Data integration of electronic medical record under administrative decentralization of medical insurance and healthcare in China: a case study

Effective Privacy Protection Strategies for Pregnancy and Gestation Information From Electronic Medical Records: Retrospective Study in a National Health Care Data Network in China

BACKGROUND

Pregnancy and gestation information is routinely recorded in electronic medical record (EMR) systems across China in various data sets. The combination of data on the number of pregnancies and gestations can imply occurrences of abortions and other pregnancy-related issues, which is important for clinical decision-making and personal privacy protection. However, the distribution of this information inside EMR is variable due to inconsistent IT structures across different EMR systems. A large-scale quantitative evaluation of the potential exposure of this sensitive information has not been previously performed, ensuring the protection of personal information is a priority, as emphasized in Chinese laws and regulations.

OBJECTIVE

This study aims to perform the first nationwide quantitative analysis of the identification sites and exposure frequency of sensitive pregnancy and gestation information. The goal is to propose strategies for effective information extraction and privacy protection related to women's health.

METHODS

This study was conducted in a national health care data network. Rule-based protocols for extracting pregnancy and gestation information were developed by a committee of experts. A total of 6 different sub-data sets of EMRs were used as schemas for data analysis and strategy proposal. The identification sites and frequencies of identification in different sub-data sets were calculated. Manual quality inspections of the extraction process were performed by 2 independent groups of reviewers on 1000 randomly selected records. Based on these statistics, strategies for effective information extraction and privacy protection were proposed.

RESULTS

The data network covered hospitalized patients from 19 hospitals in 10 provinces of China, encompassing 15,245,055 patients over an 11-year period (January 1, 2010-December 12, 2020). Among women aged 14-50 years, 70% were randomly selected from each hospital, resulting in a total of 1,110,053 patients. Of these, 688,268 female patients with sensitive reproductive information were identified. The frequencies of identification were variable, with the marriage history in admission medical records being the most frequent at 63.24%. Notably, more than 50% of female patients were identified with pregnancy and gestation history in nursing records, which is not generally considered a sub-data set rich in reproductive information. During the manual curation and review process, 1000 cases were randomly selected, and the precision and recall rates of the information extraction method both exceeded 99.5%. The privacy-protection strategies were designed with clear technical directions.

CONCLUSIONS

Significant amounts of critical information related to women's health are recorded in Chinese routine EMR systems and are distributed in various parts of the records with different frequencies. This requires a comprehensive protocol for extracting and protecting the information, which has been demonstrated to be technically feasible. Implementing a data-based strategy will enhance the protection of women's privacy and improve the accessibility of health care services.

Prevalence and clinical profiles of primary sclerosing cholangitis in China: Data from electronic medical records and systematic literature retrieval

BACKGROUND & AIMS

Epidemiology of primary sclerosing cholangitis (PSC) is lacking in China. We aimed to estimate the period prevalence and depict the clinical features of PSC in China.

METHODS

We identified and included PSC cases between 2000 and 2023 from two sources: electronic medical records (EMR) and systematical literature retrieval (SLR). The period prevalence of PSC was estimated by the multiplier method. Rate ratios (RRs) for PSC prevalence in relation to macroeconomic indicators were calculated by the negative binomial regression model.

RESULTS

A total of 1358 PSC cases were retrieved from 299 hospitals (162 from EMR and 1196 from SLR). Males accounted for 55.7 % of the PSC cases and 25.7 % had concomitant inflammatory bowel disease (IBD). The estimated period prevalence of PSC from 2000 to 2023 was 2.36 (95 % CI: 1.82, 3.34) per 100,000. Males had a numerically higher PSC prevalence than females (2.56, 95 % CI: 1.97, 3.63 vs. 2.14, 95 % CI: 1.65, 3.04 per 100,000). The highest prevalence of PSC was in East China at 4.87 (95 % CI: 3.44, 7.18) per 100,000, followed by North China at 2.94 (95 % CI: 2.33, 3.74) per 100,000, and the lowest in South China at 0.92 (95 % CI: 0.66, 1.30) per 100,000. Regional per capita GDP (RR 1.65, 95 % CI: 1.03, 2.65) and healthcare expenditure (RR 1.94, 95 % CI: 1.13, 3.38) were identified to be associated with PSC prevalence.

