Keywords
secondary use - patient–provider - data mining - workflow - medical education
Background and Significance
Background and Significance
Patient attribution is the process of attributing patient-level metrics to specific
providers,[1] which are signals of real-life provider–patient interactions (PPIs). Such attribution
is essential for measuring provider-specific quality metrics,[2] determining physician case exposure,[3]
[4] and identifying accountable care team members.[5] Attributing the care of patients to providers has been attempted through a myriad
of approaches.[6]
[7]
[8] Despite their potential wide-ranging impact, accurately deducing in-person PPIs
from within the EHR remains a challenge,[9]
[10]
[11] especially in the inpatient setting.[1] From the perspective of medical education, automated PPIs derived from EHR data
may help educators understand which patient care experiences add “educational value,”
informing educational opportunities throughout the spectrum of clinical training.[12]
[13] By determining the types of conditions, procedures, and demographics to which one
has exposure, medical educators and trainees can identify relative strengths and gaps
in clinical experience.
The growing momentum behind competency-based medical education calls for educational
structures that align with standardized goals, which can be greatly facilitated with
improved methods of automated patient attribution from medical records.[12]
[13] The Accreditation Council for Graduate Medical Education recently released its second
version of its Clinical Learning Environment Review (CLER) pathways that aim to optimize
trainee education, while providing safe and high-quality patient care.[14] Among the set of expectations outlined, the CLER pathways recommend that trainees
receive both site-levels, as well as individual-specific quality metrics, and outcome
data. This feedback necessitates accurate patient attribution to trainee providers.
Diagnostic exposure and volume among resident trainees have been reported at single
sites using organization-specific enterprise data warehouses.[15]
[16]
[17]
[18] Such information could inform gaps in clinical experiences, as well as relative
strengths.[19]
While many EHR data elements, like note authorship, care team assignment, and order
placement, may be suggestive of PPIs, such approaches to patient attribution may fall
short in depicting accurate representations of care delivery by providers who should
be primarily associated with a patient's care.[16]
[20] For example, residents may place orders on patients for whom they are not the primary
front-line clinician (FLC) during rounds when the assigned FLC is presenting the patient,
or throughout the day while the FLC is attending to other patients. Anecdotally as
noted by the authors, this is most commonly observed in the inpatient context but
may occur in resident primary care clinic or ED. Others have applied methods from
computational ethnography to EHR audit logs to describe and optimize clinical workflows
in the outpatient setting.[21]
[22] To date, only one single-center study has leveraged EHR features, such as note authorship,
ordering provider, and time-in-chart, to develop a model predicting specific resident
PPIs (rPPIs).[23] A critical need exists in the development of an approach to accurately attribute
patients to providers using common EHR data elements, including audit logs.
Objectives
In the current study, we sought to develop a method to predict pediatric rPPIs from
EHR data focusing on audit logs. We describe the validation of these models in multiple
care settings against resident-reported rPPIs and explored the nature of these interactions
through a comment analysis of residents' patient inclusion rationale.
Methods
Study Design and Recruitment
We performed a prospective cohort study of pediatric residents at The Children's Hospital
of Philadelphia in Philadelphia, Pennsylvania, a large urban academic freestanding
children's hospital in the United States with a pediatrics residency program consisting
of 153 residents, helping to staff 3 primary care clinics, 4 emergency department
(ED) teams, and 15 inpatient teams ([Fig. 1]). Residents were engaged in patient care across multiple care settings (primary
care clinics, ED, and inpatient floors) between June 1, 2018 and September 1, 2018.
Eligibility included completion of at least one shift in any of the three care settings.
