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Multidisciplinary work promotes preventive medicine and health education in primary care: A cross-sectional survey


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Background: Preventive medicine and health education are among the strategies used in coping with chronic diseases. However, it is yet to be determined what effect do personal and organizational aspects have on its' implementation in primary care. Methods: A cross-sectional survey was conducted in order to assess and compare preventive medicine and health education activities in three types of primary care models: solo working independent physicians, nurse-physician collaborations and teamwork (nurses, dietitians and social workers working alongside a physician). Questionnaires were emailed to 1203 health professionals between September and November 2015, working at Maccabi Healthcare Services, the second largest Israeli healthcare organization. Self-reported rates of health education groups conducted, proactive appointments scheduling and self-empowerment techniques use during routine appointments, were compared among the three models. Independent variables included clinic size as well as health professionals' occupation, health behaviors and training. A series of multivariate linear regressions were performed in order to identify predictors of preventive medicine and health education implementation. Computerized health records (CHR) validated our self-report data through data regarding patients' health behaviours and outcomes, including health education group registration, adherence to occult blood tests and influenza vaccinations as well as blood lipid levels. Results: Responders included physicians, nurses, dietitians and social workers working at 921 clinics (n = 516, response rate = 31%). Higher rates of proactive appointments scheduling and health education groups were found in the Teamwork and Collaboration models, compared to the Independent Physician Model. Occupation (nurses and dietitians), group facilitation training and personal screening adherence were identified as preventive medicine and health education implementation predictors. Group registration, occult blood tests, healthy population's well-controlled blood lipids as well as influenza vaccinations among chronically ill patients were all significantly higher in the Teamwork and Collaboration models, compared to the Independent Physician Model. Conclusions: The Teamwork and Collaboration models presented higher rates of preventive medicine and health education implementation as well as higher rates of patients' positive health behaviours documented in these models. This suggests multidisciplinary primary care models may contribute to population's health by enhancing preventive medicine and health education implementation alongside health professionals' characteristics.
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O R I G I N A L R E S E A R C H A R T I C L E Open Access
Multidisciplinary work promotes preventive
medicine and health education in primary
care: a cross-sectional survey
Ayelet Schor
, Lucia Bergovoy-Yellin
, Daniel Landsberger
, Tania Kolobov
and Orna Baron-Epel
Background: Preventive medicine and health education are among the strategies used in coping with chronic diseases.
However, it is yet to be determined what effect do personal and organizational aspects have on itsimplementation in
primary care.
Methods: A cross-sectional survey was conducted in order to assess and compare preventive medicine and health
education activities in three types of primary care models: solo working independent physicians, nurse-physician
collaborations and teamwork (nurses, dietitians and social workers working alongside a physician). Questionnaires were
emailed to 1203 health professionals between September and November 2015, working at Maccabi Healthcare Services,
the second largest Israeli healthcare organization.
Self-reported rates of health education groups conducted, proactive appointments scheduling and self-empowerment
techniques use during routine appointments, were compared among the three models. Independent variables included
clinic size as well as health professionalsoccupation, health behaviors and training.
A series of multivariate linear regressions were performed in order to identify predictors of preventive medicine and
health education implementation.
Computerized health records (CHR) validated our self-report data through data regarding patientshealth behaviours and
outcomes, including health education group registration, adherence to occult blood tests and influenza vaccinations as
well as blood lipid levels.
Results: Responders included physicians, nurses, dietitians and social workers working at 921 clinics (n= 516, response
rate = 31%).
Higher rates of proactive appointments scheduling and health education groups were found in the Teamwork and
Collaboration models, compared to the Independent Physician Model. Occupation (nurses and dietitians), group
facilitation training and personal screening adherence were identified as preventive medicine and health education
implementation predictors.
Group registration, occult blood tests, healthy populations well-controlled blood lipids as well as influenza vaccinations
among chronically ill patients were all significantly higher in the Teamwork and Collaboration models, compared to
the Independent Physician Model.
Conclusions: The Teamwork and Collaboration models presented higher rates of preventive medicine and health
education implementation as well as higher rates of patientspositive health behaviours documented in these models.
This suggests multidisciplinary primary care models may contribute to populations health by enhancing preventive
medicine and health education implementation alongside health professionalscharacteristics.
Keywords: Primary care model, Preventive medicine measures, Health education tools, Multidisciplinary practice
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* Correspondence:
School of Public Health, University of Haifa, Haifa, Israel
Sde-varburg, Israel
Full list of author information is available at the end of the article
Schor et al. Israel Journal of Health Policy Research (2019) 8:50
Non-adherence to medical and behavioral recommenda-
tions is common and is known to be affected by patients
and physicianscharacteristics alike [14].
Recent studies indicate that health professionals have
the ability to improve patientsadherence using various
behavioral tools and strategies for change [57].
One of the main strategies found to improve treatment
processes and access to medical care, resulting in im-
proved clinical outcomes [810] is multidisciplinary work.
It is assumed that multidisciplinary collaborations increase
the ability to accurately address a patients individual
needs, resulting in better adherence to treatment [11].
In order to achieve these goals, health professionals en-
gage in health education in an attempt to educate and ac-
tivate their patients. Health educations is defined as
creating learning opportunities designed to allow patients
to accept informed decisions and to promote positive
health behaviors that would improve their health [12].
Health education utilizes a variety of tools designed to
enhance patientsmotivation and adherence.
One of these tools is the use of health education groups,
where trained health professionals serve as group facilita-
tors, guiding the participants in the acquisition of practical
tools that promote desired behavioral changes.
These groups seem to be an effective preventive medi-
cine tool, as participation helps in a variety of behavioral
challenges such as weight loss, smoking cessation, and
self-management of chronic illnesses [13,14].
Another health education tool is the use of proactive
appointments, initiated by the health provider rather
than the patient. This allows health professionals the op-
portunity to focus on preventive medicine counseling,
perform routine check-ups and use empowerment tools,
such as motivational interviewing [1517], in order to
facilitate patientsadherence.
In spite of abundant supportive evidence indicating that
the use of preventive medicine and health education tools
reduces morbidity and mortality [18,19], itsimplementa-
tion may be complicated and ultimately depends on health
professionalsmotivation, affected by multi-level inter-
personal, and organizational factors [20].
