1
|
Vukmir RB. Medicolegal aspects of documentation and the electronic health record. Med Clin (Barc) 2024; 162:e9-e14. [PMID: 38448298 DOI: 10.1016/j.medcli.2024.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 01/16/2024] [Accepted: 01/25/2024] [Indexed: 03/08/2024]
Abstract
INTRODUCTION The busiest times in the hospital are often met by the greatest challenges in complete and comprehensive documentation of the patient care event. The near complete transition to the Electronic Health Record (EHR) was to be the solution to a host of provider documentation concerns. It is clear the EHR provides reliability, reproducibility, integration, evidence based decision-making, multidisciplinary contribution across the entire healthcare spectrum. METHODS The use of a consensus of expert opinion supplemented by focused literature review allows a balanced evidence based presentation of data. RESULTS Documentation is not a perfect tool however, as issues with efficiency, reliability, use of shortcut maneuvers and potential for increased medico-legal risk have been raised. The solution is attention to documentation detail, and creation of systems that facilitate excellence. The focus on electronic documentation systems should include continual evaluation, ongoing improvement, involvement of a multidisciplinary patient care team and vendor receptiveness to in EHR development and operations. CONCLUSION The most effective use of the EHR as a risk management tool requires documentation knowledge, targeted analysis, product improvement and co-development of clinical-commercial resource.
Collapse
|
2
|
Tolle H, Castro MDM, Wachinger J, Putri AZ, Kempf D, Denkinger CM, McMahon SA. From voice to ink (Vink): development and assessment of an automated, free-of-charge transcription tool. BMC Res Notes 2024; 17:95. [PMID: 38553773 PMCID: PMC10981346 DOI: 10.1186/s13104-024-06749-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Accepted: 03/18/2024] [Indexed: 04/01/2024] Open
Abstract
BACKGROUND Verbatim transcription of qualitative audio data is a cornerstone of analytic quality and rigor, yet the time and energy required for such transcription can drain resources, delay analysis, and hinder the timely dissemination of qualitative insights. In recent years, software programs have presented a promising mechanism to accelerate transcription, but the broad application of such programs has been constrained due to expensive licensing or "per-minute" fees, data protection concerns, and limited availability of such programs in many languages. In this article, we outline our process of adapting a free, open-source, speech-to-text algorithm (Whisper by OpenAI) into a usable and accessible tool for qualitative transcription. Our program, which we have dubbed "Vink" for voice to ink, is available under a permissive open-source license (and thus free of cost). RESULTS We conducted a proof-of-principle assessment of Vink's performance in transcribing authentic interview audio data in 14 languages. A majority of pilot-testers evaluated the software performance positively and indicated that they were likely to use the tool in their future research. Our usability assessment indicates that Vink is easy-to-use, and we performed further refinements based on pilot-tester feedback to increase user-friendliness. CONCLUSION With Vink, we hope to contribute to facilitating rigorous qualitative research processes globally by reducing time and costs associated with transcription and by expanding free-of-cost transcription software availability to more languages. With Vink running on standalone computers, data privacy issues arising within many other solutions do not apply.
Collapse
Affiliation(s)
- Hannah Tolle
- Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany.
| | - Maria Del Mar Castro
- Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
| | - Jonas Wachinger
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Agrin Zauyani Putri
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
| | - Dominic Kempf
- Scientific Software Center, Heidelberg University, Heidelberg, Germany
| | - Claudia M Denkinger
- Division of Infectious Diseases and Tropical Medicine, Center of Infectious Diseases, Heidelberg University Hospital, Heidelberg, Germany
- Partner Site Heidelberg University Hospital, German Centre for Infection Research (DZIF), Heidelberg, Germany
| | - Shannon A McMahon
- Heidelberg Institute of Global Health (HIGH), Heidelberg University Hospital, Heidelberg, Germany
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| |
Collapse
|
3
|
Seyedi S, Griner E, Corbin L, Jiang Z, Roberts K, Iacobelli L, Milloy A, Boazak M, Bahrami Rad A, Abbasi A, Cotes RO, Clifford GD. Using HIPAA (Health Insurance Portability and Accountability Act)-Compliant Transcription Services for Virtual Psychiatric Interviews: Pilot Comparison Study. JMIR Ment Health 2023; 10:e48517. [PMID: 37906217 PMCID: PMC10646674 DOI: 10.2196/48517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/25/2023] [Accepted: 09/12/2023] [Indexed: 11/02/2023] Open
Abstract
BACKGROUND Automatic speech recognition (ASR) technology is increasingly being used for transcription in clinical contexts. Although there are numerous transcription services using ASR, few studies have compared the word error rate (WER) between different transcription services among different diagnostic groups in a mental health setting. There has also been little research into the types of words ASR transcriptions mistakenly generate or omit. OBJECTIVE This study compared the WER of 3 ASR transcription services (Amazon Transcribe [Amazon.com, Inc], Zoom-Otter AI [Zoom Video Communications, Inc], and Whisper [OpenAI Inc]) in interviews across 2 different clinical categories (controls and participants experiencing a variety of mental health conditions). These ASR transcription services were also compared with a commercial human transcription service, Rev (Rev.Com, Inc). Words that were either included or excluded by the error in the transcripts were systematically analyzed by their Linguistic Inquiry and Word Count categories. METHODS Participants completed a 1-time research psychiatric interview, which was recorded on a secure server. Transcriptions created by the research team were used as the gold standard from which WER was calculated. The interviewees were categorized into either the control group (n=18) or the mental health condition group (n=47) using the Mini-International Neuropsychiatric Interview. The total sample included 65 participants. Brunner-Munzel tests were used for comparing independent sets, such as the diagnostic groupings, and Wilcoxon signed rank tests were used for correlated samples when comparing the total sample between different transcription services. RESULTS There were significant differences between each ASR transcription service's WER (P<.001). Amazon Transcribe's output exhibited significantly lower WERs compared with the Zoom-Otter AI's and Whisper's ASR. ASR performances did not significantly differ across the 2 different clinical categories within each service (P>.05). A comparison between the human transcription service output from Rev and the best-performing ASR (Amazon Transcribe) demonstrated a significant difference (P<.001), with Rev having a slightly lower median WER (7.6%, IQR 5.4%-11.35 vs 8.9%, IQR 6.9%-11.6%). Heat maps and spider plots were used to visualize the most common errors in Linguistic Inquiry and Word Count categories, which were found to be within 3 overarching categories: Conversation, Cognition, and Function. CONCLUSIONS Overall, consistent with previous literature, our results suggest that the WER between manual and automated transcription services may be narrowing as ASR services advance. These advances, coupled with decreased cost and time in receiving transcriptions, may make ASR transcriptions a more viable option within health care settings. However, more research is required to determine if errors in specific types of words impact the analysis and usability of these transcriptions, particularly for specific applications and in a variety of populations in terms of clinical diagnosis, literacy level, accent, and cultural origin.
Collapse
Affiliation(s)
- Salman Seyedi
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Emily Griner
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Lisette Corbin
- Department of Psychiatry, Duke University Health, Durham, NC, United States
| | - Zifan Jiang
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| | - Kailey Roberts
- Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, United States
| | - Luca Iacobelli
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Aaron Milloy
- Infection Prevention Department, Emory Healthcare, Atlanta, GA, United States
| | - Mina Boazak
- Animo Sano Psychiatry, Durham, NC, United States
| | - Ali Bahrami Rad
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
| | - Ahmed Abbasi
- Department of Information Technology, Analytics, and Operations, University of Notre Dame, Notre Dame, IN, United States
| | - Robert O Cotes
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, United States
| | - Gari D Clifford
- Department of Biomedical Informatics, Emory University, Atlanta, GA, United States
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA, United States
| |
Collapse
|
4
|
Lee TY, Li CC, Chou KR, Chung MH, Hsiao ST, Guo SL, Hung LY, Wu HT. Machine learning-based speech recognition system for nursing documentation - A pilot study. Int J Med Inform 2023; 178:105213. [PMID: 37690224 DOI: 10.1016/j.ijmedinf.2023.105213] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 09/01/2023] [Accepted: 09/03/2023] [Indexed: 09/12/2023]
Abstract
PURPOSE Considering the significant workload of nursing tasks, enhancing the efficiency of nursing documentation is imperative. This study aimed to evaluate the effectiveness of a machine learning-based speech recognition (SR) system in reducing the clinical workload associated with typing nursing records, implemented in a psychiatry ward. METHODS The study was conducted between July 15, 2020, and June 30, 2021, at Cheng Hsin General Hospital in Taiwan. The language corpus was based on the existing records from the hospital nursing information system. The participating ward's nursing activities, clinical conversation, and accent data were also collected for deep learning-based SR-engine training. A total of 21 nurses participated in the evaluation of the SR system. Documentation time and recognition error rate were evaluated in parallel between SR-generated records and keyboard entry over 4 sessions. Any differences between SR and keyboard transcriptions were regarded as SR errors. FINDINGS A total of 200 data were obtained from four evaluation sessions, 10 participants were asked to use SR and keyboard entry in parallel at each session and 5 entries were collected from each participant. Overall, the SR system processed 30,112 words in 32,456 s (0.928 words per second). The mean accuracy of the SR system improved after each session, from 87.06% in 1st session to 95.07% in 4th session. CONCLUSION This pilot study demonstrated our machine learning-based SR system has an acceptable recognition accuracy and may reduce the burden of documentation for nurses. However, the potential error with the SR transcription should continually be recognized and improved. Further studies are needed to improve the integration of SR in digital documentation of nursing records, in terms of both productivity and accuracy across different clinical specialties.
