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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
1,2*
, Lucia Bergovoy-Yellin
3
, Daniel Landsberger
4
, Tania Kolobov
5,6
and Orna Baron-Epel
7
Abstract
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.
Keywords: Primary care model, Preventive medicine measures, Health education tools, Multidisciplinary practice
© The Author(s). 2019 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
* Correspondence: a-schor@zahav.net.il
1
School of Public Health, University of Haifa, Haifa, Israel
2
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
https://doi.org/10.1186/s13584-019-0318-4
Background
Non-adherence to medical and behavioral recommenda-
tions is common and is known to be affected by patients
and physicians’characteristics alike [1–4].
Recent studies indicate that health professionals have
the ability to improve patients’adherence using various
behavioral tools and strategies for change [5–7].
One of the main strategies found to improve treatment
processes and access to medical care, resulting in im-
proved clinical outcomes [8–10] is multidisciplinary work.
It is assumed that multidisciplinary collaborations increase
the ability to accurately address a patient’s 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 patients’motivation 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 [15–17], in order to
facilitate patients’adherence.
In spite of abundant supportive evidence indicating that
the use of preventive medicine and health education tools
reduces morbidity and mortality [18,19], its’implementa-
tion may be complicated and ultimately depends on health
professionals’motivation, 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 country’s 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 MHS’desired clinical outcomes, such as pa-
tients’vaccinations, 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 population’s needs as well as
MHS’ability 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 clinics’assessment 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 patients’outcomes, 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 nurse’s 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 patients’main
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 patients’char-
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, its’implementation is not taken into ac-
count in routine clinic assessments, as other key
components, such as patients’medication 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.
SincepreventivemedicineishighlyencouragedbyMHS
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 Patients’Characteristics among the Three Primary Care Models
1,2
Independent physician model Team model Collaboration model
n=92 n= 264 n=30
Personnel Physicians Physicians Physicians
Nurses
dietitians Nurses
Social-worker
Clinic Ownership Physician’s clinic MHS’clinic Physician’s clinic
Nurse’s clinic
Supporting staff Physicians’choice MHS’allocation Physicians’/ Nurses’choice
Patients’Affiliation Physician Physician Physician
Nurse
Clinical Goals Physicians’goals Physicians’goals Physicians’goals
No team goals Nurses’goals
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
Patients’Gender (Male)
1,2
55.8 52.3 58.4
Patients’Age (Years)
2,3
64.3 ± 12 64.2 ± 12 63.4 ± 13
Patients’Morbidity Levels
2,3,4
1.04 ± 1.26 1.08 ± 1.27 1.10 ± 1.25
1
Percentage within the model’s population
2
p< 0.001
3
Mean ± SD
4
Charlson Score on December 31st, 2015
Schor et al. Israel Journal of Health Policy Research (2019) 8:50 Page 3 of 11
Methods
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-
fessionals’personal 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 respondents’identification.
Five weekly automatic reminders were sent to those who
did not open the questionnaire link.
Data was extracted, processed and analyzed by MHS’s
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 dietitians’work 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.
Proactivemedicinewasevaluatedbythefrequencyof
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 Professionals’Personal
Characteristics by Primary Care Model Affiliation
a, b
Variable Independent
physician model
Team
model
Collaboration
model
Value
b
p-
value
n=92 n=
264
n=30
Clinic size
(No. of
patients) (%)
Up to 400 10.6 6.2 3.3 18.35 0.001
400–1000 38.8 19.1 20
0ver 1000 50.6 74.7 76.7
Personal
characteristics (%)
Occupation
Physician
100 28.8 40 152.86 p<
0.001
Nurse 0 36.9 60
Dietitian 0 20.7 0
Social
worker
0 13.4 0
Gender
Male 56.2 15.7 32.1 47.68 p<
0.001
Female 43.8 84.3 67.9
Participation
in patient
empowerment
training
Yes 15.7 52.1 51.7 26.34 p<
0.001
No 84.3 47.9 48.3
Health
behaviors (%)
Smoking
Yes 3.8 2.9 10.7 4.03 0.13
No 96.2 97.1 89.3
Undergoing
regular
screening
Yes 75.0 84.3 78.6 3.46 0.17
No 25.0 15.7 21.4
Regular
physical
activity
Yes 73.7 61.8 41.2 4.31 0.11
No 26.3 38.2 57.1
a
Percent
b
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 frequency”repre-
sented by the highest score.