This study examined three primary care models imple-
mented by Maccabi Healthcare Services (MHS). MHS is
the second largest health maintenance organization
(HMO) in Israel, with over two million clients, repre-
senting about a quarter of the countrys population [21].
The basic primary care model, the Independent
Physician Model, was developed when MHS was
foundedin1940[22]. Independent physicians work
solo in private clinics. They are encouraged to
achieve MHSdesired clinical outcomes, such as pa-
tientsvaccinations, and their income (per capita) is
supplemented accordingly.
The second model examined is the Teamwork Model,
based on the Chronic Care Model [23,24]first imple-
mented in MHS in 2005. Teamwork clinics employ vari-
ous health professionals (physicians, nurses, dietitians
and social workers). Composition of the teams vary
among clinics, some include all four professions and
others only two (a physician and one other health pro-
fession). The type of health professions, as well as the
amount of weekly / monthly hours assigned to the team
varies according to the populations needs as well as
MHSability to supply specific demands. Thus, some
clinics are based mostly on a physician and nurse with a
few weekly / monthly hours of a dietitian and social
worker, while others revolve around a physician and
dietitian with a few nursing hours a week. Regardless of
team composition, MHS expects all Teamwork clinics to
apply multi-disciplinary work strategies, such as regular
staff meetings, conducted in order to discuss patients
treatment. Teamwork strategies, however, are not moni-
tored by MHS as a part of clinicsassessment conducted
on a regular basis, so there is no objective data as to
how common teamwork practices really are.
All Teamwork health professionals receive a monthly
salary, independent of patientsoutcomes, with the ex-
ception of physicians, who enjoy additional financial in-
centives similar to Independent physicians. As teamwork
clinics are expected to focus on preventive medicine and
patient self-management, health professionals affiliated
with these clinics are prioritized when resources are allo-
cated, for example when relevant training take place.
Attempting to provide different solutions to different
needs and limited resources, in 2013, MHS began imple-
menting the Collaboration Model. This Model stems from
the Independent Physician Model and follows its financial
model. Independent nurses collaborate with one to four
adjacent primary care independent physicians who refer
patients to their affiliated nurse when they see the need
for a nurses intervention, such as blood pressure monitor-
ing, diabetes counseling, health education on other issues
etc.On the other hand, when the independent nurses re-
quire consultations or see the need for treatment by physi-
cians (like medication changes), they will refer the patient
to their affiliated physician.
Patients choose their primary care physician and are
mostly unaware of their affiliation to a specific primary
care model. Accordingly, distributions of patientsmain
characteristics such as gender, age and morbidity levels,
defined by Charlson score [25,26] is mostly similar
among the models. Most patients, in all models are
males, Collaboration patients are a little younger with
higher morbidity levels (significance mostly stemming
from the large sample size).
Table 1presents the organizational and patientschar-
acteristics among the three primary care models.
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 2 of 11
All health professionals (physicians, nurses, dietitians
and social workers), regardless of the model they are affili-
ated with, are encouraged to acquire preventive medicine
skills and implement relevant activities in their clinics. For
that reason training is conducted within working hours
and if not, health professionals are reimbursed for their
time and training costs. In addition, professionals gain
training points upon completion, granting them with add-
itional income. Trained health professionals are free to
conduct health education group counseling in all MHS
clinics and their income is supplemented accordingly.
Health education groups are available for all patients
in multiple communal MHS facilities, regardless of the
clinical model they belong to. This allows clinics that do
not include trained health professionals but want to pur-
suit preventive medicine, to refer their patients to health
education groups conducted in a nearby clinic, or to
conduct one in their own clinic, guided by trained MHS
health professionals, not affiliated with their own clinic.
Regretfully, while the benefits of preventative medicine
and health education tools have been previously estab-
lished [18,27,28], in reality, it is applied sporadically.
Moreover, itsimplementation is not taken into ac-
count in routine clinic assessments, as other key
components, such as patientsmedication adherence
or hospitalizations are.
Acknowledging that certain organizational aspects
may affect the implementation of preventive medicine
and health education tools, this study aimed to explore
the use of such tools in various primary care models.
Our aim was to better understand what part do per-
sonal and organizational aspects play in the implementa-
tion of preventive medicine tools within the different
primary care models implemented by MHS.
in the Teamwork clinics, we assumed health professionals
affiliated with the Teamwork Model would apply preventa-
tive medicine and health education tools more widely.
Table 1 Organizational and PatientsCharacteristics among the Three Primary Care Models
Independent physician model Team model Collaboration model
n=92 n= 264 n=30
Personnel Physicians Physicians Physicians
dietitians Nurses
Clinic Ownership Physicians clinic MHSclinic Physicians clinic
Nurses clinic
Supporting staff Physicianschoice MHSallocation Physicians/ Nurseschoice
PatientsAffiliation Physician Physician Physician
Clinical Goals Physiciansgoals Physiciansgoals Physiciansgoals
No team goals Nursesgoals
Fee Policies Per capita
Supplementation according
to defined outcomes achieved
Physicians Only Physicians and Nurses
Per capita Per capita
Supplementation according to
defined outcomes achieved
Supplementation according to defined
outcomes achieved
All other professional Nurses only
Salary Supplementation according to defined
counseling activities conducted (smoking
cessation counseling, diabetic guidance etc.)
No supplementation according
to defined outcomes achieved
PatientsGender (Male)
55.8 52.3 58.4
PatientsAge (Years)
64.3 ± 12 64.2 ± 12 63.4 ± 13
PatientsMorbidity Levels
1.04 ± 1.26 1.08 ± 1.27 1.10 ± 1.25
Percentage within the models population
p< 0.001
Mean ± SD
Charlson Score on December 31st, 2015
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 3 of 11
A cross sectional survey was conducted between Sep-
tember and November 2015.
Questionnaires were emailed during 2015 to all health
professionals (1203 physicians, nurses, dietitians, and
social workers) affiliated with one of the clinics included
in this study, all of whom had been employed in the clinic
for at least 12 months prior to the data collection date. All
clinics who were operational for at least 12 months prior
to the data collection date were included in our study. Re-
spondents included 92 physicians from 594 Independent
Physician clinics, 30 professionals from 52 Collaboration
clinics, and 264 members of 273 Teamwork clinics, with
16, 78 and 43% response rates respectively, overall re-
sponse rate of 31%. (Distribution of clinic and health pro-
fessionalspersonal characteristics by primary care model
affiliation is presented in Table 2).