Collapse
Affiliation(s)
- Tso-Ying Lee
- Director of Nursing Research Center, Nursing Department, Taipei Medical University Hospital, Taipei, Taiwan; Associate Professor, School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan.
| | - Chin-Ching Li
- Assistant Professor, Department of Nursing, Mackay Medical College, New Taipei City, Taiwan
| | - Kuei-Ru Chou
- Professor, College of Nursing, Taipei Medical University, Taipei, Taiwan
| | - Min-Huey Chung
- Professor, College of Nursing, Taipei Medical University, Taipei, Taiwan
| | - Shu-Tai Hsiao
- Vice President, Taipei Medical University Hospital, Taipei, Taiwan
| | - Shu-Liu Guo
- Director of Nursing Department, Taipei Medical University Hospital, Taipei, Taiwan
| | - Lung-Yun Hung
- Head Nurse, Nursing Department, Cheng Hsin General Hospital, Taipei, Taiwan
| | - Hao-Ting Wu
- Head Nurse, Nursing Department, Cheng Hsin General Hospital, Taipei, Taiwan
| |
Collapse
|
5
|
Marchalik D, Shanafelt TD. Surgeon wellbeing in the 21st century. Br J Surg 2023; 110:1021-1022. [PMID: 37300546 DOI: 10.1093/bjs/znad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 04/28/2023] [Indexed: 06/12/2023]
Abstract
Physician time is under assault. Optimizing surgeons time and maximizing time spent on work that brings them the greatest professional fulfillment should be central tenants of these efforts.
Collapse
Affiliation(s)
- Daniel Marchalik
- MedStar Health/Georgetown University School of Medicine, Washington, DC, USA
| | - Tait D Shanafelt
- WellMD & WellPhD Center, Stanford University, Palo Alto, California, USA
| |
Collapse
|
6
|
Dinari F, Bahaadinbeigy K, Bassiri S, Mashouf E, Bastaminejad S, Moulaei K. Benefits, barriers, and facilitators of using speech recognition technology in nursing documentation and reporting: A cross-sectional study. Health Sci Rep 2023; 6:e1330. [PMID: 37313530 PMCID: PMC10259462 DOI: 10.1002/hsr2.1330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 05/18/2023] [Accepted: 05/31/2023] [Indexed: 06/15/2023] Open
Abstract
Background and Aim Nursing reports are necessary for clinical communication and provide an accurate reflection of nursing assessments, care provided, changes in clinical status, and patient-related information to support the multidisciplinary team to provide individualized care. Nurses always face challenges in recording and documenting nursing reports. Speech recognition systems (SRS), as one of the documentation technologies, can play a potential role in recording medical reports. Therefore, this study seeks to identify the barriers, benefits, and facilitators of utilizing speech recognition technology in nursing reports. Materials and Methods This cross-sectional was conducted through a researcher-made questionnaire in 2022. Invitations were sent to 200 ICU nurses working in the three educational hospitals of Imam Reza (AS), Qaem and Imam Zaman in Mashhad city (Iran), 125 of whom accepted our invitation. Finally, 73 nurses included the study based on inclusion and exclusion criteria. Data analysis was performed using SPSS 22.0. Results According to the nurses, "paperwork reduction" (3.96, ±1.96), "performance improvement" (3.96, ±0.93), and "cost reduction" (3.95, ±1.07) were the most common benefits of using the SRS. "Lack of specialized, technical, and experienced staff to teach nurses how to work with speech recognition systems" (3.59, ±1.18), "insufficient training of nurses" (3.59, ±1.11), and "need to edit and control quality and correct documents" (3.59, ±1.03) were the most common barriers to using SRS. As well as "ability to fully review documentation processes" (3.62, ±1.13), "creation of integrated data in record documentation" (3.58, ±1.15), "possibility of error correction for nurses" (3.51, ±1.16) were the most common facilitators. There was no significant relationship between nurses' demographic information and the benefits, barriers, and facilitators. Conclusions By providing information on the benefits, barriers, and facilitators of using this technology, hospital managers, nursing managers, and information technology managers of healthcare centers can make more informed decisions in selecting and implementing SRS for nursing report documentation. This will help to avoid potential challenges that may reduce the efficiency, effectiveness, and productivity of the systems.
Collapse
Affiliation(s)
- Fatemeh Dinari
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Kambiz Bahaadinbeigy
- Medical Informatics Research Center, Institute for Futures Studies in HealthKerman University of Medical SciencesKermanIran
| | - Somayyeh Bassiri
- Branch Artificial IntelligentIslamic Azad University MashhadMashhadIran
| | - Esmat Mashouf
- Department of Health Information TechnologyVarastegan Institute for Medical SciencesMashhadIran
| | - Saiyad Bastaminejad
- Department of Genetics, Faculty of ParamedicalIlam University of Medical SciencesIlamIran
| | - Khadijeh Moulaei
- Department of Health Information Technology, Faculty of ParamedicalIlam University of Medical SciencesIlamIran
| |
Collapse
|
7
|
Tran BD, Latif K, Reynolds TL, Park J, Elston Lafata J, Tai-Seale M, Zheng K. "Mm-hm," "Uh-uh": are non-lexical conversational sounds deal breakers for the ambient clinical documentation technology? J Am Med Inform Assoc 2023; 30:703-711. [PMID: 36688526 PMCID: PMC10018260 DOI: 10.1093/jamia/ocad001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 12/13/2022] [Accepted: 01/12/2023] [Indexed: 01/24/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Brian D Tran
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California, Irvine, Irvine, California, USA
- School of Medicine, University of California, Irvine, Irvine, California, USA
| | - Kareem Latif
- School of Medicine, California University of Science and Medicine, Colton, California, USA
| | - Tera L Reynolds
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, Maryland, USA
| | - Jihyun Park
- Department of Computer Science, Donald Bren School of Informatics and Computer Science, University of California, Irvine, Irvine, California, USA
| | - Jennifer Elston Lafata
- Division of Pharmaceutical Outcomes and Policy, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Center for Health Policy and Health Services Research, Henry Ford Health System, Detroit, Michigan, USA
| | - Ming Tai-Seale
- Department of Family Medicine and Public Health, School of Medicine, University of California, San Diego, La Jolla, California, USA
| | - Kai Zheng
- Department of Informatics, Donald Bren School of Informatics and Computer Science, University of California, Irvine, Irvine, California, USA
| |
Collapse
|
8
|
Onitilo AA, Shour AR, Puthoff DS, Tanimu Y, Joseph A, Sheehan MT. Evaluating the adoption of voice recognition technology for real-time dictation in a rural healthcare system: A retrospective analysis of dragon medical one. PLoS One 2023; 18:e0272545. [PMID: 36952436 PMCID: PMC10035815 DOI: 10.1371/journal.pone.0272545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 03/02/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND In 2013, Marshfield Clinic Health System (MCHS) implemented the Dragon Medical One (DMO) system provided by Nuance Management Center (NMC) for Real-Time Dictation (RTD), embracing the idea of streamlined clinic workflow, reduced dictation hours, and improved documentation legibility. Since then, MCHS has observed a trend of reduced time in documentation, however, the target goal of 100% adoption of voice recognition (VR)-based RTD has not been met. OBJECTIVE To evaluate the uptake/adoption of VR technology for RTD in MCHS, between 2018-2020. METHODS DMO data for 1,373 MCHS providers from 2018-2020 were analyzed. The study outcome was VR uptake, defined as the median number of hours each provider used VR technology to dictate patient information, and classified as no/yes. Covariates included sex, age, US-trained/international medical graduates, trend, specialty, and facility. Descriptive statistics and unadjusted and adjusted logistic regression analyses were performed. Stata/SE.version.17 was used for analyses. P-values less than/equal to 0.05 were considered statistically significant. RESULTS Of the 1,373 MCHS providers, the mean (SD) age was 48.3 (12.4) years. VR uptake was higher than no uptake (72.0% vs. 28.0%). In both unadjusted and adjusted analyses, VR uptake was 4.3 times and 7.7 times higher in 2019-2020 compared to 2018, respectively (OR:4.30,95%CI:2.44-7.46 and AOR:7.74,95%CI:2.51-23.86). VR uptake was 0.5 and 0.6 times lower among US-trained physicians compared to internationally-trained physicians (OR:0.53,95%CI:0.37-0.76 and AOR:0.58,95%CI:0.35-0.97). Uptake was 0.2 times lower among physicians aged 60/above than physicians aged 29/less (OR:0.20,95%CI:0.10-0.59, and AOR:0.17,95%CI:0.27-1.06). CONCLUSION Since 2018, VR adoption has increased significantly across MCHS. However, it was lower among US-trained physicians than among internationally-trained physicians (although internationally physicians were in minority) and lower among more senior physicians than among younger physicians. These findings provide critical information about VR trends, physician factors, and which providers could benefit from additional training to increase VR adoption in healthcare systems.
