OBJECTIVE: Ambient artificial intelligence (AI) documentation is increasingly used to draft clinical notes from patient-provider conversations, but how clinicians revise and finalize these drafts is not well understood. This qualitative content analysis study characterizes real-world edits to AI-generated drafts and identifies opportunities for improvement of AI design and the implementation process. MATERIALS AND METHODS: Eight coders analyzed clinical documentation generated by ambient AI from 200 clinical encounters. We developed an inductive coding framework with 11 codes across 3 categories: clinical content, terminology, and language style. Interrater reliability was assessed using Cohen's kappa. We then applied thematic analysis to synthesize patterns across the coded edits. RESULTS: The most frequently edited content pertained to clinical facts including orders (eg, procedures, lab tests) (40.0%), symptoms (30.3%), medication prescriptions (27.3%), and diagnosis descriptions (25.9%). In comparison, edits related to terminology use (11.6%) and language style (7.2%) were less frequent. The results of our thematic analysis show that most edits can be categorized into one of the following 5 types: to revise factual discrepancies, to add medical specialty-specific details, to express diagnostic certainties, to convert patient expressions into objective assessments recorded in medical terms, and to reorganize or condense content. CONCLUSION AND DISCUSSION: Clinicians routinely revise ambient AI drafts to modify factual details and clinical specificity. Future work on AI development and clinical implementation should emphasize specialty customization and support personalized documentation practices, alongside clinician education that promotes robust and consistent review routines to ensure documentation quality.

OBJECTIVES: Ambient clinical documentation technology uses automatic speech recognition (ASR) and natural language processing (NLP) to turn patient-clinician conversations into clinical documentation. It is a promising approach to reducing clinician burden and improving documentation quality. However, the performance of current-generation ASR remains inadequately validated. In this study, we investigated the impact of non-lexical conversational sounds (NLCS) on ASR performance. NLCS, such as Mm-hm and Uh-uh, are commonly used to convey important information in clinical conversations, for example, Mm-hm as a "yes" response from the patient to the clinician question "are you allergic to antibiotics?" MATERIALS AND METHODS: In this study, we evaluated 2 contemporary ASR engines, Google Speech-to-Text Clinical Conversation ("Google ASR"), and Amazon Transcribe Medical ("Amazon ASR"), both of which have their language models specifically tailored to clinical conversations. The empirical data used were from 36 primary care encounters. We conducted a series of quantitative and qualitative analyses to examine the word error rate (WER) and the potential impact of misrecognized NLCS on the quality of clinical documentation. RESULTS: Out of a total of 135 647 spoken words contained in the evaluation data, 3284 (2.4%) were NLCS. Among these NLCS, 76 (0.06% of total words, 2.3% of all NLCS) were used to convey clinically relevant information. The overall WER, of all spoken words, was 11.8% for Google ASR and 12.8% for Amazon ASR. However, both ASR engines demonstrated poor performance in recognizing NLCS: the WERs across frequently used NLCS were 40.8% (Google) and 57.2% (Amazon), respectively; and among the NLCS that conveyed clinically relevant information, 94.7% and 98.7%, respectively. DISCUSSION AND CONCLUSION: Current ASR solutions are not capable of properly recognizing NLCS, particularly those that convey clinically relevant information. Although the volume of NLCS in our evaluation data was very small (2.4% of the total corpus; and for NLCS that conveyed clinically relevant information: 0.06%), incorrect recognition of them could result in inaccuracies in clinical documentation and introduce new patient safety risks.

