Final Research

1

Writing Task Solution

Leidylin Guerrero

Keiser University

ENC4313-Research Writing

Diane McMurry, Ph.D.

28 June 2026

2

Solution: Ambient AI Scribes as an Intervention for EHR Documentation Burden

Transitioning from the Problem to Intervention

In the analysis above, excessive documentation burden was the most actionable of all the

factors that contribute to clinician burnout. According to Wu et al. (2024), the clinicians who did

EHR tasks during off-hours had 2.43 times the risk of experiencing burnout. The solution helps

to alleviate documentation burdens by the use of ambient AI scribes, tools that have been

enabled by voice that can capture a patient-clinician interaction and automatically generate a

draft clinical note (Leung et al., 2025; Alobayli et al., 2023). Documentation burden reduction is

essential as it directly addresses the quantifiable loss of time and cognitive effort that leads to

burnout, and can be achieved without the need to change EHR systems as a whole.

Proposed Solution

Clinical AI scribes like Nuance DAX Copilot, Abridge, Suki, and Nabla utilize natural

language processing (NLP) to capture and organize clinical interactions during real-time sessions

(Leung et al., 2025). It is not a requirement for the clinician to take part in writing a note from

scratch. It means that the AI-generated note is getting reviewed and approved by the clinician

(Olson et al., 2025). This is a direct impact on excessive documentation burden because it results

in a reduction in the after-hours charting time. It also has an impact in causing a drop in the

cognitive load for clinicians during patient visitation and allowing clinicians to pay attention to

the patient as opposed to focusing on the screen.

Evidence of Effectiveness. In a multi-center study of 263 doctors from six health

systems in the U.S., burnout rates among the clinicians who used ambient AI scribes dropped by

46.5% after 30 days (Olson et al., 2025). The research also showed very positive improvements

in cognitive task load and time spent documenting after hours. The findings from a pilot at

3

Stanford Health Care were that there were significant decreases in task load and burnout after

three months (Shah et al., 2025). In a prospective study at Hawaii Pacific Health, there was a

decrease of 76% in the amount of time clinicians spent writing notes after clinic hours (Harvey et

al., 2026; BMC Medical Informatics, 2025). A systematic review and meta-analysis indicated

moderate improvement of documentation workload and associated burnout.

Implementation in Practice. This solution would be applied at the health system level.

The IT department would need to integrate the AI scribe platform with the current EHR system,

which can take anywhere from 1 to 4 weeks. Clinicians would undergo 2.0–4.0 hours of training

to get used to the workflow and then be given a 30-day trial period (Harvey et al., 2026). This

intervention would be deployed in ambulatory care environments such as outpatient clinics in

hospitals and community practices.

How It Solves Documentation Chores. Ambient AI scribes directly tackle the

documentation burden by automating the most time-consuming part of the charting process: note

creation (Leung et al., 2025). The tool will decrease the time spent by clinicians on

documentation, which was cited as one of the biggest sources of burnout by Wu et al. (2024).

Based on the outcomes from Asgari et al.'s (2024) study, the tool also decreases the cognitive

burden by minimizing redundant data entry and distractions during patient interactions.

Feasibility Analysis

Practical Implementation Barriers. There are different licensing fees, which range

from $99 to $500+ per provider per month, and can be a burden for small practices and rural

clinics (Leung et al., 2025). They need to be integrated with the existing EHR systems, which

involves substantial IT assistance. Consistent trust and use of the tool requires the skill of the

clinician, which is dependent on the generation's comfort with technology (Shah et al., 2025).

4

AI-generated notes need to be reviewed and edited by the clinician, which will partially negate

some time savings if the notes are incorrect.

Required Resources. The successful implementation is dependent on the investment of

money for licensing, an IT team for integration and continued support, training coordinators, and

a change management leader to respond to clinician concerns (Leung et al., 2025). Additional

steps must be taken to ensure health systems are HIPAA compliant with AI vendors by signing

BAA agreements.