Collapse
Affiliation(s)
- Adedayo A Onitilo
- Cancer Care and Research Center, Department of Oncology, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - Abdul R Shour
- Cancer Care and Research Center, Department of Oncology, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - David S Puthoff
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - Yusuf Tanimu
- Cancer Care and Research Center, Department of Oncology, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
- Marshfield Clinic Research Institute, Marshfield Clinic Health System, Marshfield, Wisconsin, United States of America
| | - Adedayo Joseph
- NSIA-LUTH Cancer Center, Lagos University Teaching Hospital, Lagos, Nigeria
| | - Michael T Sheehan
- Department of Endocrinology, Marshfield Clinic, Weston, WI, United States of America
| |
Collapse
|
9
|
Jing X, Indani A, Hubig N, Min H, Gong Y, Cimino JJ, Sittig DF, Rennert L, Robinson D, Biondich P, Wright A, Nøhr C, Law T, Faxvaag A, Gimbel R. A Systematic Approach to Configuring MetaMap for Optimal Performance. Methods Inf Med 2022; 61:e51-e63. [PMID: 35613942 PMCID: PMC9788913 DOI: 10.1055/a-1862-0421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND MetaMap is a valuable tool for processing biomedical texts to identify concepts. Although MetaMap is highly configurative, configuration decisions are not straightforward. OBJECTIVE To develop a systematic, data-driven methodology for configuring MetaMap for optimal performance. METHODS MetaMap, the word2vec model, and the phrase model were used to build a pipeline. For unsupervised training, the phrase and word2vec models used abstracts related to clinical decision support as input. During testing, MetaMap was configured with the default option, one behavior option, and two behavior options. For each configuration, cosine and soft cosine similarity scores between identified entities and gold-standard terms were computed for 40 annotated abstracts (422 sentences). The similarity scores were used to calculate and compare the overall percentages of exact matches, similar matches, and missing gold-standard terms among the abstracts for each configuration. The results were manually spot-checked. The precision, recall, and F-measure (β =1) were calculated. RESULTS The percentages of exact matches and missing gold-standard terms were 0.6-0.79 and 0.09-0.3 for one behavior option, and 0.56-0.8 and 0.09-0.3 for two behavior options, respectively. The percentages of exact matches and missing terms for soft cosine similarity scores exceeded those for cosine similarity scores. The average precision, recall, and F-measure were 0.59, 0.82, and 0.68 for exact matches, and 1.00, 0.53, and 0.69 for missing terms, respectively. CONCLUSION We demonstrated a systematic approach that provides objective and accurate evidence guiding MetaMap configurations for optimizing performance. Combining objective evidence and the current practice of using principles, experience, and intuitions outperforms a single strategy in MetaMap configurations. Our methodology, reference codes, measurements, results, and workflow are valuable references for optimizing and configuring MetaMap.
Collapse
Affiliation(s)
- Xia Jing
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States,Address for correspondence Xia Jing, MD, PhD Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson UniversityEdwards Hall 511, Clemson, SC 29634United States
| | - Akash Indani
- School of Computing, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, South Carolina, United States
| | - Nina Hubig
- School of Computing, College of Engineering, Computing and Applied Sciences, Clemson University, Clemson, South Carolina, United States
| | - Hua Min
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, Virginia, United States
| | - Yang Gong
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, Texas, United States
| | - James J. Cimino
- Informatics Institute, The University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - Dean F. Sittig
- School of Biomedical Informatics, The University of Texas Health Sciences Center at Houston, Houston, Texas, United States
| | - Lior Rennert
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States
| | | | - Paul Biondich
- Department of Pediatrics, Clem McDonald Biomedical Informatics Center, Regenstrief Institute, Indiana University School of Medicine, Indianapolis, Indiana, United States
| | - Adam Wright
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Christian Nøhr
- Department of Planning, Faculty of Engineering, Aalborg University, Aalborg, Denmark
| | - Timothy Law
- Ohio Musculoskeletal and Neurologic Institute, Ohio University, Athens, Ohio, United States
| | - Arild Faxvaag
- Department of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Ronald Gimbel
- Department of Public Health Sciences, College of Behavioral, Social and Health Sciences, Clemson University, Clemson, South Carolina, United States
| |
Collapse
|
10
|
Chen JS, Baxter SL. Applications of natural language processing in ophthalmology: present and future. Front Med (Lausanne) 2022; 9:906554. [PMID: 36004369 PMCID: PMC9393550 DOI: 10.3389/fmed.2022.906554] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.
Collapse
Affiliation(s)
- Jimmy S. Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
| |
Collapse
|
11
|
Topaz M, Zolnoori M, Norful AA, Perrier A, Kostic Z, George M. Speech recognition can help evaluate shared decision making and predict medication adherence in primary care setting. PLoS One 2022; 17:e0271884. [PMID: 35925922 PMCID: PMC9352008 DOI: 10.1371/journal.pone.0271884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 07/10/2022] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Asthma is a common chronic illness affecting 19 million US adults. Inhaled corticosteroids are a safe and effective treatment for asthma, yet, medication adherence among patients remains poor. Shared decision-making, a patient activation strategy, can improve patient adherence to inhaled corticosteroids. This study aimed to explore whether audio-recorded patient-primary care provider encounters can be used to: 1. Evaluate the level of patient-perceived shared decision-making during the encounter, and 2. Predict levels of patient's inhaled corticosteroid adherence. MATERIALS AND METHODS Shared decision-making and inhaled corticosteroid adherence were assessed using the SDM Questionnaire-9 and the Medication Adherence Report Scale for Asthma (MARS-A). Speech-to-text algorithms were used to automatically transcribe 80 audio-recorded encounters between primary care providers and asthmatic patients. Machine learning algorithms (Naive Bayes, Support Vector Machines, Decision Tree) were applied to achieve the study's predictive goals. RESULTS The accuracy of automated speech-to-text transcription was relatively high (ROUGE F-score = .9). Machine learning algorithms achieved good predictive performance for shared decision-making (the highest F-score = .88 for the Naive Bayes) and inhaled corticosteroid adherence (the highest F-score = .87 for the Support Vector Machines). DISCUSSION This was the first study that trained machine learning algorithms on a dataset of audio-recorded patient-primary care provider encounters to successfully evaluate the quality of SDM and predict patient inhaled corticosteroid adherence. CONCLUSION Machine learning approaches can help primary care providers identify patients at risk for poor medication adherence and evaluate the quality of care by measuring levels of shared decision-making. Further work should explore the replicability of our results in larger samples and additional health domains.
