Keywords electronic health records - optimization - ambulatory - COVID-19 - consolidated framework
for implementation research
Background and Significance
Background and Significance
There has been an increase of adoptions of new electronic health record (her) systems
by health care institutions over the years with a goal of standardizing and improving
the quality of care that they can provide to their patients.[1 ] Throughout the process of EHR implementation, challenges may arise and ways to improve
the use and efficiency of the EHR may also be recognized. Previous studies have shown
one reason that separates “good” versus “great” EHR implementations in ambulatory
settings is the additional focus on improvement over time and standardization through
optimization.[1 ] In fact, Moon et al stressed how “such optimization following the go-live is critical
to successful implementation in ambulatory settings.”[2 ] Ultimately, finding ways to optimize EHR usage after it has undergone implementation
“is a critical step toward achieving the EHR's potential as a tool for facilitating
high quality and efficient care.”[3 ]
The focus and approach that organizations take when optimizing an EHR may differ based
on each organization's goals and priorities. While some organizations may focus optimization
efforts on patient outcomes (such as reducing blood clots and catheter-associate urinary
tract infections by putting alerts in their EHR during their optimization phase),
others may focus on optimizing the core clinical workflow at the enterprise level.[4 ] After having gone through a large structural change with implementation of a new
EHR system, in February 2020 and in the midst of the novel coronavirus, the novel
coronavirus disease 2019 (COVID-19) global pandemic,[5 ] Columbia University Irving Medical Center (CUIMC) had an immediate need to create
and adopt an optimization methodology of clinical and operational best practices to
effectively deliver the new model of patient care. The COVID-19 pandemic forced providers
and staff in CUIMC to quickly adapt and learn the new EHR system, given that the pandemic
hit only 1 month after the EHR go-live date. Given other priorities, including ways
to keep all employees safe, one can say that the implementation was an expedited process
and not a primary focus. As a result, not all stakeholders learned to utilize the
new EHR system effectively. There was great focus at CUIMC on one optimization process
identified by Moon et al which was increasing EHR efficiency by optimizing practice/process
workflows.[2 ] The design and delivery of the optimization methodology at CUIMC was studied through
the use of the Consolidated Framework for Implementation Research (CFIR) model to
ensure the maximum adoption of these best practices in the study's pilot department.
The pilot and the control groups were similar subspecialties in size, volume of patients,
types of visits, and user level of proficiency in the EHR. The pilot department was
chosen because its users are early adopters and are led by engaged and supportive
leadership.
Overview of the Consolidated Framework for Implementation Research Model
The multilayered approach of the CFIR[6 ] provides grounds for understanding how evidence-based practices are best integrated
and adopted among multiple external and internal variables. Building on Harrison's[7 ] open systems model, the CFIR model enforces evaluation of the climate, typically
defined by individuals of multiple levels of an organization, their respective interactions,
and the cumulative impact across the following five domains of the model: intervention,
outer setting, inner setting, individual characteristics, and process, as depicted
in [Fig. 1 ].
Fig. 1 The five domains of the CFIR model visualizing the inner and outer settings and the
movement from unadapted to adapted of an intervention in any given process. CFIR,
Consolidated Framework for Implementation Research.
The outer setting can be understood as the governing policies and procedures of an
organization, as well as the patients and their respective needs and resources. The
inner setting represents an organization's structural characteristics, the culture
and implementation climate, resources available to local teams and the ability of
engaged leadership to provide clear goals and feedback. The success of adaptability
of a given best practice across the course of any given process is determined by the
interaction between members of the outer setting and those of the inner setting. The
CFIR model proposes creating an environment of psychological safety, providing protected
time and venues to enable creative thinking and evaluation, and showcasing the importance
of collaboration and partnership, as the most critical competencies required of the
outer setting.[8 ]
The CFIR model also provides guidance around assessing certain intervention characteristics
such as adaptability, trial ability, relative advantage, and complexity. These factors
are critical to evaluate when introducing best practices to the inner setting members
and appropriate support structures need to be in place within the environment to enable
successful adoption.
