Improving outpatient diabetes care.
ABSTRACT More than 20% of patients in the Veterans Health Administration (VHA) have diabetes; therefore, disseminating "best practices" in outpatient diabetes care is paramount. The authors' goal was to identify such practices and the factors associated with their development. First, a national VHA diabetes registry with 2008 data identified clinical performance based on the percentage of patients with an A1c >9%. Facilities (n = 140) and community-based outpatient clinics (n = 582) were included and stratified into high, mid, and low performers. Semistructured telephone interviews (31) and site visits (5) were conducted. Low performers cited lack of teamwork between physicians and nurses and inadequate time to prepare. Better performing sites reported supportive clinical teams sharing work, time for non-face-to-face care, and innovative practices to address local needs. A knowledge management model informed our process. Notable differences between performance levels exist. "Best practices" will be disseminated across the VHA as the VHA Patient-Centered Medical Home model is implemented.
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ABSTRACT: Background Suboptimal blood glucose control among patients with type 2 diabetes continues to support the need for new pharmacologic approaches. Objective The purpose of this commentary was to highlight newly available and soon-to-be available agents that are promising tools for targeting specific pathophysiologic pathways in the management of diabetes. Methods Published evidence to support the application of novel incretin-based therapies, dipeptidyl peptidase (DPP)-4 inhibitors, sodium-glucose cotransporter (SGLT)-2 inhibitors, other oral agents and insulins for managing specific aspects of type 2 diabetes, as well as disadvantages associated with those novel medications, are discussed. Results Several new glucagon-like peptide (GLP)-1 receptor agonists with different time frames of action, although each has unique advantages and disadvantages, have been through clinical trials. Examples of these are lixisenatide and albiglutide. Currently available DPP-4 inhibitor agents, important for inhibiting the breakdown of endogenous GLP-1, have not been associated with weight gain or hypoglycemia. SGLT-2 inhibitors, which do not depend on insulin secretion or insulin action, may be advantageous in that they appear to be broadly efficacious at all stages of diabetes. New insulin analogues, such as degludec and U-500, improve glycemic control without contributing to hypoglycemia. Conclusions Advances in pharmacologic options offer the promise of improving glycemic control for longer periods, with limited glycemic fluctuations, hypoglycemia, and weight gain. However, the effectiveness of these agents ultimately depends on their availability to providers managing the health care of patients at high risk for poor diabetes outcomes and patients’ use of them as directed. Long-term effectiveness and safety trials are ongoing.Clinical Therapeutics 04/2014; · 2.59 Impact Factor
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ABSTRACT: IMPORTANCE Although serious hypoglycemia is a common adverse drug event in ambulatory care, current performance measures do not assess potential overtreatment. OBJECTIVE To identify high-risk patients who had evidence of intensive glycemic management and thus were at risk for serious hypoglycemia. DESIGN, SETTING, AND PARTICIPANTS Cross-sectional study of patients in the Veterans Health Administration receiving insulin and/or sulfonylureas in 2009. MAIN OUTCOMES AND MEASURES Intensive control was defined as the last hemoglobin A1c (HbA1c) measured in 2009 that was less than 6.0%, less than 6.5%, or less than 7.0%. The primary outcome measure was an HbA1c less than 7.0% in patients who were aged 75 years or older who had a serum creatinine value greater than 2.0 mg/dL or had a diagnosis of cognitive impairment or dementia. We also assessed the rates in patients with other significant medical, neurologic, or mental comorbid illness. Variation in rates of possible glycemic overtreatment was evaluated among 139 Veterans Health Administration facilities grouped within 21 Veteran Integrated Service Networks. RESULTS There were 652 378 patients who received insulin and/or a sulfonylurea with an HbA1c test result. Fifty percent received sulfonylurea therapy without insulin; the remainder received insulin therapy. We identified 205 857 patients (31.5%) as the denominator for the primary outcome measure; 11.3% had a last HbA1c value less than 6.0%, 28.6% less than 6.5%, and 50.0% less than 7.0%. Variation in rates by Veterans Integrated Service Network facility ranged 8.5% to 14.3%, 24.7% to 32.7%, and 46.2% to 53.4% for HbA1c less than 6.0%, less than 6.5%, and less than 7.0%, respectively. The magnitude of variation by facility was larger, with overtreatment rates ranging from 6.1% to 23.0%, 20.4% to 45.9%, and 39.7% to 65.0% for HbA1c less than 6.0%, less than 6.5%, and less than 7.0%, respectively. The maximum rate was nearly 4-fold compared with the minimum rates for HbA1c less than 6.0%, followed