An action design research to facilitate the adoption of personal health records

Adoption and user perceptions are dominant on personal health records literature and have led to a better understanding of what individuals' behaviors and perceptions are about the adoption of personal health records. However, these insights are descriptive and are not actionable to allow creating personal health records that will overcome the adoption problems identified by users. This study uses action design research to provide actionable knowledge regarding user perceptions and adoption and their application in the case of the digital allergy card. To achieve this, we conducted interviews with patients and physicians as part of the evaluation of the digital allergy card mock-up and the first prototype. As results, we provided some research proposals regarding the benefits of, levers for, and barriers to adoption of the digital allergy card that can be tested for several other personal health records.

Risk factors for clinically relevant deviations in patients’ medication lists reported by patients in personal health records: a prospective cohort study in a hospital setting

Background Personal health records have the potential to identify medication discrepancies. Although they facilitate patient empowerment and broad implementation of medication reconciliation, more medication discrepancies are identified through medication reconciliation performed by healthcare professionals. Aim We aimed to identify the factors associated with the occurrence of a clinically relevant deviation in a patient’s medication list based on a personal health record (used by patients) compared to medication reconciliation performed by a healthcare professional. Method Three- to 14 days prior to a planned admission to the Cardiology-, Internal Medicine- or Neurology Departments, at Amphia Hospital, Breda, the Netherlands, patients were invited to update their medication file in their personal health records. At admission, medication reconciliation was performed by a pharmacy technician. Deviations were determined as differences between these medication lists. Associations between patient-, setting-, and medication-related factors, and the occurrence of a clinically relevant deviation (National Coordinating Council for Medication Error Reporting and Prevention class $$\ge$$ ≥ E) were analysed. Results Of the 488 patients approached, 155 patients were included. Twenty-four clinically relevant deviations were observed. Younger patients (adjusted odds ratio (aOR) 0.94; 95%CI:0.91–0.98), patients who used individual multi-dose packaging (aOR 14.87; 95%CI:2.02–110), and patients who used $$\ge$$ ≥ 8 different medications, were at highest risk for the occurrence of a clinically relevant deviation (sensitivity 0.71; specificity 0.62; area under the curve 0.64 95%CI:0.52–0.76). Conclusion Medication reconciliation is the preferred method to identify medication discrepancies for patients with individual multi-dose packaging, and patients who used eight or more different medications.

Attribute Set-Based Boolean Keyword Search over Encrypted Personal Health Records

A personal health record (PHR) is an electronic application which enables patients to collect and share their health information. With the development of cloud computing, many PHR services have been outsourced to cloud servers. Cloud computing makes it easier for patients to manage their personal health records and makes it easier for doctors and researchers to share and access this information. However, due to the high sensitivity of PHR, a series of security protections are needed to protect them, such as encryption and access control. In this article, we propose an attribute set-based Boolean keyword search scheme, which can realize fine-grained access control and Boolean keyword search over encrypted PHR. Compared with the existing attribute-based searchable encryption, our solution can not only improve the flexibility in specifying access policies but also perform Boolean keyword search, which can meet the needs of large-scale PHR users. Furthermore, we simulate our scheme, and the experimental results show that our scheme is practical for PHR systems in cloud computing.

Perceptions of Personal Health Records in Rural Primary Health Clinics in Canada – Patient Perspectives

Personal health records are increasingly being deployed in healthcare settings. In this study we explored patients’ perceptions of personal health records in a rural community in Canada where a primary health network is being deployed. A focus group was held and data were thematically analysed. All patients used technology on a regular basis. Themes included communication and information sharing, issues with access to prior health records, data content and data control and features and functions for continuity of care. Participants expressed desire to be owners of their own record, but described instances where they might be too ill to do so. Participants were hopeful that the functions of a personal health record might help to overcome frustrations with current fragmented information and open to using technologies as part of their care process. Personal health records are promising technologies to overcome fragmented care in rural communities.

Personal health records sharing scheme based on attribute based signcryption with data integrity verifiable

The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.

The Association of Mobile Health Applications with Self-Management Behaviors among Adults with Chronic Conditions in the United States

Background: Mobile applications related to health and wellness (mHealth apps) are widely used to self-manage chronic conditions. However, research on whether mHealth apps facilitate self-management behaviors of individuals with chronic conditions is sparse. We aimed to evaluate the association of mHealth apps with different types of self-management behaviors among patients with chronic diseases in the United States. Methods: This is a cross-sectional observational study. We used data from adult participants (unweighted n = 2340) of the Health Information National Trends Survey in 2018 and 2019. We identified three self-management behaviors: (1) resource utilization using electronic personal health records; (2) treatment discussions with healthcare providers; and (3) making healthcare decisions. We analyzed the association of mHealth apps to self-management behaviors with multivariable logistic and ordinal regressions. Results: Overall, 59.8% of adults (unweighted number = 1327) used mHealth apps. Adults using mHealth apps were more likely to use personal health records (AOR = 3.11, 95% CI 2.26–4.28), contact healthcare providers using technology (AOR = 2.70, 95% CI 1.93–3.78), and make decisions on chronic disease management (AOR = 2.59, 95% CI 1.93–3.49). The mHealth apps were associated with higher levels of self-management involvement (AOR = 3.53, 95% CI 2.63–4.72). Conclusion: Among individuals with chronic conditions, having mHealth apps was associated with positive self-management behaviors.

Mechanism Design of Health Care Blockchain System Token Economy: Development Study Based on Simulated Real-World Scenarios

Background Despite the fact that the adoption rate of electronic health records has increased dramatically among high-income nations, it is still difficult to properly disseminate personal health records. Token economy, through blockchain smart contracts, can better distribute personal health records by providing incentives to patients. However, there have been very few studies regarding the particular factors that should be considered when designing incentive mechanisms in blockchain. Objective The aim of this paper is to provide 2 new mathematical models of token economy in real-world scenarios on health care blockchain platforms. Methods First, roles were set for the health care blockchain platform and its token flow. Second, 2 scenarios were introduced: collecting life-log data for an incentive program at a life insurance company to motivate customers to exercise more and recruiting participants for clinical trials of anticancer drugs. In our 2 scenarios, we assumed that there were 3 stakeholders: participants, data recipients (companies), and data providers (health care organizations). We also assumed that the incentives are initially paid out to participants by data recipients, who are focused on minimizing economic and time costs by adapting mechanism design. This concept can be seen as a part of game theory, since the willingness-to-pay of data recipients is important in maintaining the blockchain token economy. In both scenarios, the recruiting company can change the expected recruitment time and number of participants. Suppose a company considers the recruitment time to be more important than the number of participants and rewards. In that case, the company can increase the time weight and adjust cost. When the reward parameter is fixed, the corresponding expected recruitment time can be obtained. Among the reward and time pairs, the pair that minimizes the company’s cost was chosen. Finally, the optimized results were compared with the simulations and analyzed accordingly. Results To minimize the company’s costs, reward–time pairs were first collected. It was observed that the expected recruitment time decreased as rewards grew, while the rewards decreased as time cost grew. Therefore, the cost was represented by a convex curve, which made it possible to obtain a minimum—an optimal point—for both scenarios. Through sensitivity analysis, we observed that, as the time weight increased, the optimized reward increased, while the optimized time decreased. Moreover, as the number of participants increased, the optimization reward and time also increased. Conclusions In this study, we were able to model the incentive mechanism of blockchain based on a mechanism design that recruits participants through a health care blockchain platform. This study presents a basic approach to incentive modeling in personal health records, demonstrating how health care organizations and funding companies can motivate one another to join the platform.