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Pay-for-performance in health care: Evidence from a nationwide randomized experiment in the home health sector
Last registered on May 13, 2019

Pre-Trial

Trial Information
General Information
Title
Pay-for-performance in health care: Evidence from a nationwide randomized experiment in the home health sector
RCT ID
AEARCTR-0004159
Initial registration date
April 30, 2019
Last updated
May 13, 2019 11:36 PM EDT
Location(s)

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Primary Investigator
Affiliation
University of Michigan
Other Primary Investigator(s)
Additional Trial Information
Status
On going
Start date
2016-01-01
End date
2020-12-31
Secondary IDs
Abstract
This pre-specified analysis plan describes the planned approach and main analyses of a nationwide stratified cluster-randomized trial of a Medicare value-based purchasing program for home health care. The Home Health Value-Based Purchasing program was designed by the Centers for Medicare & Medicaid Services and implemented in January 2016. Randomization was conducted at the state level within nine regions. In the first year, 1,626 home health agencies from 9 states were assigned to the treatment group and 6,981 agencies from 41 states were assigned to the control group. This study will examine the effect of the intervention on health care quality in the first performance year (i.e., from January through December 2016).
External Link(s)
Registration Citation
Citation
li, jun. 2019. "Pay-for-performance in health care: Evidence from a nationwide randomized experiment in the home health sector." AEA RCT Registry. May 13. https://doi.org/10.1257/rct.4159-1.0.
Former Citation
li, jun. 2019. "Pay-for-performance in health care: Evidence from a nationwide randomized experiment in the home health sector." AEA RCT Registry. May 13. https://www.socialscienceregistry.org/trials/4159/history/46388.
Experimental Details
Interventions
Intervention(s)
The Home Health Value-Based Purchasing (HHVBP) program is a Medicare pay-for- performance program structured in a similar way as the Hospital Value-Based Purchasing Program and Skilled Nursing Facility Value-Based Purchasing Program. The program financially rewards (or penalizes) Medicare-certified home health agencies based on their performance on pre-specified measures and for reporting new quality information to CMS.
Intervention Start Date
2016-01-01
Intervention End Date
2020-12-31
Primary Outcomes
Primary Outcomes (end points)
Self-reported outcomes measures:
Improvement in Ambulation-Locomotion Improvement in Bed Transferring
Improvement in Bathing
Improvement in Dyspnea
Improvement in Pain Interfering with Activity Improvement in Management of Oral Medications
Self-reported process measures:
Pneumococcal Polysaccharide Vaccine Ever Received
Influenza Immunization Received for Current Flu Season
Drug Education on All Medications Provided to Patient/Caregiver
Patient Survey:
How often the home health team gave care in a professional way
How well did the home health team communicate with patients
Did the home health team discuss medicines, pain, and home safety with patients How do patients rate the overall care from the home health agency
Would patients recommend the home health agency to friends and family
Administrative-claims measures:
No unplanned Hospitalization
No Emergency Department Use without Hospitalization
Primary Outcomes (explanation)
The primary outcomes are constructed and calculated by the Centers for Medicare & Medicaid Services. They come from the Home Health Compare website and are derived from the Home Health Outcome and Assessment Information Set, administrative claims data, and patient experience HHCAHPS surveys.
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
In November 2015, CMS randomly selected 9 states from 9 regions to participate in the HHVBP. The program started on January 2016 and lasts through December 2020 for Massachusetts, Maryland, North Carolina, Florida, Washington, Arizona, Iowa, Nebraska, and Tennessee.
To select the participants, CMS first divided states into 9 regions based on a variety of characteristics, including geographic proximity, home health utilization rates, ownership model of agencies, the proportion of home health users that are Medicare-Medicaid dually eligible beneficiaries, and average episodes of care per agency. Next, CMS used a random number generator to select one state within each of the 9 regions to be treated.
Agencies that meet the program’s inclusion criteria within the 9 treatment states face mandatory participation. There are two inclusion criteria. First, only agencies that were Medicare certified in the baseline year (2015) and in the performance year (2016) are included. Second, agencies must have at least 5 measures that meet minimum denominator case requirements (e.g., 20 episodes for quality measures and 40 HHCAHPS surveys) in both the baseline and performance periods. Agencies that do not meet these inclusion criteria do not compete for incentive payments.
Experimental Design Details
Not available
Randomization Method
Random number generator from computer program (conducted by the Centers for Medicare & Medicaid Services).
Randomization Unit
Unit of randomization is state and within each treatment state, agencies that meet the eligibility criteria are included in the program.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
50 states
Sample size: planned number of observations
8611 home health agencies
Sample size (or number of clusters) by treatment arms
50 states (9 treatment with 1626 home health agencies, 41 control with 6981 home health agencies)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
University of Michigan Health Sciences and Behavioral Sciences
IRB Approval Date
2019-04-30
IRB Approval Number
HUM00156470
Analysis Plan
Analysis Plan Documents
HHVBP_PreAnalysisPlan

MD5: 624bf68798fed82ce4aecb9ed6584eea

SHA1: 56c2bf451979f7416a975f19eea15386b76f960f

Uploaded At: April 30, 2019