Questions were presented in Hebrew (translated to
English by the authors for publication purposes). The
questionnaires were completed anonymously via an au-
tomated system, preventing respondentsidentification.
Five weekly automatic reminders were sent to those who
did not open the questionnaire link.
Data was extracted, processed and analyzed by MHSs
Department of Health Services Research.
Respondents fully represented the health professionals af-
filiated with the clinic examined, with respect to gender,
clinic size, and occupation, as presented in Table 3.Physi-
cians and small clinics were under-represented, partly due
to the fact that some health professionals, mainly physicians
were affiliated with more than one clinic. While small clinics
may differ in their ability to implement preventative medi-
cine tools, as mentioned, MHS activities are available to all
patients, regardless of the clinic they are affiliated with.
Regretfully, a low number of dietitians took part in the
study. Since dietitianswork characteristics are more
similar to those of nurses than to social workers, they
were added to the nurses for analysis.
The dependent variables included three aspects repre-
senting the application of preventative medicine and
health education tools in the clinic.
Health education groups implementation was exam-
ined by the reported frequency of running these groups
in the clinic. Acknowledging the challenges of organizing
health education groups, our aim here was to evaluate to
what extent do health professionals implement this type
of intervention in their clinics, regardless of the type of
group conducted.
Therefore, the question was: How often are smoking
cessation groups / diabetes groups / group educational
events, conducted in the clinic?Responses ranged from
1 = never to 4 = 3 a year or more.
proactive appointments (a common organizational term)
scheduled, reported by responses to the question: How
often do you schedule proactive appointments for your pa-
tients?Responses ranged from 1 = never to 4 = regularly.
The routine use of patient empowerment tech-
niques was examined by the question: How often
Table 2 Distribution of Clinic and Health ProfessionalsPersonal
Characteristics by Primary Care Model Affiliation
a, b
Variable Independent
physician model
n=92 n=
Clinic size
(No. of
patients) (%)
Up to 400 10.6 6.2 3.3 18.35 0.001
4001000 38.8 19.1 20
0ver 1000 50.6 74.7 76.7
characteristics (%)
100 28.8 40 152.86 p<
Nurse 0 36.9 60
Dietitian 0 20.7 0
0 13.4 0
Male 56.2 15.7 32.1 47.68 p<
Female 43.8 84.3 67.9
in patient
Yes 15.7 52.1 51.7 26.34 p<
No 84.3 47.9 48.3
behaviors (%)
Yes 3.8 2.9 10.7 4.03 0.13
No 96.2 97.1 89.3
Yes 75.0 84.3 78.6 3.46 0.17
No 25.0 15.7 21.4
Yes 73.7 61.8 41.2 4.31 0.11
No 26.3 38.2 57.1
Chi-square/Fisher Test
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 4 of 11
do you use empowerment techniques during routine
appointments?Responses ranged from 1 = never to
4 = in most appointments.
Since these variables consisted of four categories and
the answers did not distribute normally, they were di-
chotomized by combining the three lower scores into
low frequency to none, and high frequencyrepre-
sented by the highest score.
These three dependent variables were based on health
professionalsself-report, as there is no existing objective
quantitative data regarding these variables.
Computerized data
In order to support the self-reported data, we added
computerized data extracted from MHScomputerized
health records (CHR) to our analysis.
We extracted data regarding 2015 prevalence rates of
chronically ill patients in each primary care model as doc-
umented in MHSchronic illness automatic registration
system [29,30], as well as registration rates to health edu-
cation groups. This enabled us to objectively assess rates
of registration to group counseling conducted.
We also examined a few health behavior outcomes doc-
umented in the CHR. This helped to gain a broader pro-
spective and to assess whether activities implemented may
indeed be associated with enhanced health outcomes. This
data included the whole cohort of patients in Maccabi (In-
dependent Physician Model n= 464,828, Team Model n=
269,844 and Collaboration Model n= 60,778).
We chose health outcomes that are included in the Is-
raeli quality indicators program, representing primary,
secondary and tertiary prevention and may be improved
with the support of trained health professionals. Among
the healthy population (MHS members not in one or
more of the chronically ill registries), we examined rates
of conducting occult blood tests and well-controlled
lipid levels. Influenza vaccination rates among diabetic
patients as well as heart diseases and high blood-
pressure patients were also examined. (Yes / No for all
variables). These health outcomes are defined by the Is-
raeli Quality Health Indicators Plan, shown to signifi-
cantly affect various health behaviors as well as
chronically ill patientshealth status [12,31].
The independent variables consisted of the respondents
personal characteristics as well as clinic characteristics.
Clinic characteristics
Included the type of model (Independent Physician /
Teamwork / Collaboration) and clinic size (small< 400 pa-
tients, medium = 4001000 patients, large>1000 patients).
Personal characteristics
Gender, occupation and personal health behaviors
(smoking, regular physical activity, and adhering to rele-
vant health screening).
Professionals participation in health education training
(motivational interviewing / smoking cessation counsel-
ing / group facilitation), as well as time passed since
completion of this training (16 months, 712 months,
1324 months, over 24 months).
Training data was analyzed as an ordinal variable.
Each type of training was given a value reflecting the
Table 3 Respondents versus MHShealth professionals affiliated with the three Primary Care Models
Gender Clinic size
Male Female Small Medium Large
Independent Physician Model Percentage among respondents 57 43 9.8 37 53.3
Percentage among MHSemployees 62.6 37.4 16.3 19.9 53.8
Team model Physicians Percentage among respondents 55.6 44.4 1.3 26.7 72
Percentage among MHSemployees 56.2 43.8 57.1 26.2 17
Nurses Percentage among respondents 4.7 95.3 1.1 5.4 93.5
Percentage among MHSemployees 2.8 96.2 3.6 47.8 60.7
Dietitian Percentage among respondents 2.2 97.8 24 34 42
Percentage among MHSemployees 3.7 96.3 16.7 28.7 54.6
Social Worker Percentage among respondents 16.7 83.3 3.4 13.8 82.8
Percentage among MHSemployees 19.3 80.7 4.2 11.4 84.3
Collaboration Model Percentage among respondents 11.8 88.2 5.6 27.8 66.7
Percentage among MHSemployees 5.2 94.8 24.2 29.5 46.3
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 5 of 11
time elapsed since completion (1 = no training, 2 = 16
months, 3 = 724 month, 4 = over 24 months).