by 2.25-fold for HbA1c less than 6.5% and less than 2-fold for HbA1c less than 7.0%. When comorbid conditions were included, 430 178 patients (65.9%) were identified as high risk. Rates of overtreatment were 10.1% for HbA1c less than 6.0%, 25.2% for less than 6.5%, and 44.3% for less than 7.0%. CONCLUSIONS AND RELEVANCE Patients with risk factors for serious hypoglycemia represent a large subset of individuals receiving hypoglycemic agents; approximately one-half had evidence of intensive treatment. A patient safety indicator derived from administrative data can identify high-risk patients for whom reevaluation of glycemic management may be appropriate, consistent with meaningful use criteria for electronic medical records.JAMA Internal Medicine 12/2013; · 13.25 Impact Factor
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ABSTRACT: Aims Patient-centered medical home (PCMH) principles including provider continuity, coordination of care, and advanced access align with healthcare needs of patients with Type II diabetes mellitus (DM-II). We investigate changes in trend for DM-II quality indicators after PCMH implementation at Southcentral Foundation, a tribal health organization in Alaska. Methods Monthly rates of DM-II incidence, hemoglobin A1c (HbA1c) measurements, and service utilization were calculated from electronic health records from 1996 to 2009. We performed interrupted time series analysis to estimate changes in trend. Results Rates of new DM-II diagnoses were stable prior to (p = 0.349) and increased after implementation (p < 0.001). DM-II rates of HbA1c screening increased, though not significantly, before (p = 0.058) and remained stable after implementation (p = 0.969). There was non-significant increasing trend in both periods for percent with average HbA1c less than 7% (53 mmol/mol; p = 0.154 and p = 0.687, respectively). Number of emergency visits increased before (p < 0.001) and decreased after implementation (p < 0.001). Number of inpatient days decreased in both periods, but not significantly (p = 0.058 and p = 0.101, respectively). Conclusions We found positive changes in DM-II quality trends following PCMH implementation of varying strength and onset of change, as well as duration of sustained trend.Primary Care Diabetes 08/2014; · 1.29 Impact Factor
University of Nebraska - Lincoln
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Public Health ResourcesPublic Health Resources
Improving Outpatient Diabetes Care
Louis Stokes Cleveland Veterans Affairs Medical Center
Veterans Affairs Nebraska-Western Iowa Health Care System,
New Jersey Veterans Affairs Healthcare System, Trenton, NJ
Clement J. Zablocki Milwaukee Veterans Affairs Medical Center and Medical College of Wisconsin, Milwaukee, WI
Louis Stokes Cleveland Veterans Affairs Medical Center
See next page for additional authors
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Kirsh, Susan; Hein, Michael; Pogach, Leonard; Schectman, Gordon; Stevenson, Lauren; Watts, Sharon; Radhakrishnan, Archana;
Chardos, John; and Aron, David, "Improving Outpatient Diabetes Care" (2012).Public Health Resources.Paper 150.
Susan Kirsh, Michael Hein, Leonard Pogach, Gordon Schectman, Lauren Stevenson, Sharon Watts, Archana
Radhakrishnan, John Chardos, and David Aron
This article is available at DigitalCommons@University of Nebraska - Lincoln:http://digitalcommons.unl.edu/
American Journal of Medical Quality
27(3) 233 –240
© 2012 by the American College of
Reprints and permission:
The care of patients with chronic disease presents many
challenges to health care systems and those who provide
care within those systems. The Veterans Health Adminis-
tration (VHA) currently serves a population with a high
prevalence of chronic disease. Diabetes is among the most
common chronic diseases and affects more than 20% of its
patients. VHA is also a large integrated health care system
that currently offers primary care services at more than
750 sites; however, quality (as measured by intermediate
outcomes of care) varies widely.1-3 VHA has great interest
in identifying best practices that can be shared across the
system. Although the term best practices generally is used,
as will be the case in this article, we recognize that how
such a practice is operationalized is heavily context depen-
dent and a more appropriate term is potentially better
practices.4-7 This project was conceptualized and informed
by a model of knowledge management that has been defined
as “an active process involving the creation of knowledge,
the intentional elicitation of knowledge, and the ability to
share knowledge across the organization.”8,9 Knowledge
may be generated both in research and in practice.10 In
this program evaluation, we sought not only to elicit
innovative or “best” practices that had been created in the
“field” but also to identify some of the factors that pro-
moted or hindered their development, with the intention of
sharing knowledge across the organization (one of the
transformational initiatives for VHA). This evaluation was
disseminated to VHA Central Office. This article describes
the design and conduct of the program evaluation.
