Impact of Health All-in-One Machines on access to healthcare of rural areas in China: an interrupted time series analysis

TL;DR

This study evaluates how Health All-in-One Machines improved healthcare access in rural China, finding significant benefits after full implementation.

Contribution

The study provides empirical evidence on the impact of Health All-in-One Machines in rural healthcare settings using a large dataset.

Findings

Health All-in-One Machines increased patient visits by 37.85% during full implementation.

Medical revenue rose by 54.03% and pharmaceutical revenue by 32.84% after full-scale use.

Medical expense per patient initially increased but showed a decreasing trend over time.

Abstract

Smart healthcare systems are expected to have a positive impact on addressing challenges in healthcare. However, the real-world adoption and widespread integration of Smart healthcare systems still face many barriers, and their clinical utility lacks empirical research with large sample sizes, particularly in rural areas. The aim of this study is to evaluate the impact of a new smart healthcare system, the Health All-in-One Machines (HAMs), on improving the health services in rural areas of China.

The data included health services information from 1,866 village clinics in Hainan, China, covering the period November 30, 2020, to April 30, 2023. The impact of Health All-in-One Machines on access to healthcare was measured using four outcome indicators: the number of patient visits, medical revenue, pharmaceutical revenue, and medical expense per patient. We conducted a three-phase interrupted time series study to explore the effects of the Health All-in-One Machines intervention on these indicators across two distinct periods: the second phase (26 weeks, adaptation period) and the third phase (74 weeks, full-scale implementation period).

The interrupted time-series analysis revealed that the Health All-in-One Machines intervention had no significant impact on outcome indicators comparing the pre-intervention period to the adaptation period. However, from the adaptation period to full implementation, significant impacts were observed. Specifically, notable level changes were observed: the number of patient visits increased by 37.85% (p < 0.01), medical revenue increased by 54.03% (p < 0.001), pharmaceutical revenue increased by 32.84% (p < 0.05), and medical expense per patient increased by 2.368 CNY (p < 0.001). Additionally, a significant trend change was observed in medical expense per patient, with a decrease of 0.15 CNY per week (p < 0.05).

This study provides empirical evidence of some positive changes in the Health All-in-One Machines intervention on the outcome indicators regarding the access to healthcare. Moreover, our analysis indicates that the Health All-in-One Machines intervention would at least take longer to take effect when implemented in large-scale rural healthcare institutions. The findings from this study provide insights for future delivery and policy making of Smart healthcare systems in rural areas.

The online version contains supplementary material available at 10.1186/s12913-025-12710-z.

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