Institutional Constraints. Certain health systems may not have the necessary budget or

technical solutions to deploy AI scribes. For larger systems, however, and hospital-owned

outpatient practices, this solution is not financially or logistically feasible, as the majority of U.S.

clinicians are now employed (Olson et al., 2025; Harvey et al., 2026). The intervention is still

considered viable as evidence shows that it generates a return on investment by lowering

burnout, increasing retention, and boosting productivity.

Counterargument

Ambient AI scribes can be opposed on several reasons by reasonable professionals. There

is limited evidence base, first of all. Although there were improvements in documentation time, a

systematic review concluded that there was "limited impact on reducing burnout" (Sarraf &

Ghasempour, 2025). A longitudinal study revealed that although the ambient AI scribes were

linked to a decrease in cognitive workload, over time, burnout actually rose (Wojda et al., 2026).

This indicates that while ambient AI scribes might be tackling symptoms of burnout, they could

be missing the potential solution to actual causes, including things like staffing problems and

organizational culture.

5

Second, there are concerns regarding the reliability and accuracy of AI-generated notes,

which can lead to resistance among clinicians using them. Second, clinicians might not trust the

accuracy and reliability of the AI-generated notes, which can hinder adoption. One randomized

study reported “occasionally” clinically significant inaccuracies (Lukac et al., 2025). Excessive

time spent editing AI notes could result in no savings of time for the clinician (Leung et al.,

2025). Third, there is an equity issue: large health systems will be using these costly tools, while

small community clinics that treat higher-risk patients are not.

Rebuttal. These are all reasonable considerations, but studies have actually shown that

ambient AI scribes can cut down on both burnout and documentation time in real-world

scenarios (Olson et al., 2025; Harvey et al., 2026). The rise in burnout described by Wojda et al.

(2026) is likely to be due to a number of other factors that will not be addressed by AI scribes.

The accuracy of AI scribe platforms is improving quickly, and most clinicians say that they are

satisfied with the quality of the notes generated (Harvey et al., 2026). Health system

partnerships, grant funding, and vendor pricing that work for smaller health systems can help

alleviate the equity issue.

Unintended Consequence

A potential but realistic side effect is that those scribes who are ambient will save

documentation time, but may add to the review and liability workloads for clinicians. AI notes

do not always get it right, and can contain hallucinations, missing information, or clinically

significant errors (Leung et al., 2025; Lukac et al., 2025). When significant time is needed for

clinicians to edit/confirm AI-generated notes, time saved may be eliminated by increased review

time. Mitigation can be achieved by: (1) choosing AI platforms with high accuracy rates; (2)

using quality assurance protocols; (3) giving guidance to the clinicians to efficiently edit; and (4)

6

a phased implementation schedule with feedback loops between the clinicians and the AI

platforms.

7

References

Alobayli, F. Y., O’Connor, S., Holloway, A., & Cresswell, K. (2023). Electronic Health Record

Stress and Burnout Among Clinicians in Hospital Settings: A Systematic Review. Digital

Health, 9(9). https://doi.org/10.1177/20552076231220241

Asgari, E., Kaur, J., Nuredini, G., Balloch, J., Taylor, A. M., Sebire, N., Robinson, R., Peters, C.,

Sridharan, S., & Pimenta, D. (2024). Impact of Electronic Health Record Use on

Cognitive Load and Burnout Among Clinicians: Narrative Review. JMIR Medical

Informatics, 12(1), e55499. https://doi.org/10.2196/55499

Harvey, C. J., Morita, J., Huynh, W., Woo, R. K., & Lee, J. P. (2026). Ambient Artificial

Intelligence Scribe Implementation in an Ambulatory Setting: Experience and Outcomes

from a Single Medical Group (Preprint). JMIR Medical Informatics, 14.

https://doi.org/10.2196/84104

Leung, T. I., Coristine, A. J., & Benis, A. (2025). AI Scribes in Health Care: Balancing