These three dependent variables were based on health
professionals’self-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 MHS’computerized
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 MHS’chronic 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 patients’health 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 = 400–1000 patients, large>1000 patients).
Personal characteristics
Gender, occupation and personal health behaviors
(smoking, regular physical activity, and adhering to rele-
vant health screening).
Training
Professional’s participation in health education training
(motivational interviewing / smoking cessation counsel-
ing / group facilitation), as well as time passed since
completion of this training (1–6 months, 7–12 months,
13–24 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 MHS’health 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 MHS’employees 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 MHS’employees 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 MHS’employees 2.8 96.2 3.6 47.8 60.7
Dietitian Percentage among respondents 2.2 97.8 24 34 42
Percentage among MHS’employees 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 MHS’employees 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 MHS’employees 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 = 1–6
months, 3 = 7–24 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 professionals’characteristics as well as for pre-
ventive medicine implementation and Pairwise contrasts
for patients’health 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.
Results
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 1.38–13.57 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 1.09–3.95).
Significant predictors of proactive appointment sched-
uling included affiliation with the Teamwork Model
(OR = 2.1, 95%CI 1.04–4.19), occupation, namely nurses
and dietitians (OR = 28.46, 95%CI 8.58–94.4) and train-
ing, specifically smoking cessation training (OR = 3.95,
95%CI 1.15–13.53). 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 professionals’ad-
herence to regular screening (OR = 2.77, 95%CI 1.30–
5.92; OR = 2.29, 95%CI 1.14–4.49 respectively).
Rates of most patients’health 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).
Discussion
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 professionals’self-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 et.al. 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 Physicians’Model were seemingly
contradictory to the low training levels reported by this
model’s 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
Independent
Physician model
n=92
Teamwork model
n= 264
Collaboration
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
more)
1.7 1.44 1.89 3.2 3.02 3.28 3.1 2.71 3.50 66.844 p<
0.001
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<
0.001
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
a
Data are presented as mean scores± standard deviation
b
ANOVA test
c
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 patients’health results, may be associated
with the different sample sizes among the primary care
models. In between two groups differences regarding pa-
tients’health 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
a
The table presents only significant predictors
b
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-
tients’health 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 clinics’routine 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.
Theresponseratewasnothigh.Physicians’under-
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
study’s conclusions.
In addition, assessing rates of preventive medicine and
health education tools implementation based on self-
report could be biased by employees’tendency to over-
report activities preformed due to the need to better fit
their own professional perception, or to meet MHS’s 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 Model’s 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.
Conclusions
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 patients’health behaviors.
Supporting professionals’training 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
a
Variables Independent physician model Team model Collaboration model Value
b
p-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
c
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
a
Percent among target population
b
Chi-square
c
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 patients’health outcomes in
future studies, may ascertain the link between preventa-
tive medicine and health education implementation and
patients’clinical outcomes in the various models.
Abbreviations
CHR: Computerized Health Records; HMO: Healthcare Maintenance
Organization; MHS: Maccabi Healthcare Services; OR: Odds Ratio
Acknowledgements
Not applicable.
Authors’contributions
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 Services’delegate. 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.
Funding
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 Services’department 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 MHS’permission.
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
1
School of Public Health, University of Haifa, Haifa, Israel.
2
Sde-varburg, Israel.
3
Department of Health Services Research and Health Economics, Maccabi
Healthcare Services, 27 Ha’Mered Street, Tel Aviv, Israel.
4
Maccabi Healthcare
Services, 3 Ha’Netsach Street, Ramat-Hasharon, Israel.
5
School of Education,
Bar-Ilan University, Ramat Gan, Israel.
6
Qyriat Bialik, Israel.
7
School of Public
Health, University of Haifa, 199 Abba khoushy Mount Carmel, 3498838 Haifa,
Israel.
Received: 5 November 2018 Accepted: 23 May 2019
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