Artificial intelligence medical scribes in allied health: a solution in search of evidence?
Laura Ryan
A * and Laetitia Hattingh A B C
A
B
C
Abstract
Artificial intelligence (AI) medical scribes (AI scribes), which ambiently record and transcribe patient–clinician interactions into structured documentation, aim to ameliorate documentation burdens, but their suitability for allied health remains unclear. AI scribes are often designed for doctors, raising concerns about accuracy, workflow integration, and applicability to allied health’s diverse documentation needs. While potential benefits include improved efficiency and patient engagement, evidence is lacking for their effectiveness in allied health settings. Risks such as AI bias, patient safety, and integration barriers may also require consideration. This paper argues that further research is needed before widespread allied health adoption, emphasising the need for discipline-specific evaluations to assess AI scribes’ viability in allied health practice.
Keywords: AI scribe, allied health, artificial intelligence (AI), clinical documentation, digital health, digital scribe, health record, healthcare technology.
References
1 Philip K. Allied health: Untapped potential in the Australian health system. Aust Health Rev 2015; 39(3): 244-7.
| Crossref | Google Scholar | PubMed |
2 McIntyre D, Chow CK. Waiting Time as an Indicator for Health Services Under Strain: A Narrative Review. Inquiry 2020; 57(1): 46958020910305.
| Crossref | Google Scholar | PubMed |
3 Rotenstein LS, Torre M, Ramos MA, Rosales RC, Guille C, Sen S. Prevalence of Burnout Among Physicians: A Systematic Review. JAMA 2018; 320(11): 1131-50.
| Crossref | Google Scholar | PubMed |
4 Yeoh SA, Kumar S, Phillips A, Li LSK. Unveiling the Exodus: A scoping review of attrition in allied health. PLoS One 2024; 19(9): e0308302.
| Crossref | Google Scholar | PubMed |
5 Brown A, El-Achkar E, Fragnito F, Gosnell S, Kelly J, Kucharski D, et al. Clinical care ratios for, and tasks undertaken by, allied health professionals: A scoping review of the literature. PLoS One 2024; 19(11): e0312435.
| Crossref | Google Scholar | PubMed |
6 Rubin B, Goldfarb R, Satele D, Graham L. Burnout and distress among allied health care professionals in a cardiovascular centre of a quaternary hospital network: a cross-sectional survey. CMAJ Open 2021; 9(1): E29-37.
| Crossref | Google Scholar | PubMed |
7 Jayakumar P, Oude Nijhuis KD, Oosterhoff JHF, Bozic KJ. Value-based Healthcare: Can Generative Artificial Intelligence and Large Language Models be a Catalyst for Value-based Healthcare? Clin Orthop Relat Res 2023; 481(10): 1890-4.
| Crossref | Google Scholar | PubMed |
8 Balloch J, Sridharan S, Oldham G, Wray J, Gough P, Robinson R, et al. Use of an ambient artificial intelligence tool to improve quality of clinical documentation. Future Healthc J 2024; 11(3): 100157.
| Crossref | Google Scholar | PubMed |
9 Misurac J, Knake LA, Blum JM. The effect of ambient artificial intelligence notes on provider burnout. Appl Clin Inform 2025; 16(2): 252-8.
| Crossref | Google Scholar |
10 Tierney AA, Gayre G, Hoberman B, Mattern B, Ballesca M, Kipnis P, et al. Ambient Artificial Intelligence Scribes to Alleviate the Burden of Clinical Documentation. NEJM Catal 2024; 5(3): CAT.23.0404.
| Crossref | Google Scholar |
11 Duggan MJ, Gervase J, Schoenbaum A, Hanson W, Howell JT, Sheinberg M, et al. Clinician Experiences With Ambient Scribe Technology to Assist With Documentation Burden and Efficiency. JAMA Open 2025; 8: e2460637.
| Crossref | Google Scholar |
12 Haberle T, Cleveland C, Snow GL, Barber C, Stookey N, Thornock C, et al. The impact of nuance DAX ambient listening AI documentation: a cohort study. J Am Med Inform Assoc 2024; 31(4): 975-9.
| Crossref | Google Scholar | PubMed |
13 Nguyen OT, Turner K, Charles D, Sprow O, Perkins R, Hong Y-R, et al. Implementing Digital Scribes to Reduce Electronic Health Record Documentation Burden Among Cancer Care Clinicians: A Mixed-Methods Pilot Study. JCO Clin Cancer Inform 2023; 7(7): e2200166.
