Intensive care management for high-risk veterans in a patient-centered medical home-do some veterans benefit more than others?

被引:8
作者
Swankoski, Kaylyn E. [1 ,2 ,7 ]
Reddy, Ashok [1 ,2 ,3 ]
Grembowski, David [1 ]
Chang, Evelyn T. [4 ,5 ,6 ]
Wong, Edwin S.
机构
[1] Univ Washington, Dept Hlth Syst & Populat Hlth, Seattle, WA USA
[2] VA Puget Sound Hlth Care Syst, Ctr Innovat Vet Ctr & Value Driven Care, Seattle, WA USA
[3] Univ Washington, Dept Med, Div Gen Internal Med, Seattle, WA USA
[4] VA Ctr Study Healthcare Innovat Implementat & Poli, Los Angeles, CA USA
[5] VA Greater Los Angeles Healthcare Syst, Dept Med, Los Angeles, CA USA
[6] Univ Calif Los Angeles, Geffen Sch Med, Dept Med, Los Angeles, CA USA
[7] Univ Washington, Hans Rosling Ctr Populat Hlth, Dept Hlth Syst & Populat Hlth, 3980 15 Ave NE, Fourth Floor, Box 351621, Seattle, WA 98195 USA
来源
HEALTHCARE-THE JOURNAL OF DELIVERY SCIENCE AND INNOVATION | 2023年 / 11卷 / 02期
基金
美国医疗保健研究与质量局;
关键词
Patient -centered medical home; Intensive management; Care management; Primary care; Vulnerable population; Veterans; HEALTH; MODEL; ACCESS; IMPLEMENTATION; ASSOCIATION; DISPARITIES; DEATH; RACE;
D O I
10.1016/j.hjdsi.2023.100677
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Background: Primary care intensive management programs utilize interdisciplinary care teams to comprehen-sively meet the complex care needs of patients at high risk for hospitalization. The mixed evidence on the effectiveness of these programs focuses on average treatment effects that may mask heterogeneous treatment effects (HTEs) among subgroups of patients. We test for HTEs by patients' demographic, economic, and social characteristics.Methods: Retrospective analysis of a VA randomized quality improvement trial. 3995 primary care patients at high risk for hospitalization were randomized to primary care intensive management (n = 1761) or usual pri-mary care (n = 1731). We estimated HTEs on ED and hospital utilization one year after randomization using model-based recursive partitioning and a pre-versus post-with control group framework. Splitting variables included administratively collected demographic characteristics, travel distance, copay exemption, risk score for future hospitalizations, history of hospital discharge against medical advice, homelessness, and multiple resi-dence ZIP codes.Results: There were no average or heterogeneous treatment effects of intensive management one year after enrollment. The recursive partitioning algorithm identified variation in effects by risk score, homelessness, and whether the patient had multiple residences in a year. Within each distinct subgroup, the effect of intensive management was not statistically significant.Conclusions: Primary care intensive management did not affect acute care use of high-risk patients on average or differentially for patients defined by various demographic, economic, and social characteristics. Implications: Reducing acute care use for high-risk patients is complex, and more work is required to identify patients positioned to benefit from intensive management programs.
引用
收藏
页数:9
相关论文
共 44 条
[1]   An Automated Model to Identify Heart Failure Patients at Risk for 30-Day Readmission or Death Using Electronic Medical Record Data [J].
Amarasingham, Ruben ;
Moore, Billy J. ;
Tabak, Ying P. ;
Drazner, Mark H. ;
Clark, Christopher A. ;
Zhang, Song ;
Reed, W. Gary ;
Swanson, Timothy S. ;
Ma, Ying ;
Halm, Ethan A. .
MEDICAL CARE, 2010, 48 (11) :981-988
[2]   REVISITING THE BEHAVIORAL-MODEL AND ACCESS TO MEDICAL-CARE - DOES IT MATTER [J].
ANDERSEN, RM .
JOURNAL OF HEALTH AND SOCIAL BEHAVIOR, 1995, 36 (01) :1-10
[3]  
[Anonymous], 2022, VA HLTH CARE COPAY R
[4]  
[Anonymous], Healthy People 2020, DOI DOI 10.1177/1527154404263265
[5]   Race, Medical Mistrust, and Segregation in Primary Care as Usual Source of Care: Findings from the Exploring Health Disparities in Integrated Communities Study [J].
Arnett, M. J. ;
Thorpe, R. J. ;
Gaskin, D. J. ;
Bowie, J. V. ;
LaVeist, T. A. .
JOURNAL OF URBAN HEALTH-BULLETIN OF THE NEW YORK ACADEMY OF MEDICINE, 2016, 93 (03) :456-467
[6]   The Oregon Experiment - Effects of Medicaid on Clinical Outcomes [J].
Baicker, Katherine ;
Taubman, Sarah L. ;
Allen, Heidi L. ;
Bernstein, Mira ;
Gruber, Jonathan H. ;
Newhouse, Joseph P. ;
Schneider, Eric C. ;
Wright, Bill J. ;
Zaslavsky, Alan M. ;
Finkelstein, Amy N. .
NEW ENGLAND JOURNAL OF MEDICINE, 2013, 368 (18) :1713-1722
[7]   Outcomes of a randomized quality improvement trial for high-risk Veterans in year two [J].
Chang, Evelyn T. ;
Yoon, Jean ;
Esmaeili, Aryan ;
Zulman, Donna M. ;
Ong, Michael K. ;
Stockdale, Susan E. ;
Jimenez, Elvira E. ;
Chu, Karen ;
Atkins, David ;
Denietolis, Angela ;
Asch, Steven M. .
HEALTH SERVICES RESEARCH, 2021, 56 :1045-1056
[8]   An operations-partnered evaluation of care redesign for high-risk patients in the Veterans Health Administration (VHA): Study protocol for the PACT Intensive Management (PIM) randomized quality improvement evaluation [J].
Chang, Evelyn T. ;
Zulman, Donna M. ;
Asch, Steven M. ;
Stockdale, Susan E. ;
Yoon, Jean ;
Ong, Michael K. ;
Lee, Martin ;
Simon, Alissa ;
Atkins, David ;
Schectman, Gordon ;
Kirsh, Susan R. ;
Rubenstein, Lisa V. .
CONTEMPORARY CLINICAL TRIALS, 2018, 69 :65-75
[9]   Exposing some important barriers to health care access in the rural USA [J].
Douthit, N. ;
Kiv, S. ;
Dwolatzky, T. ;
Biswas, S. .
PUBLIC HEALTH, 2015, 129 (06) :611-620
[10]   Computation of Standard Errors [J].
Dowd, Bryan E. ;
Greene, William H. ;
Norton, Edward C. .
HEALTH SERVICES RESEARCH, 2014, 49 (02) :731-750