![]() ![]() Finally, the large portion of time spent on documentation outside of patient encounters is tedious and repetitive 6, and heavy workloads can lead to dissatisfaction and burnout among clinicians, ultimately impacting their ability to provide patient care 7, 8, 9, 10, 11.Īrtificial intelligence (AI)-based efforts to alleviate these challenges have recently been made, including using automatic speech recognition (ASR) 12, 13, 14, 15, 16, and natural language processing (NLP) technologies to transcribe patient-clinician conversations and automatically generate clinical notes 17, 18, 19, 20. Additionally, clinicians may pay more attention to their computers and make less eye contact with patients, creating communication barriers and reducing patient satisfaction 3, 4, 5. With computer-based note-taking, information registration by typing is not as natural as free-hand note taking for some clinicians, decreasing efficiency and introducing errors 2. Some hospitals maintain the original paper copies of notes, adding burden on data administration and security. ![]() Some providers discard paper-based notes after transferring information to the EHR system, potentially losing details of their initial observations. Indeed, clinicians spend nearly two plus hours on EHR systems per hour of direct patient care 1. A large portion of such notes are unstructured and require complex transformations into computer-based documents. With paper-based note-taking, patient data must be registered first during the patient’s visit, and again during digitization. These methods have a significant impact on clinicians’ workflow and workload. Others prefer typing digital notes directly into EHR systems while talking with patients and polishing the drafts after the encounters. Some clinicians begin with free-text note-taking or structured paper forms, which are then digitized through dictation, typing into electronic health record (EHR) systems, or scanning and indexing to the patient’s records. We open source a proof-of-concept implementation that can lay the foundation for broader clinical use cases.Ĭlinicians produce a considerable amount of data when seeing a patient, including records of patient history, physical examination, lab test requests, referral reports, etc. Semi-structured interviews and trials in clinical settings rendered positive feedback from both clinicians and patients, demonstrating that AI-enabled clinical note-taking under our design improves ease and breadth of information captured during clinical visits without compromising patient-clinician interactions. The output is unobtrusively presented on mobile devices to clinicians for real-time validation and can be automatically transformed into digital formats that would be compatible with integration into electronic health record systems. PhenoPad is an intelligent clinical note-taking interface that captures free-form notes and standard phenotypic information via a variety of modalities, including speech and natural language processing techniques, handwriting recognition, and more. By surveying healthcare providers’ current note-taking practices and attitudes toward new clinical technologies, we developed a patient-centered paradigm for clinical note-taking that makes use of hybrid tablet/keyboard devices and artificial intelligence (AI) technologies. Current clinical note-taking approaches cannot capture the entirety of information available from patient encounters and detract from patient-clinician interactions. ![]()
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