Statistical analysis
The data were analyzed using the statistical program for
Health and Welfare Science for Windows (SPSS, version
22.0, Chicago, IL, USA). Descriptive data analysis included
range, mean and standard deviation for continuous vari-
ables, and frequency and percentage for categorical vari-
ables. Personal characteristics were analyzed using Chi-
square and Fisher tests.
Comparisons of means regarding health education ap-
plication among the three models were performed using
Kruskal-Wallis tests.
Between groups comparisons were conducted in order
to assess between which two groups differences were
significant. Bonferroni for distribution of clinic and
health professionalscharacteristics as well as for pre-
ventive medicine implementation and Pairwise contrasts
for patientshealth results.
Logistic regression models were conducted in order to
identify predictors of health education implementation.
The significance for all the statistical tests was set to 0.05.
The clinic and personal characteristics are presented
in Table 2.
The majority of respondents affiliated with the Team-
work and Collaboration models were women, while in the
Independent Physician Model most were men (p<0.001).
As a whole, the health professionals reported high
rates of positive health behaviors, and the differences
among the models were not statistically significant.
Reported rates of health education training were simi-
lar in the Teamwork and the Collaboration models
(about 52%), as opposed to only 16% in the Independent
Physician Model (p< 0.001).
Table 4presents the average scores of implementing
preventative medicine and health education tools in the
three models.
Overall, respondents from the multidisciplinary
models (Collaboration and Teamwork models) reported
similarly higher rates of proactive medicine and health
education tools implementation compared with the In-
dependent Physician Model.
The multidisciplinary models reported significantly
higher rates of health education group counseling con-
ducted in the clinics (mean scores of 3.2, 3.1 and 1.7 for
the Teamwork, Collaboration and Independent Phys-
ician models respectively p< 0.001).
Registration to health education group counseling
among chronically ill patients was higher in the multi-
disciplinary models. Registration rates ranged from
2.19% in the Independent Physician Model (n= 4596/
209,385), through 2.29% (n= 3436/149,412) in the Team-
work Model, to 3.05% (n= 764/24,995) in the Collabor-
ation Model (p< 0.001).
Rates of proactive appointment scheduling in the clinic
were also significantly higher in the multidisciplinary
models compared with the Independent Physician Model
(mean score of 3.8 and 3.7 and 3.0 for the Collaboration,
Teamwork and Independent Physician models respect-
ively, p<0.001).
No significant differences were observed between the
models regarding the use of empowerment techniques
(p= 0.17). However, they were higher in the multidiscip-
linary models.
Variables that may predict the implementation of pro-
active medicine tools were identified through multivari-
able linear regression models.
Table 5presents predictors of preventative medicine
and health education tools implementation.
The type of primary care model significantly predicted
health education groups counseling conducted in the
clinic. Respondents from the Teamwork Model and the
Collaboration Model were much more likely to conduct
group counseling activities in the clinics than those of the
independent physician model (OR = 6.1, 95%CI 2.63
14.13 and OR = 4.3, 95%CI 1.3813.57 respectively). The
Teamwork Model was not significantly different from the
Collaboration Model and both were significantly different
from the Independent physician Model (p<0.001).
Another significant predictor of group-counseling ac-
tivities was the type of occupation. Nurses and dietitians
were twice as likely to conduct group counseling, com-
pared to physicians (OR = 2.08, 95%CI 1.093.95).
Significant predictors of proactive appointment sched-
uling included affiliation with the Teamwork Model
(OR = 2.1, 95%CI 1.044.19), occupation, namely nurses
and dietitians (OR = 28.46, 95%CI 8.5894.4) and train-
ing, specifically smoking cessation training (OR = 3.95,
95%CI 1.1513.53). The Teamwork Model was not sig-
nificantly different from the Collaboration Model and
both were significantly different from the Independent
physician Model (p< 0.001).
Significant predictors for the use of patient empower-
ment techniques during routine appointments included
group facilitation training and health professionalsad-
herence to regular screening (OR = 2.77, 95%CI 1.30
5.92; OR = 2.29, 95%CI 1.144.49 respectively).
Rates of most patientshealth outcomes we examined
were significantly higher in the Teamwork and Collabor-
ation models (presented in Table 6).
The only variable in which the Independent Physician
Model presented similar results to those of the Team-
work Model was the influenza vaccination among dia-
betic patients (46.01 and 46.2% respectively). However,
the Collaboration model presented significantly higher
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 6 of 11
rates of diabetic influenza vaccination compared with
those of the Independent Physician Model (p< 0.018).
This study aimed at identifying factors that support or
hinder the implementation of preventive medicine and
health education tools in various primary care models.
Combining health professionalsself-report with com-
puterized objective data enabled us to gain insights re-
garding personal and organizational aspects that are
associated with the implementation of preventive medi-
cine and health education tools. While this study exam-
ined MHS, our findings are likely to be relevant to other
Israeli HMOes, as well as other countries implementing
similar primary care models.
Our results found that the health professionals affili-
ated with the multidisciplinary models engaged in more
training and implementation of preventive medicine and
health education tools compared to those affiliated with
the Independent Physician Model. More specifically, sig-
nificantly higher rates of proactive appointments sched-
uling and health education groups were found in the
Teamwork and Collaboration models, compared to the
Independent Physician Model.
This confirmed our basic hypothesis that Teamwork en-
hances the implementation of preventive medicine and
supports previous research that demonstrated Teamwork
to yield better patient adherence as well as improved clin-
ical outcomes [10].
To our surprise, while the Collaboration Model and the
Independent Physician Model operate under similar
organizational approaches, results presented by the Collab-
oration Model resembled those of the Teamwork Model.
The significant predictors of preventive medicine im-
plementation we identified can be divided into two mu-
tually interactive levels: the personal level (health
professionals) and the organizational level (MHS). These
multi-leveled interactions were previously established by
DiClemente as influencing behavioral choices
among employees, in our case, their choice to imple-
ment preventive medicine tools [20].
The significant Personal level factors we identified in-
cluded health behaviors (namely adhering to regular
health screening), training and occupation, all identified
as relevant in previous studies [3,4,15]. We found
nurses were 28.5 more times likely to schedule proactive
appointments. This is in line with previous studies dem-
onstrating the significance of the nursing profession in
proactive medicine within primary care [17]. Moreover,
preventative medicine is traditionally conducted primar-
ily by nurses in Israeli practices. This is well described in
a recent Israeli survey that found physicians perceiving
nurses as contributing to practice quality and as sharing
the responsibility for quality of care [32].