With respect to the CFIR model and this study, the pilot department is the system
of focus. Therefore, the individuals involved are to be considered the inner setting.
Everything external to that is considered the outer setting, inclusive of the organizational
governing policies and resources, the leadership, and COVID-19.
The questions of interest discussed in this paper are mentioned below:
Is the proposed framework, utilizing the CFIR model effective in guiding inner settings
to identify and execute optimization opportunities?
As a result of the optimization efforts of the inner setting, can the pilot ambulatory
clinical department improve key performance metrics when compared with a control group?
Methods
Study Design
CUIMC has an established Organization Effectiveness and Optimization (OEO) team whose
primary objective was to enable the organization to proactively meet strategic goals
by designing targeted, data-driven, and cost-effective interventions at the people,
process, and technology levels. As the internal Organization Development practitioners,
this group designed the optimization methodology with two primary objectives: (1)
utilize data to assess and monitor optimization progress, and (2) engage frontline
staff to create accountability for results in the change process. Facilitators of
the EHR optimization process included a clear vision, committed leaders, dedicated
resources, stakeholder engagement, and workflow analysis.[9 ] Within the context of the CFIR model, the organization development team was part
of the outer setting and supported the knowledge transfer and provision of tools and
resources during the optimization methodology implementation. The inner setting, comprised
of the clinical pilot department, was accountable for the execution and success of
the intervention. Understanding these two competencies, the methodology was structured
into two layers: the first layer was at the ambulatory department level where workflows
were divided into operational and clinical groups. The second layer was at the organization
level which was designed to support all ambulatory departments through the implantation
of learning models and cross-specialty collaborations ([Fig. 2 ]).
Fig. 2 The meeting structures at the department and organization level visualizing the cascading
nature of the local workgroups to the larger organization model.
Operational and Clinical Workgroups
The department level operational and clinical work streams were supported by workgroups.
The operational workgroup consisted of members of the frontline staff representing
each of the staff roles of that practice. Frontline stakeholders included medical
receptionists, practice assistants, medical assistants, and the Director of Operations.
The medical receptionists were tasked with administrative duties such as scheduling,
greeting patients, responding to messages on EHR, and checking patients in and out.
The practice assistants were responsible for surgical scheduling, referral authorizations,
work queues, and responding to messages on EHR. The medical assistants managed patient
flow through the practice and supported physicians during in-person visits by taking
patient vitals and assisting with procedures. Lastly, the Director of Operations was
responsible for operational support and clinical oversight of different practice locations.
The clinical workgroup consisted of the department's clinical lead physician and additional
EHR personalization leads who were tasked with personalizing available tools in EHR
to enable efficiencies of clinical workflows.
Members of both these workgroups served as the opinion leaders and were responsible
for executing and evaluating the optimization efforts, as it related to the CFIR model.
Both workgroups utilized an agenda and minutes for each weekly meeting for the duration
of the study. The expectations of the department leadership were to provide protected
time to the workgroups to implement and monitor changes, as well as to provide communication
channels for messaging and soliciting feedback from the rest of the department.
One of the primary goals of the workgroups was to create an environment to support
constructive feedback and to allow for full transparency of successes and lessons
learned. Additionally, both workgroups needed to maintain cross-group communication
to ensure alignment on workflows and to understand the impact of their respective
interventions.
The organization layer was designed to bring together the individual department level
workgroups to enable best practice sharing and group learning among the organization.
For the operational work stream, these events were the Transformation Collaborative
Meetings and for the clinical work stream, these events were the Cross-Specialty Provider
Group Meetings.
Optimization Methodology Phases
The process or implementation was further divided into three distinct phases as described
hereinafter.
Phase 1
The first phase was to complete a current state assessment of the prioritized workflows.
The current state assessment was performed by the OEO team and the operational workgroup.
This was done through conducting observational studies within the practice setting,
observing high performing clinicians to identify best practices, and reviewing baseline
metrics/key performance indicators (KPIs). The operational and clinical metrics, which
were reviewed, were retrieved from EHR data extracts.