CONCLUSION

Our study showed the estimated PSC prevalence varied within China, but was generally lower than that in Western countries.

Generative artificial intelligence in healthcare from the perspective of digital media: Applications, opportunities and challenges

Introduction

The emergence and application of generative artificial intelligence/large language models (hereafter GenAI LLMs) have the potential for significant impact on the healthcare industry. However, there is currently a lack of systematic research on GenAI LLMs in healthcare based on reliable data. This article aims to conduct an exploratory study of the application of GenAI LLMs (i.e., ChatGPT) in healthcare from the perspective of digital media (i.e., online news), including the application scenarios, potential opportunities, and challenges.

Methods

This research used thematic qualitative text analysis in five steps: firstly, developing main topical categories based on relevant articles; secondly, encoding the search keywords using these categories; thirdly, conducting searches for news articles via Google ; fourthly, encoding the sub-categories using the elaborate category system; and finally, conducting category-based analysis and presenting the results. Natural language processing techniques, including the TermRaider and AntConc tool, were applied in the aforementioned steps to assist in text qualitative analysis. Additionally, this study built a framework, using for analyzing the above three topics, from the perspective of five different stakeholders, including healthcare demanders and providers.

Results

This study summarizes 26 applications (e.g., provide medical advice, provide diagnosis and triage recommendations, provide mental health support, etc.), 21 opportunities (e.g., make healthcare more accessible, reduce healthcare costs, improve patients care, etc.), and 17 challenges (e.g., generate inaccurate/misleading/wrong answers, raise privacy concerns, lack of transparency, etc.), and analyzes the reasons for the formation of these key items and the links between the three research topics.

Conclusions

The application of GenAI LLMs in healthcare is primarily focused on transforming the way healthcare demanders access medical services (i.e., making it more intelligent, refined, and humane) and optimizing the processes through which healthcare providers offer medical services (i.e., simplifying, ensuring timeliness, and reducing errors). As the application becomes more widespread and deepens, GenAI LLMs is expected to have a revolutionary impact on traditional healthcare service models, but it also inevitably raises ethical and security concerns. Furthermore, GenAI LLMs applied in healthcare is still in the initial stage, which can be accelerated from a specific healthcare field (e.g., mental health) or a specific mechanism (e.g., GenAI LLMs' economic benefits allocation mechanism applied to healthcare) with empirical or clinical research.

Maximizing the Value of Real-World Data and Real-World Evidence to Accelerate Healthcare Transformation in China: Summary of External Advisory Committee Meetings

Use of real-world data (RWD) is gaining wide attention. To bridge the gap between diverse healthcare stakeholders and to leverage the impact of Chinese real-world evidence (RWE) globally, a multi-stakeholder External Advisory Committee (EAC) and EAC meetings were initiated, aiming to elucidate the current and evolving RWD landscape in China, articulate the values of RWE in ensuring Chinese patients' equitable access to affordable medicines and solutions, and identify strategic opportunities and partnerships for expansion of RWE generation in China. Chinese and international experts who are clinicians and academic researchers were selected as EAC members based on their professional background and familiarity with RWD/RWE. Three EAC meetings were held quarterly in 2023. Various topics were presented and discussed for insights and suggestions. Nine experts from China, one from South Korea, and two from Europe were selected as EAC members and attended these meetings. Experts' presentations were summarized by theme, including the RWD landscape and RWE enablement in China, as well as global development of a patient-centric ecosystem. Experts' insights and suggestions on maximizing the RWD/RWE value to accelerate healthcare transformation in China were collected. We concluded that though data access, sharing, and quality are still challenging, RWD is developing to support evidence generation in the medicinal product lifecycle, inform clinical practice, and empower patient management in China. RWD/RWE creates value, accelerates healthcare transformation, and improves patient outcomes. Fostering a patient-centric ecosystem across healthcare stakeholders and maintaining global partnerships and collaboration are essential for unlocking the power of RWD/RWE.

