Defining Team Effort Involved in Patient Care from the Primary Care Physician’s Perspective
Defining Team Effort Involved in Patient Care from the Primary Care Physician's Perspective
Andrew S. Hwang 2
Steven J. Atlas 2
Jeffrey M. Ashburner 2
Adrian H. Zai 0
Richard W. Grant 4
Clemens S. Hong 3
0 Laboratory of Computer Science, Massachusetts General Hospital , Boston, MA , USA
1 Stanford University School of Medicine , Stanford, CA , USA
2 Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital , Boston, MA , USA
3 Los Angeles County Department of Health Services , Los Angeles, CA , USA
4 Division of Research, Kaiser Permanente Northern California , Oakland, CA , USA
BACKGROUND: A better understanding of the attributes of patients who require more effort to manage may improve risk adjustment approaches and lead to more efficient resource allocation, improved patient care and health outcomes, and reduced burnout in primary care clinicians. OBJECTIVE: To identify and characterize high-effort patients from the physician's perspective. DESIGN: Cohort study. PARTICIPANTS: Ninety-nine primary care physicians in an academic primary care network. MAIN MEASURES: From a list of 100 randomly selected patients in their panels, PCPs identified patients who required a high level of team-based effort and patients they considered complex. For high-effort patients, PCPs indicated which factors influenced their decision: medical/ care coordination, behavioral health, and/or socioeconomic factors. We examined differences in patient characteristics based on PCP-defined effort and complexity. KEY RESULTS: Among 9594 eligible patients, PCPs classified 2277 (23.7 %) as high-effort and 2676 (27.9 %) as complex. Behavioral health issues were the major driver of effort in younger patients, while medical/care coordination issues predominated in older patients. Compared to low-effort patients, high-effort patients were significantly (P < 0.01 for all) more likely to have higher rates of medical (e.g. 23.2 % vs. 6.3 % for diabetes) and behavioral health problems (e.g. 9.8 % vs. 2.9 % for substance use disorder), more frequent primary care visits (10.9 vs. 6.0 visits), and higher acute care utilization rates (25.8 % vs. 7.7 % for emergency department [ED] visits and 15.0 % vs. 3.9 % for hospitalization). Almost one in five (18 %) patients who were considered high-effort were not deemed complex by the same PCPs. CONCLUSIONS: Patients defined as high-effort by their primary care physicians, not all of whom were medically complex, appear to have a high burden of psychosocial issues that may not be accounted for in current chronic disease-focused risk adjustment approaches.
primary care redesign; psychosocial; health services research; resource allocation; risk adjustment; J Gen Intern Med 32(3); 269-76 DOI; 10; 1007/s11606-016-3897-6 © Society of General Internal Medicine 2016
Provisions of the Affordable Care Act have accelerated primary
care redesign and encouraged new reimbursement approaches.1
Redesign initiatives include a focus on multidisciplinary teams
caring for primary care populations with increasing use of
population-based reimbursement strategies.2 In this context,
approaches to measuring patient-level primary care team effort
are necessary to account for variation across primary care
patient panels among clinicians and care teams.3, 4
Prior studies have used measures of patient complexity to
control for differences in the effort and resources needed to
manage a given panel of patients.5–8 These predictive models
and risk adjustment approaches focus predominately on the
measures of medical complexity, including the number and
severity of chronic conditions or prior healthcare utilization
and cost.9–11 However, behavioral health, socioeconomic, and
environmental factors that contribute to primary care team
effort may not be adequately captured in current measures of
An ideal primary care risk adjustment measure would closely
approximate the amount of clinician and primary care team effort
needed to care for an individual patient or patient panel.
Although most compensation models currently use relative value
units (RVUs) as proxies for primary care effort,15 RVUs
underestimate primary care clinician work.16, 17 In particular,
RVUbased reimbursement approaches capture only the effort related
to face-to-face patient encounters.18 Given that a substantial and
increasing proportion of primary care work occurs outside of
office visits,19–21 new measures of effort also need to capture
non-visit-based work. Patient-level factors also contribute to
primary care team effort, but these factors are often difficult to
identify from existing data sources.22 A better understanding of
the attributes of patients who require more effort to manage may
improve risk adjustment approaches, leading to more efficient
resource allocation, improved patient care and health outcomes,
and reduced burnout in primary care clinicians.23–28
Primary care physicians (PCPs) have a unique perspective
on the relative level of effort required to care for patients in
their panel, including both visit-based and non-visit-based
work, as well as patients’ psychosocial needs that affect
primary care team effort. We previously evaluated a measure of
patient complexity using physician qualitative assessments.13
In the current study, we sought to define cohorts of patients by
physician-defined effort, characterize high-effort patients by
domains of effort, and use electronically available data to
identify patient-level factors that are important drivers of
primary care team effort.