The high performing clinicians were identified with the guidance from the clinical
lead reviewing the previously mentioned metrics. The sessions were conducted by the
OEO team both in person and via video calls depending on the availability of the providers.
The OEO team met with each provider once for the duration of an hour. This process
helped in understanding the evidence strength and quality of the given workflows.
The current state process maps were reviewed and validated during the weekly workgroup
meetings. During this phase, a preproject staff survey was administered to the department
staff to identify additional opportunities for optimization and learning gaps. This
survey was designed by OEO team and was administered electronically to the department
staff.
Phase 2
The second phase was designing interventions that included the needed core components
and accounted for the adaptable periphery to ensure maximum adaptability and trial
ability. The core components included the respective clinical and operational KPIs,
and feedback from frontline staff. The adaptable periphery was defined by level of
engagement outside of the workgroup to adopt the interventions. Once created, the
intervention was piloted at a single clinical practice location (pilot department)
to optimize target workflows which were set by organizational leadership, and to bring
them closer to the desired future state. Weekly data were reviewed to monitor this
implementation and feedback was continuously solicited by the workgroup members of
their peers to understand the impact of the interventions.
Phase 3
The third phase was scaling up the adoption of the successful intervention by other
clinical practice locations with continuous data evaluation. The clinical workflows
were optimized through the Provider Group Meeting (PGM) structure: monthly sessions
designed to bring together representative providers from their respective departments
to learn best practices from each other and to communicate new system changes. Due
to the limitations brought about with the pandemic, the PGMs were conducted virtually.
In addition to the PGMs, the organization development team conducted personalization
sessions for providers when data and observations indicated opportunities for gained
efficiencies. Pre- and postproject clinical staff surveys were administered electronically
among the clinical department staff to identify additional learning gaps and opportunities
for improvement.
These surveys were entirely opinion-based and were not intended to validate the proposed
methodology. This methodology is summarized in [Fig. 3 ].
Fig. 3 Visual representation of the optimization methodology highlighting the specific details
of the different phases of the at the department level and the cascading of those
cumulative steps to the organization level.
Implementation of the Optimization Methodology in Pilot Department
The optimization methodology was first implemented within the selected pilot department
to improve a selected set of their operational and clinical workflows.
Operational Workgroup Workflows
Within this context, the outcomes were the identified workflows measured by the metrics
identified on EHR, and the goal was achieving the established targets for each respective
workflow. The operational workgroup, outlined in [Table 1 ], identified the following workflows as targets for optimization:
Table 1
Overview of evaluated operational metrics
Operational metrics
Definition
MyChart activation
Percent of patients seen in a department with an active MyChart account
eCheck-In
The percentage of arrived appointments where eCheck-in is completed. Walk-in appointments
are excluded
Schedule utilization
Displays the schedule utilization of a provider. This is calculated as all booked
time, plus late cancel time, divided by the providers' templated regular time
Average regular time
This metric indicates how much time per patient is spent updating registration information
Abbreviation: HER, electronic health record.
Source: EHR dashboard reports.
MyChart activation and usage (patient facing application).
eCheck-in (via the MyChart patient facing application).
Average registration time.
Provider schedule utilization (the ratio of time slots available to time slots used
to see patients).
Different interventions were developed to help improve the different operational workflows.
The interventions designed to increase MyChart enrollment included the utilization
of standardized scripts during previsit phone calls to patients, decreasing the overall
number of phone calls made to patients by eliminating that task from other workflows
where it was found to be duplicative, and provision of support materials that was
shared and reviewed with the staff to ensure that they were all using the most current
resources and were familiar with the contents. The interventions used to increase
the use of the electronic check-in (eCheck-in) process through the MyChart portal
and application included utilization of scripts to actively encourage patient use
of the feature, automated appointment reminder messages across the organization that
had already been updated to encourage e-check in reminders and integration of mobile
kiosk stations, and a patient greeter to try and facilitate the adoption of low-touch
methods of patient eCheck-in. The interventions utilized to reduce the average time
to complete the registration process included participation in a curated workflow
review session conducted by the enterprise training team by all department staff responsible
for registration-related functions.