OBJECTIVE: Qualitative methods are particularly well-suited to studying the complexities and contingencies that emerge in the development, preparation, and implementation of technological interventions in real-world clinical practice, and much remains to be done to use these methods to their full advantage. We aimed to analyze how qualitative methods have been used in health informatics research, focusing on objectives, populations studied, data collection, analysis methods, and fields of analytical origin. METHODS: We conducted a scoping review of original, qualitative empirical research in JAMIA from its inception in 1994 to 2019. We queried PubMed to identify relevant articles, ultimately including and extracting data from 158 articles. RESULTS: The proportion of qualitative studies increased over time, constituting 4.2% of articles published in JAMIA overall. Studies overwhelmingly used interviews, observations, grounded theory, and thematic analysis. These articles used qualitative methods to analyze health informatics systems before, after, and separate from deployment. Providers have typically been the main focus of studies, but there has been an upward trend of articles focusing on healthcare consumers. DISCUSSION: While there has been a rich tradition of qualitative inquiry in JAMIA, its scope has been limited when compared with the range of qualitative methods used in other technology-oriented fields, such as human-computer interaction, computer-supported cooperative work, and science and technology studies. CONCLUSION: We recommend increased public funding for and adoption of a broader variety of qualitative methods by scholars, practitioners, and policy makers and an expansion of the variety of participants studied. This should lead to systems that are more responsive to practical needs, improving usability, safety, and outcomes.

OBJECTIVES: To understand how medical scribes' work may contribute to alleviating clinician burnout attributable directly or indirectly to the use of health IT. MATERIALS AND METHODS: Qualitative analysis of semistructured interviews with 32 participants who had scribing experience in a variety of clinical settings. RESULTS: We identified 7 categories of clinical tasks that clinicians commonly choose to offload to medical scribes, many of which involve delegated use of health IT. These range from notes-taking and computerized data entry to foraging, assembling, and tracking information scattered across multiple clinical information systems. Some common characteristics shared among these tasks include: (1) time-consuming to perform; (2) difficult to remember or keep track of; (3) disruptive to clinical workflow, clinicians' cognitive processes, or patient-provider interactions; (4) perceived to be low-skill "clerical" work; and (5) deemed as adding no value to direct patient care. DISCUSSION: The fact that clinicians opt to "outsource" certain clinical tasks to medical scribes is a strong indication that performing these tasks is not perceived to be the best use of their time. Given that a vast majority of healthcare practices in the US do not have the luxury of affording medical scribes, the burden would inevitably fall onto clinicians' shoulders, which could be a major source for clinician burnout. CONCLUSIONS: Medical scribes help to offload a substantial amount of burden from clinicians-particularly with tasks that involve onerous interactions with health IT. Developing a better understanding of medical scribes' work provides useful insights into the sources of clinician burnout and potential solutions to it.

OBJECTIVE: Use of medical scribes reduces clinician burnout by sharing the burden of clinical documentation. However, medical scribes are cost-prohibitive for most settings, prompting a growing interest in developing ambient, speech-based technologies capable of automatically generating clinical documentation based on patient-provider conversation. Through a systematic review, we aimed to develop a thorough understanding of the work performed by medical scribes in order to inform the design of such technologies. MATERIALS AND METHODS: Relevant articles retrieved by searching in multiple literature databases. We conducted the screening process following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) in guidelines, and then analyzed the data using qualitative methods to identify recurring themes. RESULTS: The literature search returned 854 results, 65 of which met the inclusion criteria. We found that there is significant variation in scribe expectations and responsibilities across healthcare organizations; scribes also frequently adapt their work based on the provider's style and preferences. Further, scribes' job extends far beyond capturing conversation in the exam room; they also actively interact with patients and the care team and integrate data from other sources such as prior charts and lab test results. DISCUSSION: The results of this study provide several implications for designing technologies that can generate clinical documentation based on naturalistic conversations taking place in the exam room. First, a one-size-fits-all solution will be unlikely to work because of the significant variation in scribe work. Second, technology designers need to be aware of the limited role that their solution can fulfill. Third, to produce comprehensive clinical documentation, such technologies will likely have to incorporate information beyond the exam room conversation. Finally, issues of patient consent and privacy have yet to be adequately addressed, which could become paramount barriers to implementing such technologies in realistic clinical settings. CONCLUSIONS: Medical scribes perform complex and delicate work. Further research is needed to better understand their roles in a clinical setting in order to inform the development of speech-based clinical documentation technologies.