Perceived Security Risk Based on Moderating Factors for Blockchain Technology Applications in Cloud Storage to Achieve Secure Healthcare Systems.

Due to the high amount of electronic health records, hospitals have prioritized data protection. Because it uses parallel computing and is distributed, the security of the cloud cannot be guaranteed. Because of the large number of e-health records, hospitals have made data security a major concern. The cloud's security cannot be guaranteed because it uses parallel processing and is distributed. The blockchain (BC) has been deployed in the cloud to preserve and secure medical data because it is particularly prone to security breaches and attacks such as forgery, manipulation, and privacy leaks. An overview of blockchain (BC) technology in cloud storage to improve healthcare system security can be obtained by reading this paper. First, we will look at the benefits and drawbacks of using a basic cloud storage system. After that, a brief overview of blockchain cloud storage technology will be offered. Many researches have focused on using blockchain technology in healthcare systems as a possible solution to the security concerns in healthcare, resulting in tighter and more advanced security requirements being provided. This survey could lead to a blockchain-based solution for the protection of cloud-outsourced healthcare data. Evaluation and comparison of the simulation tests of the offered blockchain technology-focused studies can demonstrate integrity verification with cloud storage and medical data, data interchange with reduced computational complexity, security, and privacy protection. Because of blockchain and IT, business warfare has emerged, and governments in the Middle East have embraced it. Thus, this research focused on the qualities that influence customers' interest in and approval of blockchain technology in cloud storage for healthcare system security and the aspects that increase people's knowledge of blockchain. One way to better understand how people feel about learning how to use blockchain technology in healthcare is through the United Theory of Acceptance and Use of Technology (UTAUT). A snowball sampling method was used to select respondents in an online poll to gather data about blockchain technology in Middle Eastern poor countries. A total of 443 randomly selected responses were tested using SPSS. Blockchain adoption has been shown to be influenced by anticipation, effort expectancy, social influence (SI), facilitation factors, personal innovativeness (PInn), and a perception of security risk (PSR). Blockchain adoption and acceptance were found to be influenced by anticipation, effort expectancy, social influence (SI), facilitating conditions, personal innovativeness (PInn), and perceived security risk (PSR) during the COVID-19 pandemic, as well as providing an overview of current trends in the field and issues pertaining to significance and compatibility.

Online Medical Record Nonuse Among Patients: Data Analysis Study of the 2019 Health Information National Trends Survey.

BACKGROUND: Online medical records are being used to organize processes in clinical and outpatient settings and to forge doctor-patient communication techniques that build mutual understanding and trust. OBJECTIVE: We aimed to understand the reasons why patients tend to avoid using online medical records and to compare the perceptions that patients have of online medical records based on demographics and cancer diagnosis. METHODS: We used data from the Health Information National Trends Survey Cycle 3, a nationally representative survey, and assessed outcomes using descriptive statistics and chi-square tests. The patients (N=4328) included in the analysis had experienced an outpatient visit within the previous 12 months and had answered the online behavior question regarding their use of online medical records. RESULTS: Patients who were nonusers of online medical records consisted of 58.36% of the sample (2526/4328). The highest nonuser rates were for patients who were Hispanic (460/683, 67.35%), patients who were non-Hispanic Black (434/653, 66.46%), and patients who were older than 65 years (968/1520, 63.6%). Patients older than 65 years were less likely to use online medical records (odds ratio [OR] 1.51, 95% CI 1.24-1.84, P<.001). Patients who were White were more likely to use online medical records than patients who were Black (OR 1.71, 95% CI 1.43-2.05, P<.001) or Hispanic (OR 1.65, 95% CI 1.37-1.98, P<.001). Patients who were diagnosed with cancer were more likely to use online medical records compared to patients with no cancer (OR 1.31, 95% CI 1.11-1.55, 95% CI 1.11-1.55, P=.001). Among nonusers, older patients (≥65 years old) preferred speaking directly to their health care providers (OR 1.76, 95% CI 1.35-2.31, P<.001), were more concerned about privacy issues caused by online medical records (OR 1.79, 95% CI 1.22-2.66, P<.001), and felt uncomfortable using the online medical record systems (OR 10.55, 95% CI 6.06-19.89, P<.001) compared to those aged 18-34 years. Patients who were Black or Hispanic were more concerned about privacy issues (OR 1.42, 1.09-1.84, P=.007). CONCLUSIONS: Studies should consider social factors such as gender, race/ethnicity, and age when monitoring trends in eHealth use to ensure that eHealth use does not induce greater health status and health care disparities between people with different backgrounds and demographic characteristics.

Precision Assessment of COVID-19 Phenotypes Using Large-Scale Clinic Visit Audio Recordings: Harnessing the Power of Patient Voice.