et alAmerican Journal of Medical Quality XX(X)
1Louis Stokes Cleveland Veterans Affairs Medical Center,
2Veterans Affairs Nebraska-Western Iowa Health Care System,
Grand Island, NE
3New Jersey Veterans Affairs Healthcare System, Trenton, NJ
4University of Medicine and Dentistry of New Jersey, Newark, NJ
5Clement J. Zablocki Milwaukee Veterans Affairs Medical Center and
Medical College of Wisconsin, Milwaukee, WI
6Case Western Reserve University, Cleveland, OH
7Palo Alto Veterans Affairs Medical Center, Palo Alto, CA
8Stanford University, Stanford, CA
The authors declared no potential conflicts of interest with respect
to the research, authorship, and/or publication of this article. The
authors disclosed receipt of the following financial support for the
research, authorship, and/or publication of this article: This work was
supported in part by grants from the Department of Veteran’s Affairs
Health Services Research and Development Service, the Quality
Enhancement Research Initiative (QUERI)—Diabetes Center, and the
QUERI Center for Implementation Practice and Research Support
Susan Kirsh, MD, VA HSR&D, QUERI Center for Implementation
Practice and Research Support, Louis Stokes Cleveland VAMC
Education Office 14(W), 10701 East Blvd, Cleveland, OH 44106
Improving Outpatient Diabetes Care
Susan Kirsh, MD,1,6 Michael Hein, MD,2
Leonard Pogach, MD, MBA,3,4 Gordon Schectman, MD,5
Lauren Stevenson, PhD,1 Sharon Watts, DNP, RN-C, CDE,1,6
Archana Radhakrishnan, MD,1 John Chardos, MD,7,8
and David Aron, MD, MS1,6
More than 20% of patients in the Veterans Health Administration (VHA) have diabetes; therefore, disseminating
“best practices” in outpatient diabetes care is paramount. The authors’ goal was to identify such practices and
the factors associated with their development. First, a national VHA diabetes registry with 2008 data identified
clinical performance based on the percentage of patients with an A1c >9%. Facilities (n = 140) and community-based
outpatient clinics (n = 582) were included and stratified into high, mid, and low performers. Semistructured telephone
interviews (31) and site visits (5) were conducted. Low performers cited lack of teamwork between physicians and
nurses and inadequate time to prepare. Better performing sites reported supportive clinical teams sharing work,
time for non-face-to-face care, and innovative practices to address local needs. A knowledge management model
informed our process. Notable differences between performance levels exist. “Best practices” will be disseminated
across the VHA as the VHA Patient-Centered Medical Home model is implemented.
best practices, diabetes, disease management, quality
This article is a U.S. government work, and is not subject to copyright in the United States.
American Journal of Medical Quality 27(3)
This program evaluation used a mixed-methods approach
to identify and evaluate best practices for diabetes care.11
Facilities and community-based outpatient clinics were
stratified into high-, mid-, and low-performing sites based
on the criterion of the percentage of patients with a gly-
cosylated hemoglobin (A1c) >9%. The timeline for the
project is shown in Figure 1.
The Chronic Care Model constitutes an excellent frame-
work within which to identify structural and process com-
ponents of care that result in high quality.12,13 The degree
of implementation can be assessed by the Assessment of
Chronic Illness Care instrument only in a general sense
(eg, to compare site-specific trends in care delivery).14
Similarly, organizational surveys can identify general fac-
tors associated with better performance.15 However, if
individual practice sites are going to learn from each other,
more specific information is needed and a more granular
evaluation is necessary.
Identification of Sources
for New Knowledge
This effort recognized the transformation in care delivery
in VHA with not only increasing numbers of patients but
also a higher proportion of primary care delivery occur-
ring in community-based outpatient clinics (CBOCs).16
The majority of veterans receive preventive services and
diabetes care in primary care settings, either adjacent to
a hospital or in a CBOC. Because of the governance struc-
ture that involves Veteran Integrated Service Networks
(VISNs) that consist of a number of facilities to which
CBOCs were linked, it was important to identify variation
at the 3 different levels (VISN, facility, and CBOC) and
then to identify best practices. This included a more
in-depth evaluation at the primary care clinic level, where
the actual care for diabetes is delivered.
Identification of Variation
in Care Across Sites
Fiscal year 2008 data (the most recent available) from the
VHA Patient Care Services Diabetes Data Cube were
used to rank sites by the percentage of patients with A1c
>9%. The Diabetes Cube is a national VHA database that
includes patient-level demographics, intermediate out-
come measures and other laboratory values, as well as
medications. Analyses were limited to sites with more
than 100 patients with diabetes: 140 facilities and
582 CBOCs, each of which was included. Analyses were
stratified by size (facility vs CBOC) and location (rural vs
urban). Analyses included the population of patients with
definite diabetes (as defined by blood glucose greater
than 126 mg/dL more than once and an International
Classification of Diseases, Ninth Revision, code of
diabetes—250), subpopulations of patients with serious
mental illness, and patients who were receiving a prescrip-
tion for insulin of any type. Given recognized disparities in
chronic disease measures for patients with serious mental
illness (ie, schizophrenia, bipolar disorder, schizoaffec-
tive disorder), subpopulation analysis was evaluated for
December ‘09April ‘10 Sept.’09 July ‘10May ‘09
first ?me in
Figure 1. Timeline for the project
Kirsh et al.
this group of veterans. Because insulin use is a marker of
disease severity and duration of disease, this subpopula-
tion also was evaluated.