Collapse
Affiliation(s)
- Maxim Topaz
- School of Nursing and Data Science Institute, Columbia University, New York, New York, United States of America
- Visiting Nurse Service of New York, New York, New York, United States of America
| | - Maryam Zolnoori
- School of Nursing and Data Science Institute, Columbia University, New York, New York, United States of America
| | - Allison A. Norful
- Irving Institute for Clinical and Translational Research, Columbia University, New York, New York, United States of America
- School of Nursing, Columbia University, New York, New York, United States of America
| | - Alexis Perrier
- School of Nursing, Columbia University, New York, New York, United States of America
| | - Zoran Kostic
- Department of Electrical engineering, Columbia University, New York, New York, United States of America
| | - Maureen George
- School of Nursing, Columbia University, New York, New York, United States of America
| |
Collapse
|
12
|
Chaudhari GR, Liu T, Chen TL, Joseph GB, Vella M, Lee YJ, Vu TH, Seo Y, Rauschecker AM, McCulloch CE, Sohn JH. Application of a Domain-specific BERT for Detection of Speech Recognition Errors in Radiology Reports. Radiol Artif Intell 2022; 4:e210185. [PMID: 35923373 PMCID: PMC9344210 DOI: 10.1148/ryai.210185] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 04/11/2022] [Accepted: 05/10/2022] [Indexed: 06/15/2023]
Abstract
PURPOSE To develop radiology domain-specific bidirectional encoder representations from transformers (BERT) models that can identify speech recognition (SR) errors and suggest corrections in radiology reports. MATERIALS AND METHODS A pretrained BERT model, Clinical BioBERT, was further pretrained on a corpus of 114 008 radiology reports between April 2016 and August 2019 that were retrospectively collected from two hospitals. Next, the model was fine-tuned on a training dataset of generated insertion, deletion, and substitution errors, creating Radiology BERT. This model was retrospectively evaluated on an independent dataset of radiology reports with generated errors (n = 18 885) and on unaltered report sentences (n = 2000) and prospectively evaluated on true clinical SR errors (n = 92). Correction Radiology BERT was separately trained to suggest corrections for detected deletion and substitution errors. Area under the receiver operating characteristic curve (AUC) and bootstrapped 95% CIs were calculated for each evaluation dataset. RESULTS Radiology-specific BERT had AUC values of >.99 (95% CI: >0.99, >0.99), 0.94 (95% CI: 0.93, 0.94), 0.98 (95% CI: 0.98, 0.98), and 0.97 (95% CI: 0.97, 0.97) for detecting insertion, deletion, substitution, and all errors, respectively, on the independently generated test set. Testing on unaltered report impressions revealed a sensitivity of 82% (28 of 34; 95% CI: 70%, 93%) and specificity of 88% (1521 of 1728; 95% CI: 87%, 90%). Testing on prospective SR errors showed an accuracy of 75% (69 of 92; 95% CI: 65%, 83%). Finally, the correct word was the top suggestion for 45.6% (475 of 1041; 95% CI: 42.5%, 49.3%) of errors. CONCLUSION Radiology-specific BERT models fine-tuned on generated errors were able to identify SR errors in radiology reports and suggest corrections.Keywords: Computer Applications, Technology Assessment Supplemental material is available for this article. © RSNA, 2022See also the commentary by Abajian and Cheung in this issue.
Collapse
|
13
|
A dataset of simulated patient-physician medical interviews with a focus on respiratory cases. Sci Data 2022; 9:313. [PMID: 35710769 PMCID: PMC9203765 DOI: 10.1038/s41597-022-01423-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 05/25/2022] [Indexed: 11/08/2022] Open
Abstract
Artificial Intelligence (AI) is playing a major role in medical education, diagnosis, and outbreak detection through Natural Language Processing (NLP), machine learning models and deep learning tools. However, in order to train AI to facilitate these medical fields, well-documented and accurate medical conversations are needed. The dataset presented covers a series of medical conversations in the format of Objective Structured Clinical Examinations (OSCE), with a focus on respiratory cases in audio format and corresponding text documents. These cases were simulated, recorded, transcribed, and manually corrected with the underlying aim of providing a comprehensive set of medical conversation data to the academic and industry community. Potential applications include speech recognition detection for speech-to-text errors, training NLP models to extract symptoms, detecting diseases, or for educational purposes, including training an avatar to converse with healthcare professional students as a standardized patient during clinical examinations. The application opportunities for the presented dataset are vast, given that this calibre of data is difficult to access and costly to develop.
Collapse
|
14
|
Crandell HA, Silcox JW, Ferguson SH, Lohani M, Payne BR. The Effects of Captioning Errors, Background Noise, and Hearing Loss on Memory for Text-Captioned Speech. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2022; 65:2364-2390. [PMID: 35623337 DOI: 10.1044/2022_jslhr-21-00416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
PURPOSE Previous studies have suggested that the negative effects of acoustic challenge on speech memory can be attenuated with assistive text captions, particularly among older adults with hearing impairment. However, no studies have systematically examined the effects of text-captioning errors, which are common in automated speech recognition (ASR) systems. METHOD In two experiments, we examined memory for text-captioned speech (with and without background noise) when captions had no errors (control) or had one of three common ASR errors: substitution, deletion, or insertion errors. RESULTS In both Experiment 1 (young adults with normal hearing) and Experiment 2 (older adults with varying hearing acuity), we observed similar additive effects of caption errors and background noise, such that increased background noise and the presence of captioning errors negatively impacted memory outcomes. Notably, the negative effects of captioning errors were largest among older adults with increased hearing thresholds, suggesting that older adults with hearing loss may show an increased reliance on text captions compared to adults with normal hearing. CONCLUSION Our findings show that even a single-word error can be deleterious to memory for text-captioned speech, especially in older adults with hearing loss. Therefore, to produce the greatest benefit to memory, it is crucial that text captions are accurate.
Collapse
Affiliation(s)
| | - Jack W Silcox
- Department of Psychology, The University of Utah, Salt Lake City
| | - Sarah H Ferguson
- Department of Communication Sciences and Disorders, The University of Utah, Salt Lake City
| | - Monika Lohani
- Department of Educational Psychology, The University of Utah, Salt Lake City
| | - Brennan R Payne
- Department of Psychology, The University of Utah, Salt Lake City
- Department of Communication Sciences and Disorders, The University of Utah, Salt Lake City
| |
Collapse
|
15
|
Lee SH, Park J, Yang K, Min J, Choi J. Accuracy of Cloud-Based Speech Recognition Open Application Programming Interface for Medical Terms of Korean. J Korean Med Sci 2022; 37:e144. [PMID: 35535371 PMCID: PMC9091429 DOI: 10.3346/jkms.2022.37.e144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 04/07/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND There are limited data on the accuracy of cloud-based speech recognition (SR) open application programming interfaces (APIs) for medical terminology. This study aimed to evaluate the medical term recognition accuracy of current available cloud-based SR open APIs in Korean. METHODS We analyzed the SR accuracy of currently available cloud-based SR open APIs using real doctor-patient conversation recordings collected from an outpatient clinic at a large tertiary medical center in Korea. For each original and SR transcription, we analyzed the accuracy rate of each cloud-based SR open API (i.e., the number of medical terms in the SR transcription per number of medical terms in the original transcription). RESULTS A total of 112 doctor-patient conversation recordings were converted with three cloud-based SR open APIs (Naver Clova SR from Naver Corporation; Google Speech-to-Text from Alphabet Inc.; and Amazon Transcribe from Amazon), and each transcription was compared. Naver Clova SR (75.1%) showed the highest accuracy with the recognition of medical terms compared to the other open APIs (Google Speech-to-Text, 50.9%, P < 0.001; Amazon Transcribe, 57.9%, P < 0.001), and Amazon Transcribe demonstrated higher recognition accuracy compared to Google Speech-to-Text (P < 0.001). In the sub-analysis, Naver Clova SR showed the highest accuracy in all areas according to word classes, but the accuracy of words longer than five characters showed no statistical differences (Naver Clova SR, 52.6%; Google Speech-to-Text, 56.3%; Amazon Transcribe, 36.6%). CONCLUSION Among three current cloud-based SR open APIs, Naver Clova SR which manufactured by Korean company showed highest accuracy of medical terms in Korean, compared to Google Speech-to-Text and Amazon Transcribe. Although limitations are existing in the recognition of medical terminology, there is a lot of rooms for improvement of this promising technology by combining strengths of each SR engines.
Collapse
Affiliation(s)
- Seung-Hwa Lee
- Rehabilitation and Prevention Center, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
| | - Jungchan Park
- Department of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Kwangmo Yang
- Center for Health Promotion, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
| | - Jeongwon Min
- Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Korea
- Integrated Major in Innovative Medical Science, Seoul National University Graduate School, Seoul, Korea
| | - Jinwook Choi
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea
- Institute of Medical and Biological Engineering, Medical Research Center, Seoul National University, Seoul, Korea.
| |
Collapse
|
16
|
Peivandi S, Ahmadian L, Farokhzadian J, Jahani Y. Evaluation and comparison of errors on nursing notes created by online and offline speech recognition technology and handwritten: an interventional study. BMC Med Inform Decis Mak 2022; 22:96. [PMID: 35395798 PMCID: PMC8994328 DOI: 10.1186/s12911-022-01835-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/31/2022] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Despite the rapid expansion of electronic health records, the use of computer mouse and keyboard, challenges the data entry into these systems. Speech recognition software is one of the substitutes for the mouse and keyboard. The objective of this study was to evaluate the use of online and offline speech recognition software on spelling errors in nursing reports and to compare them with errors in handwritten reports. METHODS For this study, online and offline speech recognition software were selected and customized based on unrecognized terms by these softwares. Two groups of 35 nurses provided the admission notes of hospitalized patients upon their arrival using three data entry methods (using the handwritten method or two types of speech recognition software). After at least a month, they created the same reports using the other methods. The number of spelling errors in each method was determined. These errors were compared between the paper method and the two electronic methods before and after the correction of errors. RESULTS The lowest accuracy was related to online software with 96.4% and accuracy. On the average per report, the online method 6.76, and the offline method 4.56 generated more errors than the paper method. After correcting the errors by the participants, the number of errors in the online reports decreased by 94.75% and the number of errors in the offline reports decreased by 97.20%. The highest number of reports with errors was related to reports created by online software. CONCLUSION Although two software had relatively high accuracy, they created more errors than the paper method that can be lowered by optimizing and upgrading these softwares. The results showed that error correction by users significantly reduced the documentation errors caused by the software.