| Google Scholar |
14 Shah SJ, Devon-Sand A, Ma SP, Jeong Y, Crowell T, Smith M, et al. Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden. J Am Med Inform Assoc 2025; 32(2): 375-80.
| Crossref | Google Scholar | PubMed |
15 Cao DY, Silkey JR, Decker MC, Wanat KA. Artificial intelligence-driven digital scribes in clinical documentation: Pilot study assessing the impact on dermatologist workflow and patient encounters. JAAD Int 2024; 15: 149-51.
| Crossref | Google Scholar | PubMed |
16 Galloway JL, Munroe D, Vohra-Khullar PD, Holland C, Solis MA, Moore MA, et al. Impact of an Artificial Intelligence-Based Solution on Clinicians’ Clinical Documentation Experience: Initial Findings Using Ambient Listening Technology. J Gen Intern Med 2024; 39(13): 2625-7.
| Crossref | Google Scholar | PubMed |
17 Coiera E, Kocaballi B, Halamka J, Laranjo L. The digital scribe. NPJ Digit Med 2018; 1(1): 58.
| Crossref | Google Scholar | PubMed |
18 Mess SA, Mackey AJ, Yarowsky DE. Artificial Intelligence Scribe and Large Language Model Technology in Healthcare Documentation: Advantages, Limitations, and Recommendations. Plast Reconstr Surg Glob Open 2025; 13(1): e6450.
| Crossref | Google Scholar | PubMed |
19 Quiroz JC, Laranjo L, Kocaballi AB, Berkovsky S, Rezazadegan D, Coiera E. Challenges of developing a digital scribe to reduce clinical documentation burden. NPJ Digit Med 2019; 2(1): 114.
| Crossref | Google Scholar | PubMed |
20 Ghatnekar S, Faletsky A, Nambudiri VE. Digital scribe utility and barriers to implementation in clinical practice: a scoping review. Health Technol 2021; 11(4): 803-9.
| Crossref | Google Scholar | PubMed |
21 van Buchem MM, Boosman H, Bauer MP, Kant IMJ, Cammel SA, Steyerberg EW. The digital scribe in clinical practice: a scoping review and research agenda. NPJ Digit Med 2021; 4(1): 57.
| Crossref | Google Scholar | PubMed |
22 Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf 2019; 28(3): 231-7.
| Crossref | Google Scholar | PubMed |
23 Coiera E, Liu S. Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Rep Med 2022; 3(12): 100860.
| Crossref | Google Scholar | PubMed |
24 Kotek H, Dockum R, Sun DQ. Gender bias and stereotypes in large language models. In: Proceedings of the ACM Collective Intelligence Conference (CI '23). 2023. p. 12–24. Available at https://doi.org/10.1145/3582269.3615599. 2023;
| Google Scholar |
25 Pain T, Kingston G, Askern J, Smith R, Phillips S, Bell L. How are allied health notes used for inpatient care and clinical decision-making?: A qualitative exploration of the views of doctors, nurses and allied health professionals. Health Inf Manag 2017; 46(1): 23-31.
| Crossref | Google Scholar | PubMed |
26 Grimmer K, Lizarondo L, Kumar S, Bell E, Buist M, Weinstein P. An evidence-based framework to measure quality of allied health care. Health Res Policy Syst 2014; 12(1): 10.
| Crossref | Google Scholar | PubMed |
27 Cameron LJ, Somerville LM, Naismith CE, Watterson D, Maric V, Lannin NA. A qualitative investigation into the patient-centered goal-setting practices of allied health clinicians working in rehabilitation. Clin Rehabil 2018; 32(6): 827-40.
| Crossref | Google Scholar | PubMed |
28 Sheehan J, Laver K, Bhopti A, Rahja M, Usherwood T, Clemson L, et al. Methods and effectiveness of communication between hospital allied health and primary care practitioners: A systematic narrative review. J Multidiscip Healthc 2021; 14: 493-511.
| Crossref | Google Scholar | PubMed |
29 Beck Jepsen D, Robinson K, Ogliari G, Montero-Odasso M, Kamkar N, Ryg J, et al. Predicting falls in older adults: an umbrella review of instruments assessing gait, balance, and functional mobility. BMC Geriatr 2022; 22(1): 615-27.
| Crossref | Google Scholar | PubMed |
30 Heron RL, Eisma MC. Barriers and facilitators of disclosing domestic violence to the healthcare service: A systematic review of qualitative research. Health Soc Care Community 2021; 29(3): 612-30.
| Crossref | Google Scholar | PubMed |