Participants and Setting
We recruited staff PCPs from the Massachusetts General
Primary Care Practice-Based Research Network. We linked all
patients seen in our network in the previous 3 years to specific
PCPs using a validated algorithm.29 We invited PCPs
managing a panel of at least 100 patients in 2013 to review a list of
100 randomly selected patients using an electronic survey tool
developed in REDCap version 5.9 (Vanderbilt University,
Nashville, TN, USA). Participating PCPs received a gift card
incentive of up to $250.
After verifying that the patient was theirs, PCPs were asked,
BHow much effort does it take you and your team to care for
this patient?^ PCPs responded on a four-response scale (a lot
of effort, moderate amount of effort, little effort, no effort). We
categorized patients as high-effort (a lot or moderate amount
of effort) or low-effort (little or no effort). For high-effort
patients, PCPs indicated which factors from three effort
domains influenced their decision (Table 1). We adopted these
domains from previously published concepts,13, 30 and
modified them based on feedback from a primary care physician
advisory group in our institution. We also asked PCPs, BIn
your view, do you consider this patient a ‘complex patient’?^
Since our goal was to compare physician-defined constructs of
effort and complexity, we did not provide PCPs with a
predetermined definition of effort or complexity. Based on PCPs’
responses, we divided patients into four groups: 1) low-effort,
not complex 2) high-effort, not complex (high-effort only), 3)
low-effort, complex (complex only), and 4) high-effort,
complex. To evaluate the impact of patient age on effort
designation, we created eight age strata and assessed for changes in the
prevalence of each effort domain.
PCP variables assessed included age, gender, time since
graduation from medical school, years in practice at our
institution, total outpatient visits in the previous 3 years, panel size,
and whether they practiced at a community health center. We
derived patient-related variables from data available in an
electronic data repository over the preceding 3 years.31 Patient
demographic variables included age, gender, self-reported race
or ethnicity, language, and insurance status. We used
outpatient scheduling data to identify the number of visits to the
PCP and other practice providers, missed appointments
(Bnoshow^), and number of different providers seen. We obtained
data on admission and emergency department (ED) visits in
the prior year, comorbid conditions, medication prescriptions,
and laboratory results from the electronic medical record and
billing data (using International Classification of Diseases,
Ninth Revision [ICD-9] and Current Procedural Terminology
We examined the relationship between effort and physician
and patient panel characteristics using simple linear regression
models, with the proportion of high-effort patients as a
continuous outcome, and presented the summary statistics by
tertiles of proportion of high-effort patients. We used logistic
regression models using the general estimating equations
approach (PROC GENMOD, SAS version 9.3; SAS Institute
Inc., Cary, NC, USA) to compare the characteristics of patients
who were 1) high-effort vs. low-effort, 2) high-effort only vs.
complex only, and 3) high-effort only and complex only vs.
high-effort, complex patients, while accounting for PCP-level
clustering. We compared the prevalence of effort domains by
age strata using a chi-square trend test. We also conducted
multivariable regression analyses to identify predictors of
patients being in each of the four groups. The institutional
review board of Partners HealthCare approved our study.
Among eligible PCPs, 54 % (99/182), representing 90 % (17/
19) of network practices, reviewed their patient lists. There
were no statistically significant differences between
participating and non-participating PCPs (Online Appendix 1).
Of the 9900 patients provided to participating PCPs, PCPs
reviewed 9832 patients and designated 238 (2.4 %) as not their
patient. Among the remaining 9594 patients, PCPs classified
23.7 % as high-effort (range 3.0–55.8 %, SD 13.0 %) and
27.9 % (range 5.0–64.0 %, SD 13.5 %) as complex (Table 2).
Not all high-effort patients were deemed complex, and not all
complex patients were deemed high-effort (Fig. 1).
