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.

Drivers of expectations: Why are Norwegian general practitioners skeptical of a prospective electronic health record?

While expectations are well-known drivers of electronic health record (EHR) adoption, the drivers of expectations are more elusive. On the basis of interviews with general practitioners (GPs), we investigate how the early implementation process drives their expectations of an EHR that is being implemented in Norway. The GPs’ expectations of the prospective EHR are driven by (a) satisfying experiences with their current system, (b) the transfer of others’ experiences with the prospective EHR, (c) a sense of alignment, or lack thereof, with those in charge of the implementation process, (d) uncertainty about the inclusion of GP needs, and (e) competing technological futures. To manage expectations, starting early is important. Mismanaged expectations produce a need for convincing people to reverse their expectations. This appears to be the situation in Norway, where the GPs are currently skeptical of the prospective EHR.

Status of digitization of medical records in oncology centers in India: A survey of current status.

306 Background: Electronic medical or health record (EMR/EHR) system is yet to be universally adopted in India. In 2012 and 2016 Indian Ministry of Health published a detailed roadmap for EHR adoption. We wanted to assess the status of EMR/EHR adoption in oncology centers in India. Methods: Authors developed a short online survey to capture the use of paper charts vs. generic EMR vs. oncology specific EMR by practicing oncologists in India. The survey was shared to oncologists on June 6, 2020 through closed social media groups. Responses to the survey were collected anonymously and data aggregated for analysis. Survey will remain open till July 4, 2020. Results: At the time of abstract submission on June 16, there were 48 unique survey responses. Of those who responded to the survey, 69% were 25-45 years of age, 73% male, 71% were practicing in the state of Tamil Nadu, and 21% were practicing in a rural area or close to a small city. Oncologists from all subspecialties were represented in the survey including radiation oncology (65%), surgical oncology (25%), medical oncology (6%), and pediatric oncology/nuclear medicine (4%). About 46% had completed their training within the last 10 years, and 30% of respondents have done part of their training in foreign countries. Summary of responses to our survey is provided in the table below. Conclusions: Paper chart is still the predominant mode of clinical data capture within oncology. Administrative barriers and cost are perceived as major obstacles despite most oncologists reporting that they would very likely adapt to an onco specific EMR. [Table: see text]

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 (Preprint)

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.

Reconsidering hospital EHR adoption at the dawn of HITECH: implications of the reported 9% adoption of a “basic” EHR

Objective In 2009, a prominent national report stated that 9% of US hospitals had adopted a “basic” electronic health record (EHR) system. This statistic was widely cited and became a memetic anchor point for EHR adoption at the dawn of HITECH. However, its calculation relies on specific treatment of the data; alternative approaches may have led to a different sense of US hospitals’ EHR adoption and different subsequent public policy. Materials and Methods We reanalyzed the 2008 American Heart Association Information Technology supplement and complementary sources to produce a range of estimates of EHR adoption. Estimates included the mean and median number of EHR functionalities adopted, figures derived from an item response theory-based approach, and alternative estimates from the published literature. We then plotted an alternative definition of national progress toward hospital EHR adoption from 2008 to 2018. Results By 2008, 73% of hospitals had begun the transition to an EHR, and the majority of hospitals had adopted at least 6 of the 10 functionalities of a basic system. In the aggregate, national progress toward basic EHR adoption was 58% complete, and, when accounting for measurement error, we estimate that 30% of hospitals may have adopted a basic EHR. Discussion The approach used to develop the 9% figure resulted in an estimate at the extreme lower bound of what could be derived from the available data and likely did not reflect hospitals’ overall progress in EHR adoption. Conclusion The memetic 9% figure shaped nationwide thinking and policy making about EHR adoption; alternative representations of the data may have led to different policy.

Understanding variation in departmental adoption of Electronic Health Records: an embedded case study

Background: Electronic Health Records (EHR) are integrated software applications used by healthcare providers to create, share, retrieve, and archive patients’ health status information. Especially for large healthcare organizations, implementing Electronic Health Records organization-wide is a complex endeavour. The EHR literature generally suggests that contextual factors play a major role in adoption. We demonstrate how the work context influences adoption at the departmental level in a situation where each department has its own medical specialty or patient stream, clinical authority, and accountability. Here, the achievement of full adoption by all departments is not self-evident. Drawing on EHR implementation in a Dutch hospital, this study explores how the clinical departments’ work context characteristics contribute to their pre-implementation intended adoption and their post-implementation EHR uptake. Methods: This embedded case study allowed us to examine the EHR adoption of eight diverse clinical departments in terms of their work and socio-political context. Data collection entailed semi-structured interviews, observing meetings, document analysis, and feedback sessions to check our interpretations. We examined the context and adoption intentions before implementation and the adoption level approximately half a year after the Go Live. The comparative case analysis iterated between holistic department-level descriptions and structured data displays based on inductive and deductive coding. Results: We identified three departmental types that varied both in adoption intention and post-implementation uptake: (1) departments oriented towards the organization with an enthusiastic or compliant adoption; (2) internal-oriented departments with a selective or conditional adoption; and (3) externally oriented departments with no or low adoption with workarounds. Conclusions: We conclude that work context characteristics contribute to individual departments’ adoption of an EHR. By acknowledging departmental types that will vary in adoption intention, and especially the underlying explanatory mechanisms, we recommend that implementers acknowledge these departmental types and differentiate their strategies towards clinical departments accordingly based on their observable work context characteristics. Based on these findings, we develop propositions that contribute to the development of a department-level EHR adoption theory.