As the intended use case for our attribution method focused on resident education,
we excluded attending physicians and nurse practitioners working as FLCs. Care settings
were randomly sampled and participants were enrolled based on convenience availability
at the end of their shifts with a planned 2:1:1 sampling of inpatient:ED:clinic. We
sampled the inpatient care setting more heavily, as we anticipated greater variation
in rPPI selection, given the workflow considerations of each care setting as described
below. Enrollment continued until we achieved our target sample sizes for each care
setting based on power analyses to achieve 80% power of detecting a sensitivity greater
than 50% for detecting true rPPIs from EHR data and a specificity greater than 90%
with α = 0.05 for each model. The study was approved by the Institutional Review Board (IRB)
at The Children's Hospital of Philadelphia in Pennsylvania, United States.
Fig. 1 Pediatric residents were surveyed at the end of shifts to provide a “silver standard”
of provider–patient interactions, against which classifier predictions using EHR data
were compared. ED, emergency department; EHR, electronic health record; rPPI: resident
provider-patient interaction.
Description of Workflows
Primary Care Clinic
Residents may be scheduled for primary care half-day clinic shifts as follows: (1)
after half-day inpatient shifts, (2) before evening ED shifts, or (3) during a primary
care rotation. Prior to a clinic date, residents are assigned from three to eight
slots over 3 hours during which patients may be scheduled. Patient service representatives
fill these appointment slots, although residents may request certain patients be seen
on specific dates. Slots are typically filled with appointments prior to a clinic
date, although some slots may be reserved for walk-in appointments. Residents' names
are directly listed on appointments, even for walk-in encounters. Upon patient arrival,
residents independently take a history and physical and manage the remainder of the
visit with an attending. Following the visit and completion of documentation, residents
close the encounter, which is cosigned by the attending ([Fig. 2A]).
Fig. 2 Workflows for each resident care setting (A) primary care clinic (B) ED and (C) inpatient care settings vary widely, affecting the EHR variables relevant for a
given interaction. ED, emergency department; EHR, electronic health record; FLC, front-line
clinician; OR, operating room.
Emergency Department
At the beginning of an ED shift, residents transition the care of patients continuing
to require emergent services and assign oncoming residents to the care team for these
patients using the EHR “care team” activity. Residents continue to write orders, document,
and provide care for these patients, while also administering care to new ED patients.
Residents “sign up” for new patients by assigning themselves via the same “care team”
activity, take a physical history, place orders, and manage the visit with an attending
physician. Residents continue to provide care through either discharge or admission,
but do not close encounters ([Fig. 2B]).
Inpatient Floor
On inpatient floors, residents serve as FLCs for patients. Teams either have a senior-intern
structure composed of two senior residents and four interns or a “resintern” structure
composed of four second-year residents acting in the FLC role. Within these teams,
residents assign themselves as a patient's primary FLCs via the EHR “care team” activity.
While the assignment of daytime coverage is performed consistently, night-time coverage
is rarely updated in the EHR. As FLCs, residents write daily progress notes, place
orders, and provide direct patient care. Each patient is exclusively assigned an FLC,
but residents may care for patients on the team for whom they are not FLCs in a “cross-coverage”
role, when a patient's FLC may be in clinic, postcall, or busy with other patient
care. As a result, patients may receive care from multiple residents on the same team.
Upon patient discharge, residents write discharge summaries but to do not close encounters
([Fig. 2C]).
Identification of Resident-Reported rPPIs
Pediatric residents were approached by the study investigators (M.V.M. and A.C.D.)
and provided verbal consent, which was recorded per IRB regulations. No participant
declined to participate or refused to provide consent. Each interview lasted for approximately
10 minutes including time to describe the study, obtain, and record verbal consent
and administer the survey. Residents were able to participate on multiple shifts and
in multiple care settings. Survey responses were collected on paper and entered onto
electronic forms after data collection by the investigators. Using their EHR's report-generating
functionality, the study investigators presented participants with a list of potential
patients with whom they may have interacted based on their care setting using the
following filters:
-
Primary care clinic (clinic): all patients with a scheduled visit in the clinic during
that shift.
-
ED: all patients seen by the resident's ED care team during that shift.