Transformative Potential With Responsible Integration. JMIR Medical Informatics, 13,

e80898–e80898. https://doi.org/10.2196/80898

Lukac, P. J., Turner, W., Sitaram Vangala, Chin, A. T., Khalili, J., Shih, Y.-C. T., Sarkisian, C.,

Cheng, E. M., & Mafi, J. N. (2025). A Randomized-Clinical Trial of Two Ambient

Artificial Intelligence Scribes: Measuring Documentation Efficiency and Physician

Burnout. MedRxiv (Cold Spring Harbor Laboratory).

https://doi.org/10.1101/2025.07.10.25331333

Olson, K. D., Meeker, D., Troup, M., Barker, T. D., Nguyen, V. H., Manders, J. B., Stults, C. D.,

Jones, V. G., Shah, S. D., Shah, T., & Schwamm, L. H. (2025). Use of Ambient AI

8

Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Network

Open, 8(10), e2534976. https://doi.org/10.1001/jamanetworkopen.2025.34976

Sarraf, B., & Ghasempour, A. (2025). Impact of artificial intelligence on electronic health

record-related burnouts among healthcare professionals: systematic review. Frontiers in

Public Health, 13. https://doi.org/10.3389/fpubh.2025.1628831

Shah, S. J., Devon-Sand, A., Ma, S. P., Jeong, Y., Crowell, T., Smith, M., Liang, A. S., Delahaie,

C., Hsia, C., Shanafelt, T., Pfeffer, M. A., Sharp, C., Lin, S., & Garcia, P. (2024).

Ambient artificial intelligence scribes: physician burnout and perspectives on usability

and documentation burden. Journal of the American Medical Informatics Association,

32(2). https://doi.org/10.1093/jamia/ocae295

Wu, Y., Wu, M., Wang, C., Lin, J., Liu, J., & Liu, S. (2024). Evaluating the Prevalence of

Burnout Among Health Care Professionals Related to Electronic Health Record Use:

Systematic Review and Meta-Analysis. JMIR Medical Informatics, 12(1), e54811.

https://doi.org/10.2196/54811

1

Writing Task Solution

Leidylin Guerrero

Keiser University

ENC4313-Research Writing

Diane McMurry, Ph.D.

28 June 2026

2

Solution: Ambient AI Scribes as an Intervention for EHR Documentation Burden

Transitioning from the Problem to Intervention

In the analysis above, excessive documentation burden was the most actionable of all the

factors that contribute to clinician burnout. According to Wu et al. (2024), the clinicians who did

EHR tasks during off-hours had 2.43 times the risk of experiencing burnout. The solution helps

to alleviate documentation burdens by the use of ambient AI scribes, tools that have been

enabled by voice that can capture a patient-clinician interaction and automatically generate a

draft clinical note (Leung et al., 2025; Alobayli et al., 2023). Documentation burden reduction is

essential as it directly addresses the quantifiable loss of time and cognitive effort that leads to

burnout, and can be achieved without the need to change EHR systems as a whole.

Proposed Solution

Clinical AI scribes like Nuance DAX Copilot, Abridge, Suki, and Nabla utilize natural

language processing (NLP) to capture and organize clinical interactions during real-time sessions

(Leung et al., 2025). It is not a requirement for the clinician to take part in writing a note from

scratch. It means that the AI-generated note is getting reviewed and approved by the clinician

(Olson et al., 2025). This is a direct impact on excessive documentation burden because it results

in a reduction in the after-hours charting time. It also has an impact in causing a drop in the

cognitive load for clinicians during patient visitation and allowing clinicians to pay attention to

the patient as opposed to focusing on the screen.

Evidence of Effectiveness. In a multi-center study of 263 doctors from six health

systems in the U.S., burnout rates among the clinicians who used ambient AI scribes dropped by

46.5% after 30 days (Olson et al., 2025). The research also showed very positive improvements

in cognitive task load and time spent documenting after hours. The findings from a pilot at

3

Stanford Health Care were that there were significant decreases in task load and burnout after

three months (Shah et al., 2025). In a prospective study at Hawaii Pacific Health, there was a

decrease of 76% in the amount of time clinicians spent writing notes after clinic hours (Harvey et

al., 2026; BMC Medical Informatics, 2025). A systematic review and meta-analysis indicated

moderate improvement of documentation workload and associated burnout.