The organizational level factors demonstrated the signifi-
cance of the type of primary care model manifested by dif-
ferent combinations of health professionals affiliated with
the clinics, as well as different policies such as resources al-
located or fee, varied among the models and the profes-
sions. The significance of these aspects is addressed later.
Relevant training was highly effective as we did
find that primary care models with a higher percent-
age of trained health professionals implemented
more preventive medicine and health education
tools. Moreover, the more experienced the profes-
sionals were in group facilitation, the more likely
they were to use empowering techniques in their
routine appointments.
On the other hand, high rates of proactive appoint-
ment scheduling and health education group registration
in the Independent PhysiciansModel were seemingly
contradictory to the low training levels reported by this
models respondents.
This may be attributed to the fact that physicians affili-
ated with this model do not have nurses to depend upon
in encouraging patients to participate in relevant activ-
ities, as commonly done in Israeli multidisciplinary
teams [32], prompting them to do so on their own.
Table 4 Average Score of Preventive Medicine Tools Implementation among the Models
a, b, c
Physician model
Teamwork model
n= 264
model n=30
mean C.I 95% mean C.I 95% mean C.I 95% F Sig.
Low. Up. Low. Up. Low. Up.
Conduct group counseling activities (1 = never to 4 = three a year or
1.7 1.44 1.89 3.2 3.02 3.28 3.1 2.71 3.50 66.844 p<
Schedule proactive appointments (1 = never to 4 = regularly) 3.0 2.82 3.25 3.7 3.62 3.78 3.8 3.56 3.95 27.687 p<
Use of empowerment techniques during routine appointments (1 = never
to 4 = in most appointments)
3.0 2.81 3.23 3.2 3.09 3.30 3.3 3.02 3.60 1.773 0.17
Data are presented as mean scores± standard deviation
ANOVA test
1 = lowest frequ ency, 5 = highest frequency
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 7 of 11
The use of empowerment techniques during routine
appointments may be influenced by personal level fac-
tors, such as positive attitudes towards patient empower-
ment, while organizational factors such as clinic space
do not play a role in the use of this tool. This may ex-
plain why we found the three models did not differ
where the use of empowerment techniques in routine
appointments was concerned.
Various fee policies implemented by MHS may have also
influenced the decision to apply preventive medicine tools.
However, while financial incentives have been found to be
effective in improving processes of care and achieving tar-
geted outcomes [33] our results challenge this perception.
The Independent Physician and the Collaboration
models, both enjoying financial incentives, presented sig-
nificantly different results regarding preventive medicine.
Furthermore, in the Teamwork model, respondents re-
ported high rates of preventive medicine and health edu-
cation tools implementation despite the lack of
incentives for health professionals, other than the physi-
cians. Moreover, Teamwork nurses and dietitians were
strongly associated with higher levels of group counsel-
ing and proactive appointment scheduling. This implies
that financial incentives may not necessarily promote
the implementation of preventive medicine tools and its
specific effect require further investigation.
Result concerning preventive medicine implementa-
tion as well as patientshealth results, may be associated
with the different sample sizes among the primary care
models. In between two groups differences regarding pa-
tientshealth outcomes may be attributed to large sam-
ple sizes. Never the less, the fact that Collaboration
Table 5 Logistic Regression Analysis Presenting Predictors of Preventative Medicine Implementation in the Clinics
a, b
Variable Category OR 95% C.I. Sig.
Lower Upper
Conducting health education group counseling in the clinic
Primary care model type Independent Physician 1.000
Collaboration 4.33 1.38 13.57 0.01
Teamwork 6.09 2.63 14.13 p< 0.001
Occupation Physician 1.000
Nurse + Dietitians 2.08 1.09 3.95 0.02
Training Group facilitation 0.77 0.36 1.63 0.50
Smoking cessation 2.09 0.79 5.52 0.13
Health behaviors Adhering to regular screening 1.18 0.59 2.35 0.63
Scheduling proactive appointments
Primary care model type Independent Physician 1.000
Collaboration 0.77 0.21 2.74 0.68
Teamwork 2.09 1.04 4.19 0.03
Occupation Physician 1.000
Nurse + Dietitians 28.46 8.58 94.42 p< 0.001
Training Group facilitation 1.52 0.58 3.94 0.38
Smoking cessation 3.95 1.15 13.53 0.02
Health behaviors Adhering to regular screening 0.95 0.47 1.93 0.90
Using patient empowerment techniques in routine appointments
Primary care model type Independent Physician 1.000
Collaboration 0.63 0.21 1.81 0.39
Teamwork 0.53 0.26 1.07 0.08
Occupation Physician 1.000
Nurse + Dietitians 1.77 0.92 3.42 0.08
Training Group facilitation 2.76 1.29 5.92 0.009
Smoking cessation 0.79 0.30 2.10 0.64
Health behaviors Adhering to regular screening 2.29 1.14 4.59 0.01
The table presents only significant predictors
Variables included in the linear regression and were not significant: clinic size, gender, social worker occupation, motivational interview training, regular physical
activity, smoking
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 8 of 11
Model presented similar results to that of the Teamwork
Model, which requires considerable organizational in-
vestment, raises the need to reassess organizational pol-
icies regarding these models. Fee policies, personnel
allocation etc. should be re-evaluated in order better
utilize existing resources while maintaining optimal pa-
tientshealth outcomes.
Organizational support has been proven essential to
conduct adequate teamwork [34] and previous research
has indicated that defining unit outcomes, as well as re-
warding all members accordingly, may help engage all
members in the process and enhance interdisciplinary
collaborations [35].
Furthermore, adding the use of preventive medicine
and health education to the clinicsroutine assessments
may affirm the organizational support of such tools and
help enhance its implementation. All these may help en-
courage all team members to make better use of pre-
ventive medicine and health education. Until such a
time, as important as personal characteristics, training,
or type of primary care models are, they may not suffi-
ciently motivate health professionals to engage in pre-
ventive medicine more extensively.
This study had a few limitations.
representation may present a partial picture regarding
preventative medicine implementation in their clinics.
