机器学习在患者出院准备服务中的应用进展

doi:10.3761/j.issn.0254-1769.2024.03.018

摘要/Abstract

摘要：

随着医药卫生体制改革的深化以及就医秩序的持续优化，组织制订入出院标准，完善患者出院准备服务尤为重要。近年来，机器学习技术在医学领域的研究及应用不断深入，在处理数据及风险预测研究等方面具有独特优势。该研究从机器学习的发展进程、类型、在患者出院准备服务中的应用内容及效果、目前面临的问题等方面进行综述，以期为医护人员实施最佳临床决策提供参考，进一步完善患者出院准备服务模式。

关键词: 机器学习, 出院准备服务, 临床决策, 出院护理, 综述

Abstract:

With the deepening of the reform of the medical and health system and the continuous optimization of the medical order，it is especially important to organize the development of admission and discharge standards and improve the service of preparing patients for discharge. In recent years，the research and application of machine learning technology in the medical field has been intensifying，and it has unique advantages in processing data and risk prediction research. Therefore，this paper reviews the development process，types of machine learning，the content and effects of its application in patient discharge preparation services，and the current problems，in order to provide references for healthcare professionals to implement the best clinical decisions and further improve the patient discharge preparation service model.

Key words: Machine Learning, Discharge Preparation Services, Clinical Decision-Making, Discharge Nursing, Review

胡欢婷, 洪思思, 贾盈盈, 宋剑平. 机器学习在患者出院准备服务中的应用进展[J]. 中华护理杂志, 2024, 59(3): 378-384.

HU Huanting, HONG Sisi, JIA Yingying, SONG Jianping. Research progress on application of machine learning in discharge preparation service for patients[J]. Chinese Journal of Nursing, 2024, 59(3): 378-384.