Use of real-world evidence to support regulatory decisions on medical devices in China and a unique opportunity to gain accelerated approval in "Boao Lecheng Pilot Zone"

This article aims to summarize the development and challenges of real-world data (RWD) and real-world evidence (RWE) in China and introduce a unique opportunity for medical devices to gain accelerated regulatory approval in China by utilizing RWE generated in a free trade pilot zone "Boao Lecheng" in Hainan Province. In 2020, the National Medical Products Administration (NMPA) issued a draft guideline on the "Use of real-world data to support clinical evaluation for medical devices", suggesting that RWE derived from RWD could support clinical evaluation throughout the life cycle of a medical device. Meanwhile, the Chinese government has allowed qualified RWD collected in Boao Lecheng to support registration application of innovative medical devices and drugs in China. These medical devices and drugs should have been approved abroad, but not in China yet, and met urgent and unmet medical needs in China. The article also presents the successful story of an innovative Glaucoma drainage tube as the first medical device approved in China using RWE generated in Boao Lecheng in 2020. Although we are witnessing an increased interest in RWE, a few challenges remain, e.g., limited data accessibility and data sharing, concerns on data quality, etc. Collaborations among relevant stakeholders in the RWE research are vital to address the challenges.

An analysis of a novel Canadian pilot health information exchange to improve transitions between hospital and long-term care/skilled nursing facility

Purpose

The purpose of the article is to assess the effectiveness, compliance, adoption and lessons learnt from the pilot implementation of a data integration solution between an acute care hospital information system (HIS) and a long-term care (LTC) home electronic medical record through a case report.

Design/methodology/approach

Utilization statistics of the data integration solution were captured at one-month post implementation and again one year later for both the emergency department (ED) and LTC home. Clinician feedback from surveys and structured interviews was obtained from ED physicians and a multidisciplinary LTC group.

Findings

The authors successfully exchanged health information between a HIS and the electronic medical record (EMR) of an LTC facility in Canada. Perceived time savings were acknowledged by ED physicians, and actual time savings as high as 45 min were reported by LTC staff when completing medication reconciliation. Barriers to adoption included awareness, training efficacy and delivery models, workflow integration within existing practice and the limited number of facilities participating in the pilot. Future direction includes broader staff involvement, expanding the number of sites and re-evaluating impacts.

Practical implications

A data integration solution to exchange clinical information can make patient transfers more efficient, reduce data transcription errors, and improve the visibility of essential patient information across the continuum of care.

Originality/value

Although there has been a large effort to integrate health data across care levels in the United States and internationally, the groundwork for such integrations between interoperable systems has only just begun in Canada. The implementation of the integration between an enterprise LTC electronic medical record system and an HIS described herein is the first of its kind in Canada. Benefits and lessons learnt from this pilot will be useful for further hospital-to-LTC home interoperability work.

Healthcare data integration using machine learning: A case study evaluation with health information-seeking behavior databases

BACKGROUND

The amount of data in health care is rapidly rising, leading to multiple datasets generated for any given individual. Data integration involves mapping variables in different datasets together to form a combined dataset which can then be used to conduct different types of analyses. However, with increasing numbers of variables, manual mapping of a dataset can become inefficient. Another approach is to use text classification through machine learning to classify the variables to a schema.

OBJECTIVES

Our aim was to create and evaluate the use of machine learning methods for the integration of data from datasets across health information-seeking behavior (HISB) databases.

METHODS

Four online databases relevant to the research field were selected for integration. Two experiments were designed for dataset mapping: intra-database mapping using the one data source, and inter-database mapping to map datasets between the four databases. We compared logistic regression (LR), a random forest classifier (RFC), and neural network (NN) models by F1-score for two methods of integration. A third experiment was an ablation study that used all the available data to create a model for classifying HISB variables in a dataset.

RESULTS

In intra-database mapping, the mean F1 score for an LR classifier (0.787) was better than the RFC score (0.767) and fully connected NN (0.735). In inter-database mapping, the LR (0.245) scored best, however, this was dependent on which database was used as a training source. Using all the databases, these top three models were able to correctly classify 90-91% of the variables. Removing one dataset improved scores and resulted in a model able to correctly classify 95-96% of the HISB variables.

CONCLUSIONS

As part of data integration, a neural network can be used as an approach to map the variables of a dataset. The developed models can be used to classify the HISB terms in a database.

Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.