Since responding physicians represented all of MHS
physicians with respect to gender and clinic size (with
the exception of under-representation of small clinics,
which is less relevant, as discussed in the methods
section) we feel this bias did not profoundly flaw our
studys conclusions.
In addition, assessing rates of preventive medicine and
health education tools implementation based on self-
report could be biased by employeestendency to over-
report activities preformed due to the need to better fit
their own professional perception, or to meet MHSs ex-
pectations. The automated questionnaire system was
used to minimize this aspect as much as possible and
further validation was gained through the CHR data,
found to support our findings.
The Collaboration Models sample size was vastly dif-
ferent from the other two models. This difference is rep-
resentative of MHS clinics and does not impair
statistical conclusions deduced in this study.
This study has provided new insights regarding variables af-
fecting implementation of preventive medicine and health
education tools in primary care. We found multidisciplinary
models to be associated with higher levels of these tools
implementation. While these results were based on health
professionals self-report, it was also strongly supported by
objective organizational computerized data.
Although the organizational approach to the Collabor-
ation Model resembles that of the Independent Physi-
cians, in terms of preventive medicine application, this
model resembled the Teamwork model. This indicates
that multidisciplinary support may help promote higher
rates of preventive medicine and health education imple-
mentation as well as better patientshealth behaviors.
Supporting professionalstraining as well as acquiring
collaboration skills is essential and may help promote
the implementation of the tools acquired.
Table 6 Rates of Health Behaviours among the Three Primary Care Models
Variables Independent physician model Team model Collaboration model Value
Healthy Population
n= 464,828 n= 269,844 n= 60,778
Occult Blood Test 60.0 61.6 61.4 90.51 p< 0.001
Well-Controlled Lipid Levels
91.1 91.6 91.5 210.11 p< 0.001
Diabetic Population
n= 51,861 n= 37,455 n= 6006
Influenza Vaccination 55.2 56.0 60.4 58.04 p< 0.001
Heart Diseases Population
n= 37,036 n= 25,239 n= 4509
Influenza Vaccination 57.9 59.1 63.3 49.71 p< 0.001
High Blood-Pressure Population
n= 120,349 n= 86,629 n= 14,461
Influenza Vaccination 46.0 46.1 50.2 96.19 p< 0.001
Percent among target population
Among patients who underwent lipid levels check-ups during 2015
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 9 of 11
Our findings may assist health organizations and
policy-makers in modifying practice attributes to en-
hance preventive medicine and health education imple-
mentation in primary care.
Further examination of patientshealth outcomes in
future studies, may ascertain the link between preventa-
tive medicine and health education implementation and
patientsclinical outcomes in the various models.
CHR: Computerized Health Records; HMO: Healthcare Maintenance
Organization; MHS: Maccabi Healthcare Services; OR: Odds Ratio
Not applicable.
AS Initiated the study, formulated the questionnaires, interpreted the data
and was the major contributor in writing the manuscript. LBY Extracted and
analyzed the obtained data. DL Provided organizational and professional
support, as Maccabi Health Servicesdelegate. TK Assisted in advanced
statistical analysis and interpretation of the obtained data. OBE Assisted in
formulating the study and consolidating the final coherent message and
manuscript. All authors read and approved the final manuscript.
This work was supported by the Israeli National Institute for Health Policy
Research (Grant No. 2015/24/R). The institute was not involved in any aspect
of the study or its publication.
Availability of data and materials
The data that support the findings of this study are available from Maccabi
Health Servicesdepartment of Health Services Research, but restrictions
apply to the availability of these data, which were used under license for the
current study, and so are not publicly available. Data are however available
from the authors upon reasonable request and subject to MHSpermission.
Ethics approval and consent to participate
The study was approved by the MHS ethics committee (reference No. 2015006).
Participants filled a computerized questionnaire, indicating their consent to
take part in this study.
Consent for publication
This manuscript does not contain data from any individual person, therefor
this is not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
School of Public Health, University of Haifa, Haifa, Israel.
Sde-varburg, Israel.
Department of Health Services Research and Health Economics, Maccabi
Healthcare Services, 27 HaMered Street, Tel Aviv, Israel.
Maccabi Healthcare
Services, 3 HaNetsach Street, Ramat-Hasharon, Israel.
School of Education,
Bar-Ilan University, Ramat Gan, Israel.
Qyriat Bialik, Israel.
School of Public
Health, University of Haifa, 199 Abba khoushy Mount Carmel, 3498838 Haifa,
Received: 5 November 2018 Accepted: 23 May 2019
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Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 11 of 11
... 2 Interprofessional collaboration may address some of the shortfalls in health promotion and disease prevention by creating an overlap and sharing of the tasks among providers, especially for patients with multiple chronic conditions who often see several types of health professionals. [3][4][5] Toward that end, this clinical practice guideline is designed to offer a practical model of interprofessional collaboration for chiropractors in the delivery of clinical preventive servicesthat is, services provided by health care providers that reduce risk factors and screen for early-stage disease 6 -to adult patients with musculoskeletal conditions. Utilizing the breadth of the available health care workforce, including chiropractors, would bolster at-risk patients' exposure to health promotion messages. ...
... 22 2. No definitive clinical evidence supports a protective effect of spinal manipulation on immune system function or infectious disease prophylaxis. 52,53,56 3. Provide office and clinical staff with an infection control protocol with training on hand hygiene, personal protective equipment, and environmental (surface) 5. Take a thorough health history on all patients, including opioid and other medication use. ...
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Objective: To develop evidence-based recommendations on best practices for delivery of clinical preventive services by chiropractors and to offer practical resources to empower provider applications in practice. Design: Clinical practice guideline based on evidence-based recommendations of a panel of practitioners and experts on clinical preventive services. Methods: Synthesizing the results of a literature search for relevant clinical practice guidelines and systematic reviews, a multidisciplinary steering committee with training and experience in health promotion, clinical prevention, and/or evidence-based chiropractic practice drafted a set of recommendations. A Delphi panel of experienced practitioners and faculty, primarily but not exclusively chiropractors, rated the recommendations by using the formal consensus methodology established by the RAND Corporation/University of California. Results: The Delphi consensus process was conducted during January-February 2021. The 65-member Delphi panel reached a high level of consensus on appropriate application of clinical preventive services for screening and health promotion counseling within the chiropractic scope of practice. Interprofessional collaboration for the successful delivery of clinical preventive services was emphasized. Recommendations were made on primary, secondary, tertiary, and quaternary prevention of musculoskeletal pain. Conclusions: Application of this guideline in chiropractic practice may facilitate consistent and appropriate use of screening and preventive services and foster interprofessional collaboration to promote clinical preventive services and contribute to improved public health.