Based on the proportion of patients designated as
high-effort, there were no statistically significant
differences between PCP characteristics, while differences in
patient panel characteristics included mean age,
insurance status, and number of clinic and PCP visits (Online
Appendix 2). PCPs identified a mean of 1.7 effort
domains associated with high-effort patients. Medical/
care coordination issues were most prevalent (79.4 %),
followed by behavioral health (59.7 %) and
socioeconomic issues (31.5 %). PCPs more frequently identified
behavioral health issues in younger patients and
medical/care coordination issues in older patients, while
socioeconomic issues remained stable across age groups
(P < 0.01 for trends by decade) (Online Appendix 3).
Unadjusted Analysis Comparing Patient
Characteristics in the Different
Compared to low-effort patients, high-effort patients were
older and were more likely to be female, reside in
neighborhoods with lower median household income, be insured by
Medicare or Medicaid, have a greater number of clinic/PCP
visits, and see a greater number of different providers in their
primary care practices. Compared to low-effort patients,
higheffort patients were also significantly (P < 0.01) more likely to
have medical (e.g. diabetes [23.2 % vs. 6.4 %]), psychiatric
(e.g. bipolar disorder [4.5 % vs. 1.0 %]), and behavioral health
(e.g. substance use disorder [9.8 % vs. 2.9 %]) comorbidities
and have an ED visit (25.8 % vs. 7.7 %) or hospitalization
(15.0 % vs. 3.9 %) in the prior year (Table 3).
Compared to patients designated as complex only,
higheffort-only patients had a significantly (P < 0.05) greater
number of clinic/PCP visits, saw a greater number of different
providers in their primary care practices, and were prescribed
fewer medications. The proportion of patients with ED visits
was similar (14.7 % vs. 14.8 %, p = 0.61), but high-effort-only
patients were less likely to have had a hospitalization (4.9 %
vs. 9.3 %, p = 0.03) in the past year (Table 4).
Compared to patients designated as high-effort only or
complex only, patients who were both high-effort and
complex were older and were more likely to reside in
neighborhoods with lower median household income, have a higher
number of clinic/PCP visits, and be prescribed a higher
number of medications. In addition, compared to high-effort only
and complex only patients, patients designated as both
higheffort and complex were significantly (P < 0.05) more likely to
have acute care utilization in the past year (e.g.
hospitalizations [4.9 % and 9.3 % vs. 17.2 %]), as well as medical (e.g.
diabetes [11.8 % and 19.7 % vs. 25.7 %]), psychiatric (e.g.
depression [32.2 % and 31.5 % vs. 48.9 %]), and behavioral
health comorbidities (e.g. substance use disorder [4.4 % and
6.0 % vs. 10.9 %]) (Table 4).
Independent Predictors of the Different Effort/
The high-effort only group was independently associated
with being prescribed opiates and having increased primary
care utilization, but fewer hospitalizations. The complex
only group was independently associated with lower rates
of ED and PCP visits despite being associated with
measures of medical (e.g. being older and being prescribed more
medications) and psychosocial complexity (e.g. being
unmarried, and having high no-show rates). Lastly, the
higheffort and complex group was associated not only with the
aforementioned measures of medical and psychosocial
complexity, but also with uncontrolled diabetes (HbA1c
>9 %), behavioral health comorbidities (e.g. substance use
disorder, and anxiety/depression), as well as higher rates of
ED and PCP visits (Table 5).
In this study, we presented a new measure of primary care
team effort based on PCPs’ qualitative assessment of their
patients. We described the characteristics of patients whom
PCPs deemed to require a high level of effort to manage,
assessed the relationship between PCP-defined primary care
team effort and patient complexity, and identified
characteristics that were independently associated with high primary
care team effort. Our results suggest that PCP-defined effort
represents a unique construct that is interrelated with, but
distinct from, complexity.
Within a large academic healthcare system, PCPs
designated a quarter of their patients as high-effort, and reported
medical/care coordination, behavioral health, and social
factors as contributors to primary care team effort. Psychosocial
factors, which are typically not incorporated into traditional
risk adjustment and complexity measurement approaches,
appeared to weigh heavily in PCPs’ assessment of primary
care team effort, with two- to sixfold higher prevalence of
behavioral health problems in high-effort patients.
We previously examined medical, behavioral health, and
social factors associated with PCP-defined complexity, and
found that it was distinct from traditional comorbidity-based
measures of complexity.13 However, measures of complexity,
including our measure of PCP-defined complexity,32 do not
always correlate with the effort required to care for patients.