参考文献 45

[1]	陈冰, 刘秀文, 仲冬梅. 出院准备服务在癌症病人中的应用研究进展[J]. 护理研究, 2021, 35(23):4226-4229.
	Chen B, Liu XW, Zhong DM. Research progress on the app-lication of hospital discharge preparation service in cancer patients[J]. Chin Nurs Res, 2021, 35(23):4226-4229.
[2]	国务院办公厅. 国务院办公厅关于印发深化医药卫生体制改革2022年重点工作任务的通知[EB/OL].(2022-05-04)[2022-12-12]. http://www.gov.cn/zhengce/content/2022-05/25/content_5692209.htm
	General Office of the State Council. The General Office of the State Council issued a notice on the key tasks for deepening the reform of the medical and health system in 2022[EB/OL]. (2022-05-04)[2022-12-12]. http://www.gov.cn/zhengce/content/2022-05/25/content_5692209.htm
[3]	张晓娜, 李鑫丹, 芦鸿雁, 等. 出院准备服务对慢性阻塞性肺疾病患者干预效果的Meta分析[J]. 中华护理杂志, 2022, 57(1):42-48. DOI
	Zhang XN, Li XD, Lu HY, et al. Intervention effects of dis-charge preparation service on patients with chronic obstructive pulmonary diseases:a meta-analysis[J]. Chin J Nurs, 2022, 57(1):42-48.
[4]	孙超, 奚桓, 李峥, 等. 老年患者出院准备服务专家共识(2019版)[J]. 中华护理杂志, 2020, 55(2):220-227. DOI
	Sun C, Xi H, Li Z, et al. Expert consensus on the discharge planning for hospitalized elderly patients(version 2019)[J]. Chin J Nurs, 2020, 55(2):220-227.
[5]	乐霄, 汪晖, 王颖, 等. 出院计划实施质量评价指标的范围综述[J]. 中华护理杂志, 2021, 56(4):611-618. DOI
	Yue X, Wang H, Wang Y, et al. Review on the scope of qua-lity evaluation indicators of discharge plan implementation[J]. Chin J Nurs, 2021, 56(4):611-618.
[6]	国务院办公厅. 国务院办公厅关于促进和规范健康医疗大数据应用发展的指导意见[EB/OL].(2016-06-24)[2023-02-28]. http://www.gov.cn/zhengce/content/2016-06/24/content_5085091.htm.
	General Office of the State Council. Guiding opinions of the General Office of the State Council on promoting and standardizing the application and development of big data in health care[EB/OL].(2016-06-24)[2023-02-28]. http://www.gov.cn/zhengce/content/2016-06/24/content_5085091.htm.
[7]	Handelman GS, Kok HK, Chandra RV, et al. eDoctor:machine learning and the future of medicine[J]. J Intern Med, 2018, 284(6):603-619. DOI PMID
[8]	Levin S, Toerper M, Hamrock E, et al. Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index[J]. Ann Emerg Med, 2018, 71(5):565-574.e2. DOI URL
[9]	丁四清, 陆晶, 秦春香, 等. 数据挖掘在护理不良事件管理中的应用进展[J]. 中华护理杂志, 2019, 54(6):873-877. DOI
	Ding SQ, Lu J, Qin CX, et al. Application progress on data mining for nursing adverse event management[J]. Chin J Nurs, 2019, 54(6):873-877.
[10]	吕璐洋, 王华芬, 卢芳燕, 等. 住院患儿家庭出院准备度评估工具的研究进展[J]. 中华护理杂志, 2022, 57(4):507-512. DOI
	Lu LY, Wang HF, Lu FY, et al. Research progress of family discharge readiness assessment tools for hospitalized children[J]. Chin J Nurs, 2022, 57(4):507-512.
[11]	Causey-Upton R, Howell DM, Kitzman PH, et al. Factors influe-ncing discharge readiness after total knee replacement[J]. Orthop Nurs, 2019, 38(1):6-14. DOI PMID
[12]	Bishop JA, Javed HA, El-Bouri R, et al. Improving patient flow during infectious disease outbreaks using machine learning for real-time prediction of patient readiness for discharge[J]. PLoS One, 2021, 16(11):e0260476.
[13]	Zhang XM, Yan C, Malin BA, et al. Predicting next-day dis-charge via electronic health record access logs[J]. J Am Med Inform Assoc, 2021, 28(12):2670-2680. DOI URL
[14]	Bacchi S, Gilbert T, Gluck S, et al. Daily estimates of indivi-dual discharge likelihood with deep learning natural language processing in general medicine:a prospective and external validation study[J]. Intern Emerg Med, 2022, 17(2):411-415. DOI
[15]	Zhong HY, Poeran J, Gu A, et al. Machine learning approa-ches in predicting ambulatory same day discharge patients after total hip arthroplasty[J]. Reg Anesth Pain Med, 2021, 46(9):779-783. DOI URL
[16]	Lajevardi-Khosh A, Jalali A, Rajput KS, et al. Novel dynamic prediction of daily patient discharge in acute and critical care[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2021, 2021:2347-2352. DOI PMID
[17]	Nemati M, Ansary J, Nemati N. Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data[J]. Patterns (NY), 2020, 1(5):100074.
[18]	Ohbe H, Goto T, Nakamura K, et al. Development and valida-tion of early prediction models for new-onset functional impairment at hospital discharge of ICU admission[J]. Inten-sive Care Med, 2022, 48(6):679-689.
[19]	Inoue Y, Imura T, Tanaka R, et al. Developing a clinical pre-diction rule for gait independence at discharge in patients with stroke:a decision-tree algorithm analysis[J]. J Stroke Cere-brovasc Dis, 2022, 31(6):106441.
[20]	Betts KS, Kisely S, Alati R. Predicting neonatal respiratory dis-tress syndrome and hypoglycaemia prior to discharge:leve-raging health administrative data and machine learning[J]. J Biomed In-form, 2021, 114:103651.
[21]	Zhao CC, Bjurlin MA, Wysock JS, et al. Machine learning deci-sion support model for radical cystectomy discharge planning[J]. Urol Oncol Semin Orig Investig, 2022, 40(10):453.e9-453.e18.
[22]	Veerbeek JM, Ottiger B, Cazzoli D, et al. Speed up discharge planning at the acute stroke unit:a development and external validation study for the early prediction of discharge home[J]. Front Neurol, 2022, 13:999595. DOI URL