Choice of Sites for
Interviews and Site Visits
A purposive sample was used. High-, middle-, and low-
performing sites were identified for each of the strata.
A representative sample was chosen that included sites
from the majority of networks (VISNs). Specific atten-
tion also was paid to sites that were positive deviants17
(see Figure 2), meaning a facility that was a high-per-
forming site in a low-performing VISN. This sample
was supplemented with a convenience sample of sites
that were represented in the VHA Patient Care Services
Primary Care System Redesign Diabetes Workgroup.
A total of 31 sites were identified. Sites chosen were
heterogeneous in terms of location (urban, suburban,
and rural) and geography (South, Northeast, Midwest,
Southwest, and Northwest). In all, 67% of the 21 VISNs
were represented in the sample.
Elicitation of Knowledge
Key informant interviews were conducted with primary
care clinic directors and/or primary care leaders (physician
managers and nurse clinic managers) at sites to identify
contextual factors that might account for performance
variation and the degree to which sites had elements of the
Chronic Care Model and attributes of the Patient-Centered
Medical Home (denoted in VA as Patient Aligned Care
Team), a major initiative both in the private sector and
VHA. A semistructured 39-item interview guide (available
from the authors on request) was developed with the assis-
tance of the VA Patient Care Services Primary Care System
Redesign Diabetes Workgroup and informed by the con-
ceptual frameworks of clinical microsystems, the Chronic
Care Model, and the Patient-Centered Medical Home.
Interviews were conducted over the telephone by a clini-
cian (SK, DA, or SW); an additional individual was pres-
ent to take notes. One or more informants from each site
participated on the same call. Interviewees included pri-
mary care clinic directors in combination with other pri-
mary care physicians, and occasionally nurse practitioners,
nurse managers, and clinical pharmacists. At the conclusion
of an interview, the interviewer and note taker reviewed the
notes to ensure accuracy. Then a series of site visits were
conducted for more in-depth interviews and an assess-
ment of the facilities at a representative sample. These site
visits were conducted by a clinician (SK or DA). One or
more informants were present during these interviews
at any given time. Notes were entered onto the interview
guides and then entered into a database.
in with A1c
nts on insul
(1 2 4 6 18)
11 1111 2121 3131 4141 5151 6161 71 7181 8191 91101 101111 111121 121131 131 141141
Facili?es by Rank
Facili?es in HIGHEST
Facili?es in LOWEST
Figure 2. Variation in performance measures for diabetes (A1c <9%) at the facility level
Lowest rank is best performance. Note that 1 facility in the lowest performing network is among the highest performers.
American Journal of Medical Quality 27(3)
Quantitative. Univariate and bivariate statistics were
analyzed using SPSS 18 (IBM, Chicago, IL).
Qualitative. The database containing information derived
from the notes was compared with notes taken during
the interviews themselves. In the case of discrepancy or
uncertainty, sites were recontacted for resolution. Content
analysis was performed by thematic coding. Analysis was
carried out to identify key themes. Initial themes were
related to the elements of the Chronic Care Model and
Patient-Centered Medical Home model. We classified
practices based on 4 elements of the Chronic Care Model:
clinical information systems, decision support, delivery
system design, and self-management. A yes code for
clinical information system indicated site use of local or
national data registries or data dashboard and/or use of
electronic notes in the electronic medical record. Decision
support was coded yes for a site if it included use of the
VA/Department of Defense or American Diabetes Associa-
tion guidelines in practice (eg, embedded into electronic
notes, training on guidelines, guidelines put into local clin-
ical reminders). Delivery system design was coded yes if
sites had group visits for patients with diabetes or had a
multidisciplinary approach to starting insulin or adjusting
medications for diabetes. Self-management was coded yes
if sites had a formal diabetes self-management education
program for patients.
The total population was 2 727 795; women accounted
for 3.0%. The age distribution was as follows: <25 years,
0.01%; 25 to 34 years, 0.03%; 35 to 44years, 1.8%; 45 to
54 years, 8.8%; 55 to 64 years, 33.4%; 55 to 64 years,
25.3%; 65 to 74 years, 24.2%; 75 to 84 years, 6.2%; and
85 years and older, 3.0%. Data from facilities/CBOCs
with more than 100 patients with diabetes were used.
Overall frequency of A1c >9% was stratified by VISN,
facility, and CBOC.