The project timeframe coincided with an organizational wide initiative to optimize
provider schedule templates, to improve schedule utilization rates, and to support
an increase in postpandemic in-person visit volume.
Clinical Workgroup Workflows
The clinical workgroup, outlined in [Table 2 ], prioritized the following two clinical workflows:
Clinical patient note composition.
Turnaround time on a select set of patient messages: results, medical advice requests,
patient calls, and prescription refill authorizations.
Table 2
Overview of evaluated clinical metrics
Clinical metrics
Definition
Turnaround time–results
Average number of days a provider took to mark a results message as done
Turnaround time–medical advice request
Average number of days a provider took to mark a medical advice request as done
Manual note composition
Percentage of note content generated by the provider using manual methods
NoteWriter note composition
Percentage of note content generated by the provider using SmartBlocks, Macros or
voice recognition in a SmartBlock
SmartTools note composition
Percentage of note content generated by the provider using SmartLinks, SmartTexts,
SmartPhrase, or SmartLists
Dictation note composition
Percentage of note content generated by the provider using voice recognition (e.g.,
M*Modal fluency direct)
Copy/paste note composition
Percentage of note content generated by the provider using copied, pasteboard, copy
previous/forward
Abbreviation: HER, electronic health record.
Source: Provider efficiency profile via EHR data.
Personalization sessions and a PGM were scheduled to target improvements in these
two workflows. The personalization lead and organization development team facilitator
conducted sessions with 10 out of the 13 attending physicians in the pilot department.
The interventions focused on decreasing manual note composition through the development
of additional note templates and education on EHR functionalities to ease documentation
burden. During the PGM, content was designed to assist clinical staff adopt additional
functionalities within EHR InBasket (messaging feature), as well as develop training
content on how to use the InBasket more efficiently to improve turnaround time.
The control department, a matched surgical subspecialty department, did not have any
established workgroups conducting targeted interventions on clinical and operational
workflows utilizing the developed optimization methodology during this project period.
Results
To evaluate whether the optimization methodology was effective in improving the operation
and clinical workflows, baseline operational and clinical data were collected in May
2020, and the poststudy data were collected in August 2020. Metric data were collected
from the pilot department, and control data were captured from a comparable surgical
subspecialty department with similar volumes and clinical services.
Clinical Results
Optimization efforts were completed in August 2020, whereupon usage pattern data were
compared with preintervention (SP) time point in May 2020. An increase in SmartTool
usage was noted by 5.7% and a decrease in manual note composition was observed by
6.09%. Comparing the baseline data to the August data for the InBasket turnaround
time on patient related messages, a decrease in all message types except for medication
advice was noted. It is worth noting that the August data met and exceeded the EHR
community benchmark for turnaround time. CUIMC had set a unique target for 1-day turnaround
on all patient related messaging. All metrics, EHR community benchmarks, and CUIMC
targets can be referenced in [Table 3 ].
Table 3
Clinical metrics data for pilot and control departments
Pilot department
Control department
Baseline
Comparison
Variance from baseline
Variance from target
Baseline
Comparison
Variance from baseline
Variance from target
Turnaround time, results (d) target 7.1
5.63
3.21
−43.00%
Target met
5.22
2.69
−48.47%
Target met
Turnaround time, medical advice request (d) target 2.8
0.94
0.48
−48.70%
Target met
1.38
3.23
134.06%
−13%
Manual note composition target 8.5%
17%
11.50%
−34.08%
−26%
16.26%
14.75%
−9.29%
−42%
Note writer note composition target 8.8%
0%
0.48%
13.43%
1741%
4.37%
6.44%
47.37%
37%
Smart tools note composition target 59%
52%
55%
5.19%
7%
47.75%
52%
8.90%
13%
Dictation note composition target 6.6%
5%
6.40%
39.19%
Target met
0.00%
0.00%
0.00%
−100%
Copy/paste note composition target 16%
25%
27%
6.43%
−40%
30.27%
26%
−14.11%
−38%
Compared with the control department, the pilot department met targets for InBasket
turnaround time, time in notes, and dictation note composition. Although metrics for
manual note composition and SmartTool note composition did not meet desired targets,
the metrics were trending positively throughout the pilot department optimization
study. The control group met one metric, turnaround time for results messages which
was met in the baseline and subsequent data periods.
