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Abstract：
BACKGROUND: Since 2020, China has piloted an innovative payment method known as the Diagnosis-Intervention Packet (DIP). This study aimed to assess the impact of the DIP on inpatient volume and bed allocation and their regional distribution. This study investigated whether the DIP affects the efficiency of regional health resource utilization and contributes to disparities in health equity among regions.
METHODS: We collected data from a central province in China from 2019 to 2022. The treatment group included 508 hospitals in the pilot area (Region A, where the DIP was implemented in 2021), whereas the control group consisted of 3,728 hospitals from non-pilot areas within the same province. We employed the difference-in-differences method to analyze inpatient volume and bed resources. Additionally, we conducted a stratified analysis to examine whether the effects of DIP implementation varied across urban and rural areas or hospitals of different levels.
RESULTS: Compared with the non-pilot regions, Region A experienced a statistically significant reduction in inpatient volume of 14.3% (95% CI 0.061-0.224) and a notable decrease of 9.1% in actual available bed days (95% CI 0.041-0.141) after DIP implementation. The study revealed no evidence of patient consultations shifting from inpatient to outpatient services due to the reduction in hospital admissions in Region A after DIP implementation. Stratified analysis revealed that inpatient volume decreased by 12.4% (95% CI 0.006-0.243) in the urban areas and 14.7% in the rural areas of Region A (95% CI 0.051-0.243). At the hospital level, primary hospitals experienced the greatest impact, with a 19.0% (95% CI 0.093-0.287) decline in inpatient volume. Furthermore, primary and tertiary hospitals experienced significant reductions of 11.0% (95% CI 0.052-0.169) and 8.2% (95% CI 0.002-0.161), respectively, in actual available bed days.
CONCLUSIONS: Despite efforts to curb excessive medical service expansion in the region following DIP implementation, large hospitals continue to attract a large number of patients from primary hospitals. This weakening of primary hospitals and the subsequent influx of patients to urban areas may further limit rural patients\' access to medical services. The implementation of the DIP may raise concerns about its impact on health care equality and accessibility, particularly for underserved rural populations.
METHODS: We collected data from a central province in China from 2019 to 2022. The treatment group included 508 hospitals in the pilot area (Region A, where the DIP was implemented in 2021), whereas the control group consisted of 3,728 hospitals from non-pilot areas within the same province. We employed the difference-in-differences method to analyze inpatient volume and bed resources. Additionally, we conducted a stratified analysis to examine whether the effects of DIP implementation varied across urban and rural areas or hospitals of different levels.
RESULTS: Compared with the non-pilot regions, Region A experienced a statistically significant reduction in inpatient volume of 14.3% (95% CI 0.061-0.224) and a notable decrease of 9.1% in actual available bed days (95% CI 0.041-0.141) after DIP implementation. The study revealed no evidence of patient consultations shifting from inpatient to outpatient services due to the reduction in hospital admissions in Region A after DIP implementation. Stratified analysis revealed that inpatient volume decreased by 12.4% (95% CI 0.006-0.243) in the urban areas and 14.7% in the rural areas of Region A (95% CI 0.051-0.243). At the hospital level, primary hospitals experienced the greatest impact, with a 19.0% (95% CI 0.093-0.287) decline in inpatient volume. Furthermore, primary and tertiary hospitals experienced significant reductions of 11.0% (95% CI 0.052-0.169) and 8.2% (95% CI 0.002-0.161), respectively, in actual available bed days.
CONCLUSIONS: Despite efforts to curb excessive medical service expansion in the region following DIP implementation, large hospitals continue to attract a large number of patients from primary hospitals. This weakening of primary hospitals and the subsequent influx of patients to urban areas may further limit rural patients\' access to medical services. The implementation of the DIP may raise concerns about its impact on health care equality and accessibility, particularly for underserved rural populations.
摘要：
背景：自2020年以来，中国试行了一种创新的支付方式，称为诊断干预数据包（DIP）。本研究旨在评估DIP对住院患者数量和床位分配及其区域分布的影响。这项研究调查了DIP是否会影响区域卫生资源的利用效率，并导致区域之间卫生公平性的差异。
方法：我们从中国中部省份收集了2019年至2022年的数据。治疗组包括试点地区的508家医院（A区，DIP于2021年实施)，对照组由来自同一省份非试点地区的3,728家医院组成.我们采用差异差异方法分析了住院人数和床位资源。此外,我们进行了分层分析,以检查DIP实施的效果是否因城市和农村地区或不同级别的医院而异.
结果：与非试点地区相比，实施DIP后，A区的住院患者容量在统计学上显着减少了14.3％（95％CI0.061-0.224），实际可用卧床天数显着减少了9.1％（95％CI0.041-0.141）。研究显示，由于DIP实施后A区的住院人数减少，没有证据表明患者咨询从住院服务转移到门诊服务。分层分析显示，城市地区的住院人数减少了12.4％（95％CI0.006-0.243），农村地区的住院人数减少了14.7％（95％CI0.051-0.243）。在医院层面,基层医院经历了最大的影响，住院患者数量下降19.0%（95%CI0.093-0.287）。此外，初级和三级医院显著下降11.0%(95%CI0.052-0.169)和8.2%(95%CI0.002-0.161)，分别,在实际可用的床上天。
结论：尽管在DIP实施后努力遏制该地区医疗服务的过度扩张，大型医院继续吸引基层医院的大量患者。基层医院的削弱以及随后患者涌入城市地区可能进一步限制农村患者获得医疗服务。DIP的实施可能会引起人们对其对医疗保健平等和可及性的影响的关注，特别是对于服务不足的农村人口。
方法：我们从中国中部省份收集了2019年至2022年的数据。治疗组包括试点地区的508家医院（A区，DIP于2021年实施)，对照组由来自同一省份非试点地区的3,728家医院组成.我们采用差异差异方法分析了住院人数和床位资源。此外,我们进行了分层分析,以检查DIP实施的效果是否因城市和农村地区或不同级别的医院而异.
结果：与非试点地区相比，实施DIP后，A区的住院患者容量在统计学上显着减少了14.3％（95％CI0.061-0.224），实际可用卧床天数显着减少了9.1％（95％CI0.041-0.141）。研究显示，由于DIP实施后A区的住院人数减少，没有证据表明患者咨询从住院服务转移到门诊服务。分层分析显示，城市地区的住院人数减少了12.4％（95％CI0.006-0.243），农村地区的住院人数减少了14.7％（95％CI0.051-0.243）。在医院层面,基层医院经历了最大的影响，住院患者数量下降19.0%（95%CI0.093-0.287）。此外，初级和三级医院显著下降11.0%(95%CI0.052-0.169)和8.2%(95%CI0.002-0.161)，分别,在实际可用的床上天。
结论：尽管在DIP实施后努力遏制该地区医疗服务的过度扩张，大型医院继续吸引基层医院的大量患者。基层医院的削弱以及随后患者涌入城市地区可能进一步限制农村患者获得医疗服务。DIP的实施可能会引起人们对其对医疗保健平等和可及性的影响的关注，特别是对于服务不足的农村人口。