Overall, from 5% to >30% of patients with a diagnosis
of diabetes in facilities and CBOCs had A1c >9%. Results
for patients with diabetes who have concomitant serious
mental illness and those who use insulin are displayed in
Table 1. Sites that performed well with regard to overall
diabetes care also performed well with these 2 popula-
tions. Sites that were bottom performers also performed
poorly with these 2 populations of patients. Variation
increased as the size of the unit of analysis decreased
from VISN to facility to CBOC. There was a high corre-
lation between performance in the 2 subpopulations at the
VISN (r = .70, P < .001) and facility (r = .86, P < .001)
levels as well.
We also tried to identify positive deviants. Again, these
are high-performing facilities within low-performing VISNs.
In the highest performing network, 5 of the facilities ranked
in the top 10. A positive deviant was identified in the
lowest performing network. This facility was ranked 11th
overall (Figure 2).
Response rate. All but 2 sites that were contacted par-
ticipated in an interview (93.5%); the 2 nonparticipants
were low-performing sites. Site visits were conducted at
5 sites (2 high and 3 low performers). Key informants
included 4 primary care physicians, 1 nurse practitioner,
and 24 primary care physician clinic leaders. Five sites
had additional nursing representation, and 4 sites had
pharmacists on the call. At site visits, the interviewer met
with a similar mix of individuals. Site visits confirmed
the results of the interviews.
Staff variation. Differences were found related to orga-
nization of health services and the personnel (eg, interdis-
ciplinary teams of physicians, nurses, clinical pharmacists,
optometrists, podiatrists) who deliver them. Sites varied
in allocated resources for diabetes care (eg, staffing num-
bers and types as well as ratio of health care professionals
Characteristics of low-performing sites. A major issue cited
by physicians and other primary care providers at lower-
performing sites was insufficient support staff (nurses and
pharmacists) to perform the often needed planned visits for
diabetes care between visits to the primary care provider
(eg, medication titration), such that this type of care
Table 1. Variation in Performance Measures at Regional (VISN), Facility, and CBOC Levels for Diabetes (Percentage of Patients
With A1c <9%) for Total Population, Patients With SMI, and Those Taking Insulin
VISNVISN AverageFacilityFacility AverageCBOCCBOC Average
Patients on insulin
7.92% to 13.26%
11.54% to 15.66%
17.12% to 26.18%
4.57% to 65.63%
8.5% to 60.19%
11.73% to 70.47%
1.73% to 72.25%
4.86% to 22.48%
3.7% to 78.57%
Abbreviations: VISN, Veteran Integrated Service Network; CBOC, community-based outpatient clinic; SMI, serious mental illness.
Kirsh et al.
often did not take place. This was a particular issue at
high-volume sites; physicians felt overwhelmed with their
panel of patients in general and felt that they were unable
to spend appropriate time with patients to provide thor-
ough diabetes care. At lower-performing sites, there was
no team to provide education and care, including teaching
patients how to initiate insulin therapy. Also at lower-
performing sites, local policies prevented nurses from
following protocols for initiation and titration of insulin
and other medications. In addition, at 2 sites, individuals
highly skilled at diabetes care (nurse certified diabetes
educators) were assigned to other duties (unrelated to dia-
betes). Lower-performing sites tended to be urban and to
have higher numbers of patients with serious mental illness
and homeless patients. Although organizational barriers to
care were cited frequently, overcoming barriers of extreme
poverty also was cited as a major problem for patients at
lower-performing sites. Cultural issues related to diabetes
also were raised, especially concerning Native Americans,
African Americans, and Hispanics.
Characteristics of high-performing sites. High performers
put into practice multiple components of the Chronic Care
Model such as registries, delivery system design, and
the use of a team that was prepared for the patient visit.
Higher-performing sites had 3 or 4 elements of the
Chronic Care Model, whereas low-performing sites had
1 to 3 elements.18 This count does not include the fact
that all VA facilities use a single electronic medical record
system. Registry use facilitated identification of specific
patient populations in addition to serving as the basis for
provider audit and feedback. Multidisciplinary teams were
set up, consisting of the primary care physician or provider
(nurse practitioner or physician assistant) with nurses and
clinical pharmacists. In contrast to low-performing sites
where physicians taught insulin administration, at high-
performing sites this task was carried out by nursing or
pharmacy staff. Higher-performing sites reported strong
structural, organizational, and personnel support, lead-
ing to better diabetes care. Some sites had strong col-
laboration with health psychology in primary care. This
collaboration was cited as being “key in sharing respon-
sibilities” to provide thorough patient care. This sense
of multidisciplinary team collaboration was particularly
evident during visits to high-performing sites and absent
at low-performing sites. In addition, high performers used
forms of care design involving simultaneous presence
of individuals from multiple disciplines (eg, shared or
group medical appointments) more frequently.19 Planned
care occurred more regularly and included support for
non-face-to-face encounters. Top-performing sites also
reported using innovative practices, taking advantage of
interprofessional teams, system redesign, and population
health approaches. Groups of patients who were not per-
forming well would be identified and asked to participate
in enhanced diabetes care. Providers in the mid-to-high
performing sites used telehealth (teleconsultation and Care
Coordination Home Telehealth) more effectively by link-
ing care to an additional health care provider, other than
the primary care provider, who could change medica-
tions. Perhaps most important, care at higher-performing
sites appeared to be more patient-centered, involving and
engaging patients to promote investment in their health;
patients received reminder letters between appointments
and were taught self-management skills for their dia-
betes (including dietary changes such as carbohydrate
counting and foot exams). Because VHA is a relatively
self-contained system, few sites used additional com-
munity resources. Finally, high-performing sites reported
strong support from organizational leadership that pro-
moted the above-mentioned practices. When asked to
describe barriers to good diabetes care, high-performing
sites identified issues related to patients’ lack of ambition,
interest, and engagement; lower socioeconomic status was
a secondary concern.
