AI Article Synopsis

  • The study aims to create a framework for understanding the early development of Accountable Care Organizations (ACOs) to help with evaluation and support.
  • Data was collected from a survey of 173 ACOs associated with Medicare, Medicaid, and commercial payers between October 2012 and May 2013.
  • Three distinct types of ACOs were identified: large integrated systems, smaller physician-led practices, and moderate-sized joint groups, each with varying service scopes and performance management approaches.

Article Abstract

Objective: To develop an exploratory taxonomy of Accountable Care Organizations (ACOs) to describe and understand early ACO development and to provide a basis for technical assistance and future evaluation of performance.

Data Sources/study Setting: Data from the National Survey of Accountable Care Organizations, fielded between October 2012 and May 2013, of 173 Medicare, Medicaid, and commercial payer ACOs.

Study Design: Drawing on resource dependence and institutional theory, we develop measures of eight attributes of ACOs such as size, scope of services offered, and the use of performance accountability mechanisms. Data are analyzed using a two-step cluster analysis approach that accounts for both continuous and categorical data.

Principal Findings: We identified a reliable and internally valid three-cluster solution: larger, integrated systems that offer a broad scope of services and frequently include one or more postacute facilities; smaller, physician-led practices, centered in primary care, and that possess a relatively high degree of physician performance management; and moderately sized, joint hospital-physician and coalition-led groups that offer a moderately broad scope of services with some involvement of postacute facilities.

Conclusions: ACOs can be characterized into three distinct clusters. The taxonomy provides a framework for assessing performance, for targeting technical assistance, and for diagnosing potential antitrust violations.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4254130PMC
http://dx.doi.org/10.1111/1475-6773.12234DOI Listing

Publication Analysis

Top Keywords

accountable care
12
care organizations
12
scope services
12
taxonomy accountable
8
technical assistance
8
broad scope
8
care
4
organizations policy
4
policy practice
4
practice objective
4

Similar Publications

A core problem with the current risk-adjustment system in Medicare Advantage and accountable care organization (ACO) programs-the Hierarchical Condition Categories (HCC) model-is that the inputs (coded diagnoses) can be influenced for gain by risk-bearing plans or providers. Using existing survey data on health status (which provide less manipulable inputs), we found that the use of a hybrid risk score drawing from survey data and a scaled-back set of HCCs would, in addition to mitigating coding incentives, modestly lessen risk-selection incentives, strengthen payment incentives to deliver efficient care, allocate payment across ACOs more efficiently according to markers of population health that are not as affected by practice patterns or coding efforts, and redistribute payment in a manner that supports equity goals. Although sampling error and survey nonresponse present challenges, analyses suggest that these should not be prohibitive.

View Article and Find Full Text PDF

The purpose of this research is to describe the factors affecting hazardous chemotherapy exposure and strategies to foster chemotherapy safety among oncology nurses. Fifteen oncology nurses and 5 oncology nurse managers were recruited from 2 medical centers in the Midwest United States through convenience purposive sampling. A qualitative descriptive approach was employed.

View Article and Find Full Text PDF

Aims/hypothesis: Eating disorders are over-represented in type 1 diabetes and are associated with an increased risk of complications, but it is unclear whether type 1 diabetes affects the treatment of eating disorders. We assessed incidence and treatment of eating disorders in a nationwide sample of individuals with type 1 diabetes and diabetes-free control individuals.

Methods: Our study comprised 11,055 individuals aged <30 who had been diagnosed with type 1 diabetes in 1998-2010, and 11,055 diabetes-free control individuals matched for age, sex and hospital district.

View Article and Find Full Text PDF

Predicting the likelihood of readmission in patients with ischemic stroke: An explainable machine learning approach using common data model data.

Int J Med Inform

December 2024

Department of Health Policy and Management, School of Medicine, Kangwon National University, 510 School of Medicine Building #1 (N414), 1, Kangwondaehak-gil, Chuncheon-si, Gangwon-do 24341, Republic of Korea; Department of Preventive Medicine, Kangwon National University Hospital, 156 Baengnyeong-ro, Chuncheon-si, Gangwon-do 24289, Republic of Korea; Team of Public Medical Policy Development, Gangwon State Research Institute for People's Health, 880 Baksa-ro, Seo-myeon, Chuncheon-si, Gangwon-do 24461, Republic of Korea. Electronic address:

Background: Ischemic stroke affects 15 million people worldwide, causing five million deaths annually. Despite declining mortality rates, stroke incidence and readmission risks remain high, highlighting the need for preventing readmission to improve the quality of life of survivors. This study developed a machine-learning model to predict 90-day stroke readmission using electronic medical records converted to the common data model (CDM) from the Regional Accountable Care Hospital in Gangwon state in South Korea.

View Article and Find Full Text PDF

Objectives: This qualitative study explored the beliefs and values influencing healthcare providers' delivery of gender-affirming care (GAC) to transgender and gender-diverse (TGD) youth amidst current social and political dynamics.

Methods: The study PI conducted 43 semi-structured interviews with providers across states with varying GAC legislation. Responses from 41 providers were analyzed in this paper.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!