeexplore.ieee.org/abstract/document/8421331 [CrossRef]
- Charness G, Gneezy U, Kuhn M. Experimental methods: between-subject and within-subject design. J Econ Behav Organ 2012 Jan;81(1):1-8. [CrossRef]
- Caine K, Hanania R. Patients want granular privacy control over health information in electronic medical records. J Am Med Inform Assoc 2013 Jan 1;20(1):7-15 [FREE Full text] [CrossRef] [Medline]
- Van Slyke C, Shim JT, Johnson R, Jiang JJ. Concern for information privacy and online consumer purchasing. J Assoc Inf Sci 2006;7(6):16 [FREE Full text]
- Komiak S, Benbasat I. The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Q 2006;30(4):941-960 [FREE Full text] [CrossRef]
- Angst CM, Agarwal R. Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Q 2009 Jun;33(2):339-370 [FREE Full text]
- Zhang X, Liu S, Chen X, Wang L, Gao B, Zhu Q. Health information privacy concerns, antecedents, and information disclosure intention in online health communities. Inf Manage 2018 Jun;55(4):482-493. [CrossRef]
- O'Donnell HC, Patel V, Kern LM, Barrón Y, Teixeira P, Dhopeshwarkar R, et al. Healthcare consumers' attitudes towards physician and personal use of health information exchange. J Gen Intern Med 2011 Sep;26(9):1019-1026 [FREE Full text] [CrossRef] [Medline]
- Patel VN, Dhopeshwarkar RV, Edwards A, Barrón Y, Sparenborg J, Kaushal R. Consumer support for health information exchange and personal health records: a regional health information organization survey. J Med Syst 2012 Jun;36(3):1043-1052. [CrossRef] [Medline]
- Hair JF, Ringle CM, Sarstedt M. PLS-SEM: indeed a silver bullet. J Market Theory Pract 2014 Dec 8;19(2):139-152. [CrossRef]
- Marge M, Banerjee S, Rudnicky A. Using the Amazon Mechanical Turk for transcription of spoken language. : IEEE; 2010 Presented at: 2010 IEEE International Conference on Acoustics, Speech and Signal Processing; March 14-19, 2010; Dallas, TX, USA p. 5270-5273 URL: https://ieeexplore.ieee.org/abstract/document/5494979 [CrossRef]
- Paolacci G, Chandler J, Ipeirotis PG. Running experiments on Amazon Mechanical Turk. Judgm Decis Mak 2010;5(5):411-419 [FREE Full text]
- Behrend TS, Sharek DJ, Meade AW, Wiebe EN. The viability of crowdsourcing for survey research. Behav Res Methods 2011 Sep;43(3):800-813. [CrossRef] [Medline]
- Moder K. How to keep the Type I Error Rate in ANOVA if variances are heteroscedastic. Austr J Stat 2016 Apr 3;36(3):179. [CrossRef]
- Games PA, Keselman HJ, Rogan JC. Simultaneous pairwise multiple comparison procedures for means when sample sizes are unequal. Psychol Bull 1981;90(3):594-598. [CrossRef]
- Liu Y, Tsyvinski A. National Bureau of Economic Research. 2018. Risks and Returns of Cryptocurrency URL: https://www.nber.org/papers/w24877 [accessed 2019-06-05] [WebCite Cache]
- Tetsuya S. A microeconomic analysis of Bitcoin and illegal activities. In: Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data. Amsterdam, Netherlands: Elsevier Science; 2015:231-248.
- Notheisen B, Hawlitschek F, Weinhardt C. Breaking down the blockchain hype–towards a blockchain market engineering approach. : Association for Information Systems; 2017 Presented at: Twenty-Fifth European Conference on Information Systems (ECIS); June 5-10, 2017; Guimarães, Portugal URL: https://pdfs.semanticscholar.org/9f21/9d266984bcf1589c77080439f06d49465c69.pdf
- Connolly A, Kick A. What Differentiates Early Organization Adopters of Bitcoin From Non-Adopters? : AIS; 2015 Presented at: AMCIS 2015; August 13-15, 2015; Puerto Rico URL: https://aisel.aisnet.org/amcis2015/AdoptionofIT/GeneralPresentations/46/
- Abramova S, Böhme R. Perceived benefit and risk as multidimensional determinants of bitcoin use: a quantitative exploratory study. : AIS; 2016 Presented at: ICIS 2016; December 11-14, 2016; Dublin, Ireland URL: https://aisel.aisnet.org/icis2016/Crowdsourcing/Presentations/19/
- Möser M, Böhme R. Trends, tips, tolls: A longitudinal study of Bitcoin transaction fees. 2015 Presented at: International Conference on Financial Cryptography and Data Security; January 26-30, 2015; Berlin, Heidelberg p. 19-33.
