Background and Significance
Provider documentation is an integral part of the medical system and historically
served to communicate active disease processes, medical decision making, and treatment
plans.[1] Identifying appropriate diagnoses for a patient on a specific encounter has impact
on a wide range of factors, including billing and reimbursement, severity of illness
determination, and patient communication, with the “Open Notes Rule” mandate allowing
patients to see patient care notes.[2]
[3] Organizations dedicate resources to optimize this process, which include clinical
documentation improvement (CDI) specialists and proprietary software.[4]
[5] When additional diagnoses are identified by trained personnel, changes to the documentation
by the provider are required to finalize the process.[6] However, with the growing concerns of provider burnout, it is difficult to demand
providers to find additional time to address these coding queries outside of their
regular clinical duties.[7]
Improving documentation within the normal provider workflow is the model solution.
The introduction of documentation improvement education and dedicated personnel has
been demonstrated to significantly increase timely completion of documentation, compliance
with quality measures, more comprehensive risk assessments, and even increases in
charges made.[7]
[8]
[9] Despite these efforts in many fields, additional education related to billing and
coding has been identified as a need, especially in residency training.[10]
[11]
[12] This is difficult given the time constrains of residencies and many competing interests
for limited time. Ideally, a process to improve documentation of specific diagnoses
could occur in real-time with little to no provider interaction, based on actions
already completed in the medical record. This has been used in quality reporting and
research data collection.[13] The electronic health record (EHR) can offer opportunities to leverage results,
orders, and documented assessments to identify specific diagnoses. An emergency medicine
study leveraged prior orders for specific medications that indicated level of complexity,
but it still required further documentation from providers.[14] Furthermore, it provides additional areas for clinical decision support that fits
within the workflow of clinical care without an additional alert. We developed a novel
automated diagnosis documentation tool (AutoDx). We aimed to identify a pathway within
the EHR to assist providers with the documentation of specific diagnoses in real-time
to decrease the burden time related to documentation overall, as well as time related
to other documentation improvement processes, and to improve outcomes related to documentation.
Methods
Setting
The project was initiated at a large 660-bed urban academic hospital utilizing Epic
(Epic Care Systems, Madison, Wisconsin, United States) as its EHR. The study institution
sponsors an EHR Physician Builder program that supports physicians to make changes
to the EHR, under the supervision of the medical center's medical informatics and
information technology (IT) teams.[15] All physician builders were required to complete the formal certification program
offered by the EHR vendor before making changes to the EHR system. The AutoDx tool
was designed, developed, and implemented mainly by the physician builder group. At
the study institution, the standard process for the CDI team to address documentation
opportunities is to send queries of potential diagnoses through the EHR. These queries
can happen during the inpatient encounter or following discharge, if a secondary review
was completed. The initial recipient of the query would be the primary note writer,
whether the attending physician on direct care Hospital Medicine services or the resident
physician on many of the teaching services in the institution. Medical students did
not receive coding queries, even if they were the initial authors of a note. In cases
where residents were the primary recipient of the intervention, but declined to answer,
were significantly delayed in answering, or answered in a manner that the query sender
found to be unfavorable, then the query would be forwarded to the attending of the
services for secondary consideration.
Intervention
Development
To improve documentation, the approach taken to develop the automated documentation
tool (AutoDx) was to generate text within a provider note based on specific conditions
met within the EHR for each individual patient. AutoDx uses functionality already
within the EHR, but with a novel use case. In a simplistic summary, the process starts
with the creation of basic logic rules based on data elements within the EHR, such
as laboratory values, vital signs, or discrete flowsheet entries. The rules, either
as a single condition or combined, can be leveraged to trigger patient-specific messages,
which can then be placed within the text of a provider note.
The initial diagnoses targeted were identified based on a review of retrospective
data of the most frequently queried diagnoses by the CDI team. The messages were created
to align with the formatting being used in the note template. The logic used to generate
the message was conceptualized in three distinct archetypes ([Fig. 1]): (1) a single data element that generates a diagnosis message, such as obesity
and malnutrition; (2) a single data element plus a provider order that generates a
diagnosis message, such as hypokalemia, hypophosphatemia, and hypomagnesemia; (3)
insufficient information from a data element or order requiring provider input from
a curated list of options, such as brain edema and types of congestive heart failure.
Fig. 1 Diagnosis archetypes.