COVID-19 cases are exponentially increasing worldwide; however, its clinical phenotype remains unclear. Natural language processing (NLP) and machine learning approaches may yield key methods to rapidly identify individuals at a high risk of COVID-19 and to understand key symptoms upon clinical manifestation and presentation. Data on such symptoms may not be accurately synthesized into patient records owing to the pressing need to treat patients in overburdened health care settings. In this scenario, clinicians may focus on documenting widely reported symptoms that indicate a confirmed diagnosis of COVID-19, albeit at the expense of infrequently reported symptoms. While NLP solutions can play a key role in generating clinical phenotypes of COVID-19, they are limited by the resulting limitations in data from electronic health records (EHRs). A comprehensive record of clinic visits is required-audio recordings may be the answer. A recording of clinic visits represents a more comprehensive record of patient-reported symptoms. If done at scale, a combination of data from the EHR and recordings of clinic visits can be used to power NLP and machine learning models, thus rapidly generating a clinical phenotype of COVID-19. We propose the generation of a pipeline extending from audio or video recordings of clinic visits to establish a model that factors in clinical symptoms and predict COVID-19 incidence. With vast amounts of available data, we believe that a prediction model can be rapidly developed to promote the accurate screening of individuals at a high risk of COVID-19 and to identify patient characteristics that predict a greater risk of a more severe infection. If clinical encounters are recorded and our NLP model is adequately refined, benchtop virologic findings would be better informed. While clinic visit recordings are not the panacea for this pandemic, they are a low-cost option with many potential benefits, which have recently begun to be explored.

Comparing Methods for Record Linkage for Public Health Action: Matching Algorithm Validation Study.

BACKGROUND: Many public health departments use record linkage between surveillance data and external data sources to inform public health interventions. However, little guidance is available to inform these activities, and many health departments rely on deterministic algorithms that may miss many true matches. In the context of public health action, these missed matches lead to missed opportunities to deliver interventions and may exacerbate existing health inequities. OBJECTIVE: This study aimed to compare the performance of record linkage algorithms commonly used in public health practice. METHODS: We compared five deterministic (exact, Stenger, Ocampo 1, Ocampo 2, and Bosh) and two probabilistic record linkage algorithms (fastLink and beta record linkage [BRL]) using simulations and a real-world scenario. We simulated pairs of datasets with varying numbers of errors per record and the number of matching records between the two datasets (ie, overlap). We matched the datasets using each algorithm and calculated their recall (ie, sensitivity, the proportion of true matches identified by the algorithm) and precision (ie, positive predictive value, the proportion of matches identified by the algorithm that were true matches). We estimated the average computation time by performing a match with each algorithm 20 times while varying the size of the datasets being matched. In a real-world scenario, HIV and sexually transmitted disease surveillance data from King County, Washington, were matched to identify people living with HIV who had a syphilis diagnosis in 2017. We calculated the recall and precision of each algorithm compared with a composite standard based on the agreement in matching decisions across all the algorithms and manual review. RESULTS: In simulations, BRL and fastLink maintained a high recall at nearly all data quality levels, while being comparable with deterministic algorithms in terms of precision. Deterministic algorithms typically failed to identify matches in scenarios with low data quality. All the deterministic algorithms had a shorter average computation time than the probabilistic algorithms. BRL had the slowest overall computation time (14 min when both datasets contained 2000 records). In the real-world scenario, BRL had the lowest trade-off between recall (309/309, 100.0%) and precision (309/312, 99.0%). CONCLUSIONS: Probabilistic record linkage algorithms maximize the number of true matches identified, reducing gaps in the coverage of interventions and maximizing the reach of public health action.

Guidance for using text, email, and video communication in practices devoted to reproductive medicine.

The prevalence and ease of electronic communication, specifically email through patient portals associated with electronic medical records or via traditional enterprise email clients (e.g., Outlook) and video, have resulted in increased use for rapid communication between practitioners and their patients. Concerns regarding patient privacy and compliance with the regulations of the Health Insurance Portability and Accountability Act (HIPAA) remain a barrier to routine incorporation of electronic communication into practice. Furthermore, capital investment, implementation, and maintenance costs may provide additional barriers. These long-standing concerns have been heightened and tested by the COVID-19 pandemic. Best-practice guidelines for the secure and safe use of electronic communication with reproductive care patients are provided.

The value of federated learning during and post-COVID-19.

Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe the most recent real-world cases using the FL in both COVID-19 and non-COVID-19 scenarios and also highlight current limitations and practical challenges of FL.

Health Equity Beyond Data: Health Care Worker Perceptions of Race, Ethnicity, and Language Data Collection in Electronic Health Records.

BACKGROUND: Recent research and policy initiatives propose addressing the social determinants of health within clinical settings. One such strategy is the expansion of routine data collection on patient Race, Ethnicity, and Language (REAL) within electronic health records (EHRs). Although previous research has examined the general views of providers and patients on REAL data, few studies consider health care workers' perceptions of this data collection directly at the point of care, including how workers understand REAL data in relation to health equity. OBJECTIVE: This qualitative study examines a large integrated delivery system's implementation of REAL data collection, focusing on health care workers' understanding of REAL and its impact on data's integration within EHRs. RESULTS: Providers, staff, and administrators expressed apprehension over REAL data collection due to the following: (1) disagreement over data's significance, including the expected purpose of collecting REAL items; (2) perceived barriers to data retrieval, such as the lack of standardization across providers and national tensions over race and immigration; and (3) uncertainty regarding data's use (clinical decision making vs. system research) and dissemination (with whom the data may be shared; eg, public agencies, other providers, and insurers). CONCLUSION: Emerging racial disparities associated with COVID-19 highlight the high stakes of REAL data collection. However, numerous barriers to health equity remain. Health care workers need greater institutional support for REAL data and related EHR initiatives. Despite data collection's central importance to policy objectives of disparity reduction, data mandates alone may be insufficient for achieving health equity.

Development of an openEHR Template for COVID-19 Based on Clinical Guidelines.

BACKGROUND: The coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability. OBJECTIVE: This study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19. METHODS: The latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study. RESULTS: A total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support. CONCLUSIONS: This study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.