-
Inpatient: all patients on the inpatient care team during that shift, including patients
who were admitted, transferred, or discharged.
Participants were asked to identify patients with whom they had a “clinically meaningful
experience,” such that they would want a given patient to appear in an automated case
log. These patients were classified as “rPPI” versus “non-rPPI,” which together comprised
a “silver standard” against which we compared model outputs. This prompt was chosen
based on a short series of semistructured interviews with residents prior to data
collection, where they expressed that serving as an FLC for a patient did not necessarily
constitute a meaningful interaction, since not all interactions contain educational
value. Residents also expressed that they sometimes gleaned educational value from
patients for whom they did not serve as FLCs, particularly when they “cross-covered”
a patient in the inpatient setting. While a time motion study would have provided
objective data about residents' interactions with patients, doing so would have been
time and resource-prohibitive without fully capturing the perceived educational value
of an interaction.
To further elucidate the types of interactions, participants had with patients and
whether certain rPPIs may be more easily predicted with EHR data, participants were
encouraged to comment on their rationale for marking rPPIs next to each rPPI with
a short description. Two of the study investigators (M.V.M. and A.C.D.) independently
grouped the comments into qualitative themes based on type of interaction described
(e.g., FLC, learned about on rounds, and examined patient). Themes generated by the
investigators were then compared and resolved by consensus agreement by the investigators
(M.V.M and A.C.D.).
Development of Automated rPPIs
Variables Included in the Model
EHR data elements associated with clinical activity were identified and extracted
from the Epic Clarity database (Epic Systems, Verona, Wisconsin, United States) for
all patient encounters (both rPPIs and non-rPPIs) identified during the validation
data collection. The selection of data elements to be extracted were based on the
clinical workflows in each care setting (Description of Workflows, [Fig. 2]), residents' comments from the semistructured interviews, and previously presented
approaches to secondary use of data in the EHR.[15]
[23] In an attempt to create a broadly applicable model that was only specific to care
setting, we chose to include variables likely to be available from any EHR. These
variables included time-in-chart calculated from EHR audit logs, note authorship (signed
or shared), order placement, care team assignment, and chart closure ([Table 1]). All covariates except for time-in-chart were represented as dichotomous variables.
While patient level characteristics, such as age, complications, comorbidities, illness
severity, and length of stay, may have an effect on attribution, inclusion of such
features would limit generalizability in other care areas and thus were excluded from
the study.
Table 1
Model input variables and variable characteristics
|
Variable name
|
Data type
|
Definition
|
|
Time-in-chart
|
Continuous
|
Discrete time stamped audit logs generated by a resident within the hospital were
algorithmically converted into shifts, represented in 10-minute increments
|
|
Note authorship
|
Binary
|
The resident authored, edited, or signed ≥ 1 note in the patient's chart (1 = yes,
0 = no)
|
|
Order placement
|
Binary
|
The resident placed ≥ 1 order in the patient's chart (1 = yes, 0 = no)
|
|
Care team
|
Binary
|
The resident had a role documented in the patient's EHR care team (1 = yes, 0 = no)
|
|
Chart closure
|
Binary
|
Clinic only.
The resident in question was the provider closing the patient's chart
|
Time-in-chart was calculated from discrete audit log events generated by a given resident
in a given patient's chart, as previously described.[24] The general workflow included audit log extraction, calculation of interaction intervals,
and aggregation into total time in a patient's chart. Since the institution's EHR
records user interactions in the system as discrete timestamps, we extracted all audit
logs generated from a hospital workstation within the participant's shift with a patient
identifier attached, without filtering by interaction type (e.g., “login,” “logout,”
and “chart closure”). Shifts were defined as the period of time prior to survey administration
which contained any audit log entries for that participant. From these discrete entries,
we generated time gaps between audit log entries, representing the duration between
subsequent interactions for a given user. To exclude possible “inactive” time in a
chart, lapses greater than 30 seconds marked the end of one interval and the beginning
of another. Intervals of interaction within a patient's chart during a shift were
summed from these time gaps and represented in 10-minute increments.