Clinical data were analyzed using a nonparametric hypothesis test to determine the
statistical significance of the results. The nonparametric hypothesis test was utilized
due to the relatively small sample size, continuous data, and the inability to assume
normal data distribution. Overall, the pilot department had a statistically significant
increase in dictation (Z observed of −3.2909 with a Z critical of 1.96) and NoteWriter tool (Z observed of −3.1754 with a Z critical of 1.96) note composition from their baseline month to their observed month.
Compared with the control department, the pilot department had a statistically significant
increase in SmartTool (Z observed of −1.9821 with a Z critical of 1.96), and dictation (Z observed of −8.9013 with a Z critical of 1.96) note composition. We claim this with 95% certainty. See [Tables 4 ] and [5 ] for statistical analysis. Quantitative data from EHR cannot fully reflect the proficiency
levels across the pilot specialty. The clinical survey conducted before and after
implementation provided qualitative insights into how clinical staff perceived EHR
proficiency. The survey results were used to identify trends for optimization opportunities
across the specialty. Based on the trends, content was designed to be presented at
the PGMs. Content was presented from the Personalization Leads or Subject Matter Experts
(SME). The SMEs were identified via review of the EHR export data incorporating subjective
feedback from the Clinical Leads in the department. Observations were conducted on
the SMEs to verify workflow efficiency. The key finding from the survey responses
was an 80% perceived efficiency loss with InBasket management. This fortified prioritization
of the InBasket Management as a secondary optimization focus to note writing. In addition
to note writing and InBasket management, PGM content included reporting and billing
workflows in EHR. The PGM platform was also used to discuss recent changes and issues
from EHR upgrades. Provider optimization benefits were realized via a cyclical model
of personalization sessions, PGMs, and data monitoring.
Table 4
Nonparametric hypothesis test—control versus pilot department
Ho
There is no difference in the distribution of the two populations
Ha
There is a difference in the distribution of the two populations
Metric
Z observed
Z critical
Conclusion
Copy paste composition
0.5045
1.96
Fail to reject Ho
Manual composition
1.4775
1.96
Fail to reject Ho
NoteWriter composition
−0.5225
1.96
Fail to reject Ho
SmartTool composition
−1.9821
1.96
Reject Ho
Dictation composition
−8.9013
1.96
Reject Ho
Turnaround time for patient medical advice requests
0.7800
1.96
Fail to reject Ho
Turnaround time for patient results
−0.2523
1.96
Fail to reject Ho
Table 5
Nonparametric hypothesis test—pilot department baseline month verses observed month
Ho
There is no difference in the distribution of the two populations
Ha
There is a difference in the distribution of the two populations
Metric
Z observed
Z critical
Conclusion
Copy paste composition
−0.2887
1.96
Fail to reject Ho
Manual composition
1.5011
1.96
Fail to reject Ho
NoteWriter composition
−3.1754
1.96
Reject Ho
SmartTool composition
−0.9238
1.96
Fail to reject Ho
Dictation composition
−3.2909
1.96
Reject Ho
Turnaround time for patient medical advice requests
0.1952
1.96
Fail to reject Ho
Turnaround time for patient results
0.1732
1.96
Fail to reject Ho
Operational Results
[Table 6 ] depicts the results achieved from the operational metric workflow evaluation of
both the pilot and control departments. At the start of the project period, the pilot
department had a baseline MyChart activation rate of 39% and at the end of the period
the activation rate had increased to 49%. When comparing the project department's
variance in MyChart activation rates to the control department, it was noted that
there was a greater increase in the pilot department's activation rates. The utilization
of eCheck-in through the MyChart application was also identified as a key area for
improvement; however, the use of eCheck-in saw a decrease over the project period
(25–19% of all visits). This change coincided with the sharp increase in the department's
in-person patient visit volume (total number of video visits was relatively constant
with 265 visits in May and 223 visits in July). The control department saw a slightly
larger decrease in eCheck-in percentages (the pilot department saw a variance of −6%
compared with −11.5% in the control department).
