The Impact of Markets,
Information and Regulation on
NH Quality
Farida K. Ejaz, Ph.D., L.I.S.W.1
&
Jane Straker, Ph.D.2
Presented at
AcademyHealth’s Annual Research Meeting (ARM)
San Diego, 2004
1.
2.
Margaret Blenkner Research Institute, Benjamin Rose, 850 Euclid Avenue, Suite 1100 Cleveland,
OH 44114-3301 Fejaz@benrose.org 216.373.1660
Scripps Gerontology Center, 396 Upham Hall, Miami University, Oxford,OH 45056
strakejk@muohio.edu 513.529.5949
Overview of Presentation
• Historical overview of key sources of NH quality
information;
• Impact of NH quality information and identify
limitations;
• Current research*;
• Identify user-friendly presentations of data for
quality improvement*.
*Funded by the Commonwealth Foundation.
History of NH Quality Information





1959: Senate subcommittee identifies poor NH
care (Morris et al, 1990).
1970’s: HE&W study confirms that compliance
with regulations vary widely.
1984: Smith & Heckler confirm that Sec of HHS
is responsible for NHs to meet regulatory
standards.
1986: IOM report on NH quality is published.
1987: NH Reform Law (OBRA).
Changes in the Certification System

Prior to 1985: Certification focused on
structure & process (policies/procedures).
 After OBRA 87: The focus shifted to resident
outcomes.
– OSCAR: Stems from the annual survey and
includes facility deficiencies, staffing, and
resident characteristics.
– Complaint surveys: Data collected by
surveyors as a result of a complaint.
Focus on Resident Outcomes
–RAI: OBRA also mandated the
development of a uniform resident
assessment instrument.
–MDS: Implemented 10/1990; collected
quarterly or during a significant change in
the resident’s condition.
 Cost Reports: MDS also used to reimburse
NHs based on facility costs and resident
characteristics (RUGs/case-mix scores).
QIs & QMs from the MDS

QIs and the QMs
• Late 1990s: 24 QIs were developed to help
NHs identify areas for quality improvement;
and, to help surveyors identify residents to
include in their state surveys.
• 2002: 14 quality measures (QMs) released
for public reporting & quality improvement.
- QMs generally for consumers
- QIs are for nursing homes and surveyors.
Impact of OBRA 87

Wealth of Information: OSCAR, RAIs/MDS,
Cost Reports, the QIs, and QMs.
 2001 IOM report: Improvements in NH quality
of care (e.g. physical restraints).
 Serious problems still remain (pain, pressure
sores, malnutrition & urinary incontinence).
 Quality of life of residents has also shown
some but not remarkable improvement.
Limitations & Gaps in the Data

Data are received piece meal.
 No one data source is enough to provide a holistic
picture of quality.
 Discrepancies in data sources often stem from
different time referents & risk adjustment methods.
 Relationships between the various data sources
relatively unexamined.
Commonwealth Fund Grant on
Improving the NH Quality of Care
Study has two major goals:

To examine the relationships between various
data sources in Ohio.
 Examine provider preferences for receiving
such data to help improve NH quality.
Steps to Achieve Goals

Obtained permission to acquire 10 datasets for
research purposes.
 Computed facility level data for datasets in
which only the individual/resident level data
were available.
 Example: For each item in the resident and
family satisfaction surveys, 4 variables were
computed: 1) mean, 2) median, 3) modal
facility score, and 4) the % of negative
responses. Overall measures of satisfaction
also created.
Datasets that were Merged
1.
2.
3.
4.
5.
MDS 2.0 (last quarter of 2001 aggregated to
facility level).
OSCAR: Deficiencies and some facility
characteristics.
Complaint surveys.
Ohio Medicaid Cost Report.
Annual Survey of Long-Term Care Facilities
(ASLTCF). Collected from about 90% of
Ohio’s NHs regarding characteristics,
occupancy, turnover, charges and
reimbursement.
Merged Datasets cont..
Resident Satisfaction Surveys.
7. Family Satisfaction Surveys.
8. QMs
9. OLTCCG
10. 2000 County Census data. Census county
information was used to provide information
about each facility’s nursing home market
(competitiveness, capacity per 1,000 older
persons, etc.).
6.
Ohio’s Mega Quality Information
Dataset

Result: A Mega dataset on quality information
for Ohio’s NHs.
 Has information on over 1,000 (facilities)
cases and 1,930 variables.
 However, only about 750 facilities have data
from 8 or more sources.
Outcomes from the Mega Dataset

Models examining the relationships between
key indicators of quality from the various
datasets have been developed.
 Challenge to develop models that are simple &
examine relationships between 2-3 variables.
 Models currently being tested by statisticians at
the Scripps Gerontology Center.
Challenges Faced in Merging the
Datasets
Time-consuming.
 Tracking changes in a facility id number.
 Examining comparable data elements
from one source to another.
 The algorithms for computing federal QI
or the QMs unavailable so QMs
downloaded from NH Compare website.

Next Steps

Various reports demonstrating basic (simple)
relationships will be developed.
 Focus groups to critique reports.
 Explore whether technical assistance is needed
in interpreting reports.
 Two focus groups: HealthRays
Alliance/AOPHA; and OHCA.
Important Contributions of this
Work



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Contribute to knowledge about the information
needed and found useful by nursing home
staff.
The Mega Quality Information dataset sets a
new standard for NH quality.
Previous research on NH quality relied on one
or two sources of data.
Our success may inspire other
researchers/states to do similar work.