Classification Method and Evaluation
We created classifiers based on multivariable logistic regression, decision tree,[25] and random forest[26]
[27]
[28] models from the selected features and identified the significant covariates for
each care setting. The various classifiers performed equally well with near-identical
sensitivities, specificities, positive predictive values (PPVs), and c-statistic values. Given the equivalent characteristics of the classifiers, we chose
logistic regression as our primary classifier, as we felt that clinician educators
would likely have the most experience interpreting this classifier. All variables
were included for model development.
For all binomial regression models, the dependent variable was patient class (rPPI
vs. non-rPPI) and independent variables were the covariates listed in [Table 1]. Odds ratios (ORs) and 95% confidence intervals (CI) are presented for significant
and nonsignificant covariates. All analyses were performed in R version 3.3.3.[29] Using K-fold cross validation, we generated classifier predictions and constructed receiver
operating characteristics (ROC) curves. Concordance statistic (c-statistic) and CIs are presented for each ROC curve. Optimal threshold cut-offs were
determined by previously described methods of minimizing the costs associated with
false-positive and false-negative results[30] and 2x2 concordance tables are presented at these cutoffs.
Results
Between June 1, 2018 and September 1, 2018, we interviewed 81 unique residents over
111 shifts (24 clinic, 20 ED, and 47 inpatient shifts). From a total of 4,899 potential
rPPIs, participants identified 579 rPPIs and 4,320 non-rPPIs ([Table 2]). Of the rPPIs identified in the clinic care setting, 87.2% were well child visits,
and 12.8% were sick visit encounters.
Table 2
Description of validation data
|
Care setting
|
No. of unique resident shifts
|
No. of rPPIs
n (%)
|
No. of non-rPPIs[a]
n (%)
|
|
Clinic
|
24
|
82 (3.25)
|
2,440 (96.75)
|
|
ED
|
20
|
131 (21.65)
|
474 (78.35)
|
|
Inpatient
|
47
|
366 (20.65)
|
1,406 (79.35)
|
|
Total
|
111
|
579 (11.82)
|
4,320 (88.18)
|
Abbreviations: ED, emergency department; rPPI, resident provider–patient interaction.
a Non-rPPIs were potential rPPIs listed on a patient census for the resident's shift
that the resident excluded from the list of rPPIs.
In applying the logistic regression classifier across all care settings, increased
time-in-chart was significantly associated with a patient being classified as an rPPI
([Table 3]). For example, in clinic for each ten-minute increase in time-in-chart the odds
of being classified as an rPPI increased by a factor of 19.36 (95% CI: 4.2–278.6),
while in the ED for each 10-minute increase in time-in-chart the odds of being classified
an rPPI increased by a factor of 19.1 (95%: CI, 9.5–41.7). In the ED, authoring a
note was also significantly associated with an rPPI classification (OR= 10.8, 95%
CI: 3.6–35.9). Orders placed, note authorship, assignment on care team, and total
time-in-chart all significantly contributing to predicting rPPI in the inpatient care
setting.
Table 3
Odds ratio of being classified as PPIs in clinic, ED, and inpatient contexts by multivariable
logistic regression
|
Variable
|
Clinic OR (95% CI, p)
|
ED OR (95% CI, p)
|
Inpatient OR (95% CI, p)
|
|
Total time-in-chart[a]
|
19.36 (4.19–278.56, p < 0.01)
|
19.06 (9.53–41.65, p < 0.001)
|
2.95 (2.23–3.97, p < 0.001)
|
|
Note authorship
|
2.23 × 109 (4.58 × 10−71–NA, p = 0.99)
|
10.8 (3.55–35.93, p < 0.001)
|
2.25 (1.39–3.58, p < 0.001)
|
|
Orders placed
|
2.52 (0.10–4.15, p = 0.52)
|
1.63 (0.38–7.44, p = 0.52)
|
2.74 (1.84–4.07, p < 0.001)
|
|
Care team assignment
|
6.5 × 10−9 (NA–3.83 × 1082, p = 0.99)
|
0.60 (0.13–3.00, p = 0.52)
|
2.28 (1.39–3.72, p < 0.001)
|
|
Chart closure[b]
|
16.92 (0.13–540.72, p = 0.16)
|
–
|
–
|
Abbreviations: CI, confidence interval; ED, emergency department; NA, not available;
OR, odds ratio; PPI, provider–patient interaction.