Table 6
Operational metrics data for pilot and control departments
Pilot department
Control department
Baseline
Comparison
Variance from baseline
Variance from target
Baseline
Comparison
Variance from baseline
Variance from target
MyChart activation target 75%
38.80%
46.60%
20%
61%
57.14%
63.14%
11%
19%
eCheck in target 60%
24.78%
19.12%
−23%
68%
50.47%
38.64%
−23%
55%
Telehealth % target 30–60%
21.47%
6.08%
−72%
64.90%
30.01%
−54%
Target met
Schedule utilization target 85%
57.94%
71.56%
24%
19%
50.30%
30.01%
85%
183%
Average regular time target <5 minutes
4:44
3:51
−19%
Target met
3:14
3:53
20%
Target met
Average registration times at the start of the project period were already below the
CUIMC target of 5 minutes per patient. Overall, there was a decrease in registration
times over the course of the project period from 4 minutes and 33 seconds to 3 minutes
and 51 seconds representing a savings of 42 seconds per patient registration. This
decrease in registration time over this period also occurred at a time when patient
visit volume had increased from 1,264 visits in May to 3,721 visits in July (a 194.4%
increase over their baseline). The control department also saw an increase in visit
volume over the same period (from 2,226 visits in May to 3,980 visits in July or an
increase of 78.8%) but saw registration time per patient increase from 3 minutes 14 seconds
to 3 minutes 53 seconds (an average increase of 39 seconds per patient registration).
Schedule utilization percentage increased from 58 to 72% but it was unclear how much
of this was due to pent-up demand for in-person visits compared with optimization
of schedule templates for providers (overall visit volume increased by 194.4% for
the pilot department during this time). By comparison, the control department saw
a decrease in schedule utilization from 50% in May to 30% in July. It was not immediately
apparent why there was a decrease in schedule utilization in the control department
at a time when visit volume had increased for the control group by 79% over the project
period. Monitoring of the effectiveness of schedule optimization was ongoing and needed
further evaluation as in-person visit volumes approached prepandemic levels across
the institution.
Discussion
Using the CFIR construct to evaluate implementation of optimization methodology demonstrated
an increased likelihood of attaining optimization targets for clinical and operational
workflows when an organization creates structures to support and facilitate engaged
members of an inner setting to create workgroups that utilize essential core components
and relevant adaptable variables to design an intervention. There are numerous other
variables that contribute to these outcomes and the optimization methodology demonstrates
its utility in contributing to this mix.
Through the course of the workgroup meetings, there were several barriers identified
including time limitations and resource needs that occasionally inhibited members
from attending the weekly workgroup meetings or engaging in protected time to conduct
observations on intervention adoption. Almost all workgroup meetings were conducted
virtually which in some cases impacted the level of engagement from all users. In
addition, the rate of scaling up an intervention was sometimes impeded by competing
priorities of the department and challenges aligning priorities shared by the operational
and clinical leads. The project could have benefited from a cross-over between the
clinical and operational workgroups to ensure that department operational and clinical
leads were aligned on optimization goals. This change would enable the most efficient
use of time during workgroup meetings.
The success of interventions related to enrollment in the patient portal was determined
by technological, social, and language barriers intrinsic to the patient population
of the pilot department. Specifically, patients often required interpreter services
during outreach calls or enrollment materials and resources in their native language.
This had direct impact on the eCheck-in rate which was a metric the pilot department
continues to monitor post the project period.
The personalization sessions supported improved provider efficiency when delivered
with a targeted approach but also with on-going monitoring and touch points. The limited
amount of time the organization development team could support the project was a barrier
to longitudinal monitoring. Additionally, the department personalization leads lacked
the bandwidth for extended optimization efforts due to clinical practice demands.
