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This randomized quality improvement pilot project aims to assess whether the implementation of generative AI software for documentation, Microsoft Nuance's Digital Ambient eXperience (DAX) Copilot, enhances physician documentation efficiency and reduces burnout.
Full description
Background: While electronic health record (EHR) systems have contributed to advances in patient safety and quality of care, they have also been associated with a significant increase in documentation burden, contributing to burnout among clinicians [1]. This is particularly true for physicians with insufficient time for documentation. In some cases, it has resulted in a reduction in appointment slots to allow for additional documentation time, which in turn decreases patient access to care and physician productivity [2,3].
Microsoft Nuance© has recently announced general availability of a new generative artificial intelligence (AI) solution called Digital Ambient eXperience (DAX) DAX CoPilot [4], in which the visit is recorded with patient/parental consent, but a note is generated through the AI along with a visit transcript in near-real time that the provider can use and edit as they see fit. In addition, it allows providers to continue to use their documentation templates while adding the generative AI to "smart sections" within their note. This approach has the potential to substantially reduce documentation burden while maintaining documentation preferences of many providers.
This randomized quality improvement pilot project aims to assess whether the implementation of generative AI software for documentation, Microsoft Nuance's DAX Copilot, can enhance physician documentation efficiency and reduce burnout.
Objectives
Quality Improvement Global Aim: To increase provider documentation efficiency and reduce provider burnout related to documentation burden.
Children's Operational Goal: Determine if the cost of DAX Copilot or related vendor software is justified by reduction in proxies for physician burnout and/or could be offset by seeing more patients in the same time period to improve revenue and patient access.
Goals of the Proposed Work:
Methods: This is a randomized quality improvement project that will assess changes to proxies for provider documentation efficiency and burnout through a difference-in-differences design.
Project Participants
The project will recruit 20 providers who meet the following inclusion criteria:
Practices at Children's in a specialty supported by DAX Copilot product and with available documentation efficiency metrics from Epic's Signal product.
>0.5 clinical full time equivalent (cFTE)
2 or more half days per week on average seeing outpatients as the primary provider (not overseeing trainees or APPs).
Agrees to use the Children's EHR mobile application (Haiku) on their personal device.
Agrees to offer use of the DAX Copilot generative AI software for all patient visit encounters for the duration of the project period
Sufficient and stable EHR data on documentation efficiency from Epic's Signal product, defined as:
Participants will be identified based on characteristics such as specialty, cFTE, proportion and location of outpatient work, and Epic's Ambient Opportunity Index from Signal, which is based on normalized scores for proportion of same-day charts closed, pajama type, and characters manually typed.
Randomization will be conducted in blocks of two at the specialty level to ensure equal representation of specialties into one of the two following groups for a 3 month pilot:
Outcomes
Our primary outcomes to be obtained through Epic's Signal product will be:
"Pajama Time", defined as the average number of minutes per scheduled day spent in charting activities outside 7 AM to 5:30 PM on weekdays, time outside scheduled hours on weekends, and time on unscheduled holidays. This metric is associated with the exhaustion subscale of the Maslach Burnout Inventory [5].
"Time in Notes per Appointment" in minutes
Additional outcome metrics will include:
Progress Note Length (characters)
Note Contribution (written by provider vs. others)
Time to Appointment Closure
Proportion of notes completed using DAX Copilot generative AI software
Average patient volume per week
Pre- and post-project user responses on a modified KLAS EHR Efficiency and Satisfaction survey, a validated benchmarking tool.
Pre- and post-project patient experience scores through routinely capture NRC surveys.
Data Collection
Data will be collected from Epic© Signal and through surveys. The data will include:
Statistical Analysis Our primary analysis will be a difference-in-differences analysis for each outcome. For example, the difference between the provider's average pajama time before and after the intervention period will be calculated for all participants. We will then determine how this average differs in the AI group and in the control group to assess the difference-in-differences.
Additional analyses will include adjusted or stratified difference-in-differences analyses based on provider characteristics listed above. We will also calculate descriptive statistics to compare the outcomes and covariates between the two groups. Depending on the nature of the data, we may use run charts, t-tests, ANOVA, or other appropriate statistical methods to assess the impact of generative AI documentation software.
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Inclusion criteria
Providers:
Practices at Children's in a specialty supported by DAX Copilot product and with available documentation efficiency metrics from Epic's Signal product.
>0.5 clinical full time equivalent (cFTE)
2 or more half days per week on average seeing outpatients as the primary provider (not overseeing trainees or APPs).
Agrees to use the Children's EHR mobile application (Haiku) on their personal device.
Agrees to offer use of the DAX Copilot generative AI software for all eligible patient visit encounters for the duration of the project period
Sufficient and stable EHR data on documentation efficiency from Epic's Signal product, defined as:
Visits: In person office visits in which interactions occur in English. For example, a visit with an interpreter present who translates verbally into English during the visit would be eligible. A visit in which all interactions occur in another language (i.e. the provider speaks the language the family uses as well) would not be eligible as the AI has not been developed yet for other languages.
Exclusion criteria
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21 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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