The tool's use was expanded to other services and additional diagnoses were developed,
including those in the third archetype. These new diagnoses required input from the
provider to complete the documentation. The latest version of AutoDx contained support
for 10 specific diagnoses associated with nine different risk variables, noted in
parentheses: cerebral edema (Brain Conditions), heart failure with reduced ejection
fraction (Congestive Heart Failure), clinically significant coagulation disorders
(Coagulopathy), COVID significant decreased lymphocyte count (COVID Lymphopenia),
hypokalemia (Electrolyte Disorders), hypomagnesemia (Mineral Metabolism Disorders),
hypophosphatemia (Mineral Metabolism Disorders), obesity (Obesity), consistently significantly
elevated systolic blood pressure (Hypertensive Urgency/Emergency), and moderate or
severe malnutrition based on assessment by a registered dietician (Malnutrition).
All archetypes of AutoDx were based on logic rules configured in our base EHR functionality.
For the first two archetypes, the logic rules searched the chart for a discrete data
element (obesity or malnutrition) or a combination of data elements with order(s)
(hypomagnesemia or hypokalemia) to display formatted diagnosis. For the complete AutoDx
tool, these logic rules additionally searched for narrative result text from imaging
reports, such as computed tomography scans, magnetic resonance imaging scans, or echocardiogram,
containing key terms and patterns of change in laboratory results or vitals. When
the logic rules matched a criterion, a message displayed in the note text alerting
the authoring provider that a condition was met. The author then clicks on an embedded
hyperlink in the message text which opens a pop-up with a predefined list of possible
diagnoses based on the condition met. The author selects an appropriate diagnosis
(or declines), which files it as flowsheet data. Upon returning to the note and refreshing
the text, an inline diagnosis and brief assessment and plan is documented in the narrative.
Hard stops were added to prevent signing and completion of the note until all conditions
were addressed.
Oversight
The project was directly overseen by the medical center's IT team with executive oversight
from the institution's chief medical information officer. During the tool's development,
the CDI team conducted regular reviews to confirm that the logic elements defining
each message aligned with diagnosis criteria, ensuring the generated text accurately
reflected these criteria while seamlessly integrating into the provider note's normal
context. Providers also evaluated the logic and message to ensure they were clinically
appropriate and consistent with standard provider documentation of a similar diagnosis.
When the process was in the final phases of testing, the entire procedure was reviewed
with leadership from Compliance, Risk Management, and Legal to ensure it was appropriate
for the final medical record and to accurately be used for billing purposes. It was
then integrated into the note template for the pilot provider group. Utilization of
the tool and accurate diagnoses were monitored carefully by the clinical, IT, and
CDI teams. With the development of each new diagnosis, Physician Builders, physician
champions in the specialty, IT, and CDI evaluated both the logic and associated messages.
A summary of the logic of all conditions is summarized in the [Supplementary Appendix].
Implementation
Pilot
The first pilot phase of the AutoDx tool was with the Hospital Medicine provider group.
This group was entirely composed of attendings, whose main responsibility was clinical
care on inpatients services by themselves or in conjunction with advance practice
providers, who are integrated into the Hospital Medicine practice. Diagnoses included
in the initial version were hypokalemia, hypomagnesemia, hypophosphatemia, obesity,
and malnutrition as documented by a registered dietician. These diagnoses would populate
into the note if the conditions were met ([Fig. 2A]). In situations of electrolyte deficiency without an order for correction, a prompt
was placed to point out the diagnosis, but provider input was required to explain
why correction was not ordered. In this method, the functionality of the tool would
have to be broken to allow for edits or the provider would have to exit the note,
order the replacement, then refresh the tool within the note to update the message.
During the use of the tool, it was updated to allow providers to provide responses
for supplementation not being given, while keeping the tool functional ([Fig. 2B]). The tool was incorporated into the default note templates accessible to all users
in the Hospital Medicine group. The templates, including history and physical (H&P)
and progress notes, would automatically populate based on the note type selected.
When writing the notes, providers were still able to remove the AutoDx messages from
the note at their discretion, in a manner similar to deleting regular text or vital
signs automatically pulled into the note. A brief informational session about the
tool was conducted at a staff meeting prior to its rollout. Providers were encouraged
to use the default template both to use the tool and to allow for uniformity of note
type within the group to better facilitate transitions of care between providers.
Fig. 2 Screenshots of AutoDx in Notes (data presented in the above are imaginary).