a Total time-in-chart is represented in 10-minute increments.
b Chart closure is performed by residents only in primary care.
[Fig. 3A–C] displays the ROC curves for each care setting-specific classifier, along with concordance
tables at optimal cutoff thresholds. The clinic and ED models had similar predictive
performance, with a c-statistic of 0.999 (95% CI: 0.998–1) for the clinic model and of 0.982 (95% CI: 0.966–0.997)
for the ED model. The inpatient model had a c-statistic of 0.895 (95% CI: 0.876–0.915). Given the lower predictive performance
for the inpatient care setting model and the potential for competing priorities when
choosing a cut-off, we plot sensitivity, specificity and PPV across a range of cut-off
values ([Fig. 3D]). At an example cut-off value of 0.5, the sensitivity was 56.3%, specificity was
96.2%, and PPV was 79.5%.
Fig. 3 Receiver operator characteristic curves show excellent prediction of PPIs for the
clinic and ED submodels, while the inpatient submodel performed reasonably well. (A–C) Submodels for each care setting studied (D) Impact on sensitivity, specificity, and positive predictive value (PPV) with varying
cutoffs. Dashed line indicates optimal cut-off displayed in confusion matrix in part
(C). ED, emergency department; PPI, provider–patient interaction.
The analysis of the survey comments in the inpatient care setting showed that of 366
inpatient rPPIs, residents provided reasons for 330 (90.1%) with an average length
of 1.98 words per comment. Participants commonly identified direct involvement in
a patient's admission or transfer, or care as the FLC (55.6%, n = 183) as the rationale. Discussion of a patient on rounds (14.6%; n = 48) and patient/family interactions (14.3%; n = 47) were less frequently stated reasons for selections. Only 9.2% (n = 31) of responses were grouped into involvement in cross-coverage or escalation
of care. Another 6.1% (n = 20) of responses were grouped as miscellaneous comments not fitting into another
theme.
Discussion
In this study, we have demonstrated that an approach involving EHR data elements,
particularly audit log data, may yield salient predictors of pediatric rPPIs. Unique
compared with prior work, we validate the interpretation of these rPPIs in multiple
care settings. These commonly collected EHR data elements, including time-in-chart,
note authorship, care team assignment, and order placement, accurately predicted resident-identified
rPPIs. Time-in-chart estimated through established algorithms was the best predictor
of rPPIs in all three care settings,[22]
[24]
[31] contributing to extremely accurate (c-statistic > 0.98) predictive models in the clinic and ED care settings. Attributing
patients in the inpatient care setting was less robust (c-statistic > 0.89) and suggested the significance of other covariates in the prediction.
Data from the comment analysis of resident-reported rationales demonstrated that inpatient
providers identified rPPIs when they were directly involved in a patient's care, most
commonly during an admission, discharge, or transfer and less commonly during cross-coverage
or escalation of care.
The current study builds on the work of EHR-based patient attribution using workflow
heuristics, which highlighted several key variables, many of which overlap with the
variables we used in our models. To examine whether EHR data could predict which primary
care provider a patient should see during future appointments, Tripp et al found that
“previously scheduled provider” was the best of the predictors examined, including
the named primary care physician, placement of orders, and note writing.[32] However, the resulting model had a false positive rate >50% following both inpatient
and emergency department visits. Atlas et al augmented scheduling and billing data
with patient and physician demographic data to develop a model with a PPV of >90%.[33] However, nearly 20% of patients were inappropriately labeled as not having a provider.