Semantic categorization of Chinese eligibility criteria in clinical trials using machine learning methods

BACKGROUND

Semantic categorization analysis of clinical trials eligibility criteria based on natural language processing technology is crucial for the task of optimizing clinical trials design and building automated patient recruitment system. However, most of related researches focused on English eligibility criteria, and to the best of our knowledge, there are no researches studied the Chinese eligibility criteria. Thus in this study, we aimed to explore the semantic categories of Chinese eligibility criteria.

METHODS

We downloaded the clinical trials registration files from the website of Chinese Clinical Trial Registry (ChiCTR) and extracted both the Chinese eligibility criteria and corresponding English eligibility criteria. We represented the criteria sentences based on the Unified Medical Language System semantic types and conducted the hierarchical clustering algorithm for the induction of semantic categories. Furthermore, in order to explore the classification performance of Chinese eligibility criteria with our developed semantic categories, we implemented multiple classification algorithms, include four baseline machine learning algorithms (LR, NB, kNN, SVM), three deep learning algorithms (CNN, RNN, FastText) and two pre-trained language models (BERT, ERNIE).

RESULTS

We totally developed 44 types of semantic categories, summarized 8 topic groups, and investigated the average incidence and prevalence in 272 hepatocellular carcinoma related Chinese clinical trials. Compared with the previous proposed categories in English eligibility criteria, 13 novel categories are identified in Chinese eligibility criteria. The classification result shows that most of semantic categories performed quite well, the pre-trained language model ERNIE achieved best performance with macro-average F1 score of 0.7980 and micro-average F1 score of 0.8484.

CONCLUSION

As a pilot study of Chinese eligibility criteria analysis, we developed the 44 semantic categories by hierarchical clustering algorithms for the first times, and validated the classification capacity with multiple classification algorithms.

Adoption of Electronic Health Records (EHRs) in China During the Past 10 Years: Consecutive Survey Data Analysis and Comparison of Sino-American Challenges and Experiences

Background

The adoption rate of electronic health records (EHRs) in hospitals has become a main index to measure digitalization in medicine in each country.

Objective

This study summarizes and shares the experiences with EHR adoption in China and in the United States.

Methods

Using the 2007-2018 annual hospital survey data from the Chinese Health Information Management Association (CHIMA) and the 2008-2017 United States American Hospital Association Information Technology Supplement survey data, we compared the trends in EHR adoption rates in China and the United States. We then used the Bass model to fit these data and to analyze the modes of diffusion of EHRs in these 2 countries. Finally, using the 2007, 2010, and 2014 CHIMA and Healthcare Information and Management Systems Services survey data, we analyzed the major challenges faced by hospitals in China and the United States in developing health information technology.

Results

From 2007 to 2018, the average adoption rates of the sampled hospitals in China increased from 18.6% to 85.3%, compared to the increase from 9.4% to 96% in US hospitals from 2008 to 2017. The annual average adoption rates in Chinese and US hospitals were 6.1% and 9.6%, respectively. However, the annual average number of hospitals adopting EHRs was 1500 in China and 534 in the US, indicating that the former might require more effort. Both countries faced similar major challenges for hospital digitalization.

Conclusions

The adoption rates of hospital EHRs in China and the United States have both increased significantly in the past 10 years. The number of hospitals that adopted EHRs in China exceeded 16,000, which was 3.3 times that of the 4814 nonfederal US hospitals. This faster adoption outcome may have been a benefit of top-level design and government-led policies, particularly the inclusion of EHR adoption as an important indicator for performance evaluation and the appointment of public hospitals.

Influenza-associated disease burden in mainland China: a systematic review and meta-analysis