Middle performers and other findings. Middle-performing
sites included characteristics from both the low- and high-
performing sites. They reported increased support staff
(nurses and clinical pharmacists) when compared with
low-performing sites. Efforts focused on overcoming
patient and some system barriers. In fact, middle perform-
ers tended to focus on individual patient barriers rather
than system redesign. Both low and middle performers
cited “silos” existing between health care professionals
as a problem more frequently. Access to specialist exper-
tise varied by geographic location, with more rural sites
using telehealth to access endocrinology. Better access to
endocrine subspecialty care did not seem to be associated
with better-performing sites. All sites were aware of prac-
tice guidelines; however, most cited the guidelines of the
American Diabetes Association and few cited the Veterans
Affairs/Department of Defense guidelines. Interestingly,
regardless of performance level, sites rewarded only
physicians for meeting diabetes performance measures
as opposed to the team. There appeared to be little differ-
ence in the availability of test strips for self-monitoring
of blood glucose among sites. This evaluation occurred
prior to the national rollout of the VA Patient Portal
(MyHealtheVet), but some of the high-performing sites
were piloting secure text messaging.
Potentially better and innovative practices. Many of the
quality improvement efforts focused on implementation
of relatively well-known practices.20-26 Some are shown in
Figure 3 along with some very innovative practices that
were identified related to management of diabetes care for
homeless veterans. One site had primary care providers go
to a local homeless shelter with a laptop that was connected
wirelessly to the VHA electronic medical record. Another
site had a drop-in clinic for the homeless that offered a free
American Journal of Medical Quality 27(3)
meal and was staffed by a multidisciplinary team that
included primary care, mental health, and social work.
Our study shows that variability exists among low- to
high-performing sites that likely contributes to the differ-
ences in percentage of patients with A1c >9% and diabe-
tes care more generally.
We have used a knowledge management approach to
identify potentially better practices that could be shared
systemwide within VHA and possibly elsewhere.8 These
locally innovative practices were consistent with the
Chronic Care Model. In a systematic review, Bodenheimer
et al evaluated elements of the Chronic Care Model used
in primary care for patients with chronic illness, including
diabetes, and found that improvement in outcome mea-
sures and cost was associated with implementing a higher
number of elements.27 Other studies have had similar
findings.20-28 Our evaluation also found this to be true in
a system with a mature electronic medical record, a char-
acteristic that distinguishes VA from most US health care
systems. Our knowledge management approach identi-
fied best practices and illustrated some of the factors
associated with their development and high performance
A variety of models have been proposed for high-
performing primary care clinics.23 Most of these models
have been based on clinics/providers in private practice.
Carpiano et al described 3 major factors in family practice
offices that influenced the delivery of preventive services:
tools, teamwork, and tenacity.29 They concluded that
teamwork and tenacity are essential and necessary for
tools to be effectively employed. Feifer et al described
3 top-performing practice site archetypes (Technophiles,
the Motivated Team, and the Care Enterprise) based on
work in the Practice Partner Research Network.30 However,
the presence of a single electronic medical record makes
this model less applicable. Interestingly, although the VHA
is a hierarchical bureaucratic model with many policies
that apply to all sites of care, there is still marked variation
in implementation of the elements of the Chronic Care
Model and in the degree to which individual sites exhibit
creativity and flexibility. We also found that leadership
support, particularly at the facility level, is most cru-
cial for obtaining resources for low-performing sites to
facilitate planned care and at mid- and higher-performing
sites to support innovative ways to address a site’s unique
Some sites were located in areas with high homeless-
ness and poverty. In general, these sites had lower per-
formance than those in more demographically favorable
areas. However, sites with greater creativity were able to
develop and implement innovative solutions.31,32 The high-
est performers overcame barriers to developing and imple-
menting best practices. This required greater involvement
by leadership (eg, the strong support of primary care
clinic directors and ambulatory chiefs of staff in pro-
viding resources—staff, space, and time). Figure 4 depicts
a model of primary care system performance. Diabetes care
use of “birthday”
Drop in clinic
staffed by PCstaffed by PC
Organized of Health Care Organizedof Health Care
Decision Delivery Clinical
Support System Information
Functional and Clinical Outcomes
Organized of Health Care
Resources and Policies
Figure 3. Innovative practices aligned with the elements of the Chronic Care Model of Wagner et al18
Kirsh et al.
was identified as a priority by many sites; these sites also
identified the need for further process improvement skills,
education for all health care professionals, and sharing of
best practices across sites. Finally, many sites expressed an
interest in sharing what they have done well with other VA
sites and in being given an opportunity to learn from other
sites that have developed in areas that they have not.