Herzke et al used billing data to assign an “ownership” fraction of specific metrics
to multiple attending providers for a single hospital stay.[1] This approach cannot be applied to nonbilling providers, such as residents, and
may mask the contribution of cross-covering providers. These promising EHR-based approaches
yielded initial insights to variables important for patient attribution and provide
opportunity for improvement when focusing on rPPIs.
Relatively few studies focused on medical education have used EHR data to attribute
patients to providers.[17]
[19]
[23] Recently, Schumacher et al performed a feasibility study to attribute care of individual
patients to internal medicine interns on an inpatient service using postgraduate year,
progress note authorship, order placement, and audit log data resulting in a model
with a sensitivity approaching 80%, specificity near 98%, and a c-statistic of 91%.[23] Similarly, Sequist et al tested the feasibility of using outpatient encounters linked
to internal medicine primary care resident codes for the purposes of informing resident
education.[34] Our study reinforces the importance of similar variables with particular emphasis
on audit log data, in a separate population across multiple care settings. This finding
supports the notion that residents are more likely to report an rPPI as they spend
more time in a patient's chart. Given that physicians are likely to spend greater
amount of time in the charts of the patients for whom they are primarily caring, as
reflected in our qualitative data, the strength of association between time-in-chart
and rPPIs may hold external validity beyond resident physicians. Time-in-chart cut-offs
may vary depending on the unique characteristics of a given workflow and site-specific
validation may be necessary. The inclusion of other EHR data may help augment the
accuracy of such models in other use cases.
Our approach to patient attribution is focused on identifying patients from which
pediatric residents have gained educational value. With similar motivations, Levin
and Hron described the development of a dashboard built for pediatric residents that
utilized records of resident documentation in a patient chart as a means for patient
attribution, although validation metrics around patient attribution were not reported.[15] While asking residents to identify “clinically meaningful experiences” may limit
the utility of these specific models to medical education, the methodology employed
in this paper may be generalizable across other use cases in which robust patient
attribution methods are desired. Researchers may be able to ask participants more
or less targeted questions and revalidate to adjust model cut-offs for other needs,
such as generating individualized physician quality metrics or developing clinical
decision support for specific care team members. Together with previous work done
in this area, we hope that by characterizing a specific resident population across
multiple care settings using a common EHR data model that others may be able to begin
to test the feasibility of our approach more widely.
Future work will require demonstrated generalizability of the framework outlined here
to other specialties, provider populations, and institutions. However, perhaps more
exciting are the potential use cases that could benefit from improved attribution.
From the standpoint of medical education, curricular decisions could be influenced
at a programmatic level with data about aggregated trainee exposure, while individual
trainees could tailor their electives or self-directed learning based on exposure
gaps. Such precision education could further be accelerated by the delivery of targeted
educational material shortly after exposure for just-in-time reinforcement of learning.
At a national level, aggregated data could also start to unravel how clinical experiences
at different institutions may vary from one another. The potential applications extend
beyond medical education, as improved attribution could enhance targeted role-based
notifications and outcomes-based learning for decision support through positive deviance
models.[35] In a health care system where many patients have several providers, patients may
also directly benefit from knowing which providers are attributed to them in an automated
fashion.[36] Given the fundamental relationship between patient and provider, accurate patient
attribution has wide-ranging applications.
Limitations
There are several limitations to our work. Most importantly, our study focuses on
pediatric residents at a single institution. While we have reason to believe that
many components involved in EHR-derived rPPIs may also be important for determining
rPPIs at other locations, our study limits this conclusion. Local clinical workflows
may vary widely, making determination of rPPIs using our approach easier or harder.
Alternate workflows where trainees do not sign clinic or ED provider notes would necessitate
modification of existing models.
