数智化技术在慢病共存管理中的研究与应用

doi:10.12114/j.issn.1007-9572.2025.0470

摘要/Abstract

摘要： 多种慢病共存已成为全球公共卫生的重大挑战，传统的单病管理模式难以满足复杂的健康需求。数智化技术为慢病共存管理带来新机遇。本文系统回顾了数智化技术在慢病共存管理核心领域的应用实践，包括风险识别与预测、健康管理与患者赋能、智能决策与干预以及资源调度与管理优化。总结显示，数智化技术正推动慢病管理从"单病导向"向"多病整合"转型，为医防融合和精准健康管理提供了新范式。未来应在制度设计、数据安全、技术适配性、人才培养和伦理治理等方面系统推进，构建人机协同、可持续、有温度的智慧慢病共存管理体系。

关键词: 数智化, 慢性病, 多病共存, 卫生服务, 健康管理

Abstract:

Multimorbidity has become a major global public health challenge, and traditional single-disease management models are insufficient to address these complex health needs. Digital health technologies offer new opportunities for the management of multimorbidity. This review systematically examines the applications of digital intelligence in key domains of multimorbidity management, including risk identification and prediction, health management and patient empowerment, intelligent decision-making and intervention, as well as resource allocation and management optimization. The findings indicate that digital technologies are facilitating a shift in chronic disease management from a "single-disease orientation" toward "integrated multi-disease management", offering new paradigms for strengthening primary care-based prevention and treatment integration and precision health management. Looking forward, advancing digital health in multimorbidity care will require coordinated efforts in policy design, data security, technological adaptability, workforce development, and ethical governance to build a human-machine collaborative, sustainable and human-centered intelligent multimorbidity management system.

Key words: Digital health, Chronic diseases, Multimorbidity, Health services, Health management

于海燕,相彤,高文娟,等. 数智化技术在慢病共存管理中的研究与应用[J]. 中国全科医学, 2026, 29(21): 2933-2937. DOI: 10.12114/j.issn.1007-9572.2025.0470.
YU Haiyan,XIANG Tong,GAO Wenjuan, et al. Research and Application of Digital Health Technologies in Multimorbidity Management[J]. Chinese General Practice, 2026, 29(21): 2933-2937. DOI: 10.12114/j.issn.1007-9572.2025.0470.