For example, a medically complex patient who receives
extensive support from a caregiver at home or from a
multidisciplinary specialty care team may not require a lot of effort
from a primary care team. Conversely, a patient with few
medical comorbidities might have significant behavioral
health or social issues that require extensive attention from
the primary care team. In our study, almost one in five (18 %)
patients who were considered high-effort were not deemed
complex by the same PCPs.
Characterizing our cohort by PCP-defined effort and
complexity generated four different patient groups: 1) low-effort,
not complex; 2) high-effort, not complex (high-effort only); 3)
low-effort, complex (complex only); and 4) high-effort,
complex. Although each group likely represents a heterogeneous
group of individuals, a conceptual framework developed from
PCP-defined effort and complexity (Fig. 1) may be useful for
health systems attempting to better allocate resources and
redesign primary care to meet the quadruple aim of improving
clinical care, patient care experience, provider work
satisfaction, and costs.33
From a population perspective, high-effort complex patients
represent a group of individuals who have both medical and
psychosocial complexity that demands significant effort from
Table 3 Characteristics of Physician-Defined High-Effort vs.
primary care teams. These individuals have coexisting chronic
medical and behavioral health issues and unmet social needs
that drive high rates of acute care utilization and make them
harder to manage. They represent an ideal target for care
management programs that employ multidisciplinary teams
to address not only patients’ medical comorbidities, but also
their complex psychosocial needs.34
The high-effort only group represents the Bworried well.^35
These individuals have fewer or better-managed chronic
medical conditions and a lower rate of hospitalization than
complex patients, but present frequently to the clinic and the
ED, are more likely to be prescribed opiates, and see many
different providers. Involvement from a greater number of
providers increases the potential for care fragmentation, which
leads to increased primary care team effort in care
coordination activities. This highlights the importance of accounting
for non-visit-based work (e.g. frequency of electronic
communications with patients and other providers) in assessing
primary care team effort. Primary care teams may want to ensure
same-day or next-day access and focus on improving
continuity of care through co-management of patients with a
midlevel provider.36 Given the high prevalence of coexisting
behavioral health comorbidities among patients receiving
opioids for chronic non-cancer pain,37, 38 approaches that
promote close collaboration with mental health and addiction
specialists will also be important for this population.39, 40
Conversely, the complex only group comprises patients
with increased medical complexity as evidenced by older
age, higher number of prescribed medications, and multiple
behavioral health and social issues. However, these complex
issues do not drive frequent acute care utilization or primary
care team effort in the ways the data might predict. One
potential explanation for this discrepancy is that, unlike the
high-effort complex group, where medical and psychosocial
comorbidities coexist, this group is composed of two distinct
subgroups: medically complex older individuals with multiple
well-controlled chronic conditions, and psychosocially
complex younger, healthier individuals who are uninsured and
have high no-show rates.
Improved risk stratification models are needed to identify
high-effort patients,41 because traditional diagnosis-based
measures do not fully account for patient complexity or the
level of primary care effort required to manage patients.42 The
fact that PCPs in our study did not consider all medically
complex patients, such as those with a greater burden of
chronic diseases and higher rates of hospitalization, as
higheffort patients supports the idea that primary care risk
adjustment models must take into account demographic and
psychosocial factors as well as an assessment of available practice
resources. PCPs’ unique knowledge of their patients, their
teams, and their practice assets allows them to account for
some of these factors. However, it is time-consuming and
impractical for PCPs to review their entire panel of patients,
and incorporating PCP review into a risk adjustment approach
would introduce the possibility of gaming the system. A better
understanding of physician-reported primary care team effort
will help strengthen risk adjustment approaches and allow us
to better use quantitative data sets to distinguish between
highand low-effort patients. This will become increasingly
important as we move towards risk-adjusted population-based
Our results show that factors contributing to primary care
team effort changed with a patient’s age. Older patients were
more likely to be considered high-effort due to medical or care
coordination issues, while younger patients posed challenges
Mean age (years)
Insurance status (%)
Median household income ($)†
Did not graduate from high school (%)†
Primary care utilization
Mean clinic visits (n)
Mean PCP visits (n)
BNo-show^ rate >25 % (%)
Mean provider seen (n)
Mean prescribed medications (n)
Laboratory results (%)
Hemoglobin A1c >9 %
International normalized ratio >3.5
Substance use disorder
Clinical encounters (%)
Emergency department visits
Post-traumatic stress disorder
Substance use disorder
Selective serotonin reuptake inhibitors
*High-effort only vs. complex only
†Based on census block group data
P < 0.05 for high-effort and complex vs. high-effort only, complex only, or low-effort and not complex
§P < 0.01 for high-effort and complex vs. high-effort only, complex only, or low-effort and not complex
related to behavioral health issues. For PCPs who have closed
and aging patient panels, investment in and use of an intensive
care management team that can perform care coordination
activities and help patients manage multiple chronic
conditions will likely be most valuable.43 For PCPs with mostly
younger patients, better integration of primary care and
behavioral health teams may be more effective.44, 45
For this study, we primarily used electronically available
patient data, which allowed us to construct a conceptual
framework for effort and complexity in primary care at the
population level, but did not provide enough granularity to
further segment patients into clinically actionable subgroups.