... Também é de registrar que o apoio multidisciplinar pode auxiliar na promoção e adoção de medicina preventiva e educação em saúde (SCHOR et al., 2019). ...
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... is exercise mode can accelerate the blood circulation of the human body, promote the metabolism of the tissues, improve body function, and provide sufficient protection for the operation of other systems in the body, which has been confirmed in diseases such as chronic obstructive pulmonary disease, senile cognitive impairment, and nonalcoholic fatty liver disease [21]. Based on the previous clinical experience, this study aimed to bring an evidence-based basis for patients with mastitis through the implementation of aerobic exercise combined with health education. ...
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Objective: To explore the application value of health education combined with aerobic exercise in patients with mastitis found in physical examination. Methods: The clinical data of 100 patients with mastitis who underwent physical examination in the physical examination center of our hospital from October 2020 to October 2021 were retrospectively analyzed. According to the order of physical examination, they were equally split into experimental group and control group. The control group received the routine clinical intervention, while the experimental group received health education combined with aerobic exercise to evaluate the clinical effects of different intervention modes on patients with mastitis. Results: Compared with the control group, the experimental group after intervention achieved notably higher scores of CD-RISC, self-management ability, and mastitis-related knowledge (P < 0.001), lower scores of breast pain, skin color, and local mass diameter (P < 0.001), and a higher SF-36 score (P < 0.001). Conclusion: The clinical intervention combining health education with aerobic exercise in patients with mastitis found in the physical examination is an effective method to improve their mood state and self-management ability, and further research will help provide a good solution for such patients.
... If regular primary healthcare providers and social service providers combine to form a primary care provider network to cater to the healthcare needs of individuals in each area of a country, it would be possible for individuals to receive timely and adequate healthcare services when they need them. For example, through social services offered by the primary care provider network, individuals with functional limitations could visit a doctor and those socioeconomically disadvantaged would be more likely to be able to overcome financial barriers to healthcare (76)(77)(78)(79)(80). ...
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Purpose: We aimed to identify the strategies used to support patient behavior change by clinicians whose patients had an increase in patient activation. Methods: This mixed methods study was conducted in collaboration with Fairview Health Services, a Pioneer Accountable Care Organization. We aggregated data on the change in patient activation measure (PAM) score for 7,144 patients to the primary care clinician level. We conducted in-depth interviews with 10 clinicians whose patients' score increases were among the highest and 10 whose patients' score changes were among the lowest. Transcripts of the interviews were analyzed to identify key strategies that differentiated the clinicians whose patients had top PAM change scores. Results: Clinicians whose patients had relatively large activation increases reported using 5 key strategies to support patient behavior change (mean = 3.9 strategies): emphasizing patient ownership; partnering with patients; identifying small steps; scheduling frequent follow-up visits to cheer successes, problem solve, or both; and showing caring and concern for patients. Clinicians whose patients had lesser change in activation were far less likely to describe using these approaches (mean = 1.3 strategies). Most clinicians, regardless of group, reported developing their own approach to support patient behavior change. Those whose patients showed high activation change reported spending more time with patients on counseling and education than did those whose patients showed less improvement in activation. Conclusions: Clinicians vary in the strategies they use to promote behavior change and in the time spent with patients on such activities. The 5 key strategies used by clinicians with high patient activation change are promising approaches to supporting patient behavior change that should be tested in a larger sample of clinicians to validate their effectiveness.
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Background The Chronic Care Model (CCM) is a framework developed to redesign care delivery for individuals living with chronic diseases in primary care. The CCM and its various components have been widely adopted and evaluated, however, little is known about different primary care experiences with its implementation, and the factors that influence its successful uptake. The purpose of this review is to synthesize findings of studies that implemented the CCM in primary care, in order to identify facilitators and barriers encountered during implementation.Methods This study identified English-language, peer-reviewed research articles, describing the CCM in primary care settings. Searches were performed in three data bases: Web of Knowledge, Pubmed and Scopus. Article abstracts and titles were read based on whether they met the following inclusion criteria: 1) studies published after 2003 that described or evaluated the implementation of the CCM; 2) the care setting was primary care; 3) the target population of the study was adults over the age of 18 with chronic conditions. Studies were categorized by reference, study design and methods, participants and setting, study objective, CCM components used, and description of the intervention. The next stage of data abstraction involved qualitative analysis of cited barriers and facilitators using the Consolidating Framework for Research Implementation.ResultsThis review identified barriers and facilitators of implementation across various primary care settings in 22 studies. The major emerging themes were those related to the inner setting of the organization, the process of implementation and characteristics of the individual healthcare providers. These included: organizational culture, its structural characteristics, networks and communication, implementation climate and readiness, presence of supportive leadership, and provider attitudes and beliefs.Conclusions These findings highlight the importance of assessing organizational capacity and needs prior to and during the implementation of the CCM, as well as gaining a better understanding of health care providers¿ and organizational perspective.
The incidence and the public health importance of diabetes mellitus are growing continuously. Despite the improvement observed in the latest years, the leading cause of morbidity and mortality of diabetics are cardiovascular diseases. The diagnosis of diabetes mellitus constitutes such a high risk as the known presence of vascular disease. Diabetic dyslipidaemia is characterised by high fasting and postprandial triglyceride levels, low HDL level, and slightly elevated LDL-cholesterol with domination of atherogenic small dense LDL. These are not independent components of the atherogenic dyslipidaemia, but are closely linked to each other. Beside the known harmful effects of low HDL and small dense LDL, recent findings confirmed the atherogenicity of the triglyceride-rich lipoproteins and their remnants. It has been shown that the key of this process is the overproduction and delayed clearance of triglyceride-rich lipoproteins in the liver. In this metabolism the lipoprotein lipase has a determining role; its function is accelerated by ApoA5 and attenuated by ApoC3. The null mutations of the ApoC3 results in a reduced risk of myocardial infarction, the loss-of-function mutation of ApoA5 was associated with a 60% elevation of triglyceride level and 2.2-times increased risk of myocardial infarction. In case of diabetes mellitus, insulin resistance, obesity, metabolic syndrome and chronic kidney disease the non-HDL-cholesterol is a better marker of the risk than the LDL-cholesterol. Its value can be calculated by subtraction of HDL-cholesterol from total cholesterol. Target values of non- HDL-cholesterol can be obtained by adding 0.8 mmol/L to the LDL-cholesterol targets (this means 3.3 mmol/L in high, and 2.6 mmol/L in very high risk patients). The drugs of first choice in the treatment of diabetic dyslipidaemia are statins. Nevertheless, it is known that even if statin therapy is optimal (treated to target), a considerable residual (lipid) risk remains. For its reduction treatment of low HDL-cholesterol and high triglyceride levels is obvious by the administration of fibrates. In addition to statin therapy, fenofibrate can be recommended.
