Construction and application of an intelligent interactive discharge follow-up platform

doi:10.3761/j.issn.0254-1769.2024.15.003

Abstract

Abstract:

Objective To build an intelligent interactive discharge follow-up platform，and to explore its application effect in the management of discharge follow-up. Methods A research team was established to construct the intelligent interactive discharge follow-up platform，which includes 3 modules，namely follow-up plan customization module，follow-up execution module，and information backup and statistical analysis module. The discharge follow-up data of branch A and B of a tertiary hospital in Beijing from January to December 2022 were selected. Patients in branch A were given manual telephone follow-up by nurses，and patients in branch B were applied by the intelligent interactive discharge follow-up platform，and the follow-up efficiency of the 2 branches was compared. 100 cases of discharged patients in each of 2 hospital branches were randomly selected as research subjects，and the follow-up time and nursing manpower of 2 groups were compared. Results The follow-up rate and effective follow-up rate of branch A were 99.96% and 95.10%，while those of branch B were 99.84% and 99.66%，respectively，and the difference was statistically significant（χ²=19.028，2 081.008，P<0.001）. The opinion collection rate of branch A was 0.47%，which was higher than that of the branch B（0.01%）（χ²=249.365，P<0.001）. The time and nursing manpower spent on follow-up was even less. Conclusion The intelligent interaction discharge follow-up platform realizes human-robot multi-party intelligent interaction，which can release nursing manpower and time，improve the follow-up rate of discharged patients.

Key words: “Robot+” Application, Human-Robot Intelligent Interaction, Discharge Follow-Up, Nursing Care

摘要：

目的构建智能交互出院随访平台，并探讨其在出院患者随访管理中的应用效果。方法成立多部门协作研究团队，构建包括随访定制模块、随访执行模块、信息备份与统计分析模块3个部分的智能交互出院随访平台。选取2022年1月—12月北京市某三级甲等医院A、B院区出院随访患者，A院区患者应用智能交互出院随访平台进行随访，B院区患者采用人工电话随访，比较两院区随访效率；在两院区随机各选取100例出院患者，比较两组随访时长和人力投入情况。结果 A院区的随访率、有效随访率分别为99.96%及95.10%，B院区分别为99.84%、99.66%，两组比较，差异具有统计学意义（χ²=19.028、2 081.008，P<0.001）；A院区的意见收集率为0.47%，高于B院区的0.01%（χ²=249.365，P<0.001）；A院区随访耗费小时数与岗位人力数更少。结论智能交互出院随访平台可实现人机多方智能交互，能够节约护理人力及减少随访所用时间，提高出院患者随访率。

关键词: “机器人+”应用, 人机智能交互, 出院随访, 护理

MAO Wenping, LUO Jinkai, WANG Lihua, ZHANG Jie, REN Haiyan. Construction and application of an intelligent interactive discharge follow-up platform[J]. Chinese Journal of Nursing, 2024, 59(15): 1812-1817.

毛文平, 骆金铠, 王力华, 张杰, 任海艳. 智能交互出院随访平台的构建与应用研究[J]. 中华护理杂志, 2024, 59(15): 1812-1817.