Implementation in Practice. This solution would be applied at the health system level.

The IT department would need to integrate the AI scribe platform with the current EHR system,

which can take anywhere from 1 to 4 weeks. Clinicians would undergo 2.0–4.0 hours of training

to get used to the workflow and then be given a 30-day trial period (Harvey et al., 2026). This

intervention would be deployed in ambulatory care environments such as outpatient clinics in

hospitals and community practices.

How It Solves Documentation Chores. Ambient AI scribes directly tackle the

documentation burden by automating the most time-consuming part of the charting process: note

creation (Leung et al., 2025). The tool will decrease the time spent by clinicians on

documentation, which was cited as one of the biggest sources of burnout by Wu et al. (2024).

Based on the outcomes from Asgari et al.'s (2024) study, the tool also decreases the cognitive

burden by minimizing redundant data entry and distractions during patient interactions.

Feasibility Analysis

Practical Implementation Barriers. There are different licensing fees, which range

from $99 to $500+ per provider per month, and can be a burden for small practices and rural

clinics (Leung et al., 2025). They need to be integrated with the existing EHR systems, which

involves substantial IT assistance. Consistent trust and use of the tool requires the skill of the

clinician, which is dependent on the generation's comfort with technology (Shah et al., 2025).

4

AI-generated notes need to be reviewed and edited by the clinician, which will partially negate

some time savings if the notes are incorrect.

Required Resources. The successful implementation is dependent on the investment of

money for licensing, an IT team for integration and continued support, training coordinators, and

a change management leader to respond to clinician concerns (Leung et al., 2025). Additional

steps must be taken to ensure health systems are HIPAA compliant with AI vendors by signing

BAA agreements.

Institutional Constraints. Certain health systems may not have the necessary budget or

technical solutions to deploy AI scribes. For larger systems, however, and hospital-owned

outpatient practices, this solution is not financially or logistically feasible, as the majority of U.S.

clinicians are now employed (Olson et al., 2025; Harvey et al., 2026). The intervention is still

considered viable as evidence shows that it generates a return on investment by lowering

burnout, increasing retention, and boosting productivity.

Counterargument

Ambient AI scribes can be opposed on several reasons by reasonable professionals. There

is limited evidence base, first of all. Although there were improvements in documentation time, a

systematic review concluded that there was "limited impact on reducing burnout" (Sarraf &

Ghasempour, 2025). A longitudinal study revealed that although the ambient AI scribes were

linked to a decrease in cognitive workload, over time, burnout actually rose (Wojda et al., 2026).

This indicates that while ambient AI scribes might be tackling symptoms of burnout, they could

be missing the potential solution to actual causes, including things like staffing problems and

organizational culture.

5

Second, there are concerns regarding the reliability and accuracy of AI-generated notes,

which can lead to resistance among clinicians using them. Second, clinicians might not trust the

accuracy and reliability of the AI-generated notes, which can hinder adoption. One randomized

study reported “occasionally” clinically significant inaccuracies (Lukac et al., 2025). Excessive

time spent editing AI notes could result in no savings of time for the clinician (Leung et al.,

2025). Third, there is an equity issue: large health systems will be using these costly tools, while

small community clinics that treat higher-risk patients are not.

Rebuttal. These are all reasonable considerations, but studies have actually shown that

ambient AI scribes can cut down on both burnout and documentation time in real-world

scenarios (Olson et al., 2025; Harvey et al., 2026). The rise in burnout described by Wojda et al.

(2026) is likely to be due to a number of other factors that will not be addressed by AI scribes.

The accuracy of AI scribe platforms is improving quickly, and most clinicians say that they are

satisfied with the quality of the notes generated (Harvey et al., 2026). Health system

partnerships, grant funding, and vendor pricing that work for smaller health systems can help

alleviate the equity issue.