Collapse
Affiliation(s)
- Sahar Peivandi
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran
| | - Leila Ahmadian
- Department of Health Information Sciences, Faculty of Management and Medical Information Sciences, Kerman University of Medical Sciences, Kerman, Iran.
| | | | - Yunes Jahani
- Modeling in Health Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran
| |
Collapse
|
17
|
Lo B, Almilaji K, Jankowicz D, Sequeira L, Strudwick G, Tajirian T. Application of the i-PARIHS framework in the implementation of speech recognition technology as a way of addressing documentation burden within a mental health context. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2022; 2021:803-812. [PMID: 35308937 PMCID: PMC8861762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Documentation burden continues to be a critical issue in the adoption of comprehensive electronic health record systems. This case study demonstrates how the i-PARIHS framework can be applied to support the implementation of interventions in reducing documentation and EHR-related burden in a mental health context. As part of pre-adoption implementation activities for Speech Recognition Technology (SRT), a cross-sectional survey was conducted with physicians, residents, and fellows at an academic mental health hospital to explore their perceptions on SRT. Open-ended responses and follow-up interviews explored challenges and concerns on using SRT in practice. Through an analysis using the i-PARIHS framework, key considerations were mapped across the four components of the framework. This study demonstrates the value of applying well-established implementation frameworks, such as the i-PARIHS framework, in mitigating challenges related to documentation burden. Future studies should explore how implementation frameworks can be systematically embedded in addressing EHR-related burden.
Collapse
Affiliation(s)
- Brian Lo
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- University of Toronto, Ontario, Canada
| | - Khaled Almilaji
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Damian Jankowicz
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Lydia Sequeira
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- University of Toronto, Ontario, Canada
| | - Gillian Strudwick
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- University of Toronto, Ontario, Canada
| | - Tania Tajirian
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- University of Toronto, Ontario, Canada
| |
Collapse
|
18
|
Abstract
Accurate electronic health records are important for clinical care, research, and patient safety assurance. Correction of misspelled words is required to ensure the correct interpretation of medical records. In the Persian language, the lack of automated misspelling detection and correction system is evident in the medicine and health care. In this article, we describe the development of an automated misspelling detection and correction system for radiology and ultrasound's free texts in the Persian language. To achieve our goal, we used n-gram language model and three different types of free texts related to abdominal and pelvic ultrasound, head and neck ultrasound, and breast ultrasound reports. Our system achieved the detection performance of up to 90.29% for radiology and ultrasound's free texts with the correction accuracy of 88.56%. Results indicated that high-quality spelling correction is possible in clinical reports. The system also achieved significant savings during the documentation process and final approval of the reports in the imaging department.
Collapse
|
19
|
Mayer L, Xu D, Edwards N, Bokhart G. A Comparison of Voice Recognition Program and Traditional Keyboard Charting for Nurse Documentation. Comput Inform Nurs 2021; 40:90-94. [PMID: 34347642 DOI: 10.1097/cin.0000000000000793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
The purposes of this study are threefold: (1) compare the document times between a voice recognition system and keyboard charting, (2) compare the number of errors between the two methods, and (3) identify factors influencing documentation time. Voice recognition systems are considered a potential solution to decrease documentation time. However, little is known to what extent voice recognition systems can save nurses' documentation time. A pilot, simulation study was conducted using a voice recognition system and keyboard charting with 15 acute care nurses. A crossover method with repeated measures was utilized. Each nurse was given two simple and two complex assessment scenarios, assigned in random order, to document using both methods. Paired t-tests and multivariate linear regression models were used for data analysis. The voice recognition method saved the nurses 2.3 minutes (simple scenario) and 6.1 minutes (complex scenario) on average and was statistically significant (P < .001). There were no significant differences in errors or factors identified influencing documentation times. Eighty percent reported a preference of using voice recognition systems, and 87% agreed this method helped speed up charting. This study can show how a voice recognition system can improve documentation times compared with keyboard charting while still having thorough documentation.
Collapse
Affiliation(s)
- LeAnn Mayer
- Author Affiliations: Clinical Assistant Professor (Dr Mayer), School of Nursing, Indiana University of Fort Wayne; Assistant Professor (Dr Xu) and Professor (Dr Edwards), School of Nursing, Purdue University, West Lafayette; and Director of Research (Dr Bokhart), Lutheran Hospital, Fort Wayne, IN
| | | | | | | |
Collapse
|
20
|
Chen L, Asgari M. REFINING AUTOMATIC SPEECH RECOGNITION SYSTEM FOR OLDER ADULTS. PROCEEDINGS OF THE ... IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING. ICASSP (CONFERENCE) 2021; 2021:7003-7007. [PMID: 37351441 PMCID: PMC10286626 DOI: 10.1109/icassp39728.2021.9414207] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/24/2023]
Abstract
Building a high quality automatic speech recognition (ASR) system with limited training data has been a challenging task particularly for a narrow target population. Open-sourced ASR systems, trained on sufficient data from adults, are susceptible on seniors' speech due to acoustic mismatch between adults and seniors. With 12 hours of training data, we attempt to develop an ASR system for socially isolated seniors (80+ years old) with possible cognitive impairments. We experimentally identify that ASR for the adult population performs poorly on our target population and transfer learning (TL) can boost the system's performance. Standing on the fundamental idea of TL, tuning model parameters, we further improve the system by leveraging an attention mechanism to utilize the model's intermediate information. Our approach achieves 1.58% absolute improvements over the TL model.
Collapse
Affiliation(s)
- Liu Chen
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon, USA
| | - Meysam Asgari
- Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon, USA
| |
Collapse
|
21
|
Spinazze P, Aardoom J, Chavannes N, Kasteleyn M. The Computer Will See You Now: Overcoming Barriers to Adoption of Computer-Assisted History Taking (CAHT) in Primary Care. J Med Internet Res 2021; 23:e19306. [PMID: 33625360 PMCID: PMC7946588 DOI: 10.2196/19306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 12/23/2020] [Accepted: 01/24/2021] [Indexed: 01/10/2023] Open
Abstract
Patient health information is increasingly collected through multiple modalities, including electronic health records, wearables, and connected devices. Computer-assisted history taking could provide an additional channel to collect highly relevant, comprehensive, and accurate patient information while reducing the burden on clinicians and face-to-face consultation time. Considering restrictions to consultation time and the associated negative health outcomes, patient-provided health data outside of consultation can prove invaluable in health care delivery. Over the years, research has highlighted the numerous benefits of computer-assisted history taking; however, the limitations have proved an obstacle to adoption. In this viewpoint, we review these limitations under 4 main categories (accessibility, affordability, accuracy, and acceptability) and discuss how advances in technology, computing power, and ubiquity of personal devices offer solutions to overcoming these.
Collapse
Affiliation(s)
- Pier Spinazze
- Global Digital Health Unit, Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jiska Aardoom
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Niels Chavannes
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Marise Kasteleyn
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| |
Collapse
|
22
|
Neves M, Ševa J. An extensive review of tools for manual annotation of documents. Brief Bioinform 2021; 22:146-163. [PMID: 31838514 PMCID: PMC7820865 DOI: 10.1093/bib/bbz130] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Indexed: 12/16/2022] Open
Abstract
MOTIVATION Annotation tools are applied to build training and test corpora, which are essential for the development and evaluation of new natural language processing algorithms. Further, annotation tools are also used to extract new information for a particular use case. However, owing to the high number of existing annotation tools, finding the one that best fits particular needs is a demanding task that requires searching the scientific literature followed by installing and trying various tools. METHODS We searched for annotation tools and selected a subset of them according to five requirements with which they should comply, such as being Web-based or supporting the definition of a schema. We installed the selected tools (when necessary), carried out hands-on experiments and evaluated them using 26 criteria that covered functional and technical aspects. We defined each criterion on three levels of matches and a score for the final evaluation of the tools. RESULTS We evaluated 78 tools and selected the following 15 for a detailed evaluation: BioQRator, brat, Catma, Djangology, ezTag, FLAT, LightTag, MAT, MyMiner, PDFAnno, prodigy, tagtog, TextAE, WAT-SL and WebAnno. Full compliance with our 26 criteria ranged from only 9 up to 20 criteria, which demonstrated that some tools are comprehensive and mature enough to be used on most annotation projects. The highest score of 0.81 was obtained by WebAnno (of a maximum value of 1.0).