This study was performed in a single health care system
with a unique population of patients. The results may not
be generalizable to other health care systems or other
populations of patients. Nevertheless, VHA is a very large
system that cares for more than 2 million patients with
diabetes. Moreover, although a relatively closed system, it
still is susceptible to other influences as evidenced by the
widespread use of American Diabetes Association rather
than VHA/Department of Defense Diabetes clinical prac-
tice guidelines. Because the 2008 data were not current,
some sites may have been misclassified. Alternative and
more current data were available from the External Peer
Review Program, but the sample size of charts reviewed
was too small to have power to identify variations at the
CBOC level. Interviews were not tape-recorded, but care
was taken to have an observer present in addition to the
interviewer to assure accuracy.
In a large health care system with a mature electronic
medical record system, the presence of practice variation
not only identifies areas for improvement but also illus-
trates how known practices conduct clinical work in VA
contexts and provides a source for innovative practices.
The importance of leadership and organizational support
in improvement cannot be overemphasized. Knowledge
management efforts to share these potentially better prac-
tices and to facilitate their implementation where they
make sense locally will be the next step.
This workgroup was commissioned by Gordon Schectman, MD,
the Acting Chief Consultant for Primary Care, Patient Care
Services, VHA Central Office, Washington, DC, and led by
Michael Hein, MD, Susan Kirsh, MD, and Len Pogach, MD,
Subcommittee workgroup leaders included Latrina Wimberly,
MD, Praveen Mehta, MD, MBA, Lynda Hemann, MD, John
Chardos, MD, and Anne Emler, MD.
Physician workgroup participants included Alycia Antoine,
MD, Olawale Fashina, MD, George Malatinszky, MD, Mary
Rehs, MD, Amy Tesar, MD, Kathleen Wolner, MD, Sabrina
Felson, MD, Kenworth Holness, MD, Nicholas Masozera, MD,
Thomas Niethammer, MD, Maureen O’Hallaron, MD, Christine
Pasquariello, MD, Anna Quan, MD, Monica Sharma, MD,
Rachel Sherman, MD, Virginia Short, MD, Jacqueline Spencer,
MD, Rafael Vega-Torres, MD, Parag Dalsania, MD, Sarah
Garrison, MD, John Gilbertson, MD, Nancy Gilhooley, MD,
Prashant Phatak, MD, Steven Warlick, MD, Jane Alley, MD,
Kathryn Corrigan, MD, and Parag Dalsania, MD.
The following individuals also supported this project admin-
istratively: Michelle Montpetite and Jeneen Shell-Boyd.
The views presented in this article are solely those of the
authors and do not represent the views of the Department of
Veterans Affairs or any other agency.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
The authors disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article:
This work was supported in part by grants from the Department
of Veteran’s Affairs Health Services Research and Development
Service, the Quality Enhancement Research Initiative
(QUERI)—Diabetes Center, and the QUERI Center for
Implementation Practice and Research Support (CIPRS).
1. Miller D, Safford M, Pogach L. Who has diabetes? Best
estimates of diabetes prevalence in the Veterans Health
Administration based on computerized patient data. Diabe-
tes Care. 2004;27(suppl 2): B10-B21.
Basic Minimum Level of Staffing
Figure 4. Conceptual model illustrating factors associated
with increasing performance
Successive factors build on each other. Leadership is critical at the
level of providing the basic minimum of resources as well as in
operationalizing innovative and potentially better practices.
American Journal of Medical Quality 27(3)
2. Perlin J, Pogach L. Improving the outcomes of metabolic
conditions: managing momentum to overcome clinical iner-
tia. Ann Intern Med. 2006;144:525-527.
3. Pogach L, Holohan TV. Diabetes in the VHA: FY96 Costs
and Outcomes. Washington, DC: National Center for Cost
Containment, Department of Veterans Affairs; 1998.
4. Tucker AL, Nembhard IM, Edmondson A. Implementing
new practices: an empirical study of organizational learning
in hospital intensive care units. http://hbswk.hbs.edu/item/
5414.html. Accessed June 30, 2011.
5. Fitz-enz J. The truth about best practices: what they are
and how to apply them. Human Resource Management.