Influenza causes substantial morbidity and mortality. Many original studies have been carried out to estimate disease burden of influenza in mainland China, while the full disease burden has not yet been systematically reviewed. We did a systematic review and meta-analysis to assess the burden of influenza-associated mortality, hospitalization, and outpatient visit in mainland China. We searched 3 English and 4 Chinese databases with studies published from 2005 to 2019. Studies reporting population-based rates of mortality, hospitalization, or outpatient visit attributed to seasonal influenza were included in the analysis. Fixed-effects or random-effects model was used to calculate pooled estimates of influenza-associated mortality depending on the degree of heterogeneity. Meta-regression was applied to explore the sources of heterogeneity. Publication bias was assessed by funnel plots and Egger’s test. We identified 30 studies eligible for inclusion with 17, 8, 5 studies reporting mortality, hospitalization, and outpatient visit associated with influenza, respectively. The pooled influenza-associated all-cause mortality rates were 14.33 and 122.79 per 100,000 persons for all ages and ≥ 65 years age groups, respectively. Studies were highly heterogeneous in aspects of age group, cause of death, statistical model, geographic location, and study period, and these factors could explain 60.14% of the heterogeneity in influenza-associated mortality. No significant publication bias existed in estimates of influenza-associated all-cause mortality. Children aged < 5 years were observed with the highest rates of influenza-associated hospitalizations and ILI outpatient visits. People aged ≥ 65 years and < 5 years contribute mostly to mortality and morbidity burden due to influenza, which calls for targeted vaccination policy for older adults and younger children in mainland China.

The Chinese approach to artificial intelligence: an analysis of policy, ethics, and regulation

In July 2017, China’s State Council released the country’s strategy for developing artificial intelligence (AI), entitled ‘New Generation Artificial Intelligence Development Plan’ (新一代人工智能发展规划). This strategy outlined China’s aims to become the world leader in AI by 2030, to monetise AI into a trillion-yuan (ca. 150 billion dollars) industry, and to emerge as the driving force in defining ethical norms and standards for AI. Several reports have analysed specific aspects of China’s AI policies or have assessed the country’s technical capabilities. Instead, in this article, we focus on the socio-political background and policy debates that are shaping China’s AI strategy. In particular, we analyse the main strategic areas in which China is investing in AI and the concurrent ethical debates that are delimiting its use. By focusing on the policy backdrop, we seek to provide a more comprehensive and critical understanding of China’s AI policy by bringing together debates and analyses of a wide array of policy documents.

The Ethics of Machine Learning in Medical Sciences: Where Do We Stand Today?

Advances in Machine Learning and availability of state-of-the-art computational resources, along with digitized healthcare data, have set the stage for extensive application of artificial intelligence in the realm of diagnosis, prognosis, clinical decision support, personalized treatment options, drug development, and the field of biomedicine. Here, we discuss the application of Machine Learning algorithms in patient healthcare and dermatological domains along with the ethical complexities that are involved. In scientific studies, ethical challenges were initially not addressed proportionally (as assessed by keyword counts in PubMed) and just more recently (since 2016) this has started to improve. Few pioneering countries have created regulatory guidelines around how to respect matters of (1) privacy, (2) fairness, (3) accountability, (4) transparency and (5) conflict of interest when developing novel medical Machine Learning applications. While there is a strong promise of emerging medical applications to ultimately benefit both the patients and the medical practitioners, it is important to raise awareness on the five key ethical issues and incorporate them into medical practice in the near future.

Barriers and facilitators to data quality of electronic health records used for clinical research in China: a qualitative study

OBJECTIVES

There is an increasing trend in the use of electronic health records (EHRs) for clinical research. However, more knowledge is needed on how to assure and improve data quality. This study aimed to explore healthcare professionals' experiences and perceptions of barriers and facilitators of data quality of EHR-based studies in the Chinese context.

SETTING

Four tertiary hospitals in Beijing, China.

PARTICIPANTS

Nineteen healthcare professionals with experience in using EHR data for clinical research participated in the study.

METHODS

A qualitative study based on face-to-face semistructured interviews was conducted from March to July 2018. The interviews were audiorecorded and transcribed verbatim. Data analysis was performed using the inductive thematic analysis approach.

RESULTS

The main themes included factors related to healthcare systems, clinical documentation, EHR systems and researchers. The perceived barriers to data quality included heavy workload, staff rotations, lack of detailed information for specific research, variations in terminology, limited retrieval capabilities, large amounts of unstructured data, challenges with patient identification and matching, problems with data extraction and unfamiliar with data quality assessment. To improve data quality, suggestions from participants included: better staff training, providing monetary incentives, performing daily data verification, improving software functionality and coding structures as well as enhancing multidisciplinary cooperation.

CONCLUSIONS

These results provide a basis to begin to address current barriers and ultimately to improve validity and generalisability of research findings in China.