参考文献 38

[1]	Vos T, Lim S S, Abbafati C, et al. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1204-1222. DOI: 10.1016/S0140-6736(20)30925-9.
[2]	Fan J N, Sun Z J, Yu C Q, et al. Multimorbidity patterns and association with mortality in 0.5 million Chinese adults[J]. Chin Med J, 2022, 135(6): 648-657. DOI: 10.1097/CM9.0000000000001985.
[3]	Yarnall A J, Sayer A A, Clegg A, et al. New horizons in multimorbidity in older adults[J]. Age Ageing, 2017, 46(6): 882-888. DOI: 10.1093/ageing/afx150.
[4]	Liu X J, Song F, Liu F Y, et al. Multiple chronic conditions among older adults in China: differences in socio-demographic characteristics[J]. Heliyon, 2022, 8(10): e11129. DOI: 10.1016/j.heliyon.2022.e11129.
[5]	Zhao Y, Atun R, Oldenburg B, et al. Physical multimorbidity, health service use, and catastrophic health expenditure by socioeconomic groups in China: an analysis of population-based panel data[J]. Lancet Glob Heal, 2020, 8(6): e840-e849. DOI: 10.1016/S2214-109X(20)30127-3.
[6]	Chowdhury S R, Chandra Das D, Sunna T C, et al. Global and regional prevalence of multimorbidity in the adult population in community settings: a systematic review and meta-analysis[J]. EClinicalMedicine, 2023, 57: 101860. DOI: 10.1016/j.eclinm.2023.101860.
[7]	Jun H, Aguila E. Private insurance and mental health among older adults with multiple chronic conditions: a longitudinal analysis by race and ethnicity[J]. Int J Environ Res Public Health, 2021, 18(5): 2615. DOI: 10.3390/ijerph18052615.
[8]	Ouyang M Q, Liu B W, Qiu Z N, et al. Impact of multimorbidity on household expenditure in China: evidence from the China health and retirement longitudinal study[J]. BMC Public Heal, 2025, 25(1): 2550. DOI: 10.1186/s12889-025-23718-9.
[9]	Bhatt P, Liu J, Gong Y M, et al. Emerging artificial intelligence-empowered mHealth: scoping review[J]. JMIR Mhealth Uhealth, 2022, 10(6): e35053. DOI: 10.2196/35053.
[10]	Petretto D R, Carrogu G P, Gaviano L, et al. Telemedicine, e-health, and digital health equity: a scoping review[J]. Clin Pract Epidemiol Ment Heal, 2024, 20: e17450179279732. DOI: 10.2174/0117450179279732231211110248.
[11]	Gerber B S, Biggers A, Tilton J J, et al. Mobile health intervention in patients with type 2 diabetes: a randomized clinical trial[J]. JAMA Netw Open, 2023, 6(9): e2333629. DOI: 10.1001/jamanetworkopen.2023.33629.
[12]	Persell S D, Peprah Y A, Lipiszko D, et al. Effect of home blood pressure monitoring via a smartphone hypertension coaching application or tracking application on adults with uncontrolled hypertension: a randomized clinical trial[J]. JAMA Netw Open, 2020, 3(3): e200255. DOI: 10.1001/jamanetworkopen.2020.0255.
[13]	Aggarwal R, Sounderajah V, Martin G, et al. Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis[J]. npj Digit Med, 2021, 4: 65. DOI: 10.1038/s41746-021-00438-z.
[14]	Yap H J, Lim J J J, Tan S D, et al. Effectiveness of digital health interventions on adherence and control of hypertension: a systematic review and meta-analysis[J]. J Hypertens, 2024, 42(9): 1490-1504. DOI: 10.1097/hjh.0000000000003793.
[15]	Tu T, Schaekermann M, Palepu A, et al. Towards conversational diagnostic artificial intelligence[J]. Nature, 2025, 642(8067): 442-450. DOI: 10.1038/s41586-025-08866-7.
[16]	Eissa T, Leonardo C, Kepesidis K V, et al. Plasma infrared fingerprinting with machine learning enables single-measurement multi-phenotype health screening[J]. Cell Rep Med, 2024, 5(7): 101625. DOI: 10.1016/j.xcrm.2024.101625.
[17]	Jiang Y K, Zhao B X, Wang X P, et al. UKB-MDRMF: a multi-disease risk and multimorbidity framework based on UK biobank data[J]. Nat Commun, 2025, 16: 3767. DOI: 10.1038/s41467-025-58724-3.
[18]	Chen T, Dredze M, Weiner J P, et al. Identifying vulnerable older adult populations by contextualizing geriatric syndrome information in clinical notes of electronic health records[J]. J Am Med Inform Assoc, 2019, 26(8/9): 787-795. DOI: 10.1093/jamia/ocz093.
[19]	Loewenthal J V, Ernecoff N C, Dalal A K. Novel electronic health record strategies to identify frailty among hospitalized older adults with multiple chronic conditions[J]. J Gen Intern Med, 2025, 40(6): 1275-1279. DOI: 10.1007/s11606-024-09227-2.
