Evaluation of an intervention targeted with predictive analytics to prevent readmissions in an integrated health system: observational study

被引:7
作者
Marafino, Ben J. [1 ]
Escobar, Gabriel J. [2 ]
Baiocchi, Michael T. [3 ]
Liu, Vincent X. [2 ,4 ]
Plimier, Colleen C. [2 ]
Schuler, Alejandro [2 ,5 ]
机构
[1] Stanford Univ, Sch Med, Biomed Informat Training Program, Dept Biomed Data Sci, Stanford, CA 94305 USA
[2] Kaiser Permanente Northern Calif, Div Res, Syst Res Initiat, Oakland, CA USA
[3] Stanford Univ, Dept Med, Dept Epidemiol & Populat Hlth, Stanford, CA 94305 USA
[4] Kaiser Permanente Med Ctr, Crit Care Med, Santa Clara, CA USA
[5] Stanford Univ, Sch Med, Dept Biomed Data Sci, Stanford, CA 94305 USA
来源
BMJ-BRITISH MEDICAL JOURNAL | 2021年 / 374卷
基金
美国医疗保健研究与质量局; 美国国家卫生研究院;
关键词
HIGH-RISK PATIENTS; HOSPITAL READMISSIONS; INPATIENT DETERIORATION; REDUCTION PROGRAM; CARE; MORTALITY; RATES; RECORD; EPIDEMIOLOGY; ASSOCIATION;
D O I
10.1136/bmj.n1747
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
OBJECTIVES To determine the associations between a care coordination intervention (the Transitions Program) targeted to patients after hospital discharge and 30 day readmission and mortality in a large, integrated healthcare system. DESIGN Observational study. SETTING 21 hospitals operated by Kaiser Permanente Northern California. PARTICIPANTS 1 539 285 eligible index hospital admissions corresponding to 739 040 unique patients from June 2010 to December 2018. 411 507 patients were discharged post-implementation of the Transitions Program; 80 424 (19.5%) of these patients were at medium or high predicted risk and were assigned to receive the intervention after discharge. INTERVENTION Patients admitted to hospital were automatically assigned to be followed by the Transitions Program in the 30 days post-discharge if their predicted risk of 30 day readmission or mortality was greater than 25% on the basis of electronic health record data. MAIN OUTCOME MEASURES Non-elective hospital readmissions and all cause mortality in the 30 days after hospital discharge. RESULTS Difference-in-differences estimates indicated that the intervention was associated with significantly reduced odds of 30 day non-elective readmission (adjusted odds ratio 0.91, 95% confidence interval 0.89 to 0.93; absolute risk reduction 95% confidence interval & minus;2.5%, & minus;3.1% to & minus;2.0%) but not with the odds of 30 day post-discharge mortality (1.00, 0.95 to 1.04). Based on the regression discontinuity estimate, the association with readmission was of similar magnitude (absolute risk reduction & minus;2.7%, & minus; 3.2% to & minus;2.2%) among patients at medium risk near the risk threshold used for enrollment. However, the regression discontinuity estimate of the association with post-discharge mortality (& minus;0.7% & minus;1.4% to & minus; 0.0%) was significant and suggested benefit in this subgroup of patients. CONCLUSIONS In an integrated health system, the implementation of a comprehensive readmissions prevention intervention was associated with a reduction in 30 day readmission rates. Moreover, there was no association with 30 day post-discharge mortality, except among medium risk patients, where some evidence for benefit was found. Altogether, the study provides evidence to suggest the effectiveness of readmission prevention interventions in community settings, but further research might be required to confirm the findings beyond this setting.
引用
收藏
页数:12
相关论文
共 63 条
[11]   Methods for Evaluating Changes in Health Care Policy The Difference-in-Differences Approach [J].
Dimick, Justin B. ;
Ryan, Andrew M. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2014, 312 (22) :2401-2402
[12]   Risk-adjusting hospital inpatient mortality using automated inpatient, outpatient, and laboratory databases [J].
Escobar, Gabriel J. ;
Greene, John D. ;
Scheirer, Peter ;
Gardner, Marla N. ;
Draper, David ;
Kipnis, Patricia .
MEDICAL CARE, 2008, 46 (03) :232-239
[13]   Automated Identification of Adults at Risk for In-Hospital Clinical Deterioration [J].
Escobar, Gabriel J. ;
Liu, Vincent X. ;
Schuler, Alejandro ;
Lawson, Brian ;
Greene, John D. ;
Kipnis, Patricia .
NEW ENGLAND JOURNAL OF MEDICINE, 2020, 383 (20) :1951-1960
[14]   Multiyear Rehospitalization Rates and Hospital Outcomes in an Integrated Health Care System [J].
Escobar, Gabriel J. ;
Plimier, Colleen ;
Greene, John D. ;
Liu, Vincent ;
Kipnis, Patricia .
JAMA NETWORK OPEN, 2019, 2 (12)
[15]   Piloting Electronic Medical Record-Based Early Detection of Inpatient Deterioration in Community Hospitals [J].
Escobar, Gabriel J. ;
Turk, Benjamin J. ;
Ragins, Arona ;
Ha, Jason ;
Hoberman, Brian ;
LeVine, Steven M. ;
Ballesca, Manuel A. ;
Liu, Vincent ;
Kipnis, Patricia .
JOURNAL OF HOSPITAL MEDICINE, 2016, 11 :S18-S24
[16]   Early Detection, Prevention, and Mitigation of Critical Illness Outside Intensive Care Settings [J].
Escobar, Gabriel J. ;
Dellinger, R. Phillip .
JOURNAL OF HOSPITAL MEDICINE, 2016, 11 :S5-S10
[17]   Nonelective Rehospitalizations and Postdischarge Mortality Predictive Models Suitable for Use in Real Time [J].
Escobar, Gabriel J. ;
Ragins, Arona ;
Scheirer, Peter ;
Liu, Vincent ;
Robles, Jay ;
Kipnis, Patricia .
MEDICAL CARE, 2015, 53 (11) :916-923
[18]   Risk-adjusting Hospital Mortality Using a Comprehensive Electronic Record in an Integrated Health Care Delivery System [J].
Escobar, Gabriel J. ;
Gardner, Marla N. ;
Greene, John D. ;
Draper, David ;
Kipnis, Patricia .
MEDICAL CARE, 2013, 51 (05) :446-453
[19]   Intra-Hospital Transfers to a Higher Level of Care: Contribution to Total Hospital and Intensive Care Unit (ICU) Mortality and Length of Stay (LOS) [J].
Escobar, Gabriel J. ;
Greene, John D. ;
Gardner, Marla N. ;
Marelich, Gregory P. ;
Quick, Bryon ;
Kipnis, Patricia .
JOURNAL OF HOSPITAL MEDICINE, 2011, 6 (02) :74-80
[20]   Patient Readmission Rates For All Insurance Types After Implementation Of The Hospital Readmissions Reduction Program [J].
Ferro, Enrico G. ;
Secemsky, Eric A. ;
Wadhera, Rishi K. ;
Choi, Eunhee ;
Strom, Jordan B. ;
Wasfy, Jason H. ;
Wang, Yun ;
Shen, Changyu ;
Yeh, Robert W. .
HEALTH AFFAIRS, 2019, 38 (04) :585-593