Broader Implementation
Based on the initial success of the pilot implementation, the AutoDx tool was augmented
to meet the needs of the second implementation group, the Neuro Intensive Care Unit
(NICU). The augmentation included development of new diagnoses, including Brain Edema,
which would prompt a provider with a potential diagnosis that required more detailed
response by the provider ([Fig. 2C]). This user group included residents, primary in Neurology, plus neurointensivist
attendings. The AutoDx tool was inserted into standard note templates for NICU. Implementation
of the AutoDx tool for the NICU involved a comprehensive approach supported by the
quality lead for the unit, including development of new NICU condition-specific note
templates and multiple educational sessions with key stakeholders. The educational
sessions were tailored to reach different members of the NICU team, including residents
from neurology and neurosurgery, faculty attendings, and advanced practice providers
(APPs).
Educational sessions were tailored to accommodate the diverse roles, availability,
and documentation requirements of different NICU providers. Ensuring inclusivity,
sessions were conducted for residents in neurology and neurosurgery, faculty attendings
in neurocritical care and in one session all neurology faculty, and APPs. Recognizing
the importance of attention span and engagement, sessions for residents and APPs were
kept concise, not exceeding 20 minutes each, and conducted multiple times per week
throughout the month. This approach aimed to ensure redundancy without overwhelming
participants with lengthy didactic sessions. Additionally, sessions were integrated
into residency meetings to ensure exposure for all residents, even those not currently
rotating in the NICU.
For attending physicians, sessions were integrated into faculty and section meetings.
Attendings were provided with insights into the purpose and development of the tool
through real-time demonstrations of its functionality, emphasizing its role in resident-driven
documentation. Attendings were encouraged to embrace AutoDx by ensuring its presence
in their respective notes and to extend this practice to nonneurointensive care faculty,
considering patients' continued encounters with AutoDx notes upon transfer to other
units.
Educational sessions were not theoretical only, offering practical guidance on navigating
the AutoDx tool in clinical practice. Providers received hands-on training and troubleshooting
strategies to address implementation challenges effectively. To ensure ongoing support
and understanding, the educational sessions were scheduled in a structured manner
monthly during the implementation period. Champions from the resident teams were identified
to work with this cohort of providers to encourage use. Their involvement also was
intended to help identify and address specific concerns or challenges that arose for
the residents.
Furthermore, as a part of the implementation strategy, small batch retrospective quality
checks were conducted. These checks were designed to verify that the AutoDx tool was
being utilized correctly and that the information entered aligned with the intended
diagnostic criteria.
Study of the Intervention
Measures
The primary outcome measure of the intervention is the volume of coding queries generated
for the target diagnoses. The coding query data were provided directly from the CDI
team's internal charting which was separate from the EHR. Data on coding queries were
made available in a reliable fashion starting in April 2021, for the pilot implementation.
Utilization of the AutoDx tool was ascertained through the presence of notes from
encounters tagged with a data element distinct to AutoDx within the EHR. Post-implementation
data collection was planned for approximately 1 year after the pilot was initiated.
The values were aggregated as a proportion relative to the volume of discharges completed
by the group over the period of time being evaluated. The primary outcome measure
of the intervention was reduction in coding queries. Secondary outcomes included case-mix
index (CMI) and observed minus expected length of stay (O-E LOS) accounting for both
changes in documentation and actual clinical practice.
Analysis
Evaluation of the tool's implementation consisted of comparisons of proportions of
coding queries per patient encounter volume, as calculated by a Chi-square test, and
were evaluated over time using run charts. These changes are compared in a pre–post
fashion with the month of implementation being excluded from the analysis from both
the initial pilot and the NICU implementation. Conditions with high volumes of queries,
but not targeted by the tool, were used as a control. The specific high-volume non-AutoDx
diagnoses were unique to each implementation group. Changes in cumulative outcome
measures, including CMI and observed to expected length, were evaluated using t-test and run charts.
Results
Pilot
The pilot of AutoDx started in mid-February 2020. Coding query data were captured
from January through March 2020. The analysis compared a full month, January 2020,
pre-implementation, to a full month, March 2020, post-implementation. The Hospital
Medicine service discharged 590 patients in January and 531 in March. The number of
average coding queries per patient for AutoDx diagnoses was 0.15 pre versus 0.07 post
(p < 0.001). The highest query volume non-AutoDx diagnoses, detailed in [Table 1], averaged 0.12 per patient pre-implementation versus 0.12 post-implementation (p = 0.77). Additional months were not included in the data analysis as the institution
started a dedicated unit for the treatment of COVID-19 at the end of March 2020, which
was mainly staffed by hospitalist providers initially involved in the AutoDx pilot.[16] During the initial implementation period, no significant concerns were reported
regarding the tool, and no significant malfunction-related changes were made to the
tool. Due to the small amount of data available and the potential inclusion of COVID-19
patients in the post-implementation dataset, encounter-level outcomes were not evaluated.