Collapse
Affiliation(s)
- Mariana Neves
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| | - Jurica Ševa
- German Centre for the Protection of Laboratory Animals (BfR), German Federal Institute for Risk Assessment (BfR), Berlin, Germany
| |
Collapse
|
23
|
Exeni McAmis NE, Dunn AS, Feinn RS, Bernard AW, Trost MJ. Physician perceptions of documentation methods in electronic health records. Health Informatics J 2021; 27:1460458221989399. [PMID: 33535853 DOI: 10.1177/1460458221989399] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
This study sought to determine physician, specialty and practice factors influencing choice of method for electronic health record (EHR) documentation: direct typing (DT), electronic transcription (ET), human transcription (HT), and scribes. A survey assessing physician documentation practices was developed and distributed online. The primary outcome was the proportion of physicians using each method. Secondary outcomes were provider-rated accuracy, efficiency, and ease of navigation on a 1-5 Likert scale. Means were compared using linear mixed models with Bonferroni adjustment. The 818 respondents were mostly outpatient (46%) adult (79%) physicians, practiced for a mean 15.8 years, and used DT for EHR documentation (72%). Emergency physicians were more likely to use scribes (p < 0.0001). DT was rated less efficient than all other methods (p < 0.0001). ET was rated less accurate than DT (p < 0.001) and HT (p < 0.001). HT was rated less easy to navigate than DT (p = 0.002) and scribe (p < 0.001), and ET less than scribe (p = 0.002). Two hundred and forty-three respondents provided free-text comments that further described opinions. DT was the most commonly used EHR method but rated least efficient. Scribes were rated easy to navigate and efficient but infrequently used outside of emergency settings. Further innovation is needed to design systems responsive to all physician EHR needs.
Collapse
Affiliation(s)
| | - Andrew S Dunn
- Mount Sinai Health System, Ichan School of Medicine, Mount Sinai, USA
| | | | - Aaron W Bernard
- Quinnipiac University Frank H. Netter MD School of Medicine, USA
| | - Margaret J Trost
- University of Southern California, USA
- Children's Hospital Los Angeles, USA
| |
Collapse
|
24
|
Vosshenrich J, Nesic I, Cyriac J, Boll DT, Merkle EM, Heye T. Revealing the most common reporting errors through data mining of the report proofreading process. Eur Radiol 2020; 31:2115-2125. [PMID: 32997178 PMCID: PMC7979672 DOI: 10.1007/s00330-020-07306-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/18/2020] [Accepted: 09/16/2020] [Indexed: 11/04/2022]
Abstract
Objectives To investigate the most common errors in residents’ preliminary reports, if structured reporting impacts error types and frequencies, and to identify possible implications for resident education and patient safety. Material and methods Changes in report content were tracked by a report comparison tool on a word level and extracted for 78,625 radiology reports dictated from September 2017 to December 2018 in our department. Following data aggregation according to word stems and stratification by subspecialty (e.g., neuroradiology) and imaging modality, frequencies of additions/deletions were analyzed for findings and impression report section separately and compared between subgroups. Results Overall modifications per report averaged 4.1 words, with demonstrably higher amounts of changes for cross-sectional imaging (CT: 6.4; MRI: 6.7) than non-cross-sectional imaging (radiographs: 0.2; ultrasound: 2.8). The four most frequently changed words (right, left, one, and none) remained almost similar among all subgroups (range: 0.072–0.117 per report; once every 9–14 reports). Albeit representing only 0.02% of analyzed words, they accounted for up to 9.7% of all observed changes. Subspecialties solely using structured reporting had substantially lower change ratios in the findings report section (mean: 0.2 per report) compared with prose-style reporting subspecialties (mean: 2.0). Relative frequencies of the most changed words remained unchanged. Conclusion Residents’ most common reporting errors in all subspecialties and modalities are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). Structured reporting reduces overall error rates, but does not affect occurrence of the most common errors. Increased error awareness and measures improving report correctness and ensuring patient safety are required. Key Points • The two most common reporting errors in residents’ preliminary reports are laterality discriminator confusions (left/right) and unnoticed descriptor misregistration by speech recognition (one/none). • Structured reporting reduces the overall the error frequency in the findings report section by a factor of 10 (structured reporting: mean 0.2 per report; prose-style reporting: 2.0) but does not affect the occurrence of the two major errors. • Staff radiologist review behavior noticeably differs between radiology subspecialties. Electronic supplementary material The online version of this article (10.1007/s00330-020-07306-6) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Jan Vosshenrich
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland.
| | - Ivan Nesic
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Joshy Cyriac
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Daniel T Boll
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Elmar M Merkle
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| | - Tobias Heye
- Department of Radiology, University Hospital Basel, Petersgraben 4, 4031, Basel, Switzerland
| |
Collapse
|
25
|
Jacquemard T, Doherty CP, Fitzsimons MB. Examination and diagnosis of electronic patient records and their associated ethics: a scoping literature review. BMC Med Ethics 2020; 21:76. [PMID: 32831076 PMCID: PMC7446190 DOI: 10.1186/s12910-020-00514-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 08/03/2020] [Indexed: 02/22/2023] Open
Abstract
Background Electronic patient record (EPR) technology is a key enabler for improvements to healthcare service and management. To ensure these improvements and the means to achieve them are socially and ethically desirable, careful consideration of the ethical implications of EPRs is indicated. The purpose of this scoping review was to map the literature related to the ethics of EPR technology. The literature review was conducted to catalogue the prevalent ethical terms, to describe the associated ethical challenges and opportunities, and to identify the actors involved. By doing so, it aimed to support the future development of ethics guidance in the EPR domain. Methods To identify journal articles debating the ethics of EPRs, Scopus, Web of Science, and PubMed academic databases were queried and yielded 123 eligible articles. The following inclusion criteria were applied: articles need to be in the English language; present normative arguments and not solely empirical research; include an abstract for software analysis; and discuss EPR technology. Results The medical specialty, type of information captured and stored in EPRs, their use and functionality varied widely across the included articles. Ethical terms extracted were categorised into clusters ‘privacy’, ‘autonomy’, ‘risk/benefit’, ‘human relationships’, and ‘responsibility’. The literature shows that EPR-related ethical concerns can have both positive and negative implications, and that a wide variety of actors with rights and/or responsibilities regarding the safe and ethical adoption of the technology are involved. Conclusions While there is considerable consensus in the literature regarding EPR-related ethical principles, some of the associated challenges and opportunities remain underdiscussed. For example, much of the debate is presented in a manner more in keeping with a traditional model of healthcare and fails to take account of the multidimensional ensemble of factors at play in the EPR era and the consequent need to redefine/modify ethical norms to align with a digitally-enabled health service. Similarly, the academic discussion focuses predominantly on bioethical values. However, approaches from digital ethics may also be helpful to identify and deliberate about current and emerging EPR-related ethical concerns.
Collapse
Affiliation(s)
- Tim Jacquemard
- FutureNeuro, the SFI Research Centre for Chronic and Rare Neurological Diseases, 123 Stephen's Green, Dublin 2, Ireland.
| | - Colin P Doherty
- FutureNeuro, the SFI Research Centre for Chronic and Rare Neurological Diseases, 123 Stephen's Green, Dublin 2, Ireland.,Department of Neurology, St. James's Hospital, James's Street, Dublin 8, Ireland.,Trinity College Dublin, College Green, Dublin 2, Ireland
| | - Mary B Fitzsimons
- FutureNeuro, the SFI Research Centre for Chronic and Rare Neurological Diseases, 123 Stephen's Green, Dublin 2, Ireland
| |
Collapse
|
26
|
Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Med Inform 2020; 8:e18599. [PMID: 32706688 PMCID: PMC7414411 DOI: 10.2196/18599] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/26/2020] [Accepted: 06/13/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Artificial intelligence (AI) provides opportunities to identify the health risks of patients and thus influence patient safety outcomes. OBJECTIVE The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes. METHODS We restricted our search to the PubMed, PubMed Central, and Web of Science databases to retrieve research articles published in English between January 2009 and August 2019. We focused on quantitative studies that reported positive, negative, or intermediate changes in patient safety outcomes using AI apps, specifically those based on machine-learning algorithms and natural language processing. Quantitative studies reporting only AI performance but not its influence on patient safety outcomes were excluded from further review. RESULTS We identified 53 eligible studies, which were summarized concerning their patient safety subcategories, the most frequently used AI, and reported performance metrics. Recognized safety subcategories were clinical alarms (n=9; mainly based on decision tree models), clinical reports (n=21; based on support vector machine models), and drug safety (n=23; mainly based on decision tree models). Analysis of these 53 studies also identified two essential findings: (1) the lack of a standardized benchmark and (2) heterogeneity in AI reporting. CONCLUSIONS This systematic review indicates that AI-enabled decision support systems, when implemented correctly, can aid in enhancing patient safety by improving error detection, patient stratification, and drug management. Future work is still needed for robust validation of these systems in prospective and real-world clinical environments to understand how well AI can predict safety outcomes in health care settings.
