Topic last updated Jan. 2006
Note |
Benchmarking can improve productivity by:
Identifying
a problem:
- |
selecting
the external benchmark |
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gathering
internal data |
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identifying
variances |
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establishing
targets |
Taking
action:
- |
determining
actions |
- |
defining
responsibilities |
- |
implementing
the changes |
- |
monitoring
performance |
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How
to Make Systems Changes
for Improved Care
Three
Examples of Successful Projects
Model 1). Improvements in diabetic care as measured by A1C
after a quality improvement intervention.18
A
quality improvement project used computerized claims and laboratory
data for the private practices of nine physicians caring for Medicare
beneficiaries with diabetes in New Orleans. There were 835 patients
and 4,367 visits studied. Nine indicators evaluated three areas:
A1C testing frequency, A1C values, and frequency of office visits.
A
quality improvement intervention consisted of two components.
A. The physician component included the following:
- A
color graph that compared the physician's performance against
the other individual physicians for all of the indicators.
-
A personal contact with the physician by a knowledgeable colleague
regarding the importance of A1C monitoring.
- Patient
education tools for the physician's office- brochures, posters,
and A1C stickers.
- Scientific
literature about the validity of A1C testing.
B.
The patient component was a personal mailing from the physician to
the patient containing the following:
-
Diabetes education materials
-
An instruction to request A1C testing from the physician.
-
An offer of a free glucose monitoring meter for self-testing
of blood glucose, with a coupon that could be redeemed at local
pharmacies.
Results
showed the following significant changes:
-
Rates of opportunities for testing A1C improved from 18 to 34
percent.
-
The percentage of patients with a current A1C value improved
from 33 to 47 percent.
- The median
A1C values fell from 8.8 to 7.8 percent.
- Patients
achieving A1C less than 8 percent improved from 44 to 57 percent.
- The median
time between physician visits fell from 70 days to 60 days.
Model
2). A population-based approach to diabetes management in a primary
care setting 19
This study describes a successful population-based approach to diabetes
management in a staff model health maintenance organization in Puget
Sound. The elements of the program to improve the ability of primary
care teams to deliver population-based diabetes care included:
-
a continually updated on-line registry of diabetic patients
-
evidence-based guidelines on retinal screening, foot care, screening
for microalbuminuria, and glycemic management
-
improved support for patient self-management
-
practice redesign to encourage group visits for diabetic patients
in the primary care setting
-
decentralized expertise through a diabetes expert care team (a
diabetologist and a nurse certified diabetes educator) seeing
patients jointly with primary care teams.
Patient
and provider satisfaction improved and rates of retinal eye screening,
documented foot examinations, and testing for microalbuminuria and
hemoglobin A1c increased.
Model
3). A systematic approach to risk stratification and intervention
within a managed care environment to improve diabetes outcomes and
patient satisfaction 20
This 12-month prospective trial was conducted at primary care clinics
within a managed care organization (MCO) and involved 370 adults
with diabetes.
Measurements included:
- The
frequency of dilated eye and foot examinations, microalbuminuria
assessment, blood pressure measurement, lipid profile, and A1C
measurement
- Changes
in blood pressure, lipid levels, and A1C levels
- Changes in
patient satisfaction.
Complete
data are reported for the 193 patients who had been enrolled for
12 months; life table analysis is reported for all patients who
remained enrolled at the study's end as well as for a comparative
control group of 623 patients. For the 193 patients for whom 12-month
data were available, the number of patients in the low-risk category
(A1C <7 percent) increased by 51 percent. A total of 97 percent
of patients with an A1C >8 percent at baseline had a change in
treatment regimen. Patients at the highest risk for coronary heart
disease (LDL >130 mg/dl) decreased from 25 percent at baseline
to 20 percent. Patients with a blood pressure <130/85 mmHg increased
from 24 to 45 percent. Of these patients, 63 percent had changes
in medication. Patients and providers expressed significant increases
in satisfaction with the program.
The
program was successful in initiating the recommended changes in
the diabetic therapeutic regimen, resulting in improved glycemic
control, increased monitoring/management of diabetic complications,
and greater patient and provider satisfaction.
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