[23]	van de Sande D, van Genderen ME, Verhoef C, et al. Optimi-zing discharge after major surgery using an artificial intell-igence-based decision support tool(DESIRE):an external vali-dation study[J]. Surgery, 2022, 172(2):663-669. DOI URL
[24]	Meng QC, Liu WT, Gao PR, et al. Novel deep learning tech-nique used in management and discharge of hospitalized patients with COVID-19 in China[J]. Ther Clin Risk Manag, 2020, 16:1195-1201. DOI URL
[25]	Safavi KC, Khaniyev T, Copenhaver M, et al. Development and validation of a machine learning model to aid discharge processes for inpatient surgical care[J]. JAMA Netw Open, 2019, 2(12):e1917221.
[26]	Satyadev N, Warman PI, Seas A, et al. Machine learning for predicting discharge disposition after traumatic brain injury[J]. Neurosurgery, 2022, 90(6):768-774. DOI PMID
[27]	Valliani AA, Kim NC, Martini ML, et al. Robust prediction of non-home discharge after thoracolumbar spine surgery with ensemble machine learning and validation on a nationwide cohort[J]. World Neurosurg, 2022, 165:e83-e91.
[28]	Imura T, Iwamoto Y, Inagawa T, et al. Decision tree algorithm identifies stroke patients likely discharge home after rehabilitation using functional and environmental predictors[J]. J Stroke Cerebrovasc Dis, 2021, 30(4):105636. DOI URL
[29]	de Berker H, de Berker A, Aung H, et al. Pre-stroke disability and stroke severity as predictors of discharge destination from an acute stroke ward[J]. Clin Med(Lond), 2021, 21(2):e186-e191.
[30]	Mickle CF, Deb D. Early prediction of patient discharge dis-position in acute neurological care using machine learning[J]. BMC Health Serv Res, 2022, 22(1):1-17. DOI
[31]	Muhlestein WE, Monsour MA, Friedman GN, et al. Predicting discharge disposition following meningioma resection using a multi-institutional natural language processing model[J]. Neu-rosurgery, 2021, 88(4):838-845.
[32]	Komagamine J, Kobayashi M. Prevalence of hospitalisation cau-sed by adverse drug reactions at an internal medicine ward of a single centre in Japan:a cross-sectional study[J]. BMJ Open, 2019, 9(8):e030515.
[33]	Tan HX, Teo CHD, Ang PS, et al. Combining machine learning with a rule-based algorithm to detect and identify related entities of documented adverse drug reactions on hospital discharge summaries[J]. Drug Saf, 2022, 45(8):853-862. DOI
[34]	Knipe D, Metcalfe C, Hawton K, et al. Risk of suicide and repeat self-harm after hospital attendance for non-fatal self-harm in Sri Lanka:a cohort study[J]. Lancet Psychiatry, 2019, 6(8):659-666. DOI URL
[35]	Hawton K, Witt KG, Salisbury TLT, et al. Psychosocial inter-ventions following self-harm in adults:a systematic review and meta-analysis[J]. Lancet Psychiatry, 2016, 3(8):740-750. DOI PMID
[36]	Xu Z, Zhang Q, Yi PP. Predicting post-discharge self-harm in-cidents using disease comorbidity networks:a retrospective machine learning study[J]. J Affect Disord, 2020, 277:402-409. DOI URL
[37]	Zelkowitz RL, Jiang T, Horváth-Puhó E, et al. Predictors of nonfatal suicide attempts within 30 days of discharge from psychiatric hospitalization:sex-specific models developed using population-based registries[J]. J Affect Disord, 2022, 306:260-268. DOI URL
[38]	Shi K, Ho V, Song JJ, et al. Predicting unplanned 7-day inten-sive care unit readmissions with machine learning models for improved discharge risk assessment[J]. AMIA Jt Summits Tran-sl Sci Proc, 2022, 2022:446-455.
[39]	Chen LL, Chen SP. Prediction of readmission in patients with acute exacerbation of chronic obstructive pulmonary disease within one year after treatment and discharge[J]. BMC Pulm Med, 2021, 21(1):1-17. DOI
[40]	Howard EP, Morris JN, Schachter E, et al. Machine-learning mo-deling to predict hospital readmission following discharge to post-acute care[J]. J Am Med Dir Assoc, 2021, 22(5):1067-1072.e29. DOI URL
[41]	Symum H, Zayas-Castro J. Identifying children at readmission risk:at-admission versus traditional at-discharge readmission prediction model[J]. Healthcare (Basel), 2021, 9(10):1334.
[42]	Zhou HQ, Albrecht MA, Roberts PA, et al. Using machine lear-ning to predict paediatric 30-day unplanned hospital read-missions:a case-control retrospective analysis of medical re-cords,including written discharge documentation[J]. Aust Health Rev, 2021, 45(3):328-337. DOI URL
[43]	Stonko DP, Betzold RD, Abdou H, et al. In-hospital outcomes in autogenous vein versus synthetic graft interposition for traumatic arterial injury:a propensity-matched cohort from PROOVIT[J]. J Trauma Acute Care Surg, 2022, 92(2):407-412. DOI URL
[44]	Wang HM, Tang JX, Wu MY, et al. Application of machine learning missing data imputation techniques in clinical decision making:taking the discharge assessment of patients with spontaneous supratentorial intracerebral hemorrhage as an example[J]. BMC Med Inform Decis Mak, 2022, 22(1):13. DOI
[45]	Mahajan SM, Heidenreich P, Abbott B, et al. Predictive models for identifying risk of readmission after index hospitalization for heart failure:a systematic review[J]. Eur J Cardiovasc Nurs, 2018, 17(8):675-689. DOI URL