Overall, the methodology, framework, and strategy utilized to implement the initial
phase of optimization efforts appear to provide an effective structure that can be
replicated and scaled in a manner that would provide the organization with a significant
return of investment. Additional investment in personnel and resources may provide
a durable support structure to facilitate longitudinal optimization efforts and sustainability
across a larger number of clinical departments. Moreover, since multiple organizations
across the country are on EHR, using the CFIR model is translated to others who are
using interoperable EHRs.
Limitations
The OEO team implemented the optimization methodology with the pilot department simultaneously
with other organizational initiatives during the middle of a pandemic, thus limiting
full allocation of resources to the effort. All Provider Group Meetings were held
virtually due to the pandemic which could have impacted engagement. Ideally, these
collaborative sessions would be held in person and depending on the specific content,
as working sessions. Additionally, clinical staff familiarity with how to navigate
within the EHR environment was increasing contemporaneously with the optimization
effort, and thus some gains in efficiency would be expected from this inherent familiarity.
Analysis of data from the control department suggests that additional gains were due
to the optimization effort. Due to data limitations, direct and exact correlation
between this initiative and the improvement in the targeted clinical and operational
workflows for the pilot department cannot be determined. The assumption was that this
methodology provided a framework to the pilot department which was useful in the improvements
noted. However, this was only shown for one ambulatory department and the methodology
will need to be studied through multiple phases with additional ambulatory departments.
Additionally, the lack of longer term longitudinal assessments was a limitation to
this study.
The pilot and control department, while representing two different clinical specialties,
were selected due to comparable patient panel size, staffing ratios, and new patient
visit and follow-up visit volumes. However, by virtue of being different clinical
specialties, there are intrinsic differences, that is, a limitation of the study.
Competing priorities, even in the setting of a tuned process, sets participants' time
commitment as a limiting factor. Further evaluation should determine if this pilot
supplanted less effective optimization work with more effective optimization efforts
to assess idealized optimization protocols. Additionally, without a professional statistician
on the team, statistical analysis is limited.
Conclusion
Following the implementation of a new her, while reacting to a global pandemic, CUIMC
recognized the need to assist clinical staff in optimizing their use of EHR to adapt
to the increasing demands of a rapidly changing environment. Designing the optimization
methodology using the CFIR model ensured appropriate accounting of multiple external
and internal variables. This optimization methodology enabled staff to utilize the
knowledge and resources available to enhance their delivery of patient care. Engaging
multiple levels of an organization in a protected workspace encouraged collaboration
and partnership to develop original solutions to critical efficiency issues. Dedicating
a team to guiding the organization through the implementation of this methodology
allowed the end users to maximize their time in the engagement, focusing on creativity,
execution, and adoption. The interventions identified were clearly initiated and executed
by the inner setting but designed for the outer setting. These interventions were
successful because they accounted for the individual characteristics and tailored
processes that were effective for specific use cases. The pilot data at CUIMC has
demonstrated that utilizing the CFIR framework can be effective for guiding an organization
through identifying optimization opportunities including enhancing clinical and operational
workflows and collaborating in the implementation of interventions.
Clinical Relevance Statement
Clinical Relevance Statement
The information provided through this manuscript can provide a reference framework
for ambulatory practices to design an optimization methodology to apply to both operational
and clinical workflows. It also provides a reference on what specific key performance
indicators can be reviewed when conducting an optimization project.
Multiple Choice Questions
Multiple Choice Questions
What was a key inner setting component identified in the optimization methodology?
Data
Workgroups
Technology
Policies and procedures
Correct Answer: The correct answer is option b. Workgroups, operational and clinical workgroups comprised
of departmental frontline staff, are a key component to the first stage of the optimization
methodology.
Through what format was the clinical workflow optimization designed?
Personalization sessions
Workgroup meetings
Provider group meetings
Quarterly group meetings
Correct Answer: The correct answer is option c. Provider group meetings, these were the monthly sessions
where all providers of a department come together for best practice sharing and peer
learning.