Figures/Tables 1

References 22

[1]	孙岚. 随访管理在妇科恶性肿瘤患者术后延续护理中的应用效果[J]. 护理实践与研究, 2021, 18(18):2793-2796.
	Sun L. Application effect of follow-up management in postoperative continuous nursing care of gynecological malignant tumor patients[J]. Nurs Pract Res, 2021, 18(18):2793-2796.
[2]	李艾, 杜元太, 缪刚, 等. 基于智能引擎的甲状腺癌术后随访系统构建与实践[J]. 中国卫生信息管理杂志, 2022, 19(2):272-275.
	Li A, Du YT, Miao G, et al. Construction and practice of postoperative follow-up system for thyroid cancer patients based on intelligent engine[J]. Chin J Health Inform Manag, 2022, 19(2):272-275.
[3]	李晨菲, 刘艳, 张会敏, 等. 中老年全膝关节置换术后患者延续性护理需求及影响因素研究[J]. 中华护理教育, 2020, 17(10):924-927.
	Li CF, Liu Y, Zhang HM, et al. Investigation on the continuous nursing needs and influencing factors of middle aged and older patients after total knee arthroplasty[J]. Chin J Nurs Educ, 2020, 17(10):924-927.
[4]	北京市卫生健康委员会. 北京市改善医疗服务规范服务行为2019年行动计划[EB/OL]. (2018-12-27)[2023-02-25]. http://wjw.beijing.gov.cn/zwgk_20040/fgwj/wjwfw/201912/t20191219_1301506.html.
	Beijing Municipal Health Commission. 2019 action plan for improving medical service standards and service behavior in Beijing[EB/OL]. (2018-12-27)[2023-02-25]. http://wjw.beijing.gov.cn/zwgk_20040/fgwj/wjwfw/201912/t20191219_1301506.html.
[5]	胡蓉, 王晓燕, 陈春花, 等. 出院患者随访率低的调查研究及对策[J]. 中国现代医生, 2021, 59(4):146-149.
	Hu R, Wang XY, Chen CH, et al. Investigation on the low follow-up rate of discharged patients and countermeasures[J]. China Mod Dr, 2021, 59(4):146-149.
[6]	国务院. “机器人+”应用行动实施方案[EB/OL]. (2023-01-18)[2023-03-17]. http://www.gov.cn/zhengce/zhengceku/2023-01/19/content_5738112.htm.
	The CPC Central Committee and the State Council. Implementation plan for “Robot+” application action[EB/OL]. (2023-01-18)[2023-03-17]. http://www.gov.cn/zhengce/zhengceku/2023-01/19/content_5738112.htm.
[7]	任海艳, 王力华, 骆金铠, 等. 语音机器人在出院随访中的实践与探索[J]. 中国卫生信息管理杂志, 2021, 18(3):400-404,420.
	Ren HY, Wang LH, Luo JK, et al. Practice and exploration of speech robot in discharge follow-up[J]. Chin J Health Inform Manag, 2021, 18(3):400-404,420.
[8]	骆金铠, 张杰, 董思鑫, 等. 北京市市属医院门诊专科护理服务开展情况及患者服务需求调查[J]. 中国护理管理, 2017, 17(10):1307-1310.
	Luo JK, Zhang J, Dong SX, et al. The development and needs of specialized nursing service for outpatients in Beijing munici-pal hospital[J]. Chin Nurs Manag, 2017, 17(10):1307-1310.
[9]	王梦莹, 常陆. 提高出院患者随访成功率的护理质量改进项目分享[J]. 中西医结合心血管病电子杂志, 2019, 7(2):96-97.
	Wang MY, Chang L. Sharing of nursing quality improvement projects to improve the success rate of follow-up of discharged patients[J]. Cardiovasc Dis J Integr Tradit Chin West Med, 2019, 7(2):96-97.
[10]	胡琼丹, 陈霞, 赵方方, 等. 基于智能语音平台的脑卒中患者出院后随访模式构建及应用[J]. 军事护理, 2022, 39(12):55-57.
	Hu QD, Chen X, Zhao FF, et al. Construction and application of post-hospital follow-up model for stroke patients based on intelligent voice platform[J]. Mil Nurs, 2022, 39(12):55-57.
[11]	Huang K, Jiao ZY, Cai YJ, et al. Artificial intelligence-based intelligent surveillance for reducing nurses’ working hours in nurse-patient interaction:a two-wave study[J]. J Nurs Manag, 2022, 30(8):3817-3826.
[12]	赵雯雯, 李鹏, 卢菲, 等. 多发性骨髓瘤患者人工智能随访系统的构建及应用[J]. 中华护理杂志, 2023, 58(15):1826-1830. DOI
	Zhao WW, Li P, Lu F, et al. Construction and application of artificial intelligence follow-up system for multiple myeloma patients[J]. Chin J Nurs, 2023, 58(15):1826-1830. DOI
[13]	Hazarika I. Artificial intelligence:opportunities and implications for the health workforce[J]. Int Health, 2020, 12(4):241-245.
[14]	蒋璐璐, 王喜益, 徐洁慧, 等. 智能交互式护理信息支持系统的构建及在乳腺癌患者中的应用研究[J]. 中华护理杂志, 2023, 58(6):654-661. DOI
	Jiang LL, Wang XY, Xu JH, et al. Construction of an intelligent interactive nursing information support system and its application in young breast cancer survivors[J]. Chin J Nurs, 2023, (6):654-661.
[15]	徐亦虹, 丁珊妮, 刘晓娜, 等. 护理决策支持系统的局限性及对策[J]. 中华护理杂志, 2020, 55(3):405-409. DOI
	Xu YH, Ding SN, Liu XN, et al. The limitation of nursing decision support system and its countermeasures[J]. Chin J Nurs, 2020, 55(3):405-409.
[16]	徐雪芬, 王红燕, 郭萍萍, 等. 人工智能在慢性病患者健康管理中的应用进展[J]. 中华护理杂志, 2023, 58(9):1063-1067. DOI
	Xu XF, Wang HY, Guo PP, et al. Progress on the application of artificial intelligence in chronic disease health management[J]. Chin J Nurs, 2023, 58(9):1063-1067. DOI
[17]	Kim J, Park K, Ryu H. Social values of care robots[J]. Int J Environ Res Public Health, 2022, 19(24):16657.
[18]	Vasquez B, Moreno-Lacalle R, Soriano GP, et al. Technological machines and artificial intelligence in nursing practice[J]. Nurs Health Sci, 2023, 25(3):474-481. DOI PMID
[19]	朱建辉, 岑珏, 罗劲松, 等. 出院患者人工智能语音回访效果[J]. 解放军医院管理杂志, 2021, 28(3):214-217.
	Zhu JH, Cen J, Luo JS, et al. Effect of artificial intelligence voice return visit of discharged patients[J]. Hosp Adm J Chin PLA, 2021, 28(3):214-217.
[20]	Bian YY, Xiang YB, Tong BD, et al. Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients:exploratory quantitative and qualitative study[J]. J Med Internet Res, 2020, 22(5):e16896.
[21]	国家卫生健康委. 《三级医院评审标准(2022年版)》及其实施细则的通知[EB/OL]. (2022-12-06)[2023-02-25]. http://www.gov.cn/zhengce/zhengceku/2022-12/18/content_5732583.htm.
	National Health Commission. Notice on the evaluation standards for third level hospitals(2022 edition) and its implementation rules[EB/OL]. ( 2022-12-06)[2023-02-25]. http://www.gov.cn/zhengce/zhengceku/2022-12/18/content_5732583.htm.
[22]	师庆科, 郑涛. 大型三甲医院患者智能随访语音平台设计与应用[J]. 中国数字医学, 2021, 16(8):22-27.
	Shi QK, Zheng T. Design and application of intelligent speech follow-up system in large tertiary hospital[J]. China Digit Med, 2021, 16(8):22-27.