Collapse
Affiliation(s)
- Avishek Choudhury
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| | - Onur Asan
- School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, NJ, United States
| |
Collapse
|
27
|
Miner AS, Haque A, Fries JA, Fleming SL, Wilfley DE, Terence Wilson G, Milstein A, Jurafsky D, Arnow BA, Stewart Agras W, Fei-Fei L, Shah NH. Assessing the accuracy of automatic speech recognition for psychotherapy. NPJ Digit Med 2020; 3:82. [PMID: 32550644 PMCID: PMC7270106 DOI: 10.1038/s41746-020-0285-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Accepted: 04/30/2020] [Indexed: 01/17/2023] Open
Abstract
Accurate transcription of audio recordings in psychotherapy would improve therapy effectiveness, clinician training, and safety monitoring. Although automatic speech recognition software is commercially available, its accuracy in mental health settings has not been well described. It is unclear which metrics and thresholds are appropriate for different clinical use cases, which may range from population descriptions to individual safety monitoring. Here we show that automatic speech recognition is feasible in psychotherapy, but further improvements in accuracy are needed before widespread use. Our HIPAA-compliant automatic speech recognition system demonstrated a transcription word error rate of 25%. For depression-related utterances, sensitivity was 80% and positive predictive value was 83%. For clinician-identified harm-related sentences, the word error rate was 34%. These results suggest that automatic speech recognition may support understanding of language patterns and subgroup variation in existing treatments but may not be ready for individual-level safety surveillance.
Collapse
Affiliation(s)
- Adam S. Miner
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
- Department of Health Research and Policy, Stanford University, CA, USA
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
| | - Albert Haque
- Department of Computer Science, Stanford University, Stanford, CA USA
| | - Jason A. Fries
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
| | - Scott L. Fleming
- Department of Biomedical Data Science, Stanford University, Stanford, CA USA
| | - Denise E. Wilfley
- Departments of Psychiatry, Medicine, Pediatrics, and Psychological & Brain Sciences, Washington University in St. Louis, St. Louis, MO USA
| | - G. Terence Wilson
- Graduate School of Applied and Professional Psychology, Rutgers, the State University of New Jersey, New Brunswick, New Jersey USA
| | - Arnold Milstein
- Clinical Excellence Research Center, Stanford University, Stanford, CA USA
| | - Dan Jurafsky
- Department of Computer Science, Stanford University, Stanford, CA USA
- Department of Linguistics, Stanford University, Stanford, CA USA
| | - Bruce A. Arnow
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
| | - W. Stewart Agras
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA USA
| | - Li Fei-Fei
- Department of Computer Science, Stanford University, Stanford, CA USA
| | - Nigam H. Shah
- Center for Biomedical Informatics Research, Stanford University, Stanford, CA USA
| |
Collapse
|
28
|
Bell SK, Delbanco T, Elmore JG, Fitzgerald PS, Fossa A, Harcourt K, Leveille SG, Payne TH, Stametz RA, Walker J, DesRoches CM. Frequency and Types of Patient-Reported Errors in Electronic Health Record Ambulatory Care Notes. JAMA Netw Open 2020; 3:e205867. [PMID: 32515797 PMCID: PMC7284300 DOI: 10.1001/jamanetworkopen.2020.5867] [Citation(s) in RCA: 94] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Accepted: 03/14/2020] [Indexed: 12/18/2022] Open
Abstract
Importance As health information transparency increases, patients more often seek their health data. More than 44 million patients in the US can now readily access their ambulatory visit notes online, and the practice is increasing abroad. Few studies have assessed documentation errors that patients identify in their notes and how these may inform patient engagement and safety strategies. Objective To assess the frequency and types of errors identified by patients who read open ambulatory visit notes. Design, Setting, and Participants In this survey study, a total of 136 815 patients at 3 US health care organizations with open notes, including 79 academic and community ambulatory care practices, received invitations to an online survey from June 5 to October 20, 2017. Patients who had at least 1 ambulatory note and had logged onto the portal at least once in the past 12 months were included. Data analysis was performed from July 3, 2018, to April 27, 2020. Exposures Access to ambulatory care open notes through patient portals for up to 7 years (2010-2017). Main Outcomes and Measures Proportion of patients reporting a mistake and how serious they perceived the mistake to be, factors associated with finding errors characterized by patients as serious, and categories of patient-reported errors. Results Of 136 815 patients who received survey invitations, 29 656 (21.7%) responded and 22 889 patients (mean [SD] age, 55.16 [15.96] years; 14 447 [63.1%] female; 18 301 [80.0%] white) read 1 or more notes in the past 12 months and completed error questions. Of these patients, 4830 (21.1%) reported a perceived mistake and 2043 (42.3%) reported that the mistake was serious (somewhat serious: 1563 [32.4%]; very serious: 480 [9.9%]). In multivariable analysis, female patients (relative risk [RR], 1.79; 95% CI, 1.72-1.85), more educated patients (RR, 1.38; 95% CI, 1.29-1.48), sicker patients (RR, 1.89; 95% CI, 1.84-1.94), those aged 45 to 64 years (RR, 2.23; 95% CI, 2.06-2.42), those 65 years or older (RR, 2.00; 95% CI, 1.73-2.32), and those who read more than 1 note (2-3 notes: RR, 1.82; 95% CI, 1.34-2.47; ≥4 notes: RR, 3.09; 95% CI, 2.02-4.73) were more likely to report a mistake that they found to be serious compared with their reference groups. After categorization of patient-reported very serious mistakes, those specifically mentioning the word diagnosis or describing a specific error in current or past diagnoses were most common (98 of 356 [27.5%]), followed by inaccurate medical history (85 of 356 [23.9%]), medications or allergies (50 of 356 [14.0%]), and tests, procedures, or results (30 of 356 [8.4%]). A total of 23 (6.5%) reflected notes reportedly written on the wrong patient. Of 433 very serious errors, 255 (58.9%) included at least 1 perceived error potentially associated with the diagnostic process (eg, history, physical examination, tests, referrals, and communication). Conclusions and Relevance In this study, patients who read ambulatory notes online perceived mistakes, a substantial proportion of which they found to be serious. Older and sicker patients were twice as likely to report a serious error compared with younger and healthier patients, indicating important safety and quality implications. Sharing notes with patients may help engage them to improve record accuracy and health care safety together with practitioners.
Collapse
Affiliation(s)
- Sigall K. Bell
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Tom Delbanco
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Joann G. Elmore
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles
| | | | - Alan Fossa
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Department of Epidemiology, University of Michigan, Ann Arbor
| | - Kendall Harcourt
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Suzanne G. Leveille
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
- Department of Nursing, College of Nursing and Health Sciences, University of Massachusetts, Boston
| | - Thomas H. Payne
- Department of Medicine, University of Washington School of Medicine, Seattle
| | - Rebecca A. Stametz
- Steele Institute for Health Innovation, Geisinger, Danville, Pennsylvania
| | - Jan Walker
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Catherine M. DesRoches
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| |
Collapse
|
29
|
Joseph J, Moore ZEH, Patton D, O'Connor T, Nugent LE. The impact of implementing speech recognition technology on the accuracy and efficiency (time to complete) clinical documentation by nurses: A systematic review. J Clin Nurs 2020; 29:2125-2137. [DOI: 10.1111/jocn.15261] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 02/10/2020] [Accepted: 03/12/2020] [Indexed: 12/01/2022]
Affiliation(s)
- Joseph Joseph
- Naas General hospital Naas Ireland
- School of Nursing and Midwifery Royal College of Surgeons in Ireland Dublin 2 Ireland
| | - Zena E. H. Moore
- School of Nursing and Midwifery Royal College of Surgeons in Ireland Dublin 2 Ireland
| | - Declan Patton
- School of Nursing and Midwifery Royal College of Surgeons in Ireland Dublin 2 Ireland
| | - Tom O'Connor
- School of Nursing and Midwifery Royal College of Surgeons in Ireland Dublin 2 Ireland
| | | |
Collapse
|
30
|
Pilot trial of semi-automated medical note writing using lexeme hypotheses. Int J Med Inform 2020; 136:104095. [PMID: 32058265 DOI: 10.1016/j.ijmedinf.2020.104095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2019] [Revised: 11/14/2019] [Accepted: 02/04/2020] [Indexed: 11/24/2022]
Abstract
Clinicians write a billion free text notes per year. These notes are typically replete with errors of all types. No established automated method can extract data from this treasure trove. The practice of medicine therefore remains haphazard and chaotic, resulting in vast economic waste. The lexeme hypotheses are based on our analysis of how records are created. They enable a computer system to predict what issue a clinician will need to address next, based on the environment in which the clinician is working, and what responses the clinician has selected to date. The system uses a lexicon storing the issues (queries) and a range of responses to the issues. When the clinician selects a response, a text fragment is added to the output file. In the first phase of this work, the notes of 69 returning hemophilia patients were scrutinized, and the lexicon was expanded to 847 lexeme queries and 7995 responses to enable the construction of completed notes. The quality of lexeme-generated notes from 20 consecutive subjects was then compared to the clinicians' conventional clinic notes. The system generated grammatically correct notes. In comparison to the traditional clinic note, the lexeme-generated notes were more complete (88 % compared with 62 %), and had less typographical and grammatical errors (0.8 versus 3.5 errors per note). The system notes and traditional notes averaged about 800 words, but the traditional notes had a much wider distribution of lengths. The note-creation rate from marshalling the data to completion using the system averaged 80 wpm, twice as fast as the typical clinician can type. The lexeme method generates more complete, grammatical and organized notes faster than traditional methods. The notes are completely computerized at inception, and they incorporate prompts for clinicians to address otherwise overlooked items. This pilot justifies further exploration of this methodology.