Table 1
Hospital medicine AutoDx pilot coding queries per 100 patients pre- and postimplementation
|
Coding query
|
Query type
|
Pre (n = 590)
|
Post (n = 531)
|
Relative reduction (%)
|
p-Value
|
|
Electrolyte disturbances
|
AutoDx
|
4.6
|
0.4
|
−92
|
<0.0001
|
|
Malnutrition
|
AutoDx
|
3.9
|
3.0
|
−23
|
0.42
|
|
Obesity
|
AutoDx
|
3.9
|
2.4
|
−37
|
0.18
|
|
Mineral metabolism disorders
|
AutoDx
|
2.4
|
1.1
|
−52
|
0.12
|
|
Total
|
AutoDx
|
14.7
|
7.0
|
−53
|
<0.0001
|
|
General query
|
High Volume
|
3.1
|
4.0
|
30
|
0.41
|
|
CHF
|
High Volume
|
2.4
|
2.1
|
−13
|
0.73
|
|
Uncertain diagnosis
|
High Volume
|
1.4
|
2.3
|
67
|
0.25
|
|
CKD stage
|
High Volume
|
2.0
|
0.8
|
−63
|
0.07
|
|
Clinical validation
|
High Volume
|
1.7
|
1.1
|
−33
|
0.43
|
|
Signs/symptoms
|
High Volume
|
1.4
|
2.3
|
67
|
0.25
|
|
Total
|
High Volume
|
11.9
|
12.4
|
5
|
0.77
|
Abbreviations: CHF, congestive heart failure; CKD, chronic kidney disease.
Note: Italics represent statistical significance.
Neonatal Intensive Care Unit Implementation
The AutoDx tool was implemented for the NICU team in May 2022 for testing and utilized
clinically starting in June 2022. In the pre-implementation time frame, April 2021
to May 2022, there were 826 admissions to the NICU. During the implementation month
of June 2022, there were 69 NICU admissions. Post-implementation from July 2022 to
completion of data collection in April 2023, there were 680 NICU admissions. Following
the full implementation of AutoDx, 93.5% (636/680) of NICU admissions utilized the
tool at least in one note during an encounter. The utilization of AutoDx by month
is summarized in [Fig. 3]. Pre-implementation there were 1,280 coding queries at a rate of 1.55 queries per
admission compared with 443 coding queries at a rate of 0.65 queries per admission
(p < 0.0001) post-implementation. The proportion of AutoDx coding queries to all other
coding queries decreased from 54.9 to 37.1% (p < 0.0001). The change in specific coding query volumes is summarized in [Table 2], which includes all of the AutoDx diagnoses and the top six highest volume non-AutoDx
coding queries. Two additional diagnoses, anemia and chronic kidney disease, were
added to AutoDx during the post-implementation cycle, and are included in the overall
analysis, but are not represented in the table.
Fig. 3 NICU admissions by AutoDx Note template utilization. NICU, Neuro Intensive Care Unit.