Although our network includes a variety of practice types,
including community health centers, findings from a single
health system may not be generalizable to other primary care
networks. PCPs’ assessments were cross-sectional in nature,
while domains that drive primary care team effort and patient
complexity are likely to fluctuate over time. Utilizing new data
sources to monitor not only patients’ clinical status, but also
changes in patients’ social and behavioral determinants of
health, will be important.46 Although PCPs incorporate
knowledge of their practice and team assets in their
assessments of primary care team effort, their assessment may be
incomplete. Therefore, repeat surveys and in-depth qualitative
work with PCPs in other practice settings may help broaden
our understanding of PCPs’ perception of patient complexity
and effort. At this time, there are no standardized or validated
methods of defining primary care effort. Further research is
needed to identify important determinants of primary care
team effort (e.g. specific combinations of chronic diseases
and medications) and to develop new approaches for
measuring primary care team effort that encompass non-visit-based
work, primary care team and practice assets, and relevant
Adjusted OR [95 % CI]
Low-effort & not complex
High-effort & complex
Median household income*
Did not graduate from high school*
Primary care utilization
Number of clinic visits
Number of PCP visits
Number of providers seen
Number of prescribed medications
Hemoglobin A1c >9 %
International normalized ratio >3.5
Substance use disorder
Number of emergency department visits
Number of hospitalizations
Post-traumatic stress disorder
Substance use disorder
Selective serotonin reuptake inhibitors
*Based on census block group data
In summary, our study demonstrates that PCP’s unique
perspective on primary care team effort not only reflects the medical
complexity of patients, but also accounts for behavioral health
and socioeconomic factors impacting the level of effort required
to care for patients. Current chronic disease-focused risk
adjustment approaches often do not account for these unmeasured
components of physician effort, increasing the potential for the
unintended consequence that providers caring for patients with a
greater number of psychosocial issues will not have sufficient
resources to address patients’ needs or receive adequate
compensation for their work.47 Future studies should aim to improve
understanding of primary care team effort and to develop
approaches to better assess it. These will have important
implications for 1) efficient resource allocation, 2) healthcare redesign
that improves care and reduces physician burnout by providing
the necessary support to PCPs in both visit-based and
non-visitbased aspects of care, and 3) equitable physician compensation
through improved risk adjustment approaches.
Funding: This study was funded by Partners Community Healthcare,
Inc., and by the MGH Primary Care Operations Improvement Program.
Dr. Hong was supported by a KL2/Catalyst Medical Research
Investigator Training award from Harvard Catalyst, The Harvard Clinical and
Translational Science Center (National Center for Research Resources
and the National Center for Advancing Translational Sciences, National
Institutes of Health Award KL2 TR001100). The funding sources had
no involvement in the collection, analysis, or interpretation of the data,
or in the decision to submit the manuscript for publication.
The authors would like to thank Yuchiao Chang, PhD, for her help with
Corresponding Author: Andrew S. Hwang, MD MPH; Division of
General Internal Medicine, Department of MedicineMassachusetts
General Hospital, Gray Bigelow 730, 55 Fruit Street, Boston, MA
02114, USA (e-mail: ).
Compliance with Ethical Standards:
Conflict of Interest: Dr. Clemens Hong is the Co-Founder of Anansi
Health. Dr. Adrian Zai is the Chief Medical Informatics Officer of SRG
Technology. All other authors declare no conflict of interest.
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