[20]	Shin J E, Choi J, Lee H, et al. Protocol paper: randomized controlled trial of the smart online-to-offline model development for chronic diseases management through digital health in real world setting[J]. Trials, 2025, 26(1): 40. DOI: 10.1186/s13063-025-08735-8.
[21]	Pati S, Menon J, Rehman T, et al. Developing and assessing the "MultiLife" intervention: a mobile health-based lifestyle toolkit for cardiometabolic multimorbidity in diabetes and hypertension management-a type 1 hybrid effectiveness-implementation trial protocol[J]. BMC Public Heal, 2025, 25(1): 3. DOI: 10.1186/s12889-024-20922-x.
[22]	Gustafson D H Sr, Mares M L, Johnston D, et al. An eHealth intervention to improve quality of life, socioemotional, and health-related measures among older adults with multiple chronic conditions: randomized controlled trial[J]. JMIR Aging, 2024, 7: e59588. DOI: 10.2196/59588.
[23]	Doyle J, Murphy E, Gavin S, et al. A digital platform to support self-management of multiple chronic conditions (ProACT): findings in relation to engagement during a one-year proof-of-concept trial[J]. J Med Internet Res, 2021, 23(12): e22672. DOI: 10.2196/22672.
[24]	Sakane N, Suganuma A, Domichi M, et al. The effect of a mHealth app (KENPO-app) for specific health guidance on weight changes in adults with obesity and hypertension: pilot randomized controlled trial[J]. JMIR Mhealth Uhealth, 2023, 11: e43236. DOI: 10.2196/43236.
[25]	Jungo K T, Ansorg A K, Floriani C, et al. Optimising prescribing in older adults with multimorbidity and polypharmacy in primary care (OPTICA): cluster randomised clinical trial[J]. BMJ, 2023, 381: e074054. DOI: 10.1136/bmj-2022-074054.
[26]	Prabhakaran D, Jha D, Prieto-Merino D, et al. Effectiveness of an mHealth-based electronic decision support system for integrated management of chronic conditions in primary care: the mWellcare cluster-randomized controlled trial[J]. Circulation, 2019, 139(3): 380-391. DOI: 10.1161/CIRCULATIONAHA.118.038192.
[27]	Gholamzadeh M, Abtahi H, Safdari R. The application of knowledge-based clinical decision support systems to enhance adherence to evidence-based medicine in chronic disease[J]. J Healthc Eng, 2023, 2023: 8550905. DOI: 10.1155/2023/8550905.
[28]	El Asmar M L, Dharmayat K I, Vallejo-Vaz A J, et al. Effect of computerised, knowledge-based, clinical decision support systems on patient-reported and clinical outcomes of patients with chronic disease managed in primary care settings: a systematic review[J]. BMJ Open, 2021, 11(12): e054659. DOI: 10.1136/bmjopen-2021-054659.
[29]	Otieno P, Asiki G, Wekesah F, et al. Multimorbidity of cardiometabolic diseases: a cross-sectional study of patterns, clusters and associated risk factors in sub-Saharan Africa[J]. BMJ Open, 2023, 13(2): e064275. DOI: 10.1136/bmjopen-2022-064275.
[30]	Nguyen O T, Alishahi Tabriz A, Huo J H, et al. Impact of asynchronous electronic communication-based visits on clinical outcomes and health care delivery: systematic review[J]. J Med Internet Res, 2021, 23(5): e27531. DOI: 10.2196/27531.
[31]	Reed M, Huang J, Somers M, et al. Telemedicine versus in-person primary care: treatment and follow-up visits[J]. Ann Intern Med, 2023, 176(10): 1349-1357. DOI: 10.7326/M23-1335.
[32]	操名华, 徐明英, 黄婧仪, 等. 医共体框架下基于社区医院慢性病一体化门诊建设的慢性病管理效果探索——以星桥社区卫生服务中心为例[J]. 中国慢性病预防与控制, 2024, 32(9): 705-708.
[33]	郭晓玲, 吴浩, 刘新颖, 等. 智慧家庭医生优化协同模式的构建与实现[J]. 中国全科医学, 2017, 20(7): 784-788. DOI: 10.3969/j.issn.1007-9572.2017.07.005.
[34]	刘丹妮, 杨群娣, 喇雪娜, 等. 基于政府主导和医防融合视角下的上海市整体推进国家慢性病综合防控示范区建设主要实践与成效[J]. 中国慢性病预防与控制, 2025, 33(8): 575-578, 583. DOI: 10.16386/j.cjpccd.issn.1004-6194.20250508.0402.
[35]	Khalid N, Qayyum A, Bilal M, et al. Privacy-preserving artificial intelligence in healthcare: Techniques and applications[J]. Comput Biol Med, 2023, 158: 106848. DOI: 10.1016/j.compbiomed.2023.106848.
[36]	Xu S, Kim J, Walter J R, et al. Translational gaps and opportunities for medical wearables in digital health[J]. Sci Transl Med, 2022, 14(666): eabn6036. DOI: 10.1126/scitranslmed.abn6036.
[37]	黄亚芳, 相彤, 吴浩. 全民健康覆盖背景下的数字疗法:基层医疗的机遇与挑战[J]. 中华全科医师杂志, 2024, 23(2): 101-105. DOI: 10.3760/cma.j.cn114798-20230918-00164.
[38]	Poon C C, Zhang Y T. Perspectives on high technologies for low-cost healthcare[J]. IEEE Eng Med Biol Mag, 2008, 27(5): 42-47. DOI: 10.1109/MEMB.2008.923955.