The focus of our study on a pediatric resident population in one academic institution
may further limit generalizability to other types of providers in different settings.
As examples, primary care pediatricians, pediatric inpatient hospitalists, and pediatric
ED physicians practicing independently likely have a workflow that is more streamlined
and possibly less contiguous, resulting in different time-in-chart values. Adjustment
of cut-off points for the models may or may not capture accurate attribution in these
scenarios, and warrants further study. Though the significant variables within our
models are suspected to be universal across institutions, they may be the result of
workflows specific to this group, warranting additional work to validate this approach
more broadly. Furthermore, while direct observation of clinical workflows is considered
“gold standard” for attribution, we opted for a survey-based “silver standard” for
manually identifying rPPIs, which allowed identification of interactions with “educational
value” that would not have been identified with a time-motion study. This approach
may have been subject to recall bias and under- or overreporting. A clearer definition
when asking residents to identify rPPIs and/or the inclusion of additional variables
may lessen trade-off between sensitivity and specificity in the inpatient submodel.
However, through our comment analysis in the inpatient care setting, we were able
to elaborate on what factors play into how providers would like to attribute themselves
to patients.
Conclusion
Routinely collected EHR data, including EHR audit times, note authorship, care team
assignment, and order placement can be used to predict resident-defined rPPIs among
pediatric residents in lieu of manually documented case logs. Highly accurate methods
of patient attribution, like the one described here, have broad applications ranging
from measuring medical education to individualized clinical decision support. Future
research will be needed to determine the generalizability of this approach to other
populations of providers across other subspecialties, as well its application to other
use cases outside of medical education.
Clinical Relevance Statement
Clinical Relevance Statement
Accurate EHR-derived rPPIs have the potential to transform data-driven graduate medical
education. From a practical standpoint, rPPIs could help trigger targeted educational
content based on recent patient experiences with specific diagnoses, possible multiple
choice questions as a component in the assessment of competency, and patient experience
surveys when measuring trainees' communication skills. The methods described in this
paper advance our understanding of how EHR data may help identify rPPIs and lays the
groundwork for future work in other providers, as well as other use cases.
Multiple Choice Questions
Multiple Choice Questions
-
Which of the following is the most practical use case of accurate patient attribution?
-
Developing swim-lane diagrams for multidisciplinary clinic flow.
-
Identifying providers in need of additional elbow-to-elbow support.
-
Generating a departmental order set for standardized lead screening.
-
Identifying follow-up physicians for patients discharged from the ED.
Correct Answer: The correct answer is option d. The process of patient attribution entails matching
patients to providers responsible for their care. The potential applications are myriad,
but offer the most benefit for physician centered efforts. For example, quality improvement
initiatives looking to improve the care of an outpatient population with type-II diabetes
might use patient attribution models to determine the physicians' responsibility for
the care of these patients. Alternatively, individualized clinical decision support
systems might leverage the use of accurate patient attribution models to display certain
alerts to certain providers, based on their role in a patient's care. Of the choices
listed, answer (d) is the only option that involves targeting the correct physician
based on patient care.
-
What EHR data element is the most likely to be the strongest predictor for a highly
accurate model of patient attribution?
-
Provider training level
-
User login context.
-
EHR audit logs.
-
Order placement.
Correct Answer: The correct answer is option c. While previous studies have used provider demographic
data, like postgraduate training level, to predict provider–patient interactions,
the models performed poorly. Login context may be used to narrow possible providers
for a given patient, but does not provide data granular enough to attribute providers
to patients. Order placement may be a potential contributor to a model; however, this
may be a nonspecific action, as cross-covering providers may place orders for patients
in lieu of colleagues but would not consider themselves to be a patient's primary
care provider. EHR audit logs, particularly when time-in-chart is calculated, can
be a proxy for the amount of time and effort a provider is spending on a patient's
care and thus is likely to be a strong predictor of patient attribution.