Table 2
NICU coding queries per 100 patients pre- and post-implementation
|
Coding query
|
Query type
|
Pre (n = 829)
|
Post (n = 680)
|
Relative reduction (%)
|
p-Value
|
|
Obesity—adult
|
AutoDx
|
13.3
|
3.8
|
−71
|
<0.0001
|
|
Mineral metabolism disorders
|
AutoDx
|
9.9
|
1.8
|
−82
|
<0.0001
|
|
Brain condition
|
AutoDx
|
9.8
|
2.4
|
−76
|
<0.0001
|
|
Electrolyte disturbances
|
AutoDx
|
6.7
|
3.7
|
−45
|
0.01
|
|
CHF
|
AutoDx
|
4.7
|
1.5
|
−68
|
0.0005
|
|
Coagulopathy
|
AutoDx
|
2.9
|
1.0
|
−66
|
0.004
|
|
Hypertensive urgency/emergency
|
AutoDx
|
1.7
|
0.0
|
−100
|
0.0007
|
|
Malnutrition
|
AutoDx
|
1.1
|
0.7
|
−36
|
0.48
|
|
COVID lymphopenia
|
AutoDx
|
0.1
|
0.0
|
−100
|
0.36
|
|
Total
|
AutoDx
|
50.2
|
14.9
|
−70
|
<0.0001
|
|
Abnormal findings (not electrolyte disturbances)
|
High volume
|
9.6
|
4.1
|
−57
|
<0.0001
|
|
Clinical validation
|
High volume
|
8.7
|
6.3
|
−28
|
0.09
|
|
Uncertain diagnosis
|
High volume
|
7.7
|
3.2
|
−58
|
0.0002
|
|
Encephalopathy
|
High volume
|
6.2
|
4.0
|
−35
|
0.06
|
|
General query
|
High volume
|
5.6
|
3.5
|
−38
|
0.06
|
|
Present on admission (POA)
|
High volume
|
4.8
|
4.0
|
−17
|
0.42
|
|
Total
|
High volume
|
42.6
|
25.1
|
−41
|
<0.0001
|
Abbreviations: CHF, congestive heart failure; NICU, Neuro Intensive Care Unit.
Note: Italics represent statistical significance.
The proportion of coding queries over time as a function of patient volume is summarized
in [Fig. 4] in u-charts. Both AutoDx and Non-AutoDx queries demonstrated significant shifts
pre- and post-implementation (solid ovals). There were significant outliers in the
pre-implementation period for both groups (dotted circles), but there were only outlier
points post-intervention in the AutoDx group.
Fig. 4 u-Charts of coding query volume for AutoDx and non-AutoDx diagnoses.
The outcome measures are summarized in [Table 3]. CMI demonstrated a significant increase post-implementation (4.00 vs. 4.55, p = 0.02), but there was no significant difference in O-E LOS or in O:E mortality.
The total number of encounters with O:E mortality calculated was limited due to the
availability of data in the CDI database and not all encounters had primary diagnoses
related to a mortality risk score.
Table 3
Outcomes comparison NICU
|
Pre (n = 836)
Average (SD)
|
Post (n = 680)
Average (SD)
|
p-Value
|
|
CMI
|
4.00 (4.12)
|
4.55 (5.08)
|
0.02
|
|
O-E LOS
|
3.98 (9.45)
|
3.34 (9.99)
|
0.21
|
|
O:E Mortality[a]
|
0.152 (0.253)
|
0.177 (0.275)
|
0.10
|
Abbreviations: NICU, neonatal intensive care unit; O:E Mortality, observed to expected
mortality rate, measured as a ratio of observed deaths to expected death based on
risk model; O-E LOS, observed minus expected length of stay, measured in days; SD,
standard deviation.
Note: Italics represent statistical significance.
a Pre (n = 657); post (n = 645).
Discussion
The implementation of the AutoDx tool was associated with a significant decline in
coding queries to providers in nearly all of the targeted diagnoses in two separate
implementations. In the NICU implementation, there was also a noticeable increase
in the CMI for the targeted patient population. Neither implementations had concerns
raised regarding the accuracy or safety of the tool.
During the initial pilot implementation, only one type of targeted query demonstrated
statistically significant improvement, but all categories had a downward trend. The
limited duration of data collection secondary to changes in clinical practice from
the COVID-19 pandemic led to the analysis being underpowered. Non-AutoDx high-volume
diagnoses showed variable levels of changes, but when congregated there was no significant
change. The lack of an evaluation of AutoDx at the encounter outcome level was a limitation
to the evaluation of the efficacy of the pilot. Once clinical operations returned
to normal in Hospital Medicine, there were changes in service structure and to the
software used for creating and tracking coding queries, so a larger pre–post comparison
would not have been valid.
In the NICU implementation, two of the AutoDx diagnoses, malnutrition and COVID Lymphopenia,
did not show a significant decrease in the rate of coding queries per encounter. The
incidence of both was the lowest of all the targeted diagnoses. The baseline rate
of coding queries for malnutrition was considerably lower in the NICU than in medicine
patients over a similar timeframe, which was approximately 5%.[17] This lower level would have required a larger sample size to show significance at
the level of improvement reported. However, a similar EHR-based intervention to improve
malnutrition documentation was reported to improve diagnosis capture to 100% of opportunities
identified.[18] This study utilized similar functionality in the EHR, but had a significant focus
on ensuring appropriate documentation of nutritional status by other care providers,
nursing and dietician. On manual review of missed opportunities of AutoDx, aside from
not utilizing the tool, cases of coding queries for malnutrition were often seen when
the dietician did not document the specific malnutrition diagnosis in the flowsheet
to trigger the AutoDx message. This is an opportunity for further improvement of the
tool's overall performance.