The objective of this study was to develop a prospectively applicable method for classifying comorbid conditions which might alter the risk of mortality for use in longitudinal studies. A weighted index that takes into account the number and the seriousness of comorbid disease was developed in a cohort of 559 medical patients. The 1-yr mortality rates for the different scores were: "0", 12% (181); "1-2", 26% (225); "3-4", 52% (71); and "greater than or equal to 5", 85% (82). The index was tested for its ability to predict risk of death from comorbid disease in the second cohort of 685 patients during a 10-yr follow-up. The percent of patients who died of comorbid disease for the different scores were: "0", 8% (588); "1", 25% (54); "2", 48% (25); "greater than or equal to 3", 59% (18). With each increased level of the comorbidity index, there were stepwise increases in the cumulative mortality attributable to comorbid disease (log rank chi 2 = 165; p less than 0.0001). In this longer follow-up, age was also a predictor of mortality (p less than 0.001). The new index performed similarly to a previous system devised by Kaplan and Feinstein. The method of classifying comorbidity provides a simple, readily applicable and valid method of estimating risk of death from comorbid disease for use in longitudinal studies. Further work in larger populations is still required to refine the approach because the number of patients with any given condition in this study was relatively small.
This article presents the results of a project conducted by the Institut national d'excellence en santé et en services sociaux of Québec to develop quality of care indicators for the management of six chronic illnesses. Indicators were identified through literature searches and analysis of clinical practice guidelines (CPGs). Interdisciplinary expert panels assessed their validity and the strength of the evidence on which they were based. Representatives of patients (N = 19) and professionals (N = 29) were consulted on their relevance and acceptability. Indicators were categorized according to the Chronic Care Model (CCM). A total of 164 indicators were developed, 126 specific to the illnesses under study and 38 on processes and outcomes generic to the CCM. There was convergence between patients and professionals on the relevance of a majority of indicators. Professionals expressed concerns on the indicators measured by means of patient surveys that they considered to be too subjective. The importance given to CPGs as the main source of indicators resulted in a great number of indicators of the technical quality ofcare. Using the CCM contributed to a broader perspective of quality. The consultation process identified some of the concerns of professionals about indicator measurement, thusguidingfuture implementation initiatives.
Objectives: To conduct a systematic review of studies reporting on the development or validation of comorbidity indices using administrative health data and compare their ability to predict outcomes related to comorbidity (ie, construct validity). Study design and setting: We conducted a comprehensive literature search of MEDLINE and EMBASE, until September 2012. After title and abstract screen, relevant articles were selected for review by two independent investigators. Predictive validity and model fit were measured using c-statistic for dichotomous outcomes and R(2) for continuous outcomes. Results: Our review includes 76 articles. Two categories of comorbidity indices were identified: those identifying comorbidities based on diagnoses, using International Classification of Disease codes from hospitalization or outpatient data, and based on medications, using pharmacy data. The ability of indices studied to predict morbidity-related outcomes ranged from poor (C statistic ≤ 0.69) to excellent (C statistic >0.80) depending on the specific index, outcome measured, and study population. Diagnosis-based measures, particularly the Elixhauser Index and the Romano adaptation of the Charlson Index, resulted in higher ability to predict mortality outcomes. Medication-based indices, such as the Chronic Disease Score, demonstrated better performance for predicting health care utilization. Conclusion: A number of valid comorbidity indices derived from administrative data are available. Selection of an appropriate index should take into account the type of data available, study population, and specific outcome of interest.
Importance Type 2 diabetes mellitus is common, and treatment to correct blood glucose levels is standard. However, treatment burden starts years before treatment benefits accrue. Because guidelines often ignore treatment burden, many patients with diabetes may be overtreated.Objective To examine how treatment burden affects the benefits of intensive vs moderate glycemic control in patients with type 2 diabetes.Design, Setting, and Participants We estimated the effects of hemoglobin A1c (HbA1c) reduction on diabetes outcomes and overall quality-adjusted life years (QALYs) using a Markov simulation model. Model probabilities were based on estimates from randomized trials and observational studies. Simulated patients were based on adult patients with type 2 diabetes drawn from the National Health and Nutrition Examination Study.Interventions Glucose lowering with oral agents or insulin in type 2 diabetes.Main Outcomes and Measures Main outcomes were QALYs and reduction in risk of microvascular and cardiovascular diabetes complications.Results Assuming a low treatment burden (0.001, or 0.4 lost days per year), treatment that lowered HbA1c level by 1 percentage point provided benefits ranging from 0.77 to 0.91 QALYs for simulated patients who received a diagnosis at age 45 years to 0.08 to 0.10 QALYs for those who received a diagnosis at age 75 years. An increase in treatment burden (0.01, or 3.7 days lost per year) resulted in HbA1c level lowering being associated with more harm than benefit in those aged 75 years. Across all ages, patients who viewed treatment as more burdensome (0.025-0.05 disutility) experienced a net loss in QALYs from treatments to lower HbA1c level.Conclusions and Relevance Improving glycemic control can provide substantial benefits, especially for younger patients; however, for most patients older than 50 years with an HbA1c level less than 9% receiving metformin therapy, additional glycemic treatment usually offers at most modest benefits. Furthermore, the magnitude of benefit is sensitive to patients’ views of the treatment burden, and even small treatment adverse effects result in net harm in older patients. The current approach of broadly advocating intensive glycemic control should be reconsidered; instead, treating patients with HbA1c levels less than 9% should be individualized on the basis of estimates of benefit weighed against the patient’s views of the burdens of treatment.