6. Harrington H. The fallacy of universal best practices. TQM
7. Patton M. Evaluation, knowledge management, best prac-
tices, and high quality lessons learned. Am J Eval. 2001;22:
8. Orzano A, McInerney C, Scharf D, Tallia AF, Crabtree BF.
A knowledge management model: implications for enhanc-
ing quality in health care. J Am Soc Inform Sci Technol.
9. Berta WB, Baker G. Factors that impact the transfer and
retention of best practices for reducing error in hospitals.
Health Care Manage Rev. 2004;29:90-97.
10. Van De Ven A, Johnson P. Knowledge for theory and prac-
tice. Acad Manage Rev. 2006;31:802-821.
11. Creswell JW. Research Design: Qualitative, Quantitative,
and Mixed Methods Approaches. 2nd ed. Thousand Oaks,
12. Bodenheimer T, Wagner E, Grumbach K. Improving pri-
mary care for patients with chronic illness: the chronic care
model, part 2. JAMA. 2002;288:1909-1914.
13. Bodenheimer T, Wagner E, Grumbach K. Improving pri-
mary care for patients with chronic illness. JAMA. 2002;288:
14. Bonomi AE, Wagner E, Glasgow RE, VonKorff M. Assess-
ment of chronic illness care (ACIC): a practical tool to mea-
sure quality improvement. Health Serv Res. 2002;37:791-820.
15. Kochevar L, Yano E. Understanding health care organiza-
tion needs and context: beyond performance gaps. J Gen
Intern Med. 2006;21(suppl 2):S25-S29.
16. Perlin J, Kolodner R, Roswell R. The Veterans Health
Administration: quality, value, accountability, and informa-
tion as transforming strategies for patient-centered care. Am
J Manag Care. 2004;10:828-836.
17. Bradley E, Curry L, Ramanadham S, Rowe L, Nembhard IM,
Krumholz HM. Research in action: using positive deviance
to improve quality of health care. Implement Sci. 2009;
18. Coleman K, Austin B, Brach C, Wagner EH. Evidence on
the chronic care model in the new millennium. Health Aff
19. Kirsh S, Watts S, Pascuzzi K, et al. Shared medical appoint-
ments based on the chronic care model: a quality improvement
project to address the challenges of diabetic patients with high
cardiovascular risk. Qual Saf Health Care. 2007;16:349-353.
20. Nutting P, Dickinson W, Dickinson L, et al. Use of chronic
care model elements is associated with higher-quality care
for diabetes. Ann Fam Med. 2007;5:14-20.
21. Parchman M, Pugh J, Wang C, Romero RL. Glucose control,
self-care behaviors, and the presence of the chronic care model
in primary care clinics. Diabetes Care. 2007;30:2849-2854.
22. Scott J, Conner DA, Venohr I, et al. Effectiveness of group out-
patient visit model for chronically ill older health maintenance
organization members: a 2-year randomized trial of the cooper-
ative health care clinic. J Am Geriatr Soc. 2004;52:1463-1470.
23. Singh D. Transforming Chronic Care: Evidence for
Improving Care for People With Long-Term Conditions.
Birmingham, England: University of Birmingham Health
Services Management Center; 2005.
24. Solberg L, Crain A, Sperl-Hillen J, Hroscikoski MC,
Engebretson KI, O’Connor PJ. Care quality and imple-
mentation of the chronic care model: a quantitative study.
Ann Fam Med. 2006;4:310-316.
25. Solberg L, Asche S, Pawlson L, Scholle SH, Shih SC. Prac-
tice systems are associated with high-quality care for diabetes.
Am J Manag Care. 2008;14:85-92.
26. Sperl-Hillen J, Solberg L, Hroscrikoski MC, Crain AL,
Engebretson KI, O’Connor PJ. Do all components of the
chronic care model contribute equally to quality improve-
ment? Jt Comm J Qual Saf. 2004;30:303-309.
27. Bodenheimer T. Interventions to improve chronic illness care:
evaluating their effectiveness. Dis Manag. 2003;6:63-71.
28. Bodenheimer T, Wang M, Rundall T, et al. What are the facili-
tators and barriers in physician organizations’ use of care man-
agement processes? Jt Comm J Qual Saf. 2004;30:505-514.
29. Carpiano R, Flocke S, Frank S, Stange KC. Tools, teamwork,
and tenacity: an examination of family practice office sys-
tem influences on preventive service delivery. Prev Med.
30. Feifer C, Nemeth L, Nietert PJ, et al. Different paths to high-
quality care: three archetypes of top-performing practice sites.
Ann Fam Med. 2007;5:233-241.
31. Adler PS, Riley P, Kwon SW, Signer J, Lee B, Satrasala R.
Performance improvement capability: keys to accelerating
performance improvement in hospitals. Calif Manage Rev.
32. Markman A, Wood K. Tools for Innovation. Oxford, England:
Oxford University Press; 2009.