Two non-AutoDx high-volume diagnoses did demonstrate significant improvement following
implementation, Abnormal Findings and Uncertain Diagnosis. These are both purposefully
vague categories, but Abnormal Findings specifically does not include any queries
related to abnormal electrolytes. Near the time of implementation there was additional
emphasis placed on the primary teams within the NICU to be mindful about their documentation
and to be responsive to coding queries. The improvement in capturing the nonelectrolyte
diagnoses within AutoDx may have been another driver in this reduction of these categories
as the standardized wording used for the diagnoses would reduce the variation and
was built to be compliant with coding standards for those diagnoses. The high-volume
diagnoses, Encephalopathy and Present on Admission, which would not have been affected
by the implementation of AutoDx, showed no significant change. This refutes the impact
on overall documentation education, as encephalopathy is a frequent diagnosis on the
neurology service.[19]
[20]
[21] Present on Admission is a condition that could connect to any diagnosis, but was
not integrated as an additional explicit part of AutoDx. However, the tool was implemented
into History and Physical note templates, so the targeted conditions should have been
captured more frequently as present on initial documentation.
The method in which education was provided in the implementation cohorts was quite
different, with the Hospital Medicine group receiving very little, and the NICU receiving
a more comprehensive program. The method in which the tool was implemented within
the EHR for both cohorts was the same, through a rapid change to default note templates
and auto-population of these note templates. These different aspects of implementation
likely were the drivers of the differences in both scales of outcome, but also in
the difference in improvement in non-AutoDx diagnoses for the NICU team. As opposed
to Hospital Medicine, the NICU team had methods for dissemination of information rapidly
and with close follow-up, which lends itself well to overall improvement and not just
in the targeted diagnoses. The evaluation of implementation of another cohort at the
study institution is underway, which was completed in a similar fashion to Hospital
Medicine, and appears to be trending in a similar direction with significant targeted
query improvement, but limited change in other diagnoses.
Sustained utilization of the AutoDx tool was supported by the emergence of physician
champions within both the Hospital Medicine and NICU cohorts. These champions played
a key role in facilitating adoption among their peers. In Hospital Medicine, many
champions came from the EHR physician builder group, which formed a strong base of
support for implementation. For both groups, the champion had an informal role that
was identified by those willing to give feedback on the tool, but also had sufficient
understanding to provide at-the-shoulder support to colleagues.
One limitation of the NICU analysis is that it only included encounters of patients
admitted directly to the NICU, excluding those transferred from other services. This
methodology missed opportunities for additional capture of diagnoses using AutoDx
for patients transferred from other services. However, given the planned dissemination
of AutoDx eventually to all services, the impact of this specific population was not
the intended target of the intervention or evaluation.
Another limitation is that the primary process measure was the volume of coding queries
rather than the actual incidence of the underlying conditions and appropriate documentation.
A decrease in the frequency of coding queries should correlate with improved documentation
overall of the targeted conditions, but we did not specifically look at clinical elements
of the encounters to identify opportunities to document each of the targeted diagnoses.
The study did not have a clear quantitative balancing measure for the implementation
of AutoDx. The continued high utilization of the tool is a strong indicator of provider
satisfaction with its use as demonstrated in [Fig. 3], but this is an indirect indicator. Even with the sustained use of the tool, additional
diagnoses were developed after the initial implementation period that could reflect
missed diagnoses or redundant documentation. One of these diagnoses is anemia, which
in a large cohort of use was either listed as “Already Documented” or only “Other”
in less than 9% of all cases, indicating a >90% success rate. Informally, leadership
did not receive significant negative feedback to the use of the tool. There could
still be factors related to the time spent using the tool that might impact a provider's
satisfaction.[22] However, physician documentation completion time has been demonstrated to be highly
variable, making it an unreliable balancing measure.[23] Providers who utilized AutoDx deemed the time saved from addressing subsequent coding
queries to be far more significant than the time spent using the tool in qualitative
feedback. In ongoing evaluations of AutoDx implementation in new provider cohorts,
we have completed intake surveys and plan to survey post-implementation to capture
provider attitudes and concerns regarding the use of the tool. These results will
be disseminated once follow-up surveys are completed.
