| dc.description.abstract | Health care services received after discharge from an acute care are called post-acute
care (PAC). These services improve patient functioning and help patients for better transition
from hospitals to the community. PAC can be delivered in different settings such
as long-term care (LTC). LTC is vital for people with functional limitations. In the U.S.,
most LTC is financed by state Medicaid programs. These are administered by states and
jointly financed with state and federal funding. There are two main types of LTC delivery:
institutionalized care, dominated by the nursing home industry (NHC), and outpatient care,
provided through home and community based organizations (HCBS). HCBS is primarily
funded through Medicaid “waiver” programs that allow states to allocate some LTC funding
to non-institutionalized settings. While HCBS is the less costly option, participation
is limited by capacity shortages, and many state waiver programs have long waiting lists.
As the population ages, the demand for LTC is projected to grow significantly, and thus
HCBS capacity problems constitute a significant policy concern. This work investigates this
by formulating a bi-level stochastic game model in which a Medicaid program (the leader)
specifies the size of its waiver program, and then HBCS organizations (the followers) respond
by specifying their capacity, with LTC service demand being uncertain. We characterize the
problem and design an approximation algorithm that exploits a piecewise linear function
for computing the followers’ response function to the leader’s decision. We use a case study
based on data from the state of Texas.
Another important question in PAC studies is how to select the best providers. In
addition, it is vital to determine the factors that play roles in this decision making procedure.
Acute care managers are always looking for the best PAC providers, while they are willing not
to pay too much. In order to determine a set of best PAC’s, a multi-objective decision making approach is developed for Post-Acute Care Provider (PACP) selection. PCAP selection,
similar to other subcontracting problems, depends on multiple criteria. Besides the cost
metrics, considering service coverage requirements, readmission rate, and service quality
make the decision making more complicated.
The proposed approach provides the decision making procedure for acute care providers
subcontracting with PAC providers. This approach includes two phases. In the first phase,
providers are evaluated and assigned a comparable value based on a set of criteria. These
quality metrics are used to calculate closeness coefficients of each candidate PACP for both
short-stay and long-stay patients. These patient categories are determined by the Medicaid.
In the second phase, using the computed coefficients, we develop a multi-objective problem
that considers cost, service quality, and readmission to the hospital as objectives. The novelty
of this procedure is introducing a new view toward the provider selection problem. The
proposed approach is implemented for the PACP selection problem in the city of Houston,
TX. | |