Unintended Consequence

A potential but realistic side effect is that those scribes who are ambient will save

documentation time, but may add to the review and liability workloads for clinicians. AI notes

do not always get it right, and can contain hallucinations, missing information, or clinically

significant errors (Leung et al., 2025; Lukac et al., 2025). When significant time is needed for

clinicians to edit/confirm AI-generated notes, time saved may be eliminated by increased review

time. Mitigation can be achieved by: (1) choosing AI platforms with high accuracy rates; (2)

using quality assurance protocols; (3) giving guidance to the clinicians to efficiently edit; and (4)

6

a phased implementation schedule with feedback loops between the clinicians and the AI

platforms.

7

References

Alobayli, F. Y., O’Connor, S., Holloway, A., & Cresswell, K. (2023). Electronic Health Record

Stress and Burnout Among Clinicians in Hospital Settings: A Systematic Review. Digital

Health, 9(9). https://doi.org/10.1177/20552076231220241

Asgari, E., Kaur, J., Nuredini, G., Balloch, J., Taylor, A. M., Sebire, N., Robinson, R., Peters, C.,

Sridharan, S., & Pimenta, D. (2024). Impact of Electronic Health Record Use on

Cognitive Load and Burnout Among Clinicians: Narrative Review. JMIR Medical

Informatics, 12(1), e55499. https://doi.org/10.2196/55499

Harvey, C. J., Morita, J., Huynh, W., Woo, R. K., & Lee, J. P. (2026). Ambient Artificial

Intelligence Scribe Implementation in an Ambulatory Setting: Experience and Outcomes

from a Single Medical Group (Preprint). JMIR Medical Informatics, 14.

https://doi.org/10.2196/84104

Leung, T. I., Coristine, A. J., & Benis, A. (2025). AI Scribes in Health Care: Balancing

Transformative Potential With Responsible Integration. JMIR Medical Informatics, 13,

e80898–e80898. https://doi.org/10.2196/80898

Lukac, P. J., Turner, W., Sitaram Vangala, Chin, A. T., Khalili, J., Shih, Y.-C. T., Sarkisian, C.,

Cheng, E. M., & Mafi, J. N. (2025). A Randomized-Clinical Trial of Two Ambient

Artificial Intelligence Scribes: Measuring Documentation Efficiency and Physician

Burnout. MedRxiv (Cold Spring Harbor Laboratory).

https://doi.org/10.1101/2025.07.10.25331333

Olson, K. D., Meeker, D., Troup, M., Barker, T. D., Nguyen, V. H., Manders, J. B., Stults, C. D.,

Jones, V. G., Shah, S. D., Shah, T., & Schwamm, L. H. (2025). Use of Ambient AI

8

Scribes to Reduce Administrative Burden and Professional Burnout. JAMA Network

Open, 8(10), e2534976. https://doi.org/10.1001/jamanetworkopen.2025.34976

Sarraf, B., & Ghasempour, A. (2025). Impact of artificial intelligence on electronic health

record-related burnouts among healthcare professionals: systematic review. Frontiers in

Public Health, 13. https://doi.org/10.3389/fpubh.2025.1628831

Shah, S. J., Devon-Sand, A., Ma, S. P., Jeong, Y., Crowell, T., Smith, M., Liang, A. S., Delahaie,

C., Hsia, C., Shanafelt, T., Pfeffer, M. A., Sharp, C., Lin, S., & Garcia, P. (2024).

Ambient artificial intelligence scribes: physician burnout and perspectives on usability

and documentation burden. Journal of the American Medical Informatics Association,

32(2). https://doi.org/10.1093/jamia/ocae295

Wu, Y., Wu, M., Wang, C., Lin, J., Liu, J., & Liu, S. (2024). Evaluating the Prevalence of

Burnout Among Health Care Professionals Related to Electronic Health Record Use:

Systematic Review and Meta-Analysis. JMIR Medical Informatics, 12(1), e54811.

https://doi.org/10.2196/54811