- Komiak S, Benbasat I. Understanding customer trust in agent-mediated electronic commerce, web-mediated electronic commerce, and traditional commerce. Inf Technol Manage 2004 Jan;5(1/2):181-207. [CrossRef]
- Yeager VA, Walker D, Cole E, Mora AM, Diana ML. Factors related to health information exchange participation and use. J Med Syst 2014 Aug;38(8):78. [CrossRef] [Medline]
- Venkatesh V, Morris M, Davis G, Davis F. User acceptance of information technology: toward a unified view. MIS Q 2003;27(3):425. [CrossRef]
- Esmaeilzadeh P, Mirzaei T. Comparison of consumers' perspectives on different health information exchange (HIE) mechanisms: an experimental study. Int J Med Inform 2018 Dec;119:1-7. [CrossRef] [Medline]
- Menachemi N, Rahurkar S, Harle CA, Vest JR. The benefits of health information exchange: an updated systematic review. J Am Med Inform Assoc 2018 Sep 1;25(9):1259-1265. [CrossRef] [Medline]
- Kuo TT, Ohno-Machado L. arXiv. 2018. ModelChain: Decentralized Privacy-Preserving Healthcare Predictive Modeling Framework on Private Blockchain Networks URL: https://arxiv.org/abs/1802.01746 [accessed 2019-06-03] [WebCite Cache]
- Linn, LA, Koo MB. ONC/NIST Use of Blockchain for Healthcare and Research Workshop. Gaithersburg, Maryland: HealthIT.gov; 2016. Blockchain for health data and its potential use in health it and health care related research URL: https://www.healthit.gov/sites/default/files/11-74-ablockchainforhealthcare.pdf [accessed 2019-06-05] [WebCite Cache]
- Unertl KM, Johnson KB, Lorenzi NM. Health information exchange technology on the front lines of healthcare: workflow factors and patterns of use. J Am Med Inform Assoc 2012;19(3):392-400 [FREE Full text] [CrossRef] [Medline]
- Lam PE, Mitchell JC, Sundaram S. A formalization of HIPAA for a medical messaging system. 2009 Presented at: International Conference on Trust, Privacy and Security in Digital Business; September 3-4, 2009; Linz, Austria p. 73-85.
- McDonald C. Protecting patients in health information exchange: a defense of the HIPAA privacy rule. Health Aff (Millwood) 2009;28(2):447-449 [FREE Full text] [CrossRef] [Medline]
- Zhang P, White J, Schmidt DC, Lenz G, Rosenbloom ST. FHIRChain: applying blockchain to securely and scalably share clinical data. Comput Struct Biotechnol J 2018;16:267-278 [FREE Full text] [CrossRef] [Medline]
- Prakash P. HealthIT.gov. 2016. Adoption of blockchain to enable the scalability and adoption of accountable care URL: https://www.healthit.gov/sites/default/files/13-71-blockchain_for_healthcare_paper_final.pdf [accessed 2019-06-03] [WebCite Cache]
- Ekblaw A, Azaria A, Halamka JD, Lippman A. A Case Study for Blockchain in Healthcare:“MedRec” prototype for electronic health records and medical research data. : IEEE; 2016 Presented at: 2nd International Conference on Open & Big Data 2016; August 22-24, 2016; Vienna, Austria URL: https://pdfs.semanticscholar.org/56e6/5b469cad2f3ebd560b3a10e7346780f4ab0a.pdf
- Kiayias A, Panagiotakos G. Semantic Scholar. 2015. Speed-Security Tradeoffs in Blockchain Protocols URL: https://www.semanticscholar.org/paper/Speed-Security-Tradeo-s-in-Blockchain-Protocols-Kiayias-Panagiotakos/62646f9450a3c95e745c1d2bb056dcf851acdaad [accessed 2019-06-03] [WebCite Cache]
- Bradbury D. The problem with bitcoin. Comput Fraud Secur 2013 Nov;2013(11):5-8. [CrossRef]
- Ohno-Machado L, Bafna V, Boxwala AA, Chapman BE, Chapman WW, Chaudhuri K, iDASH team. iDASH: integrating data for analysis, anonymization, and sharing. J Am Med Inform Assoc 2012;19(2):196-201 [FREE Full text] [CrossRef] [Medline]
- Dixon BE, Zafar A, Overhage JM. A framework for evaluating the costs, effort, and value of nationwide health information exchange. J Am Med Inform Assoc 2010;17(3):295-301 [FREE Full text] [CrossRef] [Medline]
|ANOVA: analysis of variance|
|e-commerce: electronic commerce|
|EHR: electronic health record|
|HIE: health information exchange|
|HIPAA: Health Insurance Portability and Accountability Act|
|IS: information systems|
|IT: information technology|
|TAM: technology acceptance model|
|UTAUT: unified theory of acceptance and use of technology|
Edited by P Zhang, K Clauson; submitted 28.03.19; peer-reviewed by C Esposito, M Kolotylo-Kulkarni; comments to author 04.05.19; revised version received 12.05.19; accepted 20.05.19; published 20.06.19Copyright
©Pouyan Esmaeilzadeh, Tala Mirzaei. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 20.06.2019.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.