Collapse
|
31
|
A clinician survey of using speech recognition for clinical documentation in the electronic health record. Int J Med Inform 2019; 130:103938. [DOI: 10.1016/j.ijmedinf.2019.07.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Revised: 07/20/2019] [Accepted: 07/30/2019] [Indexed: 11/21/2022]
|
32
|
Palanica A, Thommandram A, Lee A, Li M, Fossat Y. Do you understand the words that are comin outta my mouth? Voice assistant comprehension of medication names. NPJ Digit Med 2019; 2:55. [PMID: 31304401 PMCID: PMC6586879 DOI: 10.1038/s41746-019-0133-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 05/30/2019] [Indexed: 11/09/2022] Open
Abstract
This study investigated the speech recognition abilities of popular voice assistants when being verbally asked about commonly dispensed medications by a variety of participants. Voice recordings of 46 participants (12 of which had a foreign accent in English) were played back to Amazon’s Alexa, Google Assistant, and Apple’s Siri for the brand- and generic names of the top 50 most dispensed medications in the United States. A repeated measures ANOVA indicated that Google Assistant achieved the highest comprehension accuracy for both brand medication names (M = 91.8%, SD = 4.2) and generic medication names (M = 84.3%, SD = 11.2), followed by Siri (brand names M = 58.5%, SD = 11.2; generic names M = 51.2%, SD = 16.0), and the lowest accuracy by Alexa (brand names M = 54.6%, SD = 10.8; generic names M = 45.5%, SD = 15.4). An interaction between voice assistant and participant accent was also found, demonstrating lower comprehension performance overall for those with a foreign accent using Siri (M = 48.8%, SD = 11.8) and Alexa (M = 41.7%, SD = 12.7), compared to participants without a foreign accent (Siri M = 57.0%, SD = 11.7; Alexa M = 53.0%, SD = 10.9). No significant difference between participant accents were found for Google Assistant. These findings show a substantial performance lead for Google Assistant compared to its voice assistant competitors when comprehending medication names, but there is still room for improvement.
Collapse
Affiliation(s)
- Adam Palanica
- Labs Department Klick Health, Klick Inc., Toronto Ontario, Canada
| | | | - Andrew Lee
- Labs Department Klick Health, Klick Inc., Toronto Ontario, Canada
| | - Michael Li
- Labs Department Klick Health, Klick Inc., Toronto Ontario, Canada
| | - Yan Fossat
- Labs Department Klick Health, Klick Inc., Toronto Ontario, Canada
| |
Collapse
|
33
|
Blackley SV, Huynh J, Wang L, Korach Z, Zhou L. Speech recognition for clinical documentation from 1990 to 2018: a systematic review. J Am Med Inform Assoc 2019; 26:324-338. [PMID: 30753666 PMCID: PMC7647182 DOI: 10.1093/jamia/ocy179] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2018] [Revised: 11/16/2018] [Accepted: 11/28/2018] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE The study sought to review recent literature regarding use of speech recognition (SR) technology for clinical documentation and to understand the impact of SR on document accuracy, provider efficiency, institutional cost, and more. MATERIALS AND METHODS We searched 10 scientific and medical literature databases to find articles about clinician use of SR for documentation published between January 1, 1990, and October 15, 2018. We annotated included articles with their research topic(s), medical domain(s), and SR system(s) evaluated and analyzed the results. RESULTS One hundred twenty-two articles were included. Forty-eight (39.3%) involved the radiology department exclusively and 10 (8.2%) involved emergency medicine; 10 (8.2%) mentioned multiple departments. Forty-eight (39.3%) articles studied productivity; 20 (16.4%) studied the effect of SR on documentation time, with mixed findings. Decreased turnaround time was reported in all 19 (15.6%) studies in which it was evaluated. Twenty-nine (23.8%) studies conducted error analyses, though various evaluation metrics were used. Reported percentage of documents with errors ranged from 4.8% to 71%; reported word error rates ranged from 7.4% to 38.7%. Seven (5.7%) studies assessed documentation-associated costs; 5 reported decreases and 2 reported increases. Many studies (44.3%) used products by Nuance Communications. Other vendors included IBM (9.0%) and Philips (6.6%); 7 (5.7%) used self-developed systems. CONCLUSION Despite widespread use of SR for clinical documentation, research on this topic remains largely heterogeneous, often using different evaluation metrics with mixed findings. Further, that SR-assisted documentation has become increasingly common in clinical settings beyond radiology warrants further investigation of its use and effectiveness in these settings.
Collapse
Affiliation(s)
- Suzanne V Blackley
- Clinical and Quality Analysis, Information Systems, Partners HealthCare, Boston, Massachusetts, USA
| | - Jessica Huynh
- General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Liqin Wang
- General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Zfania Korach
- General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| | - Li Zhou
- General Medicine and Primary Care, Brigham and Women's Hospital, Boston, Massachusetts, USA
- Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
34
|
Lee S, Mohr NM, Street WN, Nadkarni P. Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: An Overview. West J Emerg Med 2019; 20:219-227. [PMID: 30881539 PMCID: PMC6404711 DOI: 10.5811/westjem.2019.1.41244] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 12/21/2018] [Accepted: 01/01/2019] [Indexed: 12/13/2022] Open
Abstract
Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.
Collapse
Affiliation(s)
- Sangil Lee
- University of Iowa Carver College of Medicine, Department of Emergency Medicine, Iowa City, Iowa
| | - Nicholas M Mohr
- University of Iowa Carver College of Medicine, Department of Emergency Medicine, Anesthesia and Critical Care, Iowa City, Iowa
| | - W Nicholas Street
- University of Iowa Tippie College of Business, Department of Management Sciences, Iowa City, Iowa
| | - Prakash Nadkarni
- University of Iowa Carver College of Medicine, Department of Internal Medicine, Iowa City, Iowa
| |
Collapse
|
35
|
Warraich HJ, Califf RM, Krumholz HM. The digital transformation of medicine can revitalize the patient-clinician relationship. NPJ Digit Med 2018; 1:49. [PMID: 31304328 PMCID: PMC6550259 DOI: 10.1038/s41746-018-0060-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 08/09/2018] [Accepted: 08/14/2018] [Indexed: 11/09/2022] Open
Abstract
Health professionals within the medical community feel that the principles of humanism in medicine have not been a point of emphasis for information and computer technology in healthcare. There is concern that the electronic health record is eroding the patient-clinician relationship and distancing clinicians from their patients. New analytic technologies, on the contrary, by taking over repetitive and mundane tasks, can provide an avenue to make medical care more patient-centered by freeing clinicians' time, and the time of the whole clinical care team, to engage with patients. Technology such as advanced speech recognition that optimizes clinicians' workflow could revitalize the patient-clinician relationship and perhaps also improve clinician well-being. Digital phenotyping can gain invaluable additional data from patients using technology that is already used for personal reasons by the majority of patients. The digital transformation of healthcare has the potential to make healthcare more humane and personalized, however, several important steps are needed to avoid the pitfalls that have come with prior iterations of information technology in medicine such as a heightened emphasis on data security and transparency. Both patients and clinicians should be involved from the early stages of development of medical technologies to ensure that they are person-centric. Technologists and engineers developing healthcare technologies should have experiences with the delivery of healthcare and the lives of patients and clinicians. These steps are necessary to develop a common commitment to the design concept that technology and humane care are not mutually exclusive, and in fact, can be symbiotic.
Collapse
Affiliation(s)
- Haider J Warraich
- 1Department of Medicine, Cardiology Division, Duke University School of Medicine, Durham, NC USA.,2Duke Clinical Research Institute, Durham, NC USA
| | - Robert M Califf
- Duke Forge, Durham, NC USA.,4Department of Medicine, Stanford University, Palo Alto, CA USA.,Verily Life Sciences, South San Francisco, CA USA
| | - Harlan M Krumholz
- 6Section of Cardiovascular Medicine, Department of Medicine, Yale University School of Medicine, New Haven, CT USA
| |
Collapse
|